E&EG Journal Volume XXVIII, Number 1 by Association of Environmental & Engineering Geologists (AEG)

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THIS PUBLICATION IS PRINTED ON ACID-FREE PAPER Abdul Shakoor Kent State University Kent, OH 44242 ashakoor@kent.edu

EDITORS

Eric Peterson Department of Geography, Geology, and the Environment Illinois State University Normal, IL 61790 309-438-5669 ewpeter@ilstu.edu

Karen E. Smith, Editorial Assistant, kesmith6@kent.edu

Oommen, Thomas Board Chair, Michigan Technological University Sasowsky, Ira D. University of Akron

ASSOCIATE EDITORS Ackerman, Frances Ramboll Americas Engineering Solutions, Inc. Bastola, Hridaya Lehigh University Beglund, James Montana Bureau of Mines and Geology Bruckno, Brian Virginia Department of Transportation Clague, John Simon Fraser University, Canada Dee, Seth University of Nevada, Reno Fryar, Alan University of Kentucky Gardner, George Massachusetts Department of Environmental Protection

Hauser, Ernest Wright State University Keaton, Jeff AMEC Americas May, David USACE-ERDC-CHL Pope, Isaac Book Review Editor Santi, Paul Colorado School of Mines Schuster, Bob Shlemon, Roy R.J. Shlemon & Associates, Inc. Stock, Greg National Park Service Ulusay, Resat Hacettepe University, Turkey West, Terry Purdue University

Environmental & Engineering Geoscience February 2022

VOLUME XXVIII, NUMBER 1

Special Issue on Slope Stability in Memory of Jerome (Jerry) V. De Graff: Part 2 Guest Editors: Dennis Staley, Jeremy Lancaster, Alan Gallegos, Thad Wasklewicz

Submitting a Manuscript Environmental & Engineering Geoscience (E&EG), is a quarterly journal devoted to the publication of original papers that are of potential interest to hydrogeologists, environmental and engineering geologists, and geological engineers working in site selection, feasibility studies, investigations, design or construction of civil engineering projects or in waste management, groundwater, and related environmental fields. All papers are peer reviewed. The editors invite contributions concerning all aspects of environmental and engineering geology and related disciplines. Recent abstracts can be viewed under “Archive” at the web site, “http://eeg.geoscienceworld.org”. Articles that report on research, case histories and new methods, and book reviews are welcome. Discussion papers, which are critiques of printed articles and are technical in nature, may be published with replies from the original author(s). Discussion papers and replies should be concise. To submit a manuscript go to https://www.editorialmanager.com/EEG/ default.aspx. If you have not used the system before, follow the link at the bottom of the page that says New users should register for an account. Choose your own login and password. Further instructions will be available upon logging into the system. Manuscripts that do not follow the Style Guide and the Instructions for Authors will be returned. Authors do not pay any charge for color figures that are essential to the manuscript. Manuscripts of fewer than 10 pages may be published as Technical Notes. For further information, you may contact Dr. Abdul Shakoor at the editorial office.

Cover photo In memory of Jerome (Jerry) V. De Graff, 1945-2020, U.S. Forest Service, geologist, colleague, friend. Photo courtesy of the De Graff family.

Volume XXVIII, Number 1, February 2022

All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from AEG.

ADVISORY BOARD Watts, Chester “Skip” F. Radford University Hasan, Syed University of Missouri, Kansas City Nandi, Arpita East Tennessee State University

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Environmental & Engineering Geoscience Volume 28, Number 1, February 2022 Special Issue on Slope Stability in Memory of Jerome (Jerry) V. De Graff: Part 2 Guest Editors: Dennis Staley, Jeremy Lancaster, Alan Gallegos, Thad Wasklewicz Table of Contents 1

Foreword Alan Gallegos

Rockfall Kinematics from Massive Rock Cliffs: Outlier Boulders and Flyrock from Whitney Portal, California, Rockfalls Brian D. Collins, Skye C. Corbett, Elizabeth J. Horton, Alan J. Gallegos

Responses to Landslides and Landslide Mapping on the Blue Ridge Escarpment, Polk County, North Carolina, USA Richard M.Wooten, Corey M. Scheip, Jesse S. Hill, Thomas J. Douglas, David M. Korte, Bart L. Cattanach, G. Nicholas Bozdog, Sierra J. Isard

Using Radar Rainfall to Explain the Occurrence of a 2012 Soil Slip Near Mt. LeConte, TN, USA Jeffrey R. Keaton

Considering Engineering Geology Input for Probabilistic Flood Hazard Assessments Jeffrey R. Keaton

Application of a Hydrological Model for Estimating Infiltration for Debris Flow Initiation: A Case Study from the Great Smoky Mountains National Park, Tennessee Arpita Mandal, Arpita Nandi, Abdul Shakoor, Jeffrey Keaton

113

Assessment of Logistic Regression Model Performance and Physical Controls on January 9, 2018, Debris Flows, Thomas Fire, California Brian J. Swanson, Stefani G. Lukashov, Jonathon Y. Schwartz, Donald N. Lindsay, Jeremy T. Lancaster

133

Rainfall Triggering of Post-Fire Debris Flows over a 28-Year Period near El Portal, California, USA Jerome V. De Graff, Dennis M. Staley, Greg M. Stock, Kellen Takenaka, Alan L. Gallegos, Chad K. Neptune

Foreword ALAN GALLEGOS* 28270 Burrough Valley Road, Tollhouse, CA 93667, U.S. Forest Service (retired)

This is the second of two issues in the special volume of Environmental & Engineering Geoscience dedicated to honoring the life and legacy of Jerome (Jerry) V. De Graff. Jerry’s work with the U.S. Forest Service (USFS) in California gave him opportunities to influence management and consider geologic resources and hazards in managing natural resources. Jerry provided expertise in mapping and interpreting landslide terrain to mitigate damage from landslides on roads and in areas recently burned from wildfires. Jerry was a prominent presence on USFS Burn Area Emergency Response (BAER) Assessment Teams and was at the forefront to assess rockfall and debris flow hazards from wildfires. Jerry’s early work on BAER Assessment Teams used traditional methods for assessing geologic hazards, including the use of aerial photos and field review to identify evidence of past rockfalls and debris flows. Throughout this time, Jerry believed there were more efficient ways to identify debris-flow–prone areas and believed that computer models would be available someday. Jerry was always at the forefront of new technology, including the use of Geographic Information Systems and computer-based modeling. As new technology was developed, Jerry started using this technology. His work in geology brought him from the education field to a career of public service with the USFS and back to the educational field. Jerry ended his career as an Adjunct Professor at California State University where he co-led two classes with Dr. Chris Pluhar on landslides and mine reclamation. Jerry mentored many students and was well respected by his peers in the geology profession. He was a good friend to many. The second issue of this special volume includes seven papers that attest to Jerry’s interests and expertise. One of his passions was characterizing and predicting where rockfalls would occur and the geologic hazards they presented. Jerry published a 2012 Geological Society of America technical note and a 2015 paper in Environmental & Engineering Geoscience describing the challenges of evaluating and describing

rockfall hazards in wildfire areas. In this issue, Brian Collins from the U.S. Geological Survey describes an innovative method and model for characterizing rockfall hazards at a high-use national forest campground near the trailhead to Mount Whitney. Jerry worked in the Caribbean Islands through the USFS International Forestry Program and published several papers on landslides initiated from tropical storms on the islands of Dominica and Saint Thomas. Here, three papers describe landslides activated from similar prolonged precipitation events. Rick Wooten from the Kentucky Geological Survey describes an effort to characterize and map landslides in Kentucky, which experienced increased landslide activity from a year of high precipitation. Jeff Keaton from Wood Environment & Infrastructure Solutions in Los Angeles, CA, was a good friend of Jerry and provided two papers to this volume. One paper describes the use of rainfall radar to explain a soil slip in Tennessee, and the second paper makes a case for considering engineering geology for flood assessments. Jerry published many papers on debris flow hazards. Three papers continue the theme of debris flow hazards and modeling from the first issue. Arpita Mandal, from University of the West Indies, Kingston, Jamaica, characterizes infiltration rates and conditions for initiating debris flows. Brian Swanson from the California Geological Survey describes physical controls and distribution of debris flow runout from a recent case study where 23 lives were lost from several debris flow events after a post-wildfire flood event in Southern California. The last paper in this volume was Jerry’s last published paper and describes rainfall events that triggered debris flows in a small community near the California State Highway 140 entrance to Yosemite National Park. On behalf of my co-editors, Dennis Staley (U.S. Geological Survey), Jeremy Lancaster (California Geological Survey), and Thad Wasklewicz (Stantec Consulting Services), we hope you find the articles included within this special issue a fitting tribute to the life and legacy of our good friend, Jerry De Graff.

*Corresponding author email: alangallegos@netptc.net

Environmental & Engineering Geoscience, Vol. XXVIII, No. 1, February 2022, p. 1

Rockfall Kinematics from Massive Rock Cliﬀs: Outlier Boulders and Flyrock from Whitney Portal, California, Rockfalls BRIAN D. COLLINS* SKYE C. CORBETT ELIZABETH J. HORTON U.S. Geological Survey, Landslide Hazards Program, P.O. Box 158, Moﬀett Field, CA 94035

ALAN J. GALLEGOS U.S. Forest Service, Sierra National Forest, 1600 Tollhouse Road, Clovis, CA 93611

Key Terms: Rockfall, Boulder, Flyrock, Forest, Impact, Hazard, Runout, Sierra Nevada

dence in the use of a single rockfall shadow angle for estimating future rockfall hazards at the site.

ABSTRACT

INTRODUCTION

Geologic conditions and topographic setting are among the most critical factors for assessing rockfall hazards. However, other subtle features of rockfall motion may also govern the runout of rockfall debris, particularly for those sourced from massive cliffs where debris can have substantial momentum during transport. Rocks may undergo collisions with trees and talus boulders, with the latter potentially generating flyrock— launched rock pieces resulting from boulder collisions that follow distinctively different paths than the majority of debris. Collectively, these intricacies of rockfall kinematics may substantially govern the hazards expected from rockfall to both persons and infrastructure located beneath steep cliffs. Here, we investigate the kinematics, including outlier boulder and flyrock trajectories, of seismically triggered rockfalls on 24 June 2020 that damaged campground facilities near Whitney Portal, CA, a heavily used outdoor recreation gateway to the Sierra Nevada mountains. Our results, obtained in part by rockfall runout model simulations, indicate that outlier boulder trajectories resulted from opportunities provided by less steep terrain beyond the talus edge. The influence of trees, initially thought to have served a protective capacity in attenuating rockfall energy, appears to have been negligible for the large boulder volumes (>50 m3 ) mobilized, although they did potentially deflect the trajectory of flyrock debris. Rockfall outlier boulders from the event were comparable in volume and runout distance to prehistoric boulders located beyond the talus slope, thereby providing some level of confi-

Rockfalls are a hazard in many areas of the world where the intersection of infrastructure and steep cliffs places assets and lives at considerable risk. In addition to causing significant damage and cost to transportation routes and structures (Hungr et al., 1999; Guzzetti et al., 2004; and Grant et al., 2018), rockfalls also occasionally cause injuries or fatalities. The effects of rockfalls on public safety may be accentuated in outdoor recreational settings where visitors congregate to view scenery and participate in activities located near or on steep cliffs (Stock et al., 2013; Badoux et al., 2016). To improve efforts to reduce rockfall-related risks, an understanding of both the source area and triggering conditions of rockfalls, as well as the kinematics of motion and eventual deposition is needed. There has been much progress on all of these components in the past few decades, ranging from identification and assessment of source areas (Rosser et al., 2007; Abellan et al., 2010; and Matasci et al., 2018) to quantitative three-dimensional rockfall runout modeling efforts (Guzzetti et al., 2002; Agliardi and Crosta, 2003; Dorren 2003; Jaboyedoff and Labiouse, 2011; Christen et al., 2012; Dorren, 2016; and Sala et al., 2019), and to resultant hazard and risk assessments (Crosta and Agliardi, 2003; Guzzetti et al., 2003; Jaboyedoff et al., 2005; Stock et al., 2014; and Ferrari et al., 2016). However, many questions remain related to the intricacies of rockfall events, including how the kinematics of boulder transport down cliffs and slopes can interact and lead to hazards outboard of normative areas of rockfall runout (i.e., the talus slopes typically located at the base of cliffs; Evans and Hungr, 1993).

*Corresponding author email: bcollins@usgs.gov

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Collins, Corbett, Horton, and Gallegos

Hazards occurring beyond the base of talus slopes may include effects from so-called “outlier boulders” that, due to their large mass and/or velocity, have considerable momentum and lead to deposition some distance beyond the base of talus (e.g., Evans and Hungr, 1993; Wieczorek et al., 1998, 2008; and Stock et al., 2014). In addition, outliers typically have large volume that preclude them from being stopped by the characteristic scale roughness of the talus slopes over which they travel. Distal effects of rockfalls may also include smaller particles of rock that travel still farther away from talus zones along steep upwardly aimed trajectories that are substantially different compared to most other rockfall debris. These fragments are termed “flyrock” and result from impacts of rockfall on to existing talus slopes and within newly mobilized debris (Wieczorek and Snyder, 1999). Flyrock fragments are generally small in volume (typically on the order of <0.5 m3 ) compared to the bulk of rockfall debris, but technically have no upper size limit. Whereas the majority of rockfall debris generally follows the slope of the talus and is blocked or redirected slightly by trees or other boulders on the slope, flyrock may launch over or between trees and large boulders and land substantially farther from the cliff than the main rockfall deposit from which it originates. There are few studies on flyrock resulting from rockfalls (Wieczorek and Snyder, 1999; Wieczorek et al., 2008; and Giacomini et al., 2009), although much work has been conducted on the resulting size distributions of fragmenting rockfalls to which flyrock is related (e.g., Corominas et al., 2017; RuizCarulla and Corominas, 2020). Instead, the majority of research on flyrock occurrence and trajectories has resulted from investigations conducted by the mining community, who are challenged by unpredictable trajectories of blasted rock in open pit quarries (Stojadinović et al., 2011; Raina et al., 2015). Rockfall hazards beyond the base of talus slopes are traditionally addressed via simplified empirical indices such as the fahrböschung (vertical angle between the most distant rockfall blocks and the source area; Heim, 1932) or the rockfall shadow angle (vertical angle between the most distant rockfall blocks and the apex of the talus slope; Hungr and Evans, 1988), or by two- and three-dimensional numerical rockfall runout models (Pfeiffer and Bowen, 1989; Guzzetti et al., 2002; and Dorren, 2016) that deterministically solve the physical equations of motion (falling, bouncing, rolling) related to rocks moving down a slope. Although these methods provide useful guidance for conducting rockfall hazard and risk assessments, their success is dependent on the data upon which they are calibrated. Thus, detailed observations and case studies that explore the salient characteristics of rockfall kine-

matics and deposition are needed to pursue continued model improvement and subsequently to achieve better assessments of rockfall hazard and risk. To this end, we investigated a seismically triggered rockfall in the Sierra Nevada mountains of California that occurred on 24 June 2020 and caused damage to a popular outdoor recreation area (Whitney Portal; Figure 1) as a result of the deposition of debris (both outlier boulders and flyrock) reaching beyond the base of talus. Damage to the area included burial of 85 m of hiking trail at the base of the talus slope on the south side of the canyon (i.e., the Whitney Portal National Recreation Trail), destruction of campground sites from the mobilization of several large (up to 100 m3 ) boulders beyond the edge of the talus, and flyrock impacts into a parking lot and to a parked vehicle. No injuries or fatalities resulted from the rockfalls. This was a direct result of the Whitney Portal campgrounds being closed to visitors during the COVID-19 pandemic. However, at least one day-visitor hiking on the affected trail only narrowly missed being buried by the rockfall. Herein, we use the case study provided by the Whitney Portal rockfalls to investigate the kinematics of boulder trajectories and impacts to both other boulders and to existing trees on the talus slope beneath the source area cliffs. The massive (unfractured) character of the source area cliffs was of particular importance in developing our research questions regarding rockfall mobility processes. For example, whereas the protective effects of forests for mitigating rockfall runout are well known (Dorren and Berger, 2006; Dupire et al., 2016a, 2016b; and Moos et al., 2017, 2018), we were curious if existing rockfall runout models that integrate forest effects would show similar patterns of energy attenuation for large volume (hundreds of cubic meters) boulders triggered by rockfalls from a massive granitic cliff. Further, we were interested to see if existing forest patterns of adjacent talus slopes composed of massive boulders could be used as a guide for assessing future rockfall hazards from similarly destructive rockfalls. Finally, we explored the kinematics of flyrock to provide additional context for this rarely documented phenomena. EVENT SETTING Whitney Portal is located within the Inyo National Forest along the eastern escarpment of the Sierra Nevada mountains of California (Figure 1). Accessed by paved road and at an elevation of approximately 2,500 m, the area serves as the gateway and access point to popular hiking trails within the southern part of the mountain range, including the main trail to the summit of Mt. Whitney, the highest point in the con-

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Rockfall Kinematics from Massive Rock Cliﬀs

Figure 1. Map of Whitney Portal, CA, showing the 24 June 2020 rockfall source areas (SA) and the runout/deposit zone that covers approximately 2.4 hectares of the talus slope. Rockfall eﬀects include damage to a hiking trail, a campground, and a parking lot. Note that the main map image is oriented with north pointing down. The pre-event (16 July 2016) base image is from Google Earth. Tick marks reﬂect latitude and longitude in the WGS84 coordinate system. Elevations are referenced to NAVD88 in meters.

tiguous United States (4,421 m). As such, the area is heavily used and receives hundreds of thousands of visitors per year. Whitney Portal is located within the Sierra Nevada mixed-conifer forest ecologic zone (Minnich and Padgett, 2003) and is characterized by stands of red fir (Abies magnifica), white fir (Abies concolor), Jeffrey pine (Pinus jeffreyi), and pinyon pine trees (Pinus monophylla). The area experiences subzero (°C) low temperatures and abundant snowfall in the winter (December–February) and high temperatures in the 10°C–20°C range with occasional thunder-

storms in summer (June–August) (Minnich and Padgett, 2003). The topography of the eastern Sierra Nevada results from a combination of tectonics and glaciation. The region forms the western boundary of the tectonically active Basin and Range Province, which has resulted in uplift of the mountains along normal faults (e.g., Le et al., 2007), and episodes of Pleistocene glaciation have carved deep valleys that dissect the range (e.g., Moore and Moring, 2013). In the Whitney Portal region, glaciers have left behind steep rock

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Collins, Corbett, Horton, and Gallegos

Figure 2. A cross section and oblique photo of the 24 June 2020 rockfall source areas (SA) taken from the lower parking lot (see Figure 1). The cross-section line is as indicated in Figure 1. Camera orientation is to the south. Total vertical relief from the talus apex to skyline is approximately 400 m. Photo date: 7 July 2020.

walls of granodiorite (Whitney Granodiorite, 83Ma; Stone et al., 2000) that are now prone to rockfalls (Figure 2). Debris ﬂows also periodically transport material out of the various canyons with subsequent deposition to the less steep slopes of the Portal area. Although no comprehensive studies of rockfalls or other slope processes have been conducted in the area, close analogs exist elsewhere in the range, such as in Yosemite National Park located 175 km to the north (e.g., Stock et al., 2013), and catastrophic rockfalls are prevalent throughout the Sierra Nevada range (Wieczorek, 2002), including those resulting from earthquakes (e.g., Harp et al., 1984). The rockfalls at Whitney Portal were triggered by seismic shaking from the 24 June 2020, M5.8 Lone Pine earthquake epicentered 28 km to the southeast (Hauksson et al., 2020). Shaking resulted in Modiﬁed Mercalli Intensity (MMI) V intensity and esti-

mated <0.06g peak ground acceleration in the vicinity of Whitney Portal (USGS, 2020). These relatively low levels of shaking caused little damage in the vicinity of Whitney Portal aside from the rockfalls. Other rockfalls were also noted by local hikers traveling on trails to the west of Whitney Portal and were detected from satellite imagery (Hauksson et al., 2020), but overall few other site eﬀects occurred at these epicentral distances. METHODS Site Investigation We conducted a preliminary investigation of the rockfall in the days after the earthquake, including mapping the spatial extent of rockfall debris (Figure 3) and documenting its impacts to existing

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Rockfall Kinematics from Massive Rock Cliﬀs

Figure 3. An overview map of the 24 June 2020 rockfall deposit zone at Whitney Portal showing major debris tracks referenced to source location (SA; see Figure 2), locations of the talus edge and outlier boulders (B), and ﬂyrock (FR) trajectory impact locations (1, 2, 3, 4) referenced in the event description. “SB” indicates the location of an existing 450 m3 boulder around which debris split into two lobes. The map is oriented with north pointing down. The post-event (29 June 2020) base image is from Digital Globe as displayed in Google Earth.

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Collins, Corbett, Horton, and Gallegos

infrastructure. Although the campground at the base of the rockfall was closed at the time due to COVID19 pandemic precautions, the parking lots and other facilities were open for visitors and hikers. As a precaution to the possibility of additional cliff instability and aftershocks, the United States Forest Service (USFS) closed the Whitney Portal area immediately following the rockfall. The area reopened to the public 9 days later but with closure kept in place for the campground and affected trail at the base of the rockfall deposit. In early July 2020, we subsequently conducted a more comprehensive site investigation that included source zone mapping using a high-resolution spotting scope, runout zone mapping using a GPS-enabled tablet, and deposit zone mapping of individual rockfall boulders affecting infrastructure. These mapping efforts also included interviewing persons present at the time of the rockfall, documenting infrastructure impacts, and collecting remote sensing data of the runout and deposit zones (detailed in the next section). In November 2020, we returned to collect additional data required for rockfall runout modeling analyses. These field efforts included mapping positions of preevent rockfall deposits and making measurements of soil type, surface roughness, and forest characteristics. Remote Sensing Data Collection We collected high-resolution topography of the rockfall source, runout, and deposit areas using a combination of terrestrial lidar and uncrewed aerial vehicle (UAV)–based structure from motion (SfM) methods. These data provided a mechanism to make quantitative measurements of cliff and deposit zone features observed during the site investigation (e.g., Stock et al., 2018; Guerin et al., 2020). We imaged the source zone (rock cliff) topography with a Riegl Z420i laser instrument from a single location approximately 400 m from the cliff wall. This provided a mean point density of 100 points/m2 across the area of interest with adjacent features captured at lower (∼50 points/m2 ) resolution to a distance of 700 m from the laser. The three-dimensional relative positional error of the terrestrial lidar data is estimated to be 1.0 cm. We imaged the rockfall runout and deposit zones with a Ricoh GR II camera attached to a 3DR Solo UAV. Using 539 images collected at 72 m mean height above ground surface with the UAV and implementing standard Agisoft processing protocols resulted in a point cloud with 860 points/m2 mean point density over an area of 0.17 km2 . The three-dimensional relative positional error of the UAV point cloud data is estimated to be 7.2 cm. We combined the ground- and UAV-based point clouds into a single point cloud model by aligning the post-processed georeferenced UAV points with

positions provided by a differential GPS survey of the laser instrument and control target locations (three lidar control points and 10 UAV control points). This resulted in a combined model with an approximate 10-cm global coordinate error (as calculated based on a 5.25 hour static GPS survey with 1.9 cm accuracy). All subsequent measurements of rockfall source area geometry, runout zone area, and final boulder positions are based on these data. Forest Mapping To measure the spatial characteristics of the talus slope forest before and after the rockfall, we mapped the positions of all trees within the rockfall runout zone as well as adjacent talus slopes using satellite imagery (seven areas in total; Figure 4). For the prerockfall forest, we used existing ∼25-cm resolution imagery available within the Google Earth platform (images collected on 16 July 2016). We later verified these measurements with more recent 25-cm resolution Worldview-3 imagery collected on 22 May 2020. For the post-rockfall forest, we used satellite imagery of the area taken 5 days after the rockfall (29 June 2020; Worldview-3 image with 25-cm resolution). To distinguish individual trees from one another where tree crowns interwove, we conducted field work to spot check the number of individual trunks within grouped-tree zones. Using guidance from the USFS (2000), our field work also consisted of measuring the tree trunk diameter (diameter at breast height [DBH]), the tree species, and the tree health (i.e., A, B, C, D, F, with A = healthy, and F = diseased or dead) of individual trees in five zones of Whitney Portal (East 1, Rockfall, West 1, Campground, and Store; see Figure 4). We measured DBH with a diameter tape with 1-mm resolution. Given the large number of trees within the study area (∼810 remaining trees in the five zones) we selected a random subset of 10 percent of the trees for field measurement and mapped those plus the two nearest trees to each of those from the random subset. In total, we collected data on 218 trees representing approximately 27 percent of the remaining trees in five zones of Whitney Portal. Using the resultant distribution of measured DBH values for each zone, we randomly assigned DBH values to the remaining trees in each zone (including destroyed trees) to obtain a complete tree diameter dataset for subsequent modeling analyses. Talus Edge and Outlier Boulder Mapping We mapped the post-event edge of the talus slope at the base of the rockfall zone using remotely sensed imagery (i.e., grade breaks in Digital Elevation Models

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Rockfall Kinematics from Massive Rock Cliﬀs

Figure 4. A map of the Whitney Portal area showing tree mapping boundaries and mapped remaining and destroyed trees from the 2020 rockfalls. SA indicates source area locations. The talus edge was mapped only for part of the Whitney Portal area. The post-event (29 June 2020) base image is from Digital Globe referenced to NAD83, UTM Zone 11 coordinates in meters.

[DEM]) and field mapping. We also mapped the position and size of all large (greater than ∼10 m3 ) boulders from the 2020 event that were located at or beyond the talus slope edge, as well as the position of all prehistoric outlier boulders located beyond the base of the talus (i.e., within the rockfall shadow zone; Hungr and Evans, 1988; Evans and Hungr, 1993). Geomorphological interpretation was needed in some areas to distinguish between rockfall outlier boulders and debris fan deposits. In general, we distinguished rockfallrelated boulders as those that were angular and/or sufficiently large (>10 m3 ) such that transport by debris flow processes would not be expected given their relative location within the landscape. Rockfall Runout Modeling To understand the effect of forest protection on rockfall runout from the Whitney Portal rockfalls and

to simulate future rockfalls along the newly denuded slope, we conducted rockfall runout simulations using a three-dimensional probabilistic process-based rockfall trajectory model (RockyFor3D; Dorren, 2016). The model calculates sequences of rockfall motion following classical parabolic free-fall functions and subsequent collisions and rebounds on the slope surface and calculates impacts with trees as defined by input files. Rockfalls begin with zero velocity at the source cells and free fall with gravitational acceleration to initiate. The main motions are calculated using rebounds (i.e., bouncing) with rolling represented by sequences of rebounds—sliding is not allowed. The stochastic behavior of rockfall runout is captured in the model by introducing randomness in the normal and tangential coefficients of restitution (varying ± 10 percent) as well as in the directional change following an impact with the slope surface (varying 0°– 55°) and with trees (varying 0°–76°; see Dorren, 2016).

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Collins, Corbett, Horton, and Gallegos Table 1. Input parameters for RockyFor3D rockfall runout modeling. Parameter

Value

Data Source

DEM (m)

2,497–2,863

Rock density (kg/m3 ) Block height (m) Block width (m) Block length (m) Block shape Slope surface roughness (70%) (m) Slope surface roughness (20%) (m) Slope surface roughness (10%) (m) Soil type1 Tree location and diameter (cm) Tree type

2,700 1, 2, 3, 4, 5, 6; SA1/2: 7.0; SA3: 1.4 1, 2, 3, 4, 5, 6; SA1/2: 4.8; SA3: 0.9 1, 2, 3, 4, 5, 6; SA1/2: 4.2; SA3: 0.8 Rectangular prism (cuboid) 0–1.75; μ = 0.26; σ = 0.48 0–1.75; μ = 0.30; σ = 0.47 0–6.25; μ = 0.66; σ = 1.23 0, 2, 3, 4, 5, 6, 7 5–155; μ = 54; σ = 45 coniferous

Terrestrial lidar and drone-based structure-from-motion point cloud Similar to Yosemite granodiorite (Collins et al., 2020) Parameterization and outlier boulder measurements (X) Parameterization and outlier boulder measurements (Y) Parameterization and outlier boulder measurements (Z) Field observations Field measurements; Supplemental Material Field measurements; Supplemental Material Field measurements; Supplemental Material Satellite imagery and ﬁeld mapping; Supplemental Material Satellite imagery and ﬁeld mapping; Supplemental Material Field observations

Categories as defined in Dorren (2016) for the subsurface elasticity (coefficient of normal restitution): 0 = river, 2 = fine soil < 100 cm depth, 3 = scree, 4 = talus, 5 = soil covered bedrock, 6 = bedrock, 7 = asphalt. DEM = Digital Elevation Model; SA = source area; μ = mean; σ = standard deviation.

The RockyFor3D model does not provide for internal fragmentation of boulders. Although boulder fragmentation during transport is a known influence on rockfall runout (e.g., Agliardi and Crosta, 2003), implementation of this phenomena in numerical codes is still in its infancy (Zhao et al., 2017; Ruiz-Carulla and Corominas, 2020). RockyFor3D requires several input parameters (Table 1), including the topography (raster DEM); substrate characteristics (eight possible types, including soil, talus, bedrock, etc.); surface roughness over 70 percent, 20 percent, and 10 percent of sub-areas of the terrain (using the slope perpendicular mean obstacle height [MOH]); and tree distribution and characteristics (type and DBH). We used SfM and lidar data to compile a 2 m DEM over an 11.5 hectare (ha) domain (119 × 241 grid cells; Figure 4) and visually mapped substrate type from satellite images. This mapping was subsequently checked with on-site observations made during subsequent field work. For surface roughness, we visually estimated MOH via multiple transects made during our field visit. Tree data were provided by satellite image mapping and field measurements of DBH and species type (all conifers). Other required model input parameters for the RockyFor3D model are rock density (taken as 2,700 kg/m3 for typical Sierra Nevada granodiorite; e.g., Collins et al., 2020) and rock block shape released from the source area (selected as “rectangular,” i.e., a rectangular prism, in alignment with our field observations). For initial calibration simulations, we used source area rock block dimensions equivalent to the largest expected rock block size from each source area (three total [SA1, SA2, and SA3]; Table 2 and Figure 2) as determined by field measurements of the largest boulders reaching to or beyond the talus edge during the

event (Tables 1 and 3; Figure 3). These dimensions indicate “elongate” shape with long dimensions greater than the intermediate and short axes (Sneed and Folk, 1958). We also conducted parametric studies on the effect of block size by varying the source blocks using equal length cube dimensions (i.e., “compact” shape; Sneed and Folk, 1958) between 1 m and 6 m (equivalent volume of 1 m3 to 216 m3 ) based on the volume of each source area (Table 2). For source area SA3, we conducted simulations only up through a rock block size of 64 m3 to approximately match the measured total source area volume (63 m3 ). Using these inputs, we ran both singular (with one rock block launched from each source area cell) and stochastically generated simulations (using 100 simulations per source area cell) to calibrate the trajectory patterns from each of the source areas with the aim to capture the overall shape of the rockfall runout and deposition zone (Figure 3). Note that the total source area size is given as a plan-view area for the purpose of the rockfall model (Table 2); the number of corresponding raster cells approximates these areas. We then conducted simulations for the range of block size volumes (Table 1) and with various forest scenarios (with differing forest density) to identify the efficacy of the forest before and after the 2020 rockfall (Figure 4) to mitigate rockfall boulders from moving beyond the edge of the talus slope (and thus into developed areas of Whitney Portal). These comparisons were performed by running 100 simulations from each source area cell (resulting in 2,000, 5,800, and 1,900 simulations for SA1, SA2, and SA3, respectively, for the rasterized source areas) and analyzing the total number of deposited blocks (“Nr_deposited” value from the model output) that ended beyond the mapped talus edge (Figure 4). To obtain these totals and to fil-

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Rockfall Kinematics from Massive Rock Cliﬀs Table 2. Location and geometry for individual rockfall source areas. Rockfall Centroid Location1 Source Area No.

Easting (m)

Northing (m)

Elevation (m)

Height Above Talus Apex (m)

Detachment Area (m2 )

Average (range) Thickness Dimension (m)

Volume2 (m3 )

Source Raster Area (m3 ) [No. of 4 m2 Model Cells]3

SA1 SA2 SA3

389,248 389,232 389,209

4,049,426 4,049,443 4,049,487

2,795 2,760 2,693

142 107 45

81 421 63

4 (3–7) 5 (2–9) 1 (0.5–1.5)

324 2,105 63

78 [20] 227 [58] 83 [19]

1 Horizontal coordinates are referenced to the North American Datum of 1983 (NAD83), Universal Transverse Mercator (UTM) Zone 11S projection in meters. Vertical coordinates are referenced to the North American Vertical Datum of 1988 (NAVD88) in meters. 2 Volume = detachment area × average thickness (distance perpendicular to cliﬀ face). Volumetric errors primarily result from unknown source area thickness but generally vary over a factor of 2. 3 The number of model cells is calculated from the intersection of the actual plan-view polygon source area with the rockfall model raster; areas are not equivalent due to partial raster overlap with vector polygon cells.

ter out stochastically improbable trajectories, we used the “reach probability” output from the model (ranging from 0 to 100 percent), deﬁned as the number of trajectories passing through a cell divided by the total number of simulations (the total number of simulations is equal to the number of source cells multiplied by the number of simulations per cell). Using guidance from the model (Dorren, 2016), we identiﬁed only those simulation cells having greater than 1.5 percent reach probability and selected only those deposited

blocks that overlaid with those reach probability cells. We then summed the total number of the blocks that overran the edge of talus and used this metric for a comparison of forest scenarios. The stochastic ﬁltering of improbable trajectories should not be confused with the likelihood of a rockfall causing an outlier boulder that stops some distance beyond the talus. Talus boulder outliers are in some ways known quantities; they exist because large boulders have high potential energy and are larger in size than the characteristic scale of

Table 3. Location and geometry of 2020 rockfall deposit boulders and prehistoric outlier boulders. Boulder No. B1 B2 B3 B4 B5 B6 B7 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12

Volume (m3 )

Appearance1

Distance from Edge of Talus2 (m)

Observed Damage

Shadow Angle3 (deg)

1 76 22 100 248 33 43 26 63 12 132 60 16 9 16 31 43 120 125

Fresh Weathered Fresh Weathered Fresh Fresh Fresh Subangular Angular Angular Angular Subangular Angular Subangular Rounded Rounded Subangular Subrounded Angular

–9 20 1 44 0 –2 –1 7.1 37.2 7.7 5.1 10.9 4.8 4.2 5.3 3.0 38.8 26.2 20.0

None Trail, trees Trail, trees Campsites, trail, trees Trail, trees Trail, trees Trail, trees n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a

— 28 31 26 — — — 29 27 30 30 30 31 31 31 30 26 27 26

1 Appearance describes visual indications of weathered surfaces (e.g., with staining) on new (B) boulders and weathering-induced corner smoothing on old (P) boulders that may indicate relative rockfall age. 2 The edge of talus is deﬁned as the south edge of Lone Pine Creek. Distances are measured along their reconstructed rockfall trajectory to the talus edge and are not always equivalent to the shortest perpendicular distance. Negative values indicate distances south from the river (toward the cliﬀ and inboard of the talus edge). 3 Talus apex location for shadow angle determination is taken as the top of the talus from the 2020 rockfall for boulders B2, B3, B4, and P1 through P9. Talus apex for boulders P10, P11, and P12 is taken as the top of talus below a prominent rockfall chute approximately 250 m east from the top of the 2020 rockfall apex. B = rockfall deposit boulder; P = prehistoric outlier boulder; n/a = not applicable.

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Collins, Corbett, Horton, and Gallegos

the talus surface roughness (and thus do not stop on the talus). These outliers should be distinguished from those rockfall trajectories that are stochastically unlikely to occur and that result only from the existence of extreme mathematical randomness allowed by models. Examples of these latter cases are those trajectories that undergo repeated impacts with maximum normal and/or tangential coefficient of restitutions combined with the maximum or minimum resultant trajectory angle after every collision with the ground or with a tree. These combinations are unlikely to occur via natural processes and therefore do not represent expected rockfall behavior for boulders deposited either on or beyond the talus slope. RESULTS Rockfall Kinematics Our investigation of the 24 June 2020 rockfall at Whitney Portal allowed for a reconstruction of its kinematics, including source area launch trajectories, deposition of debris lobes on the talus slope beneath the cliff, mobilization of boulders beyond the base of the talus, and generation of flyrock particles that traveled well into areas of developed infrastructure. Given the multiple source areas from the cliff, as well as the possibility of remobilization of existing talus during the event, some uncertainties exist with respect to deposits sourced from particular locations. However, we use generalized geometric arguments to synthesize a plausible and likely scenario for the rockfall. Triggering and Freefall Lidar analysis, observations using a spotting scope, and examination of pre- and post-event aerial imagery show that rockfalls were triggered from three source area locations (SA1, SA2, SA3) at Whitney Portal (Figure 2). Notably, all source areas aligned near the outer “rib” of this cliff section, suggesting that possible topographic amplification of seismic waves may have influenced triggering (e.g., Ashford and Sitar, 1997; Li et al., 2019). Source areas SA1 and SA2 are located on separate vertical cliff sections above approximately 45° north-dipping ledge systems (dip direction N10°E) formed by one of several prominent but widely spaced joint sets (∼10 m, as estimated by lidar-based measurements) within the cliff. With a north-facing cliff aspect (N10°W), these ledges form dip slopes and provide opportunities for sliding block failures. We observed scrape and/or bounce marks on the ledges beneath the SA1 and SA2 source areas (Figure 2), suggesting that the ledges may have initially reoriented dislodged blocks from these locations slightly to the

Figure 5. An image of 24 June 2020 (B4) and prehistoric (P2) outlier rockfall boulders within the Mount Whitney Trailhead Campground. Boulder P2 is approximately 3 m tall. The blue dashed line indicates one of several tent sites aﬀected by the 2020 rockfalls. Photo date: 7 Nov 2020.

east before subsequently freefalling 100–140 m to the apex of the existing talus slope below (Figure 3 and Table 2). Source area SA3 is located within a lower, more westward, but structurally similar, ledge system to the upper two source areas. Mobilized debris from this source area (SA3) was conﬁned over most of its length to a rocky gully system located west of the main talus apex (Figure 3) and exhibited only minor lateral (slope perpendicular) dispersion. Geometric reconstruction of the source areas using terrestrial lidar and pre- and post-event photos indicates that the SA2 source area was the largest of the three rockfalls (Table 2) and that a total volume of ∼2,500 m3 was mobilized from all three source areas. Mobilization and Outliers After impacting the talus apex, debris from the SA1 and SA2 source areas travelled down the talus slope and split into two main lobes around an existing and prominent ∼450 m3 boulder (see “SB” in Figure 3). Whereas debris moving down the east lobe stopped 60 m above the edge of the talus (mapped as the south edge of Lone Pine Creek where the talus meets debris fan deposits emanating from canyons to the northwest), rockfall debris moving down the west lobe reached the base of the talus and sent three boulders beyond the talus edge (Figure 3). These three boulders form the outlier boulders from this event, and one (B4) caused the majority of destruction to the Mount Whitney Trailhead Campground (Figure 5). We investigated and analyzed the volumes from these boulders as well as several others located near the talus edge to provide context for the probable volumes of the

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Rockfall Kinematics from Massive Rock Cliﬀs

source area blocks triggered by the event. The results (Table 3) indicate that a range of boulder sizes (∼1–250 m3 ) reached the talus edge or beyond. Notably, the two farthest outliers (B2 and B4) from the 2020 rockfall showed indications of weathering on some sides with fresh surfaces on others, suggesting that these boulders may have formed the outer cliff face prior to the rockfall. Alternatively, they could have been remobilized from the talus slope during the event, although no obvious source of these boulders was found in preevent imagery and their overall trajectory (as evident from impact pathways down the talus slope) appears to point to at least near the top of the talus slope (Figure 6A). A comparison of the new outlier boulders (B) to prehistoric outlier boulders (P) mapped past the talus edge (Figures 5 and 7; Table 3) show considerable similarity both in volume and outlier distance (compare average volume of 54 m3 to 66 m3 volume, and 14 m to 21 m between prehistoric and 2020 event outliers, respectively), indicating that the 2020 rockfall event is similar to those that have occurred here in the past. The minimum shadow angle, βs (Hungr and Evans, 1988), calculated individually for both sets of events (2020 and prehistoric boulders) is 26° (Table 3), and there is general consistency between events (compare the mean shadow angle for all boulders from 2020 of 28° to that from the prehistoric boulders of 29°). In addition, these results show that outlier boulders tend to congregate where the topography allows longer runout. At Whitney Portal this is located in a flat, smooth area (i.e., with low surface roughness, MOH of 5 cm for 70% of the terrain) formed by a rockfall-sourced and boulder-choked section of Lone Pine Creek (Figure 7). Flyrock Trajectories The farthest-travelled deposit from the rockfall was not an outlier boulder but rather a 0.5 m3 fragment of rock that came to rest in the lower parking lot adjacent to the Mount Whitney Trailhead Campground (Figure 3, “FR1”). We identified this fragment as a piece of flyrock generated by the collision of boulders B6 and B7 with an existing 50 m3 boulder located at the base of the talus (Figure 8A). Our forensic investigation of this collision and its effects identified that the collision of these boulders likely split boulders B6 and B7 from one another and also split the 50 m3 boulder into two pieces. As a result, an overlying 300 m3 boulder translated approximately 0.75 m downhill— a testament to the energy of the impact. The flyrock, sourced as some part of these collisions (Figure 3, location 1), entered a ballistic trajectory, impacted a tree (Figure 3, location 2), and snapped it at approximately 20 m height before then impacting the lower parking

Figure 6. (A) Image looking up the talus slope crossed by boulder B4 (see also Figure 3). This single boulder may have been responsible for the destruction of tens of trees along its trajectory down the slope. (B) Many trees were snapped within a few meters of the ground, leaving splintered stumps. Photo date: (A) 7 July 2020, (B) 8 Nov 2020.

lot (Figure 3, location 3). A narrow ∼30 cm crater in the southwest corner of the lower parking lot (Figure 8B) indicates that the boulder impacted on its narrow edge, suggestive of mobility with ample angular momentum. Indications of concrete curb scraping and

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Collins, Corbett, Horton, and Gallegos

Figure 7. The relationship between topographic slope and aspect and outlier boulders along the south side of Whitney Portal (see Figure 4 for overview map). Steeply sloping east to southeast sloping terrain (bright purple and blue colors; white outline in slope/aspect key) deﬂects rockfall from traveling past the talus edge (i.e., with few outlier boulders), whereas a lower gradient east to northeast sloping terrain (muted blue and green colors; black outline in slope/aspect key) provides conditions for increased boulder mobility. Characteristics of outlier (B) and prehistoric (P) mapped boulders are provided in Table 3. Elevation contours are referenced to NAVD88 in meters.

flaking about 19 m from the initial parking lot impact indicate a second bounce occurred (Figure 3, location 4), before the flyrock came to rest an additional 17 m from the concrete curb impact (Figure 8B). The final position of the boulder was 92 m from its initial point of impact with boulders B6 and B7 and 80 m past the edge of the talus. Post-event observations by an eyewitness indicate that the rear door and side-rear window of a truck parked in the lower parking lot were also hit by flyrock from the rockfall (Figure 3, “FR2”). Given the impact marks and trajectory geometry to the truck, we inferred that two separate rocks of approximately golf-ball size (0.00004 m3 ) were likely dislodged as part of the flyrock impacting the parking lot. Forest Density at Whitney Portal Effects from a Singular Rockfall Event The 24 June 2020 rockfall destroyed approximately 190 trees as a result of debris moving down the cliff face and across the talus slope. Some of these trees

(∼20) were located on the cliff below the rockfall source zones, whereas others (∼5) were located in the campground area beyond the base of the existing talus slope (Figure 4). Within the talus zone affected by the rockfall (Rockfall zone), we identified 163 destroyed trees, which resulted in a forest density decrease from 92 trees/ha (pre-rockfall) to 43 trees/ha (post-rockfall). Thus, prior to the rockfall, the talus slope above the campground had forest density considerably more than the zone located immediately to the east (53 trees/ha; East 1 zone) but slightly less compared to the west and farther east zones (∼110 trees/ha; East 2, East 3, and West 1 zones; Table 4). We could only make tree diameter measurements of a few (10) of the destroyed trees from the broken stems that remain in the rockfall deposit zone. The DBHs of these destroyed trees range from 30 cm to 90 cm and average 60 cm. The wholesale destruction of trees exceeding half a meter in diameter (Figure 6B) is a testament to the energy of the rockfall debris and is more than likely tied to the movement of large (>10 m3 ) boulders down the slope. The height of tree stem breakage from the

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Rockfall Kinematics from Massive Rock Cliﬀs

Figure 8. Images showing the flyrock trajectory from the 24 June 2020 rockfall (see also Figure 3). Rock impacts at the base of the talus slope split and moved boulders, launching a 0.5 m3 boulder 92 m into the lower parking lot at Whitney Portal. (A) The impact of combined boulders B6 and B7 with the 50 m3 boulder resulted in translation of the 300 m3 boulder above it, splitting of B6 from B7, splitting of the 50 m3 boulder, and the ballistic trajectory of flyrock fragment FR1. The horizontal dimension of the 300 m3 boulder is approximately 10 m. (B) The flyrock bounced in the parking lot twice before coming to rest. The second bounce caused additional smaller fragments (FR2) to damage a vehicle on the east side of the parking lot. Impact locations 1, 3, and 4 are referenced to Figure 3. Photograph in (B) is from the Inyo County Sheriff’s Office; inset photograph by Brian Olson, California Geological Survey. Photo date: (A) 7 July 2020, (B) 24 June 2020 (Inset 24 June 2020).

measurements ranges from 0.5 m to 15 m and averages 4.1 m. However, this average is not likely indicative of the overall event, as most destroyed trees had little to no stem remaining in place (i.e., tree stem breakage height <0.5 m). These observations are therefore consistent with existing studies that show that stem breakage typically occurs below 1.3 m (Stokes et al., 2005). Evaluation of Long-Term Forest Structure The forest structure along the valley floor and talus slopes of Whitney Portal display distinctively different spectral distributions (Figure 9). Along the valley floor (Figure 4, Campground and Store zones), a tri-model distribution captures the flatter areas of Whitney Portal, with DBH frequency peaks at 40–70 cm, 80–110 cm, and 120–130 cm and relatively similar overall for-

est density (Table 4). On the north-facing talus slopes, the tree DBH frequencies have an additional peak and a quad-model distribution is displayed with frequency peaks at 30–40 cm, 70–90 cm, 100–110 cm, and 130– 140 cm. In addition, the overall forest densities between talus zones is more variable compared to the valley area (Table 4). The number of trees with small DBH (i.e., representing the youngest trees) are most frequent in all cases, and the overall tree size (as indicated by DBH) is about 30–40 cm larger on the valley floor compared to the talus slopes. Unfortunately, a direct comparison of tree age between mapping zones is difficult without tree-date measurements and because of the existence of more favorable growing conditions on the valley floor compared to the talus slopes. Interpretations within each of the valley and talus areas provide insight into the processes potentially

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Collins, Corbett, Horton, and Gallegos Table 4. Forest density of mapped talus zones. Talus Zone East 3 East 2 East 1 Rockfall (pre-rockfall event) Rockfall (post-rockfall event) West 1 Campground (pre-event) Campground (post-event) Store

Number of Trees

Zone Area (Hectare)

Forest Density (Trees/Hectare)

435 232 98 306 143 223 214 209 136

3.9 2.0 1.8 3.3 3.3 2.1 2.9 2.9 2.0

111 114 53 92 43 105 74 72 68

responsible for observed diﬀerences between zones. For example, within the valley area (Figure 4, Campground and Store zones), an approximately 25 cm increase in DBH of the Campground distribution results in decent alignment with the distribution for the Store zone (Figure 9, inset). This may reﬂect forest management practices in these zones, with more an-

thropogenic activity (and thus smaller, younger trees) in the Campground zone. Similarly, a comparison of the talus slope tree distributions indicates that a 10 cm increase in the DBH of the Rockfall zone trees results in good alignment with the adjacent zones to the east and west (Figure 9, inset). Given the similar growing conditions within the north-facing talus region of

Figure 9. Histogram plot of tree diameter for ﬁve zones at Whitney Portal based on post-rockfall ﬁeld measurements (N = number of measurements). Zones are delineated in Figure 4. The inset shows how alignment of data between zones is achieved with a DBH shift of +10 cm for the Rockfall zone and +25 cm for the Campground zone.

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Rockfall Kinematics from Massive Rock Cliﬀs

Figure 10. Rockfall runout model results for 100 simulations from the three source areas (SA) of the 2020 Whitney Portal rockfalls. The blue colors show the reach probability for all cells with >1.5 percent probability of a boulder traveling through or into a cell. (A) SA1 uses 20 source area cells with a block size of 141 m3 (7.0 m × 4.8 m × 4.2 m). (B) SA2 uses 58 source area cells with the identical block size as SA1. The SA2 source area was moved downslope to a dashed location to better account for the initial sliding motion likely to have occurred from this location. (C) SA3 uses 19 source area cells with a block size of 1 m3 (1.4 m × 0.9 m × 0.8 m). The domain boundary is shown in Figure 4. Results are only applicable for this scenario and do not reﬂect the actual rockfall hazard. The post-event (29 June 2020) base image is from Digital Globe.

Whitney Portal, the lower overall DBH spectral signature in the Rockfall zone is likely a result of the 2020 rockfalls removing groups of larger diameter trees. However, it may also indicate more frequent rockfall events here overall compared to neighboring zones. Rockfall Runout Modeling Simulations Our implementation of the RockyFor3D rockfall model focused on three primary goals: (1) calibration of the model to account for the general mapped trajectory pattern of the 2020 rockfalls, (2) identiﬁcation of the likely source area contributors for boulders reaching beyond the edge of talus (i.e., outlier boulders), and (3) evaluation of the role that the existing forest might have played in reducing rockfall runout. To calibrate the model, we performed both singular and stochastically-generated simulations for

boulders released from each of the three source areas (Figures 2 and 10) according to the average maximum dimension size of the talus edge boulders located beneath each source area (i.e., 141 m3 for SA1 and SA2 and 1 m3 for SA3; Table 1, Table 5). These models used the pre-rockfall event forest scenario (Table 4). Whereas rockfall trajectories from the SA1 and SA3 source areas replicated the overall rockfall runout and deposit boundary well (Figure 10), the SA2 source area resulted in rockfall trajectories traveling too far to the west and well outside the mapped deposit boundary. Inspection of the cause for this skewedness identified that the SA2 fall trajectory was launching rock blocks over the edge of the cliff to the northwest rather than sliding along the 45° dipping ledges immediately below the cliff source area where scrape and bounce marks had been mapped (Figure 2). Because sliding is not simulated within the RockyFor3D model,

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Collins, Corbett, Horton, and Gallegos Table 5. Rockfall simulation results for pre-rockfall tree scenario.

Source Area SA1 SA2 SA3

Number Block of Source Size1 Area (m3 ) Cells 141 141 1

20 58 19

Number of Simulations per Source Area Cell

Number of Blocks Deposited on Talus Slope with High Probability2 (All Simulations)

Number of Blocks Deposited Below Talus Slope With High Probability2 (All Simulations)

Number of Blocks Deposited Below Talus Slope (per Simulation)

Percent of Blocks Landing Past Edge of Talus (per Total Number of Source Area Cell Simulations)

100 100 100

1,089 3,356 1,401

700 1,804 168

7.0 18.0 1.7

35 31 9

Simulations are for rectangular prisms with dimensions 7.0 m × 4.8 m × 4.2 m for SA1 and SA2, and with dimensions 1.4 m × 0.9 m × 0.8 m for SA3. 2 High probability indicates >1.5% reach probability as deﬁned by Dorren (2016) for stochastically signiﬁcant rock block trajectories. 1

we moved the SA2 source area to the ledge located immediately below (Figure 10B) and added a 20 m initial fall height to the input model settings for SA2 to account for the approximate potential energy elevation loss resulting from this change. Although the calibration exercises resulted in a decent fit of the trajectory data to the rockfall runout and deposit boundary (compare reach probabilities and mapped deposit boundary in Figure 10), we did not modify any parameters to match the overall runout length of the trajectories (i.e., many trajectories end well past the actual boulder distribution from the 2020 rockfall). This was deliberate as we did not wish to radically or selectively change the field data used for inputs to the model. The most influential modification would likely have been to the surface roughness (MOH) data (Corona et al., 2017), since the elasticity of the substrate in the model was constrained through selection of predefined categories (Table 1) and the most appropriate value for the talus slope had already been selected. Similarly, we could have selected a smaller average block size, thereby effectively reducing the overall rockfall runout distance, but these values had also been quantified with field data for the purposes of these simulations. Thus, although we recognize that these model results may not adequately simulate the exact hazard situation that exists at Whitney Portal, they do serve to elucidate other important attributes related to rockfall source area location and runout behavior. To this end, we used the resultant rockfall trajectory output to identify the most likely source area linked to the deposition of the talus edge and outlier boulders. Examination of the event rockfall trajectory maps for each source area (Figure 10) together with the respective probability distributions of talus boulder outliers (as calculated for equal-length blocks [Figure 11] or for elongated blocks [Table 5]) indicates that the farthest outlying boulders (B2 and B4) likely initiated from the SA1 source area. Care-

ful inspection of the model output (Figure 10) indicates that the SA1 rockfall trajectories are slightly more outboard of the talus slope compared to those from SA2 (thus indicating increased probability of outlier deposition; compare 35 percent probability for an elongate source area block size of 141 m3 producing an outlier from SA1 compared to 31 percent for SA2 [Table 5 and Figure 11]). Whereas the total number of potential blocks stopping outboard of the talus slope from SA2 is more than twice that from SA1 for both compact and elongated blocks (Table 5), this is only a consequence of the greater number of source cells for SA2 (58) compared to SA1 (20) and represents the hypothetical case where the maximum block size is released from every source cell. Notably, although the SA3 source area has the highest probability for resulting in an outlier boulder (Figure 11), the model output did not yield boulders in the vicinity of the B2 and B4 outlier boulders. This is in agreement with field observations that showed that debris from the SA3 source area was only deposited on the far west side of the talus slope. Debris from the SA3 source area appeared generally smaller in volume—likely a result of the more fractured nature of the rock in the vicinity of the SA3 source area (Figure 2). Thus, large block sizes did not dominate the SA3 debris nor result in many long runout boulder trajectories. To evaluate the potential influence of the forest on the rockfall trajectories, we ran the rockfall model for three forest simulations: no forest, pre-event forest, and post-event forest (Table 4). For all scenarios, the percentage of blocks traveling past the edge of talus increases with larger block size (Figure 11). However, this relationship follows a steep linear trajectory only up to an inflection point. Above a block size of 30 to 70 m3 , the percentage of source area boulders traveling past the talus levels out. This indicates that below this threshold (i.e., inflection point), the talus slope roughness is able to stop these relatively smaller vol-

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Rockfall Kinematics from Massive Rock Cliﬀs

Figure 11. Rockfall runout model results for the three source areas (SA) of the 2020 Whitney Portal rockfalls with data points for block volumes of 1, 8, 27, 64, 125, and 216 m3 . Data show the percentage of the total source areas blocks passing the talus edge (see Figure 3) for three forest scenarios (Table 4) with 190 fewer trees in the post-rockfall forest compared to the pre-rockfall forest. The number of starting source area blocks (cells) is shown in brackets [#]; 100 simulations were used per source area cell.

ume boulders. Beyond the inflection point, the boulder size dominates the surface roughness such that all the boulders that can make it to the toe also travel past the toe. Counterintuitively, our results for each source area (Figure 11) show relatively similar distributions of rockfall trajectories passing the edge of talus regardless of the forest scenario. Model simulations with no forest did result in slightly longer runout, but only below a block size of 10 m3 can the results be considered “well behaved” in that forest scenarios with fewer trees always result in more boulders traveling beyond the edge of talus. Overall, the percentage of trajectories was not sufficiently different to conclude that the forest on the talus slope, either before or after the rockfall, has a significant effect on slowing rock blocks of such large size. In fact, in some cases, a forest scenario resulted in a higher percentage of trajectories beyond the talus slope than that with no forest. Focusing of rockfall trajectories down the steepest parts of the slope by tree collisions on either side of the runout zone may explain some of this behavior. However, the overall results are more likely related to the stochastic behavior of the model itself given the data inputs (i.e., large block volume influences and relatively low forest

density within the runout zone). Whereas additional calibration of the overall runout pattern might modify the results slightly (thereby reducing the trajectory distances of the boulders), we ﬁnd our conclusion reasonable for these types of events. Rockfalls with large block volumes falling 100+ m have tremendous potential energy and momentum, and it appears that trees did little to slow the rockfall blocks as they traveled down the talus slope. Energy calculations highlight this point. For the three source areas, each with diﬀerent volume and height above the talus (Table 2), the total potential energy (PE) can be calculated: PE = mgh

(1)

where, m = mass = ρV, with ρ = density = 2,700 kg/m3 , V = volume; g = gravitational constant (9.81 m/s2 ), and h = source area height above the talus. Summing PE for all three source areas results in a total potential energy of 7,260 MJ. Comparatively, the energy dissipated by destruction of 190 trees during the rockfall event can be estimated using a relationship with the tree stem diameter (DBH) provided by Dorren and Berger (2006), which considers only

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translational kinetic energy: Ediss,max = 38.7 × DBH2.31

(2)

where, Ediss,max is in Joules and DBH in centimeters. Here, we make the assumption of similar tree strength between Abies alba (silver fir) trees used in the Dorren and Berger (2006) experiments and the Abies magnifica (red fir) and Abies concolor (white fir) in our study, which is reasonable given their shared genus. Using the DBH values measured during field work (e.g., Figure 9) and applying a representative distribution function to the destroyed trees, we calculated a total dissipated energy of 125 MJ from tree impacts. Although this estimate is low given its dependence solely on translational kinetic energy, Dorren and Berger (2006) estimate an increase of only 0.2 percent (i.e., <1 MJ) with inclusion of the rotational kinetic energy component. Thus, trees were only responsible for dissipating less than 2 percent ( = 125 MJ/7,260 MJ) of the total potential energy of the rockfalls. KINEMATICS OF ROCKFALLS FROM MASSIVE CLIFFS Our investigation of the events associated with the 2020 Whitney Portal rockfalls identified several salient characteristics of the rockfall kinematics that are likely common for cliffs formed of massive rocks. First, we found that the rock block volume, typically scalable to the source area volume in non- or widely jointed massive rocks, plays a major role in defining the resultant trajectories down talus slopes through the development of talus slopes with high surface roughness. Slopes with high surface roughness typically result in large lateral dispersion (defined as the ratio of the maximum trajectory width at the talus toe to the slope parallel trajectory length; see for example, Crosta and Agliardi, 2004). For the Whitney Portal rockfalls, we calculated modeled dispersion ratios of 18 percent (SA3), 33 percent (SA1), and 34 percent (SA2). These latter values are at the high end of calculated ratios based on numerical simulations and are typically associated with very rough natural slopes (Agliardi and Crosta, 2003; Crosta and Agliardi, 2004). Second, we found that the role of protection forests for preventing boulders from traveling beyond the edge of existing talus slopes is likely to be minimal for large rockfall boulders emanating from cliffs composed of massive rocks. This was counter to expectations and to previous research that showed how protection forests can minimize rockfall hazard and risk through rock block energy dissipation resulting from collisions with trees (Dorren and Berger, 2006; Dupire et al., 2016a; and Moos et al., 2017, 2018). However, these previous studies were conducted for rock block volumes

that typically ranged only up to 5 m3 in volume and in forests with density up to several hundreds to thousands of trees per hectare. Our work, conducted on a rockfall with boulders up to several hundred cubic meters in volume and located in an upper montane forest zone with overall low forest density (∼50–100 trees/ha), showed little response to the existing forest, or the presence of any forest at all. In this respect, our results are consistent with the generalized trends of the previous findings (e.g., Moos et al., 2017, 2018) that have shown that the role of forests in preventing rockfall runout are substantially reduced for low forest density and high rock volume—conditions that are both met at our study site. Although this may be a result of the site-specific conditions at Whitney Portal, we suspect that these conditions are representative of other regions, including many in the Sierra Nevada of California and elsewhere worldwide, where similar conditions exist. These are thus likely to occur in massive, relatively unjointed and unfractured rocks (or those with wide joint or fracture spacing) such as batholitic granitic rocks and in settings that keep forest density at a minimum (e.g., high elevations with short growing seasons or in areas of frequent rockfall). A third conclusion reached by our study on the kinematics of massive rockfalls concerns outlier boulder transport. Whereas large-volume boulders are expected from massive rockfalls, the transport mechanism to the talus edge and beyond can be difficult to characterize. Our study found that the majority of large-volume boulders stopped before or at the edge of talus, as expected, but that a few boulders were able to mobilize considerable distances beyond the talus edge. We found strong correlations between these outliers and the topographic signature of the runout area; namely, that outlier boulders tended to mobilize via pathways where topographic slope and aspect, in addition to low surface roughness, provided opportunities for transport (Figure 7). During the 2020 event, these were manifest by the flatter topography located just above an area where prehistoric and recent large boulders partially dammed the creek running along the edge of the talus. Opportunistic topographic effects may have played a similar role in the deposition of the prehistoric outliers along the far eastern section of the talus slope (Figure 7) where the boulders intermingle with a less steep section of Lone Pine Creek. Finally, the effect of large boulder collisions occurring at the base of talus slopes is an additional important kinematic element of rockfalls triggered from massive rock cliffs. As large boulders (>100 m3 ) are more likely to be released from massive rock masses and then subsequently transported by momentum to the very base of talus slopes, their resultant deposition can result in a number of potential effects. As

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witnessed at Whitney Portal, boulders may become creek-choking elements that drive other outliers farther outboard from the talus, they may end up as outliers themselves given their considerable momentum, or they may be stopped by and undergo energetic collisions with existing boulders at the talus edge. The flyrock documented during the 2020 rockfalls is an example of the hazard associated with this latter type of talus edge collision. Flyrock occurring from collisions farther upslope on the talus may not result in deposits past the talus edge, whereas those at the talus edge can produce an entirely separate and farther-reaching set of hazards compared to outlier boulders. CONCLUSIONS AND ROCKFALL HAZARD CONSIDERATIONS The 2020 rockfalls experienced at Whitney Portal allowed us to investigate an example of a rockfall triggered from a massive rock cliff. The data gathered, analyzed, and modeled from this effort provided insight into the kinematics of these types of events, specifically in regard to the motion and interaction of large boulders with a forested slope and varied topography. We identified several important observations from this work that should aid in the consideration and assessment of rockfall hazards in these environments. Most importantly, we found that a complex set of kinematic interactions governs the rockfall depositional footprint. Interactions with other talus slope boulders, including possible talus remobilization, collisions and redirection by trees, and the propensity for flyrock generation can all be contributing factors that influence the assessment of rockfall hazards. Talus slopes are well known to be the main depositional environment for the majority of rockfall debris, but outlier boulders and flyrock potential should also be considered when planning for expected hazards. For outlier boulders, this could include defining development setback lines based on generalized approaches such as the use of the rockfall shadow angle (e.g., Evans and Hungr, 1993) or the use of statistical analysis of mapped outlier boulder locations combined with data provided from rockfall runout modeling (e.g., Stock et al., 2014). The recent and prehistoric data collected at Whitney Portal (with both similar outlier boulder volume, runout distances, and corresponding minimum rockfall shadow angles indicating consistency with the geomorphic past) highlight the significance of these types of analyses for providing relevant information for hazard assessment. Thus, this approach may provide the basis for preliminary rockfall hazard delineation for the many cliffs that surround visitor facilities at Whitney Portal. Although the minimum rockfall shadow angles

calculated at Whitney Portal (βs = 26°) are somewhat lower than identified by others (28°–35°, Hungr and Evans, 1988; see also Turner and Duffy, 2012), they do potentially highlight parallels with similar massive granitic rockfall terrain found elsewhere in the Sierra Nevada range (i.e., Yosemite Valley: 22°, Wieczorek et al., 1998; 16°, Stock et al., 2014). Mean rockfall shadow angles between these two regions (Whitney Portal: 29° and Yosemite: 25°, Stock et al., 2014) are more closely aligned. Regarding the role of forest protection on outlier boulder trajectories, our analyses showed that forests in these montane settings do little to block large boulders hundreds of cubic meters in volume from reaching the edge of the talus and beyond. Although some slowing or redirection of trajectories might occur, the overall momentum of such large boulders appears to exceed the tree-impact-related energy dissipation that could significantly change the expected trajectory of the boulders. We note, however, that this conclusion is specific to large (>100 m3 ) massive boulders and that the protection offered by forests for risks associated with smaller volume rockfalls can be considerable (Moos et al., 2018). Our results did indicate the utility of rockfall runout analyses as well as basic topographic metrics in identifying where future outlier boulders might be deposited. At Whitney Portal, these were strongly linked to places where existing topography provides opportunity. Our study did not try to constrain whether the mapped outlier boulders are outliers in the temporal sense. Although we expect that outliers are likely to be expected in any large volume event, additional work to constrain the recurrence interval of outlier boulders (e.g., using cosmogenic nuclide exposure dating methods; Stock et al., 2014; Gallach et al., 2018) could provide further insight. Comparatively, there is much less data available on either the temporal or spatial statistical probability of flyrock trajectories (Wieczorek and Snyder, 1999; Wieczorek et al., 2008; and Giacomini et al., 2009), although we found in this study that flyrock extending well past the talus zone was possible when collisions occurred between boulders near or at the talus edge. Notably, trees may have mitigated flyrock trajectories to some degree, and protection forests at the edge of the talus slope could offer at least some mitigation to flyrock. Additional work on identifying the spatial extent of flyrock from rockfall collisions may benefit from concepts and methods developed for use in open-pit mining (Raina et al., 2011; Stojadinović et al., 2011). Finally, our study identified the value of using forest density measurements to assist in the long-term evaluation of geomorphological rockfall conditions as well as related rockfall hazard assessments. By

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comparing forest density metrics, we noted the expected prevalence of rockfalls in particular talus slope zones. At Whitney Portal, the 2020 rockfall reduced the forest density in the impacted area by approximately 50 percent, which resulted in a forest density comparable to an adjacent talus zone that also showed indications of a recent large-volume rockfall. Although additional studies focused on understanding tree regrowth on talus slopes composed of such large boulders is needed (including obtaining tree ages to assist with rockfall recurrence interval calculations; e.g., Stoffel, 2006), overall patterns in forest density are still likely relevant for making general comparisons. We expect that the data provided and conclusions reached in this study will aid in further assessment of rockfall hazards at Whitney Portal and other similar massive rock cliffs subject to outlier boulder and flyrock effects.

ACKNOWLEDGMENTS We appreciate the assistance of USFS Inyo National Forest staff, including Douglas Winn, David Anderson, Mark Ingram, and Blake Englehardt, who provided logistical and informational support for our field work. We also thank Doug Thompson at the Whitney Portal Store for his description of the earthquake effects at Whitney Portal and for assistance with locating existing benchmarks for survey control. Carol Sullivan, an eyewitness to the rockfalls, provided helpful details on the extent of flyrock from the event. Ryan Gold and Alex Grant (U.S. Geological Survey [USGS]) initiated efforts to rapidly collect post-event satellite imagery of the site that assisted with our field investigation. Josip Adams (USGS) provided assistance with UAV mission planning and data postprocessing. We appreciate conversations about this work and review of an earlier version of this manuscript by Greg Stock (Yosemite National Park), as well as feedback by R. Wooten and two additional anonymous reviewers. This work was funded by the USGS Landslide Hazards Program, with additional support for E. Horton from the USGS/NAGT Cooperative Field Training Program. Any trade names mentioned in the paper are for descriptive purposes only and do not constitute endorsement by the U.S. government.

SUPPLEMENTAL MATERIAL Supplemental data from this study, including input rockfall runout ﬁles, are accessible via the USGS ScienceBase data repository at https://doi.org/10.5066/ P93TJUXH.

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M.; and Fourcaud, T., 2005, Mechanical resistance of different tree species to rockfall in the French Alps: Plant Soil, Vol. 278, pp. 107–117. https://doi.org/10.1007/s11104-0053899-3. Stone, P.; Dunne, G. C.; Moore, J. G.; and Smith, G. I., 2000, Geologic Map of the Lone Pine 15’ Quadrangle, Inyo County, California: U.S. Geological Survey Geologic Investigation Series Map I-2617, 1 plate: Electronic document, available at https://pubs.usgs.gov/imap/2617/ Turner, A. K. and Duffy, J. D., 2012, Chapter 9: Modeling and prediction of rockfall. In Turner, A. K. and Schuster, R. L. (Editors), Rockfall: Characterization and Control: Transportation Research Board of the National Academies, Washington, D.C., pp. 343–348. U.S. Geological Survey (USGS), 2020, USGS Earthquake Event Page for the M5.8 18 km SSE of Lone Pine, CA earthquake on 24 June 2020 14:40:49 UTC: Available at https://earthquake.usgs.gov/earthquakes/eventpage/ ci39493944/executive U.S. Forest Service (USFS), 2000, Chapter 10: Principles of measuring trees. In Forest Service Handbook – FSH 2409.12 Timber Cruising Handbook: U.S. Forest Service, Washington, D.C., pp. 18–48. https://www.fs.fed.us/fmsc/ftp/ measure/cruising/other/docs/FSH2409.12-2000.pdf Wieczorek, G. F., 2002, Catastrophic rockfalls and rockslides in the Sierra Nevada, USA. In: Evans S. G. and DeGraﬀ J. V. (Editors), Catastrophic Landslides: Eﬀects, Occurrence, and Mechanisms: Geological Society of America Reviews in Engineering Geology XV, Boulder, Colorado, pp. 165–190. https://doi.org/10.1130/REG15-p165. Wieczorek, G. F.; Morrissey, M. M.; Iovine, G.; and Godt, J. W., 1998, Rock-Fall Hazards in the Yosemite Valley: U.S. Geological Survey Open-File Report 98-467, 13 p. https://doi.org/10.3133/ofr98467 Wieczorek, G. F. and Snyder, J. B., 1999, Rock Falls from Glacier Point above Camp Curry, Yosemite National Park, California: U.S. Geological Survey Open-File Report 99–385, 23 p.: Electronic document, available at https://pubs.usgs.gov/of/1999/0385/report.pdf Wieczorek, G. F.; Stock, G. M.; Reichenbach, P.; Snyder, J. B.; Borchers, J. W.; and Godt, J. W., 2008, Investigation and hazard assessment of the 2003 and 2007 Staircase Falls rock falls, Yosemite National Park, California, USA: Natural Hazards Earth System Science, Vol. 8, pp. 421–432. https://doi.org/10.5194/nhess-8-421-2008. Zhao, T.; Crosta, G. B.; Utili, S.; and De Blasio, F. V., 2017, Investigation of rock fragmentation during rockfalls and rock avalanches via 3-D discrete element analyses: Journal Geophysical Research Earth Surface, Vol. 122, pp. 678–695. https://doi.org/10.1002/2016JF004060.

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Responses to Landslides and Landslide Mapping on the Blue Ridge Escarpment, Polk County, North Carolina, USA RICHARD M. WOOTEN* North Carolina Geological Survey (retired), 24 Two Oaks Drive, Fletcher, NC 28732

COREY M. SCHEIP North Carolina State University, Department of Marine, Earth, and Atmospheric Sciences, 2800 Faucette Drive, 1125 Jordan Hall, Campus Box 8208, North Carolina State University, Raleigh, NC 27695

JESSE S. HILL North Carolina Geological Survey, 2090 U.S. Highway 70, Swannanoa, NC 28778

THOMAS J. DOUGLAS North Carolina Geological Survey (retired), 21 Driftstone Circle, Arden, NC 28704

DAVID M. KORTE BART L. CATTANACH G. NICHOLAS BOZDOG SIERRA J. ISARD North Carolina Geological Survey, 2090 U.S. Highway 70, Swannanoa, NC 28778

Key Terms: Landslides, Blue Ridge Escarpment, Polk County, North Carolina ABSTRACT Landslides occur in Polk County, North Carolina, primarily along the Columbus Promontory of Blue Ridge Escarpment (BRE), which has 400 m of topographic relief and slopes typically >20°. Bedrock is characterized as late Proterozoic to early Paleozoic metamorphic rocks within Paleozoic thrust sheets. On May 18, 2018, ∼200 mm of rainfall over a 3- to 4-hour period triggered numerous debris ﬂows and slides along the BRE, causing one fatality and severe damage to homes and roads. The State Emergency Operations Center tasked the North Carolina Geological Survey to assess slope stability ahead of search and rescue operations and assess damage along the North Pacolet River valley. The loss of life and destruction from the 2018 storm and ongoing threats to infrastructure prompted us to map landslides throughout Polk County in 2019–2021 to fully document the 2018 landslides and place them in the context of past and ongoing landsliding. We mapped 920

*Corresponding author email: Richard.M.Wooten@gmail.com

varied types of landslides and attribute 241 to the 2018 storm, making it one of the largest events in North Carolina since 2004 with respect to landslide numbers and spatial frequency. The highest concentrations of landslide features in Polk County are along the slopes of the BRE, especially the Pacolet River and Green River valleys. These rivers exploit post-orogenic brittle fractures to form linear reentrants where the May 2018 and other landslides are concentrated. This article describes our landslide response and mapping efforts and relates our findings to the geomorphic and geologic framework and to past landslide events in the region. INTRODUCTION Landslides are the most common geohazard in the southern Appalachian Mountains of western North Carolina. Debris flows, dominant among landslide processes here, are triggered by rainfall on steep, soilmantled slopes (Wooten et al., 2016), and landslides have caused at least 83 fatalities since 1879 in North Carolina. Two primary components of the North Carolina Geological Survey (NCGS) geohazards program are responding to landslide events and performing county-wide landslide hazard mapping (Wooten et al., 2017). Landslide mapping by the NCGS began

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in 2005 when the North Carolina General Assembly authorized funding because of the damage and fatalities from landslides and flooding from tropical cyclones Frances and Ivan in September 2004 (Wooten et al., 2008a). From 2005 to 2011, the NCGS completed landslide hazard maps for Macon, Watauga, Buncombe, and Henderson counties in western North Carolina (Wooten et al., 2016) prior to the elimination of funding in 2011. This original mapping and subsequent improvements to the program (Bauer et al., 2012) laid the groundwork for the renewed effort in 2019. The North Carolina General Assembly authorized the NCGS to resume landslide hazard mapping in June 2018 after a major debris flow event that occurred on the Columbus Promontory of the Blue Ridge Escarpment (BRE) in Polk County in southwestern North Carolina (Figure 1). This event included over 240 landslides and caused one fatality, further affirming the need for landslide mapping efforts in the region. This article begins by describing our emergency responses to landslides in Polk County in 2018. We focus on the May 18, 2018, landslide event because the loss of life, destruction, and ongoing threats to infrastructure were the motivations for the landslide mapping throughout Polk County from 2019 to 2021. We go on to describe the landslide inventory methods, the findings from the mapping, and relationships between landslides, geomorphology, and bedrock structure. Countywide mapping allowed us to fully document the distribution and types of the 2018 landslides and place them in the context of past landslide activity, particularly with respect to bedrock structural and geomorphic controls in other similar areas where landslides are concentrated. This progression from landslide response to landslide mapping addresses our reactive and proactive efforts toward landslide loss reduction in North Carolina. HISTORICAL LANDSLIDES IN POLK COUNTY AND MOTIVATION FOR MAPPING Historical Landslides in Polk County Polk County has a long history of landslide activity prior to the May 18, 2018, event, especially in and around Howard Gap, where major transportation and utility corridors cross the crest of the BRE (Figure 1). The first major landslides reported in Howard Gap were those triggered by the July 15–16, 1916, tropical cyclone that devastated much of western North Carolina (Southern Railway Company, 1917; Witt, 2005; and Wooten et al., 2016). Scott (1972) recounts a July 18, 1916, article in the Atlanta Journal Constitution newspaper that referred to the Saluda-to-Tryon road across Howard Gap, where “landslides from the

mountains buried the road for long distances.” The numerous slope stability problems encountered during the construction of I-26 through Howard Gap and elsewhere along the Saluda grade, beginning in 1968 and continuing into the 1970s, delayed the opening of I-26 through the region (Glass, 1977; Sams and Gardner, 1974). The July 15–16, 1916, storm also triggered numerous landslides along the nearby Saluda railroad grade, disrupting a vital transportation link to the region devastated by the storm (Southern Railway Company, 1917). In 2011, the NCGS investigated an embankment failure/debris flow on the inactive Saluda railroad grade attributed to rainfall from tropical cyclone Frances or Ivan in September 2004 (Figure 2; Wooten et al., 2011). We identified 17 additional landslides of unknown vintages affecting the Saluda railroad corridor in Polk County in our recent inventory. The North Carolina Department of Transportation (NCDOT) provided the NCGS in 2008 with the approximate locations of 54 road-related landslides primarily in western Polk County. Although these data do not include the failure dates, they indicate the widespread impacts of landslides on the public transportation network through time in addition to those associated with Howard Gap. In 2009, a series of 11 road-related slope failures occurred in a now defunct mountainside development, highlighting problems associated with construction on steep slopes. Although numerous landslides occurred throughout western North Carolina during 2013 (Wooten et al., 2016), none were documented in Polk County. The May 18, 2018, storm triggered over 240 landslides in Polk County, including ones in the I-26, Howard Gap Road, and power transmission line corridors in the Saluda grade area (Wooten et al., 2019a, 2019b). In response to the 2018 damage to Howard Gap Road, the NCDOT commissioned studies along the Saluda grade I-26 corridor, a critical but vulnerable segment of the regional transportation network. Landslide data collected for that study by Appalachian Landslide Consultants, PLLC (ALC) (Bauer and Fuemmeler, 2019) have been incorporated into the NCGS landslide geodatabase used in this study. The May 18, 2018, event was the first recorded major landslide event affecting Polk County since those that occurred during the July 15–16, 1916, storm. Motivation for Mapping: 2018 Landslide Event Beginning at approximately 6:00 p.m. on the evening of May 18, 2018, a sequence of severe thunderstorms produced as much as 200 mm of rainfall over a 3to 4-hour period in Polk County (National Centers for Environmental Information, 2018; Bauer et al.,

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Figure 1. (A) Topographic relief map of the Blue Ridge Escarpment (BRE) region of western Polk County and southern Rutherford County, North Carolina. In this vicinity, the BRE is separated from the Eastern Continental Divide by up to 20 km (see inset). The Green River and North Pacolet River incise across the BRE and form reentrants to the mountain front. These high-relief zones (up to 400 m local relief) disproportionately contribute to landslide hazards in the region. (B) The Green River drains over 150 km2 of low-relief, upland topography, west of the BRE, and has larger, more developed headwaters than the North Pacolet River.

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Figure 2. (A) Cumulative rainfall departure and monthly precipitation plot of 2003–2020 rainfall data from Asheville, North Carolina (NOAA, 2021). The cumulative rainfall departure analysis and monthly precipitation indicate periods of meager and abundant rainfall, with abundant rainfall generally coincident with the occurrence of large (>100 landslides) landslide events in the region (B). The three largest landslide events in North Carolina since 1940 occurred in 2004 (>400), 2013 (>300), and 2018 (>240).

2019). At 3:29 a.m. on May 19, 2018, the State Emergency Operations Center contacted the state geologist for assistance on a landslide event with a possible fatality in a severely damaged home. The NCGS was charged with assessing slope stability and safety before an urban search and rescue (USAR) team entered the damage zone to search for the potential victim. The state geologist dispatched the senior geologists from the Asheville Regional Oﬃce to respond as outlined in the NCGS emergency landslide response chain-ofcommunication protocols (Wooten et al., 2017). Shortly after daybreak on May 19, NCGS geologists arrived at the incident response staging area to be briefed by the state and county emergency managers and integrated into the local Incident Command System for task assignments (Federal Emergency Management Administration, 2017). The staging area was located approximately 100 m east of where landslide deposits blocked U.S. Highway 176 and approximately 150 m east of the victim’s damaged home and facilitated foot access and uncrewed aerial systems (UAS)

operations (Figure 3). Rain had subsided, allowing the Broad River Volunteer Fire Department (BRVFD) to begin UAS reconnaissance flights. Emergency management teams and the NCGS conducted the following stages of activities with the corresponding assessments, actions, and decisions as summarized in Table 1: (1) UAS reconnaissance flights (optical cameras) and initial field observations, (2) assessment of foot access to the damaged home and site stability, (3) UAS (thermal imaging) locating the victim and the USAR team recovering the fatality (Figure 3C), and (4) expanded search and rescue and damage assessment along the U.S. 176 corridor. Experience-based landslide response procedures developed by the NCGS (Wooten et al., 2017) and UAS support by the BRFVD facilitated a rapid hazard assessment, coordinated search procedures, and an expedited recovery of the fatality without incident. To increase situational awareness and help plan the USAR team’s entry procedures, we utilized the real-time UAS imagery to assess current slope con-

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Figure 3. (A) Debris flows triggered by the May 18, 2018, storm originated on the steep slopes near Little Warrior Mountain on the north side of the Pacolet River valley. Coalescing debris flows channelized and deposited material to the valley floor. 2019 orthophotography base map. Contour interval = 40 m. (B) Location of search and rescue operations and fatality. Black outlines show the location of buildings prior to the debris flows. June 8, 2018, uncrewed aerial systems (UAS) image courtesy of the North Carolina Geodetic Survey. 2019 orthophotography base map. Contour interval = 10 m. (C) Thermal UAS image of rescue personnel (bright areas) during search operations. May 19, 2018, UAS image courtesy of the Broad River Fire Department. (D) UAS image from May 19, 2018, showing debris flow deposits along U.S. 176. View looking west. May 19, 2018, UAS image courtesy of the Broad River Fire Department.

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Wooten, Scheip, Hill, Douglas, Korte, Cattanach, Bozdog, and Isard Table 1. Sequence of activities, assessments, and decisions/actions in the emergency landslide response and initial damage assessment for the May 18, 2018, debris ﬂow event. Stage

Activities

(1) UAS reconnaissance ﬂights. (2) Initial ﬁeld observations

(1) NCGS and EM responder evaluate safe foot access to the home and the stability of the lower debris ﬂow tracks. (2) Real-time UAS monitoring during NCGS-EM site access.

(1) UAS ﬂight deployed with thermal imaging sensor to locate victim in the wreckage. (2) USAR team deployed. (1) Search and rescue crews and NCGS begin expanded damage assessment along the U.S. 176 corridor damage zone.

Assessments

Decisions/Actions

(1) Nature of deposits consistent with debris flow runout. (2) Two debris flow tracks along streams converged at the house site. (3) Debris flows displaced and partially buried the house. (4) Lidar DEMs show 300 m of relief with interspersed cliff lines along debris flow tracks. (5) Rain subsided; cloud cover obscured UAS views of the upper debris flow tracks. (6) Debris dams or unstable debris in the tracks that could lead to more debris flows not identified in UAS images but may exist. (1) Confirmed that debris flow tracks are in channelized drainages and runout deposits coalesce at the house. (2) The depth of sediment and large woody debris in the eastern track allow for foot access to the house. (3) The drainage divide between the two tracks is not affected by the debris flows and allows safe access to the western track. (4) Spotters for the USAR team can be positioned at a safe locations along debris flow tracks with views upslope. (1) Possible location of victim determined from thermal images. (2) USAR team positions tracked with UAS thermal image and guided to victim’s location. (3) Victim located and recovered without incident. (1) Debris flow deposits, track areas, and debris dams identified and mapped to enter into the NCGS landslide geodatabase.

(1) A hazard assessment by foot along the debris ﬂow tracks not feasible within an acceptable time frame for the USAR team to proceed. (2) NCGS to make an on-the-ground assessment of the stability of the area and the USAR team’s access to house.

(1) Incident command notified to prepare for USAR team entry into the damage zone. (2) Dispatch and brief spotters on signs of renewed debris flow activity (e.g., rapid changes in water flows or turbidity, rumbling sounds, or sounds of breaking and falling trees upslope). (3) Incident command and USAR team briefed on emergency exit route onto drainage divide between debris flow tracks. (1) Mobilize search and rescue crews for deployment along the U.S. 176 corridor damage zone.

(1) Extensive debris ﬂows and damage along a 1-km-long corridor. (2) Additional resources needed to complete damage assessment and mapping.

UAS = uncrewed aerial systems; DEM = digital elevation model; USAR = urban search and rescue; NCGS = North Carolina Geological Survey; EM = emergency management.

ditions, GPS-enabled field computers equipped with lidar-derived digital elevation models, and 2015 orthophotography for terrain analysis. The thermal imagery acquired by the UAS played a key role in the timely location of the victim (Figure 3) and limited the USAR team’s exposure time in the hazard zone. Stage 1 activities began at 7:00 a.m., and the victim was located at about 8:40 a.m. during stage 3. Immediately following the USAR team’s recovery of the victim, the NCGS began the damage and hazard assessment concurrent with search and rescue crews’ survey of the 1-km-long corridor along U.S. 176 damaged by flooding and blocked by debris flows. Over the following days, the NCGS and volunteers from ALC mapped areas impacted by debris flows and locations of potentially hazardous debris dams in debris flow tracks. Data were provided in a geographic information system (GIS) format to the incident response team. A North Carolina Forest Service heli-

copter flight on May 24, 2018, provided an overview of the scope and magnitude of the damage in the North Pacolet River valley (PRV). Preparation for Subtropical Storm Alberto During the response activities following the May 18 storms, subtropical storm Alberto formed in the southern Gulf of Mexico and was forecast to deliver heavy rainfall to southwestern North Carolina, including Polk County, over the period of May 28– 31. On May 24, NCGS and ALC geologists and meteorologists from the National Weather Service, Greenville-Spartanburg Weather Forecast Office, briefed the incident response team on the potential hazards from further flooding and landslides and uncertainties in the forecast for Alberto. A major effort by the NCDOT was still under way to clear the roads and reestablish road drainage systems after the

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May 18 storms. Debris dams remained in some debris flow tracks, diverting water and sediment onto roads and blocking culverts and ditches. The additional rain and subsequent runoff from Alberto could not only impede local traffic but also hamper emergency vehicle access. Based on these briefings and poor road conditions, the Polk County emergency manager subsequently issued a voluntary evacuation order for the PRV effective during the passage of Alberto. Aftermath of the Storms Our post-storm assessments revealed that rainfall from Alberto, now downgraded to subtropical depression, resulted in localized flooding and several roadrelated slope failures in the PRV. One cut slope failure/debris slide blocked the access road for a regional 440-kV electrical transmission line near Howard Gap. Heavier rainfall from Alberto triggered 19 damaging debris flows along the BRE in nearby McDowell and Rutherford counties (Wooten et al., 2019c, 2019d) that the NCGS investigated in response to requests from the respective county emergency managers. The early phases of landslide mapping and the damage assessment in the PRV continued through midAugust 2018 aided by additional UAS imagery acquired by the BRVFD and the North Carolina Geodetic Survey and from a North Carolina Forest Service–piloted fixed-wing aircraft. Results from these initial studies are summarized in Bauer et al. (2019), Bauer and Wooten (2019), and Wooten et al. (2019a). These early phases of mapping and damage assessment revealed that debris flows triggered by the May 18, 2018, storms in the PRV destroyed two homes and seriously damaged four others and resulted in one fatality (Figure 3). U.S. 176 and many private roads were heavily damaged and covered by the debris, requiring major repair efforts by the NCDOT and private property owners. Although the recovery of the victim was completed safely and precautions were put in place before the remnants of Alberto passed over the area, these events bring into sharp focus the need to increase societal efforts to avoid loss of life and property in similar tragedies. From our conversations with residents in the aftermath of the event, it was clear that many were not aware that they lived in locations vulnerable to debris flows and other types of landslides. Landslide hazard mapping had not been done there, so they did not know that the streams flowing off Little Warrior Mountain could transform into torrents of mud, rocks, and trees that could destroy homes. Our investigations revealed pre-existing debris flow deposits of unknown ages exposed in the tracks and runout zones of the May 18, 2018, debris flows. These deposits are evidence

that debris ﬂows had occurred previously but almost certainly not within the lifetimes of local residents. The last reports of major landslides in this area were those from the July 15–16, 1916, storm, over 100 years prior and out of recent memory. Given the loss of life, severe damage from the May 2018 storm, and ongoing landslide threats to transportation corridors and regional utility infrastructure, the NCGS and ALC began mapping in Polk County in 2019–2020 after the North Carolina General Assembly reauthorized the landslide mapping program. GEOLOGIC SETTING Bedrock Geology The southern Appalachians have a complex geologic history spanning over a billion years through multiple continental cycles of collision and rifting (e.g., Slingerland and Furlong, 1989; Thomas, 2006). Polk County lies east of the Brevard fault zone (Figure 4), a regional transpressional ductile shear zone, and within the Inner Piedmont Tugaloo Terrane, which is composed mainly of metamorphic rocks with peri-Laurentian protoliths accreted to Laurentia during a series of Paleozoic collisions during the assembly of Pangea (e.g., Hibbard et al., 2007; Merschat et al., 2012). There are multiple stacked Paleozoic ductile thrust sheets in our study area containing medium- to high-grade biotite and amphibole schists and gneisses, amphibolites, metagraywackes, and migmatites. A pervasive metamorphic foliation dips gently to moderately (mean dip = 21°) toward the ENE, with isoclinal recumbent folds and ENE-trending mineral lineations (Figure 5). Many of the rocks were mapped as part of the Mill Spring Complex (Davis and Yanagihara, 1993), which likely correlates with parts of the Tallulah Falls formation as it was mapped in the Landrum and Pea Ridge 7.5-minute quadrangles (Cattanach et al., 2013, 2016) to the south and east, respectively. Although we incorporate outcrop structural data from those studies into our analyses, establishing those bedrock relationships is beyond the scope of this study. Post-Orogenic Structures, Seismicity, and Landslides Families of post-orogenic brittle faults and fractures overprint older ductile structures throughout much of the southern Appalachians. Some of these are joint sets that correspond with E–W and ESE–WNW topographic lineament valleys that cross the regional orogenic SW–NE topography. These steep walled reentrants (i.e., transverse valleys extending into an escarpment, from Neuendorf et al., 2005) contain abundant slope failures, especially where they intersect the BRE.

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Figure 4. (A) Map of the Columbus Promontory and the Blue Ridge Escarpment (BRE) near Polk County, North Carolina. Note how the continental divide (yellow line) is west of the BRE crest. (B) Detailed map of western Polk County showing different geomorphic domains. Outlines around the Green River Gorge and the Pacolet River are the locations used in Figure 13. (C) Stacked profile of elevation and local relief across the Columbus Promontory. Local relief at the BRE is similar to the Blue Ridge mountains found west of the Brevard Fault Zone. Profiles were smoothed by applying a first-order Savitzky-Golay filter with a 667-m window length. Relief data were derived from 500-m2 kernels in 3-m increments.

Gillon et al. (2009) mapped a zone of rock slope instability in Watauga County, ∼130 km northeast of Polk County, where Paleozoic ductile faults are overprinted by high-angle ESE–WNW fractures and brittle faults. This zone aligns with the Deep Gap reentrant (Figure 1A, inset) into the BRE, a location where nearly 600 of the >2,000 landslides and debris ﬂows triggered by an August 1940 tropical cyclone are concentrated (Witt et al., 2007; Wooten et al. 2008b). Hill (2018) later mapped this zone as the Cenozoic Boone fault, a seismically active, high-angle ESE-striking fault zone that traces directly in line with the Deep Gap reentrant. The ESE-trending lineament that contains the Boone fault and Deep Gap also houses the estimated epicen-

ter of the magnitude 5.0 earthquake in Wilkes County (Stover and Coffman, 1993). In Macon County, ∼100 km west of Polk County, the Nantahala Mountains form a high NNW–SSE escarpment (Figure 1A, inset) that is cut by the E-trending Wayah reentrant, areas of concentrated debris flow activity in 2004 during tropical cyclones Frances and Ivan (Wooten et al., 2008a). The Nantahala Mountains rise over 1,100 m above the valley floor and promoted orographic enhancement of rainfall from Frances and Ivan, similar to what occurred along the BRE coincident with other major debris flow events in North Carolina and Virginia (Wooten et al., 2016, and references therein).

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Figure 5. (A) Map of western Polk County showing landslide initiation locations (red points) and previously mapped faults (blue lines). Faults modified from Garihan et al. (1993) and Davis and Yanigahara (1993). (B) Rose diagrams and lower-hemisphere stereonets (contoured poles to planes) of joints measured within Polk County, within Polk County landslides, and within slides that occurred during the May 2018 storms. These steeply dipping joint sets strike parallel to the W–E-trending Pacolet River valley and the SW–NE-trending Green River Gorge. The foliation dips gently and consistently toward east-northeast. (C) Inlay map of the Green River Gorge and its tributary Cove Creek, where numerous May 2018 slides and flows occurred within a rectilinear drainage network that is parallel to NW–SE-striking, and NE–SW-striking joint sets. (D) Frequency plot of minimum angle between joint strike and topographic aspect at landslides, with a mean value = 91°, consistent with abundant high-angle joints that acted as back-release failure surfaces at landslide initiation zones and within debris slides and flows.

Across the Columbus Promontory of South Carolina and through southwestern Polk County, NNE-, NE-, and ENE-striking brittle faults that form minor zones of cataclasite are interpreted as Mesozoic in age (Garihan et al., 1993), but the timing of fault motion is not well constrained (Figure 4). Many of these faults mapped in the Saluda 7.5-minute quadrangle (Garihan et al., 1993) may continue along strike and further into Polk County (Figure 5A). Approximately 10 km to the east and in line with the PRV, Cattanach

et al. (2013) interpreted an ENE-striking brittle rotational fault with ∼1.3 km of mappable horizontal oﬀset in the Landrum 7.5-minute quadrangle as postorogenic in age. Four major earthquakes (magnitudes ࣙ 5.0) have occurred over the past ∼160 years in North Carolina, all within topographic lineaments that intersect the BRE (Stover and Coﬀman, 1993; Stewart et al., 2020; and U.S. Geological Survey, n.d.). A magnitude 5.1 earthquake occurred near Sparta, North

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Carolina, in August 2020 along the Little River fault, an ESE-striking reverse fault that formed the first documented surface fault rupture directly linked to a modern earthquake in the southern Blue Ridge (Hill et al., 2020a) and the first evidence for motion along a postorogenic lineament that crosses the regional mountain belt. The ESE-trending Mills Gap fault zone, ∼30 km northwest of Polk County, also corresponds with a series of ESE-trending topographic lineaments (Wooten et al., 2010). It contains WNW- and ENE-striking lateral and oblique normal transtensional faults offsetting colluvium. Field evidence indicates it is a young fault, presumably Cenozoic in age. Southeast of the Mills Gap fault zone and within the same family of ESE-trending topographic lineaments is Hickory Nut Gorge, a major ESE-trending linear reentrant incising across the BRE (Figure 1A) that has experienced numerous landslides in recent and historical times (Soplata, 2016; Wooten et al., 2017, 2019c). Although there are areas of concentrated landslides within structurally controlled, ESE-trending, seismically active lineaments, there are no documented direct modern observations of seismically triggered slope failures in the southern Appalachians. Reinbold and Johnston (1987) describe shaking events near Rumbling Bald Mountain in the Hickory Nut Gorge reentrant in Rutherford County (Figure 1A). that suggest seismically induced rockfall, and Soplata (2016) postulates a coseismic origin for massive rockfall deposits mapped in detail in that area of Hickory Nut Gorge. Geomorphology Although the Appalachians are on a passive plate margin, there is abundant evidence that the modern topography has been shaped by erosional processes (e.g., Spotila et al., 2004; Prince et al., 2011), mass wasting (e.g., Lyons et al., 2014; Wooten et al., 2016), and young tectonics (Hack, 1982; Dennison and Stewart, 2001; Stewart and Dennison, 2006; Gallen et al., 2013; and Hill, 2018).The most prominent topographic feature crossing Polk County is the BRE (Figure 4), a regional zone of steep topography that extends from northern Georgia, through the Carolinas, and into Virginia, with up to 1.5 km of local relief in Mitchell and Avery counties in North Carolina. In Polk County, the BRE deviates from the Brevard fault zone and defines the terminus of the SE-protruding Columbus Promontory, with up to 400 m of local relief in a 500-m2 sampling area. Here the BRE separates high plateaus to the west from low foothills to the east (Figures 1 and 4). Much of the Columbus Promontory drains to the northwest as part of the headwaters to the French

Broad River, and some authors have delineated the western margin of the BRE as the eastern continental divide (Hack, 1982). In topographic elevation and relief proﬁles, however, the face of the escarpment is easily identiﬁed (Figure 4). Although the crest of BRE often approximates the eastern continental divide, along the Columbus Promontory it is found 5–20 km east of the divide. This creates the unique occurrence of relatively low-relief topography that drains eastward toward the steep face of the BRE. The BRE serves as the boundary between the Blue Ridge and Piedmont physiographic provinces, but the rocks underlying the Columbus Promontory are within the Piedmont geological province, which is separated from the Blue Ridge rocks by the NE-striking Brevard fault zone (Figure 4). The Polk County landscape can be summarized as low-relief uplands and low foothills separated by a steep escarpment intersected in places by linear E- and NE-trending valleys, forming deep reentrants along its sinuous trace.

Geomorphic Domains Southeast of the eastern continental divide, Polk County and the surrounding landscape can be divided into five geomorphic domains (Figure 4B): (1) low-relief highlands, (2) BRE, (3) linear reentrants, (4) monadnocks, and (5) foothills. These domains are representative landscapes with amorphous boundaries that compose the heterogeneous landscape of Polk County and elsewhere on the Columbus Promontory. Each has different geomorphic characteristics and presents its own set of slope failure hazards. In the low-relief highlands, rolling hills and meandering streams make broad valleys with wide floodplains, becoming increasingly dissected before descending the BRE. Along most of its length through Polk County, the BRE ranges from ∼750 m to ∼300 m in elevation, with over 700 m of total relief in the vicinity of Tryon Peak and White Oak Mountain (Figure 1). Typically, slopes along the BRE are greater than 20°, with sub-vertical cliff bands up to 65 m high near the upper transition from the lowrelief domain above (Figure 4). Deep linear reentrants of the E-trending PRV and the NE-trending Green River Gorge (GRG) are incised into the BRE. In the two lower-elevation geomorphic domains, the monadnocks and the foothills, slope failure hazards are much less abundant and smaller in scale than those found around the BRE. In the foothills, erosion is dominated by diffusive processes, with the exception of occasional stream bank failures.

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2018–2020 INVENTORY MAPPING Inventory Methods In recent decades, landslide mapping has relied increasingly on technological advancements like GPS and remote sensing techniques (Guzzetti et al., 2012); however, there is information that remote sensing studies will not acquire, such as the material type at the failure location (e.g., debris, earth, weathered rock). To increase our understanding of landslides in western North Carolina and to better inform physically based landslide modeling, our landslide mapping in Polk County is an iterative process of remote sensing analysis and field verification using field mapping techniques described in the section “Field Mapping Techniques.”

Remote Sensing Techniques Remote sensing for landslides relies on data captured from airborne or space-borne sensors and offers a rapid assessment of landsliding at the landscape scale. In this study, we performed remote sensing using primarily airborne lidar topographic data, aerial optical imagery, and satellite optical and near-infrared imagery. Lidar—High-resolution lidar topography data have transformed landslide mapping globally (Jaboyedoﬀ and Derron, 2020). In forested regions of the United States, these data are particularly useful because they can be reduced to bare-earth digital terrain models (DTMs). DTMs largely remove obscurities in imagery caused by dense vegetative cover, aiding in mapping surface features such as landslides (Schulz, 2004). Lidar data in North Carolina are available in two vintages: “legacy” data collected in 2004 (North Carolina Department of Public Safety [NCDPS], 2005) and quality-level 1 (QL1) data collected in 2017 (NCDPS, 2020). These data were reduced to bareearth DTMs with 6- and 0.5-m resolutions, respectively. The legacy lidar were utilized for previous landslide mapping (Wooten et al., 2007) until 2019, when the QL1 data became available. The increase in resolution changed many aspects of our landslide mapping program, but most important, it increased the efﬁciency of remote sensing analysis to locate landslides that were previously questionable or undetectable (Figure 6A and B). Aerial Imagery—North Carolina acquires aerial orthophotography on a 4-year repeat cycle (North Carolina Geographic Information Coordinating Council, 2010). In recent years, this imagery was collected with a nominal pixel resolution of 15 cm, suitable for detailed landslide mapping (Figure 6C and

D). The timing of landslides can generally be inferred to within a 4-year time period from this imagery. Available vintages of Google Earth imagery are also useful to constrain timing of landslides. Given that most landslides in North Carolina are triggered by intense rainfall, event timing can typically be further constrained to a specific rainfall event. In addition to state-sponsored aerial imagery campaigns, the use of UAS or “drones” has also increased our efficiency and accuracy in mapping landslides (Figure 6E and F). We operate a small UAS with a three-band optical camera for landslide reconnaissance and mapping purposes. Georeferenced two-dimensional orthomosaics or threedimensional digital elevation models are developed for use in mapping and analysis, typically with ArcGIS Drone2Mapphotogrammetry software. Satellite Imagery—To assist in mapping landslides from the May 18, 2018, storm, we utilized public domain, rapid repeat-cycle, multispectral satellite imagery to observe hillslope vegetation loss, a potential indicator of landslide activity. We used the HazMapper Google Earth Engine application (Scheip and Wegmann, 2021) to identify the relative changes in normalized difference vegetation index (rdNDVI) across late May 2018. Landslides identified through rdNDVI imagery analysis were recorded for either inclusion to the inventory or further field confirmation based on the confidence in the remote interpretations. These data were available in early 2019, prior to the release of the 2019 vintage aerial image, providing an early look at damage from the May 18, 2018, storm. Once the 2019 vintage image was available, rdNDVI targets were re-evaluated and modified based on the new imagery (Figure 6G and H). Field Mapping Techniques The NCGS began field mapping landslide features in 1990 (Wooten et al., 2017). In 2009, the NCGS’s field methods advanced from paper-based mapping that was then digitized into a GIS format to direct digital mapping of landslide features into a GIS landslide geodatabase using GPS-enabled, ruggedized field computers (Bauer et al., 2012). Since that time, many technological advancements have occurred with field computing equipment, namely, increased computational power, software advances, and more accurate GPS chips. Using modern equipment, we still utilize the same work flow to field verify landslide features targeted by remote sensing analysis and entering those data to our landslide geodatabase. Following remote sensing analysis, field teams visit and evaluate ambiguous landslides for characteristics such as tilted trees, ground ruptures, or other signs

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Figure 6. (A and B) Lidar-shaded relief images of a landslide in the Green River Gorge, Polk County, North Carolina, illustrating the diﬀerence in 2004–2017 lidar data sets. (C and D) Comparison of 2015 and 2019 aerial orthophotography of debris ﬂows that occurred during the May 18, 2018, landslide event. (E and F) Uncrewed aerial systems (UAS) imagery aids in mapping and monitoring the progression of landslides, such as Howard Gap landslide in Polk County (January 20, 2020, UAS images, North Carolina Geological Survey). (G) European Space Agency Sentinel-2 multispectral satellite imagery is used to identify portions of the landscape that experienced vegetation loss following the May 18, 2018, storms. (H) Landslides from panel G are visible in 2019 orthophotography.

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of historical or recent motion. Field mapping is performed directly on a base map of high-resolution lidar topography data in ESRI ArcGIS software. At each landslide location, we collect various data to characterize the landslide, with a focus on data that cannot be assessed remotely, such as material type, bedrock structural data, and sediment grain sizes and textures. With the availability of the 2017 QL1 lidar, we have increased the resolution of our mapping to >1:2,000 from previous mapping campaigns (Bauer et al., 2012). For comparison, previous landslide mapping efforts in North Carolina mapped at a resolution of >1:6,000 (Wooten et al., 2011). A large part of our mapping efforts involves calibrating interpretations between remote sensing and field observations. Types of Landslides Landslide descriptions in our inventory are in general accordance with the classification nomenclature of Cruden and Varnes (1996). Slides, flows, and falls are the most common types of landslides in Polk County (Figure 7A), accounting for 80.3%, 18.0%, and 1.0% of all slope movements, respectively. Landslides initiated primarily in debris or weathered rock (Figure 7A). Typical examples of shallow- and deep-seated landslides are shown in Figure 8A and B. Shallow translational or rotational landslides are common and often mobilize into debris flows along steep drainage networks. Deep-seated landslides are much less common in Polk County but do occur (see the section “Ongoing Landslide Activity”) and persist in the landscape, consistent with weathering characteristics of the humid climate encountered in the study area (Wieczorek, 1984; Keaton and DeGraff, 1996). Undifferentiated landslide deposits describe areas of known or suspected historical to prehistorical landslide activity but where discernible landslide features (i.e., head scarp, lateral scarps, and toes) are not obvious (Figure 8C). We term these “deposits” in our landslide geodatabase and divide deposit polygons based on different processes or relative age estimates from surface morphology parameters (e.g., surface roughness or hummock density). The 0.5-m-resolution QL1 lidar greatly aids in the identification of hummocks and depressions, which are key identifiers of these landforms in lieu of clear head scarps, lateral scarps, and toe deposits. Debris flows are common in the region, and during the May 18, 2018, event, they were both numerous and the most destructive. However, the topographic signature of prehistorical debris flows is more challenging to detect than translational or rotational hillslope failures (Lyons et al., 2014), likely causing

an under-representation of the process in our inventory. In many low-order drainages along the BRE, for example, imbricated debris packages are present in the channel banks, indicating historical and prehistorical debris flow activity. Furthermore, debris flows in 2018 deposited material onto existing debris fans formed from previous debris flow activity (Figure 8D). Rockfalls and rock slides in Polk County occur along steep cliff bands, mainly along the BRE. Our inventory includes 25 rock slides or rockfalls: 23 of these occurred along the BRE, and 19 of these occurred at ground slope angles greater than the mean slope (∼35°) of all landslide initiation zones in Polk County. Detached blocks range from the meter scale to over 30 m in the longest dimension (Figure 8E). There is no readily apparent connection between lithology and rockfall occurrence in Polk County. The nature, triggering mechanisms, and timing of large rockfalls along the BRE remain enigmatic. Inventory Summary The Polk County landslide inventory consists of 920 landslide initiation points, 685 landslide polygons (∼2.22 km2 ), and 287 undifferentiated landslide deposit polygons (∼19.9 km2 ). There are fewer polygons than points because head scarps, lateral scarps, and toe deposits were not always distinct, particularly in older landslides. For example, in many cases, well-defined initiation zones were present, while corresponding landslide polygons could not be drawn with confidence. Additionally, the uncertainty in mapping very small landslides (<100 m2 ) caused by GPS errors or poor image resolution may preclude delineating a landslide polygon. In these cases, we record the location of a landslide as a single point representing the initiation zone. Conversely, very old landslides may be obvious based on hummocky terrain, the presence of springs, and broader landscape position, but the initiation zone and exact boundaries may be difficult to determine. In these cases, we designate a polygon as a landslide deposit. Cumulatively, landslide polygons or landslide deposits cover approximately 21.5 km2 , or 3.6%, of Polk County (619 km2 ). Most of these features occur along BRE and reentrants or places with similar topographic relief (Figure 9A). Landslides are typically located along steep, highrelief terrain of the BRE with a particular focusing of landslide activity adjacent to the Green and Pacolet reentrants (Figure 9, geomorphic domain 3). Average ground slope at landslide initiation points was 35° ± 8.3° for all landslides in Polk County (N = 920; Figure 7C). The increase in landslide frequency

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Figure 7. Landslide classiﬁcations and material types from (A) Polk County and (B) May 18, 2018, landslide inventories. Designations are in accordance with Cruden and Varnes (1996). (C) Histograms of slopes at initiation zone of landslides for Polk County and the May 18, 2018, landslide event. Landslides initiated primarily high on the Blue Ridge Escarpment.

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Figure 8. Types of landslides encountered in Polk County. (A) Shallow landslides often initiate on modified slopes such as road cuts. (B) Deep-seated landslides likely include bedrock and are large in spatial extent. (C) Areas of historical to prehistorical deposition of landslide material are indicated by characteristics such as hummocks or contour reversals and indicate regions of potential instability. (D) Debris flows typically initiate high on steep slopes, channelize, and move rapidly downslope. In this example, debris was deposited on a pre-existing debris fan, evidence of repeated debris flow activity. (E) Rockfall deposits are evident in the QL1 lidar as semi-continuous trains of large boulders, from several meters to >30 m in longest dimension.

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Figure 9. Kernel density plots of spatial landslide frequency for (A) Polk County, (B) unmodified slopes in Polk County, (C) modified slopes in Polk County, and (D) the May 18, 2018, landslide event. Landslide density is lowest in eastern Polk County and highest where I-26 crosses Howard Gap, and is focused in the Green River Gorge and Pacolet River valley reentrants (location 3 in panel A) and along the face of the Blue Ridge Escarpment (location 2 in panel A). Low, medium, and high classifications assigned as 0%–25%, 25%–75%, and 75%–100% of area with landsliding, respectively.

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Figure 10. (A) Bright’s Creek landslide in northern Polk County is a large landslide complex consisting of a 44,000-m2 active portion within a 980,000-m2 composite debris deposit. (B) Howard Gap is a large landslide (45,000 m2 ) near I-26 as it crosses the Blue Ridge Escarpment. HG-1 and HG-2 are the monitoring sites referenced in the text. Photographs illustrate ground conditions near the head scarps. Left: June 4, 2018, North Carolina Geological Survey (NCGS) photo, view looking southwest. Center: June 14, 2019, NCGS photo, view looking west. Right: January 9, 2020, NCGS photo, view looking west.

near 20° is consistent with the larger geodatabase of landslides across North Carolina (n < 4,700). Kernel densities of landslides/km2 range from no landsliding, predominantly in eastern Polk County, to a maximum of 44 landslides/km2 observed in Howard Gap along the I-26 corridor, where many landslides occur on slopes modiﬁed during the construction of I-26. Regions along the Green and Pacolet reentrants are the only areas in the county

with historical landslide densities greater than 12 landslides/km2 .

Ongoing Landslide Activity We identiﬁed two large landslide complexes with areas of intermittent activity. The Bright’s Creek and Howard Gap debris slides (Figures 1A and 10)

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continue to threaten public safety, transportation networks, and public utilities and involve ongoing response eﬀorts by the NCGS, NCDOT, and utility companies. In addition to the intense rainfall from the May 2018 storms, the cumulative rainfall from these events, combined with record above-normal rainfall during the preceding months of 2018 (Figure 2), likely contributed to the reactivation of these slides. Bright’s Creek Debris–Weathered-Rock Slide The Bright’s Creek debris–weathered-rock slide is located on the SE-facing slopes of the BRE, north of the area of concentrated landslide activity from the May 18, 2018, storm (Figure 10A). Shortly after the passage of Alberto, the Polk County emergency manager contacted the NCGS because the Bright’s Creek community was concerned about large cracks that recently appeared in their roadways. An initial investigation on June 4, 2018, determined that scarps and tension cracks resulted from deep-seated slide movement that included a segment of the regional natural gas pipeline corridor. In addition to supplying natural gas to local private and commercial customers, this pipeline serves as the primary source of natural gas for the electrical power plant serving Asheville, North Carolina. Initial investigations by the NCGS and later detailed mapping by ALC delineated a 0.4-km2 area of active slide movement within a 0.98-km2 composite debris deposit comprised in part by other dormant debris slides (Bauer et al., 2020). Borehole data indicate that the active slide mass involves weathered bedrock at a depth of approximately 10 m at one location (Bauer, 2021). The utility company has subsequently undertaken ongoing monitoring and mitigation, including installation of inclinometers, strain gauges on the pipeline, and re-bedding a segment of the pipeline to accommodate slide movement. Howard Gap Debris Slide The Howard Gap debris slide is located on the south-facing slopes of the Columbus Promontory, approximately 160 m below I-26 on the Saluda Grade (Figure 10B), (Wooten et al., 2019b). Localized slope failures triggered by the May 18, 2018, storm damaged Howard Gap Road and an adjoining private property. In January 2019, the NCDOT closed a 160-m-long section of Howard Gap Road when downward vertical displacement made it impassable after heavy rainfall on December 28, 2018 (Figure 2). Polk County Emergency Management subsequently contacted the NCGS for assistance over concerns a private property owner

had about falling trees and unstable slopes above their home. The NCGS and ALC delineated an active 45,000-m2 debris slide with most activity occurring within a 2,900-m2 region of the slide. Based on evaluating preliminary lidar-derived topographic profiles of the landslide from November 2020, we estimate the maximum thickness to be 20–30 m. Movement at these depths and locations of nearby bedrock outcrops suggest that weathered bedrock may be involved in the rupture surface at depth. As of January 2021, Howard Gap Road remains closed, and the slide has damaged electrical utility lines and severed emergency water supply lines that connected the towns of Saluda and Tryon. Continued advancement of the slide presents a potential threat to a downslope home, outbuildings, and roads on private property. Additional threats to downslope areas could materialize as rapidly moving debris slides and debris flows originating from the over-steepened face of the slide mass undergo extension and internal breakup (see, e.g., Reid and Brien, 2019). Given these potential hazards, the NCGS partnered with the U.S. Geological Survey to establish two monitoring stations on the Howard Gap debris slide in June 2019. This long-term research is aimed to understand the kinematic behavior of the slide and whether motion correlates to local meteorological and hydrological conditions. Monitoring site HG-1 on the leftlateral (east) flank of the debris slide consists of a cable extension transducer (CET), a two-axis tilt meter, and a tipping bucket rain gauge. Monitoring site HG-2 on the western edge of the toe bulge consists of two survey stakes and a ground control point to measure slide displacement. CET data indicating slide displacement at HG-1 remain inconclusive, whereas meter-scale movement at HG-2 has been observed from measurements of survey stakes. Field observations have been consistent with these monitoring data, identifying increasing displacement of ground ruptures (scarps) on the over-steepened face of the slide mass above a residence and other private property. Additionally, repeat UAS flights have detected continued detachment and internal breakup of the slide mass. Differential movement continues to occur within the 45,000-m2 slide mass and has persisted into 2020. In addition to the threats posed below I-26, there are multiple slides and flows that moved in historical and modern times located upslope of I-26. Web-GIS Integration and Data Availability We partnered with the National Environmental Modeling and Analysis Center at the University of North Carolina at Asheville to design and maintain

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an interactive landslide hazards Web map (https:// landslidesncgs.org). This is a modern approach taken by other state surveys in the United States (e.g., Kentucky’s Geologic Map Service, https://kgs. uky.edu/kygeode/geomap/?layoutid = 25; Oregon’s SLIDO, https://www.oregongeology.org/slido; and Washington’s Geologic Information Portal, https:// www.dnr.wa.gov/geologyportal) to increase accessibility and usability of geological hazard information to citizens and represents a modernization of our program following the previous distribution of static digital map documents. The Web map allows users to load various base maps, parcel data, search for an address, and view metadata for individual landslide features. The Web viewer is designed to retrieve our landslide geodatabase in real time under the ArcGIS representational state transfer (REST) protocol. Data are directly available for import to ESRI software products at https://maps.deq.nc.gov/arcgis/ rest/services/DEMLR, and raw data are available on request. As further mapping is performed in North Carolina, these links will reﬂect the current version of our published database. DISCUSSION The May 18, 2018, Landslide Event The May 18, 2018, storm triggered 241 landslides in western Polk County in one of the largest landslide events in North Carolina in the past 80 years with respect to landslide density (spatial frequency). Comparable events occurred in September 2004 and in August 1940. Rainfall from tropical cyclones Frances and Ivan over September 6–17, 2004, triggered approximately 400 landslides in western North Carolina (Wooten et al., 2016) with 254 of these recorded in our landslide geodatabase distributed over a 12-county region. During August 13–17, 1940, remnants of a tropical cyclone delivered heavy rainfall, triggering over 2,000 debris ﬂows and debris slides (Wooten et al., 2008b) over a 1,197-km2 area of Watauga County. Although the Watauga County storm produced an order of magnitude greater number of landslides than the Polk County storm, the maximum landslide density from the 1940 event (20.7 landslides/km2 ) was approximately half of the observed maximum density in the 2018 event (38.9 landslides/km2 ). Because of the localized nature of the May 18, 2018, storm, the 241 landslides were limited to a 67-km2 area of western Polk County along the BRE. The increased landslide density observed in the 2018 Polk storm relative to the 1940 Watauga storm may be related to the peak rainfall rates and durations exhibited by extreme storm events, which positively

correlate with landslide activity in the southern Appalachians (Wieczorek et al., 2000, 2009; Wooten et al., 2016, 2017). Wieczorek et al. (2004) reported that a peak rainfall rate duration of 254 mm over 6 hours (42 mm/hr average) triggered over 700 debris flows during the August 13–14, 1940, storm in the Deep Gap area of Watauga County, North Carolina (Figure 1A, inset). By comparison, the May 18, 2018, storm delivered approximately 200 mm of rain over 3–4 hours (∼67 mm/hr average) (Bauer et al., 2019). The May 2018 slope failures generally consisted of shallow landslides that initiated in debris and quickly channelized into concave topography. These rapidly moving debris flows coalesced along fluvial drainage networks and, in many instances, formed long-runout debris flows (>500 m) depositing material to the floor of the PRV and GRG. Even though 241 individual landslides were attributed to this landslide event, only 162 landslide polygons were mapped, representing the amalgamation of various debris slides and debris flows into larger landslide features that were often mapped as single polygons (for mapping methods, see the section “2018–2020 Inventory Mapping”). The landslide-event magnitude (ML ) scale (Malamud et al., 2004) provides a consistent framework to compare global landslide events, much like the Richter scale is used to standardize the evaluation of global earthquake events (Richter, 1935). It is based on a consistent probability density function fitted to a frequency-area distribution of three empirical landslide inventories. An inverse gamma distribution best modeled the observed frequency area distributions (Malamud et al., 2004). Tanyas et al. (2018) updated the method by observing trends in 45 earthquake-induced landslide inventories and noted that some inventories cannot be well approximated by the inverse gamma distribution previously proposed. Following the work flow of Malamud et al. (2004) and Tanyas et al. (2018), we performed a Kolmogorov– Smirnov test between the observed frequency-area distribution for the May 2018 landslide event with a best-fit theoretical inverse gamma function. The distribution is well approximated by the inverse gamma function (p-value = 0.90). The peak of the curve represents the most numerous landslide area and is termed the rollover point. In the May 2018 event, this value is approximately 400 m2 (Figure 11). The elevated tail of the distribution and the limited number of landslides larger than the rollover point introduce too much uncertainty in estimating the power law exponent and scaling parameter of this inventory. Other studies have demonstrated power law scaling to occur only for distributions of landslides greater than approximately 1,000 m2 , which could explain why this relationship is

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Figure 11. (A) Frequency area distribution of landslides from the May 18, 2018, landslide event. A best-ﬁt inverse gamma function approximates the area distribution for landslides smaller than approximately 2,000 m2 . During mapping, the combination of coalescing debris ﬂows into singular polygons (examples in B and C) resulted in more large landslide areas in our inventory than predicted by the inverse gamma function, which has been successfully used to model landslide distributions globally. (B) Lidar-shaded relief map base. (C) 2019 orthophotography base map.

not present in the current inventory (Guzzetti et al., 2002; Malamud et al., 2004; Tanyas et al., 2018). The tail of the observed frequency-area distribution illustrates an elevated probability of large landslides (greater than 2,000 m2 ) compared to the best-fit inverse gamma function. The mapped landslide polygons range from approximately 50 m2 to 15,000 m2 . Landslides greater than approximately 2,000 m2 are typically long-runout debris flows that include multiple coalescing failures along dendritic drainage networks (Figure 11B). During inventory mapping, coalescing debris flows were typically combined into a single polygon that encompassed multiple discrete coeval landslides, inclusive of source areas, tracks, and runout. (Figure 11C). Including runout and deposits in inventory polygons can have adverse effects on the frequency-area distribution of an inventory and cause deviations from the expected distribution (Tanyas et al., 2019). The demarcation between source areas and runout zones from debris flows can be challenging to identify, and landslide deposits are often included in landslide inventory polygons (e.g., Burns and Madin, 2009; Slaughter et al., 2017). We estimate the ML as the non-linear least squares best-fit scaling constant between the observed

frequency-area distribution for the 2018 event and the theoretical inverse gamma function (ML = 2.42) (Figure 11). This is consistent with the magnitude based solely on the number of landslides (N = 241), ML = 2.38, and higher than the magnitude based on area of landsliding (A = 3.28 × 105 m2 ), ML = 2.02. Simply based on the number of landslides, the May 2018 event was a comparable order of magnitude to the September 2004 landslide event (approximate ML = 2.6) and an order of magnitude smaller than the August 1940 event (approximate ML = 3.3). To help put these ML values into perspective, Malamud et al. (2004) reported a landslide-event magnitude of ML = 3.98 for the 9,594 landslides triggered by tropical cyclone Mitch in Guatemala in 1998. Elevated Landslide Frequency in Recent Years Reliable determinations of the spatial frequency of landslides in North Carolina depend on the completeness of event-speciﬁc inventories. For the largest landslide events, relatively complete data exist in our inventory only for historically recent ones: Watauga County in 1940, tropical cyclones Frances and Ivan in 2004, and Polk County in 2018. We recognize that many

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historical landslides have been omitted from our inventory, which includes landslides dating back to 1879. Thirty-two hundred landslides (68% of total) have a known year of landslide movement. While large translational and rotational landslides may persist in humid landscapes for 104 years (Korup et al., 2010), debris flow signatures in the landscape quickly become difficult to detect (Lyons et al., 2014). Assigning a movement year to a large number of mapped historical or prehistorical landslides is often not feasible. The completeness of our inventory is highest in years with funded programs devoted to landslide mapping (2005– 2011 and 2019 to at least 2021). It is likely that our inventory includes landslides from most major landslideinducing storms over the past five decades. Rainfall, soil moisture, and elevated pore pressures are the primary hydrologic factors promoting landsliding in North Carolina (Wooten et al., 2016; Miller et al., 2018, 2019). Long time-series rainfall data have been available for many decades, while soil moisture data are more limited in western North Carolina. Rainfall records, for example, have been maintained for over a century at the Asheville Airport (National Oceanic and Atmospheric Administration [NOAA], 2021). Eschner and Patric (1982) and Neary and Swift (1987) published a rainfall threshold of 125 mm per 24 hours for triggering debris flows in eastern upland forests and the southern Appalachians, respectively. This threshold is still valid for general forecasting of landslide occurrence in North Carolina. The mechanics of rainfall triggering are well understood, whereby infiltrating rainwater increases soil pore pressure, destabilizes the soil mass, and triggers particle motion (e.g., Reid, 1994; Collins and Znidarcic, 2004). Recent work by Orland et al. (2020) and Thomas et al. (2018) demonstrated the applicability of directly monitoring and modeling soil moisture and pore pressure data to predict threshold conditions for landsliding. In partnership with the U.S. Geological Survey and the U.S. Department of Agriculture Forest Service, we have maintained soil moisture sensors and tensiometers at three sites in North Carolina since 2013–2014 (Mirus et al., 2017), so this is an area of active research. Generally, decadal-scale rainfall patterns should approximate landslide frequency. The cumulative rainfall departure from the mean in western North Carolina over the period of 2003–2020 (Figure 2A) illustrates periods of abundant and meager rainfall in the region (Weber and Stewart, 2004). The high variability of rainfall patterns in the mountains of North Carolina (Basist et al., 1994; Fuhrmann et al., 2008; Wooten et al., 2008a; and Tao and Barros, 2014) is not captured by this data set, which represents averaged rainfall across the region (NOAA, 2021). We are not evaluating the rainfall data against individual landslide

occurrences but instead against the broad pattern of landslide event frequency in western North Carolina. The May 18, 2018, storm was one of several extreme rainfall events that produced landslides in western North Carolina since 2003 that was not related to land-falling tropical cyclones (Figure 2). Landslide activity is particularly likely when extreme rainfall events occur within extended periods of above-normal rainfall, such as was the case in 2013 (Moore, 2017). Individual extreme rainfall events are typically related to low-pressure systems (Moore, 2017) or atmospheric rivers (Tao and Barros, 2014; Wooten et al., 2016 [and references therein]; and Miller et al., 2018, 2019). Nontropical, extreme rainfall events can occur in the winter, spring, and summer months, whereas land-falling tropical cyclones peak in the summer and fall months, resulting in a year-round potential for rainfall-induced landslides in western North Carolina (Figure 2). In the past two decades in North Carolina, three storms (September 2004, January 2013, and May 18, 2018) triggered at least 400, 300, and 241 landslides, respectively (Figure 2B). These events occurred during a time when western North Carolina was in a period of abundant rainfall (Figure 2A). Of the three, the 2004 landslide event was the only one triggered by a tropical cyclone, which in this case includes the passage of tropical cyclones Francis and Ivan within a 2-week period over western North Carolina (Wooten et al., 2008a). The January 14–17, 2013, storm, which initiated an extended period of above-normal rainfall (Moore, 2017), was categorized as an atmospheric river by Miller et al. (2019). The May 18, 2018, landslide event was driven by a relatively isolated, orographically enhanced convective storm. These storms can materialize with limited warning and produce extreme rainfall over localized areas. Recent examples of localized and landslide-triggering convective systems include an August 24, 2019, storm in the Nantahala Gorge, Swain County, which triggered at least 32 landslides (Hill et al., 2020b), and an April 12–13, 2020, storm near Franklin, Macon County, which triggered at least 12 landslides, including a 1.5-km-long debris flow. In addition to the storm-specific landslide events, three large landslides have activated during the abovenormal precipitation in 2018: the Bright’s Creek and Howard Gap landslides in Polk County discussed in this article and the Buffalo Creek landslide in neighboring Rutherford County (Wooten et al., 2019d). Based on the cumulative rainfall departure analysis, moisture conditions in the region have been steadily increasing since 2018, with major landslide events resulting from these periods of elevated precipitation over the preceding two decades. As moisture levels remain high, tropical cyclones and other extreme rainfall events systems have an increased potential to

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trigger landslides. Based on the well-established rainfall/landslide connection in North Carolina (Wooten et al., 2016; Moore, 2017; and Miller et al., 2018, 2019), future increases in storm intensity and frequency (e.g., Prein et al., 2017) can be expected to commensurately increase landslide frequency. Structural and Geomorphic Controls on Landslides in Polk County Outcrop structural measurements from our field mapping and bedrock mapping of the Landrum and Pea Ridge 7.5-minute quadrangles (Cattanach et al., 2013, 2016, respectively) identify three joint sets in Polk County (N = 1,565 total): a dominant E–Wstriking set and two minor sets striking NE–SW and NW–SE. The PRV (E–W trend) and the GRG (NE– SW trend) contain fractures striking parallel to the trend of the reentrants (Figure 5B, inset rose diagrams and stereonets), consistent with other reentrants along the BRE (Gillon et al., 2009; Hill, 2018). The NE–SW joint set parallel to the trend of the GRG, also parallels similarly oriented segments of the BRE with concentrated landslide activity, such as the I-26 corridor near Howard Gap and elsewhere along the Saluda Grade (Figures 1A and 5B). The positive correlation between joint strike and the general trend of the reentrants is even more evident by isolating those data to locations within landslide polygons and within landslides that failed in May 2018 (Figure 5B). Overall, foliation appears to exert a lesser influence on slope failure because the gentle to moderate dip toward the ENE dip infrequently daylights to form sliding surfaces on the slopes of the BRE and reentrants. Most landslides in Polk County initiate along the BRE, and many of these occur within the linear reentrants (Figure 9). To test the hypothesis that the lineament-parallel fractures form back-release surfaces (i.e., detachment or rupture surfaces related to main scarps or tension cracks) and lead to bedrock failure at landslide initiation points, we calculated the minimum angle between the joint strike azimuth and the surface aspect at each joint collected within the landslide polygons (Figure 5D). In an ideal failure scenario where the joints strike parallel to the surface contours, this difference angle would be 90°. These difference angles have a mean value of 91° ± 45°, which is consistent with joint-controlled back-release surfaces in downslope slab and wedge failures and strongly supports the case that the joints are influencing slope failures. These lineament-parallel joint sets are favorably oriented to serve also as detachment or rupture surfaces along the soil–rock interface in debris slide and debris flow initiation zones. In addition to fracturecontrolled failure at initiation points, this mechanism

occurs along the tracks of debris flows, where plucking of the bedrock along lineament-parallel joints contributes to increased removal of material. This analysis is consistent with field observations of continuous and often orthogonal joint sets with one set parallel to head scarps and the other oriented along linear debris flow tracks, especially in and around the PRV and GRG (Figure 12). We also observed fracture-controlled plucking of the bedrock along the main trunk stream of the Green River, indicating that this process is not limited to tributary channels. The PRV is a simpler landform than the adjacent GRG. For example, while the PRV is a relatively linear landform for the upper 10 km of the reach, the GRG is more sinuous in its path as it crosses the BRE and includes at least two nearly 90° deviations from the valley trend. Within the PRV and GRG reentrants, cross-valley geometry differences and asymmetries may help drive landslide density patterns. The PRV is steeper on the north side, presumably a reflection of the metamorphic foliation that dips 5°–30° toward the ENE, slightly into and daylighting on the south-facing slope. The steep (>60° dip) E–W-striking joints intersect the hillslopes and form large cliff faces (Figure 5B). This combination of older ductile foliation and the younger brittle joints leads to bedrock failure and abundant debris flows on the north side. The large debris flows that led to the May 2018 fatality in the PRV contain fracture-controlled cliffs perpendicular to the flow along the track and near the head scarps in colluvial catchment zones on benches formed by the intersection of steeply dipping joints and gently dipping foliation planes. The south side has relatively few debris flows but instead is mantled by a large composite of many nested landslide deposits with a total map area of 3.2 km2 . The deposits on the south side are more difficult to link to single events or source areas. There are fewer continuous cliff bands apart from the likely source areas of two anomalous rockfall deposits with numerous boulders up to 30 m wide (Figure 8E). The overall trend of the GRG presumably follows a NW–SE-striking joint set in the upper section and a more dominant NE–SW-striking joint set and likely some of the brittle faults mapped by Garihan et al. (1993) in the lower section (Figure 5A). Although we mapped more landslides on the NW side, landslides occur on both sides of the GRG. The GRG is more symmetrical in slope than the PRV, drains a larger area, and includes foothills and wide floodplains within the reentrant. The Green River has a more mature upstream drainage network compared to the North Pacolet River and includes over 150 km2 of low-relief, upland drainage area, perched above a major knickzone (elevation ∼610 m). Such a knickzone

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Figure 12. (A) Uncrewed aerial systems image looking eastward and downstream along the Green River, where E–W-striking joints control the stream morphology and allow for plucking of the bedrock. (B) Looking westward up a 2018 debris flow track on the northwest side of the Green River Gorge. The orthogonal joint sets act as back and side release surfaces. The E–W-striking joints control the stream orientation here. (C) Looking eastward up a 2018 debris flow track that removed a section of outcrop cliff band along orthogonal joint sets.

does not exist along the North Pacolet River (Figure 1B). In the upper section of the GRG that trends southeastward, there are fracture-controlled sections of the stream that follow E–W- and ESE–WNWstriking high angle joints. Visible in the ﬁeld, the E– W set of fractures that is most dominant across the study area is also the most inﬂuential structure for the

upper, detachment-limited sections of the Green River (Figure 12A). Within the lower, SW–NE stretch of the GRG, there are large tributaries whose trunk streams parallel the NW–SE-striking joint sets and the NW section of the GRG, such as Cove Creek (Figure 5C), where numerous debris ﬂows and slides occurred during the heavy rains of May 2018.

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To test for a statistical difference of hillslope metrics in the PRV and GRG, we extracted local relief and slope (in a 500-m sampling window) from a 3-mresolution digital elevation model (DEM) for the north and south sides of each reentrant (Figure 13; Table 2). None of the tests yielded equivalent means (p ࣘ 0.05), which is consistent with the general asymmetry visible in the lidar and in the style and abundance of slope failures. Ground slopes are normally distributed for each side of the PRV (26.2° ± 11.4° and 23.7° ± 10.6° for the north and south sides, respectively). Local relief is normally distributed along the south side of the PRV, but steep cliffs along the northern wall of the PRV form a bimodal distribution in the local relief (Figure 13). This secondary peak includes the highrelief terrain where many of the May 18, 2018, debris flows initiated. The GRG has more symmetrical slope profile than the PRV (23.7° ± 11.0° and 25.4° ± 11.7° for the NW and SE sides, respectively), consistent with the presence of landslides (historical and recent) on both sides of the GRG. The presence of foothills and wide floodplains near the intersection of the GRG and the BRE create a bimodal distribution of local relief for both sides of the gorge. In the GRG and PRV reentrants, debris flows and slides are most abundant where the local relief and slopes are the greatest. It is common for the slope movement features in Polk County (and elsewhere) to initiate as translational slides at the soil–bedrock/saprolite interface in hillslopes and stream banks, later transitioning into debris flows as the material travels into and along stream valleys (Iverson et al., 1997; Wooten et al., 2008a; and Rengers et al., 2020). During the May 18, 2018, landslide event, this process occurred high on the steep cliffs along the north sides of the PRV and GRG. Additionally, differential weathering of bedrock and enhanced weathering along intersecting discontinuities can influence the formation of trough- or wedgeshaped depressions in bedrock surfaces that underlie colluvial hollows and serve as debris flow initiation zones (Grant, 1988; Sas and Eaton, 2008; and Wooten et al., 2008a).

victim. Established landslide response procedures and uncrewed aerial systems supplemented by GPSenabled field computers equipped with lidar DEMs and orthophotography enabled a rapid hazard assessment, coordinated search procedures, recovery of the fatality, and initial damage assessments. The loss of life and severe damage from the storm and ongoing landslide threats to regional infrastructure, prompted the NCGS and ALC to map Polk County in 2019–2020 after the North Carolina General Assembly reauthorized the landslide mapping program. Landslide Inventory Results Our mapping revealed that the May 18, 2018, event triggered at least 241 landslides, making it the first recorded major landslide event affecting Polk County since those reported from the July 15–16, 1916, storm. County-wide mapping identified 920 debris flows, debris slides, rock slides, rockfalls, and deposits from past landslide activity that cover 22.1 km2 in areas concentrated in the western part of the county along the BRE and the reentrants incised into it. The 241 debris flows and debris slides attributed to the May 2018 event cover 0.48 km2 in areas of southwestern Polk County, concentrated on the BRE and along the slopes of the Pacolet River and Green River reentrants. The 685 landslide polygons (slope movement outlines) mapped county-wide collectively cover an area of 2.22 km2 . Composite deposits from past landslide activity mapped at 287 locations cover a much larger area of 19.9 km2 , evidence that extensive and varied masswasting processes have operated over prehistorical and historical time frames. Pre-existing debris flow deposits of unknown ages are exposed in the tracks and runout zones of the May 18, 2018, debris flows, and thick accumulations of pre-existing debris characterize the active Bright’s Creek and Howard Gap landslides. These examples demonstrate the importance of mapping past debris deposits to identify areas potentially vulnerable to future debris flow and debris slide activity. Landslide Events

SUMMARY AND CONCLUSIONS Response Synopsis On May 18, 2018, a sequence of severe thunderstorms produced as much as 200 mm of rainfall over a 3–4-hour period in Polk County, triggering numerous debris ﬂows that resulted in a fatality and severely damaged or destroyed homes. The State Emergency Operations Center tasked the NCGS with assessing slope stability and safety before a USAR team entered the landslide damage zone to search for the

To place the May 2018 storm in perspective with three other major landslide events that aﬀected western North Carolina over the past 80 years, we compared landslide numbers, event magnitudes, and landslide density (spatial frequency). From the standpoint of landslide numbers, the 241 landslides from the May 2018 convective storm in Polk County was the thirdlargest event in western North Carolina since 2004. The two larger events, both of which aﬀected multicounty regions, were the January 2013 storm (atmospheric river), which produced at least 300 landslides,

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Figure 13. (A) Bimodal distributions of local relief in the Green River Gorge show the presence of foothills and wide floodplains, primarily near the mouth of the gorge. In the Pacolet River valley (PRV), a bimodal distribution is present on the north side of the valley due to steep cliffs that correspond to a focused area of landsliding. (B) Normally distributed slopes blanket the north and south walls of each reentrant, with more asymmetry noted in the PRV due to the presence of sub-vertical cliff bands on the north valley wall.

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and tropical cyclones Frances and Ivan in September 2004, which collectively produced at least 400 landslides. In comparing the landslide event magnitudes for the May 2018 storm against the September 2004 and the August 1940 tropical cyclones, the May 2018 and September 2004 events were comparable (ML ∼ 2.4 vs. ML ∼ 2.6) and an order of magnitude smaller than the August 1940 event (ML ∼ 3.3). Although the August 13–17, 1940, tropical cyclone in the Watauga County storm produced an order of magnitude greater number of landslides (>2,000) and landslide event magnitude than the May 2018 storm, the maximum landslide density of the 2018 event (38.9 landslides/km2 ) was nearly twice that of the maximum produced by the 1940 event (20.7 landslides/km2 ). Areas with the highest landslide densities for the 2018 and 1940 events included structurally controlled, linear reentrants and the adjacent areas of the BRE. The 2018 event marked the abrupt beginning of a 2-year period of increased landslide activity in western North Carolina over the previous 4 years (2014– 2017). The cumulative rainfall departure analysis (Figure 2) shows that rainfall conditions have been steadily increasing from 2018 to 2020, with a commensurate overall increase in landslide frequency. The years 2004, 2009, and 2013 also experienced similar increases in landslide activity coincident with periods of extended above-normal rainfall. Within these periods of abovenormal rainfall, individual storms can trigger numerous rapidly moving debris slides and debris flows. Cumulative rainfall during these periods can reactivate large, slow-moving debris slides and weatheredrock slides, prime examples being the Bright’s Creek and Howard Gap landslides in Polk County that continue to threaten private property and regional infrastructure. Structural and Geomorphic Controls on Landslides We interpret our bedrock structural data to show that brittle structures influence the landscape at the landform and hillslope scales in areas of concentrated landslides on steep slopes along the BRE and particularly in the Pacolet River and Green River reentrants. The North Pacolet and the Green Rivers exploit post-orogenic, brittle fractures to form these linear reentrants, comparable to other similar landforms with concentrated landslide activity elsewhere in western North Carolina (Wooten et al., 2008, 2016, 2019c; Gillon et al., 2009; and Hill, 2018). At the hillslope scale, these lineament-parallel fractures also form back-release (i.e., detachment) surfaces that lead to bedrock failure and serve as detachment surfaces along the soil–rock interface in debris slide and debris flow initiation zones. The distributions and types

of landslides within the reentrants are related to the symmetry and steepness of their respective slope profiles and patterns of local relief. The PRV has an asymmetric slope profile (26.2° ± 11.4° and 23.7° ± 10.6° for the north and south sides, respectively). Local relief is normally distributed along the south side of the PRV, but high-relief cliff bands along the northern wall of the PRV result in a bimodal distribution with a secondary peak representing fracture controlled cliffs where many of the May 18, 2018, debris initiated (Figure 13). The lower-slope-angle south side has relatively few debris flows but instead is mantled by a large composite of many nested, pre-2018 landslide deposits of various ages. The slightly asymmetric slope profile of the GRG (23.7° ± 11.0° and 25.4° ± 11.7° for the NW and SE sides, respectively) is consistent with the concentration of landslides on both sides of the GRG. In both the PRV and GRG reentrants, debris flows and slides are most abundant where the local relief and slope are greatest. Summary Collectively, these findings have important implications for landslide response, preparedness, and loss reduction in western North Carolina. First, localized convective storms and regionally extensive lowpressure systems (e.g., atmospheric rivers) and tropical cyclones can each produce hundreds of landslides. These varied storm scenarios result in a yearround potential for major rainfall-induced landslide events, which typically occur during periods of extended above-normal rainfall in the region in recent times. Second, a geographically limited, non-tropical cyclone event, such as the one that occurred in May 2018, may produce landslide event magnitudes comparable to regionally extensive tropical cyclones, such as those experienced in September 2004. Third, localized landslide densities generated by an intense, orographically enhanced storm, such as the May 2018 event, can equal or even exceed those produced over larger areas by catastrophic tropical cyclones (e.g., in 1940). At present, advanced warnings for landfalling tropical cyclones or advancing low-pressure systems are typically on the order of days, whereas those for rapidly developing, warm-weather convective storms may be only on the order of only minutes to hours. Finally, the May 2018 storm follows the regional pattern that the BRE and reentrants incised into it are areas of concentrated landslide activity in historical times. In summary, the evolving landslide hazard program at the NCGS has a three-fold emphasis: responding to requests for technical assistance on landslides in cooperation with local, state, and federal agencies; preparing landslide hazard maps showing areas of past and

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potential future landslide activity; and making this information readily accessible to the public. Challenges remain to effectively communicate with the public and policymakers about landslide hazards. Our ability to accomplish these goals has increased with the use of high-resolution lidar, remote sensing, digital field data collection, uncrewed aerial systems, and evolving field methods. Contractual assistance from Appalachian Landslide Consultants and the National Environmental Modeling and Analysis Center is a key element of these efforts. ACKNOWLEDGMENTS We gratefully acknowledge funding of the NCGS landslide hazards program by the North Carolina General Assembly. We offer sincere thanks to the North Carolina Department of Environmental Quality and the Division of Energy, Mineral, and Land Resources for their leadership and management support of the program. Appalachian Landslide Consultants were invaluable in the mapping of Polk County during both emergency response and the county-wide inventory. Contractual support by the National Environmental Modeling and Analysis Center at the University of North Carolina at Asheville has been instrumental in development of the landslide Web map viewer. Special thanks go to the Broad River Fire Volunteer Department and Brent Hayner for their help and UAS expertise. We thank Francis Ashland and the U.S. Geological Survey for funding and installing the monitoring stations at the Howard Gap landslide and in other locations in western North Carolina. Last but not least, we are grateful to the people of Polk County for their generous access to land and the firsthand accounts of their experiences during the landslide event of 2018. This work was partially supported by the U.S. Geological Survey, National Cooperative Geologic Mapping Program, under assistance of award no. G19AC00162. The reviews and comments of three anonymous reviewers resulted in many substantial improvements to the paper. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the State of North Carolina. REFERENCES Basist, A.; Bell, G. D.; and Meentemeyer, V., 1994, Statistical relationships between topography and precipitation patterns: Journal Climate, Vol. 7, No. 9, pp. 1305–1315. Bauer, J. B., 2021, personal communication, Appalachian Landslide Consultants, Asheville, NC. Bauer, J. B. and Fuemmeler, S. F., 2019, Geologic evaluation for slope stability, Interstate 26 and Howard Gap rehab, Saluda

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Using Radar Rainfall to Explain the Occurrence of a 2012 Soil Slip Near Mt. LeConte, TN, USA JEFFREY R. KEATON* Wood Environment & Infrastructure Solutions, 6001 Rickenbacker Road, Los Angeles, CA 90040

Key Terms: Radar Rainfall, Rain Gauge, Rainfall Intensity, Soil Slip, Debris Flow ABSTRACT A storm on August 5, 2012, triggered a soil slip that mobilized into a debris flow in a relatively remote part of Great Smoky Mountains National Park, 2 km southwest of Mt. LeConte, TN. A rain gauge at Mt. LeConte Resort, recorded manually at 07:00 Eastern Time each day, reported 92.46 mm of rainfall on August 6, 2012, to the National Weather Service. Near Composite Reflectivity (NCR) and Digital Precipitation Array (DPA) products from the Knoxville, TN, NEXRAD Doppler weather radar station for August 5 and 6, 2012, were used to infer the storm’s rainfall intensity and duration details. NCR products consist of the maximum radar reflectivity value from all 14 elevation angles scanned for each 1.09 km2 cell, which users must convert to precipitation depth. DPA products are rain rate (in./hr) calculated with a National Weather Service model that uses radar reflectivity and automated rain gauge measurements to assign regional bias factors to each complete radar scan of the atmosphere surrounding the radar station using a national grid system composed of 17.12 km2 cells. Cumulative NCR inferred rain depth was 90.65 mm at the Mt. LeConte gauge location and 69.23 mm at the soil slip location. The cumulative DPA rain depth was 47.64 mm at the gauge. The maximum 15 minute NCR precipitation intensity was 39.61 mm/hr at the Mt. LeConte gauge location and 63.24 mm/hr at the soil slip location. The timing of the soil slip probably coincided with a 20-minute-long interval of maximum intensity precipitation.

cent to Alum Cave Trail, about 2 km southwest of Mt. LeConte. A park staff member reported the soil slip and debris flow to Professor Arpita Nandi, who was collaborating on hydrology-focused research in the park (Mandal et al., 2022) and wanted to use National Weather Service (NWS) weather radar to supplement a rain gauge located at the Mt. LeConte resort. The rain gauge is read manually each day at 07:00 Eastern Time and reported to the NWS. The nearest weather station with hourly readings is near Cherokee, NC, 21.5 km east of Mt. LeConte and more than 900 m lower in elevation. Weather radar has many products (https://www. weather.gov/jetstream/doppler_intro), two of which were used in this study. One product is basic measured radar reflectivity, whereas the other is a calculated product that reports precipitation rates in in./hr. These two radar products were compared with each other for the location of the Mt. LeConte rain gauge because the hydrology-focused research preferred the calculated product. The radar reflectivity product was used to estimate precipitation depth at the soil slip location and at the Cherokee rain gauge location.

INTRODUCTION A summer rainstorm in 2012 triggered a soil slip that mobilized into a debris flow near Mount Le Conte (Mt. LeConte) in the Great Smoky Mountains National Park (GSMNP) (Figures 1 and 2). The soil slip was noticed by park staff members on August 6, 2012, the morning after it happened because it was adja*Corresponding author email: jeff.keaton@woodplc.com

Figure 1. Mt. LeConte location in Great Smoky Mountains National Park. KMRX and KGSP are National Weather Service identiﬁers for the two closest weather radar stations.

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Keaton

Figure 2. U.S. Geological Survey 7.5 minute topographic quadrangle (upper map) accessed as a base map with ArcGIS 10.4 application and two excerpts of aerial photographs (lower diagrams) available through Google Earth Pro.

Precipitation intensity and duration calculated from radar reﬂectivity are described for the two rain gauge locations and at the soil slip and used to explain the timing of the soil slip. The objectives of this paper are to describe the weather radar products used to compare inferred precipitation at the Mt. LeConte and Cherokee rain gauges, use the weather products to evaluate the incremental and cumulative precipitation depths for the 1 day period ending at 07:00 Eastern Daylight Time,

EDT on August 6, 2012, and demonstrate the utility of radar rainfall intensity to explain the likely timing of the soil slip that was reported by park staﬀ members on August 6, 2012. RAIN GAUGES, SOIL SLIP, AND GEOLOGIC SETTING The rain gauge at Mt. LeConte is a nonrecording standard rain gauge (8 in. [203.2 mm], diame-

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Radar Rainfall to Explain Soil Slip Table 1. Locations of key features. Ground surface elevation is reported for rain gauge and soil slip locations; radar sensor elevation is reported for weather radar stations. Feature Mt. LeConte rain gauge Trout Branch soil slip Cherokee TN (Swain Co., NC) rain gauge KMRX NEXRAD weather radar station KGSP NEXRAD weather radar station

Latitude (°N)

Longitude (°W)

Elevation (m)

35.6550 35.6393 35.6197 36.1686 34.8833

83.4411 83.4475 83.2069 83.4019 82.2198

1,979 1,572 1,036 437 325

ter, steel) located adjacent to the LeConte Lodge (Figure 2 and Table 1). During the period of interest for this paper, it was listed as part of the NWS Cooperative Summary of Day (COOP SOD) program under Network ID GHCND:USC00406328. The station was established in 1987 and reports temperature (maximum, minimum, and at observation) and 24 hour precipitation ending at observation time, which is 07:00 Eastern Time (https://www.ncdc.noaa. gov/cdo-web/datasets/GHCND/stations/GHCND: USC00406328/detail). In keeping with descriptions of oﬃcial NWS weather stations, obstructions near the gauge are listed in the station details. Obstructions near the Mt. LeConte gauge are buildings, at distances ranging from 6 to 15 m and elevation angles of 10 to 30 degrees, and trees, at distances ranging from 7 to 27 m and elevation angles of 19 to 30 degrees. The rain gauge at Cherokee is a precision tipping bucket rain gauge that is a Remote Automatic Weather Station (RAWS) listed as “Cherokee Tennessee” (https://wrcc.dri.edu/wraws/ky_tnF.html). The geographic coordinates (Table 1) of this location plot in Swain County, NC, at a remote location approximately 19 km northeast of Cherokee, NC. The coordinates viewed in Google Earth are adjacent to a rectangular fenced feature that appears to be a weather station. This station provides hourly data at the hour for global positioning system (GPS)-synchronized local time via the Geostationary Operational Environmental Satellite (GOES), which is operated by the National Oceanic and Atmospheric Administration (NOAA). Climate data collected by RAWS consists of wind speed and direction, air temperature, fuel temperature, and moisture, relative humidity, and precipitation depth (NWCG, 2005). The Trout Branch soil slip (Figure 2) occurred adjacent to the park’s Alum Cave Trail at an elevation of 1,588 m (Table 1) on the southwest side of Peregrine Peak. This location is on the southeast divide of the Trout Branch drainage basin (Figure 2). Two aerial photos in Figure 2 show the location of the soil slip reported on August 6, 2012; the left photo is dated February 9, 2010, prior to the soil slip, whereas the right photo is dated March 18, 2013, after the soil slip

occurred. A pre-2010 soil slip appears to be present in both photos a short distance south of the soil slip reported on August 6, 2012. Review of Google Earth images suggests that neither soil slip feature existed on March 15, 1992. Vegetation patterns suggest that the southern soil slip had occurred several years prior to June 5, 2007; this suggestion is reinforced by clearer vegetation pattern in an April 16, 2008, image. The Trout Branch soil slip location is within the highlands section of the western Blue Ridge province 2 km southwest of Mt. LeConte (2,010 m elevation) and 10 km northeast of Clingmans Dome (2,025 m elevation), the highest point in GSMNP. The highlands section consists predominantly of steep slopes underlain by metasedimentary bedrock formations of Neoproterozoic age that have been faulted and folded (Southworth et al., 2012). The Trout Branch soil slip location is underlain by rocks of the Anakeesta Formation of the Great Smoky Group. The Anakeesta Formation contains a greater variety of rock types than any other formation in the highlands. Rocks of the Anakeesta Formation contain graphite, which gives them a characteristic dark color. The rocks also contain abundant sulfide minerals that produce rusty orange staining through the weathering process. The dominant rock unit is dark gray graphitic and sulfidic slate, metasiltstone, and phyllite, containing thin beds of metasandstone and metagraywacke. Dominantly fine-grained rocks of the Anakeesta Formation are well exposed at Alum Cave. Thin bodies of dark gray fine-grained metadolostone occur within the metasiltstone unit and can be recognized by their dissolution texture (Southworth et al., 2012). Rocks of the Anakeesta Formation in the vicinity of the Trout Branch soil slip dip to the southeast at 35 to 55 degrees; the dip symbol closest to the soil slip location is 45 degrees (Southworth et al., 2012). The geologic map in the vicinity of the Trout Branch soil slip shows numerous small surficial deposits labeled Qc or Qdf, representing Holocene and Pleistocene colluvium or Holocene debris flows, respectively. The location of the Trout Branch soil slip and the older soil slip to the south of the Trout Branch soil

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slip are shown on the geologic map as Za, representing the Anakeesta Formation (Southworth et al., 2012). The soil survey for the GSMNP area of the Trout Branch soil slip identifies a very stony-rock outcrop complex, known as the Luftee-Anakeesta, on 50 to 95 percent slopes (UC Davis, 2021). The geomorphic position of this soil complex is on moderately steep to very steep mountain slopes with deep, well-drained soils in high elevations. The soil complex formed in residuum that is affected by soil creep in the upper part and that weathered from low-grade metasedimentary rocks, primarily slate. The typical pedon for the Luftee very channery loam is on a forested, 65 percent south-southwest–facing slope at elevation of 1,525 m with bedrock at a depth of ∼1 m. The typical pedon for the Anakeesta channery loam is on a forested, 65 percent north-northeast–facing slope at elevation of 1,525 m with bedrock at a depth of ∼1.25 m. The Trout Branch soil slip occurred on a south-southwest–facing slope, probably in the Luftee very channery loam soil formed on graphitic and sulfidic slate of the Anakeesta Formation. Doppler Weather Radar The NEXRAD (Next Generation Weather Radar) system of WSR-88D Doppler radar stations was developed in the 1990s by the NWS (2021). Two NEXRAD weather radar stations, KMRX and KGSP, are located 59 and 138 km from Mt. LeConte (Figure 1), both of which are within the 230 km distance for short-range (termed “near” by NWS) weather radar products (https://www.ncdc.noaa.gov/data-access/ radar-data/radar-map-tool). NEXRAD products are archived and available for no-cost online retrieval at https://www.ncdc.noaa.gov/nexradinv/. Both weather radar stations were operating in precipitation mode during the storm on August 5 and 6, 2012, which consists of sixteen 360 degree scans of the atmosphere in 14 elevation angles during a nominally 5 minute cycle, which can range from 4 to 10 minutes. Digital Precipitation Array (DPA) and Near Composite Reflectivity (NCR) data for weather radar station KMRX (Figure 1) were used in this analysis. Examples of these two products are compared with each other and with Near Base Reflectivity (N0R) data in Figures 3a-3c; similar examples of these products from the KGSP radar station are presented in Figures 3d-3f. NCR shows storm structure and variability, whereas DPA is used for regional flood assessments. Users convert reflectivity values from decibels in the “Z” scale (dBZ), which is unique to weather radar, to rain rate with equations and information available from the NWS (e.g., https://www.weather.gov/ jetstream/doppler_intro).

Local mountains near Mt. LeConte block part of the lowest elevation angle of the KMRX weather radar station at the Cherokee weather station, which is as a radial pattern of radar reflectivity values in Figure 3c. The lowest elevation angle of the KGSP weather radar station (Figure 3f) appears to provide radar reflectivity coverage of the study area uninterrupted by topography. The elevation angle of the lowest weather radar scan is 0.5 degrees, which results in the centerline of the scan of the lowest KGSP station being at approximately 2,450 m altitude at the Mt. LeConte gauge location because the KGSP station is 138 km away (Figure 1). The KMRX weather radar station is 59 km from the Mt. LeConte rain gauge, resulting in a scan centerline altitude of approximately 760 m for the lowest elevation angle of the radar scan. The second, third, and fourth lowest radar scans have elevation angles of 0.9, 1.3, and 1.8 degrees, respectively, with centerline altitudes of approximately 1,220, 1,525, and 2,135 m, respectively, at the position of the Mt. LeConte rain gauge. Thus, the fourth lowest KMRX radar scan has a centerline altitude that is above the highest point of the terrain and lower than the centerline altitude of the lowest KGSP radar scan. The upper part of the third lowest KMRX radar scan has an altitude that is approximately the same as the mid-height altitude of the fourth lowest radar scan. Therefore, the closer of the two weather radar stations (KMRX) was judged to have suitable coverage for this study. DPA products (Figure 3a) display rain rate in inches per hour (upper arrow in Figure 3a and units in legend above column of colored bars), which is calculated with a NWS model that uses radar reflectivity and data from automated rain gauges in the region to assign regional bias and error factors (lower arrow in Figure 3a) to each complete radar scan using a national grid system composed of 17.12 km2 cells (NOAA, 2017). NCR products (Figure 3b) consist of the maximum (composite) reflectivity (arrow) value in dBZ units (legend above column of colored bars) from all elevation angles for each 1.09 km2 cell. The “124NM” in the line with the arrow refers to the “near” distance in nautical miles, a standard weather service unit of measure that is equal to 230 km. A few cells at the KMRX station have no data because of their proximity to the radar station and the 19.5 degree maximum elevation angle of the weather radar scans (https://www.weather.gov/ jetstream/vcp_max). N0R products (Figure 3c) consist of reflectivity values in dBZ units (upper arrow and legend in Figure 3c) for the lowest elevation angle of the radar scan (0.5 degrees, lower arrow in Figure 3c). N0R is useful for estimates of the rate of rain closest to the ground surface.

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Figure 3. Examples of NEXRAD Level III weather radar products from KMRX (a–c) and KGSP (d–f) viewed with Weather and Climate Toolkit. (a) Digital Precipitation Array (DPA, KMRX). (b) Near Composite Reflectivity (NCR, KMRX). (c) Near Base Reflectivity (N0R, KMRX). (d) Digital Precipitation Array (DPA, KGSP). (e) Near Composite Reflectivity (NCR, KGSP). (f) Near Base Reflectivity (N0R, KGSP).

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Figure 3. (continued)

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Depending on the storm velocity, composite reflectivity at any pixel in the radar coverage may measure rain that reaches the ground some distance from the pixel. The display of base reflectivity (Figure 3c) shows a radial streaked pattern caused by variability of very small droplets and the effects of shadows caused by terrain features, such as Mt. LeConte and a peak northwest of the Cherokee Tennessee RAWS. Time used in weather radar is indexed to Coordinated Universal Time (UTC) and reported as “MM/DD/YYYY HH:MM:SS Z,” which can be seen in two lines below the radar station identifier in each of the six panels in Figure 3. The letter “Z” stands for “Zulu” and denotes UTC. The lower time stamp includes “(VOL)” to indicate that it is the beginning time of the atmosphere volume coverage; the upper time stamp is the ending time for the weather radar product. All six panels in Figure 3 have a begin time of 00:01:16 Z, which is 20:01:16 EDT. Inspection of the time stamps for the six weather radar products in Figure 3 reveals that the end time for the base reflectivity product (N0R; Figure 3c) is 5 seconds later than the begin time. The end time for the DPA product (Figure 3a) is 2 minutes and 22 seconds later than the begin time. The end time for the NCR product (Figure 3b) is 4 minutes and 49 seconds later than the begin time. The base elevation angle is a single 360 degree scan of the radar transmitter/receiver, whereas DPA uses the lower seven elevation angles, and NCR is the maximum value for each azimuth and range from all 14 elevation angles. Reflectivity is a measure of size and number of water droplets in a volume of the atmosphere and is correlated to precipitation intensity, or rain rate, RR, in mm/hr. The correlation between dBZ and RR used in this study (Eq. 1a) was calculated based on a table of dBZ and RR values displayed on the NWS website (https://www.weather.gov/jetstream/refl), which corresponds nearly exactly to the default Z = 300RR1.4 (Eq. 1b) but is simpler to use in spreadsheet calculations. Reflectance higher than 51 dBZ may be an indicator of ice in the atmosphere contributing to overestimation of rain rates (Fulton, 1999). Reflectance less than 15 dBZ (<0.2 mm/hr) is neglected. The variability in precipitation shown by the NCR radar product demonstrates the utility of reflectance in discerning areas of maximum precipitation in map view. RR = exp (−4.073 + 0.1644 × dBZ) ; 15 ≤ dBZ ≤ 55

RR =

10(dBZ/10) /300

(1a)

(1/1.4) (1b)

Comparison of DPA and NCR results at Mt. LeConte for 20:01 (EDT) on August 5, 2012, illustrates the sizes and positions of the pixels for the two weather radar products (Figure 4), which remain fixed. At the conclusion of the atmosphere volume scan, the calculated DPA result for the 17.12 km2 cell containing the Mt. LeConte rain gauge was 5.79 mm/hr (Figure 4, left image) for the approximately 5 minute duration of the radar scene. The comparable NCR result for the 1.05 km2 cell containing the Mt. LeConte rain gauge was 30 dBZ (Figure 4, right image), which corresponds to a rain rate of 2.36 mm/hr for the approximately 5 minute duration of the radar scene. The DPA cell is 16.3 times larger than the NCR cell and is practical for use in hydrology studies, whereas the smaller NCR cells provide finer detail of storm variability that is useful in studies of ground surface response to precipitation events. The NCR scenes displayed in Figure 5 show the progression of the August 5, 2012, storm. The explanation of symbols for all four scenes is presented in panel D. Panels A, B, and C are spaced over a 44 minute period from 16:30 to 17:14 (EDT). Panels A and B show darker red colors at the Mt. LeConte and Trout Branch locations, whereas panel C shows darker red colors at the Cherokee gauge; darker red colors indicate high radar reflectivity, which is interpreted as higher precipitation rate. Panel D is nearly 3 hours later than panel C but only 10 minutes later than the scene in Figure 4 and shows widespread yellow and orange colors, which is moderately high radar reflectivity. STORM OF AUGUST 5 AND 6, 2012 The downloaded reflectivity data from NEXRAD WSR-88D Doppler weather radar station KMRX for August 5 and 6, 2012, were processed using the Weather and Climate Toolkit (https://www.ncdc. noaa.gov/wct/) and exported for display and analysis using ESRI ArcGIS 10.4. The Weather and Climate Toolkit viewer allowed the relevant times of the storm in the Mt. LeConte area to be identified. Reflectivity values indicated precipitation in the atmosphere at specific pixels at the time of the radar scan; however, rain may fall to the ground in adjacent or nearby pixels in fast-moving storms. The weather radar reflectivity scenes in Figure 5 suggest that the storm was distributed in convective cells of higher precipitation intensities (orange and red pixel colors) and lingered in the Mt. LeConte and Trout Branch area for approximately an hour. Therefore, a reasonable assumption is that the precipitation in the atmosphere in Figure 5 probably fell to the ground within the indicated pixel, although it is a source of error with weather radar products.

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Figure 4. Comparison of Digital Precipitation Array (DPA, left panel) and Near Composite Reﬂectivity (NCR, right panel) for scenes with the same time stamp. KMRX is Knoxville NEXRAD station.

The Cherokee RAWS data include wind. On August 5, 2012, winds were light (average <1 m/s) and variable in the early hours. The average wind speed increased to 1.8 m/s from the west about eight hours before the first rain was recorded. For seven hours before rain was recorded, the wind was from the west with average speeds between 1.8 and 2.7 m/s and maximum speeds between 3.1 and 4.9 m/s. As rain was being recorded at the Cherokee RAWS, wind was from the southwest with a maximum speed of 7.6 m/s and an average speed of 1.3 m/s. After the first hour of the storm, the winds returned to light and variable. A graph of incremental and cumulative precipitation (Figure 6) displays both measured rainfall at the Mt. LeConte and Cherokee gauge locations and estimated inferred precipitation at the gauge locations, as well as at the Trout Branch soil slip location, based on successive weather radar data sets covering the same period of time. The reflectivity values in each radar scene were converted to rain rate using Eq. 1a and then multiplied by the duration in hours since the previous radar scene to produce an equivalent inferred precipitation depth for the time increment. Cumulative inferred precipita-

tion is the running sum of the incremental precipitation depths. Precipitation amounts at the Mt. LeConte gauge, along with weather radar DPA and NCR rainfall estimates, are displayed in the upper graph in Figure 6. The gauge, read daily at 07:00 Eastern Time, represents both a daily increment and daily total of 92.46 mm; it is plotted only as cumulative precipitation. The DPA data at the Mt. LeConte location show an onset of precipitation at about 15:00 (EDT) with 0.21 mm accumulated by 15:15 (EDT) and the largest increment of 2.64 mm occurring at 16:45 (EDT). The cumulative 24 hour total precipitation at 07:00 (EDT) on August 6, 2012, was 47.64 mm. The NCR data were calculated from the reflectivity measurements and formed the basis for weather radar increments. They showed traces of inferred precipitation (i.e., reflectivity of 15 dBZ) at the Mt. LeConte location as early as 07:05 (EDT) on August 5, 2012, with an accumulation of 0.62 mm by 15:15 (EDT). The largest increment of 5.27 mm occurred at 16:50 (EDT) and corresponded to a reflectivity of 50 dBZ. The cumulative 24 hour inferred total was 90.65 mm at 07:00

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Figure 5. Comparison of four Near Composite Reﬂectivity (NCR) scenes for the storm of August 5, 2012.

(EDT) on August 6, 2012, suggesting an underestimation error of 1.8 mm or 2.0 percent. Precipitation estimates at the Trout Branch soil slip location can be provided only by the NCR data set (Figure 6, middle graph). From 07:05 (EDT) until 15:09 (EDT) on August 5, 2012, reﬂectivity values at the Trout Branch soil slip location were 5 to 10 dBZ,

indicating unsettled weather conditions, but no measurable accumulation of rainfall. Between 15:09 (EDT) and 15:46 (EDT), reﬂectivity values at the soil slip location ranged from 10 to 25 dBZ, corresponding to incremental inferred rainfall amounts of 0 to 0.10 mm, and an accumulation of 0.26 mm. Beginning at 15:51 (EDT) and lasting for 1:18 (hr:min) until 17:09 (EDT),

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Figure 6. Incremental and cumulative precipitation for the storm of August 5 and 6, 2012, at The Mt. LeConte gauge location (upper diagram), Trout Branch soil slip location (middle diagram), and Cherokee Tennessee RAWS gauge location (lower diagram).

reflectivity values were 35 dBZ or higher at the soil slip location. For over an hour, from 16:01 (EDT) until 17:05 (EDT), reflectivity values were 40 dBZ or higher, and from 16:20 (EDT) through 16:35 (EDT), reflectivity values were 50 dBZ. The maximum incremental rainfall amount, 5.27 mm, occurred four times between 16:01 (EDT) and 16:35 (EDT), and the last three times were consecutive radar time increments (Figure 6, middle graph). The cumulative total rainfall inferred from weather radar reflectivity increased by over 42

mm, from 1.16 mm at 15:56 (EDT) to 43.77 mm at 17:09 (EDT). The last radar reﬂectivity for the storm at the soil slip location occurred at 22:30 (EDT) on August 5, 2012. The 24 hour total inferred rainfall was 69.23 mm at 07:00 (EDT) on August 6, 2012. The lower graph in Figure 6 displays the Cherokee gauge and NCR data sets. The Cherokee RAWS has an automatic tipping bucket rain gauge and transmits hourly accumulations. The hourly data were plotted as quarter-hour values to preserve the scale of the

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Radar Rainfall to Explain Soil Slip Table 2a. Average return periods of partial duration series precipitation depth estimates for the August 5, 2012, storm at the Mt. LeConte gauge and the Trout Branch soil slip location (Bonnin et al., 2006). Mt. LeConte Point Precipitation Depth Data Type

Precipitation Duration

Rain gauge Weather radar NOAA Atlas 14 Weather radar NOAA Atlas 14 Weather radar NOAA Atlas 14 Weather radar NOAA Atlas 14 Weather radar NOAA Atlas 14 Weather radar NOAA Atlas 14 Weather radar NOAA Atlas 14

24 hours 24 hours 24 hours 12 hours 12 hours 6 hours 6 hours 2 hours 2 hours 60 minutes 60 minutes 30 minutes 30 minutes 15 minutes 15 minutes

Trout Branch Soil Slip

Precipitation Depth (mm)

Return Period (years)

Precipitation Depth (mm)

Return Period (years)

92.46 90.65 91 86.11 86 61.58 64 28.72 38 22.00 33 17.87 26 12.22 19

∼1.1 ∼1 1 ∼2 2 ∼1.8 2 <1 1 <1 1 <1 1 <1 1

— 69.23 91 69.23 72 68.44 64 43.78 46 40.22 40 26.98 32 15.81 19

— <1 1 <1 1 ∼3.0 2 ∼1.7 2 ∼2.1 2 ∼1.2 2 <1 1

graph for visual comparison among all three graphs in Figure 6. The storm started later at the Cherokee gauge location than at the Mt. LeConte and Trout Branch locations. The total precipitation for the 1 day (24 hour) period ending at 07:00 (EDT) on August 6, 2012, was 35.04 mm for the Cherokee gauge and 47.05 mm for the NCR radar rainfall estimate, suggesting an overestimation error of 12 mm or 34.3 percent. One of the two highest incremental readings occurred at 20:06 (EDT), near the end of the storm at the Cherokee gauge, which might be anomalous. Incremental and cumulative rainfall values at the Mt. LeConte location (Figures 6) indicate that 94 percent of the inferred DPA total and 86 percent of the inferred NCR total fell on August 5, 2012. Furthermore, 100 percent of the inferred NCR total inferred rainfall at the Trout Branch soil slip and the Cherokee RAWS locations fell on August 5, 2012. The storm of August 5 and 6, 2012, appears to have been a relatively common event, based on both the storm total at the Mt. LeConte gauge and the radar rainfall estimates at the Trout Branch soil slip location, compared to the published precipitation frequency estimates for Tennessee (Bonnin et al., 2006). The 1 year return period precipitation depth for a 24 hour storm is 91 mm for both the Mt. LeConte gauge location and the Trout Branch soil slip location (Table 2a). Similarly, the 1 year return period precipitation intensity is greater than 63 mm/hr for durations of 5, 10, and 15 minutes for both locations (Table 2b). The 2 year precipitation intensity for 60 minute intervals is 40 mm/hr, which is approximately the 60 minute radar rainfall depth value at the Trout Branch soil slip location during this storm (Table 2a).

Inferred Rainfall Intensities and Likely Debris-Flow Initiation The inferred incremental rainfall estimates at the Mt. LeConte rain gauge, Trout Branch soil slip, and Cherokee RAWS locations (Figure 6) were used to calculate precipitation intensities for durations of approximately 15 minutes (Figure 7). The radar reﬂectivity results (NCR) plotted as incremental precipitation (vertical bars) in Figure 6 are reproduced in Figure 7. Approximately 15 minute estimated rainfall intensities are plotted as open circle symbols. Weather radar reﬂectivity values were collected at nominally 5 minute intervals, which, for the period of interest, really varied between 4 and 10 minutes, with a mean interval of 5.2255 minutes and a median interval of 5.0000 minutes. Three consecutive radar increments for the period of interest ranged from 14 minutes to 22 minutes, with a mean duration of 15.6777 minutes and a median duration of 15.0000 minutes. The precipitation intensities were calculated with a spreadsheet software by summing the estimated incremental inferred rainfall in three consecutive radar increments and dividing by the actual combined duration of the three increments in hours. The highest approximately 15 minute precipitation intensity values produced by the August 5, 2012, storm at the Mt. LeConte gauge, the Trout Branch soil slip location, and the Cherokee RAWS gauge were 39.61 mm/hr, 63.24 mm/hr, and 35.15 mm/hr, respectively (Table 3). The high-intensity periods began at the Mt. LeConte gauge at or shortly after the highintensity periods concluded at the Trout Branch soil slip (Figures 6 and 7). The high-intensity period at the

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Keaton Table 2b. Average return periods, precipitation depths, and precipitation intensities for the August 5, 2012, storm at the Mt. LeConte gauge and the Trout Branch soil slip location from NOAA Atlas 14, Volume 2, Version 3 (Bonnin et al., 2006). Mt. LeConte Point Precipitation Intensity Data Type

Precipitation Duration

NOAA Atlas 14

24 hours 12 hours 6 hours 2 hours 60 minutes 30 minutes 15 minutes 10 minutes 5 minutes

NOAA Atlas 14

Trout Branch Soil Slip

Precipitation Intensity (mm/hr)

Return Period (years)

Precipitation Intensity (mm/hr)

Return Period (years)

4 6 9 19 33 53 77 92 116

1 1 1 1 1 1 1 1 1

4 6 9 19 33 53 77 93 116

1 1 1 1 1 1 1 1 1

Cherokee RAWS gauge began approximately 3 hours later than the high-intensity period at Mt. LeConte (Figure 7). Staley et al. (2015) observed that post-wildfire debris flows in the western United States have been triggered by high-intensity rainfall events that were less than 1 hour in duration (i.e., short-duration events). They defined an intensity-duration (I-D) threshold for recently burned watersheds as I = 11.6 × D−0.7 ,

(2)

where D ranges from about 0.067 hours (4 minutes) to 1 hour, and I is in mm/hr. The area around the Trout Branch soil slip and downstream track had not burned within at least a few years prior to the August 5, 2012, storm, as can be deduced from the appearance of the ground in the 2010 and 2013 aerial photo images in Figure 2. Therefore, the ground at the soil slip should have been less susceptible to debris-flow production than predicted by Eq. 2. Since rainfall I-D thresholds for soil slips in the park have not been developed, Eq. 2 was used as a benchmark for evaluating the effects of the storm on August 5 and 6, 2012, at both the Mt. LeConte rain gauge and the Trout Branch soil slip locations (Figure 8, upper and lower diagrams, respectively). Precipitation I-D thresholds for onset of soil slips or debris flows were not found in the published liter-

ature for the GSMNP area. However, an analysis of storm-triggered debris flows in the Blue Ridge of central Virginia was published by Wieczorek et al. (2000), including an I-D plot of observations for Hurricane Camille (August 1969), a major storm on June 27, 1995, and Hurricane Fran (September 1996). The ID plot (Wieczorek et al., 2000, their Figure 12) was linear and extended from 0 to 26 hours of duration and from 0 to 180 mm/hr intensity. Nine points were labeled “no debris flows,” and 30 points were labeled “debris flows.” Data from Wieczorek et al. (2000) are plotted with dark blue triangles in Figure 8 at durations exceeding 30 minutes and intensities exceeding 100 mm/hr. The shortest duration is 2 hours for a “no debris flows” data point in Wieczorek et al. (2000, their Figure 12); the maximum duration visible in Figure 8 is 1.26 hours. The threshold line separating “debris flows” and “no debris flows” in the plot published by Wieczorek et al. (2000, their Figure 12) appears to be hand drawn. A log-log equation for the threshold line was approximated for this study as I ≈ 92 × D−0.19 ; 0.8 hr ≤ D ≤ 24 hr,

(3)

where D ranges from approximately 0.8 to 24 hours, and I is in mm/hr. An early attempt at deﬁning the rainfall threshold at which shallow planar landslides rapidly disintegrate to become debris ﬂows was based on 73 published

Table 3. Summary of highest 15-minute precipitation intensity values during the August 5, 2012, storm at the Mt. LeConte gauge, the Trout Branch soil slip, and the Cherokee RAWS gauge.

Location Mt. LeConte gauge Trout Branch soil slip Trout Branch soil slip Cherokee RAWS gauge

15 Minute Intensity (mm/hr)

Ending Time (EDT)

Actual Duration (hours [minutes])

39.61 63.24 63.24 35.15

16:50 16:30 16:35 20:11

0.2500 (15) 0.2333 (14) 0.2500 (15) 0.2500 (15)

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Figure 7. Incremental precipitation and rainfall intensity for the storm of August 5 and 6, 2012, inferred from Near Composite Reﬂectivity (NCR) at the Mt. LeConte gauge location (upper diagram), Trout Branch soil slip location (middle diagram), and Cherokee Tennessee RAWS gauge location (lower diagram).

observations of rainfall intensities and durations that produced debris ﬂows on relatively undisturbed slopes (Caine, 1980). The observations were reported to be from a variety of climate zones with diﬀerent geology and topography conditions. Antecedent moisture conditions were unknown, and precipitation records were inconsistent. Caine’s (1980) resulting threshold equation, Eq. 4, is I = 14.82 × D−0.39 ; 0.1667 hr ≤ D ≤ 240 hr,

(4)

where D, in hours, ranges from approximately 10 minutes (0.1667 hours) to 10 days (240 hours), and I is in mm/hr. Caine’s (1980) general threshold is plotted in Figure 8 as a dark green long-and-short dashed line at durations greater than 10 minutes, and projected as a dark green short dashed line at durations less than 10 minutes. The symbols in both diagrams in Figure 8 are consistent for durations of approximately 5, 10, 15, 20, 25, 30, 45, and 60 minutes. The intensity and dura-

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Figure 8. Precipitation intensity and duration graphs for the Mt. LeConte gauge location (upper diagram) and Trout Branch soil slip location (lower diagram). Blue Ridge debris ﬂows in central Virginia are from hurricane and major storms (Wieczorek et al., 2000). General threshold is for catastrophic shallow landslides and durations between 10 minutes and 10 days (Caine, 1980).

tion values were determined by summing the calculated rain depths in mm over N successive reﬂectivity values and dividing by the corresponding combined duration in hours of the N measurements, where N = 1, 2, 3, 4, 5, 6, 9, and 12. The highest 5 to 15 intensity values for each approximate time interval are plotted in Figure 8. The value of the highest intensity and its time of occurrence are listed in Figure 8 for approximate durations of 5, 15, 30, and 60 minutes. The highest 15 minute precipitation intensity values with their ending times are summarized in Table 3. The highest incremental inferred rainfall amounts and maximum 15 minute inferred rain intensities at

the Mt. LeConte, Trout Branch soil slip, and Cherokee RAWS locations occurred on August 5, 2012 (Figure 7). The inferred maximum 5 minute rain intensities at the Mt. LeConte and Trout Branch soil slip locations plot essentially on the threshold line for postfire debris flows (Eq. 2; Figure 8). The post-fire debrisflow threshold trend line values (Staley et al., 2015) are exceeded by the inferred maximum rain intensities for durations longer than 5 minutes at both the Mt. LeConte and Trout Branch soil slip locations. The 92.46 mm total rainfall recorded at the Mt. LeConte gauge at 07:00 on August 6, 2012, occurred over a 24 hour period, which returns a calculated intensity of 3.85 mm/hr, which also exceeds the 1.25 mm/hr

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threshold intensity value extrapolated for a 24 hour duration using Eq. 2. The threshold line computed from the graph in Wieczorek et al. (2000) (Eq. 3) exceeds all radar rainfall intensities at all durations. Caine’s (1980) threshold line (Eq. 4) is lower than the precipitation computed from the radar rainfall, but it is parallel to its slope in log-log space. The times of the maximum estimated rain intensities at the Mt. LeConte location (Figure 8, upper graph) are 16:50 (EDT) for both 5 and 15 minute durations, 16:55 (EDT) for the 30 minute duration, and two times at 17:09 (EDT) and 17:14 (EDT) for the 60 minute duration. The calculated intensity values report the end time of the duration; therefore, the 15 minute duration ending at 16:50 (EDT) began at 16:35 (EDT). Similarly, the 30 minute duration ending at 16:55 (EDT) began at 16:25 (EDT), and the 60 minute durations ending at 17:09 (EDT) and 17:14 (EDT) began at 16:09 (EDT) and 16:14 (EDT), respectively. The importance of the time encompassed by these durations is that the high-intensity inferred rainfall occurred over a period of an hour, as can be deduced from the incremental precipitation and 15 minute duration rainfall intensity plots (Figure 7, upper graph). The times of the maximum inferred rain intensities at the Trout Branch soil slip location (Figure 8, lower graph) began about 15 minutes earlier than those at the Mt. LeConte location. The maximum inferred rain intensity for a 5 minute duration occurred five times at the Trout Branch soil slip location (16:01, 16:20, 16:25, 16:30, and 16:35 (EDT)). As expected, the maximum inferred rain intensity for a 15 minute duration had the same value as the 5 minute duration intensity and occurred twice (16:30 and 16:35 (EDT)), as can be seen in the intensity time-history plot (Figure 7, middle graph). The 30 minute duration ending at 16:50 (EDT) began at 16:20 (EDT) and includes four of the five highest 5 minute duration inferred rain intensities. The 45-minute duration ending at 16:45 (EDT) began at 16:00 (EDT), 1 minute prior to the end of the first occurrence of the highest 5 minute intensity. The 60 minute duration ending at 16:55 (EDT) began at 15:55 (EDT), which is essentially at the beginning of the first of the five highest 5 minute duration inferred rain intensities. A trend line for the highest values of the I-D plot of the August 5, 2012, storm at the Mt. LeConte location (Eq. 5; Figure 8, upper graph) is similar to Eq. 2 but with a lower gradient and higher position in log-log space: I = 24.0 × D−0.386 ,

(5)

where I is in mm/hr, and D is in hours. A two-part trend line is needed to represent the I-D plot of the

August 5, 2012, storm at the Trout Branch soil slip location (Eq. 6; Figure 8, lower graph) because the highest intensities persisted for nearly 20 minutes: 63.24; 0.06 < D ≤ 0.317 (6) , I= 40.63 × D−0.385 ; 0.317 < D < 1.12 where I is in mm/hr and D is in hours. The slope of the trend line in log-log space at durations greater than 0.317 hours (19 minutes) is essentially identical at both the Mt. LeConte and Trout Branch soil slip locations. The analysis of Doppler radar reflectivity from the KMRX weather radar station at the Mt. LeConte location suggests that the storm on August 5, 2012, produced nearly the same amount of rain (2 percent less) inferred from radar reflectivity at the Mt. LeConte rain gauge location as that recorded by the daily rain gauge (Figure 6). The highest 15 minute duration rain intensity at the Trout Branch soil slip location was approximately 60 percent greater than it was at the Mt. LeConte rain gauge location (Figure 7). The highest rain intensities persisted for 20 minutes at the Trout Branch soil slip location (Figure 8), which was four times longer than those at the Mt. LeConte gauge location. The soil slip and resulting debris flow at the Trout Branch location are likely to have initiated during the period of highest intensity rainfall, inferred from weather radar reflectivity to have been between 16:15 and 16:35 (EDT) on August 5, 2012. Use of NOAA Atlas 14 point precipitation frequency data for Tennessee (Bonnin et al., 2006) via the server (https://hdsc.nws.noaa.gov/hdsc/pfds/pfds_ map_cont.html) indicates that the August 5, 2012, storm was common, with return periods ranging from <1 year for many precipitation durations to a maximum of ∼3 years for a 6 hour duration at the Trout Branch soil slip location (Table 2). The 24 hour total precipitation depth recorded at the Mt. LeConte gauge (92.46 mm) corresponds to an average return period of ∼1.1 year; the comparable precipitation depth estimated from radar rainfall (90.65 mm) was equivalent to the depth for the 1 year return period (91 mm). The longest return period for precipitation at the Mt. LeConte location in the August 5, 2012, storm was ∼2 years for a 12 hour duration. Summary Two types of weather radar products were used to compare inferred precipitation with the values measured at the Mt. LeConte rain gauge for a storm on August 5 and 6, 2012, in the Great Smoky Mountains National Park. One of the weather radar products was used to compare inferred precipitation with the Cherokee RAWS weather station rain gauge and

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to evaluate precipitation at the location of a soil slip that was reported by a park staff member on August 6, 2012. Basic radar reflectivity (NCR) data are composite values in maximum weather radar (dBZ) units that represent columns of atmosphere in azimuth and range from NEXRAD radar stations, covering approximately 1 km2 areas (pixels) of the ground. Reflectivity values must be converted into rain rate before becoming usable for analyses. Digital precipitation array (DPA) data are values calculated automatically by a NWS model as rain rate in in./hr based on radar reflectivity and automated rain gauge data that cover over 17 km2 areas of the ground. Digital precipitation values are directly useable in regional applications. Potential errors associated with rainfall inferred from NCR data are associated with the dBZ-to-rain rate conversion calculation, the veracity of the dBZ values to represent liquid water droplets, and the assumption that the liquid droplets fall onto the ground within the pixel area. Potential errors associated with digital precipitation are fewer for regional applications and can be managed with bias and error values provided with the weather radar product. However, DPA results are broad averages that do not preserve details of storm structure or local rainfall intensities. No attempt was made to reconcile the NCR reflectivity with measured precipitation amounts. A more elaborate analysis might utilize individual elevation angles of reflectivity to determine where in the atmosphere column the highest reflectivity values were detected or evaluate an array of pixels in the radar coverage around the location of interest to infer direction and speed of air mass movement. The total precipitation measured at the Mt. LeConte gauge, which was read manually each day at 07:00 (EDT), was about 2 percent higher than the total precipitation at the gauge location inferred from the NCR data (Figure 6, upper diagram). However, the total precipitation measured at the Cherokee gauge, which automatically transmitted hourly data, was about 34 percent lower than the precipitation inferred from the NCR data at the gauge location (Figure 6, lower diagram). Precipitation inferred from NCR data showed variability in rainfall amounts that could not be compared with daily rainfall readings at the Mt. LeConte gauge; however, the variability in rainfall inferred from NCR data at the Cherokee gauge location compared reasonably well with measured hourly precipitation amounts.

CONCLUSIONS The utility of radar rainfall intensity to explain the likely timing of the soil slip that was reported by

GSMNP staff members on August 6, 2012 (Figures 7 and 8), is clear, even if specific rainfall amounts or intensity values might be inaccurate. Rain gauge values described in this paper (Figure 6) demonstrate some limitations, since the gauge near the soil slip location provided only daily totals, and the nearest gauge with hourly data was at a lower elevation and captured lower rainfall amounts and intensities. The previously published intensity-duration threshold for debris-flow initiation on recently burned slopes (Eq. 2; Figure 8) is not meant to be applied to slopes after a few years following fires. However, no similar threshold suitable for general application is available; therefore, the post-fire threshold was used for comparison in this paper. The line connecting the highest intensities for each duration at the Mt. LeConte location (Figure 8, upper diagram) in the log-log plot has a slope of −0.386, which follows a trend D−0.386 (Eq. 5). The line connecting the highest intensities for each duration higher than 19 minutes (0.317 hours) at the Trout Branch soil slip location (Figure 8, lower diagram) in the log-log plot has a nearly identical slope of −0.385, which follows a trend D−0.385 (Eq. 6). The slope of the intensity-duration trends of the radar rainfall is essentially identical to Caine’s (1980) general threshold (Eq. 4); however, the radar rainfall exceeded Caine’s (1980) threshold. The Trout Branch soil slip was reported by park staff members on August 6, 2012, and other soil slips could have occurred in the vicinity of Mt. LeConte without being reported. The August 5, 2012, storm appeared to have somewhat different characteristics at the Cherokee RAWS gauge, about 21.5 km to the east, than it did at the Mt. LeConte gauge. A line connecting the highest intensities inferred from the NCR data at the Cherokee RAWS location for each duration has a slope of −0.520 (not plotted for this paper), which is more steeply negative than that for the Mt. LeConte and Trout Branch soil slip locations, but less negative than the threshold line for recently burned slopes (Eq. 2). The slope of the line connecting maximum intensities with different durations between 0.0833 and 1.150 hours may be a characteristic of the potential for storm intensities and durations to induce soil slips and debris flows on susceptible slopes. Weather forecasts currently provide warnings for rainfall intensities; perhaps such forecasts can be expressed using rigorous probabilistic precipitation hazard terminology on a subregional basis. The intensity-duration plots (Figure 8) appear to have substantial value in assessing rainfall characteristics relevant to soil slip initiation and debris-flow mobilization. Data from automated tipping bucket

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rain gauges can provide reliable information for the locations of the gauges. Radar rainfall can provide similar information that could be normalized based on measured rainfall at gauge locations and then applied to locations of interest where soil slips have been documented and where similar slopes have remained stable. The conditions of the ground on the slope where the soil slip occurred are largely unknown. Some geological information is readily available, such as general bedrock geology and soil characteristics. Topographic information is available as digital elevation model data, but minor details typically are unavailable. The Trout Branch soil slip occurred adjacent to the Alum Cave Trail on its downhill side. Surface drainage from the trail could have been directed along an animal path or rodent burrow into the area where the soil slip occurred. Such details are highly important but require detailed geologic reconnaissance to identify and evaluate. The Trout Branch soil slip probably is a relatively unique event in that it was discovered within hours after it occurred. The storm on August 5, 2012, clearly had potential to induce soil slips and debris flows on susceptible slopes, despite the fact that it was a common event in terms of return period of precipitation depth and intensity (Tables 2 and 3). Locations where this storm triggered other soil slips or debris flows are unknown. Features of comparable importance are those locations that remained stable where precipitation occurred on slopes with similar ground conditions. Aerial photograph evidence available in Google Earth suggests that an older soil slip located a short distance south of the August 5, 2012, soil slip occurred after March 15, 1992, and several years prior to June 5, 2007. Online archives of weather radar data from the KMRX and other weather radar stations begin in 1994 and may be viewed with a convenient online viewer for dates beginning in 1995 (https://gis.ncdc.noaa.gov/maps/ncei/radar). Thus, weather radar reflectivity data might be available for an assessment of the inferred rainfall that initiated the pre-2007 soil slip near the 2012 soil slip. If so, then the inferred rainfall at the pre-2007 soil slip location would be interesting to compare with the inferred rainfall described in this paper. Radar rainfall has been useful for explaining occurrences of specific events, such as demonstrated in this paper. A greater potential value for radar rainfall might be a comparison of intensities and durations at similar locations that did and did not produce debris flows to aid in developing a precipitation-induced soil slip model and to enhance a forward-looking use of weather radar for future soil-slip and debris-flow forecasting.

REFERENCES Bonnin, G. M.; Martin, D.; Lin, B.; Parzybok, T.; Yekta, M.; and Riley, D., 2006, Precipitation-Frequency Atlas of the United States: NOAA Atlas 14, Vol. 2, Ver. 3.0, 2004 (revised 2006), National Oceanic and Atmospheric Administration, National Weather Service, Boulder, CO, 295 p., available at https://www.weather.gov/media/owp/oh/hdsc/docs/Atlas 14_Volume2.pdf (accessed January 3, 2021). Caine, N., 1980, Rainfall intensity-duration control of shallow landslides and debris flows: Geografiska Annaler, Series A, Physical Geography, Vol. 62, No. 1/2, pp. 23–27, https://doi. org/10.2307/520449 (accessed December 2021). Fulton, R. A., 1999, Sensitivity of WSR-88D rainfall estimates to rain-rate threshold and rain gauge adjustment: A flash flood case study: Weather and Forecasting, Vol. 14, pp. 604–624, https://doi.org/10.1175/1520-0434(1999)014< 0604:SOWRET>2.0.CO;2 (accessed December 2020). Mandal, A.; Nandi, A.; Shakoor, A.; and Keaton, J., 2021, Application of hydrological model for infiltration estimate for debris flow initiation using gauge and radar rainfall: A case study from Great Smoky Mountains National Park, Tennessee: Environmental and Engineering Geoscience, Vol. XXVIII, No. 1, pp. xxx, https://doi.org(?). National Oceanographic and Atmospheric Administration (NOAA), 2017, Part C WSR-88D Products and Algorithms: WSR-88D Meteorological Observations: Federal Meteorological Handbook No. 11, Office of the Federal Coordinator for Meteorological Services and Supporting Research, National Oceanographic and Atmospheric Administration, Silver Spring, MD, FCM-H11C-2017, 394 p., https://www.ofcm. gov/publications/fmh/FMH11/fmh11partC.pdf (accessed December 2020). National Weather Service (NWS), 2021, Everything You Ever Wanted to Know about the NWS WSR-88D: Electronic document, available at https://www.weather.gov/iwx/wsr_88d National Wildfire Coordinating Group (NWCG), 2005, National Fire Danger Rating System Weather Station Standards: Publication PMS 426-3, National Wildfire Coordinating Group, Washington, D.C., 26 p., https://raws.dri.edu/ documents/NFDRS_final_revmay05.pdf (accessed December 2020). Southworth, S.; Schultz, A.; Aleinikoff, J. N.; and Merschat, A. J., 2012, Geologic Map of the Great Smoky Mountains National Park Region, Tennessee and North Carolina: U.S. Geological Survey Scientific Investigations Map 2997, one sheet, scale 1:100,000, and 54 p. pamphlet. https://doi.org/10.3133/sim2997 Staley, D. M.; Gartner, J. E.; and Kean, J. W., 2015, Objective definition of rainfall intensity-duration thresholds for postfire flash floods and debris flows in the area burned by the Waldo Canyon Fire, Colorado, USA. In Lollino, G.; Giordan, D.; Crosta, G. B.; Corominas, J.; Azzam, R.; Wasowski, J.; and Sciarra, N. (Editors), Engineering Geology for Society and Territory, Vol 2: Springer International Publishing, Cham, Switzerland, pp. 621–624, https://doi.org/10.1007/978-3319-09057-3 (accessed Dec 2020). University of California at Davis (UC Davis), 2021, Soil Survey for U.S. Department of Agriculture Natural Resources Conservation Service Area TN640. Electronic document, available at https://casoilresource.lawr.ucdavis.edu/gmap/?loc=35.63930, -83.44837,z16 Wieczorek, G. F.; Morgan, B. A.; and Campbell, R. H., 2000, Debris-flow hazards in the Blue Ridge of central Virginia: Environmental & Engineering Geoscience, Vol. VI, No. 1, pp. 3–23.

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Considering Engineering Geology Input for Probabilistic Flood Hazard Assessments JEFFREY R. KEATON* Wood Environment & Infrastructure Solutions, 6001 Rickenbacker Road, Los Angeles, CA 90040

Key Terms: Annual Frequency, Uncertainty, Extreme Precipitation, Rainfall Intensity and Duration, Slope Erosion ABSTRACT Probabilistic risk assessments were developed in the 1970s as consistent approaches to assessing public health protection by nuclear-facility safety measures. Risk-informed initiatives resulted in the characterization of processes that produce extreme events (hazards) independently from the detrimental effects of such events on people or environment from facility damage (risks), as well as quantifying uncertainties. For example, large dams are designed to perform without uncontrolled reservoir release under seismic motion with 1/10,000 annual frequency. Geologic inputs for seismic hazards include ground motion sources and site response. Probabilistic flood hazards analyses are emerging in response to uncertainty about the effects of climate change, aging flood control structures, and acceptance of probabilistic seismic hazard analyses. Geologic inputs for flood hazard have focused on paleoflood hydrology from slackwater deposits and boulder bars. Procedures are available for calculating probable maximum floods, produced from the most severe combination of meteorological and hydrologic conditions, but not for assessing annual frequencies of such events. Flood routing, the domain of hydrologists and hydraulic engineers, typically stipulates channel stability. What if channels erode during extreme floods, watershed slopes are susceptible to landslides, or landslides reduce channel cross sections? Hydrologists and hydraulic engineers evaluate flood flow and water elevation effects at facilities, whereas engineering geologists need to assess slope response and mobilization of debris under extreme precipitation. Keeping slope assessments consistent with probabilistic approaches is challenging. A real location provides a hypothetical example to illustrate selected aspects of the geological approach and to utilize the results of some available tools.

*Corresponding author email: jeﬀ.keaton@woodplc.com

INTRODUCTION A hypothetical “high-hazard” facility of unidentified type at an arbitrarily selected site location in southeastern Kentucky, herein called Hypothetical, KY, provides context for a discussion about probabilistic flood hazard assessments and the role that engineering geologists should have in these assessments. The focus is on extreme events that should be considered for high-hazard facilities rather than on more common events. Case histories of modeled extreme floods resulting from 1/10,000 annual frequency precipitation events are not currently available to provide a basis for commentary on potential contributions that might come from input by engineering geologists. Therefore, this paper seeks to identify some geologic issues and topics using the characteristics of an actual location without rigorous site-specific data collection, analysis, and modeling. Data from online resources and results from available online applications enable the identification of features and characteristics that need geologic interpretation and translation to be valuable to and considered by flood modelers whose expertise is in hydrology and hydraulic engineering. The objective is to demonstrate the potential value of quantitative engineering geologic interpretation in probabilistic flood hazard assessments. Some elements of paleoflood hydrology (Costa, 1986) are mentioned in this paper, but are not a focus. The following sections of this paper describe some aspects about probabilistic hazard assessments and the basis for using extreme events for performance evaluations. References are made to nuclear facilities and large dams because probabilistic assessment procedures were developed initially for nuclear power plants and the consequences of failure of these facilities are extreme, resulting in high levels of oversight and scrutiny. Selected details about the area surrounding the Hypothetical high-hazard facility in southeastern Kentucky are summarized and then analyzed to identify aspects that appear to be relevant to extreme flood events. Aspects from this example and the role for engineering geologists in probabilistic flood hazard assessments are discussed.

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PROBABILISTIC HAZARD ASSESSMENT Probabilistic risk assessments developed for the nuclear power industry in the United States in the 1970s overcame the arbitrary and inconsistent nature of conservative approaches that relied on deterministic criteria (e.g., maximum credible earthquake) and performance assessment methods (Keller and Modarres, 2005). These were assessments of risk to public health as a consequence of the failure of safety systems caused by failure of a safety-related component, human error during plant operations, or damage to safety-related components caused by natural hazards. Refinements in probabilistic seismic hazard assessments in the 1980s and 1990s led to the first ever probabilistic ground motion maps in 1996 (Frankel et al., 1996) and their subsequent adoption in the International Building Code (IBC, 2000) and essentially all other building codes in the last 20 years. The accepted annual exceedance probability for seismic design of buildings is ∼0.0004 year–1 , which corresponds to an average return period of ∼2,500 years. This is commonly expressed in the context of a 50-year reference period as 2 percent probability of exceedance (i.e., 0.02/50 year; Wang and Ormsbee, 2005). Experience with the probabilistic basis for seismic loads in the Building Code and subsequent enhancements in probabilistic models generated widespread acceptance of probabilistic concepts, procedures, and results. A probabilistic approach was adopted for seismic evaluation of existing large dams and seismic design of new large dams (ICOLD, 2009) that is compatible with a deterministic maximum credible earthquake that has a long return period (e.g., 10–4 year–1 ). The ICOLD (2009) terminology was expanded to refer to a safety evaluation earthquake in lieu of the term “maximum design earthquake,” which may be determined with either deterministic or probabilistic approaches and has a return period of ∼10,000 years. General design criteria for nuclear power plants (U.S. NRC, 2017) includes Criterion 2: “Design bases for protection against natural phenomena. Structures, systems, and components important to safety shall be designed to withstand the effects of natural phenomena such as earthquakes, tornadoes, hurricanes, floods, tsunami, and seiches without loss of capability to perform their safety functions. The design bases for these structures, systems, and components shall reflect: (1) Appropriate consideration of the most severe of the natural phenomena that have been historically reported for the site and surrounding area, with sufficient margin for the limited accuracy, quantity, and period of time in which the historical data have been accumulated, (2) appropriate combinations of the effects of normal and accident conditions with the effects of the natural phenomena

and (3) the importance of the safety functions to be performed.”

The summary of technical characteristics and attributes of probabilistic seismic analysis in Regulatory Guide 1.200 (Table 7 in U.S. NRC, 2009) contains four bullet items with a total of 31 lines of mainly specific topics. In contrast, the summary of technical characteristics and attributes of external flood hazard analysis (Table 9 in U.S. NRC, 2009) contains two bullet items with a total of eight lines of general topics. The contrast in detail between seismic and flood processes as probabilistic hazards may be an indicator of the maturity of the approaches. The design-basis flood estimation for site characterization at nuclear power plants (Prasad et al., 2011) mentions the geologic conditions of the site several times in the context of suitability for a stationary power reactor. It also mentions geology in the context of water-control structures in the site vicinity (i.e., dams and levees) and sites located in proximity to channels that are on geologic formations in which extreme flooding could cause channel diversions toward the site. Prasad et al. (2011) also note that predictive models for channel diversions are not well established, which means that the probable maximum diversion event cannot be predicted with an ability to quantify the uncertainties. They also note the potential for erosion and deposition to occur from some flooding mechanisms, suggesting that multiple combinedeffects floods may be relevant for some sites. Historical data for extreme events are limited by the extent of the observed record, resulting in significant uncertainty in the estimated hazard magnitudes. The design-basis flood estimation reference mentions simulation models for prediction of erosion and deposition in proximity to structures of interest to forecast impacts directly on the structure. The flood estimation reference also mentions the potential for erosion of channel banks and bed in the context of sediment being deposited in a reservoir and reducing its storage capacity (Prasad et al., 2011). The term “landslide” is mentioned twice in the context of general considerations and recommendations by a Europeanbased regulatory agency (i.e., IAEA, 2011) that recognizes floods can be caused by the obstruction of river channels by landslides and other natural processes, as well as landslides or avalanches moving into waterbodies or causing tsunamis in vulnerable coastal areas. Similarly, landslides rapidly entering bodies of water are recognized as being able to generate large waves (e.g., Slingerland and Voight, 1979). The U.S. Nuclear Regulatory Commission’s Office of Nuclear Regulatory Research (NRC/RES) has hosted six Probabilistic Flood Hazard Assessment

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(PFHA) Research Workshops, the most recent of which was held February 22–25, 2021 (U.S. NRC, 2021). The 2021 workshop encompassed precipitation and flooding topics including precipitation in a small watershed, hurricane-induced storm surge and river flow in coastal areas, snowmelt-driven extreme streamflow events in inland watersheds, approaches and review guidelines for historical and paleoflood analyses, and modeling dam-breach flooding. Examples of presentations at the 2021 PFHA Research Workshop are Samothrakis et al. (2021), Weaver and CarrollSmith (2021), and Yaw (2021). The presentation by Samothrakis et al. (2021) described the probabilistic assessment of precipitation in a small watershed in eastern Tennessee and indicated that ongoing studies were focused on runoff and hydraulic aspects. The presentation by Weaver and Carroll-Smith (2021) described multi-hazard characterization of landfalling hurricanes associated with precipitation and wind. The presentation by Yaw (2021) on stochastic hydrology in the Tennessee Valley reported that “the worst case scenario may not be an exceptionally rare storm, but an unfortunately timed storm of moderate intensity.” None of the 40 presentations in the 2021 PFHA Research Workshop mentioned the effects of nearby landslides or other geologic processes on flood levels, although the runoff aspects of ongoing studies by Samothrakis et al. (2021) may include them. The high-hazard facility at Hypothetical, KY, would not be a nuclear power plant or a large dam because the topography and available space are insufficient. It could be a small modular reactor supplying local power or a facility that stores or handles regulated materials. For the present discussion of probabilistic flood hazards, the extreme flood event will have an annual exceedance probability of 10–4 , which corresponds to an average return period of 10,000 years. The structures, systems, and components important to the safety of the Hypothetical facility will have certain characteristics; however, these details are not important for the objective of this paper, which focuses on geologic processes that could contribute to a flood level that is higher than it might be if geologic effects initiated by extreme precipitation were neglected. RELEVANT STUDY AREA DETAILS AND NATURAL PHENOMENA Relevant details about the study area of the Hypothetical high-hazard facility in southeastern Kentucky (Figures 1–3) are geology and ecology, topography, soils, and vegetation. Relevant severe natural phenomena are precipitation and flooding; however, wildfire also plays a role in this assessment. The study area is in the northern part of the East Tennessee Seismic Zone,

Figure 1. The Hypothetical “high-hazard” site location.

but this detail is not relevant for the present study. The Hypothetical site boundaries do not need to be specifically defined; the study area is the watershed upstream of the site and extending downstream ∼0.4 km (Figures 2a and 3). Geology and Ecology The “high-hazard” site is located in the Central Appalachians ecoregion, which Woods et al. (2002) describe as “dissected, forested hills and mountains [that] are typically underlain by flat-lying, Pennsylvanian sandstone, shale, siltstone, conglomerate, and coal.” Bedrock geology is not particularly relevant in this study, other than it is the parent material for surficial deposits, has some resistance to erosion, and tends to be relatively stable. Coal resources were mined extensively and supported a railroad that operated in the valley for many years. The site is within a geomorphic subdivision called the Cumberland Mountains Thrust Block, which Woods et al. (2002) describe as having “high, steep ridges, hills, coves, [and] narrow valleys …Maximum elevation is greater than elsewhere in Kentucky. Forests are usually …mesophytic …but forest composition is highly variable and controlled by aspect, slope position, past usage, and degree of topographic shading. …Sedimentation from coal mines, coal washing, and logging as well as acidic mine drainage have decreased the biological integrity and productivity of surface waters. Small streams are common and have high gradients, waterfalls, many riffles, few pools, and cobble or boulder substrates.” Glacial drift has been mapped in Kentucky only along the Ohio River upstream from Louisville (McDowell and Newell, 1981), which is more than 250 km northwest of the site. Quaternary colluvium blankets the slopes, and Quaternary alluvium is

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Figure 2. (a) Detail of the hillshade base of watersheds near the Hypothetical site location. (b) Landslide and geologic units near the Hypothetical site location from the online Kentucky Geologic Map (KGS, 2020).

present in drainages and valley bottoms. The online geologic map of Kentucky (KGS, 2020) contains a landslide layer with two types of symbols in the study area: (1) red circles indicating landslides with Kentucky Geological Survey (KGS) inventory data and (2) red polygons indicating landslides mapped on 1:24,000–scale geologic quadrangles published jointly by the KGS and U.S. Geological Survey (USGS). Two landslides denoted with red circle symbols are within the watershed upstream of the Hypothetical site (Figure 2b); both are on the upslope side of Kentucky Highway 38 near the Virginia state line. The polygon landslide symbols are in the bottoms of the main valley along Clover Fork Cumberland River and several lateral drainages (Figure 2b). The four geologic quadrangles covering the watershed upstream of the Hypothetical site show polygons with the same shape and in the same locations that are mapped as alluvial fan deposits (two quadrangles; Miller and Roen, 1971, 1973); landslide debris and colluvium (one quadrangle; Froelich, 1973), or landslide debris, colluvium; and alluvial fan deposits (one quadrangle; Froelich and Stone, 1973). Therefore, the polygon landslide symbols appear to be deposits from erosion of steep hillsides, rather than earth materials that have rotated or trans-

lated as semi-intact masses and accumulated in valley bottoms where they might have the potential to affect flood flows under extreme precipitation. Topography Topography in the study area is dominated by relatively steep slopes in small valleys that drain into the Clover Fork Cumberland River (Figures 2a and 3). A series of four tiles of a 2-m bare-earth lidar-based digital elevation model (DEM) dataset was downloaded from the U.S. Department of Agriculture Natural Resources Conservation Service (NRCS 2021) Geospatial Data Gateway and mosaiced into a single layer covering the study area. The cell size of this dataset projected into NAD 83 UTM Zone 17 is 1.1 m. The ground in the study area is nearly all valley side slopes with gradients between 23 and 45 degrees (Figure 2a), a range selected based on research related to post-wildfire debris flow initiation (Staley et al., 2017) and slopes likely to have thin soil cover (Campbell, 1975). Small segments of the terrain are steeper than 45 degrees, but the corresponding pixels are difficult to discern in Figure 2a and are easier, but still difficult to discern in the Figure 2a inset map. The ele-

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Figure 3. The Hypothetical site location on topographic base with watershed outlines.

vation along a profile approximately perpendicular to ridge crests and valley bottoms (upper graph in Figure 2a) is based on DEM values extracted at 5-m intervals. The ground slope calculated with a Spatial Analyst application tool in ESRI ArcMap from the DEM at the same 5-m intervals as the elevation (lower graph in Figure 2a) is the instantaneous slope along the profile regardless of the aspect of the ground at those points. The instantaneous slope generally is between 23 and 45 degrees. The ground slope calculated along the profile line (middle graph in Figure 2a) using elevation values at a horizontal spacing of 100 m (21 5-m increments for a 100-m running average) and plotted at the midpoint distance shows the expected zero slope at ridge crests and valley bottoms, with a maximum average slope of ∼32 degrees.

Disturbed ground in the study area (Figure 3) is primarily related to coal mining activities and overhead powerline construction. Numerous roads cuts are visible in hillshade depictions of slopes in the study area (Figure 2a). Excavations for the most prominent roads in Louder Creek valley (Figure 2a) are visible as minor steps at an elevation of about 1,000 m in the slope proﬁle between ridges labeled D and E (Figure 2a). The topography of the watersheds upstream of the Hypothetical site displays valleys and ridges that trend southwest and southeast. Clover Fork Cumberland River is the through-going valley parallel to the south side of the watershed (Figure 2a). Two similar, but much smaller southwest-trending valleys are located in the northeastern part of the watershed. Three southeast-trending creeks, Heads Creek, Huﬀ

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Creek, and Louder Creek, are located in the western part of the watershed north of Clover Fork Cumberland River. The smaller southwest-trending valleys in the northeast part of the main watershed connect to the Clover Fork Cumberland River via a southeasttrending channel. The ridges appear generally linear and narrow. The slopes in the main watershed face generally in the cardinal directions: north, east, south, and west. Topographic indicators of landslide terrain, such as hummocky conditions or scalloped features, are not present. Soils The Hypothetical study area has three general soil complexes (SoilWeb, 2021) on four general slope conditions: (1) less steep ridge crests, (2) steeper eastfacing slopes, (3) steeper north-facing slopes, and (4) steeper south- and west-facing slopes. Soils on upper ridge areas are part of the Shelocta-Kimper-Cutshin complex generally on 20 to 55 percent (11 to 29 degrees) slopes. The soil series in this complex are primarily deep and well-drained soils formed in loamy colluvium or mixed colluvium from shale, siltstone, and sandstone. Permeability is moderate to moderately rapid. Typical pedon is silt loam, very channery loam, or loam with increasing rock fragments to a depth of 150 to 190 cm. Note that channery refers to thin, ﬂat, coarse fragments of sandstone, siltstone, or shale. The steeper east-facing and north-facing slopes share the same Cloverlick-Guyandotte-Highsplint complex on 20 to 80 percent (11 to 39 degrees) slopes, with some slope segments up to 100 percent (45 degrees). The soil series in this complex are primarily deep and well drained soils formed in stony, loamy colluvium weathered from sandstone, siltstone, and shale. Permeability is moderate to moderately rapid. The typical pedon is sandy loam, gravelly loam, or very channery silt loam with increasing rock fragments to a depth of 155 to 180 cm. The steeper south-facing slopes have the same soil series as the east- and north-facing slopes, with diﬀerent proportions. The soils on these slopes belong to the Highsplint-Cloverlick-Guyandotte complex on 35 to 75 percent (19 to 37 degrees) slopes. These soils are deep and well drained. The permeability is moderate to moderately rapid. The typical pedon is loam, gravelly loam, or very channery silt loam with increasing rock fragments to a depth of 155 to 180 cm. Vegetation Tree species in the study area are typical of mixed mesophytic forests on cool and dry slopes (Woods et al., 2002). The diversity of species is not as impor-

tant in this study as the extent of coverage on slopes, the deciduous nature of the trees, their root systems, and the soil moisture regime. The slopes in the Hypothetical study area are covered with mature deciduous forests, except in areas disturbed by coal mining, powerline construction, or other human activities (Figure 4). Mesophytic conditions result in trees developing extensive fibrous root systems. Extensive root systems act to stabilize the deep, loamy soil profiles, even on relatively steep slopes. The tree species in the study area typically have leaf canopies and branch networks that intercept rainfall before it reaches the forest floor. Deciduous trees produce leaf litter and duff that absorb energy from rainfall during leaf-off conditions and preserve soil moisture year-round. Precipitation In a multidisciplinary effort, the precipitation review is performed by meteorologists and hydrologists. The engineering geologist’s interest in historical precipitation is the landscape response to extreme precipitation events. In this study, historical precipitation gauge data in the Hypothetical study area were collected from the National Centers for Environmental Information of the National Oceanic and Atmospheric Administration (NOAA, 2021). Precipitation frequency estimates were collected from NOAA’s National Weather Service (NWS, 2017) using the coordinates of the Hypothetical site. Three official gauges with daily data were located within about 15 km of the Hypothetical site location (Table 1 and Figure 5). Precipitation and snow depths were plotted for the three gauges (Figure 5) and reveal that the closest gauge reported snow depth in the precipitation data column as well as in the snow depth data column, which renders the dataset unsuitable for the type of rapid analysis the engineering geologist would undertake. The daily precipitation depths plotted in Figure 5 show that the maximum is less than ∼150 mm, if the Closplint gauge is neglected. Daily snow depths exceeded 500 mm five times over a 90year period (Figure 5). The oldest gauge at Pennington Gap shows a period of no snow in the mid-1970s, which could be a period of no data during which snow depths were not reported. The Big Stone Gap gauge recorded multiple days of snow depth exceeding 250 mm, including a maximum of over 550 mm in March 2015, which was a year of widespread flooding and record cold temperature (NWS, 2015). The precipitation frequency results are partial duration series expressed as precipitation depth in millimeters and precipitation intensity in mm/hr for durations ranging from 5 minutes to 60 days and for average re-

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Figure 4. The Hypothetical site location on Google Earth images showing leaf-on (upper image) and leaf-oﬀ (lower image) forest conditions.

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Figure 5. Weather stations with daily precipitation data within about 15 km of the Hypothetical site: Map view of weather station locations (upper diagram), daily precipitation time history (middle diagram), and daily snow depth time history (lower diagram).

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Engineering Geology Input for Probabilistic Assessments Table 1. National Weather Service weather stations within ∼15 km of the Hypothetical site. (NOAA, 2021). Station Name CLOSPLINT 4 ESE, KY USa PENNINGTON GAP, VA USb BIG STONE GAP, VA USc

Network ID GHCND

Latitude; Longitude

Elevation

Start Date; End Date (yyyy-mm-dd)

Hypothetical Site Distance (km)

USC00151640 USC00446626 USC00440735

36.88333°; –83.01667° 36.7586°; –83.0105° 36.8567°; –82.7998°

548.6 m 414.5 m 446.8 m

1980-05-01; 2010-01-31 1931-07-01; 2020-11-19 1990-01-01; 2020-10-31

4.0 km 12.4 km 15.4 km

www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:USC00151640/detail www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:USC00446626/detail c www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:USC00440735/detail b

turn periods of 1 year to 1,000 years. The average return period is the reciprocal of the annual frequency or annual exceedance probability. The downloaded data were attributed to Holmes Mill, KY, which is approximately 3 km downstream along the Clover Fork Cumberland River from the Hypothetical site location. Precipitation frequency estimates for the Hypothetical site location were plotted from tabular data in the NOAA Atlas 14 Precipitation Frequency Data Server (Bonnin et al., 2006) (Figure 6). The upper diagram in Figure 6 displays the annual frequency of point precipitation depth for storms lasting 1, 10, 20, 30, and 60 days as median values and the lower and upper bounds of the 90 percent confidence interval. The largest “average recurrence interval” in Atlas 14 is 1,000 years, which corresponds to an annual frequency of 10–3 . Dotted lines project the lower and upper bounds of the 90 percent confidence interval for the 30-day duration precipitation depth to the 10,000-year average return period. A footnote to the tabular precipitation frequency estimates in Atlas 14 states that “estimates at the upper bounds are not checked against probable maximum precipitation (PMP) estimates and may be higher than currently valid PMP values.” No research in this study reviewed criteria for PMP; however, the current National Weather Service (NWS) PMP document was located (Schwartz, 1973). A note on the NWS webpage (NWS, 2015) states “NOAA’s National Weather Service …will continue to provide copies of [PMP] related documents on this site. We recognize that many of the current documents need updating. The Federal Advisory Committee on Water Information’s Subcommittee on Hydrology is examining this issue.” The NWS evaluated the effects of climate change during the period of the precipitation record used in the production of the NOAA Atlas 14 precipitation frequency estimates (Bonnin et al., 2006). They analyzed 1-day annual maximum series for shifts in mean and linear trends in mean and variance during the period of record. The results for the Hypothetical site region showed little observable or geographically consistent impact of climate change on the annual maximum series and used the entire period of record for

the computations. They state that “the estimates in this Atlas make the necessary assumption that there is no effect of climate change in future years on precipitation frequency estimates. The estimates will need to be modified if that assumption proves to be quantifiably incorrect.” Two additional point precipitation values are plotted in Figure 6 for measurements in the Pennington Gap gauge record for January 1937. These points are discussed in the next section on flooding because of a catastrophic flood that adversely affected Louisville and much of the rest of Kentucky. The lower diagram in Figure 6 is a plot of precipitation intensity against precipitation duration for durations in the NOAA Atlas 14 table ranging from 5 minutes to 24 hours. The median precipitation intensity values for the 5- to 60-minute durations and for the 1- to 24-hour durations from Atlas 14 were used to calculate regression equations, which are plotted as dark red line segments (Figure 6, lower diagram). Also plotted are precipitation intensity and duration values calculated by Wieczorek et al. (2009) that did and did not produce debris flows in the Blue Ridge part of the Central Appalachian Mountains in Virginia between 1969 and 1996. They defined a threshold that appears to be reasonably well constrained for rainfall durations between 6 to 24 hours, poorly constrained between 2 and 5 hours, and unconstrained for durations less than 2 hours (Wieczorek et al., 2009, p. 17). The Blue Ridge area is approximately 400 km northeast of the Hypothetical study area; nonetheless, the Blue Ridge area debris flow results demonstrate that precipitation intensities and durations expected to occur in the Hypothetical study area have produced debris flows in the region. For reference, a black line attributed to Staley et al. (2014) is also plotted (Figure 6, lower diagram); it is a rainfall intensity-duration threshold for post-fire flash floods and debris flows in the area burned by a specific wildfire in Colorado. This research result from the western United States is introduced because similar findings of erosion initiation following wildfire from the eastern United States are not known to the au-

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Figure 6. Point precipitation frequency estimates for Holmes Mill, KY, about 3 km downstream of the Hypothetical site location on Clover Fork Cumberland River: Point precipitation depth (upper diagram) and precipitation intensity versus duration (lower diagram).

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Engineering Geology Input for Probabilistic Assessments Table 2. List of selected significant floods in the Jackson, KY, past weather events archive (NWS, 2021a) supplemented by the floods of 1937 and 1997. Date (yyyy-mm-dd) 1937-01-13–24a 1939-07-04–05 1957-01-late & 02-early 1977-04-02–05 1997-03-probably 01b 2015-02–21 2015-03-03–04 2016-06-21 2018-02-10–11 2019-02-20–24 2020-02-06–07 a

Figure 7. A Flood Insurance Rate Map segment for the Hypothetical site location, Harlan County, KY, Panel 375 of 475, part of Map Number 21095C0375E (FEMA, 2015).

thors. The regression line for the 1,000-year return period median precipitation intensity is one order of magnitude higher than the threshold line by Staley et al. (2014) at a 1-hour precipitation duration. Flooding Surface water hydrology is the primary subject area in a probabilistic flood hazard assessment and typically is performed by hydraulic engineers and surfacewater hydrologists. Engineering geologic insight into extreme flood events would be informed by some knowledge of stream discharge from the watersheds in the study area to supplement the topographic information displayed in Figures 2a and 3. Readily available online quantitative information for sites in the United States include the national Flood Insurance Rate Map ([FIRM] FEMA, 2015) and an online streamflow statistics and spatial analysis application (USGS StreamStats, 2021). The basis for flood insurance under the U.S. program administered by the Federal Emergency Management Agency (FEMA) is an area subject to inundation by a flood that has a 1 percent chance of being equaled or exceeded during any given year, which is comparable to an annual exceedance probability of 0.01 (FEMA, 2015). The FIRM for a small area adjacent to the Hypothetical site (Figure 7) shows variable width of Zone A in Clover Fork Cumberland River and Heads Creek with Zone X adjacent to the chan-

Comment Catastrophic flash flooding affected 21 counties; mainly Ohio River Catastrophic flash flooding affected 21 counties Devastating floods Devastating flooding; 15 counties declared disaster areas In some places, worst flooding since the Great Flood of 1937 Complex winter storm and ice jam flooding Worst flooding in a decade followed by heavy snow and record cold Significant flash flooding Persistent rain brings flooding Continual rain causes flooding & rock/mudslides south of I-64 Major flooding followed by light snow

NWS, 2021b. NWS, 2021c.

nels and across Kentucky Highway 38 where it crosses Heads Creek over what appears to be a culvert when viewed virtually with Google Earth ground view; the Heads Creek channel adjacent to Highway 38 appears to be overgrown with brush and small trees. Zone A corresponds to flood hazard areas having a 1 percent annual chance of flood of undetermined depth because the area is without a base flood elevation that would enable calculation of flood depth. Zone X (Figure 7) denotes areas of “minimal flood hazard,” which is interpreted as being ground higher than Zone A. Watershed boundaries labeled in Figure 7 are in the same positions as they are in Figure 3. Summary information about significant or widespread historical floods in Harlan County, KY, was reviewed on the National Weather Service Past Weather Events Archive for Jackson, KY (NWS, 2021a). Several significant floods are listed in Table 2. Floods in 1937, 1957, 1977, 1997, 2015, 2018, 2019, and 2020 occurred in winter or early spring months. The storms that caused these floods could have involved rain-on-snow and probably involved rain-on-frozen ground, at least on north-facing slopes. Both conditions would likely have resulted in greater runoff than what similar storms would have produced during summer or fall months. Readily available reports of the Great Flood of 1937 (Table 2) focus on damage and effects in Louisville, with a comment that Louisville received 381 mm of rain from January 13 to January 24, 1937, and over 483 mm for the month of January 1937. Of the three rain gauges in the site

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Keaton Table 3. Selected peak-flow parameters from StreamStats for watersheds named in Figure 3. Peak Flow (m3 /s) Watershed Name

Drainage Area (km2 )

1% AEPa

0.5% AEPa

0.2% AEPa

36.26 19.89 0.41 2.98 0.17 0.47 3.63 0.34 0.17 0.14 0.18 0.08 0.09 3.76 0.11 0.31 0.09 0.16 0.22 0.11 0.49 0.20 0.57

169.9 115.5 9.6 34.0 5.5 10.3 38.8 8.4 5.5 4.7 5.5 3.3 3.7 39.6 4.0 8.0 3.5 5.2 6.3 4.1 10.7 6.1 11.8

198.2 135.7 11.8 40.8 6.7 12.7 46.2 10.3 6.7 5.8 6.8 4.2 4.2 47.3 5.0 9.8 4.4 6.4 7.8 5.1 13.1 7.5 14.4

238.5 164.3 14.9 50.7 8.6 16.0 57.2 13.1 8.7 7.5 8.8 5.4 5.4 58.3 6.5 12.5 5.7 8.2 10.0 6.5 16.6 9.6 18.2

Clover Fork Cumberland River below Hypothetical site Upper Clover Fork Cumberland River Louder Diversion Louder Creek Unnamed Creek R1 Unnamed Creek R2 Huﬀ Creek Unnamed Creek R3 Unnamed Creek R4 Unnamed Creek R5 Unnamed Creek R6 Unnamed Creek R7 Unnamed Creek R8 Heads Creek Unnamed Creek L1 Unnamed Creek L2 Unnamed Creek L3 Unnamed Creek L4 Unnamed Creek L5 Unnamed Creek L6 Haupts Branch Marcum Branch Lower Trace Branch a

Annual exceedance probability.

vicinity (Figure 5), only the Pennington Gap, VA, gauge was in operation in 1937. It recorded 213 mm between January 11 and January 25, 1937, and 313.5 mm for the month of January. These two precipitation depths are plotted in Figure 6 (upper diagram) at the positions of storm durations of 15 and 30 days (rain depth was 0 at the Pennington Gap gauge on January 1, 1937). The online streamﬂow statistics and spatial analysis application (StreamStats) was used to delineate the watersheds plotted in Figure 3. Selected parameters from StreamStats are summarized in Table 3 for the watersheds with labels in Figure 3. The peak ﬂows for four watersheds are plotted in Figure 8 for all of the annual exceedance probability (i.e., annual frequency) values provided in StreamStats. The watersheds selected for plotting in Figure 8 are the two largest watersheds on Clover Fork Cumberland River and the two watersheds that discharge into the river closest to the Hypothetical site (Heads Creek and Lower Trace Branch, Figure 3). The largest watershed is denoted with a white line in Figure 7 labeled Hypothetical watershed boundary, which crosses Clover Fork Cumberland River ∼500 m downstream from its junction with Heads Creek (yellow line in Figure 7). Dashed lines projecting beyond the 0.002 annual exceedance probability values for the two largest watersheds are specula-

tive but suggest that the 10–4 annual exceedance probability discharge is likely to be more than double the discharge for the 100-year ﬂood.

Figure 8. Peak ﬂow statistics for four selected watersheds in the Hypothetical site area calculated from USGS StreamStats.

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Two bridges across the Clover Fork Cumberland River, visible in Figure 9 (upper image), are each estimated to be about 18 m long. The virtual ground-level view (Figure 9, lower image) from Kentucky Highway 38 is across the bridge toward the house mentioned above as being visible in the FIRM image (Figure 7). The ground-level view gives a reasonable impression of the channel of Clover Fork Cumberland River as trapezoidal with a relatively flat bottom with what appear to be cobble- and boulder-sized fragments of durable rocks, which are likely to be sandstone or sandy siltstone based on the geologic setting. These observations allow the engineering geologist to formulate and refine an engineering geologic model of the site. The ground-level view of the bridge also includes the vegetation growing along the river channel upstream and downstream from the bridge. Vegetation and debris caught by bridges tends to impede flood flows and contribute to flooding of the immediate upstream areas. Wildfire

Figure 9. Virtual views of the Clover Fork Cumberland River valley about 0.5 km downstream from the Hypothetical site location. Upper part, Google Earth image; lower part, Google Earth ground image.

Google Earth utilities enhance understanding by providing virtual views of the field conditions (Figure 9). The virtual aerial terrain view (Figure 9, upper image) is a January 11, 2011, leaf-off image of the Clover Fork Cumberland River valley between the Hypothetical site in the upper left corner and a house on the south side of Kentucky Highway 38 and the river. This house is visible on the lower left side of the FIRM image (Figure 7). The boundary of the largest watershed is the same white line in both Figures 7 and 9. The floodplain width above the river channel is approximately 100 m; the Clover Fork Cumberland River channel gradient is about 0.01 m/m between its junction with Heads Creek and the Hypothetical watershed boundary (Figures 7 and 9).

Wildfire has become increasingly common over the past several decades as average temperatures have increased (NASA, 2019). In Kentucky, the Division of Forestry (KYDF) reports for the 2010–2019 decade that 10,280 fires burned 1,240 km2 , with 30 km2 being the largest area burned by a single fire (Table 4) (KYDF, 2021a). Arson and debris burning were the two most common causes (64.9 and 22.3 percent, respectively) of the state’s fires over a recent 10-year period, with lightning being listed as the cause of 0.4 percent of the fires (KYDF, 2021b). Fires are relatively common and mainly started by people. Fires have the potential to create a condition that enhances sediment yield, which could exacerbate the effects of extreme precipitation and subsequent flooding. Drought was attributed to the month of November 2016, which was the fourth driest on record in parts of the Cumberland Valley in Kentucky and was preceded by an abnormally dry September and October (NWS, 2016). Fall 2016 was also the warmest on record across eastern Kentucky, which contributed to numerous wildfires burning simultaneously in that part of the state. One of the Fall 2016 fires was in Harlan County, about 7 km north of the Hypothetical site location, and was responsible for burning about 30 km2 (Table 3). Fires are known to destabilize slopes, resulting in erosion and debris flows under rainfall of certain intensities and durations (Staley et al., 2014, 2017; Sankey et al., 2017; DeGraff, 2018; and Thompson et al., 2019). Initial susceptibility to debris-flow occurrence is highest for a period of about 2 years following a

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Keaton Table 4. List of wildland fires in Harlan County, KY, or Lee County, VA, between 2003 and 2019 from Wildfires in the United States (EcoWest, 2020) supplemented by information from the NWS and Kentucky Division of Forestry. Date (yyyy-mm-dd)

Reported Area Burned (km2 )

Fire Name

Hypothetical Site Distance (km)

2005-11-25 2006-12-16 2016-11-08 2016-11-monthb 2010 through 2019c

4.1 3.8 7 30 30

Highsplint Unnamed Clover Licka Pine Mountaina Largest of Decade

9 7 7.5 ∼7 ∼7

Probably same fire; could be an early name for initial fire and later name for coalesced fires. NWS, 2021d. c KYDF, 2021a. b

high-intensity wildfire with a subsequent later phase of debris-flow susceptibility caused by the decay of tree root networks on steep slopes (DeGraff, 2018). Staley et al. (2014) developed a threshold precipitation intensity-duration relationship for post-wildfire debris flows within 2 years after a fire. Staley et al. (2017) used logistic regression to quantify post-fire debrisflow likelihood in the western United States and concluded that the most accurate prediction was provided by a burn intensity parameter, the proportion of slope area steeper than 23 degrees that was burned at high or moderate severity, and a soil erodibility factor combined with the precipitation during a 15-minute period. Their analysis used precipitation depth as a multiplier for each parameter in the logistic link function, which enabled the ultimate equation to be solved for rainfall depth during a fixed duration. For example, a logistic regression based on a precipitation duration of 15 minutes enabled the 15-minute intensity to be calculated, which greatly facilitated the use of the logistic regression results in evaluating post-wildfire debris flow hazard. The use of 23 degrees as a key slope angle by Staley et al. (2017) was the basis for lower slope angle selection in Figure 2a. Sankey et al. (2017) used Monte Carlo simulation with climate, fire, and erosion models to project increases in sedimentation of more than 10 percent for about 90 percent of the watersheds studied and of more than 100 percent for more than 33 percent of the watersheds in their sample from the western United States. Their focus was on sedimentation degradation of water supply and water quality, but their method may be useful for quantifying the likelihood of sediment accumulation in narrow valleys that could be mobilized under extreme precipitation. Thompson et al. (2019) estimated physical properties of char height, litter depth, duff depth, and soil loss for non-burned conditions, prescribed fire conditions, and wildfire conditions for four national forest sites in the Central Appalachian Region. They found wildfires produced greater char heights, lower litter depths,

little difference in duff depths, and greater soil loss than the prescribed fire sites or non-burned sites. Their focus was on forest management practices, but their findings also can inform slope processes for hazard assessments. HAZARD IDENTIFICATION RESULTS Relevant details of the site, natural phenomena, and some effects caused by human activities described in the previous section provide a basis for considering potential input that engineering geologists might contribute to probabilistic assessments of flood hazards. The hazard in this regard is the primary characteristic of inundation related to the water surface elevation and potential secondary characteristics related to erosion by water impinging on the site or facilities at the site. From a flood routing perspective, events or processes that have the potential to raise the flood level at a particular site could occur to the main channel upstream of the site, to the main channel downstream of the site, or from a tributary or side slope to the main channel where the site is located. Upstream events include failure of water impoundments (reservoir or tank), mine tailings impoundments, and major water conveyance facilities (canals or large-capacity pipelines). Downstream events include landslides that partially or completely block the main channel or debris flows that enter the main channel from a slope or tributary. At or near the site location, a debris flow or landslide from the opposite side of the main channel could deflect the flow toward the site or substantially reduce the channel cross section, resulting in deeper flow, or a rock fall or rapid landslide could create a water wave directed toward the site. Each possible mode of earth movement that has the potential to affect the water surface elevation near the site or direct a wave toward the site needs to be assessed for credibility. Those that have some credibility must be further assessed for likelihood

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in quantitative or semi-quantitative terms. Credible modes of earth movement that are part of a sequence of events also must considered in terms of conditional probabilities, even if those probabilities are qualitative. The engineering geologist does not work in isolation but participates as a member of a team in a workshop framework that allows each idea to be scrutinized and discussed until all credible modes are identified and described in terms of the range of effects and corresponding probabilities of occurrence under conditions, such as the 10,000-year precipitation event. The site of the Hypothetical “high-hazard” facility is located in an ecoregion of flat-lying sedimentary sandstone, shale, siltstone, conglomerate, and coal that is dissected by steep, narrow valleys with narrow, linear ridge crests. Soils on the slopes are deep (>1m) and moderately permeable. Vegetation is mature deciduous forest except where disturbed by coal mining, powerline construction, or other human activities. The mesophytic moisture regime results in trees having extensive fibrous root systems that help stabilize soil on slopes that are inclined on average between 27 and 32 degrees (Figure 2a). The region is susceptible to wildfire. After severe wildfire resulting in tree mortality, the fibrous root systems may persist for several years before decaying to the point of losing its soil-stabilizing qualities (DeGraff, 2018). Readily available reports of the Great Flood of 1937 (Table 2) focus on damage and effects in Louisville, with a comment that Louisville received 381 mm of rain from January 13 to January 24, 1937, and over 483 mm for the month of January 1937. Of the three rain gauges in the site vicinity (Figure 5), only the Pennington Gap, VA, gauge was in operation at that time; it recorded 213 mm between January 11 and January 25, 1937, and 313.5 mm between January 2 and January 31, 1937. Blue circle symbols plotted in Figure 6 indicate annual frequencies or average return periods as if the Pennington Gap gauge was representative of the Holmes Mill, KY, area. The 15-day precipitation depth corresponds to an annual frequency of about 0.3 or an average return period of about 3 years; the 30-day precipitation depth annual frequency is slightly greater than 0.2, which is less than a 5-year average return period. The magnitude of flooding associated with the January 1937 storm may have been a result of frozen ground conditions causing higher runoff or rain falling on snow, contributing snowmelt water to the flood (Graybeal and Leathers, 2006). The floodplain visible in Figure 9 (both images) and indicated to at least some degree in the FIRM (Figure 7) suggests that prehistoric flows in Clover Fork Cumberland River may have been larger than historical flows. Application of Quaternary stratigraphy and chronology may be able to distinguish deposits

of paleoflood events or determine the current stage of the fluvial system relative to an equilibrium condition. Erosion or deposition features may be present on the valley sides above the floodplain of Clover Fork Cumberland River, which may represent evidence of past flood discharge. Roads cut into the valley-side slopes are visible in the shaded relief depictions of 1.1-m pixel size lidarbased topography and enhanced with colorized slopes steeper than 23 degrees (Figure 2a). Roads of this type are constructed by balanced cut-and-fill, typically with poorly compacted fill on the downhill sides. Drainage control features may be present, but typically these roads discharge stormwater at intermittent, uncontrolled locations. The potential impact on slope processes of a 10,000-year precipitation event is challenging to imagine and describe in a way that preserves uncertainty in a quantitative or at least rigorous way. An embankment at the mouth of Louder Creek Diversion (Figures 2a inset and 3) appears to encroach into the floodplain of Clover Fork Cumberland River and create an artificial “narrow” at this point of the valley. An erosion gully at the downstream interface between the embankment and natural ground on its west side is also visible (Figure 2a inset). The effects of a 10,000-year precipitation event needs to be analyzed at the location of the erosion gully and the toe of the embankment adjacent to the river channel in a way that preserves uncertainty. Furthermore, the effects of embankment erosion or failure on flood levels at the Hypothetical site needs to be quantified in a way that preserves uncertainty. “Landslides” is one of the items listed in the 31 lines of seismic hazard topics in Regulatory Guide 1.200 (Table 7 in U.S. NRC, 2009). As experience with probabilistic flood hazard assessments grows, it is reasonable to expect that landslides and other slope processes may be added to the list of topics in Regulatory Guide 1.200 (Table 9 in U.S. NRC, 2009). DISCUSSION A general paper on the methods of analyzing uncertainty in performance assessments (Mahadevan and Sarkar, 2009) was published as an attachment to a research project sponsored by the International Atomic Energy Agency regarding the performance of cementitious materials in the long-term storage and disposal of radioactive waste. This paper considered sources of uncertainty to be physical variability (also referred to as aleatory uncertainty), data uncertainty (also referred to as epistemic uncertainty), and model error (resulting from approximate mathematical expressions of system behavior and numerical computation ap-

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Keaton

proximations). Mahadevan and Sarkar’s (2009) paper also identified four types of analyses to quantify performance assessment uncertainties: (1) inputs into performance assessment models, (2) propagation of input uncertainty through performance assessment models, (3) model error quantified through verification and validation activities, and (4) probabilistic performance assessment. This level of rigorous analysis in complex predictive geotechnical models may be emerging (e.g., Strenk and Wartman, 2011). Engineering geologic models (e.g., Parry et al., 2014) tend to focus on conditions based on geological knowledge, such as distribution of geologic formations, surficial deposits, structural elements (e.g., contacts, faults, and folds), and age relationships, initially in a conceptual way to identify what might be encountered at a site and subsequently in a constrained way based on observations and investigations. Parry et al. (2014) note that “the uncertainty associated with the choice of geological details on which to base a conceptual model is very different from the uncertainty associated with the location of a geological boundary within 3D space for an observational model. By acknowledging these different [conceptual, observational, and analytical] approaches, the different types of uncertainty within the model can be appreciated and hopefully understood.” Quantifying uncertainty in geologic mapping, interpretation, and model development may be a challenge for professional geologists, but probabilistic approaches make it necessary, at least at some level, such as to provide an opinion of the range of sediment volume eroded from long, steep slopes in response to extreme precipitation. Given the occurrence of an extreme precipitation event, the condition of the slope might be frozen for a few weeks or months of a year, depending on the aspect of the slope, or it might be thawed and within a year after being severely burned in a wildfire. An event tree approach allows process outcomes to be tracked through each option of a branching system by having probabilities assigned to each possible branch. Identifying all meaningful branches and assigning reasonable probabilities to each of them is what is needed to produce a probabilistic result. Reasonable probabilities consist of a best estimate and a range or a lower bound probability and an upper bound probability. CONCLUSIONS The descriptions of site details in which geology is relevant for assessing extreme flood hazards at the Hypothetical “high-hazard” site help to identify the important role engineering geologists have in probabilistic flood hazard assessments. Ultimately, the flood hazard at the Hypothetical site must be expressed as the

annual probability of flood flow exceeding a designated elevation and the hydraulic parameters (velocity and depth) associated with flow reaching that elevation. An upstream reservoir release can be dismissed as a source of flooding at the site; therefore, the source of potential flooding will be precipitation. The paths that floodwaters take are controlled by upstream topography, which has the potential to change during an extreme flood event through slope and channel processes of erosion and deposition. Runoff from winter precipitation may fall on frozen ground, which should be relatively resistant to erosion, at least initially. These processes can include landslides or debris flows emerging from side valleys and encroaching on the main river channel. The effects of flood flow at the site will be controlled by the resistance of earth materials and engineered features to scour and erosion. Engineering geologic field assessments might include examination of some or all of the following features and conditions:

r Stratigraphy of ﬂoodplain deposits along the reach

r r r r r r

of the Clover Fork Cumberland River local to the site and the possible presence of knickpoints in the main channel, Geomorphic features along the interface between the floodplain and slopes that might indicate past flood-stage discharge or debris flows from slopes or side valleys, Stratigraphy of channel deposits along creeks in tributary valleys, Characteristics of and stream incisions into deposits in mouths of tributary valleys, Features such as embankments and gullies (examples labeled in Figure 2a inset), Characteristics of slope deposits exposed in cuts for roads graded across the steep slopes, Characteristics and stratigraphy exposed in erosion features on the sidehill roads, Geomorphic features along major and minor channels and on slopes in the Clover Lick or Pine Mountain burn area as surrogates for what might be expected following a wildfire on watershed slopes in the Hypothetical site drainage area.

Subjective interpretations, such as “reasonable” or “conservative” assumptions or estimates by individual engineering geologists or other professionals, should be avoided unless they are associated with quantified and defendable uncertainties and frequencies. Currently, case histories related to prediction of the magnitude of the 10,000-year flood appear to be limited to the U.S. NRC Probabilistic Flood Hazard Assessment Research Workshops. The 2021 Workshop also included a probabilistic assessment of flooding enhanced by snowmelt. Future workshops provide opportunities

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for presentations by engineering geologists to discuss enhanced sediment yield and slope movements under extreme precipitation without and with effects of wildfire and frozen ground. Where judgment must be used because data are unavailable or out of range, methods for opinions from qualified and knowledgeable individuals (i.e., experts) should follow a process to preserve uncertainties and quantify them. The nuclear power industry formalized the use of experts for probabilistic seismic hazard assessments in the 1990s (Budnitz et al., 1997) and updated guidelines for implementation 20 years later (Ake et al., 2018). Engineering geologists should embrace rigorous probabilistic approaches and learn to recognize deterministic assumptions, whether pessimistic or optimistic, as sources of error without quantified uncertainties. The geologic and geotechnical effects of extreme precipitation events, with or without wildfire or frozen ground, are difficult to imagine, let alone bracket the probabilities of enhanced sediment yield and landslides given their occurrence. Sediment yield and landslides have the potential to adversely impact flood levels; this potential must be quantified in probabilistic terms. This paper attempts to raise awareness of this potential and the opportunity for engineering geologists to participate in developing an approach to a solution. REFERENCES Ake, J.; Munson, C.; Stamatakos, J.; Juckett, M.; Coppersmith, K.; and Bommer, J., 2018, Updated Guidelines for SSHAC Hazard Studies: U.S. Nuclear Regulatory Commission NUREG-2213, 145 p.: Electronic document, available at https://www.nrc.gov/docs/ML1828/ML18282A082.pdf Bonnin, G. M.; Martin, D.; Lin, B.; Parzybok, T.; Yekta, M.; and Riley, D., 2006, Precipitation-Frequency Atlas of the United States: National Oceanic and Atmospheric Administration, National Weather Service, NOAA Atlas 14, Vol. 2 Version 3.0, 295 p.: Electronic document, available at https://www.weather.gov/media/owp/oh/hdsc/docs/ Atlas14_Volume2.pdf Budnitz, R. J.; Apostolakis, G.; Boore, D. M.; Cluff, L. S.; Coppersmith, K. J.; Cornell, C. A.; and Morris, P. A., 1997, Recommendations for Probabilistic Seismic Hazard Analysis: Guidance on Uncertainty and Use of Experts: U.S. Nuclear Regulatory Commission NUREG/CR-6372 and UCRL-ID-122160, Vol. 1, Main Report, 28 p.: Electronic document, available at https://www.nrc.gov/docs/ML0800/ ML080090003.pdf Campbell, R. H., 1975, Soil Slips, Debris Flows, and Rainstorms in the Santa Monica Mountains and Vicinity, Southern California: U.S. Geological Survey Professional Paper 851, 51 p. https://doi.org/10.3133/pp851. Costa, J. E., 1986, A history of paleoflood hydrology in the United States, 1800—1970: Eos Transactions American Geophysical Union, Vol. 67, No. 17, pp. 425–430, doi:10.1029/EO067i017p00425-02.

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hurricanes. In 6th Annual Probabilistic Flood Hazard Assessment PFHA Workshop 2021, U.S. Nuclear Regulatory Commission,: Electronic document (presentation slides), available at https://www.nrc.gov/docs/ML2106/ML21064A422.pdf Wieczorek, G. F.; Eaton, L. S.; Morgan, B. A.; Wooten, R. M.; and Morrissey, M., 2009, An Examination of Selected Historical Rainfall-Induced Debris-Flow Events Within the Central and Southern Appalachian Mountains of the Eastern United States: U.S. Geological Survey Open-File Report 2009–1155, 25 p.: Electronic document, available at https://pubs.usgs.gov/of/2009/1155/pdf/ofr2009-1155. pdf Woods, A. J.; Omernik, J. M.; Martin, W. H.; Pond, G. J.; Andrews, W. M.; Call, S. M.; Comstock, J. A.; and Taylor, D. D., 2002, Ecoregions of Kentucky: Electronic document, available at ftp://newftp.epa.gov/EPADataCommons/ORD/ Ecoregions/ky/ky_front.pdf Yaw, M., 2021, Stochastic hydrology in the Tennessee Valley. In 6th Annual Probabilistic Flood Hazard Assessment PFHA Workshop 2021, U.S. Nuclear Regulatory Commission: Electronic document (presentation slides), available at https://www.nrc.gov/docs/ML2106/ML21064A437. pdf

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Application of a Hydrological Model for Estimating Inﬁltration for Debris Flow Initiation: A Case Study from the Great Smoky Mountains National Park, Tennessee ARPITA MANDAL* Department of Geography and Geology, University of the West Indies, Mona Campus, Kingston, Jamaica

ARPITA NANDI Department of Geosciences, East Tennessee State University, Johnson City, TN 37614

ABDUL SHAKOOR Department of Geology, Kent State University, Kent, OH 44240

JEFFREY KEATON Wood Environment & Infrastructure Solutions, Inc., 6001 Rickenbacker Road, Los Angeles, CA 90040

Key Terms: Rainfall–Runoﬀ Simulation, Inﬁltration, Rain Gauge, Weather Radar, Hydrological Modeling, Debris Flows

for the gauge and from 15 mm to 0.14 mm for radar, indicating essentially saturated conditions on the day of the debris ﬂow.

ABSTRACT

INTRODUCTION

Debris flows occur frequently in remote areas of Great Smoky Mountains National Park, Tennessee. Rainfall gauges are not adequate for modeling infiltration required for triggering debris flows. Weather radar, providing frequently updated, continuous coverage, is a valuable tool for estimating rainfall intensity, duration, runoff, and infiltration. Daily rainfall from a sole gauge was compared with hourly rainfall from the Digital Precipitation Array weather radar product to model infiltration on August 5, 2012, the day before a debris flow was known to have occurred in the 91-km2 West Prong Little Pigeon River watershed. Additionally, both gauge and radar data were used for rainfall–runoff–infiltration modeling for a 42-day period in July and August 2012. Runoff and infiltration were simulated using the conventional semi-distributed hydrological model HEC-HMS. A local bias correction of radar rainfall at the gauge location improved correlation between the radar rainfall and the gauge data. Peak daily rainfall for the August 5 storm was 93 mm (gauge) and 98 mm (radar), whereas average daily rainfall for the 42-day period was 10 mm and 7.75 mm, respectively. Over the study period, simulated daily infiltration declined from 28 mm to 0.5 mm

Debris flows are rapid slope movements that pose a hazard to life, property, and environment throughout the United States. They occur due to the combined effect of intrinsic factors, such as bedrock, soil type, geologic structure, slope angle, and land cover, and extrinsic triggering factors, such as rainfall, snowmelt, and earthquakes. The rainfall in an area produces variable soil moisture, surface runoff, and infiltration. Quantifying the amounts of infiltration and surface runoff from rainfall events can aid in understanding debris flow triggers. Infiltration during and following heavy rainfall could increase pore-water pressure to a threshold value and initiate sliding/debris flows. The absence of rain and stream flow gauges in many watersheds, prone to slope failures, makes it difficult to estimate infiltration from rainfall events. Researchers typically use local rain gauges for assessing runoff and infiltration at the watershed level (Rahimi et al., 2003; Aleotti, 2004). Since rain gauges provide point estimates of rainfall, using gauge data from the surrounding areas requires spatial interpolation methods such as kriging (Gandin, 1965; Matheron, 1973). Modeling results may be unsatisfactory in the absence of a network of relatively closely spaced rain gauges, which is rare in most remote watersheds (Chang et al., 2009). The absence of rain gauges in reasonable proximity

*Corresponding author email: arpita.mandal@uwimona.edu.jm

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Mandal, Nandi, Shakoor, and Keaton

of debris flows, combined with spatial variability of rainfall, indicates the need for alternative sources of rainfall information for runoff and infiltration analyses in hydrological modeling (Morrissey et al., 1995; Chang et al., 2009). Recent studies point out that radar rainfall data should be used in ungauged areas and should offer an important spatial and temporal reference in gauged areas (Keaton, 2017; Rossi et al., 2017). Furthermore, radar rainfall data is useful in hilly areas where variables such as elevation, slope angle, and slope aspect impose significant limitations on rain gauges to provide representative rainfall data (Hossain et al., 2004). Details on uses of gauge and weather radar data in hydrological modeling, as well as the benefits and challenges regarding both types of data, can be found in the work of Cole and Moore (2008). Comparison of Radar and Rain Gauge Data The Next Generation Weather Radar (NEXRAD) system of Doppler radar stations was developed by the National Weather Service of the National Oceanic and Atmospheric Administration (NOAA), U.S. Department of Commerce, in the 1990s to provide rainfall data where rain gauge data are unavailable (https://www.ncdc.noaa.gov/data-access/radardata). Three levels of NEXRAD products are available, with Level III including over 75 data products as digital files, color images, and acetate overlay copies at state, county, and city scales, that help weather analysts forecast weather. The Digital Precipitation Array (DPA) is a commonly used NEXRAD Level III product for 1-hour rainfall accumulation on the Hydrologic Rainfall Analysis Project (HRAP) grid system (cell size 4.7625 km by 4.7625 km) (NOAA, 2017). Several studies have compared rain gauge and NEXRAD Level III data from paired locations (Lott and Sittel, 1996; Pereira Filho et al., 1998; Jayakrishnan et al., 2004; Moon et al., 2004; Westcott and Knapp, 2006; Xie et al., 2006; Wang et al., 2013; and Keaton, 2017). These studies concluded that NEXRAD Level III data products either underestimate or overestimate the rainfall in comparison to gauge rainfall, or they show a good correlation with the gauge data. Considering varying results of the above-mentioned studies, this research used both radar rainfall and rain gauge data for estimating runoff and infiltration associated with debris flows. Usually, rain gauge observations are used for bias correction of initial radar rainfall estimates for increased accuracy of radar rainfall. Different bias corrections, such as local bias correction (Michelson and Koistinen, 2000), mean field bias correction (Zhang et al., 2004), rain-rate magnitude bias correction (Habib et al., 2008), spatio-temporal bias correction (Mandapaka et al., 2010), and range-

dependent bias correction (Krajewski et al., 2011), are found in the literature. In this study, a local bias correction was used for the radar rainfall products.

Hydrological Modeling of Debris Flows The Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS), developed by U.S. Army Corps of Engineers (USACE) (2013), is a widely used method for estimating runoff and infiltration for gauged and ungauged watersheds. Robayo et al. (2004) and Knebl et al. (2005) used HEC-HMS for regional-scale modeling of San Antonio, Texas. Research by Gul et al. (2010) showed the combined usage of hydrologic and hydraulic models (HEC-HMS and Hydrologic Engineering Center–River Analysis System [HEC-RAS]) for simulation of extreme events for the Bostanli River basin in Turkey. Mandal et al. (2013, 2016) described application of hydrological modeling for flood peak flow estimation for watersheds in Jamaica using data from rain gauges. Similar work, involving GIS-based hydrological modeling and HEC-HMS, to compute peak discharges from rainfall events has been conducted by Olivera and Maidment (1999), Anderson (2000), Usul and Yilmaz (2002), and Noorbakhsh et al. (2005). Regarding application of HEC-HMS, Jošt and Matjaž (2006) used it to calculate runoff volume of precipitation required for debris flow initiation. Wei et al. (2018) used HEC-HMS modeling in debris flow– prone areas to validate the accuracy of runoff calculations by comparing the calculated results and measured data. Wang et al. (2015) and Zhang et al. (2017) applied HEC-HMS to calculate the amount of runoff in debris flow–prone areas. In Shuida gully in Sichuan Province, China, Wang et al. (2015) used HEC-HMS to simulate the rainstorm for a debris flow and calculated the peak discharge for various rainfall return periods. Zhang et al. (2017) used the basin model of a debris flow gully in HEC-HMS, which performed calculations under different types of rainfall. The results showed that the peak flow rate positively correlated with the arrival time of the peak of rainfall. Hydrological modeling for debris flow studies tends to focus more on infiltration and less on surface runoff. Higher rates of infiltration can result in higher pore pressures in soil/rock, resulting in initiation of debris flow events (Wang et al., 1997; Guo et al., 2016). For watersheds susceptible to debris flows but lacking stream gauges or in situ infiltration measurements, hydrological modeling is an effective means of estimating the runoff and infiltration amounts as well as time to peak flow. Simulating different intense rainfall events aids in predicting infiltration and runoff depths, whose

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Hydrological Model Application for Debris Flows

temporal distribution can help predict the possible debris flow initiation time. Debris Flow Hazards in the Southeastern Appalachian Region Debris flows are a common occurrence in southeastern Appalachians, the location of Great Smoky Mountains National Park (GRSM). A severe storm in August 1938 produced more than 300 mm of rain within a 4-hour period (75 mm/hr), causing more than 100 debris flows within the GRSM (Scott, 1972). Bogucki (1976) identified numerous debris flow scars in the GRSM and attributed them to a September 1951 rainstorm. About 50% of the debris flows occurred in the Alum Cave Creek watershed on the southwest flank of Mt. Le Conte, damaging roads and hiking trails to Mt. Le Conte (Bogucki, 1976). In the GRSM area, debris flows result from a complex interaction between various rock and soil types, joint geometries, precipitation intensities, topographic profiles, and hydrological conditions (Moore, 1986). Clark (1987) stated the importance of additional research to determine debris flow triggering factors in the Appalachians and suggested that high-intensity rainfall events were likely the primary causative factor. Ryan (1989) studied visible debris flow impacts to trees, performed tree-ring analysis, and estimated the average recurrence interval of debris flows in Mt. Le Conte area to be 13.8 years. When ground conditions are conducive, rainfall from cloudburst, hurricanes, and storms can trigger fastmoving flows (Wieczorek et al., 2000). In 2004, Hurricanes Frances and Ivan triggered at least 155 landslides and debris flows in North Carolina, causing 10 fatalities (Wooten et al., 2008). OBJECTIVES This study applies hydrological modeling for estimating infiltration amounts required for debris flow initiation in a small watershed within GRSM using both gauge and radar rainfall data. The hypothesis of the research is that infiltration during and following a heavy rainfall event could increase pore-water pressure to a threshold value and initiate debris flows. The study is the first attempt at using hydrological modeling for a better understanding of debris flow triggering rainwater infiltration in the GRSM area. The specific objectives of the study are to (1) evaluate the use of weather radar in debris flow studies in the West Prong Little Pigeon River (WPLPR) watershed, GRSM, by comparing the performance of radar-based rainfall and rain gauge data and (2) estimate the change in soil infiltration required for initiation of debris flows in WPLPR using hydrological modeling. The merit of the study

lies in the attempt to estimate debris flow triggering rainfall, runoff, and infiltration in an ungauged watershed in the absence of high-resolution spatial and temporal rainfall data and lack of stream gauges or in situ infiltration measurements. STUDY AREA The study area lies in the southeastern section of the WPLPR watershed, GRSM (Figure 1). The watershed covers an area of approximately of 91 km2 . This area was chosen because of its popularity and relative accessibility within the park, the increasing number of tourists, and recent debris flow incidents making it potentially unsafe for the tourists (Nandi and Shakoor, 2016). The WPLPR watershed, ranging in elevation from approximately 640 m to 2,024 m, includes Mt. Le Conte, Newfound Gap, and Tennessee State Highway 441, the main road for travel through the watershed (Figure 1). The WPLPR watershed receives about 170 mm of average monthly summer precipitation and 180 mm of average monthly water content from winter snow melt at higher elevations. The landscape is covered with a mixture of evergreen and deciduous forests. With frequent freeze-thaw cycles in winter and average monthly rainfall exceeding 100 mm in April and May, spring is usually a prime time for mass movements, such as rockfalls (Bates et al., 2018). On the other hand, thunderstorms and hurricanes are common from June to November, a period coinciding with the Atlantic hurricane season, that bring in shortduration, high-intensity rainfall events, thus increasing the potential for mass movement activity; the maximum number of debris flows occur in the month of August (Ryan, 1989; Wieczorek et al., 2000). A total of 252 past debris flows have been reported in the study area, using current and historical aerial photographs and satellite imageries ranging from 1995 to 2014, most of which occurred on steep slopes below ridges at higher elevation (>1,500 m) (Figure 2). After a series of high-intensity rainfall events during the summer of 2012, a debris flow occurred on August 6 near Trout Branch (35°38 21.34 N, 83°26 50.83 W), at an elevation of 1,588 m, that left a 0.83-km-long track (Mandal and Nandi, 2017). The debris flow started above the Alum Cave Bluffs Trail, about 2.5 km from the trailhead along Highway 441, in the heavily weathered saprolite of the Anakeesta Formation. The debris flow pathway had an average slope of 46° in a southwest direction, with the flow ultimately entering Trout Branch through dense spruce–fir forest (Figure 1). The rocks in the WPLPR watershed are metasedimentary units of the Great Smoky Group of late Precambrian age, consisting of primarily Thunderhead

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Figure 1. (a) A 1.5-km-long Trout Branch debris ﬂow initiated in the Alum Cave Trail within the WPLPR watershed, (b) the Great Smoky Mountains National Park boundary, and (c) the WPLPR watershed with catchment boundaries.

Sandstone and Anakeesta Formation (Bogucki, 1976) (Figure 3). The Thunderhead Sandstone consists of conglomerate and coarse-grained meta-sandstone inter-bedded with graphitic meta-siltstone and slate. The Anakeesta Formation contains a variety of rock types, including slate, meta-siltstone, phyllite, metasandstone, chloritoid schist, and greywacke (Ryan, 1989). A small patch of ankerite-rich meta-sandstone and sandy dolomite, with chloritoid meta-siltstone, is also present. The Copperhill Formation, with metasiltstone and meta-greywacke along with a small

exposure of Elkmont Sandstone, is also present in the study area. The Anakeesta Formation is particularly prone to mass wasting because of its heterogeneous lithology dominated by slate, numerous discontinuities, and intense weathering along the ridges of the study area. The study area is bound by thrust faults. The Mingus fault, located east of the study area, is a west-trending, high-angle reverse fault that is exposed within the Anakeesta Formation. The southeasttrending, right-lateral Oconaluftee fault dips toward the southwest in the western part of the study area. The

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Figure 2. Digital elevation model of the WPLPR watershed showing surface drainage and locations of debris ﬂows. The red circle on top of the ﬁgure shows the location of the rain gauge station at Mt. Le Conte. Source of the DEM: USGS 3DEP of 10-m resolution.

Oconaluftee fault separates the Anakeesta Formation from the Copperhill Formation (Bogucki, 1976). The WPLPR watershed contains a variety of soils, ranging in texture from sand and sandy loam to silty-clayey loam (U.S. Department of Agriculture [USDA], 2015). METHODOLOGY Precipitation Input for Hydrological Modeling Two types of rainfall were used for the study: gauge rainfall and radar rainfall. The gauge at Mt. Le Conte is located adjacent to the WPLPR watershed (Figure 2). Recorded rainfall values were acquired from the global historical climatology network (GHCN) maintained by the NOAA National Environmental Satellite, Data, and Information Service. The gauge is read manually each day at 07:00 (http://www. highonleconte.com/daily-posts/weather-station) and reported to the National Weather Service, where it has been archived since 1988 under the name “MOUNT LECONTE, TN US” with the Network ID “GHCND:USC00406328.” The oﬃcial station location is 35.655° latitude, −83.4411° longitude, and at 1,979.1-m elevation. The daily rainfall from January 2012 to August 2012 was used to identify the general trend of hourly rainfall including the Trout Branch debris ﬂow event of August 6; however, daily rainfall from July 2 to August 12, 2012, was used for hydrological model-

ing. For the radar rainfall, 1-hour DPA ASCII files were obtained from the NOAA National Climate Data Center (https://www.ncdc.noaa.gov/nexradinv). The DPA estimated 1-hour precipitation accumulations, an eight-bit product with 255 possible precipitation values on the HRAP grid. Figure 4 shows a sequence of four radar rainfall scenes in the study area for August 5, 2012, from a 10-hour-duration storm before the August 6, 2012, debris flow event. The 4- to 6-minute radar hourly rainfall rates in Coordinated Universal Time date and time were converted into the local Eastern Daylight Time date and time. The 4- to 6-minute rates were converted to incremental hourly rain depth and then summed to give the daily depth for the rainy days from July to August. To keep the DPA results consistent with the gauge rainfall data, the daily rain depths were considered to represent a 24-hour period from 07:01 on Day 1 to 07:00 on Day 2 and attributed to Day 1. A mean bias adjustment factor of 2.17 was derived from the ratio of daily gauge rainfall and accumulated daily DPA radar rainfall at the Mt. Le Conte gauge location for the days that had rainfall events during the study period. Thereafter, a final radar rainfall estimate was obtained by multiplying the initial daily DPA radar rainfall estimate with the mean bias adjustment factor (2.17). This local bias factor is much higher than the NEXRAD regional bias factor assigned to all 7,327 cells in the 166,190-km2 DPA area for the radar station because it applied only to the five or six cells that

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Figure 3. Geologic map of the study area and the vicinity.

covered the Mt. Le Conte rain gauge and the adjacent 91-km2 WPLPR watershed. The bias-adjusted daily radar rainfall rates were compared with the corresponding rain gauge data. The Spearman rho correlation coefficient was used to evaluate the statistical correlation between gauge data and adjusted radar rainfall estimates. The 24-hour daily rainfall for July and August 2012 from both sources was used for runoff simulations in HEC-HMS, and a comparison was made between the outputs for catchments and stream junctions of interest. Hydrological Modeling In this study, we investigate the debris flow– triggering rainfall–runoff–infiltration relationship using HEC-HMS model simulations for the August 6,

2012, Trout Branch debris flow event at Mt. Le Conte in the WPLPR watershed. Hydrological modeling for the WPLPR watershed was used because of the lack of measured stream flow and infiltration data. Additionally, the absence of adequate number of rain gauges in the debris flow–prone watershed required infiltration and runoff for various rainfall events to be estimated. Moreover, the temporal resolution of the data from the sole existing rain gauge was not sufficient to pick up the duration of the storm that initiated the August 2012 debris flow event, indicating the need for spatial and temporal information to be obtained from another source. A NEXRAD Level III DPA product for the months of July and August 2012 was used for this purpose. While past debris flows in WPLPR were mapped in previous studies, the initiation dates were not available in the existing records; therefore, the study was

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Figure 4. Four-frame sequence of NEXRAD Level III DPA product for the study area during passage of rainstorm on August 5, 2012.

limited to the August 6, 2012, event because it was reported by the park officials, following a severe rainstorm event on August 5, 2012 (Figure 4). Hydrological modeling was performed to estimate runoff and infiltration from rain gauge and radar rainfall from July 2012 to August 2012. The modeling tools involved HEC-HMS 4.2, HEC GeoHMS 10.2, and the Arc Hydro tools of ArcGIS 10.6 as a pre-processor for watershed delineation. HEC-HMS simulates the runoff and infiltration for a watershed at catchment and sub-catchment levels using rainfall, catchment area, catchment elevation and slope, and soil and land use types as the primary input parameters. The model is a four-component system involving the basin model (physical parameters for each catchment, such as elevation, area, soil, land use, basin lag time, and stream network), meteorological model (rainfall and

snowmelt), control simulation (time for simulation), and the time-series component, which comprises the available rainfall and or snowmelt data of different durations (5 minutes to 24 hours). Figure 5 illustrates the different components of the model in the form of a flowchart. The boxes in Figure 5 provide the full description of the model following the work of Mandal et al. (2013, 2016). The watershed model, created in HEC-GeoHMS and Arc Hydro, was transported to HEC-HMS and used as the basin model. The meteorological model consisted of the gauge and radar rainfall. The model assumed portioning equal amounts of rainfall to each catchment from the sole rain gauge and the radar rainfall values. The DPA radar product is delivered as an array of values each of which represents hourly precipitation rate in an area of 17.12 km2 . Therefore, the

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were collected from National Park Service’s (2015) Integrated Resource Management Applications portal. We used the standard CN table for the state of Tennessee (Tennessee Department of Transportation, 2010). The weighted CN for each sub-basin was then computed using Eq. 1: Ai CNi (1) CN = Ai

Figure 5. Flowchart showing the steps involved in developing the hydrological model along with its diﬀerent components (basin model, meteorological model, time series, and control speciﬁcations for the simulation) in HEC-GeoHMS and HEC-HMS.

91-km2 area of the watershed is equivalent to about 5.3 DPA cells. The shape of the watershed and orientation of the cells, however, result in the watershed being represented by three DPA cells. The runoff and infiltration were computed for the rainfall as recorded by the rain gauge at Mt. Le Conte and the bias-adjusted DPA values. HEC-HMS computes infiltration (or loss) from rainfall using the Natural Resources Conservation Service US-SCS curve number (CN) (U.S. Soil Conservation Service, 1986) as well as the Green and Ampt (1911) method. The SCS method is most appropriate for estimating peak flows and infiltration for storm events and is built into the HEC-HMS model under the basin model component where the user enters the CN for each sub-basin or catchment. The relationship between accumulated rainfall depth and accumulated runoff in the SCS method was first derived by Mockus (1964) from experimental studies of numerous soils under different land use conditions (agricultural, urban, and rural). The CN (or the runoff coefficient) is a dimensionless number that indicates the runoff potential for a characteristic combination of soil and land cover types as based on their infiltration capacity. It is assigned based on a combination of soil types, classified in terms of hydrologic soil groups (HSG), and the land use for each sub-basin. The soil data obtained from the USDA (2015) were used to classify soils into HSGs A–D based on their texture, composition, and internal drainage (infiltration) characteristics. HSG A has the lowest runoff potential and includes sandy loam and gravel. HSG B has moderate runoff potential and includes clay and sand. HSGs C and D represent clayey loam and clay, respectively, with high runoff potential. The land use data for the study area

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where CN = area-weighted CN for each subcatchment or sub-basin, Ai = area of the sub-basin, and CNi = CN for the sub-basin. Higher values of CN imply higher surface runoff and greater potential for flooding. Runoff was computed using the relationship between accumulated rainfall and runoff, derived by Mockus (1964) for storm events of 1-day duration or less. The peak discharge or runoff was computed by using Eq. 2: Q=

(P − Ia )2 (P − Ia + S)

(2)

where Q = runoff in inches, P = rainfall in inches, S = maximum soil water storage potential, and Ia = initial abstraction, which accounts for the interception of rainfall by vegetation as well as transpiration and evaporation. The infiltration was computed internally in the HEC-HMS model by subtracting the runoff as estimated from Eq. 2 from the rainfall values. The lag time, the time difference between occurrence of peak rainfall and peak discharge, and time of concentration for flow for different catchments were computed using Eq. 3 (Mockus, 1964): 0.7 − 10 + 1 L0.8 1,000 CN (3) Tc = 1,900 Y 0.5 where Tc = time of concentration in hours, L = length of the flow path in feet, CN = runoff curve number, and Y = average slope of the basin in percent. It should be noted that Eqs. 1–3 were developed using the U.S. customary system of units; HEC-HMS converts the units to the International System of Units during computation. Developing the Meteorological Model The meteorological model was created in HECHMS by using the daily rainfall data as recorded at the Mt. Le Conte gauging station (Figure 2) as well as the adjusted NEXRAD III radar rainfall (Figure 4). The radar rainfall data were extracted from the same location as the Mt. Le Conte gauging station because of limited spatial variability of the few biascorrected DPA cells over the watershed. The values

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Figure 6. Primary layers used in the creation of catchments and drainage network: (a) DEM, (b) ﬂow direction, (c) catchment raster, (d) catchment polygon, and (e) basin model with drainage network, debris ﬂow locations, and the symbology for sub-basin, junction, reach, reservoir, and sink, as developed in HEC-GeoHMS.

were summed to provide the daily precipitation depth values so that the time interval of both data sets remained identical. Delineating Catchment Areas The basin model, as developed in HEC-GeoHMS, uses the digital elevation model (DEM) of the watershed (Figure 2) as the base layer and, using the steps under the terrain processing module of Arc Hydro, extracts different catchments and sub-catchments from the watershed based on different water flow directions with variations in slope and elevation of the DEM. It also uses the DEM to extract the drainage network (the main channel and the junctions or nodes of tributaries) and slopes (USACE, 2013). The DEM of the WPLPR watershed was obtained from the National Park Service. Flow direction is in the steepest direction of the slope downstream for each cell of the DEM raster and was designated by numbers referring to ordinal directions. Flow accumulation is the flow from all cells up-

stream of a user-defined accumulation point based on raster cell size. Cells with high flow accumulation are areas of concentrated flow. Streamlines were extracted from the flow accumulation cells, and catchments were delineated around the streamline cells. The final catchments were produced by merging common drainage junction points to minimize the number of sub-catchments. Figure 6a–d shows steps

Table 1. Summary of the amounts of rainfall, inﬁltration, and runoﬀ, based on HEC-HMS results for Catchments A and B, using gauge and radar data.

Catchment A Rainfall (mm) Infiltration depth (mm) Runoff depth (mm) Catchment B Rainfall (mm) Infiltration depth (mm) Runoff depth (mm)

Rain Gauge

Radar

424 48 369

192 55 144

424 50 365

192 54 140

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Figure 7. Map of the WPLPR watershed showing the locations of debris ﬂows along with (a) the hydrologic soil types, (b) the land cover, and (c) the average CN for diﬀerent catchments.

in the creation of the catchment model, and Figure 6e shows the ﬁnal basin model with sub-basin, reach (river channel), and junctions (outlets) assigned in HEC-GeoHMS. The basin model was transferred to HEC-HMS for the ﬂow simulations. The areaweighted CN, calculated using Eq. 1, was assigned to each sub-basin in the basin model (Figure 7).

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RESULTS Gauge and Radar Rainfall Estimates The Mt. Le Conte rain gauge data from May 2012 to August 2012 showed a monthly rainfall of 161 mm for May, 72 mm for June, 321 mm for July, and 192 mm for August. The maximum daily rainfall recorded for

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Figure 7. (continued)

May–June was approximately 30 mm compared to 46 mm and 93 mm for July and August, respectively (Figure 8). Thus, July and August were the wettest months during the study period. This indicates that the ground was relatively dry during May and June but became wetter during July and August, leading to a gradual decrease in inﬁltration. The peak daily rainfall at the Mt. Le Conte gauge was 93 mm, as recorded on August 6 (note the gauge is read only once in 24 hours, at

07:00, so the rain from an afternoon–evening storm on August 5, as shown in Figure 4, would be recorded the morning of August 6). The radar rainfall estimates at the Mt. Le Conte gauge location were extracted only for the months of July and August 2012. Based on weather radar, 13 storm events passed over the area during this time. Daily rainfall amount increased during the period between July 2, 2012, and August 5, 2012 (Figure 8).

Figure 8. July–August 2012 daily rainfall reported for the rain gauge at Mt. Le Conte and NEXRAD III as calculated for DPA products for the rain gauge location.

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Both gauge and bias-adjusted radar estimates of daily rainfall showed comparable results. The biasadjusted radar rainfall for August 5, 2012, the day before the Trout Branch debris flow occurred, was 98 mm (4.083 mm/hr) compared to the gauge rainfall of 93 mm (3.875 mm/hr). The radar rainfall for August 5 at the gauge location indicated a storm duration of 10 hours from afternoon to midnight, with the high intensity occurring between 17:25 and 20:25. The highest approximately 15-minute intensity values at the Mt. LeConte gauge location were 36.66 mm/hr at 16:50 and 16:55 on August 5, 2012. Both high-intensity intervals had durations of 0.333 hours (20 minutes). The highest approximately 15-minute intensity values at the Trout Branch soil slip location were 63.24 mm/hr at 16:30 and 16:35 on August 5, 2012. Both highintensity durations were 0.233 and 0.25 hours (14 and 15 minutes). The radar data revealed the onset, peak, and passage of the storm (Figure 4), whereas the daily rain gauge, of course, was unable to capture storm details. The correlation between gauge data and bias-adjusted radar estimates showed a moderately good Spearman rho correlation coefficient of 0.64 (p < 0.05). The rainfall data from both the radar and the rain gauge (Figure 8) were used as an input for the runoff and infiltration estimation.

Influence of HSG, Land Use, and Curve Number on Debris Flow Distribution Most of the watershed is composed of soils belonging to HSG B, specifically the northern, north-eastern, and central sections (Figure 7a). These soils consist of primarily sand and sandy loam to silty-clayey loam texture and are characterized by low runoff potential and high infiltration rate (USDA, 2009). On the other hand, soils belonging to HSG C, having a higher runoff potential, are present at lower elevations, above the channel beds, and have the second-largest number of debris flow occurrences. The land-cover map for the watershed (Figure 7b) shows that urbanization is limited in the watershed and that debris flows occur in forested areas ranging from evergreen forests to woody wetlands. Figure 7c shows the CN, weighted with respect to the catchment area using a combination of soil type and land use (Figure 7a and b). Most of the debris flows occur in catchments with CN ranging from 59 to 62, comprising the highland areas, as opposed to catchments with CN > 62, representing downstream sections of stream channels, that is, areas of high surface runoff (Figure 7c). A lower CN indicates a higher rate of infiltration, which could lead to ground saturation and initiation of debris flows.

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Rainfall–Runoff Simulation Results HEC-HMS was used to perform rainfall–runoff simulations for the WPLPR watershed, using the SCS loss estimation method, the basin model (Figure 6e), and the daily rainfall data from rain gauge and radar (Figure 8) for the Trout Branch debris flow on the Alum Cave trail along with the other debris flows (Figures 1 and 2). The model produced runoff hydrographs for every catchment and stream junction. However, only the simulation results for Catchment A (containing the Trout Branch debris flow), Catchment B (below the Mt. Le Conte rainfall gauge station), and stream outlets C and D (upstream and downstream junctions) (Figure 9a) are discussed because they are relevant and representative. Tables 1 summarizes the results of the simulation, showing the infiltration and runoff depths, for Catchments A and B using gauge and bias-adjusted radar rainfall estimates. Catchment A, located downstream, shows an increase in runoff as compared to Catchment B for both data sets. Slightly elevated infiltration in Catchment B could be attributed to the presence of a dry and bare ridge in the catchment, as observed in the land use data. Although the two data sets show difference in the amounts of runoff, the infiltration amounts differ by only 2–3 mm. The significant difference in the amounts of runoff between the two data sets is due to the difference in the input rainfall data from rain gauge and radar estimates. The rain gauge records the total rainfall in a 24-hour period, while the radar captures the rainfall during the onset and passage of the storm. Hence, the temporal variation inherent in the radar data leads to the difference in the simulation results. The model outputs for the months of July and August show similar trends for both catchments and both rainfall data sets, with peak runoff initially occurring 1–2 days after the peak rainfall, accounting for the basin lag time, which was reduced toward the later part of the study period (Figures 10 and 11). The peak runoffs for both catchments were observed to occur around August 6 for both the rain gauge– and the radar rainfall–derived outputs, as expected. Both data sets showed higher amounts of infiltration during early July, which gradually decreased during late July and early August, indicating continuous ground saturation with additional rainfall throughout the duration of the study period. Catchment A had peak discharge values of 0.712 m3 /s and 0.736 m3 /s for rain gauge and radar rainfall, respectively (Figure 10), whereas Catchment B had peak discharge values of 0.682 m3 /s and 0.703 m3 /s for the same rainfall data sets (Figure 11). Outlet D, located at the downstream end of the WPLPR watershed, shows an estimated peak runoff

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Figure 9. Basin model showing (a) locations of catchments of interest (A and B) and outlets (C and D); (b) discharge hydrograph, simulated using rainfall from radar and rain gauge, for Outlet C; and (c) discharge hydrograph for Outlet D.

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Figure 10. Discharge hydrographs, rainfall, and inﬁltration depths for Catchment A, using rainfall from rain gauge (top) and radar (bottom). Refer to Figure 9a for location of Catchment A.

of 33 m3 /s for the gauge rainfall as compared to 34 m3 /s for the radar rainfall. Outlet C, located at the downstream end of the Trout Branch watershed, shows a peak runoﬀ of 0.68 m3 /s for the gauge rainfall and 0.7 m3 /s for the radar rainfall. Since stream runoﬀ relies on slope and topography, the stream junctions at 106

lower elevation have a higher volume of discharge, as they include flows from the upstream catchments and tributaries. Thus, Outlet D, located near the mouth of the main channel, shows a much higher runoff than Outlet C, located in the highlands. The trends in the two runoff hydrographs for the two outlets are

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Figure 11. Discharge hydrographs, rainfall, and inﬁltration depths for Catchment B, using rainfall from rain gauge (top) and radar (bottom). Refer to Figure 9a for location of Catchment B.

similar with the peak discharges for the two data sets differing by ∼1 m3 /s, thus showing a good correlation between the runoff from the rain gauge and local bias-corrected radar, with the radarbased results showing slightly higher values of peak runoff.

DISCUSSION Hydrological modeling of the WPLPR watershed, using HEC-HMS, is a new initiative for this study area to assess the progressive reduction in soil inﬁltration because of a storm event. The reduction in

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infiltration indicates ground saturation and an increase in the soil pore-water pressure to a point where the slope becomes unstable, resulting in debris flows. The study area has only one rain gauge at Mt. Le Conte, and it is recorded manually only at 07:00 each day. Its precipitation depth represents a 24-hour rainfall accumulation. For watersheds such as WPLPR, weather radar products can be useful substitutes for rainfall data and can provide considerably more information about spatial and temporal precipitation than data from a single gauge if accurate bias corrections can be applied. The present work provides a comparison of the infiltration and runoff estimates using a single gauge adjacent to WPLPR and a radar rainfall product adjusted for local bias. Comparison between the two data sets also showed rainfall on the same days, but, overall, the radar showed slightly less rainfall than the gauge, as found by other researchers (Neary et al., 2004; Sebastianelli et al., 2010). Nevertheless, the rainfall trends are consistent for both data sets. Previous studies have shown that elevated pore pressures could initiate debris flows during heavy precipitation where initially the infiltration rates are high, resulting in nearly saturated condition and, consequently, slope failures (Wang et al., 1997; Guo et al., 2016). A study by Tang (2013) pointed out that degree of soil saturation of a drainage basin is a good indicator to evaluate the soil water conditions during generation of debris flows. The study indicated that, on average, about 70% soil saturation of a basin could be critical for debris flow generation. However, this percentage can vary with the underlying conditions of a study area (Tang, 2013). The other mechanism of debris flow initiation is runoff related, where excess runoff washes debris along pre-existing channel beds. For example, Rengers et al. (2020) and Tang et al. (2020) observed runoff-generated debris flows after wildfires in the southwestern United States. In the present study, results for both data sets indicate that catchments at higher elevation (Catchment B) exhibited a higher amount of total monthly infiltration as compared to catchments downstream (Catchment A). Analysis of daily rainfall–runoff showed infiltration values reaching maximum soon after the peak rainfall, followed by a steady decline in infiltration and an increase in surface runoff. Both gauge and radar rainfall showed that the month of July had the highest total and daily peak rainfall and runoff. Both data sets showed higher amounts of infiltration during early July that gradually decreased during late July and early August. This pattern suggests that a large amount of rainfall could decrease soil infiltration because of the elevated soil moisture content, thus increasing pore pressure, which is the most likely cause of the August 6, 2012, landslide, and subsequent debris flow.

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Limitations of the Study Three major limitations were encountered during this study. The first limitation is the lack of a network of rain gauge stations in the vicinity of the watershed where the debris flow occurred. There is only one rain gauge station in the GRSM region, located at Mt. Le Conte, which coincidentally happens to be near the catchments with maximum number of debris flows. The next closest gauge is 22 km to the east, in Cherokee, Tennessee, a remote automated weather station at an elevation of 1,036 m (https://wrcc.dri.edu/wraws/ky_tnF.html) that records hourly data. This weather station was judged to be too far from the WPLPR watershed to be used in the analysis. Its daily rainfall accumulation for August 5 and 6 was 38.6 mm, indicating that rainfall during the storm was not uniform across the region. Additional rain gauge stations in reasonable proximity to the WPLPR watershed probably would have allowed for better correlation between rainfall and runoff for each catchment and a better understanding of the rainfall–runoff–infiltration relationship with respect to debris flow initiation. The present study used the rainfall data from one gauge as representing uniformly distributed, non-varying precipitation over the entire 91 km2 area of the watershed. The second limitation is the absence of stream gauges or in situ measurements of infiltration or runoff for calibration of the model. The area lacks perennial streams or rivers, and, as such, no stream gauges are available to measure daily flow. Thus, calibration of the model was restricted to only rainfall input in the present study. The exact date-to-date and time-to-time correlation of rainfall and debris flow initiation was not possible from previous debris flow records, as there is no record of the exact dates and times of historical debris flows. Therefore, the study was limited to only the August 6, 2012, debris flow event, as it was the only known debris flow with an exact date of occurrence. The third limitation is not knowing the exact timing of the August 6, 2012, debris flow event. This study concluded the increased soil moisture led to soil saturation and greater runoff, which was the most likely cause of the debris flow. This conclusion could have been strengthened if the exact timing of the debris flow were known. CONCLUSIONS The results of this study lead to the following conclusions: 1. This study is the first attempt at using HECHMS for surface runoff and infiltration focusing

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primarily on infiltration in an area of the Great Smoky Mountains where rain and stream-flow gauges are absent, making it difficult to estimate runoff and infiltration thresholds for landslide initiation from rainfall events. The application of hydrological modeling in ungauged watersheds provides an opportunity to develop an early warning system for slope failures as well as flooding. The temporal resolution of the data from a sole existing rain gauge, near Mt. Le Conte, is not adequate to identify the duration of the storm that initiated the August 6, 2012, debris flow event, necessitating the use of the NEXRAD Level III rainfall product. The rain gauge and the local bias-adjusted NEXRAD rainfall product for the study area compare reasonably well, exhibiting a moderately significant statistical correlation. For the study area (WPLPR watershed, GRSM), HEC-HMS has proved to be a viable hydrological modeling tool for estimating runoff and infiltration depths for a given amount of rainfall using both the rain gauge and the radar data. The radar rainfall prior to the August 6, 2012, debris flow event at the gauge location indicated a period of 10 hours of rainfall from afternoon to midnight, with the highest intensity rain of 63.24 mm/hr for 15 minutes. Both rain gauge and NEXRAD Level III rainfall simulations reveal that as runoff peaked around August 5, 2012, following the July and August storms, the consequent decrease in infiltration and associated increase in pore pressure most likely initiated the Trout Branch landslide, which mobilized into a debris flow.

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Assessment of Logistic Regression Model Performance and Physical Controls on January 9, 2018, Debris Flows, Thomas Fire, California BRIAN J. SWANSON* California Geological Survey, 320 West 4th Street, Suite 850, Los Angeles, CA 90013

STEFANI G. LUKASHOV California Geological Survey, 801 K Street, Sacramento, CA 95820

JONATHON Y. SCHWARTZ U.S. Forest Service, 1190, East Ojai Avenue, Ojai, CA 93023

DONALD N. LINDSAY California Geological Survey, 6105 Airport Road, Redding, CA 96002

JEREMY T. LANCASTER California Geological Survey, 801 K Street, Sacramento, CA 95820

Key Terms: Post-Fire Debris Flow, Rainfall Intensity, Morphometric Factors, Geologic Factors, Watershed Area, Montecito, Atmospheric River, Logistic Regression ABSTRACT The 2017–2018 Thomas Fire burned 281,893 acres of land in southeastern Santa Barbara County and southwestern Ventura County. An atmospheric river storm impacted the region on January 9, 2018, producing intense rainfall in the western and northern portions of the burned area and triggering numerous post-fire debris flows (PFDFs). The most destructive and deadly flows inundated the town of Montecito, where 23 people died. Debris flow source and inundation mapping data across the fire provide a rare opportunity to assess the interplay between rainfall intensity, watershed characteristics, geologic conditions, and resulting PFDF occurrence. Mapped data are compared to spatially explicit analyses of 857 drainage basins modeled with the U.S. Geological Survey (USGS) logistic regression model (LRM) for PFDF prediction using 15-minute rainfall thresholds at 50 and 90 percent (P50 and P90) probabilities of exceedance. Results indicate that the LRM successfully predicted nearly every PFDF reaching the basin pour point. However, overall model accuracy was lowered by numerous false-positive responses, *Corresponding author email: brian.swanson@conservation.ca.gov

even where rainfall depths were far above LRM thresholds. Analyses of basins where rainfall was above P50 thresholds reveal a strong correlation between high falsepositive responses and basins experiencing rainfall of less than about 150 to 200 percent of USGS thresholds. These false positives occurred in basins with small (0.02– 0.05 km2 ), steep (ࣙ23°) burned areas and in basins underlain by relatively weak geologic units that weather to produce few boulders. Identified relationships provide a basis for refining and improving existing PFDF hazard assessment modeling.

INTRODUCTION It is well established that fire-induced loss of vegetation cover and alteration of soils dramatically increase the probability and volume of potential postfire debris flows (PFDFs) and flooding during intense, short-duration rainfall following the fire, which may adversely impact development downstream of burned watersheds (e.g., Eaton, 1935; Cleveland, 1973; Wells, 1987; Shuirman and Slosson, 1992; Cannon and Reneau, 2000; U.S. Geological Survey [USGS], 2005; Kean et al., 2011; and De Graff, 2014). Rapid PFDF hazard assessments by emergency response teams in the western United States commonly rely on modeling by the USGS Landslide Hazards Program based on the logistic regression methodology developed by Staley et al. (2016) to help quantify potential hazard

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to life safety and property (values at risk) and assist communities in their emergency response planning. The Thomas Fire burned 281,893 acres in southeastern Santa Barbara County and adjoining portions of southwestern Ventura County between December 4, 2017, and January 12, 2018, and was the largest fire recorded within California at the time. The USGS National Landslide Hazards Program team conducted a rapid evaluation of potential debris flow hazard to aid the state and federal PFDF hazard assessments (California Department of Forestry and Fire Protection [CAL FIRE], 2018; U.S. Department of Agriculture Forest Service, 2018) that revealed high probability for debris flows at the 1-year recurrence interval in the mountainous regions of the burned area (Figure 1). While rainfall intensity thresholds for debris flow likelihood varied between modeled basins, a 15-minute-duration rainfall intensity (i15) of 24 mm/hr (6-mm depth in 15 minutes) was adopted as the fire-wide average by the Thomas Fire Watershed Emergency Response Team (WERT) mobilized to evaluate post-fire hazards from the Thomas Fire (CAL FIRE, 2018). Early on the morning of January 9, 2018, a lowmagnitude atmospheric river with an embedded narrow cold frontal rainband (NCFR) struck the burned area with the heaviest rains concentrated in Santa Barbara County and the northern mountainous areas of the burned area in Ventura County (Oakley et al., 2018; Lukashov et al., 2019). Rainfall intensities were extreme in the Santa Ynez Mountains and Juncal Canyon in Santa Barbara County, far exceeding USGS thresholds with recurrence intervals greater than 50 years at some rain gages for 5- and 15-minute durations. As the storm progressed eastward into Ventura County, the NCFR began to dissipate in the southern and eastern portions of the burned area (Figure 2). The heavier rains triggered debris flows and sediment-laden flows in many of the steep burned watersheds within the fire perimeter. The most destructive of these flows originated on the southern flank of the Santa Ynez Mountains and rapidly coalesced into the major canyons that drain southward into the communities of Montecito, Summerland, and Carpinteria. The flows inundated alluvial fan areas in the Montecito and Carpinteria piedmonts, killing 23 people, damaging or destroying 558 structures, and resulting in direct and indirect costs exceeding $1 billion (Laber, 2018; Lancaster et al., 2021). The distribution of debris flows and impacts in the inundated portions of Montecito and the Santa Barbara Coastal Plain were documented in Kean et al. (2019) and Lancaster et al. (2021), respectively, and additional data on the conditions in the source areas are provided in Keller et al. (2020). Lukashov et al. (2019) expanded

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debris flow mapping across the entire burned area, revealing the occurrence of numerous large debris flows in the Juncal Creek and Matilija Creek watersheds and smaller flows in the Nordhoff Ridge area north of Ojai (Figure 3). Few debris flows were identified in the southern and eastern portions of the fire. The distribution and size of debris flows was observed to broadly correspond with variations in rainfall intensity across the burn area as noted in Lukashov et al. (2019) but not assessed in detail. The goal of this study is to assess physical controls on the distribution of debris flows across the burned area. To this end, we focus on assessing the performance of the USGS logistic regression model (LRM) (Staley et al., 2016, 2017) and its empirically derived, independent parameters (soil burn severity, slope gradient, and soil erosion susceptibility) with respect to observed 15-minute-duration rainfall depths and PFDF initiation rainfall thresholds. We then evaluate the influence of two additional parameters, watershed area and geologic source units, on PFDF occurrence. Background Factors Controlling Debris Flow Initiation Debris flows are classified as containing greater than about 50 percent sediment load by volume that exhibit viscous, non-Newtonian flow behavior (Pierson and Costa, 1987; Lancaster et al., 2015). Debris flows can entrain large boulders, creating a coarse surge front consisting of a fluidized fine-grained matrix and a solid phase of rock and woody material (Iverson, 1997, 2014). Debris flow occurrence is generally associated with three dominant controlling factors (e.g., Takahashi, 1981): 1. Water input: Under unburned conditions, total and antecedent rainfall and buildup of soil pore-water pressure are the primary water-input drivers in the formation of infiltration-driven debris flows (e.g., Campbell, 1975). Under post-fire conditions, highintensity rainfall at sub-hourly durations is a primary driver of sudden, runoff-driven debris flows without the requirement of antecedent moisture (e.g., Moody and Martin, 2001; Cannon et al., 2008; Kean et al., 2011; and Staley et al., 2017). 2. Watershed morphometrics: Multiple watershed characteristics have been interpreted to influence the formation and propagation of debris flows, including mean catchment slope, watershed area, relief, relief ratio, Melton’s ruggedness number (R), planimetric length (PL), drainage density, slope curvature, channel gradient, and fan gradient (e.g.,

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Logistic Regression Model Performance, 2018 Thomas Fire Debris Flows

Figure 1. Debris flow probability map for 1,736 basins developed by the USGS for rapid assessment of the Thomas Fire area (outlined in red) prior to the January 9 debris flows; northern “Unburned Area” defines area within overall fire perimeter that did not burn in the Thomas Fire. The USGS model predicted high PFDF probabilities (60–100 percent) for basins within the Santa Ynez Mountains, Matilija Creek watershed, Nordhoff Ridge, and areas north of Santa Paula Ridge at a 24-mm/hr intensity for a 15-minute duration (6-mm depth). Source: https://landslides.usgs.gov/hazards/postfire_debrisflow/detail.php?objectid = 198.

Jackson et al., 1987; Rickenmann and Zimmermann, 1993; Wieczorek et al., 1997; Coe et al., 2003; Hungr et al., 2007; and Horton et al., 2008). Wilford et al. (2004) classified flow type by R in the source basin and reported that debris flows commonly occur where R exceeds 0.6 and where PL is less than 2.7 km; Welsh and Davies (2011) derived a lower R threshold of 0.5. However, under burned conditions, Gartner et al. (2014) relate PFDF response to basins with a wide range of R values from 0.12 to 1.03, with an average of 0.51. 3. Sediment supply: The supply, grain size distribution, and susceptibility to erosion of sediment in source canyon slopes and channels constitute the third primary controlling factor, the occurrence of which is further controlled by the lithology, consistency, and degree of fracturing of the underlying geologic units (e.g., Spittler, 1995; Bovis and Jakob, 1999; and Staley et al., 2014). In burned areas, the formation of runoff-initiated debris flows is promoted by the depth of loose, fire-disaggregated surface material and hydrophobic conditions

(related to soil burn severity) and the development of dry ravel, the presence of alluvial debris within channels, and the availability of cobble- to bouldersize particles (e.g., DeBano et al., 1979; Wells, 1987; Wohl and Pearthree, 1991; Spittler, 1995; Cannon and Reneau, 2000; Kean et al., 2013; and DiBiase and Lamb, 2020). Previous studies have also noted that the enhanced potential for runoff-generated debris flows decreases with time following wildfire as vegetation recovers and soil properties return to pre-fire conditions (e.g., Thomas et al., 2021), although the potential for shallow infiltration-driven, post-fire flows may peak later in the recovery cycle (e.g., Rengers et al., 2020). PFDF Model Overview The USGS developed multi-variate LRMs to predict PFDF probability in the western United States and southern California (as summarized in Staley et al., 2017) using combinations of parameters,

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Figure 2. Map showing contoured distribution of classified peak 15-minute rainfall depths interpolated from records at 40 gages on January 9, 2018, rain gage sites and associated peak 15-minute depths at each gage. Rainfall contours graphically illustrate the breaks analyzed in this article, including the fire-wide PFDF threshold depth of 6 mm in 15 minutes (24-mm/hr intensity) adopted prior to the debris flow event by the USGS and WERT. As illustrated, the most intense rainfall occurred in Santa Barbara County.

including Melton’s number, relief, slope gradients, average gradient of burned terrain, and peak storm intensity (mm/hr) (Cannon, 2000, 2001; Cannon et al., 2003, 2010; Rupert et al., 2003, 2008; and Staley et al., 2013). The current USGS debris flow probability LRM used to assess the Thomas Fire was developed by Staley et al. (2016, 2017) and is based on 1,550 records from post-fire rainfall events. This revised and simplified method relies on three controlling parameters in the upslope areas of each modeled basin, each multiplied by rainfall accumulation to ensure that the likelihood of occurrence approaches 0 percent during periods of no rain (see eq. 5 from Staley et al., 2017): 1. Proportion of upslope area with moderate to high burn severity and gradients ࣙ23° (42.4 percent) 2. Average difference normalized burn ratio (dNBR) ÷ 1,000 3. Erodibility of fine fraction (<2.0 mm) of the soil (soil KF-factor) The LRM is based on topographic data obtained from a 10-m USGS digital elevation model (DEM)

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available nationwide and is limited to basins ranging from 0.02 to 8 km2 (∼5–2,000 acres) in area. The model is considered applicable for conditions in the first year or two after fire (Staley et al., 2016). Rainfall data used to develop the LRM were obtained (without interpolation) from nearby gages within distances of up to 4 km from the debris flow observation point, and rainfall accumulations and peak storm intensities were calculated using a backward differencing approach (Kean et al., 2011). Peak 15-minute rainfall accumulations ranging from 0.4 to 28 mm (1.6– 112-mm/hr intensity) were considered in the model. The peak rainfall intensity with a 15-minute duration (15-minute accumulation) was found to best predict PFDF (Staley et al., 2016). Soil burn severity for the Thomas Fire was derived from field-validated Burned Area Reflectance Classification (BARC) imagery derived from the dNBR. Soil properties were derived from Schwartz and Alexander (1995). The model assesses potential PFDF occurrence at fixed probability levels with respect to defined basin pour points (basin mouths) and for individual segments

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Logistic Regression Model Performance, 2018 Thomas Fire Debris Flows

Figure 3. Map showing distribution of debris flows mapped by Lukashov et al. (2019) relative to contours of peak 15-minute rainfall. The five watershed regions defined in the text are illustrated in the index map, and boundaries are outlined with dashed gray lines on the main map. Almost all debris flows occurred in areas with peak rainfall depths greater than the fire-wide 15-minute threshold, except locally in the Ojai Valley area. No debris flows were identified in the Sulphur Mountain and Red Mountain areas, where rainfall was above the fire-wide threshold.

within the basins. Potential run-out and flow paths beyond the pour points are not currently evaluated by the model, although this is a topic of current study (e.g., Bessette-Kirton et al., 2019). Previous Evaluations of the USGS Model for the Thomas Fire A non-linear methodology to assess the probability of PFDF was developed by Kern et al. (2017) and Addison et al. (2019) using machine learning. Addison (2018) applied the method to the Santa Barbara Coastal Plain portion of the Thomas Fire and concluded that both the non-linear model and the USGS LRM performed well in identifying the highrisk basins that produced debris flows on January 9, 2018, but reported that the LRM produced more false positives than the non-linear model. The Addison study utilized 6,611 independently generated basins, an averaged 30-minute rainfall intensity obtained from five gages in the western portion of the burned area where rainfall was extreme, and USGS basin probabil-

ities for a 40-mm/hr event across the burned area and used damage reports only in the Montecito area to validate mapping based on dNDVI differencing. In contrast to Addison (2018), this study uses the same 1,736 basins defined by the USGS for the Thomas Fire, 15-minute rainfall depths without interpolation (in accordance with the preferred USGS model duration) from 40 rain gages spread across the burned area, spatially explicit USGS rainfall thresholds for each basin, and field-validated debris flow mapping based on a combination of high-resolution lidar and imagery, which results in a more robust data set and uniform comparison. Physical Setting Topography/Geomorphology The Thomas Fire burn area encompassed roughly 1,141 km2 (440 mi2 ) of land with ridgeline elevations ranging from about 3,500 ft (1,067 m) to 4,800 ft (1,463 m) along the crest of the Santa Ynez Moun-

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tains range to almost 5,600 ft (1,705 m) at Nordhoff Ridge north of Ojai and reaching a maximum of 6,010 ft (1,832 m) at Monte Arido on the western side of Matilija Creek. The burn area is subdivided into five primary watershed regions based on physiographic characteristics and response to the January 9 storm (Figure 3): (1) Santa Barbara, the southern flank of the Santa Ynez Mountains eastward to Rincon Creek; (2) Juncal, Juncal Creek on the northern flank of the Santa Ynez Mountains and southwest of Old Man Mountain; (3) Matilija, the Matilija Creek watershed upstream of Matilija Springs; (4) Ojai, the Ojai and Upper Ojai valleys on the southern flank of Nordhoff Ridge; and (5) Other, the remainder of the burned area, which generally experienced lower rainfall intensity than the other areas. The Santa Barbara Coastal Plain consists of piedmont landforms including large, steep coalescing alluvial fans at the mountain front and more gently sloping fans near the coast (Lancaster et al., 2021). The upper fans are typically entrenched where they emerge from narrow canyons. The mountain front at Montecito steps back from the coast relative to its position to the east at Carpinteria, and fan morphology is interrupted by the eastern, discontinuous extension of Mission Ridge, which experienced uplift during the late Quaternary along the Mission Ridge fault and is now cut by antecedent channels (Minor et al., 2009; Kean et al., 2019). Watershed lengths for the dominant canyons progressively increase eastward from Montecito, ranging from about 3.7 km at Montecito to 9 km at Rincon Creek (Lancaster et al., 2021). Fan gradients on the upper piedmont at Montecito range from 5.3 percent downstream of larger source basins up to 12 percent for steeper, smaller basins and decrease to a range of 2.7–3.1 percent on the lower piedmont fans south of Mission Ridge. Upper piedmont fan gradients in the Carpinteria area range from 1.8 to 10 percent, with steeper gradients associated with smaller basins. Individual alluvial fan areas are typically 1 km2 or less but range up to 2.8 km2 below Santa Monica Canyon. In the Juncal Creek drainage and upper Santa Ynez River basin, drainages on the northern flank of the Santa Ynez Mountains are steep and relatively short and drain directly into an axial valley. Alluvial fans are therefore fairly small (0.005 km2 on average) and commonly steep, with slope gradients ranging up to 21.3 percent. Watersheds in upper Juncal Canyon are variable but generally either produce small, steep fans or cut through old, abandoned fans before discharging into the axial valley. The Matilija Creek watershed consists of the main fork, which is over 7 km long, and several large tribu-

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tary forks. Smaller catchments drain into the axial valleys of the larger forks of Matilija Creek, producing relatively small fans (0.001–0.05 km2 ) with slope gradients ranging from 3.7 to 12.5 percent that have been modified by flows in the axial valleys. Basins on the southern flank of Nordhoff Ridge drain to a series of large coalescing fan deposits that underlie much of the Ojai and Upper Ojai valleys. Upper fan slope gradients range from 6.6 to 9.9 percent, and individual fans range up to 2.8 km2 in area. The alluvial fans commonly contain bouldery debris flow deposits but are commonly entrenched near the mountain front. Climate and Vegetation The study area has a Mediterranean climate with warm, dry summers and cool, wet winters where precipitation occurs almost entirely as rain, with ephemeral snow occurring in the higher elevations during colder storms. Summer monsoon-driven rain and associated thunderstorms are uncommon, but heavy rainfall can occur in the winter months associated with atmospheric river storms from the Pacific Ocean (Dettinger et al., 2011; Ralph and Dettinger, 2012; and Oakley et al., 2017). These atmospheric river storms commonly account for the majority of annual rainfall accumulation in the study area. Mean annual rainfall in the burn area varies from about 45.0 cm (17.7 in.) in the Santa Barbara Coastal Plain up to 75.4 cm (29.7 in.) in the upper Juncal/Santa Ynez watershed but is quite variable from year to year. Rainfall totals and intensity can also vary across the area due to orographic effects, with heavier rain occurring on higher mountain ranges. The climate supports dominantly heavy mixed chaparral within the burned area with the remainder of the area composed primarily of coastal scrub, coastal oak woodland, annual grassland, valley foothill riparian, and montane hardwood-conifer vegetation types (CAL FIRE, 2018). Geology The Thomas Fire burn area is located within both the western portion of the west-trending Transverse Ranges Geomorphic Province and the transitional area into the adjacent northwest-trending Coast Ranges Geomorphic Province to the northwest (California Geological Survey [CGS], 2002). The mountain ridgelines mimic the general trend of late Neogene and Quaternary folding and faulting, which formed in response transpressional tectonic relationships along the San Andreas Fault boundary between the North American and Pacific lithospheric plates north of the study area.

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Figure 4. Geologic map of Thomas Fire area, simplified from the geologic map in CAL FIRE (2018), illustrating five selected geologic unit groups based on age, lithology, and induration; peak rainfall contours shown for reference. Most debris flows occurred in basins underlain by Group 1 Jurassic and Eocene rocks.

Bedrock units exposed within the burn area are primarily sedimentary in origin, range in age from Jurassic to Pleistocene, and have been uplifted and steeply tilted. For the purpose of this study, geologic units have been organized and merged into five groups based on similar lithologic, induration, and age characteristics as shown on the geologic map in Figure 4, which has been simplified from the geologic map compiled in the WERT report (CAL FIRE, 2018). Previously mapped landslides have been merged with the underlying source unit and are not distinguished for purposes of assessing runoff-driven PFDFs. DATA SOURCES AND METHODOLOGY Event Rainfall Data Raw rainfall data for the January 9, 2018, storm were obtained from 40, 0.01-in.-increment, tippingbucket gages maintained by the Santa Barbara County Public Works Department (10 gages) and Ventura County Watershed Protection District (30 gages).

The raw data was processed to determine 15-minuteduration rainfall depths using a backward diﬀerence procedure similar to the procedure described in Kean et al. (2011) to calculate rainfall accumulation preceding each tipping-bucket record. The maximum rainfall accumulation and associated end time were then used to identify the peak 15-minute depth during the storm period (Figure 2). In the absence of monitoring instrumentation to document the timing of PFDF initiation in each basin studied, peak rainfall depths are assumed to represent the PFDF triggering rainfall. However, the authors recognize that the actual triggering rate may have been lower, occurring either before or after the peak, a phenomenon noted in Staley et al. (2013). Within most of the burned area where PFDF response occurred, the peak 15-minute rainfall occurred in an eastward progression associated with the NCFR from about 3:30 to 5:00 AM (PST) on January 9. Rapid onset of runoﬀ and inundation relative to timing of peak rainfall was documented in the Montecito area, which supports the inference that rainfall

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durations of 15 minutes or less drove the occurrence and severity of PFDFs where rainfall was extreme. Farther northeast in the Juncal Canyon and Matilija Canyon areas, 15-minute rainfall depths decreased but remained above USGS thresholds, but the timing of debris flows is not well documented. Peak 15-minute depths recorded at each tippingbucket gage in 0.01-in. increments, regardless of time during the storm, were converted to metric units, plotted, and interpolated with the inverse-weighted distance method in ArcGIS (Chen et al., 2017). Resulting rainfall depth contours and classified ranges of rainfall depth in intervening areas are shown in Figure 2. The 6-mm peak 15-minute rainfall depth contour coincides with the 24-mm/hr i15 rainfall intensity-duration adopted as the fire-wide threshold by the WERT (CAL FIRE, 2018); spatially explicit USGS basin thresholds are not constrained by this contour line. Rainfall depths were initially interpolated for each USGS basin centroid for preliminary analyses. However, due to uncertainties in interpolation between widely spaced gages, local rapid lateral variation in rainfall intensity, and lack of consistent correlation between rainfall data and National Weather Service radar returns, the un-adjusted rainfall data from the nearest rain gage for each basin is used in our analyses. This standard is also consistent with the procedure used to develop the USGS debris flow probability models. The peak 15-minute rainfall depth of 26.16 mm recorded at the Doulton Tunnel gage up-gradient of Montecito is the highest observed during the January 9 event, which is within the limits of the training set used to develop the USGS model. Event Debris Flow Data The extent of debris flow inundation in the Montecito area and Santa Barbara Coastal Plain was documented in Kean et al. (2019) and Lancaster et al. (2021), respectively, and Lukashov et al. (2019) extended mapping across the entire Thomas Fire burned area. Lukashov et al. (2019) used confidence criteria of definite, probable, and queried to classify remotely mapped PFDFs, which were then field validated at 161 mapped PFDF sites spanning the width of the burned area, and the distribution of flows was illustrated by cumulative mapped PFDF track lengths classified per 1-km2 grids. This study considers only the definite and probable PFDF segments from Lukashov et al. (2019), the distributions of which are illustrated in Figure 3. Mapping was based on post-event, 1m-resolution Digital Global imagery, lidar collected by Towill Inc. processed to 0.5-m-resolution hillshade, and 5-cm-resolution aerial photography. The smallest recognizable watershed areas upstream of mapped

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initiation points for the defined minimum 1.5-m flow width mapped by Lukashov et al. (2019) ranged from about 0.00016 km2 (0.04 acre) to 0.011 km2 (2.75 acres). USGS LRM Data Modeling for the Thomas Fire defined parameters and PFDF probabilities for 1,736 basins within the burned area. However, not all of these basins are located in proximity to a rain gage that could be used to assess PFDF triggering thresholds. Staley et al. (2016) note that basins within a maximum distance of 4 km from a rain gage were used to develop the model. For this study, only basins with centroids located within 2.5 km of a rain gage were retained for analysis due to spatial variability in rainfall intensity observed in gage data and weather radar; one exception was made for basins up-gradient of the Montecito area and eastward to Santa Monica Canyon, where weather radar data indicate that rainfall intensity was uniformly very high, and therefore all basins were retained for analysis. Basins with poor visibility in post-storm imagery or poor correlation between mapped segments and USGS-defined basins were also filtered out, leaving a total of 857 basins for evaluation, of which 171 experienced debris flows. All analyses in this study are based on spatially explicit data from the USGS model rather than the fire-wide rainfall thresholds, such as values adopted for emergency response planning. Data on basin morphology were derived from the database generated by the USGS for the Thomas Fire assessment where possible to improve direct comparison of observed PFDF with the USGS model. Catchment areas evaluated in this study refer either to the steep burned portion of a given basin with slope areas inclined ࣙ23° (42.4 percent) that were burned at moderate or high severity or to total basin area as defined in the USGS model for the Thomas Fire. Spatially explicit rainfall thresholds producing 50 and 90 percent event probabilities (P50 and P90 of Staley et al., 2017) for each basin were compared with observed rainfall depths (i.e., rainfall depths modeled to produce debris flows 50 and 90 percent of the time in a given basin). The P50 thresholds were chosen as the primary focus of this study because this exceedance level is commonly adopted for use in rapid emergency hazard assessments where a higher level of conservatism is considered. However, P90 thresholds were also analyzed as a more reasonable value to test the ability of the model to predict PFDF occurrence. The USGS LRM incrementally generated a total of 43,337 channel segments within the Thomas Fire burned area. Considering the large number of USGSdefined segments and difficulties encountered in

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Figure 5. (A) Example of abundant sandstone boulders, many exceeding 1-m diameter, in San Ysidro Creek following the January 9, 2018, event with cliffs of steeply dipping Coldwater Formation sandstone beds on the left channel bank. (B) The largest known boulder mobilized by January 9 debris flows, 5.5 × 8 × 9.5 m in dimension, with cliffs of source Matilija Formation sandstone beds on horizon, Matilija Creek; large boulders in background are rounded and represent ancient flow deposits rather than rockfall from distant cliffs. Photos by Brian Swanson.

correlating observed PFDF segments mapped on highresolution base maps with USGS-modeled segments generated from a coarse, 10-m-resolution DEM, a full assessment of the USGS segment model was not considered feasible for this study. Geologic Data Occurrence of destructive debris flows and flows that leave evidence of the event (e.g., levees, terminal boulder lobes, and deposits with inverse grading, or poorly sorted matrix with coarse, floating clasts) typically require the presence of large (cobble and boulder size) debris in the source watersheds. This requires that resistant geologic units underlie the source basins that weather to produce large debris, a factor not specifically identified in the Staley et al. (2016) model. Also, fine-grained source material such as silty and clayey soil and ash may be an important component that promotes the buildup of pore pressures within the flow matrix, which allows for large debris to become buoyant. Within the Thomas Fire burned area, Eocene sandstones of the Matilija and Coldwater formations are strongly cemented, and outcrops weather to form large boulders, commonly larger than 1 m in diameter and locally approaching 10 m in maximum dimension (Figure 5). Fine-grained shales and siltstones of the interleaved Juncal Formation and Cozy Dell Shale weather to form residual colluvial soils of sand and gravel fragments within a fine-grained matrix on hillslopes. Relationships between geologic unit and rill formation above Montecito are discussed by Alessio et al. (2018) and Keller et al. (2020), and the combination of both indurated and fine-grained source units may be a significant influence on the formation of

the event debris flows. The Sespe Formation also contains both resistant sandstone and conglomerate beds and weak mudstone interbeds. Younger units exposed in the burned area, such as the Pico Formation, are less resistant and typically produce relatively few boulders. The numerous geologic units exposed within the burned area have been merged into five groups in Figure 4 with similar induration and age characteristics for purposes of analysis in this study. (see Townsend et al., 2021 for discussion of related rock strengths). Group 1 (the oldest group) consists primarily of Eocene marine strata along with local Cretaceous strata and Franciscan Group metamorphic rocks, which underlie most of the steep mountainous areas in the northern half of the burned area. Eocene strata are the most extensively exposed of this group and typically support steep terrain due to the recency of uplift, a very steep bedding inclination, and the presence of indurated sandstone beds, particularly in the Matilija and Coldwater formations. Weaker siltstone and shale strata of the Juncal Formation and Cozy Dell Shale are interbedded with the indurated sandstone units. A thick band of Eocene strata has been tilted on end along an easterly trend from the Santa Ynez Mountains through the north-central portion of the burn area to Nordhoff Ridge, known locally as the Matilija overturn. Group 1 bedrock underlies many of the source areas that generated debris flows on January 9, 2018. Group 2 consists solely of the late Oligoceneto early Miocene-age non-marine Sespe Formation, made up of indurated sandstone and conglomerate beds as well as interbedded softer mudstone. Group 3 consists of Miocene-age units, including the Vaqueros Formation, Rincon Shale, Monterey Formation, “Temblor Sandstone,” Modelo Formation, and

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Sisquoc Formation. These units are dominantly fine grained and relatively weak compared to the Eocene formations exposed to the north. Group 4 consists of Pliocene- and Pleistocene-age strata, including the Pico, Santa Barbara, Casitas, and Saugus formations, which are exposed in the southeastern portion of the burn area. These formations are poorly indurated in general, and the Pico Formation, which is widely exposed in the southeastern portion of the burned area, contains few resistant interbeds. Group 5 consists of the younger Quaternary-age sediments that make up alluvial valley fill, alluvial fans, and older terrace deposits, which are of only local consequence as a PFDF source. However, fan deposits downstream of source areas underlain by Eocene strata and portions of the Sespe Formation commonly contain boulder-laden deposits indicative of past debris flow occurrence.

Analytical Methods Model performance is assessed statistically using receiver operating characteristic (ROC) analysis methods (as summarized in Staley et al., 2016; Addison, 2018) to compare modeled response with observed conditions as defined by the following four possible outcomes of a binary classifier model defined in a 2 × 2 confusion matrix: true positive (TP; above threshold, PFDF occurred), false positive (FP; above threshold, no PFDF), true negative (TN; below threshold, no PFDF), and false negative (FN; below threshold, but PFDF occurred). To relate the distribution of debris flows across the burned area with corresponding USGS-defined basins, PFDF occurrence was initially classified into three categories: (1) PFDF occurred and reached and/or continued beyond the defined basin pour point, (2) PFDF occurred but stopped or transitioned to sediment-laden flows upstream of the pour point, and (3) no PFDF occurred. As past USGS modeling considered only PFDFs that reached the pour point for purposes of model calibration and such flows are more likely to adversely impact development, basins with PFDFs that stopped upstream of the pour point are assigned as “no PFDF” in this study for strict comparison with the USGS model. Five ROC statistical relationships are assessed to evaluate the performance of the P50 and P90 USGS PFDF model results in predicting the distribution of Thomas Fire debris flows: 1. Sensitivity: fraction of PFDF locations that were correctly predicted, defined as TP/(TP + FN); range 0–1.0; 1.0 = perfect prediction 2. Specificity: fraction of PFDF locations that did not produce PFDFs and were correctly predicted by

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the model, defined as TN/(TN + FP); range 0–1.0; 1.0 = perfect prediction 3. Accuracy: overall performance of model in correctly distinguishing between basins with debris flows or no debris flows, defined as (TP + TN)/(TP + TN + FP + FN); range 0–1.0; 1.0 = perfect prediction 4. Kappa: overall performance that also corrects for chance agreement, defined by Cohen (1960) as (Accuracy − random accuracy)/(1 − random accuracy); range −1.0–+1.0 5. Threat score: overall performance of the model ignoring TN PFDF response, defined as TP/(TP + FP + FN); range 0–1.0; 1.0 = perfect prediction RESULTS AND DISCUSSION A total of 857 basins across the burned area within 2.5 km of a rain gage are assessed; 171 of these basins produced debris flows on January 9, of which 120 (70 percent) reached the defined basin pour point (Table 1). Basins are grouped into five watershed regions based on similarities in topographic, rainfall, and debris flow response conditions and by geologic groups for purposes of analysis. The upper section of Table 1 summarizes the number of basins, observed debris flow response, and rainfall depths normalized by percent of USGS LRM rainfall threshold at each basin and averaged by watershed region. The second part of Table 1 presents ROC parameters and performance of the USGS LRM by watershed region and geologic group at P50 and P90 probability factors. The distribution of basins with respect to P50 rainfall thresholds and whether the basins produced PFDFs that reached the pour point, terminated upstream of the pour point, or did not produce PFDFs is illustrated in Figure 6. Rainfall Intensity Thresholds Rainfall Intensity–Duration Relationships Rainfall intensity–duration relationships indicate minimum hourly rainfall intensity thresholds of PFDF occurrence at 5-, 15-, 30-, and 60-minute durations of 27.4, 20.3, 14.7, and 13.2 mm/hr, respectively (See “lower limit” in Figure 7). However, numerous FPs occur in the same range of rainfall intensities as PFDF occurrence, illustrating the variability between rainfall and PFDF response and the apparent trend of overprediction in the current USGS model. Observed Rainfall and Predicted LRM PFDF Thresholds Rainfall depths on January 9 across all 857 basins averaged 163 percent of the LRM P50 thresholds

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Logistic Regression Model Performance, 2018 Thomas Fire Debris Flows Table 1. Thomas Fire basin statistics by watershed region quantifying PFDF response, rainfall depths relative to USGS P50 thresholds by PFDF response, and ROC parameters and statistical analyses for USGS P50, P75, and P90 rainfall thresholds. Watershed Region

Number of Basins by PFDF Response Basins > USGS P50 Threshold

Average Rainfall Depth by % of P50 Threshold

Total Basins

PFDF Reached PP

PFDF Upstream of PP

Basins with No PFDF

857 74 54 197 41 491

120 35 22 54 5 4

51 16 8 16 5 6

686 23 24 127 31 481

ROC parameters

Model Evaluated Thomas Fire Santa Barbara Juncal Canyon Matilija Canyon Ojai/Nordhoﬀ Ridge Other areas Group 1: Jurassic to Eocene Group 2: Sespe Formation Group 3: Miocene Group 4: Plio-Pleistocene Group 5: Quaternary

118 35 22 54 4 3 112 5 0 0 1

USGS P50 Results (Assuming PFDF Upstream of PP Is Considered as No PFDF) 377 360 2 0.98 0.49 0.56 0.20 37 2 0 1.00 0.05 0.50 0.05 32 0 0 1.00 0.00 0.41 0.00 141 2 0 1.00 0.01 0.28 0.01 12 24 1 0.80 0.67 0.68 0.24 155 332 1 0.75 0.68 0.68 0.02 227 52 1 0.99 0.19 0.42 0.11 44 50 0 1.00 0.53 0.56 0.10 31 50 0 0.00 0.62 0.62 0.00 29 175 1 0.00 0.86 0.85 − 0.01 46 33 0 1.00 0.42 0.43 0.02

0.24 0.49 0.41 0.28 0.24 0.02 0.33 0.10 0.00 0.00 0.02

112 35 22 52 1 2 107 4 0 0 1

USGS P90 Results (Assuming PFDF Upstream of PP Is Considered as No PFDF) 219 518 8 0.93 0.70 0.74 0.29 25 14 0 1.00 0.36 0.66 0.35 32 0 0 1.00 0.00 0.41 0.00 118 25 2 0.96 0.17 0.39 0.08 2 34 4 0.20 0.94 0.85 0.17 42 445 2 0.50 0.91 0.91 0.07 155 124 6 0.95 0.44 0.59 0.28 12 82 1 0.80 0.87 0.87 0.33 16 65 0 0.00 0.80 0.80 0.00 8 196 1 0.00 0.96 0.96 − 0.01 28 51 0 1.00 0.65 0.65 0.04

0.33 0.58 0.41 0.30 0.14 0.04 0.40 0.24 0.00 0.00 0.03

Basin Summary Thomas Fire Santa Barbara Juncal Canyon Matilija Canyon Ojai/Nordhoﬀ Ridge Other areas

495 72 54 195 16 158 Sensitivity

All Basins 163 265 449 239 94 91 Speciﬁcity

PFDF Reached PP

PFDF Upstream of PP

315 331 463 275 137 130 Accuracy

No PFDF

251 250 448 239 120 129 Kappa

129 174 436 223 83 90 Threat score

PFDF = post-ﬁre debris ﬂow; USGS = U.S. Geological Survey; P50 = 50% probability; ROC = receiver operating characteristic; P75 = 75% probability; P90 = 90% probability; PP = pour point; TP = true positive; FP = false positive; TN = true negative; FN = false negative.

defined at each basin, and 495 basins experienced rainfall greater than the LRM P50 threshold. More specifically, rainfall depths were extreme in the Santa Barbara, Juncal, and Matilija watershed regions with respect to USGS P50 thresholds, with depths ranging from 239 to 449 percent of threshold estimates on average. Rainfall was most extreme in areas where debris flows occurred, as expected, but were still well above threshold on average, even where no debris flows were observed. Rainfall depths dropped below P50 thresholds on average in the Ojai and Other areas overall but were 120 to 137 percent of P50 thresholds on average where debris flows were observed. The relationship between estimated USGS LRM P50 thresholds and observed rainfall depths is graphically presented for each basin in Figure 8, with PFDF response symbolized. Figure 8A illustrates basins

where PFDFs reached the pour point, and Figure 8B illustrates basins where no PFDF occurred or the PFDF did not reach the pour point. This figure illustrates three important relationships: (1) Nearly all basins where PFDF reached the pour point had 15minute peak rainfall intensities greater than the LRM threshold, (2) many basins with peak rainfall above the LRM threshold did not produce debris flows that reached the pour point, and (3) many FP responses occurred in the Other watershed region (Figure 6). The number of basins classified by ranges of percent of observed peak 15-minute rainfall depths with respect to basin-specific, spatially explicit LRM P50 rainfall thresholds is illustrated in Figure 9A. No basins within 125 percent of LRM thresholds produced a PFDF reaching the pour point, and only one in every 14 basins on average produced PFDFs

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Figure 6. Map illustrating the distribution of PFDF response for 857 USGS-deﬁned basins relative to spatially explicit (rather than ﬁre-wide average) USGS P50 rainfall thresholds. TP PFDF responses are concentrated in the western Santa Ynez Mountains, Juncal Canyon, and Matilija Creek regions, and TN PFDF responses are concentrated in the southeastern (Other) portion of the burned area; FP responses are spread across the burn area, including in several very small basins in the western area.

reaching the pour point where rainfall was 125 to 150 percent of the LRM threshold. The ratio of TP to FP response steadily increases with increasing rainfall but does not exceed 50 percent until observed rainfall approaches 300 percent of the threshold. These results support that current USGS P50 model thresholds are conservative with respect to PFDF response at the Thomas Fire. The relationship of PFDF response to rainfall thresholds for the 335 basins where rainfall was above the LRM P90 threshold is illustrated in Figure 9B. An FP PFDF response is clustered where rainfall was within 200 percent of the LRM threshold. In contrast with the P50 thresholds, TP responses occur across the range of rainfall. Performance of the USGS LRM The results of ROC analyses for LRM P50 thresholds indicate a nearly perfect sensitivity of 0.98, as only two of the 120 burned basins that produced debris ﬂows reaching the pour point experienced rainfall

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intensity below the USGS P50 threshold (i.e., 118 TP and two FN responses). This is higher than the values reported by Addison (2018) for either the Staley et al. (2016) model or Addison’s non-linear model runs. Sensitivity is perfect in the Santa Barbara, Juncal, and Matilija watershed regions at P50 thresholds and values of 0.8 and 0.75 in the Ojai and Other watershed regions due to one FN response in each area. Sensitivity declines locally at P90 thresholds. However, as noted above, rainfall rates were far above USGS thresholds in most areas, so the defined thresholds are not well tested. Additional sorting of the available data identified only 64 basins where rainfall intensities across the burned area were within 10 percent of estimated LRM P50 thresholds, 50 of which were in the Other watershed region. Of the 64 basins, 35 are below P50 thresholds, one of which produced the only debris flow that reached the pour point. In summary, for basins experiencing rainfall within 10 percent of P50 threshold, the LRM P50 model correctly predicted true-negative (TN) responses in 34 of 35 basins and over-predicted

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Figure 7. Log plot of hourly rainfall intensity against duration illustrating PFDF response at 5-, 15-, 30-, and 60-minute durations; basins with PFDFs reaching the pour point staggered from those that did not. Line deﬁnes lower limit of rainfall intensity that produced PFDFs reaching the pour point.

Figure 8. Observed peak 15-minute rainfall depths plotted against spatially explicit USGS P50 15-minute rainfall thresholds showing relationships based on 857 USGS basins. (A) Responses at basins where PFDFs reached pour point. (B) Rainfall thresholds were far exceeded in the Santa Barbara, Juncal, and Matilija regions; however, numerous FP PFDF responses are apparent in (B).

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Figure 9. Histograms plotting a number of basins classiﬁed by percent of observed rainfall with respect to USGS thresholds for basins that received rainfall above USGS thresholds; P50 thresholds shown in (A) and P90 thresholds shown in (B). No PFDFs reached pour point with rainfall within 125 percent of P50 thresholds; TP responses are more evenly distributed at P90 thresholds, with most FP responses limited to rainfall within 200 percent of the threshold.

FP PFDF responses in the other 29 basins, indicating conservatism in the model and a specificity of 0.54, which is slightly higher than analyses of all basins (0.49 in Table 1). Considering all 857 basins, specificity at P50 thresholds was 0.49 but varies widely from nearly 0 in the Santa Barbara, Juncal, and Matilija watershed regions to 0.67–0.68 in the Ojai and Other areas (Table 1). The data indicate that the P50 LRM generated many FP predictions throughout the burned area, but the specificity varies due to the relatively high number of TN responses in the Ojai and Other watershed regions in comparison with the other regions evaluated, which correlates to the distribution of lower rainfall intensities relative to threshold values. Specificity generally improves across the burned area and at each subregion at P90 thresholds (Table 1). The overall performance of the model, as defined by accuracy, is 0.56 for the fire as a whole at P50 thresholds (Table 1), which is lower than reported by Addison (2018) for both the USGS model and the nonlinear model. The accuracy varies from a minimum of 0.28 at Matilija, where there was a relatively large proportion of FP responses, to a maximum of 0.68 in the Ojai and Other areas due to a relatively large proportion of TN responses. An additional measure of overall performance is provided in the “Kappa” column of Table 1. Considering all data, kappa values range from 0.20 at P50 up to 0.29 at P90 thresholds, which represents only fair agreement with the model per the criteria presented in Cohen (1960) and slightly below the range of reasonable agreement of 0.3–0.5 per Kuhn and Johnson (2013). Kappa is much more variable within the five defined watershed regions.

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The USGS adopted the threat score as a good indicator of model performance, and Staley et al. (2016) reported a value of 0.40 for “all records” for the preferred 15-minute-duration model. In contrast, the P50 LRM produces a threat score of 0.24 for PFDF response to the January 9 storm. This relatively low score reﬂects the large number of FN responses and the absence of TN responses in the equation, for which the model did well in assessing. Threat scores across the burned area increase to 0.33 at P90 thresholds. The threat score varies by watershed region and ranges up to 0.49 at P50 thresholds and 0.58 at P90 thresholds in the Santa Barbara region. However, the very low scores calculated in the Other watershed region due to the relatively large number of FP results lowers the overall results substantially. Discussion of Other Parameters Evaluation of the performance of the USGS PFDF model reveals the following three primary results: 1. Model parameters of soil burn severity, watershed steepness, soil conditions, and 15-minute rainfall intensity thresholds correctly predicted the occurrence of nearly every PFDF that reached the basin pour point in the Thomas Fire on January 9, 2018, as indicated by near perfect sensitivity values. This success is critical for use in post-ﬁre hazard assessments. 2. Numerous FP responses lowered model performance below threat scores initially obtained by Staley et al. (2016) and indicate substantial conservatism in the model results for the Thomas Fire.

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3. Rainfall intensity was far above basin-specific, spatially explicit USGS thresholds at almost all basins where debris flows occurred, which indicates the USGS rainfall thresholds are not well constrained relative to the January 9 event; thresholds may therefore be conservative (lower than needed) at these TP basins as well as at the FP basins. The relatively high occurrence of FP results with respect to the USGS model indicates that other factors not incorporated in the USGS model may influence the predictability of PFDF response within the Thomas Fire. Numerous factors and combinations of factors may contribute to the noted disparities, but for the limited scope of this article, the potential influence of watershed area and underlying geologic conditions is further investigated. Additional analyses focus on watershed response for the 495 basins where observed rainfall was greater than the associated USGS-defined P50 rainfall thresholds, as the number of PFDFs that occurred where rainfall was below threshold (FN) is negligible, and basins exhibiting no response below USGS thresholds (TN) are already correctly predicted with the model.

Watershed Area The number of basins exhibiting either no PFDF (FP), PFDF upstream of the pour point (also FP for this analysis), or PFDF to the pour point (TP) are classified with histograms into eight ranges of watershed area in Figure 10A and B. A clear relationship between PFDF occurrence and increasing total watershed area is apparent in Figure 10A. The number of FP responses and ratio of FP to TP responses decline systematically with increasing watershed area from a maximum ratio of about 8:1 for basins between 0.02 and 0.05 km2 to about 2.6:1 at basins between 1 and 2 km2 . Conversely, the number of basins with TP response remains relatively uniform for all classified watershed area ranges. These results suggest that total watershed area is an important independent variable influencing the formation of PFDFs. Classified ranges of watershed area burned at moderate or high soil burn severity with slopes ࣙ23° (as defined in the USGS LRM) rather than total basin area are illustrated in Figure 10B. This plot shows a strong correlation between FP response and basins with steep burned areas of less than 0.05 km2 ; 313 of the 495 basins are of this size, and 266 of the 313 basins (85 percent) did not produce a PFDF that reached the pour point. It should be noted that some mapping bias may exist in the data set, as smaller basins may have produced debris flows that did not meet the minimum

1.5-m debris flow width criteria defined in Lukashov et al. (2019). Influence of Geology Statistical analyses are conducted with PFDF response correlated to the five selected geologic unit Groups (Figure 4) and are provided in Table 1. Of the total 857-basin data set, 392 are underlain dominantly by Group 1 geologic units of Jurassic to Eocene age, 205 are underlain by Group 4 Plio-Pleistocene units, and the remainder are fairly evenly divided between Groups 2, 3, and 5. Group 1 geologic units underlie 113 of the 120 basins that produced PFDF reaching the pour point; five of the 120 basins are underlain by Group 2, and one of the 120 basins is in a Group 5 basin. Sensitivity for individual groups ranges from 0.95 to 1.0 at P50 and P90 thresholds, except for a 0.8 value for P90 thresholds in Group 2. Specificity is more variable at P50 thresholds, ranging from 0.19 in Group 1 to 0.86 in Group 4, reflecting the large number of TN responses relative to FPs in Group 4 and the opposite response in Group 1 basins. Threat scores range from 0.33 to 0.40 at P50 to P90 thresholds for Group 1 basins, indicating reasonable correlation, but are much lower for basins underlain by Group 2–5 geologic units. Model correlation with kappa is poor at P50 thresholds for all groups, with a maximum of 0.11 for Group 1. The number of basins underlain by each geologic unit group is illustrated relative to PFDF response and average rainfall depth in Figure 10C. As previously noted, almost all observed TP response occurred within Group 1 and to a lesser degree in Group 2 units, both of which weather to produce resistant large debris and fine sediment. The ratio of FP to TP is 2:1 for basins underlain by Group 1 rocks and 9:1 for basins underlain by Group 2. In contrast, the ratio is ࣙ47:1 for Groups 3–5, which are underlain by weaker rock units that weather to produce relatively little large debris. This includes the Pico Formation, which underlies much of the hillside area north of the city of Ventura, and fine-grained rocks of the Rincon Shale and Modelo Formation. The correlation of higher PFDF occurrence with geologic Groups 1 and 2 that can generate both indurated rock debris and fine sediments in comparison with weaker rock in Groups 3–5 suggests a strong control on PFDF occurrence by geologic conditions. It is also notable that average 15-minute rainfall depths for each group are illustrated for each group by PFDF response in Figure 10C. Average rainfall depths range from about 8 to 16 mm for all PFDF responses, but there is not a clear trend in rainfall depths between the groups that explains the positive PFDF response. However, bias may exist in the data set because

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Figure 10. Histograms plotting number of basins with rainfall above USGS P50 threshold classiﬁed by PFDF response with respect to three parameters: (A) total basin area, (B) steep burned basin area (as deﬁned in USGS LRM), and (C) geologic unit and average peak rainfall depth by PFDF response by geologic group. (D) Subset of basins with steep burned area >0.05 km2 by geologic unit. High FP-to-TP PFDF ratios are associated with total basin areas of <1 km2 and with steep burned basin areas of <0.05 km2 . Most PFDFs occurred in Group 1 geologic units, although average rainfall was only modestly higher than in other groups. The number of basins showing FP PFDF response and ratio with FP to TP decreases substantially for basins with steep burned areas of >0.05 km2 .

the weaker units may produce fine-grained debris flows that lack diagnostic boulder levees and lobes for identification in remotely mapped areas. To further assess the influence of geology on PFDF occurrence, Figure 10D illustrates the distribution of the 182 basins identified in Figure 10B as having a steep burned area of greater than 0.05 km2 and muchreduced FP PFDF response. The ratio of TP to FP PFDF response increases for both Group 1 and Group 2 basins, and the lone TP basin in Group 5 is filtered out, increasing the apparent relationship between PFDF occurrence and geologic units containing both indurated, debris-producing rock and finegrained rock. CONCLUSIONS AND FURTHER STUDIES Evaluation of PFDF response in the Thomas Fire indicates that the USGS logistic regression model performed well in predicting the debris flows of January 9, 2018. However, numerous FP PFDF responses, despite the occurrence of rainfall rates far above P50 and P90 thresholds, suggest significant conservatism

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in the model and that other factors or model adjustment should be considered to improve model performance. Debris flow initiation generally occurred at 15-minute rainfall depths greater than about 150 to 200 percent of LRM P50 thresholds. Two additional factors were found to correlate with high FP watershed response: basins watersheds with steep burned areas of less than 0.05 km2 and basins underlain by relatively weak geologic units that generate limited volumes of large-diameter debris, a principal ingredient in debris flow formation. It is hoped that relationships between PFDF occurrence and observed rainfall, watershed area, and geologic conditions identified in this study will help retain accurate identification of high-hazard watersheds obtained with the current LRM model, reduce overprediction in areas of lower hazard, and thereby improve overall prediction of PFDF hazards. However, numerous interacting factors control the occurrence of PFDF, and more rigorous studies of controlling parameters are warranted. Additional topics of study include re-evaluation of controlling rainfall intensities and durations, more detailed evaluation of controlling

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geologic conditions on a regional scale, relationships between rainfall intensity and minimum watershed area above observed PFDF initiation point, correlation of channel gradient at PFDF terminus with rainfall intensity and watershed characteristics, and characterization of debris flow run-out from the Montecito debris flow event. ACKNOWLEDGMENTS The authors would like to acknowledge the three anonymous peer reviewers for their constructive comments, Jason Kean and Dennis Staley at the USGS Landslide Hazards Program for providing insights on factors controlling PFDF and providing partial funding for this study through an Intergovernmental Personnel Act agreement between the CGS and the USGS Geologic Hazards Science Center, atmospheric scientist Nina Oakley at the UCSD Scripps Center for Western Weather and Water Extremes, and Anita Carney of the CGS for assisting with GIS expertise and figure preparation. REFERENCES Addison, P., 2018, Application of Remote Sensing and Machine Learning Modeling to Post-Wildfire Debris Flow Risks: Open Access Dissertation, Michigan Technological University, 78 p. https://doi.org/10.37099/mtu.dc.etdr/703 Addison, P.; Oommen, T.; and Sha, Q., 2019, Assessment of post-wildfire debris flow occurrence using classifier tree: Geomatics, Natural Hazards Risk, Vol. 10, No. 1, pp. 505–518. http://dx.doi.org/10.1080/19475705.2018.1530306 Alessio, P.; Dunne, T.; and Morell, K., 2018, The contribution of post-wildfire rilling to generation of the 2018 Montecito, CA debris flows: Quantification and interpretation of rill geometries and patterns: American Geophysical Union, Fall Meeting, Abstract #H21F-03. Bessette-Kirton, E. K.; Kean, J. W.; Coe, J. A.; Rengers, F. K.; and Staley, D. M., 2019, An evaluation of debris-flow runout model accuracy and complexity in Montecito, CA: Towards a framework for regional inundation-hazard forecasting: In Kean, J. W.; Coe, J. A.; Santi, P. M.; and Guillen, B. K. (Editors), Debris-Flow Hazards Mitigation: Mechanics, Monitoring, Modeling, and Assessment: Proceedings of the Seventh International Conference on Debris-Flow Hazards Mitigation: Golden, CO, Association of Environmental and Engineering Geologists, pp. 257–264. Bovis, M. J. and Jakob, M., 1999, The role of debris supply conditions in predicting debris flow activity: Earth Surface Processes Landforms, Vol. 24, pp. 1039–1054. https://doi.org/10.1002/ (SICI)1096-9837(199910)24:11%3C1039::AID-ESP29%3E3. 0.CO;2-U California Department of Forestry and Fire Protection (CAL FIRE), 2018, Thomas Fire Watershed Emergency Response Team—Final Report: CA-VNC-103156, 172 p. California Geological Survey (CGS), 2002, California Geomorphic Provinces: California Department of Conservation, California Geological Survey, Note 36.

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THIS PUBLICATION IS PRINTED ON ACID-FREE PAPER Abdul Shakoor Kent State University Kent, OH 44242 ashakoor@kent.edu

EDITORS

Eric Peterson Department of Geography, Geology, and the Environment Illinois State University Normal, IL 61790 309-438-5669 ewpeter@ilstu.edu

Karen E. Smith, Editorial Assistant, kesmith6@kent.edu

Oommen, Thomas Board Chair, Michigan Technological University Sasowsky, Ira D. University of Akron

Environmental & Engineering Geoscience February 2022

VOLUME XXVIII, NUMBER 1

Special Issue on Slope Stability in Memory of Jerome (Jerry) V. De Graff: Part 2 Guest Editors: Dennis Staley, Jeremy Lancaster, Alan Gallegos, Thad Wasklewicz

Cover photo In memory of Jerome (Jerry) V. De Graff, 1945-2020, U.S. Forest Service, geologist, colleague, friend. Photo courtesy of the De Graff family.

Volume XXVIII, Number 1, February 2022

ADVISORY BOARD Watts, Chester “Skip” F. Radford University Hasan, Syed University of Missouri, Kansas City Nandi, Arpita East Tennessee State University

ENVIRONMENTAL & ENGINEERING GEOSCIENCE