EEG November 2019 Volume 25, Number 4 by Association of Environmental & Engineering Geologists (AEG)

EDITORIAL OFFICE: Environmental & Engineering Geoscience journal, Department of Geology, Kent State University, Kent, OH 44242, U.S.A. phone: 330-672-2968, fax: 330-672-7949, ashakoor@kent.edu. CLAIMS: Claims for damaged or not received issues will be honored for 6 months from date of publication. AEG members should contact AEG, 201 East Main St., Suite 1405, Lexington, KY 40507. Phone: 844-331-7867. GSA members who are not members of AEG should contact the GSA Member Service center. All claims must be submitted in writing. POSTMASTER: Send address changes to AEG, 201 East Main St., Suite 1405, Lexington, KY 40507. Phone: 844-331-7867. Include both old and new addresses, with ZIP code. Canada agreement number PM40063731. Return undeliverable Canadian addresses to Station A P.O. Box 54, Windsor, ON N9A 6J5 Email: returnsil@imexpb.com. DISCLAIMER NOTICE: Authors alone are responsible for views expressed inarticles. Advertisers and their agencies are solely responsible for the content of all advertisements printed and also assume responsibility for any claims arising therefrom against the publisher. AEG and Environmental & Engineering Geoscience reserve the right to reject any advertising copy. SUBSCRIPTIONS: Member subscriptions: AEG members automatically receive digital access to the journal as part of their AEG membership dues. Members may order print subscriptions for $60 per year. GSA members who are not members of AEG may order for $60 per year on their annual GSA dues statement or by contacting GSA. Nonmember subscriptions are $295 and may be ordered from the subscription department of either organization. A postage differential of $10 may apply to nonmember subscribers outside the United States, Canada, and Pan America. Contact AEG at 844-331-7867; contact GSA Subscription Services, Geological Society of America, P.O. Box 9140, Boulder, CO 80301. Single copies are $75.00 each. Requests for single copies should be sent to AEG, 201 East Main St., Suite 1405, Lexington, KY 40507. © 2019 by the Association of Environmental and Engineering Geologists

THIS PUBLICATION IS PRINTED ON ACID-FREE PAPER Abdul Shakoor Department of Geology Kent State University Kent, OH 44242 330-672-2968 ashakoor@kent.edu

EDITORS

Brian G. Katz Florida Department of Environmental Protection 2600 Blair Stone Rd. Tallahassee, FL 32399 850-245-8233 eegeditorbkatz@gmail.com

Sasowsky, Ira D. University of Akron Katz, Brian G. Florida Department of Environmental Protection Shakoor, Abdul Kent State University

ASSOCIATE EDITORS Brankman, Charles, Consultant Boston MA Bruckno, Brian Virginia Department of Transportation Clague, John J. Simon Fraser University, Canada De Graff, Jerome V. California State University, Fresno Fryar, Alan University of Kentucky Hauser, Ernest Wright State University Hutchinson, Jean Queens University, Canada Keaton, Jeff AMEC Americas Marinos, Vassillis Aristotle University of Thessaloniki, Greece

McBride, John Brigham Young University Mwakanyamale, Kisa Illinois State Geological Survey Santi, Paul Colorado School of Mines Dee, Seth University of Nevada, Reno Shlemon, Roy R.J. Shlemon & Associates, Inc. Stephenson, William U.S. Geological Survey Stock, Greg National Park Service Sukop, Michael Florida International University Ulusay, Resat Hacettepe University, Turkey Watts, Chester F. “Skip,” Radford University West, Terry Purdue University

Environmental & Engineering Geoscience November 2019 VOLUME XXV, NUMBER 4

SUBMISSION OF MANUSCRIPTS 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 http://eeg.allentrack.net. 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. Please carefully read the “Instructions for Authors”. 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 Inspection and drainage gallery of Ermenek Dam, Turkey. Ermenek Dam is a double-curvature arch dam with a height of 218 m. There are three galleries at different levels within the dam. The dam has been operated since 2012... see article on page 345. Photo courtesy of Melih Calamak.

Volume XXV, Number 4, November 2019

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.

EDITORIAL BOARD Watts, Chester “Skip” F. Radford University, Chair Hasan, Syed University of Missouri, Kansas City Nandi, Arpita East Tennessee State University Oommen, Thomas Michigan Technological University

ENVIRONMENTAL & ENGINEERING GEOSCIENCE

Environmental & Engineering Geoscience (ISSN 1078-7275) is published quarterly by the Association of Environmental & Engineering Geologists (AEG) and the Geological Society of America (GSA). Periodicals postage paid at AEG, 201 East Main St., Suite 1405, Lexington, KY 40507 and additional mailing offices.

THE JOINT PUBLICATION OF THE ASSOCIATION OF ENVIRONMENTAL AND ENGINEERING GEOLOGISTS AND THE GEOLOGICAL SOCIETY OF AMERICA SERVING PROFESSIONALS IN ENGINEERING GEOLOGY, ENVIRONMENTAL GEOLOGY, AND HYDROGEOLOGY

THIS PUBLICATION IS PRINTED ON ACID-FREE PAPER Abdul Shakoor Department of Geology Kent State University Kent, OH 44242 330-672-2968 ashakoor@kent.edu

EDITORS

Brian G. Katz Florida Department of Environmental Protection 2600 Blair Stone Rd. Tallahassee, FL 32399 850-245-8233 eegeditorbkatz@gmail.com

Sasowsky, Ira D. University of Akron Katz, Brian G. Florida Department of Environmental Protection Shakoor, Abdul Kent State University

Environmental & Engineering Geoscience November 2019 VOLUME XXV, NUMBER 4

Volume XXV, Number 4, November 2019

ENVIRONMENTAL & ENGINEERING GEOSCIENCE

Environmental & Engineering Geoscience Volume 25, Number 4, November 2019 Table of Contents 255

Digital Surface Model-Aided Quantitative Geologic Rockfall Rating System (QG-RRS) Yonathan Admassu

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Evaluating Landslide Remediation Methods Used in the Carpathian Mountains, Poland Zbigniew Bednarczyk

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Improvement of Internal Stability of Alluvial Clay from Famagusta Bay, Cyprus, Using Copolymer of Butyl Acrylate and Styrene Mohammad Reza Golhashem and Eris Uygar

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Analysis of Landslide Kinematics Using Multi-Temporal Unmanned Aerial Vehicle Imagery, La Honda, California Jordan A. Carey, Nicholas Pinter, Alexandra J. Pickering, Carol S. Prentice, and Stephen B. DeLong

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Influence of Salt Tectonics on Fault Displacements and Submarine Slope Failures from Algeria to Sardinia Julia A. Yeakley, Abdul Shakoor, and William Johnson

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Quality Appraisal of Groundwater in Arid Regions Using Probabilistic and Deterministic Approaches Milad Ebrahimi, Hamidreza Kazemi, Majid Ehteshami, and Thomas D. Rockaway

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On the Optimum Layout of a Drainage Gallery in Concrete Gravity Dams on Isotropic Foundation Tameem Daghestani, Melih Calamak, and Ali Melih Yanmaz

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Assessment of Open-Source Software, QGIS, to Estimate Hurricane Matthew Flood Extent in Robeson County, North Carolina, Using Unsupervised Classification Cortney Cameron and Chibuike Madumere

Digital Surface Model-Aided Quantitative Geologic Rockfall Rating System (QG-RRS) YONATHAN ADMASSU* Department of Geology and Environmental Science, James Madison University, Harrisonburg, VA 22807

Key Terms: Digital Surface Models, Rockfall, Rockfall Hazard Rating, Terrestrial LiDAR, Structure from Motion ABSTRACT Rockfalls are one of the most common types of slope failures that affect cut slopes along roadways in mountainous regions. The Rockfall Hazard Rating System (RHRS), started in Oregon and adopted by various U.S. states, is used to rate cut slopes with respect to their likelihood of releasing rockfalls. Existing rating systems use semi-quantitative approaches to rate geological and non-geological factors. The main geologic factors are favorability/unfavorability of orientation of discontinuities with respect to the orientation of slope faces and likelihood of differential weathering leading to undercutting of strong rock units. Digital surface models (DSMs) derived from light detection and ranging (LiDAR) and photogrammetry have been used to remotely characterize rock mass. This research introduces an expanded application of DSMs to quantify geologic factors that contribute to the likelihood of rockfall events. The method is hence referred to as the Quantitative Geologic Rockfall Rating System (QG-RRS). Four DSMbased parameters, A, B, C, and D, have been identified to evaluate geologic factors. These parameters quantify the likelihood of discontinuity orientationcontrolled failures (parameter A), the degree of undercutting (parameter B), rockfall activity based on rockfall release surfaces (parameter C), and rockfall volume from rockfall voids (parameter D). This rating system, although not inclusive of other non-geological factors, appears to provide reproducible quantitative estimation of geologic factors that control rockfall generation.

INTRODUCTION Rockfalls from cut slopes along highways are hazardous to motorists and can cause damage to road*Corresponding author email: admassyx@jmu.edu

ways. Rockfall events are controlled by rock mass properties (rock type and discontinuity characteristics) and external causes such as rainfall, snowmelt, groundwater seepage, surface water, weathering, erosion freeze-thaw cycles, tree root wedging, and, disturbance by animals and earthquakes (Higgins and Andrew, 2012). Proactive measures to control and manage rockfall-related hazards have been proposed, including performing rock slope inventories, rating rock slopes with respect to rockfall generation, identifying and developing rockfall mitigation projects, and routinely reviewing rockfall sites with a regular update of database information (Pierson and Van Vickle, 1993; Pierson and Turner, 2012). A rockfall is a slope hazard, but the degree of the hazard posed has to be evaluated in terms of risk factors. The risk factors include geologic and non-geologic factors that contribute to the likelihood of rockfall generation and other factors that consider the impact of a rockfall event. For example, a given slope may be rated high risk due to the presence of unfavorable geologic factors, but its impact on the roadway and motorists is minimal if there is a wide rockfall catchment ditch. Oregon’s Department of Transportation introduced the first methodology, known as the Rockfall Hazard Rating System (RHRS), for rating the rock slopes with respect to rockfall generation (Pierson and Van Vickle, 1993). Oregon’s RHRS is a numerical system of rating slope geometry (slope height, catchment ditch), geologic factors, hydrologic factors, rockfall impact factors (decision site distance, roadway width, block size), climate, and rockfall history (Table 1). Each factor is numerically rated based on exponential scores of 3, 9, 27, and 81, where higher scores indicate higher risk. The overall RHRS is the sum total score of individual factors. Eighteen U.S. states have adopted Oregon’s RHRS (Russell et al., 2008). Others, such as Arizona, Colorado, Idaho, New Jersey, Vermont, New York, Ohio, and Tennessee, have modified it to fit their unique geologic attributes (Stover, 1992; NYSDOT, 2015; Eliassen and Ingraham, 2000; Fish and Lane, 2002; Miller, 2003; Shakoor and Martin, 2005; and Vandewater et al., 2005). One main shortcoming of the RHRS is its rating of all factors equally

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Admassu Table 1. Oregon’s Rockfall Hazard Rating System (RHRS) after Pierson and Van Vickle (1993). Score Category

3 Points

Slope height Ditch eﬀectiveness Average vehicle risk percent Decision sight distance Roadway width (ft) Geologic character Case 1 Structural condition

9 Points

27 Points

81 Points

25 ft (7.6 m) Good catchment 25% of the time Adequate sight 44

50 ft (15.2 m) Moderate catchment 50% of the time Moderate sight 36

75 ft (22.8 m) Limited catchment 75% of the time Limited sight 28

100 ft (30.5 m) No catchment 100% of the time Very limited sight 20

Discontinuous joints, favorable orientation Rough, irregular

Discontinuous joints, random orientation Undulating

Discontinuous joints, adverse orientation Planar

Continuous joints, adverse orientation Clay inﬁlling, slickensided

Few diﬀerential erosion features Small diﬀerence

Occasional diﬀerential erosion features Moderate diﬀerence

Many diﬀerential erosion features Large diﬀerence

Major diﬀerential erosion features Extreme diﬀerence

Climate and presence of water on slope

1 ft/3 cubic yards (0.3 m/2.3 m3 ) Low to moderate precipitation; no freezing periods; no water on slope

2 ft/6 cubic yards (0.6 m/4.5 m3 ) Moderate precipitation or short freezing periods or intermittent water on slope

3 ft/9 cubic yards (0.9 m/6.9 m3 ) High precipitation or long freezing periods or continual water on slope

Rockfall history

Few falls

Occasional falls

Many falls

4 ft/12 cubic yards (1.2 m/9.2 m3 ) High precipitation and long freezing periods or continual water on slope and freezing periods Constant falls

Rock friction Case 2 Structural condition Diﬀerence in erosion rates Block size/volume of rockfall

without giving higher weights to more important factors, such as geology (Russell et al., 2008; Santi et al., 2009). Another commonly cited problem is the use of subjective terminologies to rate the different factors that often lead to inconsistent rating depending on who performed the evaluation (Russell et al., 2008; Santi et al., 2009). For example, the boundary between 3 points assigned for “few differential erosion features” and 9 points for “occasional differential erosion features” can be very vague. The Missouri DOT separates consequences and risk factors related to rockfall hazard (Maerz et al., 2005; Youssef and Maerz, 2010). Some parameters, such as unfavorable geologic conditions, may increase the risk of rockfall occurrence, but the effectiveness of the catchment ditch is related to the consequence of a rockfall event. The overall Missouri RHRS provides scores reflecting both risk and consequence of failure (Maerz et al., 2005; Youssef and Maerz, 2010). Saroglou et al. (2012) identified risk factors belonging to four differently weighted categories: parameter A—slope geometry (25 percent weight), parameter B—geological condition (25 percent weight), parameter C—triggering factors such as seismicity and rainfall (10 percent weight), and parameter D—consequences of a rockfall (40 percent weight).

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High-resolution digital surface models (DSMs), primarily derived from terrestrial light detection and ranging (LiDAR) scanning (TLS), and photogrammetry have become powerful tools for rock mass characterization. Both methods generate a cloud of points (each having x, y, z values) representing the surface of a scanned object, which in this case is a rock slope. The point cloud can then be converted into a triangulated mesh, from which geological measurements can be obtained. A DSM of a slope defines a slope as a mesh made up of triangles, each of which is the bestfit plane for a subset of the point cloud. TLS shoots a laser onto a target and records the returned laser to calculate the x, y, z coordinates of every point from which the laser beam bounces. Its abilities to scan steep inaccessible slopes, reducing the risk to personnel, and create a permanent record of slope surfaces have made TLS an attractive technique for evaluating rock slopes (Andrew et al., 2012). Some limitations of TLS cited by Andrew et al. (2012) include error during registering the point cloud, effect of atmospheric conditions on the laser, and effect of surface conditions on the returning laser. The TLS scanner is tripod-mounted but can also be mounted on a moving vehicle, allowing faster data acquisition with lower point cloud density (Andrew et al., 2012). Photogrammetry, on the

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other hand, uses overlapping photographs to generate point clouds. Various photogrammetry algorithms exist, but, recently, structure from motion (SFM) has gained popularity because of the simple pre-processing required and the user friendliness of the software such as Photoscan Agisoft (www.agisoft.com). The cost of SFM software is comparatively much lower than a TLS scanner. The main disadvantage of SFM photogrammetry is the fact that it cannot scan behind vegetation, unlike TLS, which can shoot laser through openings in between leaves and branches. By filtering out the first returns of the reflected laser, points returning from vegetation can be cancelled out, leaving second returns that represent the surface behind the vegetation. SFM obtained from unmanned aerial vehicles (UAVs) presents an even more exciting opportunity to evaluate inaccessible slopes. Point cloud data from either SFM or TLS can also have RGB (redgreen-blue) color values. Both TLS and photogrammetry suffer from the effects of occlusion of higher parts of a slope that fall under shadows due to the fixed position of the TLS scanner or photogrammetry camera. Various researchers have shown the use of TLS and photogrammetry for discontinuity characterization (orientation, spacing, roughness) (Kemeny and Turner, 2008; Lato et al., 2009a; Sturzenegger and Stead, 2009; Nguyen et al., 2011; Lato and Vöge, 2012; and Vöge et al., 2013). Based on discontinuity orientations derived from TLS, rockfall hazard evaluation can be performed (Lato et al., 2009b; Nguyen et al., 2011). Lato et al. (2009b) and Nguyen et al. (2011) also showed the use of successive LiDAR scans from different time periods to identify sites of rockfall release based on change detection. Lato and Vöge (2012) provided a detailed methodology for rockfall hazard evaluation based on TLS/airborne LiDAR-derived geometric slope profile, structural kinematics, discontinuity spacing, discrete block modelling, and rockfall rollout distances. DSMs of slope faces from TLS and SFM photogrammetry allow quantitative evaluation of favorability/unfavorability of discontinuities and other geologic factors, including degree of undercutting, and the presences/size of rockfall voids. Quantitative rating of the likelihood of rockfall occurrence based on geologic factors is more reproducible and less subject to inconsistency. The objective of this research is to present a quantitative geologic rockfall rating system (QG-RRS) methodology based on quantifiable geologic factors measured from DSMs. The QG-RRS method attempts to provide a more consistent rockfall likelihood evaluation by taking into consideration all or most of the relevant geologic parameters.

GEOLOGIC FACTORS CAUSING ROCKFALLS The main geologic conditions leading to rockfall generation are the presence of (1) unfavorably oriented discontinuities with respect to slope orientation, (2) weak, easily erodible rock units interbedded with stronger layers, promoting undercutting-induced rockfalls (Shakoor and Weber, 1988; Admassu et al., 2012), or (3) unconsolidated cobble/boulder deposits. Discontinuities are natural planar breaks in rocks such as bedding planes, joints, foliation, and fault planes. The orientation of discontinuities with respect to the slope may lead to plane, wedge, and/or toppling failure. Plane and wedge failures occur when a block of rock slides along a discontinuity plane(s) as a result of driving forces exceeding resisting forces. Plane failures occur along a single discontinuity plane, whereas wedge failures require two intersecting planes. The kinematic preconditions for sliding of blocks during plane and wedge failure are when discontinuity planes or their intersections reach the surface (daylight) on the slope face and their dip angles or plunges of intersection lines are steeper than the friction angle. Goodman (1989) defined toppling to occur when (90 − σ (dip of discontinuity) + j (friction) is less than (slope angle) and the strike of discontinuity is subparallel with the slope. The potential for discontinuity-controlled rock slope failures is traditionally evaluated by plotting discontinuity orientation, slope face orientation, and a friction circle on a stereonet. Existing RHRS systems rate a slope’s likelihood of releasing rockfalls based on structural condition (orientation of discontinuities, continuity of discontinuities, presence of differential erosional features) in conjunction with friction condition (discontinuity surface roughness) of discontinuities (Table 1). In addition to discontinuity orientation-controlled release of rockfalls, undercutting-induced rockfalls are also common (Figure 1). These failures are common where the geology is characterized by interlayered strong and weak sedimentary layers that are prone to differential weathering. Differential weathering will result in faster weathering of weak, erodible, flat-lying layers (shales, mudstones, claystones) undercutting an overlying resistant rock layer. As a result, the undercut layer loses support from the weathered and eroded underlying layer and eventually releases rockfalls (Figure 1). Undercutting-induced failures are possible only when the depth of undercutting (length into the slope face) exceeds the spacing between the discontinuities (Figure 1). Similar modes of failure can occur for rocks with varying joint spacing, where highly jointed rocks can undercut rocks that are more widely jointed.

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Figure 1. Undercutting-induced rockfalls.

EVALUATING GEOLOGIC FACTORS USING DSM Identifying Discontinuities Discontinuity data (orientation, continuity persistence, and roughness) have traditionally been collected manually using a transit compass and observation in the field in accordance with the detailed line survey method (Piteau and Martin, 1977). In the past 8 to 10 years, LiDAR and photogrammetry have increasingly been used to remotely characterize discontinuities and evaluate rock mass properties. Computer software such as Split-FX (www.spliteng.com) performs manual or automated discontinuity mapping from TLS and photogrammetry (Kemeny and Turner, 2008). Lato and Vöge (2012) and Vöge et al. (2013) introduced the software PlaneDetect that can automatically map discontinuity surfaces. Bias against some discontinuity orientations should be corrected in accordance with Terzaghi (1965) and Lato et al. (2009a). Point cloud data generated from either TLS or SFM can be exported to the Split-FX software to generate a triangulated mesh DSM (Figure 2a and b). SplitFX calculates the centroid location, area, and orientation of each triangle within the triangulated mesh. Split-FX can then automatically find patches that are flat surfaces, which may represent discontinuities

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(Figure 2c). Patches (discontinuities) are selected based on the maximum variation of neighboring triangles and a specified minimum area. Split-FX also allows the user to manually identify patches either by inserting a line along a fracture trace or outlining a polygon around a flat surface (Figure 2c). Dip direction, dip, unit vectors, and area of each patch (discontinuity) can be exported from Split-FX as .csv or .txt files. Discontinuity data should be plotted on a stereonet, and discontinuity sets should be identified. Split-FX can then export orientation (dip direction/dip), area, and centroid location (x, y, z) of each patch (discontinuity). The orientation of all triangles representing the slope surface can also be exported. Rating Geologic Factors The main geologic factors controlling the release of rockfalls are the occurrence of structurally controlled and undercutting-induced slope failures. Evidence for past rockfall activity and volume of rockfalls should also be estimated quantitatively. The workflow of the DSM-aided quantitative QG-RRS includes the following steps: (1) Quantify the kinematic potential for structurally controlled slope failures that generate rockfalls. Based on the orientation of identified

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Figure 2. Diagrams showing (a) point cloud (2,014,815 points) from a site in western Ireland, (b) mesh from point cloud, and (c) patches (with yellow boundaries) and fracture traces (purple ellipsoids).

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discontinuities, the kinematic potentials for plane, wedge, and toppling failure can be evaluated quantitatively (Admassu and Shakoor, 2013). Parameter A is used to quantify kinematic potential for rockfall release based on discontinuity orientation with respect to slope orientation. (2) Quantify the likelihood of undercutting-induced rockfalls. Interlayered strong/weak rocks are susceptible for undercutting of the strong rock layers by the weak layers, causing the undercut rock to lose support and eventually release rockfalls. Undercutting initially exposes the base of the undercut rock unit as a sub-horizontal surface. The number of exposed sub-horizontal surfaces and their individual areas can be used as a proxy to quantify the likelihood of undercutting-induced rockfalls. The area of individual sub-horizontal planes is proportional to the depth of undercutting. The maximum depth of undercutting before a rockfall occurs depends on joint spacing of the undercut rock unit. The depth of undercutting should be compared with discontinuity spacing to evaluate how close a site of undercutting is to releasing a rockfall. Parameter B is used to quantify the likelihood of undercutting-induced rockfalls from dimensions of undercut surfaces. (3) Estimate rockfall events from identified areas of previous rockfall source sites. Rockfall source sites are defined as exposed voids resulting from the release of rockfalls. Such voids are bounded by at least three discontinuity surfaces. Based on the number of such discontinuities, parameter C estimates past rockfall events. (4) Estimate volume of rockfalls. The volume of rockfalls is mainly controlled by discontinuity spacing. Parameter D is used to quantify rockfall block volumes from volumetric dimensions of exposed rockfall voids. Parameter A: Kinematic Potential for Structurally Controlled Rockfall Releases—The likelihood of rockfall generation depends on how many of the discontinuity planes and their intersections are steeper than the friction angle and gentler than the slope face, such that they can possibly daylight on the slope face. Once planar surfaces representing discontinuities have been identified, software packages such as DipAnalyst (www.dipanalyst.com) or DIPS (www.rocscience.com) can be used to calculate failure indices, which are ratios of discontinuity planes/intersections that can cause plane/toppling or wedge failures to the total number of discontinuity planes/intersections (Admassu and Shakoor, 2013). Discontinuity data are, however. subject to bias depending on the orientation of the cut slope. The three failure indices are defined as follows:

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Figure 3. Cartoon model showing daylighting surfaces that are steeper than discontinuities.

(1) Plane Failure Index = Total number of discontinuities that can meet plane failure criteria/Total number of discontinuities. (2) Wedge Failure Index = Total number of discontinuity intersections that can meet wedge failure criteria/Total number of discontinuity intersections. (3) Toppling Failure Index = Total number of discontinuities that meet toppling failure criteria/Total number of discontinuities. Parameter A primarily quantifies kinematic potentials for failure based on two sub-parameters, A1 and A2 . A1 is the highest failure index value in percent (among the plane, wedge, and toppling failures). An ideal vertical slope is used for kinematic analysis to consider the worst possible scenario. Friction values should be provided based on experience or laboratory results. A joint roughness coefficient (JRC) correction can be applied for rough discontinuities. A2 , on the other hand, quantifies the percentage of slope surfaces that can cause daylighting conditions for all discontinuity orientations that show failure potential according to Markland’s test (Hoek and Bray, 1981). Daylighting slope surfaces are surfaces that are steep enough to make discontinuities or their intersections intersect with the slope surface (Figure 3). Based on the orientation of each of the triangles of the mesh draped over the point cloud (Figure 2), slope surfaces that cause daylighting of discontinuities or their intersections can be identified. Selection of daylighting slope surfaces is based on a given threshold of the failure index value. For example, for the highest failure index calculated, if the selected threshold value is 10 percent, the orientations of slope surfaces that can cause greater than 10 percent failure potential (plane, wedge, or toppling) are considered as potential daylighting slope surfaces. Such surfaces are selected to estimate A2 . Since rockfalls from higher elevations have more energy, increasing the

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Parameter B has two parts, B1 and B2 . Parameter B = B1 × B2 × 100,

Figure 4. Cartoon model showing sub-horizontal undercut surfaces. Uli is the length of undercutting layer i, whereas Ti is the thickness of undercutting layer i.

risk of a rockfall hazard, individual daylighting slope surfaces should be weighted based on the elevation of their geometric center or their centroid. A2 is calculated as the total area of daylighting slope surfaces and their corresponding centroid elevation (Z). The centroid elevation is normalized by dividing it by a chosen benchmark height of 100 ft (30.5 m), which is the highest slope rated by most RHRS. Therefore, Parameter A = A1 × A2 × 100,

(1)

where A1 = highest plane, wedge, or toppling failure index in percent for a vertical slope face; A2 = (surface area of individual daylighting slope surfaces based on a threshold kinematic potential) × Zi /100)/(slope length × slope height), where Zi is the centroid elevation in feet of individual slope surfaces that cause daylighting conditions. Zi is divided by 100 ft or 30.5 m (to evaluate elevation based on a benchmark height of 100 ft or 30.5 m). To calculate A1 and A2 , the orientation of discontinuities (patches) and areas/centroid locations of each patch can be exported from Split-FX in the form of a spreadsheet. Kinematic analysis on the discontinuities is then performed using DipAnalyst to determine A1 , and potential daylighting slope surfaces can be selected from the exported spreadsheet. Parameter B: Undercutting-Induced Rockfalls— Undercutting-induced rockfalls are common where rock units are subject to differential weathering, such as sedimentary units where strong rock units are interlayered with weaker mudrocks (Figure 4). Differentially jointed rock masses may also lead to undercutting-induced rockfalls. The presence of undercut rock units indicates future likelihood of failure. Sites of undercutting are usually exposed as horizontal surfaces. Horizontal discontinuities along with their corresponding areas can be selected from patches identified in Split-FX. Rockfalls due to undercutting occur when the depth of undercutting exceeds the average spacing of joints of the undercut unit. Therefore, the likelihood of releasing rockfalls is a function of not only depth of undercutting, but also joint spacing.

(2)

B1 quantiﬁes the proportion of a given slope that consists of rock layers that can potentially cause undercutting-induced rockfalls. B1 is calculated by summing the products of length and the thickness of the potentially undercutting layers and dividing this by the total vertical area of the slope face (slope length × slope height). High B1 values indicate several potentially undercutting layers or few but thick undercutting layers. B1 = (U li × Ti ) /slope length × slope height, (3) where Uli is the length of undercut layer i on the slope face, and Ti is thickness of undercut layer i. B2 evaluates how close undercut surfaces are to failing based on the maximum depth of possible undercutting before a rock block is released. This depends on the discontinuity spacing of the undercut unit. B2 compares the areas of exposed undercut surfaces to the maximum possible area of undercut surfaces before a rockfall is released. The maximum dimension of undercut surfaces is controlled by the spacing of discontinuities. Also, as undercutting from higher elevation can generate more energetic rockfalls, B2 is weighted based on elevation. B2 = (Ahsi × Zi /100) / ([Ahsi /Wund ] × Wund ) ,

(4)

where Ahsi is area of individual horizontal surfaces; Zi is the centroid the elevation of the ﬂat discontinuity in feet normalized to a 100 ft (30.5 m) height; Wund is the average maximum depth of undercutting based on joint spacing; and Ahsi /Wund is an approximate length of individual horizontal surfaces. To calculate B1 , the length and thickness of each undercut layer are measured using measurement tools in Split-FX. To calculate B2 , sub-horizontal discontinuities or patches are selected based on shallow dip amounts (0 to 10 degrees). Joint spacing can be measured on the colorized point cloud using tools in Split-FX. Parameter C: Rockfall Release Surfaces—Rockfall events leave voids on a slope face. The voids are bounded by at least three discontinuity surfaces (Figure 5). Parameter C estimates history of rockfall activity and future likelihood based on the presence of rockfall voids. Slopes that rank high in parameter A have a high percentage of unfavorable discontinuities, but if the discontinuities have low persistence or high friction angle, rockfalls would be rare. Such slopes would then have low rating in terms of parameter C. The orientations of at least three surfaces (J1 , J2 , J3 ) bounding a rockfall void need to be identiﬁed

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Parameter D estimates the volume of rockfalls from the areas of at least three discontinuity surfaces (J1a , J2a , J3a ) bounding rockfalls (Figure 5). As a predictor of rock block volume in terms of areas of discontinuities, the product of the average area of each bounding surface, J1a , J2a , J3a , is calculated. This product is in m6 and should be compared to a standard value of 1 m6 for constant comparison between sites. Parameter D = [(J1a Mean × J2a Mean × J3a Mean )/1 m6 ] (6) × 100, where J1a , J2a , and J3a are the areas of discontinuity surfaces bounding a rockfall. For slopes with undercutting layers, Figure 5. Cartoon showing a rockfall void and bounding surfaces (J1 , J2 , and J3 ). J1 and J3 serve as sliding surfaces, whereas J2 is a release surface.

based on field experience and visual inspection of the point cloud data. Two of the three discontinuities (J1 and J3 ) can intersect, forming a “daylighting” wedge, but a third surface, J2 , has to be present to release a rockfall, since not all intersecting discontinuities will release rockfalls if they are discontinuous or have high friction. When a rockfall is released, sliding occurs along the J1 and J3 surfaces, and detachment from the J2 surface is necessary (Figure 5). Therefore, the presence of J2 indicates that release of rockfalls is possible. J2 is oriented nearly parallel to the slope face, indicating past rockfall events, and its frequency is also a predictor of future rockfall releases. Such surfaces are termed “rockfall release surfaces” and need to be quantified as parameter C. Parameter C = [ (J2ai × Zi /100) /slope length (5) × slope height] × 100, where J2ai is area of J2 surfaces, and Zi is the centroid elevation in feet of an individual release surface in feet. J2ai is normalized by its height (Z)/100 ft (or Z/30.5 m). This will give higher weight for release surfaces from higher levels of the slope cut. The product of slope length and height gives is an estimate of the size of a cut slope. To calculate parameter C, the orientation of J2 has to be identified first, and discontinuities with such orientations should be selected from the discontinuities (patches) exported from Split-FX. Parameter D: Volume of Rockfall Blocks—The volume of rockfalls is an important parameter that is usually determined from physically measuring rockfalls found in catchment ditches. The dimensions of rockfalls are governed by joint spacing in three dimensions.

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Parameter D = [(Ahsi /Wund )Mean × Wund Mean × Ti Mean ]/1 m3 ] × 100,

(7)

where Ahsi is the area of individual horizontal surfaces; Ti is thickness of the undercutting layer; Wund is the maximum depth of undercutting based on joint spacing; and Ahsi /Wund is an approximate length of individual horizontal surfaces. QUANTITATIVE GEOLOGIC ROCKFALL RATING SYSTEM (QG-RRS) The original rockfall hazard rating is based on geologic, climatic, slope design, and traffic conditions. However, the DSM-aided QG-RRS is based mainly on geologic factors that affect the likelihood of rockfall occurrence. The QG-RRS is based on the result of the four parameters discussed above. In addition, the age of the cut slope should be taken into consideration, since QG-RRS parameters such as B, C, and D are affected by the age of the slope cuts. Older slope cuts would have more release surfaces and deeper undercutting. Therefore, it is reasonable to qualify QG-RRS values by providing class designations I, II, III, IV, and V based on age (class I: 0–25 years, class II: 25–50 years, class III: 50–75 years, class IV: 75–100 years, class V: > 100 years) to make comparison of QG-RRS values. Due to varying geologic attributes, different scenarios may arise. For a given cut slope, kinematic analysis may show a high likelihood for failure, but due to the nature of the discontinuities (non-persistent discontinuities and rough discontinuities), rockfalls may not occur, resulting in higher rating of parameter A but lower parameter C. Therefore, cut slopes consisting of uniform rocks (having similar resistance to weathering) that are prone to discontinuity orientationcontrolled failure should be evaluated in terms of both parameters A and C. Both types of cut slopes should also be evaluated in terms of the size of rockfalls,

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parameter D. Other cut slopes may only be aﬀected by undercutting-induced rockfall generation. The proposed QG-RRS is multipronged. For cut slopes consisting predominantly of strong rock units, and where the main cause of slope failure is structurally controlled, both parameter A and parameter C should be used alongside Parameter D. QG-RRSUniform strong units with structurally controlled failure = A + C + D, (8) where the maximum QGRRSUniform strong units with structurally controlled failure score may reach 300. Cut slopes consisting of interlayered sedimentary units subject to undercutting-induced rockfalls should be rated in terms of undercutting potentials and past rockfall events. Therefore, for cut slopes consisting interlayered rock units subject to diﬀerential weathering, parameters B and D should be used. QG-RRSInterlayered rocks = B + D,

(9)

where the maximum QG-RRSInterlayered rocks score can reach 200. CASE STUDIES Afton Mountain Cut: Interstate 64 The Afton mountain cut slope is on the west-bound section of Interstate 64 in Virginia (mile marker 101) with a slope aspect of 165 degrees (Figures 6 and 7). The rock unit is a late Proterozoic meta-basalt, the Catoctin Formation, which has a well-developed, highly persistent south-dipping foliation and subvertical orthogonal joints. It is evident that rockfalls occur due to plane failures along the south-dipping foliation. The cut slope was scanned with a Trimble TX5 scanner. Split-FX was used to convert the point cloud into a mesh of triangles. Patches representing discontinuities were generated (Figure 8). Discontinuity data were also collected using a transit compass. Two joint sets, J1 (foliation) and J2 , with mean dip direction/dip values of 164°/44° and 167°/83°, respectively, are visible on the stereonet plot of patches extracted from the point cloud (Figure 9). A third set, not visible from the LiDAR scans (due to the scanner orientation) but measured in the ﬁeld, has mean dip direction of 61° and mean dip of 77°. Evaluating QG-RRS Parameter A—Parameter A was calculated using Eq. 1. Kinematic analysis (using DipAnalyst software) based on the identiﬁed patches and using a friction angle of 37 degrees (estimated by sliding rock blocks)

showed 0.62, 0.47, and 0 for plane, wedge, and toppling failure indices, respectively (Figure 9). Hence, the A1 value, the highest failure index, is 0.62 (plane failure). By plotting discontinuities, slope face, and friction circle on a stereonet in DipAnalyst, the slope face orientation that can cause 10 percent or greater of plane failures was found to be a slope angle of >35 degrees and slope aspect of 145 to 185 degrees (Figure 10). After the selection of slope surfaces satisfying the daylighting condition was made, (slope surface area that can cause daylighting conditions × Zi 30.5) was calculated to be 25.5 m2 . Slope length and slope height were measured on the DSM using the Split-FX software as 30.2 m and 10.3 m, respectively. Therefore, A2 = (surface area of individual daylighting surfaces based on a threshold kinematic potential) ×Zi /100 (slope length × slope height) ; A2 = 25.5 m2 /311 m2 = 0.08; Parameter A = 0.62 × 0.09 × 100 = 5.6. Parameter B—Since there were no undercutting related problems observed, a value of zero was assigned for parameter B. Parameter C—Rockfall release sites were observed on the slope face (Figure 7). Release surfaces can visually be identiﬁed on site photographs or in the point cloud. The release surfaces are near vertical (>65°) with dip directions between 135 and 175 (Figure 11a and b). Patches with such orientations were selected, and the (J2ai × Zi 30.5) value was calculated to be 0.17 m2 , where the slope length × slope height is 311 m2 . Parameter C = [ (J2ai × Zi /100) /slope length ×slope height] × 100; Parameter C = 0.17 m2 /311 m2 × 100 = 0.05. Parameter D—The Afton mountain cut slope has released several rockfalls. The catchment ditch appears inadequate, and a rockfall fence has been erected. Rockfall sizes are controlled by three discontinuity sets. Discontinuities identiﬁed in Split-FX were plotted on a stereonet (DipAnalyst), and three sets of discontinuities were characterized. One set is foliation with average dip direction of 168° and average spacing of 0.61 ft (0.19 m). Two subvertical joint sets, one trending sub-parallel (mean dip direction = 149°) to the slope face (average spacing of 1.27 ft [0.39 m]) and another perpendicular (mean dip direction = 061°) to the slope face (average spacing 1.34 ft [0.41 m]), were recorded. The corresponding mean surface areas of bounding surfaces J1a , J2a , and J3a are 0.7 m2 , 0.8 m2 , and 1.7 m2 . To calculate parameter D, J1a × J2a × J3a

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Figure 6. Location map of Afton Mountain and U.S. Highway 33 slope cuts.

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Figure 7. The Afton Mountain cut along west-bound I-64 in Virginia, USA. Rockfalls are released due to plane failure, and release surfaces shown are sub-parallel to the roadway.

equals 0.95 m6 . Therefore, Parameter D = (J1a Mean × J2a Mean × J3a Mean ) /1 m6 ×100; Parameter D = 0.95 m6 /1 m6 × 100 = 95. Afton Mountain QG-RRS The Afton cut slope predominantly consists of strong rock units, and the main cause of slope failure is structurally controlled. It was constructed in 1964, making it a class III slope in terms of age. Therefore, the QG-RRS for the Afton cut slope is calculated as: QG-RRS = A + C + D; QG-RRS = 5.6 + 0.05 + 95 = 101.6 − III.

This value can be rated as moderate, because the maximum QG-RRS value can potentially exceed 300. The moderate value reﬂects the gentle slope angle, which is nearly parallel with the main discontinuity, and therefore fewer daylighting surfaces are present. U.S. Highway 33 Cut Evaluating QG-RRS A cut slope on U.S. Highway 33 consisting of interlayered sandstone and shale units belonging to the Hampshire Formation of Devonian age was chosen as a site representing slopes with undercutting-induced rockfall problems (Figure 12). The cuts were made in the 1940s, making it a class III cut slope in terms of

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Figure 8. Terrestrial laser scanning (TLS) using a Trimble TX5 scanner of the Afton Mountain cut showing (a) colorized point cloud, (b) mesh created (Split-FX), and (c) patches shown with yellow boundaries (Split-FX).

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Figure 10. (a) Triangulated mesh from cut slope along I-64 highway in Virginia and (b) daylighting surfaces shown in brown. Color was added to the mesh using ArcGIS for better display.

Parameter B—At the U.S. Highway 33 cut, discontinuities identified in Split-FX were exported into a spreadsheet. Out of the all identified discontinuities, 29 subhorizontal surfaces, which are sites of undercutting, were identified (Figure 12). To evaluate parameter B, total length ( Uli ) and thickness ( Ti ) of the single undercutting unit were determined to be 16.1 m and 5.1 m, respectively. The slope length and height were 10.9 m and 9.9 m, respectively. The average spacing of joints parallel to the slope face (Wund ) that control depth of undercutting was measured on a georeferenced point cloud as 0.55 m. Therefore,

Figure 9. (a) Poles to discontinuities showing two distinct joint sets, and (b) results of kinematic analysis using DipAnalyst software (www.dipanalyst.com).

age. The section of U.S. Highway 33 where it crosses the Allegheny Mountains in western Virginia is prone to releasing undercutting-induced rockfalls due to the interlayering of strong layers with soft erodible layers. There is no evidence for discontinuity orientationcontrolled slope failures. Therefore, parameter B and D, are the most relevant.

B1 = (U li × Ti ) /slope length × slope height; B1 = 16.1 m × 5.1 m/10.9 m × 9.9 m = 82.1 m2 /107.9 m2 = 0.76; B2 = (Ahsi × Zi 30.5) / ([Ahsi /Wund ] × Wund ) ; (Ahsi × Zi 30.5) = 0.19 m2 ; ([Ahsi /Wund ] × Wund ) = 4.9 m2 ; B2 = 0.04; Parameter B = B1 × B2 × 100 = 0.76 × 0.04 × 100 = 3.04. Parameter D—Rockfalls induced by undercutting were observed in the catchment ditch. The discontinuities that control the release of rockfalls are bedding

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calculated as: QG-RRS = B + D; QG-RRS = 3.3 + 5.9 = 9.2 − III. This value is very low out of a maximum of 200. This is because of the minimum amount of undercutting that has taken place and the short heights of sites of undercutting. SUMMARY AND DISCUSSION

Figure 11. (a) Triangulated mesh from cut slope along I-64 highway in Virginia and (b) release surfaces shown in brown. Color was added to the mesh using ArcGIS for better display.

planes and joint sets that are parallel and perpendicular to the slope face. The average bedding thickness (Ti Mean ), mean area of undercut horizontal surfaces (Ahsi ), and average depth of undercutting (Wund ) were determined to be 0.36 m, 0.17 m2 , and 0.55 m, respectively. The value of (Ahsi /Wund )Mean was determined as 0.31 m. Linear measurements of Ti and Wund were made on the point cloud using Split-FX. The area of the sub-horizontal surfaces was extracted from the discontinuity data exported from Split-FX. Therefore, Parameter D = [(Ahsi /Wund )Mean × Wund Mean ×Ti Mean ]/1 m3 ] × 100; Parameter D = [0.3 m × 0.55 m × 0.36 m/1 m3 ] ×100 = 5.9. U.S. Highway 33 QG-RRS The U.S. Highway 33 cut slope consists of interlayered rock units subject to diﬀerential weathering. Therefore, the QG-RRS for U.S. Highway 33 was

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The DSM-aided QG-RRS offers a novel method to use high-resolution DSMs for rating the likelihood of rockfall generation. It mainly characterizes four simple geologic parameters, which provide quantitative evaluations of the kinematic potential for structurally controlled rockfalls (parameter A) and undercuttinginduced rockfalls (parameter B). It is also capable of quantifying past rockfall events (parameter C) and estimating rockfall volume (parameter D) based on rockfall voids. Incorporation of elevation values in all QG-RRS calculations are useful, because elevation directly correlates with rockfall energy. The inputs for these parameters are derived from DSMs that are easily measurable from Split-FX software outputs, which include dip direction, dip, area, and centroid elevation of discontinuities. Slope length and height can also easily be determined from DSMs. Geologic parameters are the most important rockfall parameters, as the other non-geologic risk factors can promote rockfall generation but cannot cause rockfalls independently. DSM-based evaluation of geologic parameters is safer for surveying inaccessible parts of a slope and is more efficient than manual data collection. The QG-RRS allows quantification of geologic parameters, making the results more reproducible and less subject to the evaluator’s experience. The QG-RRS is a remote-sensing method that is still far from being completely automated or able to replace the role of a professional in the process. Deriving inputs for QG-RRS from DSMs requires geological/geotechnical engineering experience. Defining the orientation of undercut surfaces, number and thicknesses of undercutting layers, and orientations of discontinuity surfaces bounding rockfalls requires the judgement of geological/geotechnical engineering personnel. Friction angle values need to be determined in the laboratory or assigned based on prior experience. Therefore, the QG-RRS should strictly be defined as a DSM-aided rockfall rating system method that is not a stand-alone remote-sensing methodology. It also needs further testing and refining at several slope cuts representing varying geological scenarios to make it widely acceptable.

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Figure 12. (a) Photograph of a road cut along U.S. Highway 33 in Virginia showing undercutting, (b) mesh created from a LiDAR-derived point cloud, and (c) undercut horizontal surfaces selected on the mesh. Color was added to b and c using ArcGIS for better display.

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The QG-RRS is not comparable to the RHRS because it does not handle all the risk factors contributing to rockfall hazard. The purpose of the QG-RRS is to efficiently quantify geologic risk factors that contribute to rockfall generation using the fast-growing availability of high-resolution DSMs. QG-RRS normalizes geologic parameters based on height to account for rockfall energy that is related to the consequence of a rockfall event. Slopes rated high in the QG-RRS can be selected for close monitoring or rockfall hazard evaluation. The availability of LiDAR from mobile platforms and SFM from UAVs provides the opportunity for faster data acquisition and multitemporal scanning and evaluation of slopes. Through multitemporal QGRRS evaluation, the long-term performance of cut slopes can be documented. Some disadvantages of DSM-aided QG-RRS include the high cost of the TLS instrument and the necessary software. The use of multiple software packages to produce point clouds, extract discontinuity data, perform kinematic analysis, and ultimately evaluate QG-RRS appears to be cumbersome. Better software capable of performing the QG-RRS workflow in one batch will in the future streamline the process and pave the way for a more automated QG-RRS evaluation. CONCLUSIONS The DSM-aided QG-RRS mainly based on geologic parameters is a simplistic approach to a quantitative rockfall likelihood rating method. DSM-aided QGRRS is performed safely and is more representative because discontinuities from higher and inaccessible parts of a slope can be measured. It is a simple approach offering a method that gives more weight to geological parameters and provides quantitative results. LiDAR and SFM photogrammetry from mobile platforms such as vehicles or UAVs can expedite the process of data collection, allowing continuous scanning of miles of slope cuts. The proposed QG-RRS needs to be tested at different slopes with variable geologic configurations. REFERENCES Admassu, Y. and Shakoor, A., 2013, DipAnalyst: A computer program for quantitative kinematic analysis of rock slope failures: Computers and Geosciences, Vol. 54, pp. 196–202. Admassu, Y.; Shakoor, A.; and Wells, N., 2012, Evaluating selected factors affecting the depth of undercutting in rocks subject to differential weathering: Engineering Geology, Vol. 124, pp. 1–11. Andrew, R.; Arndt, B.; and Turner, A., 2012, Instrumentation and monitoring technology. In Turner, K. A. and Schuster, R. L. (Editors), Rockfall: Characterization and Control:

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Transportation Research Board, National Research Council, Washington, D.C., pp. 212–276. Fish, M. and Lane, R., 2002. GIS and New Hampshire rock cut management. New Hampshire DOT Research Record, Concord, New Hampshire, 19p. Elliasen, T. D. and Ingraham, P. C., 2000. Review, rockfall hazard rating and repair of aging interstate rock cuts in northeastern Vermont: 51st Annual Highway Geology Symposium Proceedings, Seattle, Washington, pp. 233–243. Goodman, R. E., 1989, Introduction to Rock Mechanics: John Wiley & Sons, New York, NY, 562 p. Higgins, J. D. and Andrew, R.D., 2012, Rockfall types and causes. In Turner, K. A. and Schuster, R. L. (Editors), Rockfall: Characterization and Control: Transportation Research Board, National Research Council, Washington, D.C., pp. 21–53. Hoek, E. and Bray, J. W., 1981. Rock Slope Engineering, The Institute of Mining and Metallurgy, London, England, 358 pp. Kemeny, J. and Turner, K., 2008, Ground-Based LiDAR: Rock Slope Mapping and Assessment: Central Federal Lands Highway Technology Deployment Initiatives and Partnership Program, Federal Highway Administration, Lakewood, CO. Lato, M.; Diederichs, M. S.; Hutchinson, D. J.; and Harrap, R., 2009a, Optimization of LiDAR scanning and processing for automated structural evaluation of discontinuities in rockmasses: International Journal of Rock Mechanics and Mining Sciences, Vol. 46, No. 1, pp. 194–199. Lato, M.; Hutchinson, J.; Diederichs, M.; Ball, D.; and Harrap, R., 2009b, Engineering monitoring of rockfall hazards along transportation corridors: Using mobile terrestrial LiDAR: Natural Hazards Earth Systems Science, Vol. 9, No. 3, pp. 935–946. Lato, M. J. and Vöge, M., 2012, Automated mapping of rock discontinuities in 3D LiDAR and photogrammetry models: International Journal of Rock Mechanics and Mining Sciences, Vol. 54, pp. 150–158. Maerz, N. H.; Youssef, A.; and Frennessey, T. W., 2005, New risk-consequence rockfall hazard rating system for Missouri highways using digital image analysis: Environmental and Engineering Geoscience, Vol. 11, No. 3, pp. 229–249. Miller, S. M., 2003, Development and Implementation of the Idaho Highway Slope Instability and Management System: Idaho Transportation Department Report #N03-07. New York State Department of Transportation, 2015, Rock slope rating procedure. Geotechnical Engineering Bureau, report # GEM-15, 49p. Nguyen, H. T.; Fernandez-Steeger, T. M.; Wiatr, T.; Rodrigues, D.; and Azzam, R., 2011, Use of terrestrial laser scanning for engineering geological applications on volcanic rock slopes—An example from Madeira Island (Portugal): Natural Hazards Earth Systems Science, Vol. 11, No. 3, pp. 807–817. Pierson, L. A. and Turner, A. K., 2012, Implementation of rock slope management systems. In Turner, K. A. and Schuster, R. L. (Editors), Rockfall: Characterization and Control: Transportation Research Board, National Research Council, Washington, D.C., pp. 72–110. Pierson, L. A. and Van Vickle, R., 1993, Rockfall Hazard Rating System Participant’s Manual: Federal Highway Administration Publication SA-93-057, 104 p. Piteau, D. R. and Martin, D. C., 1977, Description of detail line engineering geology mapping method. In Rock Slope Engineering, Part G: Federal Highway Administration Reference Manual FHWA-13-97-208.

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Quantitative Geologic Rockfall Rating System Russell, C. P.; Santi, P. M.; and Higgins, J. D., 2008, Modification and Statistical Analysis of the Colorado Rockfall Hazard Rating System: Colorado Department of Transportation (CDOT) DTD Applied Research and Innovation Branch Report No. CDOT-2008-7. Santi, P. M.; Russell, C. P.; Higgins, J. D.; and Spriet, J. I., 2009, Modiﬁcation and statistical analysis of the Colorado rockfall hazard rating system: Engineering Geology, Vol. 104, pp. 55–65. Saroglou, H.; Marinos, V.; and Tsiambaos, G., 2012, Rockfall hazard and risk assessment: an example from a high promontory at the historical site of Monemvasia, Greece: Natural Hazards and Earth Systems Sciences, Vol. 12, pp. 1823–1836. Shakoor, A. and Martin, J. W., 2005, Development of a Rockfall Hazard Rating Matrix for the State of Ohio: Federal Highway Administration Report No. FHWA/OH-2005/005. Shakoor, A. and Weber, M. W., 1988, Role of shale undercutting in promoting rock falls and wedge failures along Interstate 77: Environmental and Engineering Geoscience, Vol. 25, No. 2, pp. 219–234.

Stover, B. K., 1992, Highway Rockfall Research Report: Colorado Geological Survey Special Publication 37. Sturzenegger, M. and Stead, D., 2009, Close-range terrestrial digital photogrammetry and terrestrial laser scanning for discontinuity characterization on rock cuts: Engineering Geology, Vol. 106, No. 3, pp. 163–182. Terzaghi, R. D., 1965, Sources of error in joint surveys: Geotechnique, Vol. 15, pp. 287–304. Vandewater, C. J.; Dunne, W.; Mauldon, M.; Drumm, E.; and Bateman, V., 2005, Classifying and assessing the geologic contribution to rockfall hazard: Environmental and Engineering Geoscience, Vol. 11, No. 2, pp. 141–154. Vöge, M.; Lato, M. J.; and Diederichs, M. S., 2013, Automated rockmass discontinuity mapping from 3dimensional surface data: Engineering Geology, Vol. 164, pp. 155–162. Youssef, A. M. and Maerz, N. H., 2010, Development, justiﬁcation, and veriﬁcation of a rock fall hazard rating system: Bulletin of Engineering Geology and Environment, Vol. 71, pp. 171–186.

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Evaluating Landslide Remediation Methods Used in the Carpathian Mountains, Poland ZBIGNIEW BEDNARCZYK* Poltegor Institute of Opencast Mining, Parkowa 25, 51-616 Wroclaw, Poland

Key Terms: Engineering Geology, Landslides, Instrumentation, Modeling, Remediation ABSTRACT The aim of this study was to evaluate the results of landslide remediation in the Polish Carpathians. The research for safeguarding the roads and infrastructure was conducted in the years 2005–2018 in nine landslide areas. The interpretation of engineering geology conditions was complex due to the soil-rock nature of the flysch sediment. Movements were activated after heavy rainfalls. In two cases, triggers were connected with the undercutting of the slope or external loading. The research methods included mapping, drilling, index, oedometer, direct shear tests, ground-penetrating radar scanning, and numerical modeling. To date, 15–59 series of inclinometer and piezometer network readings in 30 locations have been taken. Three online stations have been delivering continuous, nearly real-time data since May 2010. Displacements before the remediation ranged from a few millimeters to several centimeters. The proposed remediation methods included piles, micropiles, anchors, retaining walls, and drainage systems. Six stabilization projects were prepared and checked using the limit equilibrium method and finite element method modeling. The research shows that in five landslide areas, the proposed remedial works were effective. Two other partial stabilization works limited the scope of the movements but did not eliminate the risk. At two locations, only temporary repairs were conducted. Proper identification of the landslide triggers and activity is standard for the recognition of counteraction possibilities and could lower stabilization costs. The selected methods delivered data for remedial decisions. However, effective remediation of an active Carpathian landslide is difficult. It requires individually calibrated investigations, representative monitoring, and careful design of stabilization.

*Corresponding author email: zbyszbed@gmail.com

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INTRODUCTION Landslides are widespread phenomena responsible for economic and environmental losses in many countries (Petley, 2012). In the Polish Carpathians, they are common due to morphological, geological, and climatic conditions. More than 95% of landslides in Poland occur in the Carpathians (Chowaniec et al., 2015). Landslides usually have several internal causes, including geology and physical parameters. They can also be induced by people, such as when a slope is undercut during road construction. External triggers are connected mainly with extreme or intense rainfalls that reduce the strength of slope material. The rapid infiltration of rainfall, causing soil saturation and a temporary rise in pore-water pressures, is generally believed to be the mechanism by which most shallow landslides are generated during storms (Wieczorek 1996; Baum et al., 2003). At investigated flysch landslides, the main triggers representing external activation were extreme rainfalls and snowmelt (Dickau et al., 1996; Starkel, 2011; and Gil and Dlugosz, 2004). However, causes connected with human activity have also been observed. The remediation of landslides after damage to infrastructure in recent years is becoming more and more important for local communities. Intensification of landsliding in the Polish Carpathians is also caused by deforestation, changes in land use, reductions of the area occupied by agriculture, increased anthropopressure, loading or undercutting of slopes by roads and infrastructure, a lack of surface drainage and culverts under roads, inadequate regulation of rivers, a lack of reservoir maintenance, and levees without proper protection of floodplains. Shallow landslides are most often observed at slopes covered by residual soil detritus. However, some landslides are connected to deeper lowstrength flysch layers or structural surfaces (Bober, 1984). In some areas, landslides have occupied 30 to 40 percent of slopes and are the dominant process in mountain reliefs (Kotarba, 1986). The basic types of landslides are rotational with a relatively shallow and concave cylindrical slip surface (Raczkowski, 2002). Others are translational or compound. Rockfall, lateral spreading, and mudflow have also observed in these areas (Zabuski et al., 1999; Margielewski, 2004).

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Heavy rainfalls result in the full saturation of slope covers and the partial liquefaction of soils (Gorczyca, 2004). Slides initially occur along structural, geological surfaces and are often reactivated. Deep landslides with a slip surface depth exceeding 30 m are relatively rare (Sawicki, 1918; Nowalnicki, 1976). An increasing amount of infrastructure, including new transportation routes and private buildings, is being constructed in areas that have been previously avoided or used only for agricultural production because of their instability. According to data from the Polish Geological Survey, which is a part of the Polish Geological Institute (PGI), there is one landslide for every square kilometer and every 5 km of public road in the Polish Carpathians (Raczkowski, 2002). The damage to roads, private property, and other existing infrastructure caused by landslides is registered every year. As a result of catastrophic rainfalls in 1997, 2001, and 2010, many new landslides were formed, and old ones were reactivated on an unprecedented scale. The landslides caused an economic loss of €50 million in 2001. This amount increased by €2.9 billion in 2010 after floods in southern Poland. The National Building Inspection Authority estimates that in June 2010 alone, 2,269 buildings were damaged and 560 were destroyed (Polish Ministry of the Environment, 2010). The first inventories of landslides in the Carpathian region, which were conducted in 1968–1969 by the PGI, documented more than 8,500 landslides in the Carpathians and around 2,400 in other areas. They also calculated 2,970 landslide-threatened buildings, 49 railway lines, and 1,072 roads. The landslide risk area registered in the 1960s amounted to 671.8 km2 , 369 km2 of which included active landslides (Polish Ministry of the Environment, 2010). In order to counteract the landslide phenomena, the Polish government and the PGI initiated the SOPO Landslide Counteraction Framework Project in 2004 (SOPO, 2017). The primary aim of this nationwide project was to find and recognize all landslide-prone areas in Poland and provide assistance for regional planning. In addition to landslide mapping, the project also included the creation of a special Web-based database, site investigations, recognition of counteraction possibilities, and designs of remediation works for civil engineering stabilization projects. Under this project, over 60,000 landslides have been mapped and inserted in the special open-access Webbased landslide database (Grabowski et al., 2010). The 500 most dangerous landslides traversed by roads or near important infrastructure were selected by the PGI for site investigations. The SOPO project also included the installation of monitoring instrumentation at 100 landslides. Although the landslide research was performed for many years, the problem of the identification of mass movement triggers has often been

underestimated in the practice of Polish geological engineering. It is difficult to identify the causes of flysch landslide activation due to a complex geological structure and a variety of triggers. Flysch deposits on the slopes are characterized by many thin layers of marine sedimentation. The main internal landslide-prone factors are connected with a relatively steep slope morphology, the low strength of claystones, and a geology characterized by many faults and folds. In some parts, the claystones had mechanical parameters characteristic for weak cohesive soils. Sandstones usually occurred as thin, more permeable strata inside clayey layers and allowed for water infiltration. In some cases, flysch slope failures could occur on structural surfaces or bedding planes. The cause of some examples of ineffective landslide remediation in Polish Carpathians often stems from a lack of essential professional monitoring before, during, and after remediation works. Sometimes the only investigatory methods used were rotary or auger drilling. Counteractions are usually conducted in a relatively short period of time, as short as a few months. Simple investigations and remediation methods can be used at small landslides; however, detailed site investigations and monitoring methods are necessary for large and complex landslides. These methods are more expensive and time consuming but could significantly improve the effectiveness of remediation works (Dunnicliff, 1993; Senneset, 1998). The stability of slopes could be improved in several ways. The most important is the correct identification of triggers that affect the stability of the slopes (Hutchinson, 1983). The identification of these factors is essential for effective, individually designed remediation. However, for large, complex, and reactivated landslides, these factors are usually difficult to identify. Therefore, there is a significant necessity to conduct preventive monitoring measures in advance in important areas. This should lower the danger to public roads and infrastructure, improve the quality of remediation, and lower costs. Appropriately selected monitoring methods must be carried out well before remediation (Schuster and Krizek, 1988; Sowers and Royster, 1988; Dunnicliff, 1993; Mikelsen 1996; and Soesters and Weston, 1996). In some sources, this is defined as 10–12 representative measurements every month over at least one year (Cornforth, 2005). The author of this article had the opportunity to investigate 25 landslides and participate in the design of seven landslide road reconstructions (Bednarczyk, 2008, 2013). The proposed methods were included in the design of stabilization projects or preliminary counteraction concepts. Due to the high cost of stabilization, only temporary repairs were performed on

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Bednarczyk Table 1. Landslide area parameters. Area No.

Landslide Location, Landslide No.

Road/Infrastructure at Risk (m)

Landslide Volume (m3 )

Landslide Depth (m)

Landslide Classiﬁcation

Landslide Type

Remedial Works

Szymbark, 1–6

800

2.20

1.3–15.0

Debris slide

Reactivated

Szymbark, 7 and 8 Bystra, 9–11 Bystra, 12–14

450 200 600

0.52 2.32 2.25

6.0–8.0 8.0–12.0 6.0–12.0

Debris slide Debris slide Debris slide

Reactivated Reactivated Reactivated

Biesnik, 15

500

2.41

9.0–20.0

Debris slide

Reactivated

Yes/partial 2009x Nox Nox No/design for landslide no. 12* Nox

Sekowa, 16 Wapienne, 17

150 400

0.50 1.86

2.7–5.1 2.5–9.0

Debris slide Debris slide

Reactivated Reactivated

Strzeszyn, 18 Sitnica, 19 Lubatowa, 20 Dukla, 21 Tarnawa, 22 Zarebki, 23 Sitarzowka, 24

100 50 150 100 300 800 100

0.31 0.10 0.82 0.48 0.91 0.30 1.60

1.4–6.0 1.2–5.0 12–14.3 8.0–16.0 10.0–15.0 6.0–8.0 8.0–20.0

Debris slide Debris slide Debris slide Debris slide Debris slide Debris slide Debris slide

Reactivated Reactivated Reactivated New 2004 New 2006 Reactivated New 2010

Strachocina, 25

150

0.57

11.0–18.0

Debris slide

Potential

II III IV V VI VII VIII

Yes/2007* No/temporal repairs 2008x Yes 2008* Yes 2008 * Yes 2008x Yes 2007 Yes/2008* Nox No/temporal repairs 2012 Yes 2012x

The author took part in preparation: x = general remediation concepts; * = remedial design projects. 1 m = 39.4 in.

two roads. In one case, slope investigation was conducted for the design infrastructure at a landslideprone slope for the Polish Oil and Gas Company (Bednarczyk and Szynkiewicz, 2009). One project was realized for the protection of the cultural heritage of Saint John Chapel in Dukla (Bednarczyk, 2014). The main purpose of the research was to define the possibilities for and methods of landslide remedial works. In order to fill the knowledge gap, landslide behavior was studied before, during, and after stabilization. Other objectives were connected with control of remedial works and early warnings. The chosen methods of investigation and monitoring helped in the detection of sliding zone depths, ground movement directions, and velocities. This article includes an overview of the methods that were implemented and the conclusions that resulted from the flysch landslide research. LANDSLIDE LOCALIZATION AND GEOLOGY The investigated landslides are located in the Beskid Niski and Beskid Sredni mountain ranges and the Carpathian Foreland in southeastern Poland. The slopes have a height of 400–850 m above sea level and an inclination of 5º to 19º. The flysch layers are up to 7 km deep, formed 130–200 million years ago in a deep sea. The stratified structure of flysch sedimentation consists of many thin layers of claystone, sandstone, and siltstone. Its thickness varies between a few millimeters and several meters. These deposits were

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folded and moved up to 30 km to the north during the Alpine Orogenesis. The geology of flysch sedimentation is characterized by many structural faults and discontinuities. Intensive erosion in river valleys and the high groundwater level during the Holocene era formed a thick residual deposit and activated numerous ground movements. The basic parameters are presented in Table 1, while localizations are given in Figure 1. Landslide volumes varied from 0.1 to 2.2 million cubic meters. Ground movement depths ranged from a few meters to nearly 20 m. In the investigated areas, flysch peaks and ridges usually consist of stiffer sandstone. Slopes are often derived mainly from low-strength Oligocene claystone with shallow groundwater levels. The layers often dip in the slope inclination. Mass movement constitutes the debris slide type of landslide (Cruden and Varnes, 1996). The movements are usually extremely slow, up to 16 mm a year (classification Cruden and Varnes, 1996). Colluvium is usually a heterogeneous mixture of highly saturated and disintegrated claystone and crushed stiff sandstone (Figure 2). Rainfalls, floods, snow melting, pore pressure fluctuations, and the undercutting of some of the landslide toes by roads, erosion, and static or dynamic loads are the main external factors enhancing sliding activity (Bednarczyk, 2013). Other internal factors include relatively steep slopes and erosion processes in river valleys (Gil and Dlugosz 2006; Starkel, 2011). The

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Figure 1. Locations of investigated landslide areas. Roman numerals I through X indicate landslide areas described in Table 1 and text. Landslide map (Dziewanski and Czajka, 2001).

interlayers of sandstone often allow for water infiltration through its fractures and faults. Saturated claystone fragments inside colluvium have the mechanical parameters of weak cohesive soil with high plasticity. The fissures and cracks promote the penetration of rainfall water, which weakens the strength parameters and mechanical anisotropy of the claystone layers. The chemical weathering processes cause changes in the texture of the laminated clayey soils. Using an X-ray diffractometer, illite-smectite minerals have been identified in the mineralogical composition of clayey layers (Gaszynska-Freiwald, 2009). Most of the investigated landslides have been reactivated many times (area nos. I–VI). Two were newly

formed (area nos. VII–IX). The landslides posed risks for important public roads at lengths of 50–800 m. The roads traversed diﬀerent parts of the landslides. They were usually localized at the landslide toe, often close to the rivers (area nos. I, II, V, VI, VIII, and IX). Four landslides were formed directly upslope of roads (area nos. III, IV, V, and IX). In one case, the central part of the landslide was traversed by the road (area no. III). One project was connected with safeguarding industrial infrastructure for the Polish Oil and Gas Company (area no. X). Another was performed for the safeguarding of Saint John Chapel in Dukla at area no. VII (Bednarczyk, 2014). During the investigations, serious damage to roads, private property, and other

Figure 2. Example cross section for landslide no. 5 in Szymbark, area no I.

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Bednarczyk

infrastructure was recorded. This happened in June 2004 and May 2006 after heavy rainfalls. The most extensive damage was observed in May–June 2010 following rainfalls of over 200–300 mm and a flood in southern Poland. SITE INVESTIGATIONS AND INSTRUMENTATION The site investigation program was dependent on the risk posed by each landslide, the volume of unstable mass, and observed displacements. It included the preparation of geological engineering reports in risk areas (2005–2008). Research methods included high-quality boreholes, undisturbed sampling, Global Positioning System–real-time kinematic (GPSRTK) mapping, and ground-penetrating radar (GPR) near-surface geophysical scanning. Conventional instrumentation was realized in boreholes for the site investigation (2005–2014). Online ground movement and pore pressure monitoring systems were installed in specially drilled boreholes (2010). Examples of results from the investigations conducted for 25 landslides are given below. Mapping, Drilling, and Sampling An update of the actual landslide terrain morphology was very important due to the lack of current landslide maps and significant morphological changes caused by ground movements in recent years. In some areas, GPS-RTK measurements have been implemented due to their efficiency and low cost in relation to conventional mapping. The GPS measurements were found to be an effective method of landslide mapping with a horizontal and vertical accuracy of 1 cm in post-processing. However, some difficulties occurred in the forest areas. In the areas of remediation works, standard geodesy surveying was also used. Drilling allowed for the recognition of stratification layers and inclination inside the slopes. For this reason, over 800 m of core drilling was performed using the dual-core diamond-impregnated apparatus to a diameter of 132 mm and depth of 9–30 m. During these works, over 200 soil samples were taken for laboratory tests. The relatively high yield of the core (80 to 90 percent) allowed for descriptions of landslide profiles, cross sections, and, in some cases, recognition of the sliding surface depth. Geophysical Scanning Additional data connected with slope stratification and colluvium depths were detected using 2D GPR RAMAC (random access method of accounting and

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Figure 3. The results of GPR scanning at landslide no. 22, with examples of counteraction works.

control) scanning (Figure 3). This allowed for more accurate detection of landslide depth by measurements of changes in the dielectric properties of colluvium and bedrock layers. The depths of the GPR survey using 100-MHz antennas were as deep as 10–18 m, depending on the local conditions. Scanning usually did not have very high resolutions at depths below 15 m but allowed for general landslide depth and internal structure recognition. Lower-frequency 100-MHz unshielded antennas allowed for relatively deeper penetration. The scanning was calibrated in lines between the boreholes. Scanning with 100-MHz unshielded antennas enabled a general recognition of colluvium layers, internal structures, and bedrock depth for all of the presented landslides at a total length of more than 30 km. The best scanning results were obtained to a depth of about 10–20 m, depending on the local conditions (Bednarczyk and Szynkiewicz, 2009). However, in some cases, radargrams showed reflections down to a depth of 45 m. This geophysical method was found to be very economical and useful for the general recognition of landslide colluvium. However, interpretation of the GPR profiles should be carefully constrained by borehole data. In the GPR method, the initially interpreted layer depths are not the direct result of scanning and require the usage of correlation coefficients in the interpretation software. The characteristic values of the dielectric constant, attenuation (dBm−1 ) permittivity, and permeability need to be added for every selected layer of different types of rocks and soils (sandstone, mudstone, claystone, and colluvium) (Daniels, 2004). Ground Vision software was used for data interpretation. Visualization of the GPR results included the filtration of scanning profiles using colors. On the cross sections, the yellow color indicated mudstone and gray claystone. The interpretation also included an indication of faults and sliding surfaces.

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Figure 4. Manual inclinometer monitoring results, landslide no. 1.

At landslide no. 22 in Tarnawa (area no. VIII), GPR scanning detected landslide deposits localized to a depth of 15 m, which corresponded well with saturated colluvium in the boreholes. GPR profiling was essential for the construction of geotechnical cross sections. It allowed for the recognition of landslide colluvium and the inclinations of layers and faults. For example, at landslide no. 16, under a public road, colluvium was recognized at depths of 2.8–4.5 m. Interpretation using the GPR method and the boreholes in the sandy and clayey layers was helpful in the identification of zones prone to water infiltration. However, it should be noted that the identification of colluvium was possible through GPR correlation with detailed site investigation and monitoring results. With the GPR method, layer depths are the result only of interpretation and should be calibrated very carefully. The GPR method had some limitations in places like forests or locations close to power supply lines. It is also important to note that the interpretation of the results of GPR flysch landslide scanning was not easy and could made only by experienced users of this equipment.

projects. Boreholes drilled inside the site investigations were also used for inclinometer, piezometer, and pore pressure transducer instrumentation. Conventional monitoring methods were used for standard inclinometer, piezometer, and pore pressure monitoring before, during, and after remediation (Figures 4 and 5). The network included 30 monitoring locations, consisting of a 70-mm ABS inclinometer depth of 7–21 m and a standpipe piezometer depth of 5 m. For

Monitoring Instrumentation Nearly 500 m of inclinometer casing was installed for monitoring road and infrastructure remediation

Figure 5. Inclinometer and piezometer monitoring results, landslide no. 6.

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Figure 6. Online early warning station, located on landslide no. 6.

pore pressure monitoring, pneumatic and piezoelectric transducers were located at a depth of 5–10 m. In 2010, the first online early warning system in Poland was installed at landslide area no. I (Figure 6). Four field stations were built at landslides nos. 1–6 over the public Szymbark-to-Szalowa road. This included two online Shape Accel Array (SAA) field stations, an in-place inclinometer, and a meteorological station. The SAA 3D systems consisted of tilt sensors every 0.5 m to depths of 12 and 16 m. They included a total of 66 tilt sensors and allowed for the measurements of a much greater displacement range to compare with the standard inclinometers. This type of system could withstand displacements even, in some cases, up to 500 mm. Another automatic in-place inclinometer system with three uniaxial sensors and a length of 14 m was also installed. The ground movement–measuring devices were supplemented by three automatic pore pressure and groundwater-level transducers and an online weather station with measurements of rainfall, air temperature, air pressure, and air humidity. Nearly realtime measurements were implemented to detect risk conditions for the public road below the landslide area (Figure 7). RESULTS OF LABORATORY TESTS To define the geotechnical parameters of colluvium, index, compressibility, and strength tests were performed. The index tests included grain size, moisture content, plastic and liquid limit, bulk density, and content of organic/bituminous material. To measure the compressibility of colluvium soils, an IL oedometer test was performed. However, the preparation of samples for the oedometer and strength tests was complicated due to the occurrence of crushed rock particles in the soil samples. Therefore, the strength tests were limited to simple direct shear tests in the shear box

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Figure 7. Cumulative displacement plot (mm), May 2010– December 2013, landslide no. 6.

apparatus and only a limited number of isotropically consolidated undrained and isotropically consolidated drained triaxial tests. The index tests detected that the colluvium represented silty clay, loam, gravelly loam, claystone, and sandstone (bedrock). The investigated soil samples had a natural moisture content of 18 to 37 percent, varied in cohesion from 6.5 to 10 kPa, and had angles of internal friction of 11º to 15º The clayey colluvium was characterized by a very high compressibility. The highest values of the moisture content and plasticity were observed in samples taken near the slip surface at depths of approximately 2–10 m. At landslide no. 16 in Sekowa, samples were taken at a depth of about 2.5 m, while at landslide nos. 1–6 in Szymbark, samples were taken at a depth of 10 m. RESULTS OF MONITORING MEASUREMENTS The ground movement and groundwater conditions had been examined on the basis of systematic, conventional measurements every few months from 2006 to 2018, a period of over 12 years. In the ﬁrst 5 years, scheduled manual inclinometer and pneumatic pore pressure readings were taken more often, every 30–45 days, and then two times a year. Automatic pore pressure piezoelectric transducer readings had been performed every 6 hours since 2005. Some of these monitoring data were used for the preparation of remediation concepts or projects, while others were used for studies of landslide activity and control of stabilization works. The measurements allowed for the general veriﬁcation of representative values of displacements, including their directions and depths. The observed ground movements varied from 50 to 500 mm in 12 years’ time. In some cases, displacements reached critical values for the

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inclinometers, meaning it was possible to measure the displacements only above the shallowest sliding surface. At landslide no. 17 in Wapienne (area no. III), displacements of 400 mm caused damage to inclinometer casings at a depth of 2.5 m. One inclinometer casing was also nearly damaged in landslide nos. 1–6 in Szymbark (area no. I) by a displacement of 150–180 mm at a depth of 11 m (Figure 4). The greatest displacement occurred after the highest pore pressure variations. In this case, the pore pressure rose to 60–65 kPa and then decreased to 40–45 kPa. The largest incremental displacement occurred during the stabilization of landslide nos. 1–6 (area no. I) in 2009. At that time, the displacement reached 53.9 mm (Figure 5). High values of displacements were noted in May 2006 (11.2 mm) and March 2007 (5.3 mm). In May–June 2006, relatively large monthly displacements of 12 mm were also observed at landslide no. 16 in Sekowa (area no. II) before the remediation works. Ground displacements were usually activated after a period when the pore pressure on the slip surface reached 65 kPa and then dropped to 40 kPa. The pore pressure variations had better correlation with mass movements than the groundwater-level fluctuations, which indicates that the pore pressures throughout the landslide are not purely hydrostatic. In all the locations, an increase of the displacements was observed in May–June 2010. However, this was also dependent on the spatial distribution of rainfall and the implemented remediation works. The online system installed in landslide nos. 1–6 (area no. I) in 2010 delivered more comprehensive data. It has multiple 3D displacement sensors and very small reading intervals: (1) 10 minutes for rainfall and other meteorological data, (2) 1 hour for pore pressure, and (3) 6 hours for the 3D displacement sensors. The online monitoring system registered record-high rainfalls just after the installation in May–June 2010. The reading from the automatic weather station on June 2 reached 100 mm in three hours’ time. During 40 months of system operation, the total SAA displacement reached 30–50 mm and a depth of 12–15 m. The largest deformation of inclinometer casings occurred in May–June 2010 and June–August 2011. The initial threshold values for an early warning were defined as a daily displacement higher than 1 cm and rainfall higher than 100 mm per day. Conventional and online measurements were conducted until the end of 2018. Further operation of the system depends on possible financing. Two online inclinometer stations have already been damaged by displacements larger than 500 m. Three manual inclinometer installations were affected by high displacements and are no longer able to measure the full inclinometer profile. In some other locations, it will probably no longer be possible to collect data for the

foreseeable future due to the observed ground movement sizes. RESULTS OF NUMERICAL MODELING Slope stability was checked using limit equilibrium method (LEM) numerical modeling. The factor of safety (FoS) was calculated before and after remediation. The strength parameters for the analysis were implemented as the lowest values from the laboratory tests and comparable experience. LEM analysis was conducted using the Janbu, Bishop, and MorgensternPrice methods based on the most probable circular slip surface and comparing them with the monitoring results. For example, in landslide nos. 1–6 in Szymbark (area no. I), two layers characterized by different parameters were selected. The first colluvium layer had a specific weight of 14 kN/m3 , saturated specific weight of 16 kN/m3 , effective friction angle of 9º, and effective cohesion of 11 kN/m2 . The second claystone bedrock layer had a specific weight of 16 kN/m3 , saturated weight of 20 kN/m3 , effective friction angle of 40º, and effective cohesion of 29 kN/m2 . Limit equilibrium analyses showed that the analyzed slopes (landslide nos. 1–6) had a low FoS (0.68–1.2). Very low values of FoS (0.7–0.8) were calculated for lower slope parts at the landslide tongues. Finite element method (FEM) modeling was implemented to predict possible displacements and compare them with the monitoring results (Figure 8). The modeling included external factors, such as static or dynamic loads, shallow groundwater levels, and preferred slip surfaces. However, it was not possible to include the same triggers because in some cases the instability was caused by other external factors. For example, at landslide nos. 1–6 in Szymbark (area no. I), the causes included undercutting of the landslide head during road construction in World War II and the exclusion of the land from agricultural production in the 1990s. At landslide no. 18 in Strzeszyn (area no. IV), it was difficult to identify the slope saturation by the springs. The numerical modeling adopted simplified geometrical models of the slopes and their main parameters. The proposed counteraction methods were tested using the classical LEM based on the relative factor of safety. It was desired that after full remediation, the FoS should be higher than 1.5. However, the value of FoS calculated using the Bishop method at landslide no. 5 in Szymbark (area no. I) after partial remediation was 1.26 (Figure 9). The most probable slip surface was located outside the road area after stabilization. At landslide no. 18 in Strzeszyn (area no. IV), the pre-stabilization values of FoS = 1.2– 1.31 increased to 1.53–1.64 after stabilization. After

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Figure 8. FEM analysis, predicted displacements, landslide no. 5.

remediation at both landslides, the most probable rotational slip surfaces (FoS ࣘ 1.3) were localized outside the public roads. At landslide area no. I, the least safe area was located at the upper part of the landslide, and at landslide area no. IV, it was located at the lower part of the landslide. Fortunately, these low-FoS zones had no significant influence on the public roads. Analysis using FEM modeling by SoilVision software took into account the results of the monitoring measurements. In this way, the definition of the boundary conditions

was more representative. The ﬁnal FEM mesh indicated that without the stabilization works, the stresses could pose a danger to the public road. The applied approach allowed for the analysis of the inﬂuence of high pore water pressure on the reduction of shear strength. The modeling for landslide no. 16 in Sekowa (area no. II) detected that the expected total displacement could reach up to 12 cm without remediation. At this landslide, the FoS was slightly above 1.13 before stabilization and 1.58 following stabilization (Figure 10).

Figure 9. LEM analysis after counteraction, landslide no. 5, FoS = 1.26.

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Figure 10. LEM slope stability anal., Bishop method, stabilized landslide no. 16, FoS = 1.58.

A similar factor of safety was obtained after the stabilization of landslide no. 22 in Tarnawa (Figure 10). For active landslide no. 24, the analysis detected a very low FoS (0.89). The analysis performed for the foundations of the industrial infrastructure of the Polish Oil and Gas Company in Strachocina detected FoS = 1.52 after the design of the stabilization works (Figure 11).

DESIGN OF REMEDIAL PROJECTS The choice of the appropriate stabilization method was based on the observed slide phenomena, site investigations, and monitoring results. These included landslide size, depth, ground displacement, and groundwater conditions. It was important to use the stabilization methods directly dedicated to slope instability triggers. The basic protections prevented the slopes from absorbing rainwater and directed it to the nearest watercourse. The slopes were covered by grass or other low-permeability layers. All the recesses, marsh, and water reservoirs were drained. In some cases, underground waters that irrigated the landslide areas were drained by a system of wells or horizontal drains. In order to improve the stability of the slopes,

various retaining wall systems or buttresses were used (Figure 12). These were usually situated at the fore, or toes, of the landslide. Often, the slope gradient was relieved by an intermediate level supported by retaining walls, piles, micropiles, or anchors. These methods were adapted to the parameters of the particular landslide and the depth of the bedrock layers. These included surface and internal drainage systems, drilled piles, continuous flight auger (CFA) piles, micropiles, anchors, gabion-type retaining walls, and different types of surface and internal drainage. The specification of the remedial method was dependent on road localization and other local conditions. Protected infrastructure or roads were located in the lower, middle, or upper parts of landslides. At landslide area nos. II, IV, V, and VI, the roads were located close to the rivers at the head part of the landslide. Therefore, the retaining gabion walls were built in the highstrength bedrock layers along the rivers. These safeguarded the roads against ground movements and riverbanks against erosion processes. In some cases, gabions were additionally built on the pile foundations. In landslide area no. II, the gabion retaining wall along the river was built on 300 piles drilled to a depth of 6 m at a distance of 200 m. In this landslide area, two

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Figure 11. LEM slope stability analysis, Janbu method, stabilized landslide no. 16, FoS = 1.52.

retaining walls on pile foundations below and above the road were constructed. The second retaining wall with a length of over 30 m was set above the road on 60 vertically drilled piles 300 mm in diameter in three rows to a depth of 11 m. On landslide nos. 1–6 (area no. I), the gabion retaining wall was built along the river only to protect its banks. The head part of this landslide was partially stabilized by an anchor with a length of 6–20 m installed into the bedrock. The slope protection on this landslide was accomplished with Geobrugg high-tensile wire mesh supported by anchors (Figure 13). At the large and dangerous landslide area no. VI, CFA piles with a diameter of 600 mm were drilled into the bedrock in six rows above the road and three rows below the road to a depth of 15 m. The upper parts of the piles were connected by a special supporting construction of reinforced concrete. Diﬀerential localization of the retaining walls was required at Strzeszyn, Dukla, and Strachocina (area nos. IV, VII, and X) due

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to the need for protection of the slope above the landslide zone. The retaining walls on these landslides were founded on 300–600-mm-diameter piles drilled into the bedrock. On landslide no. 18 in Strzeszyn (area no. IV), a gabion retaining wall was built on a foundation of 43 drilled piles to a depth of 15–17 m. The 600-mm-diameter piles were situated in two rows, and the upper parts were connected in the reinforced concrete construction. The ﬁrst row was tilted by 11 degrees, and the second was vertical. The remediation also included a new culvert under the road, a surface, and internal horizontal drainage system at a length of 25 m equipped with ceramic Poltegor-type ﬁlters. For landslide no. 21 in Dukla, the remedial project was prepared to safeguard Dukla’s historic Saint John Chapel. The remediation was designed by the Building Research Institute ITB Warsaw. It included internal and surface drainage of the slope and construction of a retaining wall on a micropile foundation to a depth of 10–12 m. The drainage system was built as a system

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Figure 12. Design of pile retaining wall, landslide no. 25 in Strachocina (area no. X). A-A = longitudinal cross sections; B-B = transverse cross sections. Six landslide remediation projects were designed and implemented (Bednarczyk, 2008). The remedial works for area nos. I, II, IV, V, VI, VII, VIII, IX, and X are listed in Table 2.

of trenches filled with stones and geotextiles, collecting rainwater downhill. The renovation of the chapel also included the construction of a retaining wall and a cave with two water intakes below the chapel (Bednarczyk, 2014). In all the road stabilization projects, it was important to lead the rainwater outside the landslide zone. This was realized by building new surface drainage systems and new culverts under the roads that were beneficial to the slope stability. The drainage system at landslide no. 16 in Sekowa had a length of 300–500 m. However, the local road district decided to cut its size for economic reasons. This was not beneficial to the slope stability. The start time of the remediation works also played an important role in

the effectiveness of the stabilization. During stabilization works in late autumn, mass movements increased to 60 mm at landslide no. 16 following intense rainfalls (Figures 14 and 15; Table 2). These movements appeared to be dangerous during the stabilization. It was hoped that their impact on the stabilization effort was low, and this was confirmed to be true during further monitoring measurements over the next 10 years. It was detected that the size of the observed movement at landslide no. 16 was reduced to ±5 mm within 11 years’ time. This is also reflected in the values of the groundwater pore pressure, reduced from 45 kPa before the stabilization works to 30 kPa post-stabilization. The depth of the groundwater also

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Bednarczyk

reached deeper levels, from 1.3 to 2.2 m. The values of pore pressure after remediation decreased in some places at the Szymbark landslide from 50 to 15 kPa. However, despite the partial stabilization at this landslide, a displacement of 13–20 mm occurred. This increased following the record rainfall in May–June 2010 during the ﬂood in southern Poland. DISCUSSION

Figure 13. Partial stabilization of landslide nos. 1–6 in Szymbark (area no. 1).

Figure 14. Stabilization works at landslide no. 16 in Sekowa (area no. II).

Figure 15. Control of stabilization works at landslide no. 16 in Sekowa (area no. II).

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The methods used for stabilization reduced the risk and secured threatened public roads; they were checked by control monitoring measurements following the remedial works and compared with displacements before and during stabilization (Table 2). The values of the observed movements ranged from 1 to 138 mm before the stabilization works. In some cases, they increased to more than 61 mm during the stabilization works, often caused by conducting construction works at the end of the year in adverse weather conditions. The displacement was usually reduced to a few millimeters after remediation. However, extremely high displacements of 40 cm–2 m (registered by tape) were observed in May–June 2010 at landslide no. 17 in Strzeszyn (area mo. IV). It was hoped that the ground movements occurred below the safeguarded road area. The partial stabilization that was implemented at landslide nos. 1–6 in Szymbark (area no. I) has not fully safeguarded the risk area. This was confirmed by record-high displacements of 10 mm, which were recorded by an automatic early warning system after high precipitation on June 4, 2010. At landslide area nos. III and IX, only temporary low-cost road repairs were decided on. Stabilization was fully effective at landslide no. 16 in Sekowa (area no. II), landslide no. 20 in Lubatowa (area no. VI), landslide no. 22 in Tarnawa (area no. VIII), and landslide no. 25 (area no. X). Following the remedial works, displacements varied within only a few millimeters. At landslide no. 17 in Strzeszyn (area no. IV), landslide stabilization safeguarded the road area. However, the lower part of the landslide was activated in June 2010 after extreme precipitation of over 100 mm/m2 . At the partially stabilized landslide Nos. 1–6 in Szymbark (area no. I), the scope of the movements was limited, but ground displacements were still observed. The largest displacements of over 70 mm in 1 month were registered during the remediation works at the Sekowa landslide. The acceleration of movements occurred due to the undercutting of the landslide’s lower part during stabilization in the rainy autumn season. However, after stabilization, these displacements were reduced to a few millimeters. The research showed that stabilization was difficult at this landslide area and should be continuously observed.

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Evaluating Landslide Remediation Methods

Figure 16. Comparison of conventional landslide monitoring results.

The interpretation of the conditions of flysch landslide engineering geology required the careful design implementation of individually designed investigation methods. The Carpathian landslides were reactivated many times, and therefore it is difficult to predict their real behavior and activity without precise monitoring data. Serious geotechnical problems caused by flysch sediments occur widely in many other European countries. The interpretation of landslide triggers based on in situ monitoring, laboratory tests, and numerical modeling is very important (Cano and Tomas, 2013; Berti et al., 2017; and Marjanović et al., 2018). The conclusions from the research indicate that investigation methods should be chosen with respect to landslide geology type, size, and activity. Possible remedial works should be identified after comprehensive investigations and a reasonably long period of monitoring measurements. This is especially important in the Polish Carpathians, where these measurements were not widely performed. In the presented projects, the author conducted monitoring measurements for a relatively long period of time. This was possible due to the other ongoing projects in the surrounding areas. Some of the landslide locations were controlled before, during, and after remediation. Conventional monitoring was performed in 30 places over 12 years, beginning in January 2006. A comparison of all the conventional monitoring results for each of the 25 investigated landslides is presented in Figure 16. The movement depths varied from 3 to 18.2 m, and the observed cumulative displacements ranged from 2 to 186 mm. The highest pore pressure values at the slip surfaces were 35–100 kPa. The groundwater level depths varied from 0.2 to 4.9 m. However, relatively significant time lags between the measurements made representative interpretation of the measured parameters difficult. The results detected the

cumulative displacements of 2–186 mm at depths of 3–18 m. The ground movements registered after heavy rainfalls were accompanied by shallow groundwater levels and significant pore pressure variations. Intensive rainfalls and pore pressure fluctuations were the main activating triggers. Usually, the activation of movements occurred within 24–48 hours after heavy rainfall and an increase of pore pressure up to 100 kPa. However, the number of monitoring points was limited. In the author’s opinion, pore pressure variations, in some cases higher than 100%, could be used as an important early warning indicator for landslides. The monitoring results that were performed were beneficial for the design and control of landslide stabilization works. The monitoring results also helped in the definition of boundary conditions in the slope stability analysis. Numerical modeling allowed for the prediction of the expected ranges of displacements and control of stabilization methods. Before counteraction, displacements varied from a few millimeters to 138 mm. During the stabilization, movements increased from a few millimeters up to 61 mm. Hopefully, movements were limited to a few millimeters 4–11 years after the stabilization, except for the landslide in Szymbark. The stabilization or remediation methods that were implemented were fully effective at five landslides in Dukla, Sekowa, Lubatowa, Tarnawa, and Strachocina. Displacement after stabilization varied within only a few millimeters. At two landslides in Szymbark and Strzeszyn, the remediation works partially safeguarded the road area but were not fully effective in places far from the roads. At two other landslides in Sitarzowka and Wapienne, temporary road repairs did not protect against landslide activity. However, further limited use of these roads was possible. CONCLUSIONS The research conclusions show that flysch landslide remediation should be designed very carefully. Monitoring should be carried out in advance over a reasonably long period of time, at least a full year, because of high costs and the potential to avoid possible mistakes and improve the effectiveness of the design works. The implemented research methods of GPR scanning, drilling of high-quality boreholes, monitoring, and modeling could help in the design of more effective and reliable stabilization works. Stabilization should be controlled through monitoring. Instrumentation types and the minimum amount of required monitoring should be chosen carefully by engineering geologists. In places with important infrastructure or roads that are threatened by landslides, the implementation of online systems could lower the landslide risk

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285

286

Dukla, 21

VII

2007–2018

0.82

0.10

0.91

Lubatowa, 20

2007–2018

0.31

VIII Tarnawa Dolna, 22 2006–2018

Sitnica, 19

2007–2018

0.50

2.20

0.48

Strzeszyn, 18

2006–2018

10–15

8–16

12–14.3

1.2–5

1.4–6

2.7–5.1

1.3–15

2007

2006

2009

2008

2007

2009

3–5 (275)

No data

56.6 (117)

4.7 (368)

1.8 (369)

26 (607)

3–138 (1342)

5–110 (3,237)

Displacement/Time after Stabilization, mm/(d) Stabilization Methods

Partial stabilization of landslide head part with road—gabion retaining walls along the river, anchors, internal and surface drainage systems, new culverts 61 (154) 5 (3,332) Stabilization of lower landslide part—gabion wall on pile foundation along the river, retaining wall on pile foundation above the road, surface drainage systems 0.6–1 (117) 2–6 (3,526) near the Stabilization of road road, 400–2000 below above the landslide road zone—gabion wall on pile foundation protecting the road, internal and surface drainage system, new culvert 6.5 (127) 2.5 (3,538) Stabilization of lower landslide part with road—gabion wall above the road, gabion wall along the river, new culvert >15 (166), inclinometer 6.1 (3,236), repaired Stabilization of lower damage inclinometer landslide part with road—piles, internal and surface drainage system, new culvert No data 3.8 (4,672) Stabilization of chapel above the landslide area—retaining wall on micropile foundation above the landslide, surface and internal drainage systems 1–2 (227) 5.6 (3,854) Stabilization of road crossing central landslide area and buildings in risk zone—retaining wall piles on pile foundation, anchors, gabion walls along the river, gabion walls on pile foundation, internal and surface drainage system

7–54 (91)

Depth, No. of Monitoring Displacement/Time Displacement/Time Monitoring Volume, m (1 m = 39.4; Stabilization Measurement before during 3 Date Series Stabilization, mm/(d) Stabilization, mm/(d) Time million m 1 mm = 0.0394 in.)

2006–2018

Sekowa, 16

Szymbark, 1–6

Landslide Location, Landslide No.

Area. No.

Table 2. Landslide remedial works.

Bednarczyk

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0.57 Strachocina, 25 2008–2018 X

11–18

2009

2–5 (2,955) 4–6 (531) 3–8 (320)

Temporal stabilization of lower landslide part by concrete retaining wall built with Larsen elements Stabilization of area above the landslide for foundation design—three retaining walls on pile foundations, anchors, internal and surface drainage systems No data No data >100 1.6 Sitarzowka, 24 IX

8–20

2012

—

Displacement/Time after Stabilization, mm/(d) Displacement/Time during Stabilization, mm/(d) Landslide Depth, No. of Monitoring Displacement/Time Area. Location, Monitoring Volume, m (1 m = 39.4; Stabilization Measurement before Date Series Stabilization, mm/(d) No. Landslide No. Time million m3 1 mm = 0.0394 in.)

Table 2. Continued.

Stabilization Methods

Evaluating Landslide Remediation Methods

and provide an early warning. The online system installed in Szymbark was the first of its kind in Poland. It allowed for the continuous observation of landslide behavior and triggers as well as the definition of early warning levels. The results of the research were delivered to the local road authorities and owners of the infrastructure. This information contained details about the threats in selected locations. The remediation of large and active Carpathian flysch landslides is not an easy task and is not always possible because of economic concerns. The decision requires high-quality engineering geology data and the recognition of landslide size and activity. The research detected that the full stabilization of some of the studied landslides could be difficult or even impossible due to the size, depth, and scope of the movements. Other landslides located in areas of high importance must be carefully monitored in advance of stabilization works. This monitoring is especially important because of the cost of remedial works. Careful attention should be paid to the interpretation of monitoring results. ACKNOWLEDGMENTS The author would like to acknowledge the European Investment Bank and the Polish State budget for financing within the SOPO Landslide Counteraction Project. The installation of the first online monitoring system in the Polish Carpathians would not have been possible without the EU Innovative Economy Project UDA-POIG.01.03.01-00-043/08, financed by the European Agency for Regional Development. I would like to express my special thanks to the local authorities in Gorlice, Zembrzyce, Sekowa, Dukla, and Krosno as well as the Polish Oil and Gas Company in Sanok, the Polish Geological Survey, the Carpathian Branch (PGI) in Krakow, and the Institute of Geography and Spatial Organization Polish Academy of Sciences Research Station in Szymbark (PAS) for their cooperation in the presented research. I would like to extend my special thanks to Dr. Wojciech Raczkowski (PGI), Prof. Eugeniusz Gil (PAS), and the reviewers of this manuscript for their valuable comments and their willingness to share their experience.

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Improvement of Internal Stability of Alluvial Clay from Famagusta Bay, Cyprus, Using Copolymer of Butyl Acrylate and Styrene MOHAMMAD REZA GOLHASHEM* ERIS UYGAR Department of Civil Engineering, Engineering Faculty, Eastern Mediterranean University, Famagusta, North Cyprus, via Mersin 10 Turkey

Key Terms: Internal Stability, Erosion, Aqueous Polymer, Alluvial Clay ABSTRACT The internal stability of alluvial clays may be significantly compromised during a heavy rainfall due to infiltration of surface water causing sudden inundation, softening, and loss of erosion resistance or mechanical strength. Most of the available stabilization methods for clay soils employ pozzolanic or other cementitious binders, creating a chemically bound clay-admixture matrix. These admixtures commonly require a curing period after placement and compaction. Alternatively, aqueous polymers can be used in diluted form without any need for a curing period. Aqueous polymers can form agglomerations of clay particles enclosed in a matrix of polymer chains, held together by electrostatic and hydrogen bonding, improving erosion resistance. In this research, an aqueous polymer, namely, copolymer of butyl acrylate and styrene (CBAS), is mixed with alluvial clay sampled from Famagusta Bay, Cyprus, and the clay stability test is performed as a basis for assessing the degree of improvement on erosion resistance. A time-dependent approach for the evaluation of test results is followed to increase the accuracy of the analysis of the actual behavior observed during the test. A significant improvement in the erosion resistance is observed in treated test specimens. The mode of collapse of specimens during the clay soil stability test when aqueous polymer is used also changed from being gradual cracking and slaking to explosive. The swelling behavior and the effect of drying on the erosion resistance are also observed in the testing program. X-ray diffraction analysis and Fourier transform infrared spectroscopy are performed for observation of the effect of CBAS on microstructural interactions, such as electrostatic bonding and changes in soil fabric.

*Corresponding author email: mohammad.golhashem@cc.emu.edu.tr

INTRODUCTION Alluvial clays are considered to be problematic due to their composition, variation in their deposition, and the nature of source rock characteristics. Such soils may also have a high volume change character with an affinity to attract moisture, which may lead to distress in the structures, highways, slopes, and so on (AlHomoud et al., 1995: Basma et al., 1998; Inyang et al., 2007; and Miao et al., 2017). Various methods have been considered by geotechnical engineers for surficial stabilization of clays. Among all methods used in practice, it is common to utilize shallow mixing with a stabilizing agent (pozzolanic or chemical admixture) followed by compaction. The intent is to improve the interactions within the clay fabric and the interactions of these with other ions present in the medium such that permeability, shear strength, and overall durability of the compacted clay are improved (Lahalih and Ahmed, 1998; Inyang et al., 2007; Yazdandoust and Yasrobi, 2010; Maaitah, 2012; Soltani, 2016; and Tajdini et al., 2017). Based on the properties of a particular clay and the surrounding environmental conditions, various stabilization agents might be considered. The main goal of the stabilization is to ensure the internal stability of the clay fabric by controlling the water adsorption (Hudyma and Avar, 2006; Anagnostopoulos, 2007; Estabragh et al., 2011; Seco et al., 2011; Huang and Liu, 2012; and Miao et al., 2017). The internal stability of clay is strongly correlated with the stability of the fabric and agglomerations, which can be achieved by the stabilization of the macropores with a filler or by provision of cementitious (chemical or electrostatic) reactions in macro- and micropores. The use of stabilizing agents, such as cement, fly ash, and lime for improvement of clay internal stability, has been well documented, successfully providing stability through both mechanisms. The use of other nontraditional stabilizers, such as enzymes, resin, and polymeric fibers, has also been reported (Anagnostopoulos, 2007; Estabragh et al., 2011; Liu et al., 2011; Arasan et al., 2017; Ganapathy et al., 2017; and Pu et al., 2018).

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Golhashem and Uygar

In recent years, as the awareness of environmental sustainability concerns has increased, the use of aqueous polymers as a new approach to prevent environmental problems caused by the instability of clay soils and provide soil stability has started to attract more attention (Gamble, 1971; Bae et al., 2006; Seco et al., 2011; Yilmaz et al., 2012; Zezin et al., 2015; Soltani, 2016; Tajdini et al., 2017; and Xiao et al., 2017). Aqueous polymers can easily be diluted in water and applied in shallow mixing and compaction of clays for provision of engineered fills (Azzam, 2014; Rezaeimalek et al., 2018). Compared to the method of applying traditional additives in dry form, a diluted aqueous polymer has the advantage of being effectively absorbed by the clay. This eases the mixing process prior to compaction and eliminates the need for a curing period after application (Inyang et al., 2007). Aqueous polymers can also provide effective dust control (Bae et al., 2006; Xu et al., 2017; and Ding et al., 2018). The internal stability of an expansive clay treated with an aqueous polymer was studied by Liu et al. (2009), who observed considerable improvement of the integrity of the clay treated with a sprayed polymer solution. Liu et al. (2009) reported that when a surface coating of crumbs treated by a polymer is attained, it is likely to observe an explosive mode of collapse on adsorption of water molecules during the clay stability test. Hence, the ability of an aqueous polymer to restrain clay aggregate during adsorption of water is an important factor in assessing its effectiveness. With the control of water adsorption of the clay aggregates, internal stability of the clay structure, on saturation, is improved (Liu et al., 2009, 2011; Seco et al., 2011). However, it is also critical to note that the stability of the treated clay on drying is also important, that is, when there is significant loss of moisture. Inyang and Bae (2005) studied the water adsorption of a clay treated with aqueous polymer, indicating reduction in the drying rate and the rate of crack formation. Yazdandoust and Yasrobi (2010) provided supporting evidence in the results from a polymer-stabilized expansive clay subjected to swell-shrink cycles. They reported that the presence of polymer helps clay particles to move closer together after each swell-shrink cycle. Aqueous polymers may also provide a permanent improvement on clay stability due to their absorbency onto clay microstructure, improving swelling behavior (Williams et al., 1968; Inyang and Bae, 2005; and Inyang et al., 2007). Clay stability is traditionally measured by various methods (Bronick and Lal, 2005; Liu et al., 2009; Cañasveras et al., 2010; and Jozefaciuk and Czachor, 2014). but omits the impact of soil compaction. In this research, the internal stability and erosion resistance of a compacted alluvial clay treated with

290

aqueous polymer, namely, copolymer of butyl acrylate and styrene (CBAS), is studied through the clay stability test. The internal stability of the clay will vary depending on its initial water content, as this directly affects both the quantity and the rate of potential water adsorption. Hence, the testing strategy is designed in a way to consider both the variation in the amount of aqueous polymer added and the initial water content of the specimens. Another important aspect of internal stability measurements is the collapse mode on inundation. The collapse mode is considered to provide indirect information as to how well the polymer is absorbed by the clay macropores and microstructure. Therefore, the collapse mode is carefully observed during the clay stability test using video recordings to enable evaluation of any change in performance of the specimens against inundation. In addition, testing for internal stability is carried out for up to 1 hour, and the analysis method is modified to enable assessments to be carried out at any time to determine time-dependent changes in the internal stability of specimens. The clay stability tests are carried out using specimens of identical size, prepared following the same compactive effort and initial water content. The change in physical properties such as density and void ratio are measured as the specimens are allowed to dry through phases from plastic limit to shrinkage limit. One-dimensional swell measurements are also carried out to investigate the impact of the treatment carried out on the volume change characteristics of the test specimens. In order to complement the discussions on the interactions formed at the clay-polymer interface and the activity of the polymer added, electrical conductivity and ion concentration measurements are performed on the untreated and treated clay slurry. Further analyses using Fourier transform infrared spectroscopy (FT-IR) and X-ray diffraction (XRD) of the test specimens are also carried out. MATERIALS AND METHODS General Testing Strategy In order to assess the effectiveness of CBAS for improving the internal stability of clay, a test program comprising test specimens treated with various concentrations of CBAS is implemented. For this purpose, the clay stability test is considered to be a practical, quick, reliable method (Liu et al., 2009). All tests are performed on 25 approximately identical specimens for each group: Gr-1, untreated clay; Gr-2, clay treated with 0.5 percent CBAS; Gr-3, clay treated with 1 percent CBAS; and Gr-4, clay treated with 2 percent CBAS.

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Improvement of Internal Stability of Alluvial Clay

The effect of moisture content changes on the treated clay is considered to be another important aspect of studying the effectiveness of the CBAS. Therefore, subgroups are formed to enable measurement of the effect of drying on the internal stability. In addition, the volume change behavior is studied using onedimensional swell tests. In order to investigate the microlevel interaction between CBAS and clay, XRD and FT-IR tests are performed on untreated and treated samples. XRD analysis is used to define the mineralogy of the alluvial clay and to look for new compounds that might have formed after the treatment. FT-IR analysis is performed to further investigate bond formation.

CBAS CBAS is generally used in the industry as part of the treatment for decorative coatings and as an effective adhesive. It has high alkali resistance and low water adsorption and is non-hazardous and inert. CBAS is considered to be an effective admixture that can interact within the clay through two mechanisms: (1) by forming films or webs around clay agglomerations in macropores or (2) by forming continuous or discontinuous films or webs within the clay fabric when it is thoroughly adsorbed into clay microstructure. It is also important to note that, depending on the ion concentration around a clay double diffuse layer, CBAS is likely to be absorbed in between clay particles by electrostatic interaction. The influence of the resulting film or web of CBAS, despite its being flexible, is likely to help keep the clay agglomerations and clay fabric together against internal forces that will develop on water adsorption. This can reduce and slow down the volume change (potential) of the clay, and it can also be expected that there would be improvements against softening as well as wind and water erosion. CBAS would improve accumulation of clay agglomerations and promote aggregate formation, the stability of which are key for wind and water erosion resistance (Williams et al., 1968; Barthès and Roose, 2002; Niewczas and WitkowskaWalczak, 2005; and Xiao et al., 2017). The general chemical formulation of CBAS is presented in Figure 1. CBAS is a waterborne (aqueous) polymer. It has a solid content of 50 ± 1 percent, a pH value of 7.0–9.0, a viscosity of 2,400–4,600 mPa·s, and a density of 1.02 g/cm3 . It has medium viscosity compared to other polymers of similar type, and as it is a copolymer comprised of hydrophilic and hydrophobic blocks, it has higher penetrability into and around clay agglomerations, providing effective adsorption onto these surfaces (Panova et al., 2017).

Figure 1. Chemical formulation of CBAS.

In order to analyze the interaction between the clay and CBAS, test specimens are produced in slurry form and tested for the resulting electrical conductivity and the concentration of total dissolved solids (total dissolved anions and cations [TDS]) using a Bante900 Multiparameter Water Quality Meter instrument. The specimens are prepared as one part of dry clay in grams to five parts of distilled water in milliliters, including CBAS added as a percentage of the dry weight of clay. The results are presented in Figure 2. Timebased measurements show that the interaction between clay and polymer forms almost instantly. Due to the anionic character of the CBAS, it is considered that there is electrostatic interaction between CBAS and the readily soluble cations in clay and water molecules. The interaction is such that even with the slightest addition of CBAS (0.5 percent), there is a significant reduction of electrical conductivity and TDS. The drop in electrical conductivity is considered to be due to the formation of polymer chains leading to an increase in the tortuosity within the soil fabric. However, as the quantity of CBAS is increased, it is observed that there is a smooth rise in the electrical conductivity measurements, which is interpreted to be caused by the increase in the electrical conductivity of the pore fluid due to the CBAS added.

Figure 2. Electrical conductivity and total dissolved solids with respect to CBAS for Famagusta alluvial clay.

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Figure 3. Change in Atterberg limits due to addition of CBAS for Famagusta alluvial clay.

Alluvial Clay The clay samples used in this research were selected from surficial alluvial fan deposits that are extensively present along the shorelines of Famagusta, Cyprus. The index properties of the alluvial clay are measured as specific gravity = 2.69 (in accordance with ASTM D854-14), shrinkage limit = 19 percent (in accordance with BS 1377-2:90), plastic limit = 30 percent, and liquid limit = 64 percent (in accordance with ASTM D4318-17e1). The soil classification in accordance with ASTM D2487-17 is obtained as CH (highly plastic clay), composed of 62 percent clay and 38 percent silt. The standard Proctor test results indicated that the optimum moisture content and maximum dry density for the clay are approximately 25 percent and 1.56 g/cm3 , respectively. The plasticity of the treated clay is considered to be important, as it affects the workability while mixing and during compaction. Traditionally, it is considered that a stabilization method lowering the plasticity and reducing the water retention of the clay would suffice very well for engineering applications (Petry and Little, 2002). Hence, the treated clay is tested for the change in its plasticity properties with the increase in the percentage (by dry mass of clay) of CBAS diluted in distilled water. The results are presented in Figure 3. The results indicate a modest change in the plasticity of the clay when CBAS is added up to approximately 2 percent by dry mass of clay, beyond which there is a small rise in both the liquid limit and the plastic limit. Sample Preparation for Clay Stability Test CBAS (percent dry mass of the clay) is diluted in distilled water and added to form specimens of each test group. In order to ensure that the test specimens are approximately identical, they are extruded from

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Figure 4. Change in void ratio with respect to water content during drying process for Famagusta alluvial clay.

samples compacted to the water content at plastic limit using standard Proctor energy in accordance with ASTM D698-12e2. The test specimens are cylindrical in shape and trimmed to a diameter of 38 mm and a height of 10.5 mm. After trimming to the test size, all test specimens are placed in a vacuum desiccator for 24 hours. For the preparation of subgroups at various initial water contents, the test specimens are subjected to vacuum drying in the desiccator and weighed at regular intervals of 1 to 2 hours until the required water content for testing is obtained. During vacuum drying, silica gel is placed at the bottom of the desiccator to help absorb the moisture from the specimens. At the end of this process, four sub-groups of test specimens are formed with the following approximate initial water contents: 11, 16, 22.5, and 28 percent. This provided a range of initial water contents between plastic phase and solid to semi-solid phase based on Atterberg limits, which are considered to be representative of the moisture variation that can be observed for superﬁcial soils through seasonal changes. In order to aid comparison of diﬀerences in the results for untreated and treated specimens, an additional group of treated specimens with 5 percent CBAS is also tested. The data obtained on the change of void ratio of specimens with respect to change in water content during the drying process are presented in Figure 4. As observed from the data, the drying characteristics of the specimens varied due to the addition of CBAS such that the increase in the percentage CBAS resulted in a decrease in the change of void volume as the specimens are allowed to dry. Hence, at the same water content, this change in behavior allowed for a greater volume to be occupied by voids within the specimens. In other words, this measurement shows indirectly that the addition of polymer reduces the shrinkage of clay during drying by supporting the void volume (macropores)

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Figure 5. Illustration of clay stability test setup.

internally within the fabric of the clay. This provides a positive practical implication such that the reduced shrinkage would also mean fewer desiccation cracks and increased erosion resistance and stability on inundation.

as provided in the following equations: f i=1 ai ci + 100aα Kf = ; 25 ci =

Methodology and Analysis of Clay Stability Test The methodology of the clay stability test was modified to enable measurements to be taken for a longer period of time to improve precision in the assessment of the performance of treated clay specimens, as even at low levels of treatment, almost all of these specimens remained intact during the first 10 minutes of the tests carried out. An illustration of the clay stability test setup is presented in Figure 5. The test method involves immersion of the 25 test specimens (all at once) in distilled water and recording observations on the stability of the test specimens with respect to time. In addition, a review is also performed by observing the tests for a second time from video recordings to enable a fair evaluation of the collapse behavior of the specimens. In order to enable calculation of the clay stability index over a longer period of time, observations at time periods of 0–10, 10–20, and 20–30 minutes are noted. The observations include recording the number of collapsed samples within each set of 25 samples. These data provided an opportunity to create a clay stability index assessment based on time, which are referred to as K10 , K20 , and K30 . Measurement of the clay stability index at various time periods enabled evaluation of the internal stability for long-term behavior. It also increased the precision of measurements by allowing a fair comparison between the treated specimens groups. Accordingly, the equation for calculation of clay stability index (Liu et al., 2009) is modified to allow calculation of the clay stability index for various time periods

100 100 + (ti − 1) , 2t f tf

(1) (2)

where Kf = the clay stability index in percentage and f is the time at which the clay stability index is calculated, ti = time in minutes and increases with 1-minute intervals from t1 = 1 minute to the time at which the clay stability index is calculated, tf = time at which the clay stability index is calculated, ai = number of samples that collapsed at time interval between ti−1 and ti , aα = number of samples that remained stable at the end of the test at time tf , and ci = percentage weight of number of collapses observed at time interval between ti−1 and ti , in the clay stability index calculated at time tf . The stability index, Kf , is calculated as a function of the percentage of samples that remain stable at the time when it is calculated. A drop in the value of Kf , with time at which it is calculated, shows that, as the test progressed, a fewer number of test specimens would remain stable. As can be observed from Eq. 1, as the time period at which the clay stability index is calculated increases, the impact of the collapses observed at earlier stages of the test on the clay stability index reduces. This provided the opportunity to perform a fair assessment of long-term clay stability by calculation, taking into account the number of collapses in the later stages of the test as well. For the same data, if the clay stability index were calculated for 10, 20, and 30 minutes using Eqs. 1 and 2 and assuming no change in the stability of the specimens after 10 minutes, the results would reﬂect a drop in the stability over time. Hence, it can be concluded that earlier collapse reﬂects a lower clay stability index.

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Golhashem and Uygar Table 1. Clay stability test results for Famagusta alluvial clay. Time-Assessed Clay Stability Index and Group K10 Gr-1 Gr-2 Gr-3 Gr-4 K20 Gr-1 Gr-2 Gr-3 Gr-4 K30 Gr-1 Gr-2 Gr-3 Gr-4

Initial Moisture Content (%) 11

81.6 73.8 93.8 92.2

75 97.2 97.4 100

63.9 59.1 83.5 87.7 52.5 52.7 77 85.1

22.5

Percentage of Explosive Collapse 28

22.5

73 100 100 100

70 100 100 100

10 50 100 100

8 42 33 —

0 — — —

61.5 90.6 92 100

57.2 100 100 100

53.7 100 100 100

25 50 78 100

8 50 50 —

0 — — —

57 83.8 88.1 100

53.6 100 100 100

45.2 100 100 100

29 60 78 100

8 0 60 —

0 — — —

Gr-1 = untreated clay; Gr-2 = clay with 0.5% CBAS; Gr-3 = clay with 1% CBAS; Gr-4 = clay with 2% CBAS.

The mode of collapse of individual specimens observed during the test is also noted for each time period. Two modes of collapse are considered: gradual cracking and explosive. The former is when a specimen cracks and disperses or slakes gradually, with a tendency to keep a resemblance of its original shape even after collapse. The latter mode of collapse is one that doesn’t show a clear indication of collapse until it happens suddenly. It includes major cracking and release of air bubbles, often followed by complete disintegration (Liu et al., 2011). From the test results collected, the mode of collapse observed is quantified by calculating the percentage of specimens collapsed by the explosive mode within the total specimens collapsed. The modes of collapse observed can be used to assess how the CBAS interacted with the clay fabric. Gradual cracking occurs when a film or web of polymer is formed in the macropores limiting the water ingress in between clay particle associations. On the other hand, the explosive mode is associated with the fine establishment of the polymer in the form of a film or web within the clay particle clusters. One-Dimensional Swell Test In order to elaborate more on the effect of CBAS in the volume change behavior of the test specimens, the one-dimensional swell test is performed on four test groups of specimens in accordance with ASTM D4546-14e1 by considering free swell method under a 7-kPa surcharge. A control group of untreated specimens, two groups of specimens treated with a low percentage of CBAS (2% and 5%), and a group of specimens treated with a high percentage of CBAS (10%)

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are used in the tests. All test specimens are prepared at a dry density corresponding to −2 percent on the dry side of the optimum moisture content, which is approximately 22.5 percent, following the standard Proctor compaction effort in line with the test specimens prepared for the internal stability measurements. This initial state for test groups will provide a representative laboratory behavior for the performance of CBAS for a typical engineered fill. As part of this test, change in the specimen height on inundation with distilled water is measured with respect to time, and plots of the ratio of change in height ( H) to original height ( H0 ) versus logarithm of time (log t) are studied for quantification of the performance of the treatment at various percentages. RESULTS AND ANALYSIS Internal Stability The results of the clay stability tests are presented in Table 1. The calculated K values indicate that the test specimens treated with CBAS have improved stability, with an increasing trend as the percentage addition of the CBAS is increased. Even with the minute amount added in Gr-2, the clay stability index is improved considerably for a given initial water content. In order to highlight the degree of improvement for treated test specimens (Gr-2 to Gr-4) with respect to control group (Gr-1) specimens, the ratio of the clay stability index of treated specimens to the clay stability index of the control group specimens is calculated. As depicted in Figure 6A–C, the improvement is more prevalent as the time period at which the K value

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Figure 6. Variation of the clay stability index at (A) 10 minutes, (B) 20 minutes, and (C) 30 minutes with respect to CBAS for Famagusta alluvial clay.

Figure 7. Variation of the clay stability index at (A) 10 minutes, (B) 20 minutes, and (C) 30 minutes with respect to initial water content for Famagusta alluvial clay.

calculated increases, conﬁrming the improved longterm stability of the treated test specimens. The impact of the initial water content on the test results is notable. In Figure 7A–C, the plot of the test results indicates that the internal stability of all treated test specimens is improved with respect to the increase in the initial water content prior to testing. It is also interesting that CBAS seems to be most eﬀective for Gr-2 test

specimens, which consists of only 0.5 percent of CBAS by dry mass of clay. As control specimens became less stable with increasing initial water content, the treated samples had an improved performance, reﬂecting an improvement in resistance against inundation even at high initial water content levels. When these results are examined from another point of view, that is, with respect to a decrease in the

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initial water content, it can be observed that there is an improvement in internal stability for the control specimens. It can be postulated that the improvement in the performance of control specimens is due to the increase in the affinity of the clay mineral layers to adsorb water, which initially will be tolerated well by the clay microstructure, causing an apparent stability in the clay-water electrolyte system. However, with the degree of saturation of the test specimens increasing, the adsorbed ions on the diffuse double layer around the clay particles will lead to expansion of the interparticle pore space and increase in the repulsion forces between clay particles (Mitchell and Soga, 2005). This phenomenon reduces the stability of the clay structure as depicted by a drop in the clay stability index when it is measured at various time periods as K10 , K20 , and K30 in the clay stability test. For treated specimens, CBAS addition leads to a reduction in the interaction between the clay particles and particle associations with the water, and as a result, the clay fabric will be affected less. However, an increase in the absorbed water will lead to an increased interaction between the clay-water-polymer electrolyte system. Hence, the increased interaction is reflected as a reduction in the clay stability index. In this mechanism, it is notable that the reduction in the clay stability index decreases as the polymer added increases, which would imply that as the amount of polymer is increased, lower initial water contents can be tolerated for the same level of internal stability. This is especially important, as the polymer treatment in civil engineering projects can be performed for superficial clays, which may be exposed to significant moisture changes. A general trend was observed through the clay stability test in terms of mode of collapse such that the test specimens with higher CBAS content tended to reflect the explosive mode of collapse more than the other test specimens. The initial water content of the specimens did not appear to have a clear impact on the mode of collapse. However, it is observed that explosive collapse is more common with the specimens having lower initial water content (water contents 11% and 16%). On the other hand, the time period at which the internal stability is evaluated seems to have a correlation with the collapse mode such that most of the collapses observed after 10 minutes are recorded to be due to gradual cracking and slaking rather than the explosive mode of collapse. It is observed that the interaction between CBAS and clay is effective on the collapse mode. For treated specimens, the formation of a web of CBAS chains within the macropores may have generated an initial resistance against cracking and slaking immediately after the immersion of specimens in water. As the test progressed, the adsorbed water–induced volume increase in the specimens

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Figure 8. One-dimensional swell curves for Famagusta alluvial clay.

created an increase in internal stress and caused the explosive mode of collapse in most of the treated specimens. The observed behavior shows that an effective web of polymer chains was formed around clay fabric as well in most of the specimens. One-Dimensional Swell Test The stages observed from a typical one-dimensional swell test on clay soil (initial, primary, and secondary swells) are defined by Nagaraj et al. (2010). The results from the one-dimensional swell tests are presented in Figure 8 and are interpreted with respect to initial, primary, and secondary swell behavior. The data obtained indicate that the initial swelling of untreated specimens and specimens with low percentages of CBAS (2% and 5%) are quite similar, while the initial swell of the highpercentage CBAS group is significantly less. This initial difference in the swelling behavior corresponds to the stages in which the macropores start to become saturated; it also indicates that the response of the film or web of CBAS to the internal stresses created by water adsorption is not as stiff unless CBAS is introduced beyond a certain level, which in this case is approximately 10 percent. However, during the primary swell stage, there is an increasing trend in the improvement observed with respect to the percentage of CBAS added. It is interesting to note that the percentage of improvement measured in the swell is approximately equal in both stages of swell for the high-percentage CBAS group (approximately 50% with respect to the untreated specimen). It is also notable that as the percentage of CBAS is increased, the primary swell stage started to become less obvious, which means that the rate of primary swell is reduced. The highpercentage CBAS curve (10% CBAS) indicates similar primary and secondary swell rates, both of which seem to be similar with the secondary swell rate of the untreated specimen. These observations indicate that the CBAS is fairly effective overall after the initial stages of the swell, at the end of which the specimens can be

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regarded as fully saturated. However, after this stage there seems to be little or negligible improvement during secondary swelling. This is due to the high overall internal stresses created by the water adsorption during swelling, which is likely to have caused exceedance of the strength of the film or web of CBAS formed. As a result of this, the rate of secondary swell remained similar to the rate observed for the untreated specimen. Another reason for this may be that the secondary swelling, as the part of swell that occurs after saturation of the macropores and ongoing water absorption through the micropores, is not improved, as the CBAS is not effectively absorbed in the micropores. When the one-dimensional swell test results are compared with the clay stability test results, it can be stated that the CBAS addition is very effective in the latter even at low levels of treatment. However, higher percentages of CBAS are required in order to observe a significant improvement in the former. The reason for this behavior can be explained as follows: (1) the mechanism of internal stresses generated when swelling takes place in confined, one-dimensional form is different than the mechanism of internal stresses generated in the clay stability test, where three-dimensional swell is allowed in an unconfined manner, and (2) onedimensional swell test results are interpreted at lower strain levels compared to the clay stability test results, in which collapse of specimens was observed related to a failure condition (large strain). Microstructure The X-ray diffraction tests are performed by using a by Bruker D8 XRD instrument with a coppersealed tube X-ray source producing Cu kα radiation at a wavelength of 1.5406 Å from a generator operating at 40 keV and 40 mA. A parallel beam of monochromatic X-ray radiation is produced by the use of a Göbel mirror optic (primary optic). The diffracted X-rays are recorded on a scintillation counter detector located behind a set of long Soller slits/parallel foils. All XRD tests are performed using air-dried specimens. The results of the XRD test are used to determine the mineralogy of the specimens. The results are presented in Figures 9 and 10. The clay mineral constituent of the soil specimens used is determined to be kaolinite; a number of analyses were done to increase the confidence on the results. However, it should be noted that, as it is confirmed indirectly with the soil plasticity and swelling tests, it is likely that other clay mineral constituents, such as mixed-layer clays, are also present in the specimens, causing an expansive behavior. The results indicate only minute changes in the reflection intensity, confirming that there is no new crystal formation. However, the minute changes in the

Figure 9. X-ray diﬀraction analysis of untreated Famagusta alluvial clay (start: 5.0º; end: 90.02º; step time: 1 second; temperature: 25ºC).

Figure 10. X-ray diﬀraction analysis of treated Famagusta alluvial clay (start: 5.0º; end: 90.02º; step time: 1 second; temperature: 25ºC).

reﬂection intensities may also mean that the mineral crystal size is slightly aﬀected by the treatment. There is potential attraction between the cations in clay and the anionic CBAS, resulting in the formation of electrostatic bonds. In order to further investigate these bonds, an FT-IR test is conducted on the specimens using a PerkinElmer UATR Two instrument. All test specimens are prepared under wet conditions. FT-IR test results are presented in Figure 11. As depicted in Figure 11, at wave numbers 1,730 and 2,958, carbonyl group, carbon-hydrogen bonds, and

Figure 11. Fourier transform infrared spectroscopy results for Famagusta alluvial clay.

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other electrostatic bonds are formed in the treated clay; when CBAS is diluted in water and mixed with wet clay, hydrogen bonds are formed between the anionic polymer chains and the hydrogen atoms of the water molecules, creating a dipole. Then the resulting negative charge on the other side of the dipole is likely to have caused the formation of electrostatic bonds with the cations in the clay, which, in turn, are attached to negatively charged clay minerals through further electrostatic bonding. Reduction of water content during drying causes the hydrogen bonds to detach as water molecules are removed from macropores. However, bonds formed in micropores remain attached at the diffuse double layer of clay particle surfaces. Therefore, CBAS may interact with the clay fabric even when the treated clay loses moisture, providing improvement for internal stability and erosion resistance. CONCLUSION Based on the results, it can be concluded that specimens treated with the aqueous polymer are significantly improved, even when the degree of treatment is as low as 0.5 percent by dry mass of the clay. The improvement in the clay stability index, K, is as high as two-fold when the results are compared with the results from untreated test specimens. The specimens tested in slurry form for the measurement of clay-polymer-water electrolyte system properties confirmed that the type of aqueous polymer used does not require a curing period. The interaction was detectable through the drop in electrical conductivity and TDS data. The measurements taken during specimen preparation indicated that the drying curves of polymertreated specimens and control specimens differ such that the treated specimens allow for a greater void volume at the same water content. This is considered to be due to the support provided by polymer chains, within the treated specimens, against collapse of the macropores during drying. The polymer chains lead to less shrinkage and fewer desiccation cracks, improving the internal stability of compacted clay on inundation. The clay stability tests depend on the initial water content of the test specimens because as the initial water content increases, the internal stability also improves. Reduction in initial water content caused lower internal stability for both untreated and treated test specimens. Most of the treated test specimens did not collapse in the clay stability tests. The clay stability test is performed by following a time-based methodology when calculating the clay stability index, K. The results indicated that the longterm behavior of the test specimens is determined more

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accurately by using computations of K at 10, 20, and 30 minutes. The mode of collapse is influenced by the water absorption rate linked with the initial water content. Time-dependent evaluation of the mode of collapse behavior indicated that the specimens are more likely to collapse by gradual cracking for K20 and K30 measurements. The specimens tended to increase in the explosive mode of collapse as the polymer content increased, which is considered to be associated with the effective entrapment of clay particles by polymer chains. The one-dimensional swelling behavior results indicated that, compared to the clay stability tests results, a higher percentage of CBAS is required for effective treatment of volume change behavior. This is likely due to the need for resistance against the internal tensile stresses generated by water absorption. XRD and FT-IR tests showed that there are no chemical interactions between the polymer and the clay, which indicates that the stability is improved through hydrogen and electrostatic bonding. REFERENCES Al-Homoud, A. S.; Basma, A. A.; Husein Malkawi, A. I.; and Al Bashabsheh, M. A., 1995, Cyclic swelling behavior of clays: Journal of Geotechnical Engineering, Vol. 121, No. 7, pp. 562–565. https://doi.org/10.1061/(ASCE)07339410(1995)121:7(562). Anagnostopoulos, C. A., 2007, Cement–clay grouts modified with acrylic resin or methyl methacrylate ester: Physical and mechanical properties: Construction and Building Materials, Vol. 21, No. 2, pp. 252–257. https://doi.org/10.1016/j.conbuildmat.2005.12.007. Arasan, S.; Bagherinia, M.; Akbulut, R. K.; and Zaimoglu, A. S., 2017, Utilization of polymers to improve soft clayey soils using the deep mixing method: Environmental and Engineering Geoscience, Vol. 23, No. 1, pp. 1–12. https://doi.org/10.2113/gseegeosci.23.1.1. ASTM D698-12e2, 2012, Standard Test Methods for Laboratory Compaction Characteristics of Soil Using Standard Effort (12 400 ft-lbf/ft3 (600 kN-m/m3 ): ASTM International, West Conshohocken, PA. http://www.astm.org/cgibin/resolver.cgi?D698-12e2. ASTM D4546-14e1, 2014, Standard Test Methods for OneDimensional Swell or Collapse of Soils: ASTM International, West Conshohocken, PA. https://doi.org/10.1520/D454614E01. ASTM D854-14, 2014, Standard Test Methods for Specific Gravity of Soil Solids by Water Pycnometer: ASTM International, West Conshohocken, PA. https://doi.org/10.1520/D0854-14. ASTM D2487-17, 2017, Standard Practice for Classification of Soils for Engineering Purposes (Unified Soil Classification System): ASTM International, West Conshohocken, PA. https://doi.org/10.1520/D2487-17. ASTM D4318-17e1, 2017, Standard Test Methods for Liquid Limit, Plastic Limit and Plasticity Index of Soils: ASTM International, West Conshohocken, PA. https://doi.org/10.1520/D4318-17E01. Azzam, W. R., 2014, Utilization of polymer stabilization for improvement of clay microstructures: Applied Clay Science,

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Analysis of Landslide Kinematics Using Multi-Temporal Unmanned Aerial Vehicle Imagery, La Honda, California JORDAN A. CAREY* NICHOLAS PINTER Department of Earth and Planetary Sciences, One Shields Avenue, University of California, Davis, CA 95616

ALEXANDRA J. PICKERING CAROL S. PRENTICE STEPHEN B. DELONG U.S. Geological Survey, 345 Middlefield Road, MS 977, Menlo Park, CA 94025

Key Terms: Landslide, UAV, Structure-from-Motion, DEM, Geomorphology, Remote Sensing

monitor active geomorphic processes in emergent situations where high-resolution digital topography is needed in near-real-time.

ABSTRACT The combination of unmanned aerial vehicle (UAV) photography with structure-from-motion (SfM) digital photogrammetry provides a quickly deployable and costeﬀective method for monitoring geomorphic change, particularly for hazards such as landslides. The Scenic Drive landslide is a deep-seated slope failure in La Honda, CA, with episodic activity in 1998 and 2005–06. Heavy rainfall during 2016–17 initiated movement of a new and separate landslide directly upslope of the existing Scenic Drive landslide, damaging three residences. We acquired imagery of the Upper Scenic Drive landslide beginning 2 days after initial motion using a global positioning system–enabled UAV. We used this imagery to generate seven digital elevation models (DEMs) between January and May 2017, with spatial resolutions of ∼3–10 cm/pixel. We compared these DEMs with each other and with available light detection and ranging (LiDAR) data to assess landslide kinematics, including horizontal displacement vectors, rates of motion, and total mass redistribution, and to test the accuracy and applicability of UAV/SfM-derived measurements. We estimated the maximum horizontal displacement of the slide was at least 5 m during the monitoring period and calculated that ∼3,000 m3 of material was displaced by the landslide. Comparing the UAV-derived topography with synchronous terrestrial LiDAR scanning showed that accuracies of the two techniques are comparable, generally within 0.05 m horizontally and within 0.20 m vertically in unvegetated areas. This study demonstrates the capability of combining UAV and SfM to map and

* Corresponding author email: jacarey@ucdavis.edu

INTRODUCTION Landslides in steep topography can pose a serious threat to population and infrastructure, sometimes leading to significant economic losses and casualties (Wartman et al., 2015). Landslide studies include landslide inventories, susceptibility maps and analyses, predictive models, and active landslide monitoring, with the overall goal of reducing the risk associated with landslide hazards. Recent landslide studies have benefited from improvements in remotesensing technology, especially high-resolution topographic surveys (e.g., McKean and Roering, 2004; Corsini et al., 2009; Roering et al., 2009; Bull et al., 2010; DeLong et al., 2012; Lucieer et al., 2014a; and Tarolli, 2014). Common techniques to develop highresolution topographic data include terrestrial and airborne light detection and ranging (LiDAR), which allows for the creation of high-resolution (ࣘ1 m pixel spacing) digital elevation models (DEMs). LiDAR measures the distance to the target using laser pulses and their return times and has the ability to retrieve multiple returns in vegetated areas. LiDAR data can be acquired from the ground or aerially from a plane, helicopter, or unmanned aerial vehicle (UAV). Aerial LiDAR and terrestrial LiDAR have been particularly powerful tools for landslide studies; however, both are costly, and aerial LiDAR requires a larger operating platform and rarely can be acquired and processed quickly enough for rapid disaster response. Recently, the combination of UAVs and structure-from-motion (SfM) digital photogrammetry has provided a quickly deployable and cost-effective method for monitoring geomorphic change and hazard assessment in

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localized regions. UAV-derived digital surface models (DSMs) can achieve high resolutions (pixel sizes of 1– 15 cm) and high accuracy (<10 cm), providing a valuable geomorphic tool where rapid deployment is a high priority. UAV-based remote sensing and photogrammetry using SfM can achieve resolutions comparable or even better than LiDAR-derived DEMs (Westoby et al., 2012; Fonstad et al., 2013) and have proven to be useful for a number of geomorphic applications (e.g., Javernick et al., 2014; Lucieer et al., 2014b; Smith, 2014; Gomez et al., 2015; Woodget et al., 2015; and Shugar et al., 2017). Javernick et al. (2014) developed a UAV/SfM workflow to create DEMs in a moderately vegetated, complex, braided, fluvial environment. Their DEMs had horizontal error of 0.04 m and vertical error of 0.10 m (in non-vegetated areas) from photos taken 600–800 m above ground level, suggesting that this UAV/SfM workflow is suitable for geomorphic change detection and hydrodynamic modeling. Lucieer et al. (2014b) used UAV/SfM to monitor the health of Antarctic moss beds. Through DEM differencing and manual feature tracking, Immerzeel et al. (2014) calculated mass loss and surface velocity of a Himalayan glacier. Smith (2014) used groundbased photography and SfM to estimate peak discharges of flash floods and found that SfM utilized with ground control is accurate to within 0.1 m of traditional differential global positioning system (GPS) surveys. Miřijovský and Langhammer (2015) conducted multi-temporal UAV monitoring on a mountain stream, providing a detailed analysis of bank erosion and volumes of material carried away from a stream bank. Woodget et al. (2015), used SfM to survey and quantify exposed and submerged fluvial topography in clear water. Errors for submerged features ranged between 0.016 m and 0.089 m and increased with depth (Woodget et al., 2015). Shugar et al. (2017) used UAV-generated DEMs in conjunction with hydrologic measurements to document the retreat of Kaskawulsh glacier and to illustrate how the retreat significantly altered drainage patterns in the region due to stream piracy. Another promising application for use of UAVs and SfM is landslide monitoring (Niethammer et al., 2011; Stumpf et al., 2013; Lucieer et al., 2014a; Peterman, 2015; Turner et al., 2015; and Fernández et al., 2016). Niethammer et al. (2011) used UAV-derived imagery to measure fissures and displacements on an active landslide. Expanding on this work, Stumpf et al. (2013) developed an automated method to detect landslide fissures from UAV-derived images. Lucieer et al. (2014a) and Turner et al. (2015) used UAV/SfM to create a three-dimensional (3D) model of active landslides and calculate displacements using

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COSI-Corr software. Fernández et al. (2016) used a similar workflow to monitor an active landslide, using differential DEMs to calculate vertical displacements and measure horizontal displacements using high-resolution orthophotos. Similarly, Niethammer et al. (2011), Stumpf et al. (2013), Lucieer et al. (2014a), Peterman (2015), and Turner et al. (2015) all have used UAV/SfM products for documenting horizontal displacements and displaced volumes. When comparing LiDAR- or UAV/SfM-derived DEMs, the uncertainty within each individual DEM and the accuracy of the alignment between multiple DEMs are crucial factors that are required to obtain accurate displacement and volumetric calculations. Landslide monitoring and differencing using UAV/SfM-derived DEMs apply the same concepts as for LiDAR-derived DEMs. Repeat surveys can be conducted to determine landslide kinematics and calculate volumetric changes (Stumpf et al., 2015; Turner et al., 2015) and displacement rates (Niethammer et al., 2011; Lucieer et al., 2014a; Stumpf et al., 2015; and Turner et al., 2015). Assessing 3D movements within a landslide using a UAV is difficult because features used to track movement may undergo rotational movement and may be “lost” beneath the surface (Lucieer et al., 2014a). Some methods are being developed to align DEMs using topographic grids or LiDAR point clouds (Martha et al., 2010; Immerzeel et al., 2014; Lucieer et al., 2014a; and Turner et al., 2015); however, such work is still in progress. Several recent studies have assessed the data quality and potential errors within the SfM workflow (Harwin and Lucieer, 2012; Turner et al., 2012; Westoby et al., 2012; Fonstad et al., 2013; Hugenholtz et al., 2013; and Tonkin et al., 2014). New UAVs are equipped with navigation-grade GPS (∼5–10 m), but this position uncertainty is too great for many geomorphic applications, and most cases of landslide change detection in particular. A major step in reducing DEM uncertainty is the use of surveyed ground control points (GCPs). SfM software will create DEMs without GCPs (Niethammer et al., 2010; Agisoft, 2014), but GCPs improve both the precision and accuracy of the resulting DEMs. The aims of this study were to (1) further demonstrate the potential of UAV-SfM techniques in geomorphic studies and hazards management, (2) quantify landslide displacements during the 2016–17 Upper Scenic Drive failure, (3) quantify displaced landslide volumes by differencing UAV-derived multi-temporal DEMs, and (4) identify the importance of prolonged and/or high-intensity rainfall in the triggering process of the Upper Scenic Drive landslide. We also

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Landslide History

Figure 1. Map showing location of the Upper Scenic Drive landslide in Northern California.

compared the accuracy between UAV- and LiDARderived DEMs. STUDY SITE During the winter of 2016–17, high-intensity and prolonged rainfall triggered numerous slope failures across northern California. The Upper Scenic Drive landslide is in La Honda, CA, east of Highway 84, in the Santa Cruz Mountains of San Mateo County (Figure 1). La Honda is a small, unincorporated residential community, with three residential structures located within the landslide and ﬁve additional structures directly adjacent to the landslide (Figure 2). The area is underlain by the Purisima Formation, a late Miocene and Pliocene sequence of shallow-marine interbedded ﬁne sandstones and mudstones, dipping gently to the southwest (Brabb et al., 1998; Wells et al., 2005). The Purisima Formation unconformably overlies submarine volcanic breccia of the Oligocene and Miocene Mindego Basalt in this area (Brabb et al., 1998; Wells et al., 2005). The residential region of La Honda was previously mapped as a probable ancient landslide deposit (Brabb and Pampeyan, 1972).

Deep-seated landslides occurred along Scenic Drive in La Honda during the winter of 1997–98, with movement recurring and the landslide expanding during the winters of 2004–05 and 2005–06 (Jayko et al., 1998; Wells et al., 2005, 2006). The initial slide began in late January of 1998 after heavy El Niño rainfall (200 mm of rainfall between January 31 and February 9), with continued movement until March 1998. The slide was approximately 145 m long and 110 m wide in 1998 (Figure 2), and it ranged from 6 to 8.5 m deep (Jayko et al., 1998). Movement of the slide in 1998 was predominantly to the southwest, with maximum rates of 0.20 m/d during February 20–26, 1998, measured using tape measure and ground nails (Jayko et al., 1998). The slide consisted of extensional features in the northeast upslope zone and compressional features in the southwest downslope zone, separated by a hinge zone (Jayko et al., 1998). The headscarp varied in height between 1 and 4 m, while the toe was characterized by a 1.2–1.5 m bulge (Jayko et al., 1998; Upp, 1998). In late February of 2005, the 1998 slide began to remobilize and subsequently expanded to the east and south. The slide evolved to be roughly triangular-shaped, with lateral margins migrating and expanding approximately 200 m on each side (Wells et al., 2005). Maximum displacement rates of up to 0.31 m/d were recorded in mid-April, with total maximum horizontal displacements up to 5.31 m along Scenic Drive measured using a differential GPS system (Wells et al., 2005). Movement on the slide stopped during the summer months and started again in December 2005, continuing into June 2006 (Wells et al., 2006). Movement of the 2006 slide occurred in the eastern and southern portions of the 2005 slide, extending further in both directions (Figure 2). Topographic differencing between May 2005 and July 2006 surveys showed that the headscarp graben in the eastern portion of the slide subsided approximately 6 m, with a corresponding growth in the toe of the slide (Wells et al., 2006). The combination of these three slide events led to the destruction of eight homes and damage to portions of Scenic Drive and Recreation Drive. Between 2009 and 2014, $6.3 million were spent on repairing Scenic Drive and reinforcing the hillslope (Noack, 2014). The 2016–17 Upper Scenic Drive landslide was located adjacent and upslope of the previous ones (Figure 2). Over the course of 2016–17 slide activity, three homes were evacuated and “red-tagged” by San Mateo County, along with significant destruction to portions of Scenic Drive.

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Figure 2. Map showing historical landslide episodes along Scenic Drive (Jayko et al., 1998; Wells et al., 2005, 2006). Dashed white line shows extent of Figure 3. Base orthophoto is from March 11 UAV survey. Two-meter contours are from January 27 LiDAR survey.

Residents ﬁrst noticed movement of the Upper Scenic Drive landslide on January 10, 2017. Movement continued throughout January and slowed beginning in February. The slide was triangular and covered an area of approximately 10,000 m2 (Figure 3). An ∼3-m-high, ∼100-m-long headscarp developed along the northeast portion of the slide. The direction of movement was towards the west-southwest, with two prominent NW-SE–striking extensional grabens and numerous tension cracks across the landslide body. The western extensional graben stretched approximately 12 m across and was ∼3 m deep. Near the toe of the landslide (and above the headscarp of the previous landslides), three separate areas collapsed downslope. The western and largest collapse occurred between January 21 and January 27, 2017, and a second, smaller collapse occurred between January 27 and February 13, 2017. The third collapse occurred between February 13 and March 11, 2017, within the trees at the eastern portion of the toe (not visible from aerial imagery).

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METHODS UAV Platform and Sensor For this study, we used a DJI Phantom 3 Professional quadcopter UAV. This 1,280 g UAV is equipped with on-board GPS and camera gimbal. We collected photographic imagery using a 12.4 megapixel camera with a Sony EXMOR 1/2.3 complementary metal-oxide semiconductor sensor. The camera is equipped with a 20 mm lens with a field of view of 94°. While many studies utilize time-lapse for image capture, we used manual single-shot capture to increase image capture rate. UAV flight times are limited by battery life and ranged in this study from about 18 to 23 minutes per battery, depending on the ascent and descent time during the flight. The on-board GPS of the DJI Phantom 3 provides position accuracies of ∼5–10 m for initial geotagging of photos (see https://www.dji.com/phantom3-pro/info). UAV-derived imagery was acquired

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Figure 3. Morphologic features of the 2017 Scenic Drive landslide. Arrows on tension crack indicate relative direction of movement. The basemap hillshade is sourced from January 27 LiDAR survey (Pickering et al., 2018). Relative movement of the slide is to the WSW. Mapping is based on ﬁeld observations, SfM mapping, and LiDAR mapping.

with ∼60–70 percent overlap and ∼30 percent sidelap. Data Collection We conducted seven ﬁeld surveys between January 12, 2017, and May 9, 2017, referenced hereafter by the survey date (Table 1). Prior to image acquisition, we placed multiple targets across the ﬁeld site to provide GCPs for georeferencing. We constructed these targets from plastic tarps and duct tape, and they were approximately 0.5 × 0.5 m in size. We distributed 26 targets Table 1. UAV survey summary for the Scenic Drive landslide. Survey Date January 12, 2017 January 17, 2017 January 21, 2017 January 27, 2017 February 13, 2017 March 11, 2017 May 9, 2017

Interval (days)

Weather Conditions

— 5 4 6 17 26 59

Cloudy Sunny, clear Cloudy, overcast Sunny, dry, light wind Partly cloudy, dry, light wind Sunny, dry Sunny, dry

across the field site during the January 27 survey and left them in position through the May 9 survey. We repeatedly surveyed the GCP targets using a Leica Viva GS15 real-time kinematic (RTK) GPS (reported horizontal and vertical accuracies < 2 cm) and surveyed in Universal Transverse Mercator (UTM) North American Datum 1983 (NAD83) zone 10N. We did not use targets for the January 12, January 17, and January 21 surveys. To georeference these surveys, we identified stable points (sewers, road markers, etc.) that were unaffected by the landslide across the field area, and we used these stable points to generate additional GCPs for all seven surveys. Aerial photographs for the first three surveys (January 12, January 17, and January 21) were collected by the La Honda Fire Brigade using the same UAV model. The average flying height of the UAV during the first three surveys was ∼200 m above ground level (but varied significantly). The final four surveys (January 27, February 13, March 11, and May 9) were conducted by the author (J. A. Carey) who is a certified remote pilot per Federal Aviation Administration (FAA) Part 107. These surveys averaged ∼37 m flying height. Images were collected in

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Carey, Pinter, Pickering, Prentice, and DeLong Table 2. Agisoft Photoscan workflow and parameters used for each step of the SfM analysis. Workﬂow

Speciﬁc Parameter Settings

1. Add photos 2. Align photos/Build sparse point cloud 3. Build dense cloud 4. Build mesh 5. Georeference GCPs 6. Build DEM 7. Build orthophoto 8. Classify point cloud

Pre-selected based on image quality Accuracy: high Pair pre-selection: generic and reference Quality: medium, high, or ultra-high Depth ﬁltering: aggressive Surface type: height ﬁeld Source data: dense point cloud Face count: high Interpolation: enable UTM NAD83 zone 10N Source: dense point cloud Source: DEM Max angle: 15° Max distance: 1 m

JPEG format, as this file type can be written to memory card much faster than RAW format. For our purposes, the increased writing speed of JPEG images outweighed the slightly greater image quality of RAW format, and JPEG resolutions were sufficient for this study. Camera angle was typically at nadir; however, some oblique photos (∼45°) were also taken during each survey to increase point density in areas covered by high vegetation. Terrestrial LiDAR data were collected concurrently with UAV surveys on January 27 and May 9, 2017, using a Riegl VZ-400 terrestrial laser scanner. The 2017 surveys used real-time positioning information fed from the Leica Viva GS15 GNSS RTK system to the Riegl VZ400 scanner, allowing for initial centimeter-scale alignment of individual scan positions. Individual point clouds consisting of several million points were collected, internally aligned with the multi-station adjustment tool within Riegl RiScan Pro to a single merged point cloud using a modified iterative closest point algorithm (Besl and McKay, 1992), filtered, and georeferenced using Riegl RiScan Pro. The point clouds were then classified by height using Terrasolid Terrascan software and gridded with Golden Software Surfer 14 to produce 0.05-m-resolution DEMs (Pickering et al., 2018). The authors recognize that some of the UAV flights described above, performed by others, do not meet today’s FAA rules and regulations. Readers are advised to become familiar with, and meet, all FAA Part 107 requirements for small unmanned aerial systems (sUAS) operations before engaging in similar studies. Structure from Motion: DEM and Orthophoto Generation We processed images collected in this study using Agisoft Photoscan Professional version 1.3 (Photoscan). Photoscan uses SfM techniques to align photos, build a dense point cloud, and export a DSM and orthomosaic. See Figure 4 for a general workflow and Table 2 for specific parameter settings. Like traditional photogrammetry, SfM uses images from multiple

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viewpoints to reconstruct 3D geometry of a surface. However, SfM uses image matching algorithms that allow for automated, random image acquisition. The three primary steps of the SfM workflow include (1) image acquisition, keypoint extraction, and alignment; (2) 3D scene reconstruction; and (3) post-processing and DEM generation (Westoby et al., 2012). Alignment of photos begins by using scale invariant feature transform (SIFT) object recognition (Lowe, 2004). SIFT detects features in each image that are invariant to image scaling and rotation, and partially invariant to changes in light conditions and camera viewpoint. The number of these keypoints (matched features between images) depends on image texture and resolution (Westoby et al., 2012). The 3D scene reconstruction determines the positions of keypoint features and camera position, orientation, and lens distortion (Turner et al., 2015), resulting in topographic point clouds. Post-processing typically involves transforming the point cloud from a relative to absolute coordinate system through the use of GCPs. The resulting topographic model is not necessarily a DEM due to the incorporation of off-ground features, such as vegetation, and is more aptly identified as a DSM. We imported photographs into Photoscan in JPEG format, eliminating lower-quality photos (typically photos that appeared blurry or had contrast issues). Each photo was geotagged with latitude and longitude in the JPEG EXIF header by the on-board UAV GPS unit. After import, the photographs were aligned through feature matching (using SIFT) and bundle block adjustment. Block adjustment transforms image positions based on image overlap, camera position, and keypoints, distributing errors among images and keypoints. Photoscan first creates a “sparse cloud” of common feature points between multiple photographs in X, Y, and Z coordinates and then builds a “dense cloud,” which is a full-resolution model of the topography being surveyed. Photoscan offers several options for dense-cloud resolution, depending on the number of photos and number of sparse-cloud points. A triangular mesh is then created to solidify the dense point cloud.

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Figure 4. General workﬂow in Agisoft Photoscan showing primary imports and exported products. Imagery depicted here is from March 11 survey.

Next, between 9 and 21 GCPs are used to georeference the 3D models. We manually identiﬁed the GCPs in all photographs in which a GCP marker appeared. Photoscan then automatically compiled photographs that contained the locations of each identiﬁed GCP. Manual adjustment was necessary to accurately place each marker with the corresponding GCP. After repeating this step for each GCP, we imported the RTK GPS coordinates of each GCP marker and paired them with the corresponding GCPs. Checkpoints, which are GCPs not used for alignment, were used for accuracy assessment. One DSM per survey was created using each georeferenced dense point cloud and exported as a GeoTIFF in the UTM NAD83 zone 10N coordinate system. The DSMs created during this study all had pixel resolutions <0.1 m (see Figure 5 and Table 3) and 3D root mean square errors (RMSE) between 0.114 and 1.178

(Table 4). Each DSM and its corresponding photography were combined to create an orthophoto, which is a composite image of the individual photographs that has been mosaicked together and adjusted for topographic relief and camera distortion (Lucieer et al., 2014a). We also used Photoscan to classify the point clouds into two classes: ground and everything else (vegetation, structures, etc.). Using the classification tool within Photoscan, the dense cloud was divided into cells of 25 m2 (based on large tree-covered areas), and in each of these areas, the lowest point was detected. Triangulation of these points, within Photoscan, gave an approximation of the ground surface in the covered regions (Agisoft, 2014). Following this classification, we built a mesh from the new point cloud containing only ground points, and from this mesh we derived a bare-ground, classified DSM.

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Carey, Pinter, Pickering, Prentice, and DeLong Table 3. Summary of SfM statistics and DEM resolutions. Quality setting for dense cloud creation was chosen based on computing power. May 9 survey continually failed on “high”-quality setting, likely due to complex vegetation (long grass). The first three surveys did not have the initial intention of being used for SfM survey, so significantly fewer photos were taken. No. of Photos Mean Flight Height (m) Survey Area (m2 ) Quality Setting No. of Points DEM Resolution (cm/pixel)

Survey January 12 January 17 January 21 January 27 February 13 March 11 May 9

25 35 56 739 716 861 873

163 286 146 37.1 36.3 36.3 38.7

64,800 182,000 82,700 48,800 41,000 52,500 61,300

Point Cloud Alignment Accurate change detection and volumetric measurements require precise alignment of the DSMs being differenced. Misalignment can occur because each DSM is georeferenced individually (with slight changes to GCP target locations in each survey), and the most likely misalignments are in the Z-axis (i.e., co-registration or vertical datum issues) (Turner et al., 2015). Additional misalignments can include rotational or scale differences. In this study, we used CloudCompare, an open-source, 3D point cloud analysis software, to check for misalignments in our DSMs. The dense point clouds were exported from Photoscan into CloudCompare and aligned following the procedure outlined by Turner et al. (2015). We created masks for each of the dense point clouds being compared (1) to ensure that clouds covered the same extent and (2) to exclude the active slide area so only stable areas were tested for misalignment (Turner et al., 2015). We compared the alignment of each point cloud to the Mar. 11 point cloud (which we defined as a reference), and the transformation matrices (rotational, translational, and scale parameters) were estimated for each point cloud and aligned accordingly. Horizontal Surface Displacement We measured horizontal surface displacements manually by comparing features in multiple

Ultra-high Ultra-high Ultra-high High High High Medium

32,979,943 32,973,047 58,694,822 95,971,171 124,925,315 124,206,812 25,946,254

3.87 7.53 3.86 2.73 2.23 2.69 5.52

generations of orthophotographs that we manually aligned and georeferenced in ArcMap 10.4. To minimize error propagation, all orthophotographs were aligned and georeferenced to the March 11 survey, resulting in RMSE less than 0.3 m, with the exception of the February 13 survey. We used distinct surface features to track horizontal surface displacement, including vegetation, sewers, roads, and structures. We identified a total of 37 points that we tracked between each survey. Additionally, we created vectors for the same 37 points between the January 12 and March 11 surveys to calculate a simplified, single vector of total movement. Vertical Displacement/Volume Change To estimate the volume change between subsequent DSMs, we created multiple DEMs/DSMs of difference (DoDs) by subtracting each older DSM from the more recent DSM (Wheaton, 2008; Wheaton et al., 2010). This provides a new raster representing the change in pixel heights between the two rasters. Each pair of DSMs must have the same pixel resolution and spatial extent before they can be analyzed. Therefore, spatial extent values and resolutions of the DSMs were modified, including cropping spatial extents and slightly down-sampled resolutions. Based on the average vertical RMSE (∼0.23 m) (see Table 4), we conservatively excluded changes <0.40 m between pairs

Table 4. Summary of checkpoints and associated spatial errors within DSMs and orthophotos.

Survey January 12 January 17 January 21 January 27 February 13 March 11 May 9

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No. of Photos Used (Photos Taken)

No. of GCPs

No. of Checkpoints

X RMSE (m)

Y RMSE (m)

Z RMSE (m)

3D RMSE (m)

25 (25) 33 (35) 55 (56) 739 (2,089) 716 (1,689) 861 (1,232) 873 (877)

9 9 15 21 20 19 16

4 4 5 6 7 8 5

0.147 0.126 0.045 0.043 0.649 0.179 0.072

0.178 0.074 0.093 0.051 0.556 0.083 0.092

0.306 0.398 0.048 0.132 0.810 0.430 0.140

0.383 0.424 0.114 0.148 1.178 0.473 0.182

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exported outside of the landslide boundary; rather, material was just rearranged within the landslide. Therefore, we would expect the net diﬀerence between depletion and accumulation to equal zero. RESULTS Accuracy of DSMs

Figure 5. Comparison of the hillshade models produced using (A) San Mateo County airborne LiDAR 4-m-resolution DEM and (B) our UAV/SfM-derived, 0.03-m-resolution DSM.

of surveys to limit the amount of noise in the change detection. To quantify the amount of material displaced, the height diﬀerence per pixel was multiplied by the area of the pixel and then summed, providing volumes in cubic meters (Daehne and Corsini, 2013). This calculation is not a measure of true mobilized volumes, but rather a measure of mass redistribution within the landslide, as we only observed changes at the surface, not taking into account below-ground displacements. At the Upper Scenic Drive landslide, little material was

We assessed the accuracies of DSMs and orthophotos produced for this study using GCP checkpoints. Following Turner et al. (2015), we used approximately 30 percent of the GCPs as checkpoints in each survey. We measured differences in X, Y, and Z coordinates between each checkpoint on the orthophoto and the same point measured using RTK GPS, and we calculated RMSE for each model (Table 4). RMSE is a measure of the standard deviation of the residuals (RTK GPS location − SfM location). The remaining GCPs (used to build the DSM) had horizontal (XY) RMSE values that ranged from 5 to 10 cm in each of the surveys, and vertical (Z) RMSEs that ranged from 10 to 20 cm reported within Photoscan. The one exception was the February 13 survey, which had X, Y, and Z RMSE values of 60, 50, and 80 cm, respectively. This may be a result of poorly measured GCPs during this survey, but it is more likely a product of the partly cloudy weather conditions during the survey. The Photoscan algorithm relies on matching similar points, and the difference between cloudy and sunny skies can make alignment difficult by significantly changing the darkness of an object or feature. For this reason, we excluded the February 13 survey from horizontal and vertical displacement measurements. Additionally, we compared the accuracy of the DSM derived from the January 27 SfM survey with a DEM from a LiDAR survey conducted on the same day. To do this, we compared each model against the survey points from the RTK GPS. We manually identified 12 RTK GPS checkpoints in both the January 27 SfM hillshade and terrestrial LiDAR surveys. We then subtracted the RTK GPS X, Y, and Z values from both the SfM and LiDAR surveys to assess the deviations of the measured values from RTK GPS. Mean horizontal differences between the RTK GPS points and the SfM and LiDAR points were 0.020 m and 0.059 m, respectively. Mean vertical differences were −0.041 m for LiDAR and −0.116 m for SfM compared to the RTK GPS. The horizontal and vertical accuracies of both the SfM survey and the LiDAR survey are both reasonable, with all mean differences <0.12 m. The largest deviations from the RTK GPS were in the vertical accuracies of the SfM survey. Tonkin et al. (2014) found that vertical errors in SfM-derived DEMs were highest in steep, vegetated terrain.

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Figure 6. Map of elevation diﬀerences between the January 27 LiDAR DEM and the January 27 SfM-derived DSM. Maps show results of no threshold (A), 0.15 m threshold (B), 0.20 m threshold (C), and 0.40 m threshold (D). Base map is January 27 LiDAR DEM.

To further compare SfM with LiDAR, we created DoDs, in ArcGIS, with thresholds of 0.15 m, 0.2 m, and 0.4 m (to ignore changes less than specified value) and no threshold, by subtracting the January 27 DSM survey from the January 27 LiDAR DEM (Figure 6). Differences should theoretically come to zero, because the two surveys were taken simultaneously. The DoD with no threshold suggested uniform “erosion” across the survey area, indicating that Z values in the SfM survey were uniformly higher than the LiDAR survey (Figure 6). With a 0.20 m threshold applied, the majority of the erroneous “erosion” disappeared, showing that most Z values were uniformly <0.20 m different than the LiDAR survey (Figure 6). Regions with dense vegetation and structures resulted in larger differences, both negative and positive. Horizontal Surface Displacements The majority of movement on the slide occurred in mid-late January 2017. Figure 7 shows displacement vectors that indicate the magnitude and direction of landslide motion between the January 12 and March 11 orthophotos. Displacement magnitudes

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varied across the slide, whereas the directions of displacement were all oriented towards the westsouthwest. Horizontal displacements reached a maximum of 5.4 m in the central portion of the slide, measured over the duration of monitoring. The smallest horizontal displacements were measured around the periphery of the slide. These displacement vectors were all minimum estimates of total motion, because models accounted only for movement beginning on January 12, while movement was ﬁrst detected by residents on January 10. However, our study yielded similar horizontal displacement results to Pickering et al. (2019), who used pre- and post-motion satellite imagery between 2016 and 2017. Landslide displacement and rates were not uniform over the time interval of our study. Based on the measured horizontal displacements at each survey interval (Table 5), the majority of movement on the slide occurred between January 17 and January 27. Average horizontal displacement rates across the landslide between the January 17 and January 21 surveys were 0.40 m/d, slowing to 0.30 m/d between January 21 and January 27. Horizontal displacements continued to slow between January 27 and March 11, with an

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Figure 7. Landslide displacement map between January 12 and March 11, 2017, based on objects within the slide. Length of vector corresponds to degree of horizontal displacement. Vectors are shown on March 11 orthophoto. Black line indicates the landslide boundary.

Table 5. Summary statistics of the 37 horizontal displacement measurements. Displacement (m)

Rate (m/d)

Survey

Min

Max

Mean

Standard Deviation

Min

Max

Mean

Standard Deviation

January 12–January 17 January 17–January 21 January 21–January 27 January 27–March 11 March 11–May 9

0.14 0.32 0.59 0.04 0.01

0.38 2.36 2.61 0.55 0.24

0.26 1.62 1.82 0.24 0.07

0.07 0.54 0.43 0.10 0.05

0.027 0.079 0.098 0.001 0.000

0.076 0.590 0.435 0.013 0.004

0.052 0.405 0.303 0.006 0.001

0.013 0.135 0.072 0.002 0.001

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Carey, Pinter, Pickering, Prentice, and DeLong Table 6. Summary of volume calculations performed within the Geomorphic Change Detection plugin for ArcGIS. A threshold of 0.4 m was applied to all surveys. Survey January 12–January 21 January 21–January 27 January 27–March 11 Total January 12–March 11

Total Volume of Erosion (m3 )

Total Volume of Deposition (m3 )

Net Volume Diﬀerence (m3 )

1,624 ± 836 1,530 ± 661 276 ± 170 3,430 3,359

963 ± 499 1,218 ± 685 276 ± 175 2,457 2,575

−661 −312 0 −973 −784

average rate of 0.24 m/d and a maximum displacement of 0.55 m. Between the March 11 and May 9 surveys, the majority of displacement values were <0.10 m, which is at or below RMSE values. Maximum displacement between the March 11 and May 9 surveys was measured at 0.24 m. Therefore, we conclude that movement stopped on the majority of the slide before March 11, with small movement up to 0.24 m thereafter near the toe of the landslide. Average net displacement over the course of the monitoring period was ∼4 m. Table 5 shows average and maximum horizontal displacement values for each time interval. Vertical Displacements/Mass Redistribution Change detection to measure vertical displacement and volume measurements was carried out by subtraction of classified DSMs, creating DoDs. Given the inherent uncertainty in an individual DSM, one of the challenges involving DoDs is deciphering between real geomorphic change and noise (Wheaton et al., 2010). To minimize this error, DSMs were aligned using RTK GPS (described previously), and a minimum threshold was applied based on the RMSE. Displaced volume calculations presented here were calculated between pairs of surveys: the January 12, January 21, January 27, and March 11 surveys (Table 6). There were significant differences in the quality of the DSMs used in this study. An example change-detection map is presented in Figure 8, showing net vertical displacements between the January 12 and March 11 surveys. Areas in blue highlight regions that lost material, and areas in red highlight regions that gained material. Regions of no color represent areas that fell below the 0.40 m threshold and are recorded as no change. We would expect the net vertical displacement to equal zero, as little material was being carried out of the system (by a stream for example). Areas of elevation loss primarily occurred near the headscarp and in extensional grabens, while areas of elevation gain occurred near the toe of the slide. We found a similar pattern in the January 12–January 21 and January 21–January 27 DoDs. We calculated mass redistribution for each DoD, and

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results are shown in Table 6. Total mass redistribution in the 2017 Upper Scenic Drive landslide was ∼3,000 m3 of displaced material (3,359 m3 of volume “loss” and 2,575 m3 of volume “gain”). Pickering et al. (2019) conducted a similar analysis by diﬀerencing an aerial LiDAR survey from 2005 and a terrestrial LiDAR survey from 2017. Our SfM change-detection results show general agreement with the LiDAR change detection from Pickering et al. (2019). Rainfall Measurements Landslide motion of the 2017 Upper Scenic Drive landslide was associated with prolonged rainfall. A rain gauge located approximately 2 km to the southeast was used to assess hourly rainfall during the winter of 2016–17 (Figure 9). Between October 2016 and March 2017, La Honda received 1093 mm of precipitation and a total of 1210 mm for the 2017 water year. For comparison, long-term records from 1993 to 2016 averaged 715 mm/water year; the 1998 and 2005 water years received 1017 mm and 850 mm, respectively. Peak rainfall intensities on late January 8 and early January 9 were up to 5.6 mm/hr. Sustained, heavy rainfall between early morning January 7 and January 9, 2017, resulted in a total of 88.4 mm. Only 5.33 mm of rainfall occurred between the January 12 and January 17 surveys, corresponding with the much slower displacement rates during that period. Mean daily average displacement rates were highest between the January 17 and January 21 surveys (0.41 m/d), corresponding with the highest mean daily rainfall rates during the study period (averaging 21.01 mm/d). The highest intensity of rainfall (11.7 mm/hr) of the season occurred on January 18, and both rainfall and displacements decreased thereafter (Figure 9). DISCUSSION Accuracy The primary limitation of a SfM study is that, unlike LiDAR, an optical sensor cannot penetrate

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Figure 8. Vertical displacement map between January 12 and March 11 “active slide area” DSMs. Background is March 11 DSM. Blue areas show decrease in ground-surface elevation, while red areas show increases in ground-surface elevation. Topographic and vertical-change proﬁles along cross-sections B-B and C-C between January 12 and March 11 DODs are shown. Blue lines represent elevation on March 11, and red lines represent elevation change between January 12 and March 11.

vegetation. Therefore, the ground surface cannot always be extracted from a SfM point cloud. We attempted to classify out the vegetation in this study by classifying ground points and building a mesh; however, vegetated areas still proved to contain the highest uncertainties. Throughout the monitoring period, growth of grass added uncertainty to our measurements. By the May 9 survey, grass across the ﬁeld area was up to 2 m tall near the toe of the landslide, creating signiﬁcant uncertainty in our photogrammetry analysis. For this reason, we excluded the May 9 survey from our change detection analysis.

In addition, the overall quality of any SfM survey depends on the number of 3D points and amount of photograph overlap in the model, which is related to the number of photographs. The January 12, January 17, and January 21 surveys used signiﬁcantly fewer photographs, and thus contained fewer points in the dense point cloud than the other surveys. Although DSM resolutions and accuracies in these three surveys are still comparable with the remaining surveys, visual inspection of the hillshade model (a DEM product) reveals the quality diﬀerence. DEM artifacts can create discrepancies in volume displacement measurements;

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Figure 9. Cumulative rainfall and rainfall intensity from December 1, 2016, to May 15, 2017. Star indicates timing reported by residents of ﬁrst movement of the 2017 Upper Scenic Drive landslide. Black dots indicate survey dates.

therefore, the January 17 survey was eliminated from our change detection. Comparison of 2017 Motion with Previous Activity In this study, we estimated horizontal displacements by tracking various surface features across the landslide between orthophotographs. The largest horizontal velocities occurred near the central toe portion of the slide. Near the headscarp, horizontal motion was small, likely because either this region was dominated by vertical motion, or horizontal motion primarily occurred prior to the January 12 survey. In late February of 1998, peak velocities on the Scenic Drive landslide averaged 0.20 m/d (February 20–26, 1998) (Jayko et al., 1998), compared to 0.41 m/d average over 4 days in 2017. Horizontal displacements for the reactivated slide in 2005 averaged up to 0.31 m/d (Wells et al., 2005). Total horizontal displacement of the 2005 landslide was comparable to the 2017 slide, with maximum displacements measured at 5.31 m (Wells et al., 2005) (compared to 5.4 m on the 2017 slide). Horizontal displacements recorded during the 2006 reactivation were signiﬁcantly larger than all the other nearby slides. Between January and May of 2006, the slide moved more than 20 m, signiﬁcantly displacing both Scenic and Recreation Drives (Wells et al., 2006).

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Scarp heights in 1998 and 2017 were comparable, both measured at 3+ m. However, scarp heights on the 2005 and 2006 slides were signiﬁcantly larger than in 2017, measured at 7+ m. Cores taken during the 1998 slide indicated a slide depth of ∼8–9 m along a soft, wet clay gouge (Upp, 1998). Below this gouge, more intact sandstone of the Purisima Formation was encountered in boreholes (Upp, 1998). While no cores were conducted in association with this study, it is likely that the 2017 landslide reached a similar depth into the Purisima Formation as the 1998 slide, based on similar slide area, total displacement magnitude, scarp heights, and underlying geology. Rainfall Comparison and Triggering This region of California has a Mediterranean climate characterized by hot, dry summers and mild, typically wet winters. The central California coast’s water year typically includes three phases: (1) wetting, (2) excess moisture, and (3) drying (Jayko et al., 1998). Wetting occurs during the fall, when dry, shallow soil typically absorbs any moisture with little to no runoﬀ and no recharge into deeper layers (Jayko et al., 1998). Landslides as a result of precipitation during this time are unlikely (Jayko et al., 1998). The excess moisture phase occurs during the winter with

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prolonged and/or intense rainfall. Shallow soils become saturated, surface runoff is common, and deeper layers begin to receive excess rainfall through recharge (Jayko et al., 1998). Landslides are most likely to mobilize during this phase. The drying phase occurs during the spring and summer, when soil moisture decreases as rainfall declines below evapotranspiration rates, and any additional rainfall can be absorbed into shallow layers (Jayko et al., 1998). Landslides are typically not triggered during this time; however, landslides that initiated during the winter may continue to move. The winter of 2016–17 was the second wettest on record between the months of October and March, and the wettest on record between October and February since 1895, when record keeping began (Gomez, 2017). A direct relationship can be observed between the amount of rainfall and landslide displacement when antecedent rainfall conditions have sufficiently saturated the underlying soil. In 2016–17, antecedent rainfall in December of 159 mm likely saturated the ground, and intense and prolonged rainfall during the first 2 weeks of January 2017 immediately preceded and presumably triggered the 2017 motion of the Upper Scenic Drive landslide (Figure 9). Peak rainfall intensities on late January 8 and early January 9 were up to 5.6 mm/hr. A likely more important factor in triggering the landslide was the sustained rainfall between early morning January 7 and January 9, 2017, totaling 88.4 mm. Mitigation and Future Landslide Hazard Although motion of the 2017 Upper Scenic Drive landslide ceased at the end of the 2017 rainy season, the potential for future motion and damage on the slide is high, given the lack of permanent mitigation measures and proximity to numerous structures. In 2014, permanent mitigation measures (two large stitch pier retaining walls, a series of tie-backs emplaced along part of the headscarp to stabilize a section of upper Scenic Drive, extensive sub-surface and surface drains, and recontouring of much of the landslide surface) were completed as a result of previous slide activity. These measures were successful in stopping movement along the preexisting slide areas during the winter of 2016–17. During the active period of the 2017 slide, residents completed a number of temporary measures to mitigate landslide movement. A drainage trench was dug in the western extensional graben, to eliminate the ponding of water. Additional temporary pipes were placed around the slide to move water downslope. Large tarps covered much of the eastern portion of the landslide throughout the winter of 2017. A temporary water bar was also installed by San Mateo County during the winter to divert road drainage

away from the slide. A culvert was installed during the summer of 2017 to permanently divert this road drainage. Additionally, a drainage ditch along Woodland Vista, upslope of the slide, was lined with asphalt. CONCLUSIONS In this study, we used a UAV coupled with SfM photogrammetry to monitor an active landslide in La Honda, CA. We conducted seven surveys between January and May 2017. Each survey included field mapping, the collection of UAV aerial imagery, and measurement of GCPs using an RTK GPS. Orthophotos, 3D point clouds, and DEMs were created using SfM digital photogrammetry, with spatial resolutions ranging from ∼3 to 10 cm/pixel. We also correlated displacements with total rainfall and rainfall intensity measurements. We estimated horizontal landslide displacements by tracking features in SfM-generated orthophotos across the landslide. We measured a maximum horizontal displacement over the monitoring period of 5.4 m near the central toe portion of the slide. The creation of DoDs, through DEM subtraction, yielded a redistributed mass totaling ∼3,000 m3 of material over a 3 month period. Comparisons between the LiDAR DEM and SfM DSM showed that the two survey methods are comparable in the horizontal direction within 0.05 m. In the vertical direction, LiDAR and SfM are comparable within 0.20 m in unvegetated areas. This comparison between ground-based LiDAR and SfM shows that the UAV/SfM workflow is suitable for monitoring active mass-wasting processes in regions where landslides pose a direct threat to the surrounding community. The Upper Scenic Drive landslide is located within an area of unstable slopes, and future landslides within the region are likely. Slide motion within the region is driven by prolonged rainfall that saturates the landslide mass. We suggest that continued monitoring of the region be conducted during the winter months. The ability of the SfM method to accurately measure geomorphic change is largely dependent on the amount of vegetation within the study area, as vegetated areas can create large errors in the vertical dimension. While SfM is not capable of replacing airborne and terrestrial LiDAR, specifically in vegetated settings, it can be used as a quick, flexible alternative to create high-resolution topographic data. ACKNOWLEDGMENTS This research was supported by a Geological Society of America Student Research Grant and University of California–Davis Earth and Planetary Sciences

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Durrell Grant. Thanks go to the landowners of La Honda for providing access to their property, and Ari Delay, of the La Honda Fire Brigade, who shared a number of aerial and ground-based photographs to help aid this research. The manuscript was greatly improved by the comments of Elizabeth Haddon and Francis Rengers. Any use of trade, ﬁrm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. government.

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Turner, D.; Lucieer, A.; and de Jong, S. M., 2015, Time series analysis of landslide dynamics using an unmanned aerial vehicle (UAV): Remote Sensing, Vol. 7, pp. 1736–1757, doi:10.3390/rs70201736. Turner, D.; Lucieer, A.; and Watson, C., 2012, An automated technique for generating georectified mosaics from ultra-high resolution unmanned aerial vehicle (UAV) imagery, based on Structure from Motion (SFM) point clouds: Remote Sensing, Vol. 4, pp. 1392–1410, doi:10.3390/rs4051392. Upp, R. R., 1998, Geotechnical Investigation, Scenic Drive Landslide, San Mateo County, California: Report to San Mateo County, Upp Geotechnology, pp. 1–94. Wartman, J.; Montgomery, D. R.; Anderson, S. A.; Keaton, J. R.; Benot, J.; dela Chapelle, J.; and Gilbert, R., 2015, The 22 March 2014 Oso landslide, Washington, USA: Geomorphology, Vol. 253, pp. 275–288, doi:10.1016/j.geomorph.2015.10.022. Wells, R. E.; Rymer, M. J.; Prentice, C. S.; and Wheeler, K. L., 2005, Map Showing Features and Displacements of the Scenic Drive Landslide, La Honda, California, during the Period March 31–May 7, 2005: U.S. Geological Survey Open-File Report 2005-1191, 1 p. Wells, R. E.; Rymer, M. J.; Prentice, C. S.; and Wheeler, K. L., 2006, Map Showing Features and Displacements of the Scenic Drive Landslide, La Honda, California, during the Period March 31, 2005–November 5, 2006: U.S. Geological Survey Open-File Report 2006-1397, 2 p. Westoby, M. J.; Brasington, J.; Glasser, N. F.; Hambrey, M. J.; and Reynolds, J. M., 2012, “Structure-fromMotion” photogrammetry: A low-cost, effective tool for geoscience applications: Geomorphology, Vol. 179, pp. 300– 314, doi:10.1016/j.geomorph.2012.08.021. Wheaton, J. M., 2008, Uncertainty in Morphological Sediment Budgeting of Rivers: Unpublished Ph.D. Thesis, University of Southampton, Southampton, U.K., 412 p. Wheaton, J. M.; Brasington, J.; Darby, S. E.; and Sear, D. A., 2010, Accounting for uncertainty in DEMs from repeat topographic surveys: Improved sediment budgets: Earth Surface Processes and Landforms, Vol. 35, pp. 136–156, doi:10.1002/esp.1886. Woodget, A. S.; Carbonneau, P. E.; Visser, F.; and Maddock, I. P., 2015, Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry: Earth Surface Processes and Landforms, Vol. 40, pp. 47–64, doi:10.1002/esp.3613.

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Inﬂuence of Salt Tectonics on Fault Displacements and Submarine Slope Failures from Algeria to Sardinia JULIA A. YEAKLEY Gannett Fleming Engineers and Architects, P.C., 3770 Embassy Parkway, Akron, OH 44333

ABDUL SHAKOOR* Department of Geology, Kent State University, Kent, OH 44242

WILLIAM JOHNSON Rhea Engineers and Consultants, 441 Mars-Valencia Road, Valencia, PA 16059

Key Terms: Salt Tectonics, Diapirs, Fault Displacements, Submarine Slope Failures, Seismic Profiles ABSTRACT We used previously obtained marine geophysical and geotechnical data for the proposed Galsi pipeline route from Algeria to Sardinia to analyze the buried salt distribution, rates of fault displacements, and frequency and lateral extent of submarine slope failures. Crossing the convergent African/Nubian–European plate boundary, the southern section of the pipeline route traverses continental shelves and slopes of Algeria and Sardinia as well as the Algerian abyssal plain of the western Mediterranean. Deeply buried Messinian-aged salt is present throughout this area. Being less dense and more buoyant than the overburden sediment, the salt tends to flow upward to form diapiric structures that, in turn, result in the formation of faults and landslides in the overlying sediment. Measured offsets from seismic profiles of different resolutions were compared with predicted sediment age at depth of each offset, yielding an average rate of fault displacement of 1.5 cm/kiloyear (ky). The highest rates of displacement are along the Cagliari slope near Sardinia (2.5-2.7 cm/ky) and near the convergent plate boundary (2.3 cm/ky). Utilizing the same geophysical data, the frequency and lateral extent of submarine slope failures in the study area can also be linked to the distribution of salt and the influence of salt tectonics. Turbidity currents and hyperpycnal flows are present within the Algerian basin, whereas local debris flows, landslide runouts, and channelized debris flows are present along the Sardinian slope. The low sedimentation rates, determined in this study, suggest that the most recent slope failures related to salt tectonics occurred more than 12,000 years ago. *Corresponding author email: ashakoor@kent.edu.

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INTRODUCTION The purpose of this study is to investigate the effects of salt tectonics on fault displacements and submarine slope failures between Algeria and Sardinia. To accomplish this, we used data from marine surveys undertaken for the southern portion of the proposed Galsi pipeline, extending from the northeastern coast of Algeria to the southwestern tip of Sardinia (Figure 1). Physiographically, the pipeline route crosses the continental shelves and slopes of Algeria and Sardinia as well as the abyssal plain of the western Mediterranean (Nicholls, 2011). Geologically, the route crosses the convergent African/Nubian– European plate boundary along the edge of the Algerian continental shelf (Kherroubi et al., 2009; Meghraoui and Pondrelli, 2012) and the deeply buried Messinian-aged (late Miocene, about 5.4 million years ago [Ma]) salt, emplaced during the Messinian Salinity Crisis (MSC), whose location is not precisely known (Sage et al., 2005). The upward movement of salt in the deep Algerian-Balearic basin of the western Mediterranean results in the formation of faults and occurrence of large-scale slope failures in the overlying sediment (Deverchère et al., 2007). The Galsi pipeline data provide information needed for determining the nature and origin of diapiric structures, the location of faults associated with salt tectonics, rates of fault displacements, and the nature, extent, and ages of submarine slope failures. SEAFLOOR DEFORMATION, SALT DEPOSITION, AND TECTONICS The deformation of the seaﬂoor related to salt movement in the study area includes domes, depressions, faults, slope failures, and pockmarks (crater-like features associated with ﬂuid venting, gas expulsion, or

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Figure 1. Physiographic setting of southern portion of Galsi pipeline, from Algeria to Sardinia. Locations of the ﬁve UHR lines discussed in this study are shown in yellow (courtesy of Galsi SpA).

dissolution of salt caused by upward movement of diapiric structures). About 22–21 Ma, a block of continental crust, presently known as Sardinia, rotated away from France and reached its current location about 16–15 Ma (Rosenbaum et al., 2005). The Sardinia Channel, connecting the Algero-Provençal basin and the southwestern Tyrrhenian abyssal plain southeast of Sardinia, is a result of a compressional event with crustal thickening followed by extension and thinning (Mascle et al., 2001). This is significant because this area would normally be a part of the abyssal plain region if not for this crustal block and, consequently, has minimal sources of terrigenous sediment to collect along the slope (Johnson et al., 2011). With minimal sediment accumulation, seafloor features are better preserved, and sediment layers represent a large amount of time. The MSC affected the entire Mediterranean over a relatively short period of time (5.96–5.33 Ma) when the Mediterranean–Atlantic connection progressively closed (Garcia-Castellanos and Villasenor, 2011). This lack of connectivity between the Atlantic and the Mediterranean during the MSC most likely resulted from tectonic uplift of the Gibraltar arc seaway as well as global sea-level changes, both of which controlled— and still control—the inflow of water to the

Mediterranean (Garcia-Castellanos and Villasenor, 2011). Sea level in the Mediterranean dropped by as much as 1.5 km due to sub-aerial erosion along the margins. Evaporite deposition (anhydrite, halite, and potash), reaching a thickness of as much as 2 km, occurred in the deep basin, whereas carbonates were deposited in shallower regions (Pawlewicz, 2004; Bertoni and Cartwright, 2015). The thickness of mobile Messinian salt in the Algerian basin is approximately 1 km (Mauffret, 2007; Dal Cin et al., 2016). Overlying the evaporites is a Pliocene and Quaternary sequence of clastic sediments. Post MSC, rapid eustatic rise in sea level and flooding of the Mediterranean resulted in low sedimentation rates (Cita et al., 1978). Studies on the Gulf of Lion in the western Mediterranean documented a two-stage Zanclean transgression or infilling of the Mediterranean around 5.33 Ma (Bache et al., 2009). Primarily, the sea level first rose slowly, smoothing margins by wave abrasion, and later rose faster, preserving features of the MSC (García-Castellanos and Villasenor, 2011). According to Mauffret (2007), the distribution of thick salt generally corresponds to the location of the abyssal plain. However, a study of the continental slope, south of Sardinia, by Johnson et al. (2011) shows that thick salt is present at depths much shallower than the abyssal plain (Figure 2). The central Mediterranean is an area where detailed studies on the distribution of salt are scarce and where the stresses that have caused diapiric structures to form are not well defined or understood. The formation and movement of the late Miocene salt, originating from the cyclical flooding of the Mediterranean Sea by opening and closing of the Strait of Gibraltar, is key to understanding the processes responsible for the present-day seafloor morphology. Diapiric structures are commonly associated with passive margins that display thick layers of evaporites, dominated by halite, deposited during and/or immediately after continental rifting (Fort and Brun, 2011). Such salt basins lack stability, as salt is very weak and is able to flow under very low differential stresses, even at surface temperature conditions (Fort and Brun, 2011). The movement of salt (halokinesis) in passive margin basins is typically due to density inversion and differential loading and is influenced by the slope at the base of the salt layer (van der Pluijm and Marshak, 2004). In these passive environments, where there is no evidence of regional tectonic effects, the observed deformations are usually gravity driven. However, the situation along the Galsi pipeline route is not as straightforward. The Algerian margin corresponds to the plate boundary between the European and African plates with the Algerian basin to the north and an Alpinetype belt, called Maghrebides, to the south that formed

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Figure 2. South-to-north geologic cross section along the Galsi pipeline route, generated from the UHR seismic lines used in this study with focus on Messinian salt. The estimated rates of fault displacement and the corresponding UHR lines are shown on top of the cross section for later reference. (Note: This article presents data only for UHR lines 6, 8, 12, 13a, and 15 a; data regarding fault movement for all seismic lines can be found in Yeakley, 2018.)

from the subduction and closure of the Tethyan Ocean beneath the European plate during the Miocene (Leprêtre et al., 2013). Along the Algerian margin, there is an uplifted area with squeezed salt walls and anticlines aligned with the northwest-to-southeast crustal compressional stress direction that is associated with African–European plate interaction. The location of this area, as identified in recent studies by Kherroubi et al. (2009) and Domzig et al. (2010), is denoted by the red line in Figure 2. The nature of salt tectonics within the abyssal plain and the Sardinian continental slope is essentially unknown and is the focus of this study. Previous work on the Galsi pipeline data identified not only that evaporites are present along the Sardinian continental shelf and continental slope, away from the abyssal plain, but also that the movement of evaporites affects the geotechnical conditions along the seafloor, manifested as faulting and submarine slope failures (Johnson et al., 2011). The overall slope has different morphological characteristics that depend primarily on the position and degree of deformation from the upwelling salt as well as the thickness of overlying sediment (Johnson et al., 2011). However, previous studies did not investigate the details of the nature of salt movement in this area, especially the mechanisms and rates of movement. This study analyzes salt movement in terms of diapiric structures, the rates of displacement along the faults associated with diapiric structures, and the time between slope failure events caused by salt movement.

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RESEARCH METHODS Types of Data Used in the Study Geophysical Data Deep, two-dimensional, ultra-high-resolution (UHR) seismic profiles as well as much shallower, higherresolution Chirp sub-bottom profiles (SBPs) were used in this study. The UHR seismic reflection data were acquired by Fugro (a geotechnical firm) using a proprietary air gun system equipped with 24 channels, 12-fold acquisition, a 48-Hz low-cut filter, and an 824-Hz high-cut filter to obtain a penetration depth of more than 1 km below the seabed. The Chirp seismic profiles were obtained by a high-resolution autonomous underwater vehicle. They are named so after the “Chirping” technique, which consists of varying the amplitude and frequency of an emitted pulse in a fixed pattern in an attempt to reduce error once the received and emitted signals are cross correlated (McGee, 1995). The Chirp system operated between 2 and 7 kHz. Both UHR and Chirp profiles were analyzed using a free Russian software SeiSEE capable of viewing any SGY file. For analyzing the UHR data, the velocity of sound in seawater (1,500 m/s) (Reynolds, 2011) was used to estimate depth within unconsolidated sediments, with 100 ms of two-way travel time being approximately equivalent to 75 m. This assumes that the wave velocity in seawater is the same as in the sediments. Since we were interested in evaluating sediment deformations at the surface or shallow subsurface and since, based on geotechnical data, the

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near-surface sediments are mostly unconsolidated, assuming the velocity of wave in water to be the same as in the sediment is reasonable. Contrary to the name, UHR seismic profiles do not have nearly the resolution as the Chirp sub-bottom profiles, but they penetrate much deeper. Both UHR and Chirp profiles were used to understand the effects of salt tectonics and were interpreted together to understand how the deeper Messinian salt structures affect the shallow seafloor in terms of faulting and mass movements. Geotechnical Data Fifty-five coreholes were drilled along the proposed pipeline route. Corehole logs were prepared and core samples collected. Penetration resistance of soil sediment was measured using a piezo-cone penetration test and a T-bar test. Both tests were used to estimate the undrained shear strength of the fine-grained soils. The SEACALF cone penetrometer, with a frame generating 50 kN of force, and the STARCOR fixed-piston corer, with a 20-m core barrel, were also used. Vibrocoring was used for recovering undisturbed soil samples of sands and stiffer clays. Only 15 out of the 55 coreholes had useful carbon-14 dates for calculating relative sedimentation rates near the available UHR and Chirp seismic profiles. Bathymetric Data Multibeam echo sounder bathymetry data, obtained using Kongsberg Simrad EM300 and EM3000 systems with nominal frequencies of 30 and 300 kHz, respectively, supplemented by a Teledyne Reson SeaBat 8160 system operating at a nominal frequency of 50 kHz, were used to produce the base maps for the pipeline surveys. Seismic profiles are useful for identification and analysis of faults, folds, and failure deposits. However, depressions and other seafloor surface features, such as pockmarks, scarps, and failure runout distances, are more easily identified using sidescan sonar and digital elevation maps. For this study, high-resolution multibeam echo sounder bathymetric images supplement the seismic reflection data. Sidescan sonar transmits sound waves and analyzes the return signal or echo that bounces off the seafloor. The strength of the return echo is continuously recorded, creating an image of the seafloor (National Oceanic and Atmospheric Administration, 2017). Bathymetric images and side-scan sonar data allow accurate mapping of seafloor objects and locations for use in visualizing and correlating data (Brissette and Clarke, 1999). Surfer 10 mapping and analysis software was used to grid/layer bathymetric and side-scan sonar images of

Figure 3. Locations of UHR seismic lines and the associated Chirp proﬁles and coreholes superimposed on multibeam bathymetry image and shaded relief map. (a) Southern half of Algeria-to-Sardinia route. (b) Northern half of Algeria-to-Sardinia route.

the seafloor as well as to plot locations of seismic lines and coreholes used in this study (Figure 3). Plotting such locations allowed for cross correlation of all of the collected data. Determining the Sedimentation Rates We determined sedimentation rates from carbon14 dated samples obtained from different depths of the coreholes drilled in the seafloor. The ages of the sediment, determined by the carbon-14 method, were plotted against the corresponding depths, with the horizontal axis representing the age and the vertical axis

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Figure 4. Relationship between depth of corehole 15a-1 and carbon-14 dates used to determine the average sedimentation rate (denoted by dashed line) associated with the UHR 15a line.

representing the depth (or sediment thickness). The slope of the linear trend, connecting the data points, was used to estimate an average rate at which the sediment was deposited in that area, assuming a constant rate of deposition. For example, samples from depths of 32, 128, 177, and 1,097 cm from corehole 15a-1, associated with the UHR 15a line, resulted in ages of 1.54, 8.07, 12.91, and 47.35 ky, respectively, yielding an average sedimentation rate of 22 cm/ky (Figure 4). Calculations for determining the sedimentation rate from all 15 coreholes can be found in Yeakley (2018). Where coreholes were not present, the sedimentation rates were extrapolated between coreholes, assuming consistency. Data from diﬀerent coreholes show that higher rates of sedimentation occur closer to Algeria and decrease northward toward Sardinia (Figure 5). However, overall sedimentation rates along the entire area of interest are low. From dated core samples, it is possible to identify how much sediment accumulated in a given area within a certain time. This information was then used to roughly determine the age of sediment near faults, calculate rates of fault displacements, and estimate time intervals between slope failures. Identifying Salt Diapirs Salt diapirs form in diﬀerent ways. Active diapirism occurs when diapirs pierce through fault zones formed

Figure 5. Plot of sedimentation rates based on carbon-14 dated data from corehole samples showing an overall low rate of sedimentation along the proposed route with higher rates in the south (near Algeria) and lower rates in the north (near Sardinia).

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due to rising salt, passive diapirism is when a diapir develops in response to sediment buildup, and reactive diapirism is when a diapir rises through a fault zone formed due to regional extension (Karam and Mitra, 2016). Diapiric structures associated with salt movement are present in every UHR seismic profile analyzed in this study, as indicated by the strong reflective signal of the Messinian erosional surface. Salt is nonporous and lacks the ability to compress. Therefore, salt does not increase in density when buried but rather tends to behave like a fluid and rise, especially under increased temperature and pressure. When denser sediment overlies less dense salt, density inversion takes place, and the salt becomes buoyant (van der Pluijm and Marshak, 2004). Heavier sediment tends to accumulate in depressions, and salt continues to move away to form ridges and diapiric or dome-like structures. Salt diapirs push on overlying sediments, causing anticlines and faults to form, the features used to identify diapirs. As the pressure from below increases, so does the potential for active piercement. Once a diapir pierces the sediment, passive diapirism takes over while the overburden resists the driving forces of the rising salt. Therefore, a thin overburden sediment above a diapiric ridge is more susceptible to active piercement through differential loading because of a decrease in resistance and an increase in driving force (Shultz-Ela et al., 1993). Differential loading creates greater vertical load on different parts of the salt diapirs and can squeeze salt from areas of higher pressure to areas of lower pressure (van der Pluijm and Marshak, 2004). If salt diapirs continue to push on the overlying sediments, slopes steepen and can result in slope failure events. RESULTS Influence of Salt Tectonics on Faulting and Fault Displacements The most dominant effect of salt on the overlying sediment is faulting caused by the upward movement of diapiric salt structures. These faults are visible on UHR seismic profiles and were quantitatively analyzed using higher-resolution Chirp seismic profiles in conjunction with carbon-14 dated corehole data. For the sake of brevity, only three seismic lines, with salt-related faulting, are discussed below. Locations along the seismic profiles are given in terms of the seismic source locations, referred to as shotpoints, during data acquisition. Since the original nomenclature of Chirp lines and coreholes involves long and cumbersome terms to keep track of, we used a modified nomenclature, matching the UHR lines nomenclature, for ease of reference (Table 1).

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Salt Tectonics Table 1. Original and modified nomenclature of Chirp profiles and coreholes associated with the UHR seismic lines. The modified nomenclature is used in this study. UHR Line Nomenclature

Associated Chirp Proﬁle Nomenclature

Modiﬁed Chirp Proﬁle Nomenclature

Associated Corehole Nomenclature

Modiﬁed Corehole Nomenclature

UHR 6 UHR 8 UHR 12 UHR 13a

ASF_2c1.01 xl_39c.01 asz6_1ya HR_ASF_3-1 ASGH_5-2 HR_ASB_0-2

Chirp 6 Chirp 8 Chirp 12 Chirp 13a-1 Chirp 13a-2 Chirp 15a

L-BC-OSAS-29 L-SC-OSAS-18 L-BC-OSAS-08 L-BC-OSAS-11 L-GC-OSAS-20A L-BC-OSAS-59 L-SC-OSAS-56A

Corehole 6 Corehole 8 Corehole 12 Corehole 13a-1 Corehole 13a-2 Corehole 15a-1 Corehole 15a-2

UHR 15a

UHR 15a Line The UHR 15a line is associated with Chirp proﬁle 15a (running nearly parallel to each other) as well as with coreholes 15a-1 and 15a-2 (Figure 3a). Age dating of corehole 15a-1 samples yielded an average sedimentation rate of 22 cm/ky (Figure 4). Close to corehole 15a-1, faults are present along the UHR 15a line between shotpoints 1600 and 1750 (Figure 6). The associated 15a Chirp line shows two faults designated as Fault 1 and Fault 2, with Fault 1 located at shotpoint 10250 (Figure 7a) and Fault 2 located between shotpoints 15200 and 15400 (Figure 7b). The average rates of displacement calculated for Fault 1 and Fault 2 are 2.5 and 1.8 cm/ky, respectively (Table 2). These rates are slightly higher than the overall average rate of movement along faults near the proposed pipeline (1.5 cm/ky). Local sedimenta-

Figure 6. Faults along the UHR 15a seismic line.

tion rate was used to estimate age at a certain depth where offset in strata had occurred by the fault. The offset divided by the estimated age provided the rate of fault movement. Increased offset with depth is typical of salt-related movement, assumed to be due to slow creep (Table 2). Fault 1 and Fault 2 show a continuous creep, comparable to salt tectonics, and it may be that the only driving force for these surface faults is from halokinesis, as they are not deep-rooted. They may represent tensional conditions forming at the crest of an active salt dome. Both faults appear to be growth faults, which are normal faults that develop and continue to move during sedimentation and typically have thicker strata on the downthrown side. They take millions of years to mature and start with sediment deposited on top of a thick evaporite layer.

Figure 7. Chirp 15a seismic proﬁle, associated with the UHR 15a line, showing the presence of: Fault 1 (a) and Fault 2 (b).

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Yeakley, Shakoor, and Johnson Table 2. Depths and oﬀsets of fault segments used for estimating the rates of movement for Fault 1 and Fault 2 along Chirp proﬁle 15a.

Fault 1

Depth of Fault Segment (cm)

Oﬀset of Fault Segment (cm)

Estimated Age of Sediment (ky)

Estimated Rate of Fault Movement (cm/ky)

400 700 950 1,250

50 75 90 150

18 32 43 57

1,400 1,850 2,225

100 150 200

64 84 101

2.8 2.4 2.1 2.6 2.5 1.6 1.8 2.0 1.8

Average rate of fault movement 2

Average rate of fault movement

of movement along these faults cannot be accurately assessed due to the surrounding failure deposits. UHR 12 Line

Figure 8. UHR 6 seismic proﬁle displaying diapiric piercement and multiple faults caused by salt tectonics.

UHR 6 Line The UHR 6 seismic line is oriented from south to north and corresponds to Chirp 6 seismic line as well as the nearby corehole 6 (Figure 3b). In UHR 6 seismic profile, faults are closest to the surface of the seafloor near shotpoint 1300 but are also present at deeper levels between shotpoints 700 and 1300 (Figure 8). Faults are also observed within the Chirp 6 seismic profile near shotpoints 3700 and 4000 (Yeakley, 2018). Based on the corehole 6 information, the calculated sedimentation rate is 17.3 cm/ky (Table 3). However, the rate

The UHR 12 seismic line runs east to west and crosses the south to north Chirp 12 seismic line near shotpoint 600 on UHR 12 and shotpoint 6200 on Chirp 12 (Figure 3b). Corehole 12 is located south of the intersection of the two seismic lines but is the closest available corehole (Figure 3b). Faulting present in UHR 12 is buried deep between shotpoints 400 and 600, whereas both shallow and deep faults are visible between shotpoints 800 and 900 (Figure 9). A detailed analysis of Chirp 12 seismic proﬁle shows the presence of two faults associated with salt diapirism, referred to as Fault 1 and Fault 2, with Fault 1 located near shotpoint 4000 and Fault 2 located between shotpoints 3600 and 3700 (Figure 10). Fault 1 is a reverse fault and Fault 2 a normal fault. Although halokinesis generally produces normal faults, it is obvious from Figure 10 that salt tectonics can result in both compressional stresses required for the formation of reverse faults and tensional stresses required for the

Table 3. Average sedimentation rates determined from carbon-14 dated coreholes 6, 12, and 13a-2 located in the vicinities of UHR lines 6, 12, and 13a.

Corehole 6 12 13a-2

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Rate of Sedimentation (cm/ky)

404,221 415,736 411,335

4,258,920 4,279,218 4,265,174

17.3 4.6 5.0

Figure 9. UHR 12 seismic proﬁle showing both shallow and deep faulting.

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Figure 10. Chirp 12 seismic proﬁle, associated with the UHR 12 seismic line showing the presence of two faults, designated as Fault 1 and Fault 2.

formation of normal faults at a short distance from each other. Dates from corehole 12 indicate a sedimentation rate of approximately 4.6 cm/ky (Table 3). Using this sedimentation rate, rates of displacement along Faults 1 and 2 in Chirp 12 seismic profile were calculated as 4.0 and 0.9 cm/ky, respectively. The rate of displacement along Fault 1 is significantly higher than the overall average rate of fault movement (1.5 cm/ky) near the proposed pipeline. Overall, the rates of fault displacement are low with the highest rates of displacement for faults along the Cagliari slope near Sardinia (2.5–2.7 cm/ky) and near the convergent plate boundary (2.3 cm/ky) (Figure 2). While halokinesis appears to influence the seafloor, these low rates of displacement suggest that the salt diapirs in the area are rising at a slow rate.

Influence of Salt Tectonics on Slope Failures Submarine slope failures, pervasive along the Mediterranean continental margins, can be a major hazard to underwater pipelines (Urgeles and Camerlenghi, 2013). Due to decreased frictional resistance to sliding, because of pore pressure, submarine slope failures can occur on much gentler slopes and can spread for much greater distances than terrestrial slope failures. The presence of widespread Messinian salt at relatively shallow depths promotes fluid flow and deformation due to salt diapirism within the continental margins (Urgeles and Camerlenghi, 2013). Data collected for the proposed pipeline indicate that faults associated with halokinesis dominate near the Algerian continental slope, turbidity currents and hyperpycnal flows are present within the Algerian basin, and local debris flows, landslide runouts, and channelized debris flows are present along the Cagliari slope (Johnson et al., 2011).

Figure 11. Chirp 6 seismic proﬁle, associated with the UHR 6 seismic line showing the presence of three landslide deposits in the northern portion (a) and ﬁve landslide deposits in the southern portion (b).

This study identifies several erosion channels, failure scarps, and failure deposits within the upper Cagliari slope south of Sardinia. The slope failures resulted from slow movement of the underlying salt. Also, many of the runout channels and scarps look fresh, as if the failure events occurred recently. However, due to lack of aerial weathering, most traces of slope failures are significantly older (>12,000 years) than they appear. The age of a submarine slope failure was determined by the age of sediment covering the failure deposits, which, in turn, was determined from the calculated sedimentation rates. The following sections describe selected examples of UHR and Chirp profiles where failure events are present. UHR 6 Line The UHR 6 line is associated with Chirp 6 profile (Figure 3). The Chirp profile shows more failure events than faults in the shallow subsurface. The abundance of these failures is evident from the northern and southern portions of the Chirp seismic profile (Figure 11). In the northern portion of the Chirp 6 profile, there are three failure events, varying from 100 to 500 cm thick, which occurred over the past 58,000 years (Figure 11a). Based on the sedimentation rate of 17.3 cm/ky (Table 3), the most recent slope failure occurred about 12,000 years ago, and the recurrence intervals between failures are 17,000 years and

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Yeakley, Shakoor, and Johnson Table 4. Ages of three failure events in the northern portion and five failure events in the southern portion of Chirp 6 seismic profile as calculated by the best interpretation of undisturbed sedimentary cover. Chirp 6 Seismic Profile Northern portion

Southern portion

a b

Slope Failure Event

Failure Deposit Thickness (cm)

Sediment Cover Thickness (cm)a

Age (ky)b

1 2 3 1 2 3 4 5

100 300 500 250 250 300 500 400

200 500 1,000 200 700 750 1,200 1,400

12 29 58 12 40 43 69 81

Excluding thickness of any slope failure deposits considered to have occurred instantaneously. Assuming average sedimentation rate of 17.3 cm/ky.

29,000 years (Table 4). In the southern portion of Chirp 6 proﬁle, there are ﬁve failure events, between 250 and 400 cm thick, which occurred within the past 81,000 years (Figure 11b). Based on the estimated sedimentation rate of 17.3 cm/ky (Table 3), the most recent failure event occurred roughly 12,000 years ago, and the events occurred anywhere between 3,000 and 28,000 years apart from one another (Table 4).

UHR 13a Line The UHR 13a seismic line is associated with Chirp 8 and Chirp 13a seismic lines and with coreholes 8, 13a1, and 13a-2 (Figure 3b). Corehole 13a-1 did not have useful carbon-14 data for use in this study. The UHR 13a seismic profile shows clear evidence of diapiric piercement around shotpoint 1900 (Yeakley, 2018), which was correlated with mass movement events to the north and south. Due to low sedimentation rate and the absence of aerial erosion, failure features near UHR 13a can be mapped on Chirp profile, side-scan sonar image, and bathymetric map (Figure 12). The oldest failure event (Event 1) in the northern portion of Chirp profile 13a is about 400 cm thick and has approximately 600–700 cm of stratified sediment on top of it, not including the thickness of the shallower failure deposits, which are assumed to have occurred instantaneously (Figure 13a). Based on the average sedimentation rate of about 5.0 cm/ky from coreholes 8 and 13a-2 (Table 3; Yeakley, 2018), the first failure (Event 1) occurred about 130,000 years before present (Table 5). The second failure event (Event 2) is 450 cm thick and is covered with about 200–250 cm of stratified sediment to the surface, placing its age to about 45,000 years before present (Table 5). The most recent event in this area (Event 3) is draped by about 100 cm of sediment, making it about 20,000 years old (Table 5). The recurrence interval between

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Figure 12. Bathymetric image, with UHR 8, UHR 13a, Chirp 8, Chirp 13a-1, corehole 8, and corehole 13a-2 superimposed showing areas aﬀected by slope failures (in red).

failure events in this portion varies between 20,000 and 90,000 years. In the southern portion of Chirp proﬁle 13a, six different failure events were observed (Figure 13b). Using the same sedimentation rate of roughly 5.0 cm/ky, the youngest failure event in the southern portion, with a thickness of 250 cm and a sedimentary cover of 60 cm, occurred 12,000 ago, whereas the oldest failure event, 400 cm thick with a 320 cm of sedimentary cover, not including more recent failure deposits, has an approximate age of 64,000 years (Table 5). The recurrence intervals vary from 4,000 to 16,000 years.

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Figure 13. Chirp 13a-1 seismic proďŹ le, associated with the UHR 13a-1 seismic line, showing the presence of three landslide deposits in the northern portion (a) and six landslide deposits in the southern portion (b).

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Yeakley, Shakoor, and Johnson Table 5. Ages of three failure events in the northern portion and six failure events in the southern portion of chirp 13a-1 seismic proﬁle as calculated by the best interpretation of undisturbed sedimentary cover. Chirp 13a Seismic Proﬁle Northern portion

Southern portion

a b

Slope Failure Event

Failure Deposit Thickness (cm)

Sediment Cover Thickness (cm)a

Age (ky)b

1 2 3 1 2 3 4 5 6

400 450 350 400 150 500 200 200 250

650 225 100 320 280 200 140 80 60

130 45 20 64 56 40 28 16 12

Excluding thickness of any slope failure deposits considered to be instantaneous. Assuming average sedimentation rate of 5 cm/ky.

DISCUSSION Evidence of shallow faulting is present in all the seismic proﬁles obtained along the proposed pipeline but is more dominant near Algeria. In contrast, more slope failure events aﬀect the Cagliari slope near Sardinia. The greater number of slope failures on the Cagliari slope is most likely due to the shallower nature of the salt present. The active margin, near Algeria, versus the passive margin, near Sardinia, is also a key factor that contributes to variable amounts of fault displacement, rates of displacement, and slope failure events in relation to salt diapirism. The average rate of movement along all faults analyzed in this study is 1.5 cm/ky, with the highest rates of displacement found along the Cagliari slope near Sardinia (2.5– 2.7 cm/ky) and near the convergent plate boundary (2.3 cm/ky), where tectonic activity may be increasing the rate at which the salt is being squeezed and forced upward (Figure 2). The data collected for the GALSI pipeline show how the movement of an active salt dome at depth manifests at the surface in the form of many small normal faults. The fault movement near the convergent plate boundary may be related to the compressional tectonic activity. Comparing passive versus active margins, faults near the active margin of Algeria and the convergent zone as well as those near the passive margin of Sardinia exhibit higher rates of displacement. The higher rates of displacement near the passive margin are due to either the absence of a sedimentary cover or a thin sedimentary cover. Using UHR lines 7–12 for the Cagliari slope, we estimated locations and extents of near-surface salt diapirs (green-shaded areas) as well as those buried by sediment (yellow-shaded areas) (Figure 14). The Cagliari slope is more susceptible to deformation from underlying salt movement because it takes less force to deform the overlying thin sediment. Even though salt tecton-

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ics have a significant effect on the seafloor deformation along the proposed pipeline route between Algeria and Sardinia, the rate of salt movement appears to be quite slow. Multiple small slope failures linked to the upward movement of salt diapirs near the Cagliari slope occurred more than 12,000 years ago. The amount of time between failure events varies between 4,000 and 85,000 years. The large amount of time between failure events corroborates the slow rates of salt movement and consequently of fault displacement along the proposed pipeline route.

Figure 14. Predicted locations of salt diapirs on the Cagliari slope, south of Sardinia, as indicated by UHR lines 7–12.

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For proper evaluation of the results and scope of this study, it is important to consider its limitations. The rates of movement along faults and the amount of time between submarine slope failure events were determined during the study. However, because of the availability of only two-dimensional seismic data, neither the horizontal extent of the faults nor the aerial extent or volume of slope failures could be determined. Availability of three-dimensional seismic data could provide more comprehensive information for future studies. The study also makes assumptions in order to estimate fault displacement rates and the frequency of submarine slope failures. These assumptions constitute additional limitations of the study. It was assumed that minimal movement of failure deposits occurred since their accumulation, allowing their depth-based ages to be representative. Furthermore, a constant sedimentation rate was assumed in this study. However, studies on the Gulf of Lion in the western Mediterranean note that the Zanclean transgression (infilling of the Mediterranean at 5.33 Ma) happened in two stages (Bache et al., 2009). During the first stage, the sea level rose slowly, smoothing margins by wave abrasion, whereas during the second stage, sea level rose faster, preserving features of the MSC (García et al., 2011). Assuming a constant rate of sedimentation ignores the effects of these two stages. It is important to note that Quaternary accumulation of sediment has been increasing and has since reached rates similar to those before the MSC (Cita et al., 1978). This study does not account for such fluctuations in sedimentation rate, sea level changes related to glacial and interglacial cycles, and the associated sediment load fluctuations and climatically driven continental loads. In addition, the sedimentation rates based on corehole data are accurate only for the areas where the coreholes were drilled. Away from the corehole, the sedimentation rate could be different, and the area could be affected by different seafloor features. Depths at which dates are calculated from may have been impacted by multiple slide events, and the information presented may not be as straightforward as it appears. Nevertheless, the overall interpretation that small slope failures in this area happened infrequently but persistently is valid and expected from long-term slow salt movement. Finally, no corrections were made to the data to account for the compaction of sediment over time. CONCLUSIONS The proposed route of the Galsi pipeline has many faults associated with halokinesis that exhibit higher rates of displacement near the compressional zone of the boundary between the European and African continental plates and on the Sardinian slope. The com-

pression near the plate boundary is the most likely cause for salt to squeeze and for diapiric structures to rise upward into the overlying sediment at a faster rate. The shallower diapirs near Sardinia is the probable cause of higher rates of displacement along faults on the Sardinian slope. Although the rates of displacement are relatively higher in these areas, the overall rate of movement is low (1.5 cm/ky). Submarine slope failures are frequent along the Sardinian (Cagliari) slope, where piercing of diapiric structures occurs because of thinner sediment cover, resulting in the steepening of slopes. Due to a low sedimentation rate and the lack of aerial erosion, submarine failure scarps, deposits, and runout features are well preserved and can be mapped even after thousands of years. ACKNOWLEDGMENTS All seismic profiles and corehole data used in this study are courtesy of Galsi Pipeline Project. Offshore geophysical data were provided by D’Appolonia, Pittsburgh, PA, with permission from James Nicholls, marine geoscientist, formerly with the Galsi Project and currently with Flintshire Geoscience Ltd, Port Erin, British Isles. This project would not have been possible if it were not for James Nicholls. Thank you James Nicholls for the opportunity to work with such unique and extensive data. REFERENCES Bache, F.; Olivet, J. L.; Gorini, C.; Rabineau, M.; Baztan, J.; Aslanian, D.; and Suc, J. P., 2009, Messinian erosional and salinity crises: View from the Provence Basin (Gulf of Lions, Western Mediterranean): Earth and Planetary Science Letters, Vol. 286, Issue 1, pp. 139–157. Bertoni, C. and Cartwright, J., 2015, Messinian evaporites and fluid flow: Marine and Petroleum Geology, Vol. 66, Pt. 1, pp. 165–176. Brissette, M. B. and Clarke, J. E., 1999, Side scan versus multibeam echosounder object detection: A comparative analysis: International Hydrographic Review, Monaco, Vol. 76, Issue 2, pp. 21–34. Cita, M. B.; Ryan, W. B. F.; and Kidd, R. B., 1978, Sedimentation Rates in Neogene Deep-Sea Sediments from the Mediterranean and Geodynamic Implications of Their Changes: Initial Reports of the Deep-Sea Drilling Project, pp. 991–1002 Dal Cin, M.; Del Ben, A.; Mocnik, A.; Accaino, F.; Geletti, R.; Wardell, N.; Zgur, F.; and Camerlenghi, A., 2016, Seismic imaging of Late Miocene (Messinian) evaporites from Western Mediterranean back-arc basins: Petroleum Geoscience, doi:10.1144/petgeo2015-096. Deverchère, J.; Yelles, K.; Domzig, A.; Mercier De Lépinay, B.; Cattaneo, A.; Gullier, V.; and Kherroubi, A., 2007, Overall Tectonic Pattern of the Algerian Margin: Evidence for Active Folding and Thrusting from the 2003 and 2005 MARADJA Cruises: Rapports et ProcèsVerbaux des Réunions de la Commission Internationale

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McGee, T. M., 1995, High-resolution marine reflection profiling for engineering environmental purposes, Part A: Acquiring analogue seismic signals: Journal of Applied Geophysics, Vol. 33, pp. 271–285. Meghraoui, M. and Pondrelli, S., 2012, Active faulting and transpression tectonics along the plate boundary in North Africa: Annals of Geophysics, Vol. 55, No. 5, doi:10.4401/ag4970. National Oceanic and Atmospheric Administration, 2017, U.S. Department of Commerce, Office of Coast Survey. Nicholls, J., 2011, Survey assesses geohazards for record subsea pipeline: Offshore Magazine, http://www.offshore-mag.com/ articles/print/volume-71/issue-1/flowlines-__pipelines/surveyassesses-geohazards-for-record-subsea-pipeline.html. Pawlewicz, M., 2004, The Pre-Messinian Total Petroleum System of the Provence Basin, Western Mediterranean Sea: USGS Bulletin 2204-A, http://pubs.usgs.gov/bul/b2204-a/b2204a.html. Reynolds, J. M. 2011. An Introduction to Applied and Environmental Geophysics, 2nd ed.: John Wiley and Sons, West Sussex, U.K., 710 p. Rosenbaum, G.; Regenauer-Lieb, K.; and Weinberg, R., 2005, Continental extension: From core complexes to rigid block faulting: Geology, Vol. 33, pp. 609–612. Sage, F.; Gronefeld, G. V.; Déverchère, J.; Gaullier, V.; Maillard, A.; and Gorini, C., 2005, Seismic evidence for Messinian detrital deposits at the western Sardinia margin, Northwest Mediterranean: Marine and Petroleum Geology, Vol. 22, pp. 757–773. Shultz-Ela, D. D.; Jackson, M. P. A.; and Vendeville, B. C., 1993, Mechanisms of active salt diapirism: Tectonophysics, Vol. 228, pp. 275–312. Urgeles, R. and Carmerlenghi, A., 2013, Submarine landslides of the Mediterranean Sea: Trigger mechanisms, dynamics, and frequency-magnitude distribution: Journal of Geophysical Research, Earth Surface, Vol. 118, pp. 2600–2618. van der Pluijm, B. A. and Marshak, S., 2004, Earth Structure: An Introduction to Structural Geology and Tectonics, 2nd ed.: W. W. Norton & Company, Inc., 500 Fifth Avenue, New York, N.Y. 10110, pp. 26–30. Yeakley, Y. A., 2018, Influence of Salt Tectonics on Seafloor Morphology from Algeria to Sardinia: MS Thesis, Kent State University, Kent, OH, 121 p.

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Quality Appraisal of Groundwater in Arid Regions Using Probabilistic and Deterministic Approaches MILAD EBRAHIMI* Louisville and Jeﬀerson County Metropolitan Sewer District, 700 West Liberty Street, Louisville, KY 40203

HAMIDREZA KAZEMI Center for Infrastructure Research, University of Louisville, 132 Eastern Parkway, Louisville, KY 40292

MAJID EHTESHAMI Department of Civil and Environmental Engineering, K. N. Toosi University of Technology, 1346 Vali-Asr St. Mirdamad Boulevard, Tehran, Iran

THOMAS D. ROCKAWAY Center for Infrastructure Research, University of Louisville, 132 Eastern Parkway, Louisville, KY 40292

Key Terms: Groundwater Quality, Factor Analysis, Cluster Analysis, Water Quality Index, Multi-Hazard Risk Assessment ABSTRACT This study explores using probabilistic and deterministic approaches for evaluating the quality of groundwater resources. The proposed methodology first used the probabilistic approach, which included multivariate statistical analysis, to classify the groundwater’s physiochemical characteristics. Then, building on the obtained results, the deterministic approach, which included hydrochemistry analyses, was applied for comprehensive assessment of groundwater quality for different applications. To present this multidisciplinary approach, a basin located in an arid region was studied. Considering the results from correlation and principal component analyses, along with hierarchical Q-mode cluster analysis, chloride salts dissolution was identified within the aquifer. Further application of the deterministic approach revealed degradation of groundwater quality throughout the basin, possibly due to the saltwater intrusion. By developing the water quality index and a multi-hazard risk assessment methodology, the suitability of groundwater for human consumption and irrigation purposes was assessed. The obtained results were compared with two other studies conducted on aquifers under similar arid climate conditions. This comparison indicated that qual-

*Corresponding author email: milad.ebrahimi@louisvillemsd.org

ity of groundwater resources within arid regions is prone to degradation from salinization. The combined consideration of probabilistic and deterministic approaches provided an eﬀective means for comprehensive evaluation of groundwater quality across diﬀerent aquifers or within one.

INTRODUCTION As populations expand and urbanization increases, the associated water demands put signiﬁcant pressures on natural water resources. Groundwater, as a common source for residential, agricultural, and industrial demands, has been subjected to tremendous deterioration, especially in arid regions. To provide a sustainable source, understanding the temporal and spatial ﬂuctuation of water quality is essential considering climate changes and local environmental pressures. Thus, for arid regions with low rainfall rates and limited groundwater resources, a robust groundwater quality investigation is crucial to promulgate regulations for better environmental management and human health protection. For arid regions, many studies have been conducted to understand the groundwater chemistry processes and establish procedures necessary for water quality assessments (Meng and Maynard, 2001; Kim et al., 2005; Vasanthavigar et al., 2010; Iranmanesh et al., 2014; Moya et al., 2015; and Niu et al., 2017). In general, these studies are either deterministic or probabilistic in their origin.

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For the probabilistic approach, statistical algorithms, such as correlation, clustering, factor analysis, and measurement uncertainty, are used to provide a classification scheme for categorizing the physicochemical properties of aquifer and to identify the anthropogenic sources of contamination governing groundwater quality deterioration (Güler et al., 2002; Belkhiri et al., 2011; Rastogi and Sinha, 2011; Machiwal and Jha, 2015; and Wator ˛ et al., 2016). For the deterministic approach, a geochemical analysis of groundwater samples within the aquifer is conducted by using traditional graphical methods and diagrams, such as Piper, Wilcox, or US Salinity Laboratory Staff plot (Herczeg et al., 2001; Yidana and Yidana, 2010; Jamshidzadeh and Mirbagheri, 2011; Ebrahimi et al., 2016; and Zaidi et al., 2016). These studies evaluated the suitability of samples for drinking and irrigation purposes by comparing groundwater physicochemical parameters with pre-established quality standards and indicators. Both types of studies have presented significant information regarding the aquifer’s quality condition. The probabilistic approach, while strong for comparison purposes, does not provide meticulous results based on individual quality indicators, especially for waters under severe adverse environmental impacts. The deterministic approach delivers results pertaining to individual sampling wells. However, it is challenging to compare the overall groundwater quality conditions across several basins or for temporal analyses by using this approach alone. The combination of statistical methods in conjunction with the traditional water quality assessment techniques can provide a rigorous procedure to draw meaningful results of the overall groundwater quality for the investigated basins. The objective of this study is to utilize a methodology that combines the probabilistic and deterministic approaches for assessing aquifer’s quality condition. For this purpose, a 300-km2 basin under arid weather conditions was selected as the case study. The intent of this work was to identify: (1) groundwater classification scheme, (2) processes governing the groundwater chemistry, (3) hydrochemical characteristics of groundwater, and (4) suitability of the groundwater for drinking and agricultural purposes.

METHODS The proposed methodology ﬁrst uses the probabilistic approach to classify wells based on physicochemical properties of groundwater. Then, based on the obtained results, the deterministic approach is applied for comprehensive assessment of groundwater quality for diﬀerent applications.

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For the probabilistic approach, the multivariate statistical techniques were applied. To determine if the specific parameters were statistically correlated, Pearson product moment correlation analysis was performed (Cohen et al., 2013). To classify sampling wells into finite statistically distinct hydrochemical groups based on their similarities, cluster analysis (CA) was used (Kaufman and Rousseeuw, 2009). To simultaneously evaluate the correlations among several variables and to reduce the total data set dimension, principal component analysis (PCA) was conducted (Mackiewicz and Ratajczak, 1993). By concurrent use of probabilistic and deterministic approaches, the processes responsible for groundwater quality deterioration were evaluated. The deterministic approach included traditional groundwater classification techniques. To investigate the suitability of groundwater for drinking, the standards by the World Health Organization (WHO) were exercised. Additionally, the water quality index (WQI) was developed to score the combined influences of individual quality variables on the overall groundwater quality for human consumption. Finally, to assess the suitability of groundwater for agricultural activities, a hazardbased study of groundwater mineral compounds was conducted. The Study Area The 300-km2 Shiraz basin lies in south-west Iran. The plain lies between longitudes 520 29 and 520 36 E and latitudes 290 33 and 290 36 N. The superficial plain of the study area ranges from 1,400 to 3,100 m above mean sea level. The basin is surrounded on the north and northwest by mountains. On the south, it is located in the vicinity of the 250-km2 Maharloo Salt Lake, which has dominant water salinity compositions of sodium-chloride-magnesium and sodiumsulfate; see Figure 1. The Shiraz basin is an alluvial aquifer with sequences of sand and clay. It includes a surface unconfined aquifer and a deep aquifer with approximate depths of 40 and 160 m, respectively. The geological formation of the plain is characterized by shales and gypsiferous marls near the ground surface and with sandstone and conglomerate at depth. Based on 50 years of recorded data, the average annual temperature for this plain is 18.2ºC, and the mean annual precipitation is 338 mm. The 106-mm minimum and 578-mm maximum annual recorded precipitations occurred in 1983 and 1995, respectively. Groundwater Sampling The groundwater quality study in the Shiraz basin included 310 samples collected from 23 wells

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Groundwater Appraisal in Arid Regions Table 1. Descriptive statistics of the groundwater physicochemical parameters. Parameter (unit)

Mean

Median

Minimum

Maximum

Standard Deviation

pH TDS (mg/L) EC (µs/cm) TH (mg CaCO3 /L) Ca (mg/L) Na (mg/L) K (mg/L) Mg (mg/L) CO3 (mg/L) HCO3 (mg/L) SO4 (mg/L) Cl (mg/L)

7.6 2,324.1 3,125.6 1,302.2 228.3 304.5 7.0 180.5 2.9 424.4 648.9 657.0

7.4 1,395.0 2,064.0 962.5 183.6 132.9 3.9 112.4 0.0a 396.6 366.0 257.0

6.9 412.0 596.0 182.5 40.8 21.8 1.0 20.0 0.0a 131.2 18.3 61.2

9.8 11,800.0 10,107 4,950.0 688.5 3,408.5 41.6 808.0 28.5 732.2 2,945.7 6,859.6

0.7 2,525.6 2,660.7 1,197.7 172.2 694 9.4 211.5 6.9 185.4 747.7 1,410.9

Below detection limit.

during 2014. To determine the physicochemical parameters, all collected samples were tested per the standard methods (Rice et al., 2012). The statistical summary of diﬀerent quality parameters and major ions is presented in Table 1. All parameters were found to be non-normally distributed based on the calculated signiﬁcance level of less than 0.05. ANALYSIS AND DISCUSSION Probabilistic Assessment of Groundwater Quality For probabilistic assessment of the groundwater, a multivariate statistical procedure was employed. The proposed approach, which includes correlation, cluster, and principal component analyses, is a controlling mathematical method for categorizing and interpreting large data sets in environmental monitoring programs (Liu et al., 2003; Niaki and Jahani, 2013). The

Figure 1. Study area.

numerical analyses performed in this section were conducted by using the statistical software SPSS (Statistical Package for Social Science, version 13.0). Within all analyses, variables were normalized to mean zero and unit variance to prevent misclassiﬁcations arising from diﬀerent parameter scales.

Correlation Analysis Correlation analysis is a technique to measure the relationship between chosen variables. The established coefficient, ranging from negative 1 to positive 1, is the degree of the dependency in the same direction (positive values) or in the opposite direction (negative values) (Cohen et al., 2013). The Pearson productmoment correlation analysis was applied to each pair of Shiraz groundwater quality parameters (Table 2). As expected, a significant positive correlation was observed between total dissolved solids (TDS) and electrical conductivity (EC), which indicates that the same underlying process has influenced both parameters. Also, TDS content exhibited high correlations with total hardness (TH), Ca, K, Mg, SO4 , and Cl, which are the main elements contributing to groundwater salinity. The TH value exhibited considerable positive correlations with Ca, Na, K, Mg, Cl, and SO4 . It can be interpreted that the groundwater hardness is due mainly to saline compounds resulting from those elements (Udayalaxmi et al., 2010). Na and Cl possessed a considerable high positive correlation, which suggests the dissolution of chloride salts within the study area (Belkhiri et al., 2011). Strong correlations between Cl and Ca, Na, K, and Mg refer to the dissolution of evaporates. Also, the observed high correlation between Cl and SO4 indicates the impact of agricultural activities on the groundwater vulnerability (Dhanasekarapandian et al., 2016).

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Ebrahimi, Kazemi, Ehteshami, and Rockaway Table 2. Correlation matrix of the groundwater physicochemical parameters.

pH TDS EC TH Ca Na K Mg CO3 HCO3 SO4 Cl

TDS

CO3

HCO3

SO4

1.00 − 0.07 − 0.06 − 0.21 − 0.30 0.41 − 0.15 − 0.15 0.92 − 0.63 − 0.15 0.20

— 1.00 0.99 0.97 0.90 0.67 0.96 0.96 − 0.10 0.21 0.94 0.87

— — 1.00 0.96 0.90 0.68 0.95 0.95 − 0.09 0.20 0.93 0.87

— — — 1.00 0.95 0.46 0.98 0.98 − 0.23 0.34 0.96 0.74

— — — — 1.00 0.36 0.89 0.86 − 0.29 0.42 0.94 0.59

— — — — — 1.00 0.50 0.50 0.35 − 0.31 0.47 0.89

— — — — — — 1.00 0.99 − 0.15 0.26 0.94 0.78

— — — — — — — 1.00 − 0.18 0.27 0.92 0.80

— — — — — — — — 1.00 − 0.57 − 0.17 0.15

— — — — — — — — — 1.00 0.16 − 0.02

— — — — — — — — — — 1.00 0.67

— — — — — — — — — — — 1.00

Bold represents that the correlation is signiﬁcant at 0.05 level.

Cluster Analysis (CA) CA is a technique for classifying samples into a set of finite groups based on their specific similarities. The derived groups represent the overall correspondence of variables in the data set (Massart et al., 1983). There are two types of clustering methods: R-mode and Q-mode (Caliński and Harabasz, 1974). The Q-mode method works to group similar samples, each containing the same number of variables, whereas the R-mode method works to reduce the total number of variables by categorizing them into a smaller number. For the purpose of this study, the Q-mode hierarchical cluster analysis was performed to identify statistically distinct hydrochemical groups of sampling wells. To formulate the clustering approach, the Ward algorithmic method was carried out (Ward, 1963). This method is based on the analysis of variance to split different clusters. To measure the distance between clusters, the Euclidean distance method was employed (Davis and Sampson, 1986). This approach organizes the data set and represents the results with a dendrogram. As a result, four clusters (A, B, C, and D) were distinguished for the Shiraz aquifer; see Figure 2. Cluster A, comprising 27% of sampling wells, covered mostly the middle parts of the basin, adjacent to the urban areas; see Figure 3. Cluster B included 23% of the wells and encompassed the northwest regions of the basin. Cluster C contained 23% of the sampling wells and expanded on the southeast areas of the basin nearby the Salt Lake. Cluster D consisted of 27% of the samples and covered mostly the mid-south of the study area.

ited number of uncorrelated components. The resultant components present a specific variance percentage of all studied variables with minimal information loss (Ebrahimi et al., 2017). The overall characteristic of the data set can be sufficiently described by considering the components with high variance percentages (loadings) (Alberto et al., 2001; Bayo and López-Castellanos, 2016). For the Shiraz aquifer, PCA was applied on the 12 physicochemical parameters to extract the principal components corresponding to different sources of variation. The Kaiser rule of eigenvalues greater than one and the varimax normalization method for orthogonal factor rotation were used for this procedure (Kaiser, 1974). As a result, two components were generated, which cumulatively accounted for 88.8% of initial data

Principal Component Analysis (PCA) To investigate the interrelationships among large groups of variables, PCA can be applied (Jolliﬀe, 2002). This technique identiﬁes variables that are correlated with each other and converts them into a lim-

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Figure 2. Hierarchical dendrogram cluster map for the sampling wells.

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Figure 3. Spatial distribution of clusters within the study area.

set variance. Thus, with a minimal information loss of 11.2%, 12 groundwater physicochemical parameters were reduced to two components; see Table 3. The ﬁrst component, accounting for 64.4% of the variance, contained absolute loading for TDS, EC,

Table 3. Rotated component matrix with factor loadings (>0.4).a Principal Component Attribute

PC1

PC2

pH EC TDS Ca Na K Mg CO3 HCO3 SO4 Cl TH Eigenvalue Initial variance percentage (loading) Cumulative variance percentage

— 1.00 1.00 0.89 0.67 0.97 0.96 — — 0.94 0.87 0.97 7.77 64.37 64.37

0.93 — — — 0.58 — — 0.90 − 0.78 — — — 2.88 24.39 88.76

Rotation converged in eight iterations.

TH, cationic ions, SO4 , and Cl. This component can be considered as an indicator for natural weathering of the minerals (Belkhiri et al., 2011). The second component, which accounted for 24.4% of the total variance, comprised absolute significant loadings for pH, CO3 , and HCO3 . This component is an indicator for natural processes and water–rock interactions, such as dissolution of carbonate minerals in the presence of soil CO2 (Belkhiri et al., 2011). The orthogonal rotation plot of the samples on the two principal factors represented reasonable separations along the axes; see Figure 4. High separation of data points from cluster C indicates that hydrochemical variations of the samples within this cluster were greater than those from the other clusters. This can be attributed to the effect of the Salt Lake located in the vicinity of cluster C. Also, the highest positive loadings of PC1, which contains high positive scores on the salinity-related constituents, were observed for cluster C. In conclusion, by considering the results from CA and PCA, along with hierarchical Q-mode cluster analysis, locations of chloride salt dissolution were identified within the aquifer. More specifically, the areas adjacent to the Salt Lake were found to be potentially susceptible to groundwater salinization.

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Ebrahimi, Kazemi, Ehteshami, and Rockaway

Figure 4. PCA scores from samples from diﬀerent clusters.

Deterministic Assessment of Groundwater Quality The obtained results from the probabilistic approach in the previous section were used as the basis for the deterministic assessment of the groundwater. This combined approach evaluates the groundwater’s hydrochemical characteristics throughout the study area. Additionally, the groundwater suitability potential for drinking and irrigation purposes was comprehensively weighed by developing a WQI and a multi-hazard risk assessment. Chemical Composition Assessment Hydrogeochemical studies evaluate the processes responsible for groundwater quality vulnerability. Four different classification methods were selected to recognize hydrochemical types of groundwater. Each method was applied separately on the previously identified clusters within the Shiraz basin. To determine the groundwater ionic order, the average abundances of anion and cation concentrations were compared. To categorize the hydrochemical facies of water samples, the Domenico classification was considered (Domenico, 1972). To graphically present the composition of major ions and relationships between the dissolved constituents, the Piper trilinear diagram was used (Piper, 1944). Finally, to assess the chemical categories of groundwater samples, the Chadha diagram was analyzed (Chadha, 1999). Ebrahimi et al. (2016) have previously reviewed each of the aforementioned methods in detail. The average ionic abundancy from samples of each individual well was calculated and then averaged for

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each cluster. For cluster A, the cationic and anionic compositions were found to be dominated by Ca and HCO3 , respectively; see Figure 5. The dominant ionic variation within cluster B was determined as Na-Cl. Thus, the overall groundwater composition at the west and middle regions of the basin was characterized as Na-Ca-Cl-HCO3 . The samples from cluster C, which depicted the Mg-SO4 type of water, showed the highest ion concentration abundances. Cluster D presented lower concentration fluctuations compared to cluster C; however, it was also found to have the same dominant water type. Almost all magnesium-related saline compounds might exist within the samples from cluster D. Using the Domenico classification, the groundwater cationic facies in most parts of the study area were determined as Ca-Na; see Table 4. The chloride-sulfatebicarbonate was found as the major anionic facies of the groundwater throughout the basin. However, some wells within clusters A and C contained HCO3 -Cl-SO4 and Cl-SO4 types of anionic hydrochemicals, respectively. Considering the Piper diagram, most of the sampling wells were characterized as the Ca-Cl type of water; see Figure 6 and Table 5. However, the Ca-Mg-Cl type of water were found for 33% and 60% of the samples from clusters A and B, respectively. Considering the Chadha diagram, the Ca-Mg-Cl type of water was observed for all the clusters; see Figure 7 and Table 6. Fifty percent of the samples for cluster A and 20% from cluster B fell under the CaMg-HCO3 subdivision. Also, the Na-Cl type of water was confirmed for 40% and 20% of the samples within clusters B and C, respectively. In addition, it was concluded that for the groundwater constitutes in the Shiraz basin, alkaline earths exceeded alkali metals and strong acidic anions exceeded weak acidic anions. In conclusion, from the aforementioned classification techniques, the overall groundwater chemical composition was found to comprise mainly chloridebased saline compounds.

Drinking Water Quality Assessment To investigate the suitability of groundwater for human consumption, major water quality constituents should be inspected for their compliance with preestablished standards. For the Shiraz basin, the standards deﬁned by WHO (2011) was used; see Table 7. In addition, to identify the sources of contamination as well as the contaminant transport pattern across the study area, the special distributions of water quality parameters were studied using GIS-based variograms; see Figure 8.

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Figure 5. Average abundances of ions.

Table 4. Domenico classiﬁcation of groundwater hydrochemical facies. Percentage of Constituents

Cation facies Calcium-magnesium (Ca-Mg) Calcium-sodium (Ca-Na) Sodium-calcium (Na-Ca) Sodium-potassium (Na-K) Anion facies Bicarbonate (HCO3 ) Bicarbonate-chloride-sulfate (HCO3 -Cl-SO4 ) Chloride-sulfate-bicarbonate (Cl-SO4 -HCO3 ) Chloride-sulfate (Cl-SO4 )

Cluster, %

Ca + Mg

Na + K

HCO3 + CO3

Cl + SO4

90–100 50–90 10–50 0–10

0–10 10–50 50–90 90–100

— — — —

17 83 — —

— 60 40 —

— 80 20 —

— 100 — —

— — — —

90–100 50–90 10–50 0–10

0–10 10–50 50–90 90–100

— 50 50 —

— 20 80 —

— — 40 60

— — 100 —

Table 5. Hydrochemical classiﬁcation of groundwater based on the Piper diagram. Cluster, % Subdivision 1 2 3 4 5 6

Facies

CaHCO3 Na-Cl Mixed Ca-Na-HCO3 Mixed Ca-Mg-Cl Ca-Cl NaHCO3

— — — 33 67 —

— 20 — 60 20 —

— — — — 100 —

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Ebrahimi, Kazemi, Ehteshami, and Rockaway Table 6. Hydrochemical classiﬁcation of groundwater based on Chadha diagram. Cluster, % Subdivision 1 2 3 4 5 6 7 8

Classiﬁcation

Alkaline earths exceed alkali metals Alkali metals exceed alkaline earths Weak acidic anions exceed strong acidic anions Strong acidic anions exceed weak acidic anions Ca-Mg-HCO3 Ca-Mg-Cl Na-Cl Na-HCO3

— — — — 50 50 — —

— — — — 20 40 40 —

— — — — — 80 20 —

— — — — — 100 — —

Analysis of the results from Table 7 revealed high non-compliance with the standard limits within clusters C and D. Most of the observed pH values fell within the recommended range. From a taste aspect, WHO restricts the consumption of water with a TDS content higher than 1,000 mg/L. Also, high TDS concentrations may result in scale formations in household appliances and water pipes. The majority of the studied samples exhibited TDS values greater than the recommended limit. Same results were observed for EC content. EC values greater than 3,000 µs/cm indicate enrichment of salts in the groundwater, which was observed for clusters C and D; see Figure 8(c). WHO has speciﬁed a maximum allowable limit of 500 mg/L for TH in drinking water. Almost all samples showed TH values higher than 300 mg/L. Thus,

they can be classified as the hard-water type, which has a corrosion potential for distribution systems. WHO has proposed 250 mg Cl/L as the taste threshold. The groundwater of the mid-south and southeast regions contained Cl values higher than the recommended limit. High observed Cl concentrations within those areas confirm the previous assumption of groundwater salinization. As for the sodium value, same results were interpreted. High SO4 concentrations, observed mainly in the central and southeast parts of the basin (see Fig. 8[g]), can cause a laxative effect in water consumers.

Figure 6. The Piper diagram of the samples.

Figure 7. Chadha’s hydrochemical classiﬁcation diagram.

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Water Quality Index (WQI) The WQI is a dimensionless number that cumulatively expresses the quality of an aggregated set of measured physicochemical parameters from diﬀerent samples in a given area (Hallock, 2002). The lesser values indicate that the quality of water is more adapted with the pre-established standards proposed by WHO. The established WQI, as a variable indicator, enables decision makers to distinguish diﬀerent groundwater sources based on their suitability for drinking purposes (Bordalo et al., 2006).

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Figure 8. Spatial distribution of quality parameters within the study area. (a) pH. (b) TDS. (c) EC. (d) TH. (e) Cl. (f) Na. (g) SO4 . (h) WQI.

Table 7. Assessment of groundwater drinking suitability based on the WHO standard. Cluster, % Samples Exceeded the Limit Parameter (Unit) pH EC (µs/cm) TDS (mg/L) TH (mg/L) Cl (mg/L) Na (mg/L) SO4 (mg/L)

WHO Standard

Samples Range

6.5–8.5 1,500 1,000 500 250 200 250

6.9–9.8 596–10,107 412–6,885 182.5–4,950 61.1–1,648.4 21.8–740.2 18.2–2945.7

— 83 83 100 17 — 50

20 20 20 20 20 — 80

20 100 100 100 80 40 40

— 100 100 100 67 50 83

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Ebrahimi, Kazemi, Ehteshami, and Rockaway Table 8. Relative weights of groundwater quality parameters. Parameter (Unit) pH TDS (mg/L) TH (mg/L) Ca (mg/L) Na (mg/L) SO4 (mg/L) Cl (mg/L)

Assigned Weight (wi)

Relative Weight (Wi)

2 5 5 2 3 4 4 ࢣwi = 25

0.08 0.2 0.2 0.08 0.12 0.16 0.16 ࢣWi = 1

For all sampling wells within the Shiraz basin, the WQI was calculated based on the method proposed by Yidana et al. (2010). All parameters (n) were assigned a weight (wi) on a scale of 1 to 5, based on their inﬂuence on drinking water quality and human health; see Table 8. The relative weight value (Wi) and the quality rating scale (qi) for each parameter were calculated using Eqs. 1 and 2, in which Ci and Si are the measured concentration and the WHO standard for each parameter, respectively. Finally, the WQI for an individual well was then expressed as the sum of the subindex (SIi) of all parameters by using Eqs. 3 and 4. wi . Wi = n i=1 wi qi =

Ci × 100. Si

SIi = Wi × qi. n WQI = i=1 SIi .

(1)

(2) (3) (4)

The groundwater can be categorized into five classes based on the calculated WQI; see Table 9 (Sahu and Sikdar, 2008). For the Shiraz basin, the computed indexes ranged from 80 to 661, with an average value of 251; see Figure 8(h). It can be interpreted that for the clusters A and B, located farther from the Salt Lake, none of the samples exhibited unfit quality for drinking, while all samples from cluster C and 83% of the sampling wells from cluster D were determined to have water with very poor to unpotable quality. For the wells located in the vicinity of the Salt Lake, the water quality was significantly degraded. However, for those located in the north or west parts of the basin, the suitability of groundwater remained acceptable. Agricultural Water Quality Assessment The most important factor affecting the suitability of groundwater for agricultural applications is the salinity level (Todd and Larry, 2005). The presence of

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Figure 9. Wilcox diagram.

saline compounds in irrigation water can adversely affect the soil structure and its vegetation capability. To have an overall assessment regarding the suitability of the groundwater for agricultural activities in the Shiraz aquifer, the Wilcox diagram (Wilcox, 1948) was used, which considers the combined eﬀect of sodium percentage and electrical conductivity values; see Figure 9. Most of the sampling wells from clusters A and B were categorized as good to permissible water types. All samples from cluster C were found to be unsuitable for irrigation activities, while most of the samples from cluster D were classiﬁed as doubtful water. Thus, it can be concluded that the irrigation quality of groundwater within the Shiraz basin was partially degraded.

Multi-Hazard Risk Assessment The mineral compounds have an essential role in the groundwater’s agricultural applicability potential. Hazardous levels of diﬀerent quality indicators, including salinity, sodium, alkalinity, lime deposition, bicarbonate, and chloride, can prohibit cultivation of crops sensitive to saline water. In addition, it may even lead to further adverse impacts, such as lower rates of soil permeability, plugging of irrigation systems, and foliar burns (Ebrahimi et al., 2016). Thus, it is necessary to further investigate the eﬀects of the mentioned indicators. As a result, a methodological approach was developed to evaluate the hazard potentials associated with high levels of various quality indicators. For the

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Groundwater Appraisal in Arid Regions Table 9. Classiﬁcation of groundwater drinking suitability based on the WQI. Cluster, % WQI <50 50–100 100–200 200–300 >300

Description

Excellent Good Poor Very poor Unﬁt for drinking

— — 83 17 —

— 40 40 20 —

— — — 40 60

— — 17 50 33

Shiraz basin, the proposed multi-hazard risk assessment was applied; see Table 10. All samples from cluster C, located at the vicinity of the Salt Lake, were classified as high-saline water and not suitable for irrigation. This confirmed the obtained results from analyzing the Wilcox diagram. Also, medium to medium-high salinity was observed in other parts of the basin. These water types are not suitable for sensitive plants to saline compounds. Considering the sodium percentage (Na%), most of the samples were identified to have very low to low sodium

hazards, while samples from cluster B fell under the range of medium to medium-high hazard classes. High levels of sodium in irrigation water can limit a soil’s permeability (Raju, 2007). Using the residual sodium carbonate (RSC) content identiﬁed low sodium hazard for all sampling wells. Thus, monitoring the inﬁltration rates and the soil’s pH level would not be necessary (Hopkins et al., 2007). Analyzing the sodium absorption ratio (SAR), all samples were categorized as low-alkalinity hazard classes. Considering the Cl content, the majority of the

Table 10. Irrigation based multi-hazard risk assessment. Cluster, % Parameter

Range

Salt

EC <0.25 0.25–0.75 0.75–2.0 2.0–3.0 >3.0

Sodium (based on sodium percentage), Na%

Sodium bicarbonate, RSC

Alkalinity, SAR

Chloride, Cl

Bicarbonate, HCO3

Lime deposition, mg lime/L

TDS <160 160–480 480–1,280 1,280–1,920 >1,920 <20 20–40 40–60 60–80 >80 <0 0–1.0 1.0–2.5 >2.5 <10 10–18 18–26 >26 <70 70–140 140–350 >350 <1.5 1.5–7.5 >7.5 <2 2.0–3.0 3.0–4.0 >4

Hazard Potential

Very low Low Medium Mid-high High Very low Low Medium Mid-high High Low Medium High Very high Low Medium Mid-high High Low Medium Mid-high High Low Medium High Low Medium Mid-high High

— — 50 50 — 67 33 — — — 100 — — — 100 — — — — — 100 — — 33 67 — — — 100

— 20 60 20 — 20 — 60 20 — 100 — — — 100 — — — — 20 80 — — 100 — — — 60 40

— — — — 100 60 20 20 — — 100 — — — 100 — — — — — 40 60 — 60 40 — — 40 60

— — 17 67 16 67 33 — — — 100 — — — 100 — — — 16 — 50 34 — 33 67 — — — 100

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Ebrahimi, Kazemi, Ehteshami, and Rockaway Table 11. Overall groundwater quality assessment for three diﬀerent basins. Basin, % Evaluation step Hydrochemical facies: Piper

Drinking-based assessment: WQI

Agriculture-based assessment: Wilcox

Classiﬁcation

Shiraz

Damghan

Kashan

CaHCO3 Na-Cl Mixed Ca-Na-HCO3 Mixed Ca-Mg-Cl Ca-Cl NaHCO3 Excellent Good Poor Very poor Unsuitable Excellent Good Permissible Doubtful Unsuitable

— — — 27 73 — — 9 36 32 23 4 33 9 27 27

— 33 — 40 27 — — — 46 27 27 — — 33 27 40

— 71 29 — — — — — 10 33 57 — 9 5 19 67

sampling wells were classified as medium-high chloride hazard. Irrigation with these water types can result in foliar burns on crops. Also, high-chloride hazard classes were determined for 60% and 34% of the samples from clusters C and D, respectively. Thus, the assumption of groundwater salinization within those regions of the study area can be confirmed. Irrigation with high-chloride water can lead to significant negative impacts on agricultural products. Finally, the potential of plugging in irrigation systems was evaluated by using the lime deposition index. Almost all samples showed medium-high to high lime deposition hazard risks. The irrigation limit of 0.5 cm/hr is suggested for these water sources (Hopkins et al., 2007). Comparison with Previous Studies To further present the practicality of the proposed methodology, the overall dynamic of the groundwater quality in the Shiraz basin was compared with two previously studied basins: Kashan and Damghan. Both basins make up similar aquifers and lithology of rocks and are under similar climate conditions. The Kashan basin, with an area of about 7,083 km2 , is located in central Iran (Baghvand et al., 2010; Jamshidzadeh and Mirbagheri, 2011). Due to its proximity to the Central Desert, this basin has a dry climate, and groundwater is the only source of water for agricultural, industrial, and domestic activities. The Damghan basin, with an area of about 5,865kKm2 , is located in northeast of Iran (Ebrahimi et al., 2016). Due to arid and hot climate conditions, groundwater is the only source for all sectors of the economy.

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The groundwater quality conditions of the Kashan and Damghan basins were reanalyzed based on the performed methodology in this study; see Table 11. First, the chemical composition of each basin was investigated by using the Piper diagram. Then the suitability of groundwater for drinking purposes was determined based on the developed WQI. Finally, the suitability of groundwater for agricultural applications was assessed by considering the Wilcox classification. According to the Piper cataloging, most of the groundwater samples within the Shiraz and Damghan basins exhibited the Ca-Cl and mixed Ca-Mg-Cl type of hydrochemical facies, while for the Kashan aquifer, the Na-Cl type of water was found to be dominant. It can be inferred that Cl type of saline compounds largely contributed to the groundwater chemical composition in all studied cases. By developing the WQI, similar drinking water quality was observed for the Shiraz and Damghan basins. Within both aquifers, approximately 25% of the samples were found to be unfit for drinking. However, the groundwater quality within the Kashan basin was determined to be significantly degraded compared to that from the other two basins. More than half of the groundwater resources from the Kashan basin did not meet the quality limitations and were categorized as unpotable water. One of the benefits of using the WQI method is the ease of comparing the overall drinking quality of groundwater across several basins, which was presented here. Based on the Wilcox classification, the groundwater within the Shiraz basin was determined to be more suitable for irrigation compared to that of the other two basins. Similar to the WQI results, the Kashan basin was found to be the most

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degraded basin, and approximately 70% of its samples were determined to be unsuitable for agricultural applications. The overall obtained groundwater quality results agree with the identified hydrochemical facies. Although groundwater types from all the basins were affected by the Cl compounds, the one with dominant Na-Cl type (Kashan basin) was found to be the most degraded, possibly due to extreme saltwater intrusion into this aquifer, as reported by Jamshidzadeh et al. (2011). SUMMARY AND CONCLUSIONS This study demonstrated the effectiveness of the combined consideration of probabilistic and deterministic approaches for a robust groundwater quality evaluation. Application of the probabilistic approach, which included multivariate statistical analysis, provided a classification scheme for categorizing the physicochemical properties of aquifer. Further application of the deterministic approach, which was built on the obtained results from the conducted probabilistic analysis, led to a comprehensive evaluation of the groundwater quality. The developed multihazard-based procedure was found to be an inclusive tool for irrigation groundwater risk assessment. Additionally, the WQI proved to be an effective method for assessing the overall drinking quality of groundwater. By using a consistent criterion, this methodology was found to be specifically suitable for comparing the overall groundwater quality conditions across different aquifers or for a temporal assessment within one aquifer. Application of the proposed methodology on a basin with an arid climate identified chloride-based saline compounds throughout the aquifer. The results indicated that less than a third of sampling wells contained potable water and that only half of the study area comprised suitable groundwater for irrigation. Finally, the overall groundwater quality condition of this case study was compared with that from two other basins located in similar arid regions. It was concluded that the studied groundwater resources were prone to quality degradations, possibly due to salinization. The presented methodology in this study provides environmental analysts and governmental decision makers with a comprehensive tool for evaluating current and future quality conditions within any given aquifer. REFERENCES Alberto, W. D.; del Pilar, M. D.; Valeria, A. M.; Fabiana, P. S.; Cecilia, H. A.; and de los Ángeles, B. M., 2001, Pattern

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Ebrahimi, Kazemi, Ehteshami, and Rockaway Jamshidzadeh, Z. and Mirbagheri, S., 2011, Evaluation of groundwater quantity and quality in the Kashan Basin, Central Iran: Desalination, Vol. 270, No. 1, pp. 23–30. Jolliffe, I., 2002, Principal Component Analysis: Wiley Online Library. Kaiser, H. F., 1974, An index of factorial simplicity: Psychometrika, Vol. 39, No. 1, pp. 31–36. Kaufman, L. and Rousseeuw, P. J., 2009, Finding Groups in Data: An Introduction to Cluster Analysis: Wiley, New York. Kim, J. H.; Kim, R. H.; Lee, J.; Cheong, T. J.; Yum, B. W.; and Chang, H. W., 2005, Multivariate statistical analysis to identify the major factors governing groundwater quality in the coastal area of Kimje, South Korea: Hydrological Processes, Vol. 19, No. 6, pp. 1261–1276. Liu, W.; Li, X.; Shen, Z.; Wang, D.; Wai, O.; and Li, Y., 2003, Multivariate statistical study of heavy metal enrichment in sediments of the Pearl River Estuary: Environmental Pollution, Vol. 121, No. 3, pp. 377–388. Machiwal, D. and Jha, M. K., 2015, Identifying sources of groundwater contamination in a hard-rock aquifer system using multivariate statistical analyses and GIS-based geostatistical modeling techniques: Journal of Hydrology: Regional Studies, Vol. 4, pp. 80–110. Mackiewicz, A. and Ratajczak, W., 1993, Principal components analysis (PCA): Computers and Geosciences, Vol. 19, pp. 303– 342. Massart, D. L.; and Kaufman, L., 1983, The Interpretation of Analytical Chemical Data by the Use of Cluster Analysis: Wiley, New York. Meng, S. X. and Maynard, J. B., 2001, Use of statistical analysis to formulate conceptual models of geochemical behavior: Water chemical data from the Botucatu aquifer in Sao Paulo state, Brazil: Journal of Hydrology, Vol. 250, No. 1, pp. 78–97. Moya, C. E.; Raiber, M.; Taulis, M.; and Cox, M. E., 2015, Hydrochemical evolution and groundwater ﬂow processes in the Galilee and Eromanga basins, Great Artesian Basin, Australia: A multivariate statistical approach: Science of the Total Environment, Vol. 508, pp. 411–426. Niaki, S. T. A. and Jahani, P., 2013, The economic design of multivariate binomial EWMA VSSI control charts: Journal of Applied Statistics, Vol. 40, No. 6, pp. 1301–1318. Niu, B.; Wang, H.; Loáiciga, H. A.; Hong, S.; and Shao, W., 2017, Temporal variations of groundwater quality in the Western Jianghan Plain, China: Science of the Total Environment, Vol. 578, pp. 542–550. Piper, A. M., 1944, A graphic procedure in the geochemical interpretation of water analyses: Eos, Transactions, American Geophysical Union, Vol. 25, No. 6, pp. 914–928.

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Raju, N. J., 2007, Hydrogeochemical parameters for assessment of groundwater quality in the upper Gunjanaeru River basin, Cuddapah District, Andhra Pradesh, South India: Environmental Geology, Vol. 52, No. 6, pp. 1067–1074. Rastogi, G. K. and Sinha, D., 2011, A novel approach to water quality management through correlation study: Journal of Environmental Research and Development, Vol. 5, No. 4, pp. 1029, 1035. Rice, E. W.; Baird, R. B.; Eaton, A. D.; and Clesceri, L. S., (Editors), 2012, Standard Methods for the Examination of Water and Wastewater, 22nd ed.: American Public Health Association, American Water Works Association, Water Environment Federation, Denver, CO. 1496 p. Sahu, P. and Sikdar, P., 2008, Hydrochemical framework of the aquifer in and around East Kolkata Wetlands, West Bengal, India: Environmental Geology, Vol. 55, No. 4, pp. 823– 835. Todd, D. K. and Mays, L. W., 2005, Groundwater Hydrology: Wiley, New York. Udayalaxmi, G.; Himabindu, D.; and Ramadass, G., 2010, Geochemical evaluation of groundwater quality in selected areas of Hyderabad, AP, India: Indian Journal of Science and Technology, Vol. 3, No. 5, pp. 546–553. Vasanthavigar, M.; Srinivasamoorthy, K.; Vijayaragavan, K.; Ganthi, R. R.; Chidambaram, S.; Anandhan, P.; Manivannan, R.; and Vasudevan, S., 2010, Application of water quality index for groundwater quality assessment: Thirumanimuttar sub-basin, Tamilnadu, India: Environmental Monitoring and Assessment, Vol. 171, No. 1–4, pp. 595–609. Ward, J. H., Jr., 1963, Hierarchical grouping to optimize an objective function: Journal of the American Statistical Association, Vol. 58, No. 301, pp. 236–244. Wator, ˛ K.; Kmiecik, E.; and Tomaszewska, B., 2016, Assessing medicinal qualities of groundwater from the Busko-Zdrój area (Poland) using the probabilistic method: Environmental Earth Sciences, Vol. 75, No. 9, p. 804. Wilcox, L. V., 1948, The Quality of Water for Irrigation Use: U.S. Department of Agriculture, Economic Research Service, Washington, DC. World Health Organization, 2011, Guidelines for DrinkingWater Quality, 4th ed.: World Health Organization, Geneva. Yidana, S. M. and Yidana, A., 2010, Assessing water quality using water quality index and multivariate analysis: Environmental Earth Sciences, Vol. 59, No. 7, pp. 1461–1473. Zaidi, F. K.; Mogren, S.; Mukhopadhyay, M.; and Ibrahim, E., 2016, Evaluation of groundwater chemistry and its impact on drinking and irrigation water quality in the eastern part of the Central Arabian graben and trough system, Saudi Arabia: Journal of African Earth Sciences, Vol. 120, pp. 208–219.

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On the Optimum Layout of a Drainage Gallery in Concrete Gravity Dams on Isotropic Foundation TAMEEM DAGHESTANI Universiteler Mah., Dumlupinar Blv. No. 1, Department of Civil Engineering, Middle East Technical University, Ankara 06800, Turkey

MELIH CALAMAK* 300 Main St., Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29208

ALI MELIH YANMAZ Universiteler Mah., Dumlupinar Blv. No. 1, Department of Civil Engineering, Middle East Technical University, Ankara 06800, Turkey

Key Terms: Concrete Gravity Dam, Drainage Gallery, Uplift, Seepage, Performance ABSTRACT This study investigates the optimum location of drainage galleries of concrete gravity dams under usual, unusual, and extreme loading conditions. The uplift and the stress distributions were computed using the gravity method and the beam theory, whereas the leakage rate into the gallery was determined by the finite-element method. The results show that the presence of a drainage gallery can reduce the uplift by over 60 percent compared to a non-drained case. The suitable gallery location shifts toward the upstream if the drain diameter increases and the spacing decreases. This is consistent with previous research. As the gallery is moved toward the downstream, the crack length decreases; however, this increases the uplift. The most effective solution is found to be placing the gallery 10 percent of the base width away from the heel and at the downstream water level or below and adding post-tension cables on the downstream side. This option also yields lower pumping costs by reducing the pump head. INTRODUCTION Seepage through the foundation of a gravity dam creates an uplift force that causes a reduction in the effective weight of the dam and increases the potential failure risk of the structure. Therefore, uplift reduction via a proper drainage system is of utmost importance. Drains embedded into the foundation from a gallery *Corresponding author email: calamak@cec.sc.edu

can collect water by means of the pressure difference. This reduces the pore water pressures in the foundation and hence decreases the uplift force. These drains are connected to a gallery located in the dam that runs across the entire length of the structure. A typical foundation drain and the drainage gallery are shown in Figure 1. Along the gallery, there exists a side channel that collects and conveys water entering the gallery from the foundation drains and leakage through the upstream face and the abutments. The channel is connected to a sump well to pump the collected water out of the dam. The most common method is to pump the collected water to an elevation higher than that of the dam outlet to transport the water by gravity toward the downstream. There exists limited information on the effects of the layout of drainage galleries on concrete gravity dams. The U.S. Bureau of Reclamation (USBR; 1976) and the U.S. Army Corps of Engineers (USACE; 1995) recommended a minimum distance of 1.52 m between the gallery floor and the foundation level and between the upstream face of the dam and the upstream wall of the gallery to reduce local stress concentrations. Goodman et al. (1983) presented an analytical solution for the flow through a horizontal crack and beneath the dam and showed that the drains have a large influence on the magnitude and distribution of uplift through the cracks located close to the upstream face. The maximum uplift reduction was observed when the drainage gallery was located between one-fifth and one-half of the length of the horizontal crack from its upstream end. The analytical solution presented by Amadei et al. (1989) was used to model the internal uplift pressure due to cracks in old concrete dams. The influences of the crack properties, the ratio of piezometric head to reservoir head, and the drain size and spacing on the

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Figure 1. (a) The cross-sectional view and characteristic dimensions of a drainage gallery and a concrete gravity dam; (b) the plan view of the foundation drains.

uplift were investigated. It was found that the drains located at the crack longitudinal center were the most effective in reducing the uplift. Chawla et al. (1990) studied the optimum location of drains in concrete dams, considering their ability in reducing the uplift pressures. The study considered equally spaced drains of uniform diameter. An analytical solution based on seepage theory was applied on a hypothetical dam, and it was found that the uplift pressure decreased as the spacing between drains was reduced and the diameter was increased. It was also shown that when the spacing of drains increased and the diameter decreased, the optimum horizontal location of the gallery shifted toward the downstream. Amadei and Illangasekare (1992) assessed the stability of concrete gravity dams with finite cracks at the base using the strength of material approach under usual loading conditions. The results showed that the drain effectiveness increased as the cracks propagated further into the dam in the horizontal direction and as the lateral width of the cracks decreased. In addition, the drains could reduce the magnitude of minimum allowable compressive stress

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and the magnitude of the uplift when the crack intersected the drains. El-Razek and Elela (2001) experimentally obtained the optimum position of vertical drainage galleries underneath concrete gravity dams. To this end, a sand model was set up, and uplift pressures beneath the dam model were recorded using piezometers for various upstream water levels and horizontal positions of the gallery. The study found that the optimum horizontal location of the gallery was at 50 percent of the base width away from the upstream face of the dam. El-Razek and Elela (2002) extended the previous research using the same methodology and studied the impact of the drain diameter, spacing, and the penetration depth on uplift pressures. Their results showed that the most inﬂuential parameter of the uplift pressure was the penetration depth of the drain (i.e., it can reduce uplift by up to 40 percent). It was followed by the drain spacing and diameter, with uplift reductions of 25 percent and 11 percent, respectively. Zee et al. (2011) estimated the variation of pore water pressures and seepage rates from the dam body and foundation in concrete gravity dams with

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drainage wells. Their study simulated the seepage and estimated piezometric heads of an existing dam having different combinations of drain diameters, spacing, and locations using the finite-difference method. It was found that the drain wells near the upstream face reduced the pore water pressures. The larger drain diameters, smaller spacing, and further distances from the upstream face provided the highest uplift reduction. Nourani et al. (2017) have studied the effects of the distance of the drains from the upstream face, the spacing between the drains, and the drain diameter on uplift force reduction using SEEP/W software that is based on the finite-element method. Their study considered a hypothetical dam, and the results showed that drains become ineffective if they are placed too close to the upstream face and if they are moved further away from the upstream face. The optimum location of the drain, which resulted in the minimum uplift pressure, was presented in design charts and with algebraic equations. The study showed that as the drain diameter decreased and/or the spacing between the drains was increased, the location of minimum uplift shifted toward the downstream side. Previous studies have shown that the drainage system has a great impact on reducing the uplift under the usual loading conditions. However, the effects of drains on internal stresses and the cracks generated at the dam toe are still unknown. In addition, how the seepage rate into the drainage gallery from the foundation changes with respect to drain properties has not been investigated, and the relation between the pumping schedule and the gallery location has not been addressed before. The current study investigates the impact of both horizontal and vertical locations, size, and spacing of the drainage gallery on uplift force reduction, development of internal stresses, crack generation and propagation, and the seepage rate into the gallery in concrete gravity dams under usual, unusual, and extreme loading conditions. The uplift forces and the internal stresses are determined using the gravity method based on the limit equilibrium and the beam theory, whereas the leakage rate into the gallery is assessed by the finite-element method. A hypothetical dam is used to determine the effects of drainage properties. Then the findings are applied to a case study to compare the optimal location of the drainage gallery based on the obtained results with the existing drainage properties in the dam. METHODS AND TOOLS Stability, Uplift Force, and Stress Analyses A dam is subject to various forces (i.e., weight, hydrostatic, uplift, earthquake, etc.) during its lifetime. It

is considered safe and stable if it can withstand the impact of all acting forces. These forces could lead to the failure of the dam by causing the dam to overturn or slide. In this study, the dam safety against overturning and sliding is checked using the rigid body equilibrium technique (Leclerc et al., 2001). With this method, all applied forces and moments acting on a potential failure plane are determined according to the free body diagram, and all the unknowns are calculated using equilibrium equations based on Newton’s Third Law of Motion. The uplift pressure computation includes the upstream and downstream water levels, the vertical and horizontal locations of the gallery, and the efficiency of the drains. USACE (1995), USBR (1987) and the Federal Energy Regulatory Commission (FERC; 2016) have various assumptions on the distribution shape and computation of the uplift pressure. In this study, the USACE (1995) guideline is used, and the uplift pressures are computed using Eqs. 1 and 2, which are valid when the drain is located below and above the downstream water level, respectively. Uplift pressure distribution is shown in Figure 1a. In this figure, γw is the specific weight of water. B−X H3 = K (H1 − H2 ) + H2 ; (1) B B−X H3 = K (H1 − H2 ) + H2 − Y + Y , (2) B where H1 and H2 are the water elevations at the upstream and downstream sides, respectively; H3 is the uplift intensity at the line of the drains; B is the base width of the dam; X is the horizontal distance of the drain from the upstream face; Y is the vertical distance of the drain from the dam base; and K is the efficiency of the drains, which is a function of the following: (1) the horizontal position of the drain, (2) the drain diameter, (3) the spacing between the drain centers, and (4) the length of the joint at which the efficiency is computed (Leclerc et al., 2001). Stresses develop inside the dam due to acting forces. In addition, the presence of a drainage gallery creates a hollow section in the dam, which leads to an increase in the localized internal stresses around the gallery. Therefore, the internal stresses should be computed at any potential location and checked if they are under the maximum allowable limit. In this study, the elastic beam theory (Leclerc et al., 2001) is used to compute the stresses developing in a concrete gravity dam. The theory states that the internal moment is related to the displacement and the slope of its elastic curve by the following simplified non-linear second-order differential equation (Hibbeler, 2015). d 2v M = , (3) EI dx2

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where M is the internal moment, E is the modulus of elasticity, I is the moment of inertia, v is the axis along which the force is applied, and x is the axis through the element that is perpendicular to the applied force. The slope of the moment is the shear force, V (x), and the slope of the shear is the intensity of the distributed load, w(x). Eqs. 4 and 5 are used to determine V (x) and w(x), respectively: d 2v d V (x) = (4) EI 2 ; dx dx d2 d 2v w (x) = (5) EI . dx2 dx2 Eq. 5 is also known as the Euler-Bernoulli equation and is used to relate the applied load to the deflection of the beam. The gravity method assumes that a dam is made up of several vertical elements separated by contraction joints. Each element carries its own load to the foundation without transferring the load to or from another element. The following equation based on shallow beam theory describes the stresses developing in the dam (Leclerc et al., 2003; Yanmaz, 2018): V Mc ± , (6) σ= A I where σ is the stress, V is the vectorial summation of the vertical forces, M is the moment about the midsection of the beam, I is the moment of inertia, and A is the area of the beam, and c is the distance from the mid-section to the point at which stresses are determined. In the current study, a computer program, CADAM (Leclerc et al., 2003), is used for all stability, uplift, and stress analyses. It has been successfully used in similar previous research (Shori et al., 2012; Bretas et al., 2014; and Løkke and Chopra, 2015), and it can perform stability analysis under different loading conditions, compute uplift pressures and forces, and compute the internal stresses using the gravity method. Detailed information about the program can be found in Leclerc et al. (2003). Seepage Analysis Two-dimensional seepage through soils is governed by Darcy’s law, and it is defined with the differential equation given below (Richards, 1931; Papagianakis and Fredlund, 1984): ∂ ∂θ ∂H ∂H ∂ kx + ky +Q= , (7) ∂x ∂x ∂y ∂y ∂t in which H is the total head; Q is the applied boundary flux; kx and ky are the hydraulic conductivity in the horizontal and vertical directions, respectively; θ is the 348

volumetric water content; and t is the time. Eq. 7 shows that the rate of change of flow at a point in time in the horizontal and vertical directions, in addition to the external flux applied, is equivalent to the change in storage of the soil system (i.e., the rate of change of volumetric water content with respect to time; GeoSlope Int. Ltd., 2013). The Galerkin approach can be applied to obtain an integral form of Eq. 7, which can be solved using a robust and common technique, the finite-element method:

[B] [C] [B] dA H + τ τ A N T dL, t = qτ T

λ N T N dA {H },

(8)

where B is the gradient matrix; C is the matrix of hydraulic conductivity; N is the interpolating function vector; H is the nodal head vector; τ is the element thickness; λ is a storage term; q is the unit flux; and A and L indicate summation over the area and edge of element, respectively (Geo-Slope Int. Ltd., 2013). The solution of Eq. 8 using boundary conditions yields the total head at finite-element nodes and pore water pressures in the element regions. SEEP/W (Geo-Slope Int. Ltd., 2013) is a software that can solve this equation using the finite-element method, and it has been commonly used in related previous research (Calamak and Yanmaz, 2017, 2018; Calamak et al., 2017; and Liu et al., 2017). It is adopted in this study to mathematically simulate the flow beneath the gravity dam and to estimate the flow entering the foundation drainage. APPLICATION STUDY AND THE ANALYSES The Effect of Dam Height Variation The application study is focused on determining the effects of drain location, size, and spacing on the uplift force at the dam base and internal stresses. Before this, a preliminary analysis is conducted to understand the impacts of dam dimensions on the uplift force. To this end, three hypothetical dam cases having heights of 50 m, 100 m, and 200 m are considered. The base widths of dams are taken as 80 percent of the corresponding dam height. The upstream and downstream water levels are taken as 75 percent and 2 percent of the dam height, respectively, to illustrate the usual loading conditions for each dam configuration. The dams are named as Dam 1, 2, and 3 and are presented in Table 1 with their characteristic dimensions. A definition sketch showing the dimensions can be seen in Figure 1a. The crest thickness, tc , and the height of top section, H* , of gravity dams are determined using the following equations, based on regression analysis of geometric properties of several existing gravity dams

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Optimum Layout of a Gallery in Dams Table 1. The dimensions of the considered dams. Model Name Dam 1 Dam 2 Dam 3

H (m)

H* (m)

tc (m)

B (m)

50 100 200

5.38 10.75 21.5

4.77 7.14 11.89

40 80 160

(Yanmaz, 2018): tc = 0.0475H + 2.392; H ∗ = 0.1075H,

(9) (10)

in which H, tc , and H* are measured in meters. First, the uplift force is determined for the non-drained case. Then a foundation drain and a drainage gallery are added. The foundation drain diameter (labeled D in Figure 1b) and spacing (labeled S in Figure 1b) are set as 0.25 m and 3 m, respectively. For each dam, both the horizontal location (labeled X in Figure 1a) and vertical location (labeled Y in Figure 1a) of the drain are varied. These are done while keeping one location constant and varying the other (e.g., X was fixed for Y variations). Both locations are varied between 1, 2, 3, 4, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, and 100 percent of the corresponding dimension, which is the dam height for Y and the base width for X. The location being kept constant is fixed at 1 percent of the corresponding dimension. For example, for Dam 2, Y is varied for an X fixed at 0.8 m, whereas X is varied for a Y kept at 1 m. The compressive and tensile strengths of the concrete are taken as 30 MPa and 3 MPa, respectively (Beser, 2005). For each dam, the loading combination included the dead load, hydrostatic loads at the upstream and the downstream, and the uplift pressures. Figure 2a and b show the percent reduction in the uplift force at the dam base for each of the Y and X

percent locations, respectively, for various dam heights. The percent reduction corresponds to the percent change of the uplift force between the non-drained case and each drained case. The results showed that the presence of a drainage gallery can significantly reduce the uplift compared to the non-drained case. The larger dams (i.e., higher and wider) have a higher uplift percent reduction. As the height of the dam and/or the base width increase, the dam retains more water, and this increases the magnitude of the uplift force and the percent reduction of uplift. The maximum reduction in the uplift is observed when the drain is located close to the heel of the dam in a horizontal distance of 4 percent to 10 percent of the dam base width from the heel. The percent uplift reduction gets its maximum value if the gallery is located at the same level or below the downstream water level (i.e., Y is less than or equal to 2 percent of H). In addition, within this range the uplift reduction becomes constant. The percent reduction of uplift decreases at a linear rate as the gallery is moved vertically away from the base of the dam (see Figure 2a). The reason for the linear behavior here is that the drain effectiveness does not depend on the vertical position of the gallery; it depends on its horizontal location. Therefore, the change in Figure 2b is nonlinear. The reduction in the uplift becomes zero when Y is 75 percent of the dam height or X is 100 percent of the dam base width. These values correspond to a very high level and far downstream points and lead to an ineffective drainage system. The Effect of Drain Size and Spacing Dam 2 is considered under usual loading conditions to test the impact of the foundation drain size and spacing. To this end, various diameters, which are

Figure 2. The percent reduction of uplift for (a) Y and (b) X variations under usual loading of Dams 1, 2, and 3.

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Figure 3. The percent reduction of uplift for Y and X variations under usual loading of Dam 2 when (a–b) S = 5 m and (c–d) D = 0.10 m.

0.10 m, 0.25 m, 0.50 m, 0.75 m, and 1.00 m, and spacing values, which are 1 m, 2 m, 3 m, 4 m, and 5 m, are considered. These values are taken with reference to Chawla et al. (1990) and Yanmaz (2018). Each drain size is coupled with each of the spacing values, and 25 scenarios are created. Then each scenario is

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tested using the same procedure followed for the dam height variation analyses (i.e., Y is varied for constant X, and vice versa). Figure 3a and b show the percent reduction in the uplift force for each of the Y and X percent locations considered, respectively, for a drain spacing of 5 m

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for various diameters, whereas Figure 3c and d show the same for a drain diameter of 0.10 m for various spacing. The results indicate that as the drain diameter increases, the uplift decreases and the percent reduction increases. In addition, smaller spaces between the drains (i.e., more drains per unit length) lead to reduced uplift force. Furthermore, as the diameter increases or the spacing between drains decreases, the maximum percent reduction points for the uplift shift toward the upstream. This is consistent with the results of Chawla et al. (1990). The maximum uplift reduction was observed for drainage galleries located in a horizontal location, X, of 4 percent to 15 percent of the dam base width and a vertical position, Y, that is below the downstream water level. This observation also agrees with the suggested minimum distance of 1.52 m provided in the literature (USBR, 1976; USACE, 1995). In the scope of the study, the internal stresses developed at the upstream and downstream faces of the dam are also computed at each joint level, and the effects of various locations and geometric configurations of the drainage system on the internal stress distributions are investigated. The stress distribution between the upstream and the downstream faces is assumed to be linear, and interpolation is used to determine the internal stresses at each joint level. Figure 4a and c correspond to the stresses at the dam heel for Y variations for a drain spacing of 5 m and a diameter of 0.10 m, respectively. It is seen that as the foundation drain diameter increases and the drain spacing decreases the stresses increase. This is attributed to increased hollow spaces that lead to lower concrete area. Figure 4b and d correspond to the heel stresses for X variations for a drain spacing of 5 m and a diameter of 0.10 m, respectively. The stresses increase as the spacing between the drains decrease and the drain diameter increases. Similarly, this effect can be attributed to the reduced area of concrete. It is seen that the stresses at the downstream side are not affected by either the foundation drain diameter or spacing, and they are not provided in the study. Along with the usual loading condition, unusual and extreme loading conditions are considered for Dam 2 as well. The upstream water level is taken as 100 percent and 75 percent of the dam height for unusual loading and extreme loading, respectively. For the extreme loading case, the pseudo-static approach is used, and the peak ground accelerations are taken as 0.30 g and 0.20 g, corresponding to the horizontal and vertical peak ground accelerations, respectively (Beser, 2005). It is seen that for the unusual loading case, the uplift force variation and internal stresses for different foundation drain sizes and spacing are very similar to those presented for the usual loading case. Thus,

these are not provided separately in this study. The uplift force and the percent reduction in it are slightly higher since there is more water present in the reservoir due to flooding. However, for the unusual loading case base cracking is observed. The cracks are observed to initiate from the dam heel and propagate toward the downstream horizontally. Table 2 presents the lengths of the cracks at the base level as the percentage of the dam base width for Dam 2 for foundation galleries placed at various horizontal locations when the gallery height is taken as 1 percent of the total dam height. A base crack having a length of 42.3 percent of the base width is observed to occur for the nondrained case. No cracking is observed if the drainage gallery is horizontally located at distances between 1 percent and 50 percent of the base width from the heel. When the gallery is moved further downstream, cracking starts again; its length increases and approaches its maximum value. For the extreme loading condition, the uplift force variation for various drain diameters and spacing is almost the same as those presented for the usual loading case. Therefore, these data are not presented separately in the study. However, it was found that for the extreme loading condition, contrary to unusual loading case, the base cracks initiate from the dam toe and extend toward the upstream horizontally. Figure 5 shows the length of cracks as the percentage of dam base width for various horizontal locations of the drainage gallery for the extreme loading. The results show that the crack length decreases as the drain diameter increases and the spacing decreases. The crack length becomes minimum when the gallery is placed at 40 percent of the dam base width away from the upstream. However, if the gallery is placed at this location, the uplift force at the base will be much higher compared to values at a location closer to the upstream face. Therefore, placing the drainage gallery at this location is not feasible with regard to the uplift. A horizontal location between 10 percent and 20 percent of the dam base width can be selected to minimize both cracking and the uplift force. The Effect of Post-Tension Cables The results showed that cracks are generated at the base of Dam 2 for unusual and extreme loading conditions. To this end, inclined post-tension cables (see Figure 1a) are added close to the dam toe to reduce the cracks at the base during the extreme loading condition. Seven wire strands with a diameter of 15.24 mm (ASTM, 2017) having a minimum ultimate strength of 260.7 kPa (ArcelorMittal, 2018) are used for this purpose. The cables are stressed to 60 percent of the minimum ultimate strength (i.e., 156.4 kPa). Four different inclination angles, θ (i.e., 45°, 60°, 75°, and 90°), are

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Figure 4. Internal stresses at the upstream for Y and X variations when (a–b) S = 5 m and (c–d) D = 0.10 m for usual loading of Dam 2.

considered. The elevation of the cables measured from the dam base, y, is taken to be between 1 m to 10 m with 1 m of increment. The drainage gallery is placed at an elevation, Y, of 1.0 m (i.e., 1 percent of the dam height), and a horizontal distance, X, of 4 m (i.e., 10 percent of the base width, measured from the heel). It

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should be noted that this horizontal location is found to be the optimum gallery location with regard to the uplift force reduction (see Figure 3b and d); however, it also yielded certain cracks at the base of the dam (see Figure 5). The foundation drain diameter is set as 0.25 m, with 3-m spacing between drain centers. Figure 6

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Optimum Layout of a Gallery in Dams Table 2. The percentage of crack lengths for various horizontal locations of the gallery for unusual loading of Dam 2.

X as Percentage of B No drainage 1–50 60 70 80 90 100

Crack Length as the Percentage of the Dam Base Width 42.3 No cracking occurs 2.5 20.0 32.6 40.0 42.3

shows the amount of the reduction in the crack length generated at the downstream for the post-tension cabled case of Dam 2 under the extreme loading condition. The reduction in the crack length of each case is measured with respect to the crack length of the case with no cables. It is seen that the post-tension cable can reduce the crack length by about 1.8 percent. The results show that the largest reduction is obtained when the cables are close to the dam base (i.e., at an elevation, y, of 1 m) and are vertically placed (i.e., at an inclination angle of 90°). It should be noted that the overall reduction in the crack length for the anchorage positions tested is relatively small. In the scope of this study, post-tension cables were also placed in the crest, close to the upstream face. However, these led to an increase in the crack length generated at the downstream side and therefore are not provided in the study. It is possible that there might be other, more eﬀective cable positions for reducing the crack length, including positions that were not considered in the analysis. The Seepage from the Foundation into the Gallery The amount of water seeping into the gallery is needed to calculate the dimensions of the sump well

Figure 5. The crack lengths as the percentage of dam base width for X variations under extreme loading of Dam 2.

Figure 6. %Reduction in the crack length for various inclinations and positions of post-tension cables for extreme loading of Dam 2.

where the water will be collected and to determine the pump power required to remove the collected water out of the dam. In this study, the amount of water seeping into the gallery for its different locations is computed with SEEP/W for Dam 2 under usual loading conditions. The foundation material is assumed to be homogeneous and isotropic, fully saturated sandy loam with a hydraulic conductivity of 1 × 10−5 m s−1 and a saturated volumetric water content of 0.4 m3 m−3 (Carsel and Parrish, 1988). The dam body is assumed to be impervious, and the edges of the dam in contact with the water are defined as “interface layers.” In the finite-element model, the maximum element size is taken as 1 m. This yielded 7,421 nodes and 7,156 elements in the model. The upstream water level is set at 75 m as a constant head boundary condition, and no tailwater is assumed to maximize the head difference and the seepage. The downstream side of the foundation is flagged as a potential seepage face and assigned a free surface boundary condition, in which the total head is equal to elevation head. Lastly, the foundation drain opening is considered as a zero-pressure head boundary condition. The seepage rate and the pore water pressures are obtained at five different points. Points 1, 2, and 3 are located at the heel, the mid-base width of the dam, and the toe, respectively, whereas Points 4 and 5 are located 0.1 m away from the left and right side of the foundation drain, respectively. The locations of these points can be seen in Figure 1a with markers P1–P5. The drain diameter is taken as 0.25 m, and the impact of the location of the gallery is investigated. Figure 7a and b show the variation of seepage rate into the drain and pore water pressure with respect to various horizontal positions of the gallery, respectively. The results show that seepage rate decreases as the gallery is moved toward downstream, except at Point 3, which has very low pore water pressures and is

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Figure 7. (a) The seepage rate and (b) pore water pressure at the foundation of Dam 2 for various horizontal locations of the gallery.

located at the dam toe. In both figures, sharp changes are observed for Point 2 when the gallery is placed at 50 percent of the base width away from the upstream face. In this case, Point 2 coincides with the drain. In the entrance of the foundation drain, high hydraulic gradients occur, and this results in a higher seepage rate and lower pore water pressures there. In the scope of this study, additional seepage analyses are conducted for varying vertical positions of the gallery and drain diameters as well. The results of these analyses show that the vertical position of the gallery has almost no effect on the seepage rate into the gallery and the pore water pressures. It is also seen that when the foundation drain diameter is increased, higher seepage rates and lower pore water pressures are obtained in the dam foundation. The detailed presentation of these findings can be found in Daghestani (2018). CASE STUDY: PORSUK DAM The findings of the application study are applied to an existing dam to compare a previous drainage gallery design with the recommendations of the current study. For this purpose, Porsuk Dam, located 25 km southwest of Eskisehir, Turkey, is taken into consideration. It is a concrete gravity dam with a height and base width of 49.7 m and 39.4 m, respectively. In the dam, there exists a vertically placed foundation drainage system having the following proper-

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ties: Y = 3.07 m, X = 7.01 m, D = 0.12 m, and S = 2 m (Ural and Ungan, 1967). The foundation of Porsuk Dam is composed of peridotite containing serpentinized veins and decomposition zones near the surface. The geologic formation lies within the Northern Ophiolite Belt of Anatolia (Ilhan, 1976). The peridotites are dark green, generally massive, and fresh. The dominant rock type is harzburgite, which is characterized by distinct light green pyroxenes and dark green olivines (Doyuran et al., 1993). The properties of the dam and other input data used for the stability, stress, and seepage modeling are presented in Table 3. For the analyses, four scenarios are considered. Scenario 1 corresponds to the original conditions of the dam. This study found that the maximum reduction of uplift occurs when the foundation gallery is located 10 percent of the base width away from the heel. Considering this, in Scenario 2 the drain is located 3.94 m away from the heel, accomplished by keeping its height the same as in the original case. It was also shown that a vertically placed post-tension cable at the downstream side of the dam helps with the reduction of cracks. Therefore, in Scenario 3 a post-tension cable having an angle of 90°, a diameter of 15.24 mm, and the minimum breaking strength of 260.7 kPa is placed at an elevation, y, of 1 m from the dam toe. Scenario 4 uses the same posttension cable added in Scenario 3; however, the gallery is placed slightly further downstream at an X of 11.8

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Optimum Layout of a Gallery in Dams Table 3. The data for simulation of Porsuk Dam (Beser, 2005). Parameter

Value

H tc H* B Normal reservoir water depth Maximum reservoir water depth Tailwater depth Dam outlet elevation Geological composition of the foundation Hydraulic conductivity of the foundation Speciﬁc weight of concrete Compressive strength of concrete Tensile strength of concrete Internal friction angle Peak Residual Cohesion of lift joint Allowable compressive stress in concrete Allowable compressive stress at foundation Allowable shear stress at foundation Horizontal peak ground acceleration Vertical peak ground acceleration

49.70 m 4.50 m 8.64 m 39.40 m 45.60 m 48.20 m 6.00 m 16.20 m Peroditite 6 × 10−7 m s−1 24 kN m−3 30 MPa 3 MPa 55° 45° 931 kPa 3.75 MPa 4.00 MPa 1.50 MPa 0.30 g 0.20 g

percent of B. This place is found to be the closest point to the optimum horizontal location of the gallery regarding uplift force reduction and yielding a smaller crack length than that of the original case introduced in Scenario 1. The vertical position of the gallery is not changed, since the original location is below the downstream water level, which is found to be the best vertical position in this study, and any gallery located above the downstream water level will lead to an increase in the uplift force and the cracks generated. Table 4 summarizes the scenarios considered and shows the results for the uplift force and crack lengths and the reductions in these compared to the original case of the dam. The results showed that the existing design of Porsuk Dam is proper; the existing gallery location provides a good balance between the reduction of uplift force and the cracks generated at the base. However, Scenario 3 results in a higher uplift reduction with a slight increase in the crack length. Furthermore, Scenario 4 presents a gallery location between the existing one and that proposed in Scenario 3, allowing for a higher uplift reduction than the existing location and lower crack length than the existing location, with the help of an installed post-tension cable. Therefore, Scenario 4 is found to be the best, considering both uplift force and crack reduction under the assumed conditions. The collected water in the drainage gallery should be pumped out, and pumping is one of the major cost items in the operation of a concrete gravity dam.

In the scope of this study, the effects of the drainage gallery location on the pumping schedule are also investigated. A sump well, in which the drained water is collected and pumped out, is placed just at the bottom of the drainage gallery of Porsuk Dam. The dimensions of the sump well are taken as 3 m × 3 m × 2.5 m, considering the suggestion of Chauhan et al. (2008). In a preliminary analysis, the seepage rate into the drain foundation of the dam is found to be 7.2 × 10−4 l s−1 , which is negligibly low. However, there will be some contribution to the flow taking place in the drainage gallery from the leakage through the upstream face and the abutments. Scuero et al. (2015) stated that the average leakage rate through the geomembrane covered the upstream face of a 188-mhigh roller compacted concrete dam is 2.0 l s−1 . Porsuk Dam has no protective impervious material at its upstream face. Weber and Zornberg (2007) stated that the hydraulic conductivity of the geomembrane is around 1 × 10−15 m s−1 , while the hydraulic conductivity of concrete is 1.5 × 10−12 m s−1 (Zee et al., 2011). Therefore, a greater leakage rate through the upstream face is expected for Porsuk Dam. In addition, a contribution from the leakage through the abutments is expected. Therefore, the given leakage rate in the literature is doubled, and 4.0 l s−1 is assumed as the total flow rate into the sump well. In the analysis, the vertical positions of the drainage gallery and sump well are varied, whereas their horizontal location is kept constant, since the contribution from the foundation drain is negligibly small. Two alternative vertical positions are considered for them: X = 4 m and 6 m. The lowest vertical position is taken as 4 m to keep a 1.50-m distance between the bottom of the sump well and the foundation. The alternate elevation is taken as 6 m, which is the downstream water level of the dam. For the pumping cost estimation and scheduling, the following are considered: (1) the pump efficiency is 80 percent; (2) the water is pumped to an elevation that is higher than that of the outlet elevation (i.e., 16.2 m for Porsuk Dam) to allow the gravity to transport the water; (3) the duration of pumping is obtained by dividing the volume of the sump well by the net flow (i.e., difference between the pumping and inflow rates); (4) the unit price of electricity is estimated to be 7.14 cent kWh−1 (Turkish Statistical Institute, 2017); and (5) the initial costs for various pump powers are obtained from Varan Pompa (2018). The daily cost is estimated by multiplying the cost of each pumping session by the number of pumping sessions in a day. The number of sessions is calculated by assuming that the sump well is filled and then emptied. The sump well is considered to empty with three different discharges, Q : 5.00 l s−1 , 6.67 l s−1 , and 8.33 l s−1 . The annual cost of the pumping operation is computed as the

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Daghestani, Calamak, and Yanmaz Table 4. The eﬀects of post-tension cables on the reduction of uplift force and crack generation for Porsuk Dam. Usual Loading Scenario 1 2 3 4

Unusual Loading

Extreme Loading

Post-Tension Cable

X (m)

Uplift (kN m−1 )

Uplift Reduction (%)

Uplift (kN m−1 )

Uplift Reduction (%)

Percent Crack Length

Crack Length Reduction

No No Yes Yes

7.01 3.94 3.94 4.65

4,211.5 3,981.0 3,981.0 3,995.6

— 5.5 5.5 5.1

4,335.7 4,090.1 4,090.1 4,105.7

— 5.7 5.7 5.3

5.08 6.90 5.53 5.07

— − 35.83 − 8.90 0.16

summation of the daily cost for a year and the product of initial cost of the pump and the capital recovery factor, which is taken as 0.1 (Yanmaz, 2018). The daily pumping cost depends on (1) the pump power, which is a function of pumping discharge, head, and eﬃciency; (2) the duration of pumping, which is a function of pumping discharge; (3) the number of pump operations in a day; and (4) the unit price of electricity. The variation of the annual pumping cost with respect to the pump head is presented in Figure 8. It is found that even though pumping at a higher discharge rate requires a more powerful and expensive pump, it costs less annually, since the duration of pumping decreases with it. Among all cases, the cheapest option is to place the gallery and the sump well at the closest possible elevation to the dam outlet elevation (i.e., 6 m for Porsuk Dam) to minimize the pumping head and to choose a higher pumping rate (i.e., 8.33 l s−1 for Porsuk Dam) to minimize the pumping duration. However, it should be noted that the gallery elevation should be below the downstream water level to minimize the uplift force at the base of the dam.

CONCLUSIONS In this study, the impacts of both vertical and horizontal locations of the drainage gallery within concrete gravity dams as well as the foundation drain size and spacing on uplift force development, stresses within the dam, and crack generation were studied. While previous studies considered the impacts of these variables only for the usual loading condition, this study tested various scenarios for all usual, unusual, and extreme loading conditions to provide a more comprehensive investigation and generalized results. The uplift force and internal stresses were estimated numerically using the gravity method based on the limit equilibrium and the beam theory, and the seepage beneath the dam and into the foundation drain is computed using the finite-element method. First, the analyses were conducted on a hypothetical concrete gravity dam to obtain generalized results in an application study. Then the findings were implemented in a case study to compare the performance of the actual drainage system in the dam with the proposed one. Finally, the effects of the vertical position of the drainage gallery and the sump well on pumping cost were investigated. The results and findings of the study led to the following conclusions:

r The presence of a gallery can reduce the uplift by over 60 percent.

r Placing the gallery at 10 percent of the base width

Figure 8. The variation of the annual pumping cost for Porsuk Dam for diﬀerent pump heads, discharges, and locations of the drainage gallery.

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away from the upstream face generates the lowest uplift force, on average. This best horizontal location shifts toward the upstream when (1) the height of the dam increases; (2) the diameter of the foundation drain increases; and (3) the spacing between the drains decreases. r The lowest uplift force is generated when the gallery is vertically placed close to the foundation. The best vertical position for it is found to be the downstream water elevation or below this level. However, it should be kept in mind that there should be a minimum distance of 1.52-m spacing between the gallery and the foundation level.

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r Larger drain diameters and smaller spacing between drain centerlines increase the internal stresses and shift the gallery position giving the minimum uplift force toward the upstream face. r The maximum crack length reduction is obtained when the gallery is at a horizontal distance of 40 percent of the base width from the upstream face. Moving the drain galleries toward upstream generates longer crack lengths. However, under extreme loading conditions, in which cracks form on the downstream side of the dam, with the addition of posttension cables along the downstream face the crack length can be reduced. The greatest crack reduction is obtained when the cables are close to the dam toe and vertically placed. r Placing the gallery as close as possible to the dam outlet elevation reduces the pump head and thus the pumping cost. Running the pump with a higher discharge rate reduces the duration of pumping and the operation cost as well. Generalization of the above results requires further research. In a future study, pre-existing cracks in the dam, the temperature variation, any possible transient boundary conditions (including rapid fill and drawdown), and the effects of the size of the gallery should be considered. In addition, the foundation resistance from dam embedment may be included. The analyses in this study were based on a simplified distribution of uplift treated as an external force with a homogeneous and isotropic foundation. The uplift can be simulated as a body force rather than an external one, and the complete geology of the foundation can be considered for more realistic results.

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Assessment of Open-Source Software, QGIS, to Estimate Hurricane Matthew Flood Extent in Robeson County, North Carolina, Using Unsupervised Classiﬁcation CORTNEY CAMERON1, * CHIBUIKE MADUMERE2 Department of Earth, Environmental and Geospatial Sciences, North Carolina Central University, Durham, NC 27707

Key Terms: Flooding, Remote Sensing, Natural Hazards, Emergency Management, Open Source ABSTRACT The spatial extent of flooding caused by Hurricane Matthew in Robeson County, NC, in October 2016 was investigated by comparing two Landsat-8 images (one flood and one non-flood) following K-means unsupervised classification for each in both ENVI, a proprietary software, and QGIS with Orfeo Toolbox, a free and open-source software. In this study, unsupervised classification was capable of rapidly producing regional maps, but poor accuracy constrained practical application. Of particular note, the open-source setup performed on par with the proprietary option for each of the classifications. Overall, remote sensing techniques using opensource software show promise in helping aid workers to cost-effectively conduct post-event analyses and relief efforts. INTRODUCTION During and immediately after a storm event, remotely sensed images, such as aerial imagery, can allow researchers to safely and cost-effectively identify hard-hit areas in order to prioritize relief efforts. Additionally, post-event analyses can help researchers map flooding and damage patterns to inform emergency preparedness planning (Jeyaseelan, 2003; Sanyal and Lu, 2004; and Jain et al., 2005). However, leading commercial remote sensing software packages, which range in price from hundreds to thousands of dollars, can cause cost concerns for budget-restricted organizations, creating a need for researchers to design, evaluate, and implement open-source alternatives (Teeuw 1 Present address: Resource Evaluation Section, Southwest Florida Water Management District, Brooksville, FL 34604 2 Present address: Infrastructure and Environmental Systems Program, University of North Carolina at Charlotte, Charlotte, NC 28223 *Corresponding author email: ccamero6@eagles.nccu.edu

et al., 2013). At the same time, open-source solutions that require the user to have some coding knowledge, such as scripts or programs written entirely in the R or Python languages, may face resistance at these organizations due to perceptions regarding their lack of ease of use. On the other hand, open-source QGIS (QGIS, 2016) and its various supported add-on packages and plugins may be suitable for general purpose usage at public and non-profit agencies (Friedrich, 2014). Accordingly, the main objective of this article is to investigate QGIS for assessing post-storm flooding and other general applications using flooding in Robeson County as a case study. At the time of its landfall in 2016, Hurricane Matthew was the most expensive Atlantic hurricane for the United States since 2012, and, with over a thousand fatalities, the deadliest hurricane since 2005 (Muhr et al., 2016; note that recent storms have surpassed these records). Matthew, which reached hurricane status on September 29 in the Caribbean Sea, made landfall in South Carolina as a category one hurricane on October 8, then weakened, tracked eastward, and lost hurricane status on October 9 (Muhr et al., 2016). However, for days to weeks following the storm, extensive flooding persisted up to 200 km (200 mi) inland. Several deaths throughout the Carolinas were linked to the storm (Bruton and Johnson, 2016; Ray, 2016). Robeson County, one of the state’s poorest counties, suffered extensive flooding from Matthew. The county contains an area of 2,460 km2 supporting over 130,000 residents, a third of whom live in poverty, with a per capita income of under $16,000 (USCB, 2010; Quillin, 2016). Therefore, many residents in Robeson are especially vulnerable to negative socioeconomic outcomes caused by natural hazards. For example, lower income residents often have fewer options for evacuation before or during a storm or for relocation afterwards, and 2 year’s after Matthew’s passage, many homes in Robeson were still afflicted with Matthew-related damage when Hurricane Florence arrived in September 2018 (Colman and Cusick, 2018; McGee, 2018).

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Additionally, as a Coastal Plains county, Robeson could experience climate change–driven increases in the recurrence or intensity of landfalling large storms (Gutmann et al., 2018; Lim et al., 2018; and Trenberth et al., 2018). Thus, Matthew (and subsequent storms) underscore the need for accurate, cost-effective flood mapping after storm events, with remote sensing technologies well positioned to meet this need (Sanyal and Lu, 2004; Schumann et al., 2009). To that end, this article uses Landsat-8 imagery with QGIS with Orfeo Toolbox (QGIS, 2016), which together provide a graphical user interface for various geospatial analyses, to perform a preliminary investigation of flooding extent in Robeson County in the days following Hurricane Matthew’s passage. These results are compared to results obtained in ENVI, the proprietary industry standard software. Additionally, previous inundation estimates for all and parts of Robeson are available, respectively, from North Carolina Emergency Management (NCEM, 2016), using digitization of aerial and satellite imagery, and from the U.S. Geological Survey (USGS; Musser et al., 2017), using high-water markers; these results are summarized later for comparison. The overall goal is to qualitatively assess the viability of using open-source software as compared to a proprietary solution, with a lesser emphasis on assessing an approach for unsupervised classification for rapid regional flood mapping. METHODS The Landsat-8 satellite orbits the Earth every 99 minutes (14 times per day), providing imagery of every point on Earth every 16 days. Landsat-8 data are available free of charge through the USGS’s EarthExplorer (USGS, 2016). Robeson County was selected for analyses because contemporary news reports indicate that it suffered extensive flooding (Quillin, 2016) and because clear pre- and post-flood images were available for the county. Landsat-8 provides 11 bands; bands 2 to 7 are used in this study, all having a resolution of 30 m. These bands span the electromagnetic spectrum from 0.45 to 2.29 μm, or are visible through infrared light (Roy et al., 2014); band 2 corresponds to blue light, band 3 to green light, band 4 to red light, band 5 to near-infrared light, and bands 6 and 7 to short-wave infrared light. Two Landsat-8 satellite images of the eastern Carolinas (path 16; row 36), one for October 13, 2016, and one for October 29, 2016, were obtained through EarthExplorer (USGS, 2016). The images selected contain less than 10 percent cloud coverage and were clipped using an outline of Robeson County obtained from the North Carolina Department of Transportation (NCDOT, 2016).

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The October 13 image represents a flood stage (Bruton and Johnson, 2016; Ray, 2016), while the October 29 image is used as a non-flood reference image. The images show gross visual differences in apparent water coverage without any additional processing, supporting flood and non-flood designations (Figures 1 and 2). Several area USGS river discharge stations provide further support for these designations. For example, at USGS station 0210550 on the Cape Fear River near Tarheel, NC, the mean daily discharge for October during the 1937 to 2018 period-of-record is ∼3,060 ft3 /s. In 2016, however, mean daily discharge reached ∼23,000 ft3 /s on October 13, compared to ∼2,450 ft3 /s on October 29 (USGS, 2019). Additionally, the proximity of the two selected dates minimizes the influence of seasonal and other non-storm temporal fluctuations of water surface area. Prior to classification, atmospheric correction was applied to stacked images using the QGIS (version 2.18.0) Semi-Automatic Classification Plugin (SCP; version 5.2.0), which uses DOS1 with some parameters provided in the Landsat-8 image metadata file (see Congedo and Munafo, 2012, 2014; Zou and Lin, 2013; and Congedo, 2017). Then, for each image, K-means unsupervised classification was performed in QGIS with Orfeo Toolbox (Christophe et al., 2008; Teodoro et al., 2012; and QGIS, 2016), with settings of a maximum of 25 classes (above 25, few pixels went into new classes, so 25 was retained); a training set size of 1,000; a maximum number of iterations of 1,000; and a default convergence threshold of 0.0001. The K-means approach divides pixels into k groups (in this case, 25) such that each pixel falls into the cluster with the nearest mean (Shiudkar and Takmare, 2017). Several algorithms exist, but conceptually, clusters are iteratively drawn to locate clusters that maximize differences between groups while minimizing differences within groups (Shiudkar and Takmare, 2017). Classes were then manually combined into four land cover categories—open water, vegetation (e.g., forests, wetlands), open land (e.g., fields, bare soil, crops), and developed (e.g., buildings, urban areas, residential areas, roads)—based on visual assessment of raw images combined with, for non-flood images, comparison with the National Land Cover Dataset (NLCD; Homer et al., 2015). A similar analysis was repeated using ENVI (version 5), except that 1) atmospheric correction was applied to each image using ENVI’s QUick Atmospheric Correction (QUAC; Bernstein et al., 2012), and 2) ENVI’s default change threshold of 0.05 was retained. Both DOS1 and QUAC determine correction parameters based on observed pixel spectra in the image; however, QUAC generally produces reflectance spectra within 10 percent of ground truth and typically provides

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better results than does the simpler DOS1 method (Bernstein et al., 2012; Guo et al., 2014). A cursory accuracy assessment was performed for each of the flood and non-flood classification maps using 108 randomly placed points, with no weighting given by land cover type. The points were then manually classified for the flood and non-flood images, with the NLCD used to verify classification for the non-flood images. Given the low number of points, the accuracy assessment is mainly useful in qualitatively comparing the performance of the two programs rather than in providing a final determination of model accuracy. RESULTS Classification results are shown in Figures 1 and 2 and in Table 1. Accuracy assessments are shown as confusion matrices in Tables 2 through 5. Confusion matrices describe the performance of a classification model for each class (Congalton, 1991). The rows display counts of model classifications by class, while columns show the same for the reference classification. The producer’s accuracy indicates the complement of how frequently pixels were erroneously excluded from a class (false negatives), while the user’s accuracy indicates the complement of how often pixels were erroneously included in a class (false positives). Overall accuracy describes the overall proportion of pixels correctly classified as compared to the reference set. For all sets, the kappa coefficient (a measure of agreement between classification and reference values, where 0 is worst and 1 is perfect) was between 0.5 and 0.6, which is generally considered indicative of moderate performance (Landis and Koch, 1977), while overall accuracy was ∼0.7. The accuracy assessment further showed a statistically significant difference between model and reference values for QGIS and ENVI flood stage classifications for water (correspondent to model underdetection) and vegetation (correspondent to model overdetection) (Tables 4 and 5). However, at an alpha of 0.05, no differences were found between the accuracies of QGIS and ENVI classifications, except for the case of development (where the latter finding is likely an artifact of the small number of points assessed). DISCUSSION Our results provide a first-order assessment of flooding extent in Robeson County following Hurricane Matthew sufficient to qualitatively compare QGIS with ENVI. Inherent uncertainties in our results prevent an extensive quantitative assessment but provide useful qualitative information sufficient for the com-

parison of these open-source and commercial software. Notably, per the QGIS-derived data, as much as ∼170 km2 of land surface area was covered by water during flooding, with an estimated 45 km2 of developed areas inundated (Figure 1 and Table 1). However, as evidenced by the accuracy assessment and comparison with NCEM (2016) and Musser et al. (2017), based on the tendency for the classifications to underpredict open water areas during flooding, a significantly larger portion of land may have been inundated. Using 95 percent confidence intervals about the reference proportion and the difference between the model and reference proportions (assuming the points were representative of the overall classified population), flooding, to varying degrees, may have affected as much as 50 percent, or 1,230 km2 , of the county. However, this estimate, which is closer to but greater than the estimate by NCEM (2016), is constrained by the low number of points tested for accuracy. By assuming inter-image uncertainties are comparable, changes rather than absolutes may be illustrative, and to that end, several trends are suggested in the classification maps. First, the decrease in land area classified as “development” and “vegetation” from the non-flood to flood stage likely corresponds with the concomitant increase in water coverage (Table 1 and Figure 2). Second, the decrease in area classified as “vegetation” during flooding could also result from changes in vegetation moisture content during flooding. Third, the increase in area classified as “open land” is speculated to be driven by inaccurate classification within images and inconsistent classification between images (e.g., Ceccato et al., 2001; Zarco-Tejada et al., 2003). Using additional classifications, such as saturated soil or wetlands, might help to alleviate this issue. For the flood stage, NCEM (2016) and Musser et al. (2017) found, using considerably more detailed and accurate methods, that nearly 100 percent of the city of Lumberton was inundated, a finding neither QGIS nor ENVI replicated (Figure 2). Furthermore, the digitization effort by NCEM (2016) found that about 32 percent of Robeson was subjected to water, while our classifications found 9.2 percent (QGIS) and 4.1 percent (ENVI). For the non-flood stage, NCLD reports that 0.5% of Robeson is open water, lower than the values found by our classifications: 2.2 percent (QGIS) and 1.5 percent (ENVI). Specifically comparing the output of unsupervised K-means classification in QGIS against ENVI, overall land cover changes (in terms of negative and positive) showed the same trend (but different magnitudes) for each land cover group, although the changes do not exceed uncertainties associated with the analysis. A possible reason for differences in magnitude

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Figure 1. Grayscale, true-color Landsat-8 images (top; A, B) and classified maps for QGIS (C, D) and ENVI (E, F) of Robeson County (H) for non-flood (A, C, E) and flood (B, D, F, G) stages. NCEM flood extent map in G shows only flood (black). In the classified maps (C–F), black is water, dark gray is vegetation or open land, and light gray is developed.

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Figure 2. Grayscale, true-color Landsat-8 images (top; A, B) and classified maps for QGIS (C, D) and ENVI (E, F) of selected area (Lumberton) of Robeson County (G) for non-flood (A, C, E) and flood (B, D, F) stages. In the classified maps (C–F), black is water, dark gray is vegetation, gray is open land, and light gray is developed.

Table 1. Results of unsupervised K-means classification in QGIS with Orfeo toolbox reported as land cover percentages for non-flood (October 29, 2016) and flood (October 13, 2016) stages in Robeson County, NC.

Non-Flood (%) Land Cover Water Vegetation Open land Development

Change During Flooding (%)

Flood (%)

QGIS

ENVI

QGIS

ENVI

QGIS

ENVI

2.2 56.4 30.6 10.8

1.5 51.9 45.5 1.0

9.2 45.1 36.7 9.0

4.1 49.0 46.2 0.8

+7.0 − 11.3 +6.1 − 1.8

+2.6 − 2.9 +0.7 − 0.2

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Cameron and Madumere Table 2. Confusion matrix for non-flood (October 29, 2016) stage in Robeson County, NC, following unsupervised K-means classification in QGIS. The kappa coefficient is 0.53. Bold indicates a statistically significant difference between model and reference values for the category using a two-sample test for equality of proportions at an alpha of 0.05. Reference values are in columns and model values in rows. Classification Water Vegetation Open land Development Total Producer’s accuracy

Water

Vegetation

Open Land

Development

Total

User’s Accuracy

6 1 1 0 8 0.75

4 46 2 1 53 0.87

0 8 25 8 38 0.58

0 1 6 2 9 0.21

10 56 31 11 108 0

0.60 0.82 0.71 0.18 0 0.70

stems from differences in the built-in atmospheric correction methods and default convergence settings. Despite this, given consideration of uncertainties, the accuracy assessment suggests that the outputs between the two were statistically equivalent, which is consistent with the usage of the same classification approach (K-means) for each. Accordingly, overall accuracies were similar at a moderate ∼0.7 (that is, an estimated 70 percent of pixels were accurately classified as compared to reference data) for all classifications. However, reliance on overall accuracy for model performance may be insufficient depending on the intended purpose. For flood relief and rescue, if the model classified the correct percentage of a class overall but that percentage was allocated to the wrong pixels, rescue efforts could be diverted to the wrong areas. To that the end, the method suffered from significant accuracy variability across classes (Tables 2 through 5), which, along with its disagreement with NCEM (2016), Musser et al. (2017), and the NLCD, underscores its shortcomings. Foremost, the method underperformed at distinguishing between development (which, because of its variability, is notoriously difficult to classify) and open land. However, in practice, these two land cover types are essential to distinguish, as rescue workers would target populated developed areas over open land areas. Additionally, the method underdetected water during the flood stage. Supervised training, including the in-

tegration of other data sets (such as parcel information available to local governments), could help to improve this outcome, at the tradeoff of additional time required for analyses. Results could likely further be improved by isolating only those areas immediately adjacent to known open water (per, for example, the NLCD). Finally, some error was undoubtedly introduced during the manual combination of classes, as well as in the manual classification of points used to check the model accuracy. However, the low number of points constrains the accuracy assessment, and it is possible the poor results seen for the water and development are at least somewhat a reflection of the low sample size tested for accuracy in those categories. As another limitation, the specific method is constrained by cloud cover. Indeed, in this study, a flood stage image was available during continued flooding, but this admittedly ultimately stems from—as is often the case with natural disasters—chance. Usage of actively sensed satellite data could serve to reduce or eliminate cloud cover issues, but at the time of writing, there exists, to the authors’ knowledge, no freely and readily available actively sensed satellite data with appropriate spatial and temporal resolution for flood mapping. For non-flood classifications, land cover data sets such as the NLCD provide options. Overall, the described concerns largely limit the method as is to rudimentary but rapid post-event analyses. While the specific approach tested here for

Table 3. Confusion matrix for non-flood (October 29, 2016) stage in Robeson County, NC, following unsupervised K-means classification in ENVI. The kappa coefficient is 0.55. Bold indicates a statistically significant difference between model and reference values for the category using a two-sample test for equality of proportions at an alpha of 0.05. Reference values are in columns and model values in rows. Classification Water Vegetation Open land Development Total Producer’s accuracy

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Water

Vegetation

Open Land

Development

Total

User’s Accuracy

3 4 1 0 8 0.38

4 46 3 0 53 0.87

0 7 29 2 38 0.70

0 2 6 1 9 0.10

7 59 39 3 108 0

0.43 0.78 0.74 0.33 0 0.73

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Unsupervised Classification in QGIS Table 4. Confusion matrix for flood (October 13, 2016) stage in Robeson County, NC, following unsupervised K-means classification in QGIS. The kappa coefficient is 0.56. Bold indicates a statistically significant difference between model and reference values for the category using a two-sample test for equality of proportions at an alpha of 0.05. Reference values are in columns and model values in rows. Classification

Water

Vegetation

Open Land

Development

Total

User’s Accuracy

Water Vegetation Open land Development Total Producer’s accuracy

11 13 0 0 24 0.46

0 32 0 2 34 0.94

0 8 29 5 42 0.69

0 1 4 3 8 0.38

11 54 33 10 108 0

1 0.59 0.88 0.30 0 0.69

regional flood mapping does provide rapid results (which at gross scale appear reasonable; Figure 2), local-scale accuracy is unsatisfactory, limiting the feasibility for relief efforts. Using more detailed approaches, however, QGIS has been previously successfully evaluated for flood risk and hazard mapping (Samela et al., 2018), and incorporating elevation data has been shown to enhance Landsat-based flood mapping (Wang et al., 2012). The main outcome of this study, then, is to demonstrate that QGIS, especially when used in combination with GRASS (Neteler et al., 2012) or Orfeo Toolbox or (both of which are readily linked with the software), offers many built-in features that are functionally equivalent to proprietary solutions; we found that its speed and accuracy matched industry-standard software. As an open-source software package with a graphical user interface, its implementation can provide a cost-effective and relatively user-friendly option for budget-restricted organizations, without sacrificing key functionality. Overall, we feel that this and other work reveal the potential for open-source approaches, such as QGIS, for flood and other mapping utilities using satellite imagery and other data (such as digital elevation models). Furthermore, we use just one option of no doubt hundreds of free or open-source possibilities—R, Python, and Google Earth Engine among them—and even QGIS alone supports a robust plugins library of hundreds. In fact, while atmospheric

correction was performed in this study using SCP, both GRASS and Orfeo Toolbox readily support the same (DOS1) and other corrections, as well as many other tools and classiﬁcation algorithms. Accordingly, countless articles have documented success with open-source software and remote sensing (Teeuw et al., 2013); yet despite this potential, the experience of the authors remains that many public agencies, at least in the United States, rely on proprietary software with minimal training devoted to opensource solutions. The authors hope that this article joins others in demonstrating the promise of opensource alternatives and encouraging agencies to consider training in and, as appropriate, adoption thereof, as complementary to existing commercial solutions. Indeed, between writing this article and its submittal for publication, one of the researchers changed institutions and was unable to access ENVI for further analyses using the software—illustrative of the barriers that reliance on proprietary software, however high performing, can introduce. By comparison, outside of staﬃng and hardware costs, using Landsat, Sentinel2, or other freely available imagery with QGIS (particularly with GRASS or Orfeo Toolbox) and other open-source packages provides a virtually no-cost mapping option. As possible reasons for limited adoption include lack of awareness of, limited research performed using, and fewer training and support options available for open-source software, continued research

Table 5. Confusion matrix for flood (October 13, 2016) stage in Robeson County, NC, following unsupervised K-means classification in ENVI. The kappa coefficient is 0.57. Bold indicates a statistically significant difference between model and reference values for the category using a two-sample test for equality of proportions at an alpha of 0.05. Reference values are in columns and model values in rows. Classification

Water

Vegetation

Open Land

Development

Total

User’s Accuracy

Water Vegetation Open land Development Total Producer’s accuracy

8 16 0 0 24 0.33

0 33 2 0 34 0.94

0 5 35 1 42 0.85

0 1 6 1 8 0.13

8 55 43 2 108 0

1 0.60 0.81 0.50 0 0.71

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