Environmental & Engineering Geoscience by Association of Environmental & Engineering Geologists (AEG)

Environmental & Engineering Geoscience FEBRUARY 2015

VOLUME XXI, NUMBER 1

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

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

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Cover photo Cal Trans employee on California Route 2, within the San Gabriel Mountains north of Los Angeles, CA, September 17, 2006, during the Station Fire. Dry ravel is actively forming a debris cone encroaching on the traffic lane. Dust rises as sand-sized particles to rocks nearly a foot in diameter cascade from the steep slope. Photo Credit: U.S. Forest Service, Burned Area Emergency Response (BAER) Team geologist, Jonathan Swartz.

Environmental & Engineering Geoscience Volume 21, Number 1, February 2015 Table of Contents

Simulations of Potential Future Conditions in the Cache Critical Groundwater Area, Arkansas Haveen M. Rashid, Brian R. Clark, Hanan H. Mahdi, Hanadi S. Rifai, and Haydar J. Al-Shukri

Uncertainty Associated with Evaluating Rockfall Hazard to Roads in Burned Areas Jerome V. De Graff, Bill Shelmerdine, Alan Gallegos, and David Annis

Complex Landslide Triggered in an Eocene Volcanic-Volcaniclastic Succession along Sutherland River, British Columbia, Canada Andre´e Blais-Stevens, Marten Geertsema, James W. Schwab, and Theo W. J. Van Asch

Modeling the Northern Coastline of Yucatan, Mexico, with GENESIS Roger Gonza´lez-Herrera, Alfonso Solı´s-Pimentel, Carlos Zetina-Moguel, and Ismael Marin˜o-Tapia

Collection and Application of Outcrop Measurements in Glacial Materials for Geo-Engineering and Hydrogeology along the Vermilion River, East-Central Illinois Christopher J. Stohr, Andrew J. Stumpf, and Barbara J. Stiff

Simulations of Potential Future Conditions in the Cache Critical Groundwater Area, Arkansas HAVEEN M. RASHID Dams and Water Resources Department, Faculty of Engineering, University of Sulaimani, Sulaymaniyah, Iraq; and Department of Applied Science, University of Arkansas, 2801 South University Avenue, Little Rock, AR 72204

BRIAN R. CLARK U.S. Geological Survey, Arkansas Water Science Center, Fayetteville Field Office, 700 West Research Boulevard, MS 36, Fayetteville, AR 72701

HANAN H. MAHDI Graduate Institute of Technology, University of Arkansas, 2801 South University Avenue, Little Rock, AR 72204

HANADI S. RIFAI Civil and Environmental Engineering Department, University of Houston, Room N107, Engineering Building 1, Houston, TX 77204-4003

HAYDAR J. AL-SHUKRI Department of Applied Science, University of Arkansas, 2801 South University Avenue, Little Rock, AR 72204

Key Terms: Modeling, Aquifer, Calibration, Pilot Point, MODFLOW

ABSTRACT A three-dimensional finite-difference model for part of the Mississippi River Valley alluvial aquifer in the Cache Critical Groundwater Area of eastern Arkansas was constructed to simulate potential future conditions of groundwater flow. The objectives of this study were to test different pilot point distributions to find reasonable estimates of aquifer properties for the alluvial aquifer, to simulate flux from rivers, and to demonstrate how changes in pumping rates for different scenarios affect areas of long-term water-level declines over time. The model was calibrated using the parameter estimation code. Additional calibration was achieved using pilot points with regularization and singular value decomposition. Pilot point parameter values were estimated at a number of discrete locations in the study area to obtain reasonable estimates of aquifer properties. Nine pumping scenarios for the years 2011 to 2020 were tested and compared to the simulated water-level heads from 2010. Hydraulic conductivity values from pilot point calibration ranged between 42 and 173 m/d. Specific yield values ranged

between 0.19 and 0.337. Recharge rates ranged between 0.00009 and 0.0006 m/d. The model was calibrated using 2,322 hydraulic head measurements for the years 2000 to 2010 from 150 observation wells located in the study area. For all scenarios, the volume of water depleted ranged between 5.7 and 23.3 percent, except in Scenario 2 (minimum pumping rates), in which the volume increased by 2.5 percent.

INTRODUCTION The Mississippi River Valley alluvial aquifer, often termed the ‘‘alluvial aquifer,’’ is a water-bearing assemblage consisting of gravels and sands that underlies about 82,879 km2 of Missouri, Kentucky, Tennessee, Mississippi, Louisiana, and Arkansas (Czarnecki et al., 2002). In eastern Arkansas, the alluvial aquifer occurs in an area generally 80 to 201 km wide by about 402 km long adjacent to the Mississippi River (Czarnecki et al., 2002). Crowley’s Ridge, which trends approximately north to south in northeastern Arkansas, separates the alluvial aquifer into two parts. The ridge rises 30 to 76 m above the surrounding alluvial plain, is about 241 km in length, and averages about 4.8 km wide in the southern half and 16 km wide in the northern half (Gonthier and Mahon, 1993).

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Figure 1. Cache Critical Groundwater Area (top) and the current study area (bottom).

Pumping of groundwater from the alluvial aquifer for agriculture started in the early 1900s in the Grand Prairie area for the irrigation of rice and soybeans. The first documentation of water-level declines in the alluvial aquifer was in 1927 (Engler et al., 1945; Czarnecki, 2010). Long-term water-level measurements in the alluvial aquifer show an average annual decline of 0.3 m/yr in some areas (Freiwald, 2005; Schrader, 2010). Because of the heavy demands placed on the aquifer for irrigation, two major cones of depression have formed in the potentiometric

surface of the alluvial aquifer in an area referred to as the Cache Critical Groundwater Area. The first cone of depression is in Poinsett and Cross Counties, and the second is in the St. Francis, Lee, and Monroe Counties. The Cache Critical Groundwater Area was designated in 2009 by the Arkansas Natural Resources Commission. The designation was made because of water-level declines to below 50 percent of the original saturated thickness of the alluvial aquifer (Arkansas Natural Resources Commission, 2011).

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Figure 2. Representation of land surface over the model area.

Objectives of the Study A numerical model of groundwater flow of the Mississippi River Valley alluvial aquifer in the Cache Critical Groundwater Area was developed that facilitated simulation of an 11-year period from 2000 to 2010 and various forecast scenarios from 2011 to 2020. The objectives of the study were to test different pilot point distributions to estimate aquifer properties for the alluvial aquifer, to simulate flux from rivers, and to demonstrate how changes in pumping rates for different scenarios affect the depleted area over time. Description of Study Area The study and model area is 6,869 km2 and extends from Crowley’s Ridge on the east, west to the Cache River, north to the Arkansas State line, and south to Lee County (Figure 1). This allows model boundaries

to be far enough away from major pumping areas to permit a reasonable comparison to existing conditions within the model area. The model domain includes parts of Clay, Greene, Craighead, Cross, Poinsett, St. Francis, Lee, Monroe, Woodruff, and Jackson Counties and is bounded between latitudes 34u399010 to 36u299530N and longitudes 90u109560 to 91u239420W. Parts of three rivers are located within the study area: the Cache River, the L’Anguille River, and the Black River. The northeastern corner of the model grid is located at 36u299530N latitude and 90u109560W longitude. Land surface altitudes range from 109 m to 47 m above National Geodetic Vertical Datum (NGVD) of 1929, from north to south in the study area (Figure 2). Mean annual precipitation for the years 2000 to 2010 is 1,219 mm (PRISM Group, 2012). The average annual temperature for the area is approximately 60uF (15.5uC) (Broom and Lyford, 1981; PRISM Group, 2012). The dominant land use (almost 90 percent of the area) in the area consists of cultivated crops such as

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Rashid, Clark, Mahdi, Rifai, and Al-Shukri Table 1. Details of the previous models and the current model. Steady State Cell Size No. of (SS)/Transient (km2) Layers (TR)

Author and Year

Calibration Method

Software Used

Observed Data Used for Calibration

Broom and Lyford (1981) Ackerman (1989) Mahon and Poynter (1993) Reed (2003)

23 65 2.6 2.6

1 3 1 2

SS/TR SS TR SS/TR

Manual Manual Manual PEST/manual

Gillip and Czarnecki (2009) Clark and Hart (2009)

2.6 2.6

2 13

TR SS/TR

Current model

0.5

PEST/manual Ucode 2005/manual/ PEST PEST and pilot point MODFLOW 2000 2000–2010 (GWVistas)

SIP method 1911–1978 1.5 MODFLOW 1984 1972 2.86 MODFLOW 1988 1972, 1982 1.5 to 2.33 MODFLOW 2000 1972, 1982, 1992, 1.84 1998 MODFLOW 2000 1998–2005 2.5 MODFLOW 2005 1870–2007 7.06

RMSE 5 root mean square error; SIP 5 strongly implicit procedure; PEST 5 Parameter Estimation Code (Doherty, 2010a).

Figure 3. Thickness of the alluvial aquifer model layer in meters.

RMSE (m)

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1.18

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Figure 4. Hydraulic head altitude of year 2000 represents the initial water-level condition in meters.

rice, soybeans, cotton, corn, sorghum, and wheat (U.S. Department of Agriculture, NRCS, 2011). Two proposed irrigation project areas, L’Anguille River and Bayou DeView, of approximately 500 and 427 km2, respectively, are located in part of each of the Craighead and Poinsett Counties (Czarnecki et al., 2003; U.S. Department of Agriculture, NRCS, 2011). Water use in the study area is dominantly for irrigation (Holland, 2007). The alluvial aquifer is composed of alluvial and terrace deposits of Quaternary age (Ackerman, 1989). Lithologically, Quaternary alluvial and terrace deposits are similar, consisting of unconsolidated sediments that grade from gravel and coarse sand in the lower sections to silt and clay in the upper sections. The total thickness of the alluvial aquifer ranges from 15 to 50 m and consists of coarse sand and gravel deposits. The upper part of the alluvial aquifer (clay cap) consists of clay, silt, and fine-grained sand that are generally

3–15 m thick (Czarnecki et al., 2002). The alluvial aquifer for most of the study area is unconfined, as documented in earlier studies (Czarnecki et al., 2002; Reed, 2003). Generally, within the study area, lateral flow of groundwater occurs from the north and west and flows toward the south and east. Crowley’s Ridge, which coincides with the easternmost part of the study area, is an erosional remnant of deposits of Tertiary age trending north to south. Crowley’s Ridge is a prominent topographic feature compared to the lowrelief surface of the Mississippi Alluvial Plain and forms a physical barrier to groundwater flow in the alluvial aquifer (Schrader, 2010). Several groundwater flow models have been constructed to simulate regional groundwater flow in the alluvial aquifer. Broom and Lyford (1981) developed a two-dimensional digital model of the alluvial aquifer. Ackerman (1989) constructed a three-layer finitedifference model to simulate two-dimensional steady-

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Figure 5. Boundary condition types used in the study area.

state flow in the aquifer for the year 1972. Mahon and Poynter (1993) developed two separate models: one for the area north of the Arkansas River and one for the area south of the Arkansas River. Reed (2003) constructed a digital model of the alluvial aquifer in eastern Arkansas based on the model developed by Mahon and Poynter (1993) to simulate groundwater flow for the period from 1918 to 2049. Gillip and Czarnecki (2009) published a validation of the Reed (2003) groundwater flow model that was updated with 1998–2005 water-use and water-level data. Clark and Hart (2009) developed the Mississippi Embayment Regional Aquifer. Table 1 show details of the previous models and the current model. METHODS A numerical finite-difference model was constructed using Groundwater Vistas (version 6.18), which

provides a Windows graphical interface of MODFLOW. The MODFLOW 2000 (Harbaugh et al., 2000; Hill et al., 2000) and the Preconditioned Conjugate-Gradient Method (PCG2) solver (Hill, 1990) were used for simulation. The software was used to solve the three-dimensional groundwater flow governing Eq. 1 (Anderson and Woessner, 1992). L Lh L Lh L Lh Lh K K K ð1Þ z z {R~Ss Lx Lx Ly Ly Lz Lz Lt where K is hydraulic conductivity; h is piezometric head; R is volumetric flux per unit volume (representing source/sink terms); t is time; x, y, and z axes are assumed to be parallel to the major axes of the hydraulic conductivity; and Ss is specific storage coefficient. The developed groundwater flow model simulates 12,078 irrigation wells located in the study area that were pumped between 2000 and 2010. All wells were

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Figure 6. Location of observation wells used in the calibration process and associated residuals (multiple residuals per well).

imported individually and represented using a well package of MODFLOW; the summation of pumping for each model cell also was accomplished within MODFLOW. Groundwater pumping from the alluvial aquifer for irrigation is seasonal, occurring mainly from April to September (spring–summer), with little to no pumping from October to March (fall–winter). Most of the 10 counties located in the study area use groundwater at a rate of between 0.38 and 1.5 million m3/d, except for Clay, Poinsett, and Cross Counties, which have an estimated groundwater use in the range of 1.5 to 4.5 million m3/d (Holland, 2007). Model Discretization The finite-difference grid used in the current model consists of 294 rows, 149 columns, and a single layer with varying thickness by cell. Each model cell represents 0.5 km2 in area. The model simulation

represents 11 years (2000 to 2010) using 23 transient stress periods. Stress periods 2 to 23 are each 6 months in length to accommodate irrigation pumping occurring from April to September and the lack of irrigation from October to March. All stress periods are divided into six time steps (each time step represents a month in length) except for stress period 1, which has three time steps. River Package The river package uses stream bed conductance (COND) to account for the length (L) and width (W) of the river channel in the cell, the thickness of the river bed sediments (M), and their vertical hydraulic conductivity (Kv) (Anderson and Woessner, 1992), thus: COND~

Kv LW , M

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terrace deposits comprising the alluvial aquifer in eastern Arkansas (Pugh et al., 1997) and 2) thickness of the Mississippi River Valley confining unit in eastern Arkansas (Gonthier and Mahon, 1993); then the thicknesses were added together to derive the layer thickness. The top of the alluvial aquifer was assumed to be land surface, and the bottom of the alluvial aquifer was calculated by subtracting the thickness of the aquifer from the top of the aquifer. Figure 3 shows the thickness of the model layer that ranged between 15 and 64 m. One layer was used to simulate the alluvial aquifer. While this layer thickness includes the clay of the upper part of the aquifer, this inclusion is inconsequential in terms of aquifer properties because the average depth of water is below the clay layer (Reed, 2003). Thus, when simulated as a convertible layer, transmissivity, storage, and other head-dependent calculations are based on the simulated water level rather than on the top of the aquifer layer. Model Parameters Figure 7. PEST parameter sensitivity of hydraulic conductivity, specific yield of the entire model area, and the vertical hydraulic conductivity of three major rivers.

where Kv was taken as 0.1 m/d for the Cache River and 0.05 m/d for both the L’Anguille and Black Rivers as an initial vertical hydraulic conductivity; W was taken as the average width of the rivers measured from TOPO software (version 3.4.3) as 19 m, 30 m, and 52 m for L’Anguille, Black, and Cache Rivers, respectively; and M was taken as 1 m. The total lengths (within the study area) of the simulated rivers were 62,049 m, 115,450 m, and 254,792 m for the Black, L’Anguille, and Cache Rivers, respectively. The bottom of the rivers was taken as the same altitude of the datum of the gauges. The river stage data, taken from six gauges located on the rivers (Figure 2), were downloaded for the years 2000 to 2010 from the National Hydrography Dataset Plus. The rate of leakage (Qriver) between the river and the aquifer is calculated from the stream bed conductance, head in the river (Hriver), and head in the aquifer (h) (Anderson and Woessner, 1992), thus: Qriver 5 COND (Hriver 2 h), for h . bottom of the stream bed (RBOT). The leakage rate is calculated from Qriver 5 COND (Hriver 2 RBOT) for h # RBOT. Layer Thickness The layer thickness of the alluvial aquifer was estimated using a Geographic Information System (Arc GIS10) by digitizing the two thickness contour maps: 1) thickness of the Quaternary alluvial and

The initial input model parameters, such as hydraulic conductivity and specific yield, assumed to be homogeneous and isotropic, were 70 m/d (Pugh, 2008) and 0.3 (dimensionless) (Broom and Lyford, 1981; Anderson and Woessner, 1992; and Clark and Hart, 2009), respectively. The recharge to the aquifer occurs mainly from infiltration of precipitation through the upper fine-grained materials. Groundwater flow from the adjacent and underlying aquifers is assumed to be negligible and was neglected (Mahon and Poynter, 1993). The recharge rates of previous model simulations in the alluvial aquifer ranged from 0.000055 to 0.00028 m/d (Ackerman, 1989; Mahon and Ludwig, 1989; and Clark and Hart, 2009). The initial recharge rate for this model was assumed to be a uniform rate of 0.00015 m/d. Potentiometric Surfaces and Initial Water-Level Condition Two aerially extensive cones of depression have formed in the potentiometric surface in the Cache Critical Groundwater Area (Figure 4). One cone of depression occurs in Poinsett and Cross Counties (northern cone), and the second is in St. Francis, Lee, and Monroe Counties (southern cone). The potentiometric surface contours indicate that groundwater flows toward the south and east, except where flow is affected by groundwater withdrawals, such as in the areas of the cones of depression. More recently, the northern cone has expanded farther south into Cross County (Schrader, 2010). The potentiometric surface

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Figure 8. Pilot point distributions in the study area; (A) method 1, (B) method 2, and (C) method 3.

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Rashid, Clark, Mahdi, Rifai, and Al-Shukri Table 2. Final parameter estimation from pilot point methods.

Method

Total Number and Type of Pilot Point

Kx (m/d)

Specific Yield

1 1 2 2 3 3

654_PH 654_PV 921_PH 921_PV 450_PH 450_PV

42–173 41–175 43–172 43–180 43–169 43–169

0.1920.337 0.18520.337 0.18320.333 0.18320.335 0.18720.344 0.18720.348

Recharge (m/d) 8.7 8.3 8.4 8.4 6.9 6.8

3 3 3 3 3 3

1025–6 1025–6 1025–6 1025–6 1025–6 1025–6

3 3 3 3 3 3

1024 1024 1024 1024 1024 1024

Residual Mean (m)

RMS Error (m)

20.27 20.35 20.36 20.35 20.38 20.39

1.18 1.24 1.23 1.23 1.28 1.28

PH 5 preferred homogeneity; PV 5 preferred value.

for the year 2000 was used as an initial water-level condition for the numerical model (Figure 4). Boundary Conditions In general, boundary conditions are mathematical statements specifying the dependent variable (head) or the derivative of the dependent variable (flux) at

the boundaries of the model domain. Boundary conditions used in the model (Figure 5) consist of the constant head boundary condition for the northern and southern boundaries of the model. The specified head for the northern boundary changes temporally and ranges from 85 to 88 m. The southern specified head boundary changes temporally and spatially and ranges between 45 and 57 m. For the

Figure 9. Horizontal hydraulic conductivity (m/d) results from pilot point calibration.

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Figure 10. Specific yield results from pilot point calibration.

eastern boundary of the model, which coincides with Crowleyâ&#x20AC;&#x2122;s Ridge, the no-flow boundary condition was applied because the ridge functions as a groundwater flow barrier. For most of the western boundary of the area, the river boundary condition was applied (the Cache River being the actual boundary) using the MODFLOW river package. Calibration Calibration is the process of adjusting model input parameter values to match the simulated values to the field observations. Simulated heads were compared to 2,322 hydraulic head observations from 150 observation wells completed in Quaternary alluvium and terrace deposits located in the study area (Figure 6). The model was calibrated in two phases. The first phase used the parameter estimation code (PEST) process (Doherty, 2010a, 2010b), which assumes the study area is homogeneous and isotropic and was

used to determine the sensitivity of model results to overall aquifer properties. In the second phase, a pilot point technique was used in three different ways, described in the following section, to evaluate pilot point distribution effects on model calibration. The parameters estimated in the first phase included the horizontal hydraulic conductivity, the specific yield, and the river conductance for all three simulated rivers. The most sensitive parameters were specific yield (sy), hydraulic conductivity (kx), and river vertical hydraulic conductivity for the Cache River (rv3), Lâ&#x20AC;&#x2122;Anguille River (rv2), and Black River (rv1), respectively (Figure 7). Thus, pilot point calibration was undertaken to improve the spatial distribution, and consequently the calibration, of these variables. Pilot Points In the second phase of model calibration, pilot points were used with regularization and Singular

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Figure 11. Recharge rate (m/d) results from pilot point calibration.

Value Decomposition assist to improve the spatial variation in parameter values and the model calibration. The aim of using pilot points is to provide an intermediate approach for characterizing heterogeneity in groundwater models between direct representation of cell by cell variability and reduction of parameterization to a relatively few homogeneous zones (Doherty et al., 2010). Pilot points allow for greater flexibility in the spatial assignment of the aquifer properties. Each point at a specified location can be assigned a value of a hydraulic property, which can change throughout the calibration process. A hydraulic property value for each model cell is interpolated based on the values of surrounding pilot points, which can serve to spatially vary the properties in a gradational manner, rather than as fixed discrete zones of hydraulic properties. For more information on pilot points and geostatistical methods associated with their use see Doherty (2013).

Pilot point parameter values were estimated at a number of discrete locations distributed throughout the model domain, and these parameter values were then spatially interpolated to the cells of the model grid using a kriging spatial interpolation method (Doherty, 2010a). Three different distributions of the pilot points were used (Rashid et al., 2013) (Figure 8), as follows: 1) observation triangulation method using preferred homogeneity regularization in which a triangle for each neighboring observation well was constructed and pilot points were specified at the center of each triangle (Rumbaugh and Rumbaugh, 2011); 2) similar to method 1; however, the pilot points were specified at the midpoints of each side of a given triangle; and 3) pilot points specified exactly at the same location of observation wells. For all three methods, additional pilot points were included to fill in gaps (areas that did not have pilot points within a 7-km radius). The total numbers of pilot points were 654, 921, and 450 for methods 1, 2, and 3, respectively (Figure 8). For each

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Figure 12. Vertical hydraulic conductivity (m/d) of the river bed from PEST calibration.

of the parameter groups—hydraulic conductivity, specific yield, and recharge—218, 307, and 150 pilot points were used in methods 1, 2, and 3, respectively. The pilot point distribution method 1 using the observation triangulation method (654 pilot points) with preferred homogeneity regularization was chosen as the final model calibration because of the lower root mean square error (RMSE) in comparison with those of the other pilot point distributions, although differences in RMSE among the three methods were relatively small, on the order of 0.1 m (Table 2). RESULTS The final parameter estimates for the calibrated model (Figures 9 through 11) were considered reasonable estimates based on the simulations that were completed as well as on previous studies for the material type and condition found in the alluvial aquifer. Horizontal hydraulic conductivity ranged from 42 to

173 m/d (Figure 9). Values of specific yield ranged from 0.19 to 0.337 (Figure 10). The maximum value of 0.377 for specific yield is slightly higher than the estimated value (0.3) used in the prior studies for the area; however, this value was considered to be reasonable based on the material type of the aquifer (Anderson and Woessner, 1992). Calibrated recharge rates ranged from 0.00009 to 0.0006 m/d (Figure 11), which brackets the average value used in previous models. The difference in recharge values is likely related to the lack of observation wells in these areas, which consequently affects the number of pilot points in such areas. The final values of river bed vertical hydraulic conductivity range from 0.007 to 0.1 m/d (Figure 12). River flux for the three simulated rivers indicates that the Black, L’Anguille, and Cache Rivers discharge 17, 21, and 59 percent, respectively, of the total stream flow flux to the aquifer. Parameter estimation with the three methods of pilots points produced visually similar distribution of properties overall.

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Figure 13. 1:1 Best-fit line of observed versus simulated head in meters.

Hydraulic Head Observations and Error Simulated heads were compared to 2,322 observed hydraulic head measurements from 150 observation wells. The simulated head values show a correlation coefficient of 0.99 to observed heads along a 1:1

best-fit line (Figure 13). Of the 2,322 observations used for calibration, the residuals of 2,227 observations (or about 96 percent of all observations) ranged between 2.5 and 22.5 m. The maximum residual was 4.7 m, and the minimum residual was 24.3 m (Figure 6).

Figure 14. Mass balance summary for the entire model simulation.

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Figure 15. Simulated head altitude for the year 2010 base model.

RMSE was determined using the equation RMSE~Â˝Sum(ho {hs )2 =n 0:5

Figure 16. Simulated head altitude for the year 2020 (Scenario 1).

1,374 residuals (59.18 percent) were less than zero (over-prediction), and 948 residuals (40.82 percent) were greater than zero (under-prediction).

(ho {hs ) is residual in meters Mass Balance where ho is observed hydraulic head in meters; hs is simulated hydraulic head in meters, and n is number of observations. The average value of the RMSE for the first phase of calibration was 1.64 m, whereas the values of the RMSE for the second phase ranged from 0.94 m in 2002 to 1.45 m in 2008, with an average of 1.18 m over a range of observed hydraulic head of 48.84 m (the range equals the difference between the highest and lowest observed hydraulic head). The mean of residuals indicates model bias depending on the magnitude and direction of the mean away from zero (Clark and Hart, 2009). The closer the mean to zero, the less model bias occurs. A positive mean indicates that the model tends to under-predict, and a negative mean indicates the model tends to over-predict. The mean residual for the entire model simulation was 20.27 m, which indicates a slight bias of simulated heads to over-predict the observed hydraulic heads. Out of 2,322 observations,

The mass balance summary, which indicates changes in storage, for all inflow to the aquifer and outflow from the aquifer for the entire model simulation (23 stress periods) is shown in Figure 14. Positive rates indicate inflows to the aquifer, and negative rates indicate outflows from the aquifer. The percent error between the inflow to the aquifer and outflow from the aquifer was equal to 23.79 3 1025. Withdrawal Scenarios Future forecasts for the years 2011 to 2020 were tested using nine different pumping scenarios. The simulated heads for pumping scenarios were compared with the simulated head results from the model simulation for the year 2010 base model (Figure 15). The first three scenarios represented maximum (Scenario 1), minimum (Scenario 2), and average

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Figure 17. Simulated head altitude for the year 2020 (Scenario 2).

Figure 18. Simulated head altitude for the year 2020 (Scenario 3).

(Scenario 3) pumping rates from irrigation wells. Scenario 4 simulated pumping representative of a prolonged dry period (precipitation less than the mean annual precipitation in the study area of 1,219 mm). Pumping rates from the dry years of 2000, 2003, 2005, and 2010 were averaged and held constant from 2011 to 2020. Scenario 5 simulated pumping representative of a prolonged wet period (annual precipitation greater than the mean annual precipitation in the study area of 1,219 mm) (PRISM Group, 2012). Pumping rates from the wet years of 2001, 2004, 2006, 2008, and 2009 were averaged and held constant from 2011 to 2020. The next three scenarios represented pumping rates specified as the same as the year 2010 (Scenario 6), a 2 percent increase from the pumping rate of 2010 (Scenario 7), and a 2 percent decrease from the pumping rate of 2010 (Scenario 8). Scenario 9 simulated the extrapolation of pumping from 2011 to 2020. The extrapolation was based on fitting a curve through pumping rates of wells from 2000 to 2011 with at least 5 years of data and with the additional requirement that the data included 2009 and 2010 pumping information. For most scenarios, the groundwater level altitudes in the areas of the cones of depression further declined, creating dry cells, which are equivalent to

areas of groundwater depletion in this study. Scenario 1 produced the largest area (52 km2) of groundwater depletion (Figure 16). From the results, the lowest contour value of the northern cone in the year 2010 base model was 38 m above the NGVD of 1929, whereas this value was 23 m in Scenario 1, 41 m in Scenario 2, and 32 m in Scenario 3 (Figures 15 through 18). The simulated hydraulic head altitudes for Scenarios 3 through 5 indicated that the lowest contour values of the northern and southern cone were 32 m, 32 m, and 35 m, respectively (Table 3). The simulated head altitudes for Scenarios 6 through 9 indicated that the lowest contour values of the northern and southern cone were 32, 29, 35, and 35 m, respectively, as shown in (Table 3). Simulation of a prolonged dry period, similar to the dry periods of 2000, 2003, 2005, and 2010, indicated groundwater depletion in the northern cone of over 18 percent compared to the base scenario. In contrast, simulation of wet periods 2001, 2004, 2006, 2008, and 2009 indicated groundwater depletion in the northern cone of over 8 percent when compared to the base scenario. An estimate of the volume of water stored in the alluvial aquifer can be made by calculating the thickness of the saturated zone for each different scenario (simulated head at each scenario minus

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25.9

bottom of the alluvial aquifer, multiplied by the specific yield; Clark et al., 2011). The percentage of volume of water available in the aquifer compared to the base scenario decreased by 23.3 percent in Scenario 1. For the nine tested scenarios, the volume of water depleted ranged between 5.7 and 23.3 percent, except in Scenario 2 (minimum pumping rate), in which the volume increased by 2.5 percent. Table 3 shows detailed information related to the simulation results for all scenarios in comparison with the base scenario (year 2010). In addition, the model simulations indicated the period of time between 2011 and 2020, in which areas of the alluvial aquifer are depleted, as represented by dry cells.

29.5 27.4

25.7

53,314 51,274 52,444

53,420

35 21.1–7.2 35 2013 1 8 7.3 5.8 7.8 29 21.6–9.0 29 2013 2 10 8.3 19.3 7.5 32 22.5–7.9 32 2014 1 8 6.9 0.0 7.6

35 22.8–5.9 35 2016 1 5 5.5 220.2 7.8

S7 S6

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27.8 28.0 28.3 S 5 scenario. 1 Northern cone 5 cone in Poinsett and Cross Counties. 2 Southern cone 5 cone in St. Francis, Lee, and Monroe Counties.

223.3 0.0

2.5

52,208 52,086 51,957 43,425 56,636

58,064

35 21.8–7.7 32 2012 1 5 7.4 6.4 7.6 32 22.2–8.1 32 2013 3 9 7.2 4.2 7.6 32 21.4–7.9 32 2016 2 8 7.5 8.1 7.6 23 20.22–20.7 20 2012 6 104 12.4 78.6 6.3 38 0 38 N/A N/A N/A 6.9 0.0 8.2

Lowest contour value for northern cone1 (m) Range in the difference in simulated head (m) Lowest contour value for southern cone2 (m) Dry cell start year No. of dry cells as started Total no. of dry cells at year 2020 Total pumping rate (million m3) Percent increase/decrease of pumping rate Mean head of water stored in the aquifer (m) Volume of water stored in the aquifer (million m3) Percent decrease/increase of stored volume of water

41 24.3–2.6 41 N/A N/A N/A 3.5 250.0 8.5

S4 S3 S2 S1 Base Information

Table 3. Simulation results of all scenarios.

CONCLUSIONS A three-dimensional finite-difference model of part of the Mississippi River Valley alluvial aquifer in the Cache Critical Groundwater Area in eastern Arkansas was constructed to simulate potential future conditions of groundwater heads at various pumping rates. The objectives of this study were to test different pilot point distributions to find reasonable estimates of aquifer properties for the alluvial aquifer, to simulate flux from rivers and to demonstrate how changes in pumping rates for different scenarios affect the depleted area over time. Three different distributions of the pilot points were used for model calibration: 1) observation triangulation method, in which a triangle for each neighboring observation well was drawn and pilot points were set in the center of each triangle; 2) a method similar to method 1; however, the pilot points were set at the midpoints of each side of a given triangle; and 3) pilot points were set at exactly the same location of the observation wells. Hydraulic conductivity values from pilot point calibration ranged between 42 and 173 m/d. Specific yield values ranged between 0.19 and 0.337. Recharge rates ranged between 0.00009 and 0.0006 m/d. The final parameter estimates of the calibrated model are considered reasonable estimates based on previous studies for the material type and condition found in the alluvial aquifer. Nine pumping scenarios for the years 2011 to 2020 were tested and compared to the simulated water-level head from 2010. Simulation of a prolonged dry period, similar to the dry periods of 2000, 2003, 2005, and 2010, indicates groundwater depletion in the northern cone of over 18 percent compared to the base scenario; in contrast, simulation of wet periods 2001, 2004, 2006, 2008, and 2009 indicates groundwater depletion in the northern cone of over 8 percent compared to the base scenario. For all scenarios the volume of water depleted ranged between 5.7 and 23.3 percent, except in Scenario 2

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(minimum pumping rates), in which the volume increased by 2.5 percent. From the perspective of the withdrawal scenarios, this model may be used to add to the knowledge of system response under various pumping and climate conditions. From the perspective of the calibration method, the method introduces potential realization of hydraulic properties that may be confirmed or refuted by future aquifer tests or models. This model is an improvement over previous models, especially with regard to the precision (or accuracy of calibration statistics) if offers.

REFERENCES ACKERMAN, D. J., 1989, Hydrology of the Mississippi River Valley Alluvial Aquifer, South-Central United States—A Preliminary Assessment of the Regional Flow System: U.S. Geological Survey Water Resources Investigation Report 88-4028, 80 p. ANDERSON, M. P. AND WOESSNER, W. W., 1992, Applied Groundwater Modeling Simulation of Flow and Advective Transport: Academic Press, Inc., San Diego, CA. ARKANSAS NATURAL RESOURCES COMMISSION, 2011, Arkansas Ground-Water Protection and Management Report: Electronic document, available at http://www.anrc.arkansas.gov/ groundwater/2011_gw_report.pdf BROOM, M. E. AND LYFORD, F. P., 1981, Alluvial Aquifer of the Cache and St. Francis River Basin, Northeastern Arkansas: Open-File Report 81-476, 48 p. CLARK, B. R. AND HART, R. M., 2009, The Mississippi Embayment Regional Aquifer Study (MERAS): Documentation of a Groundwater Flow Model Constructed to Access Water Availability in the Mississippi Embayment: U.S. Geological Survey Scientific Investigations Report 2009-5172, 61 p. CLARK, B. R.; HART, R. M.; AND GURDAK, J. J., 2011, Groundwater Availability of the Mississippi Embayment: U.S. Geological Survey Professional Paper 1785, 62 p. CZARNECKI, J. B., 2010, Groundwater-Flow Assessment of the Mississippi River Valley Alluvial Aquifer of Northeastern Arkansas: U.S. Geological Survey Scientific Investigations Report 2010-5210, 33 p. CZARNECKI, J. B.; CLARK, B. R.; AND REED, T. B., 2003, Conjunctive Use Optimization Model of the Mississippi River Valley Alluvial Aquifer of Northeastern Arkansas: U.S. Geological Survey Water Resources Investigation Report 03-4230, 29 p. CZARNECKI, J. B.; HAYS, P. D.; AND MCKEE, P. W., 2002, The Mississippi River Valley Alluvial Aquifer in Arkansas: A Sustainable Water Resource: U.S. Geological Survey Fact Sheet 041-02: Electronic document, available at http://pubs. er.usgs.gov/publication/fs04102 DOHERTY, J., 2013, Groundwater Data Utilities, Part A: Overview: Watermark Numerical Computing, May, 2013: Electronic document, available at http://www.pesthomepage.org/Downloads. php DOHERTY, J. E., 2010a, PEST Model-Independent Parameter Estimation User Manual: 5th ed., with Slight Additions, Brisbane, Australia, Watermark Numerical Computing: Electronic document, available at http://wi.water.usgs.gov/models/ pestcommander/PC_pubs.html DOHERTY, J. E., 2010b, Addendum to the PEST Manual, Brisbane, Australia, Watermark Numerical Computing: Electronic

document, available at http://wi.water.usgs.gov/models/ pestcommander/PC_pubs.html DOHERTY, J. E.; FIENEN, M. N.; AND HUNT, R. J., 2010, Approaches to Highly Parameterized Inversion: Pilot-Point Theory, Guidelines, and Research Directions: U.S. Geological Survey Scientific Investigations Report 2010-5168, 38 p. ENGLER, K.; THOMPSON, D. G.; AND KAZMANN, R. G., 1945, Ground Water Supplies for Rice Irrigation in Grand Prairie Region, Arkansas: University of Arkansas, Agriculture Experiment Station Bulletin No. 457, 56 p. FREIWALD, D. A., 2005, Ground-Water Models of the Alluvial and Sparta Aquifers: Management Tools for a Sustainable Resource: U.S. Geological Survey Fact Sheet 2005-3008, 4 p. GILLIP, J. A. AND CZARNECKI, J. B., 2009, Validation of a GroundWater Flow Model of the Mississippi River Valley Alluvial Aquifer Using Water-Level and Water-Use Data for 1998– 2005 and Evaluation of Water-Use Scenarios: U.S. Geological Survey Scientific Investigations Report 2009-5040, 22 p. GONTHIER, G. J. AND MAHON, G. L., 1993, Thickness of the Mississippi River Valley Confining Unit in Eastern Arkansas: U.S. Geological Survey Water-Resources Investigations Report 92-4121, 4 sheets. HARBAUGH, A. W.; BANTA, E. R.; HILL, M. C.; AND MCDONALD, M. G., 2000, MODFLOW-2000, The U.S. Geological Survey Modular Ground-Water Model-User Guide to Modularization Concepts and the Ground-Water Flow Process: U.S. Geological Survey Open-File Report 00-92, 121 p. HILL, M. C., 1990, Preconditioned Conjugate-Gradient 2(PCG2), A Computer Program for Solving Ground-Water Flow Equations: U.S. Geological Survey Water-Resources Investigations Report 90-4048, 25 p. HILL, M. C.; BANTA, E. R.; HARBAUGH, A. W.; AND ANDERMAN, E. R., 2000, MODFLOW-2000, The U.S. Geological Survey Modular Ground-Water Model-User Guide to the Observation, Sensitivity, and Parameter-Estimation Processes and Three Post-Processing Programs: U.S. Geological Survey OpenFile Report 00-184, 209 p. HOLLAND, T. W., 2007, Water Use in Arkansas, 2005: U.S. Geological Survey Scientific Investigations Report 2007-5241, 33 p. MAHON, G. L. AND LUDWIG, A. H., 1989, Simulation of Ground_Water Flow in the Mississippi River Valley Alluvial Aquifer in Eastern Arkansas: U.S. Geological Survey WaterResources Investigations Report 1989-4145, 88 p. MAHON, G. L. AND POYNTER, D. T., 1993, Development, Calibration, and Testing of Groundwater Flow Models for the Mississippi River Valley Alluvial Aquifer in Eastern Arkansas Using One-Square Mile Cells: U.S. Geological Survey Water Resources Investigation Report 92-4106, 33 p. PRISM GROUP, 2012, PRISM Group: Electronic document, available at http://www.prism.oregonstate.edu/products/ matrix.phtml PUGH, A. L., 2008, Summary of Aquifer Test Data for Arkansas 1940–2006: U.S. Geological Survey Scientific Investigations Report 2008-5149, 33 p. PUGH, A. L.; WESTERFIELD, P. W.; AND POYNTER, D. T., 1997, Thickness of Quaternary Alluvial and Terrace Deposits Comprising the Mississippi River Valley Alluvial Aquifer in Eastern Arkansas: U.S. Geological Survey Water-Resources Investigations Report 97-4049, 1 map; 81 3 66 cm on sheet 92 3 71 cm, folded in envelope 31 3 23 cm. RASHID, H. M.; AL-SHUKRI, H. J.; AND MAHDI, H. H., 2013, Pilot Point Calibration of the Ground Water Flow Model of the Mississippi River Valley Alluvial Aquifer of Cache Area. MODFLOW and More 2013: Translating Science into Practice—Conference Proceedings: Integrated Groundwater Modeling Center (IGWMC), Colorado School of Mines.

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Simulations of Future Conditions in Arkansas REED, T. B., 2003, Recalibration of a Ground-Water Model of the Mississippi River Valley Alluvial Aquifer of Northeast Arkansas, 1918–1998, with Simulations of Water Levels Caused by Projected Ground-Water Withdrawals through 2049: U.S. Geological Survey Water Resources Investigations Report 2003-4109, 58 p. RUMBAUGH, J. O. AND RUMBAUGH, D. B., 2011, Guide to Using Groundwater Vistas, version 6: Environmental Simulations, Inc., Reinholds, PA.

SCHRADER, T. P., 2010, Water Levels and Selected Water-Quality Conditions in the Mississippi River Valley Alluvial Aquifer in Eastern Arkansas, 2008: U.S. Geological Survey WaterResources Scientific Investigations Report 2010-5140, 71 p. U.S. DEPARTMENT OF AGRICULTURE, NRCS, 2011, Irrigation Projects: Electronic document, available at http://www.ar. nrcs.usda.gov/programs/watersheds_irrigation.html

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Uncertainty Associated with Evaluating Rockfall Hazard to Roads in Burned Areas JEROME V. DE GRAFF1 USDA Forest Service, 1600 Tollhouse Road, Clovis, CA 93611

BILL SHELMERDINE Olympic National Forest, 1835 Black Lake Boulevard, SW, Olympia, WA 98512

ALAN GALLEGOS USDA Forest Service, 1600 Tollhouse Road, Clovis, CA 93611

DAVID ANNIS Eldorado National Forest, 100 Forni Road, Placerville, CA 95667

Key Terms: Rockfall, Wildfires, Roads, Western USA, Natural Hazards

ABSTRACT During and following wildfires affecting steep mountain slopes, there can be an increase in rockfall activity usually taking the form of individual rocks, and occasionally, groups of rocks rolling, sliding or bouncing downslope. This increase results from removal of stabilizing vegetation, downed wood, and organics within the soil matrix as well as increase in erosional processes such as dry ravel. The hazard posed to vehicles is difficult to assess because of uncertainty manifested in several ways. First, there is uncertainty in defining the road segments that will be impacted by increased rockfall activity. Second, it is difficult to quantify the size, number, and/or travel behavior of rocks which may impact a given road segment. Finally, there is uncertainty as to how long increased rockfall activity may persist after a wildfire. Between 2007 and 2013, some insight into the first two uncertainty issues was provided by observed rockfall on roads within eight different wildfires in California and Idaho. This insight provided an efficient and effective means to prioritize rapid assessment for rockfall hazard for a large number of roads within the 2013 Rim Fire in the central Sierra Nevada, California. Data on the third rockfall uncertainty issue, persistence, was developed for a road on the Olympic National Forest in Washington. Monitoring of rocks accumulating on the road at sixteen sites between July 2006 and April 2007 recorded 3,463

Corresponding author email: 45nyutca@sbcglobal.net.

rocks with the number of rocks found to decrease over time. INTRODUCTION Since the 1980s, wildfires occurring in the western United States have increased on the basis of either area being burned (Stephens, 2005) or frequency (Westerling et al., 2006). Much of this increasing trend can be attributed to climatic control (Littell et al., 2009). Commonly, western wildfires are concentrated in mountainous landscapes often involving land administered by Federal agencies including the Forest Service, National Park Service and Bureau of Land Management (Figure 1). The mountainous areas of the western United States are largely rural in character with fewer roads than are found within the major valleys and plains bounding them. The steep slopes limit most of the Interstate and State highways to certain corridors across the mountain ranges. Local roads are typically more numerous and exist to access mountain communities, residences, energy development sites, ski resorts and other recreational facilities, mining operations, and to carry out land management activities such as timber harvest and fire suppression. Many roads in these mountainous areas are subject to landslide impacts which interfere with their intended uses, threaten public safety, and impose significant hardship on road users (De Graff and Cunningham, 1982; De Graff et al., 1984; Cannon et al., 2001; Harp et al., 2008; and Beukelman and Erickson, 2012). Landslide activity can be greater after a wildfire, increasing the risk posed to roads within the burned area. The landslide types commonly associated with

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Figure 1. Map showing where Federal agencies are responsible for land management including wildfire-related actions within the United States. Most of the Federally-managed land is found in the mountainous western states as national parks and monuments, national forests and grasslands, or land administered by the Bureau of Land Management.

burned watersheds posing the most hazard are debris flows and rockfalls (Cannon et al., 2010b; De Graff and Gallegos, 2012; and Santi et al., 2013). Our understanding of the post-fire risks from debris flows has undergone significant improvement in recent decades (De Graff et al., 2007, 2013). It is possible to define increased post-fire debris flow risk within affected watershed basins in terms of the probability of occurrence and volume, and the areas downstream where inundation might take place (Cannon et al., 2010a). In contrast, rockfall behavior following wildfires is poorly understood, resulting in significant uncertainty in assessing risk from this mass wasting process (De Graff and Gallegos, 2012). This uncertainty is especially problematic for assessing the risk to roads and road users. Assessing rockfall risk is important not only because roads are generally present within burned watersheds but also because of the potential

for injuries and fatalities resulting from rockfall occurrence affecting those roads. This paper examines some initial data we used to reduce the uncertainty in our assessment of rockfall hazard following a wildfire. This includes information developed to define possible higher risk road segments, its application to the rapid post-fire assessment process, and some insight into the persistence of this increased rockfall hazard within a burned area. ROCKFALL HAZARD FROM BURNED AREAS Rockfall is commonly envisioned as being a mass of rock detached from a steep natural or constructed slope (Varnes, 1978); it free-falls, slides, rolls and bounces to a lower, flatter slope where it comes to rest. Often, it is spread as large individual blocks like the classic natural features seen on the valley floor within Yosemite National Park (Cordes et al., 2013).

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Figure 2. Half of the boulder which mobilized at the Dutch Creek Work Center, Boise National Forest during the 2012 Trinity Ridge Fire. The boulder rolled down a slope to the right of this view before bounding across the access road to the position seen in the photo. The other half continued rolling to the left of this view. The truck being driven by the observers was moved to near the boulder for scale (Ivan Erskine, U.S. Forest Service).

Figure 3. One of the switchbacks on the Saddle Springs Road within area burned by the 2010 Canyon Fire on the Sequoia National Forest, Piute Mountains, California. This shows the accumulation of rocks present in April 2011 prior to re-opening the road to public access after the winter storm events. Motorists driving too fast or inattentively could have insufficient time to stop before impacting rocks (Jerome De Graff, U.S. Forest Service).

Rockfall from large individual boulders or multiple large rocks can also be generated from slopes mantled by glacial, fluvial, or colluvial deposits. This is often in response to the erosional loss of the fine-grained matrix surrounding these large rock blocks (Turner and Jayprakash, 2012). Turner and Jayprakash (2012) point out that while large rockfalls can block transportation corridors for days, rockfalls involving relatively small volumes can pose significant hazards to travelers, recreationists, and workers. Moving rocks, even small ones, can impact and cause significant damage to vehicles traveling along a road. The driverâ&#x20AC;&#x2122;s response to a sudden impact by even a small rock may cause loss of control and result in a single or multi-vehicle crash. It is also possible for injury or fatalities to result from rocks directly hitting moving vehicles (Turner and Jayprakash, 2012). An incident which occurred during the 2012 fire-fighting operations for the Trinity Ridge Fire on the Boise National Forest, Idaho illustrates the real human threat posed by rockfall. U.S. Forest Service Fire Management Officer Ivan Erskine and a fellow member of the team managing the fire-suppression activities on this wildfire were at the Dutch Creek work center. The wildfire had burned the slope around this Forest Service facility two days earlier and they were photo-documenting the structures that had burned. Just before departing in their truck, a large boulder estimated to be 1.4 by 1.2 by 0.6 m rolled down an adjacent slope where it bounced across the access road. Upon impact, it split into two halves with one remaining at the impact point and the

other half rolling about 22 m beyond the impact point before coming to rest (Figure 2). Mr. Erskine estimated the difference in time between their observing this event at a safe distance and their driving the truck across the path of this bouncing boulder was a matter of seconds (Erskine, 2012). Impact from a boulder this size would have caused serious injury (or worse). Single large rocks or multiple small rocks deposited on roads may also pose problems for road use. The impact of rocks during deposition may cause pitting and damage to pavement. The size of individual rocks or just their number may be sufficient to actually block passage. Vehicles driving over even small angular rocks on the road risk damaging tires. Because many of the roads within a mountainous burned area will have switchbacks along steep sections or frequent curves limiting sight distance, drivers can come upon rocks obstructing the road unexpectedly (Figure 3). Motorists attempting to avoid impact can take actions that result in accidents (Turner and Jayprakash, 2012). In the mountainous western United States, rockfalls, like debris flows, are not confined only to burned areas (Turner and Jayprakash, 2012). What is notable is the increase in rockfall activity associated with wildfire occurrence. This increase is noticeable starting at the time the slopes are being burned (Santi et al., 2013), which poses an immediate hazard to fire fighters (Swanson, 1981). It is sometimes necessary for teams managing fire suppression efforts to request road maintenance equipment to clear rock from roads

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being used by crew trucks and water tankers to avoid vehicle damage and ensure safe driving conditions (Lemmon, 2011; LeBlanc, 2013). In the days and weeks following wildfires, the rockfall activity continues even in the absence of storm events but at a decreased rate. Road segments which are closed to use prior to the first seasonal wet period after the wildfire often have significant accumulations of rock requiring removal before reopening for use. This indicates that increased wildfireinduced rockfall results from the immediate effects of the fire on the landscape. It also suggests that those effects may persist for weeks and months and can be influenced by precipitation events. LINKING BURNED CONDITIONS TO INCREASED ROCKFALL The most obvious changed conditions attributable to fire involve vegetation, the organic and litter layer, and the upper centimeters of the underlying mineral soil. Within the perimeter of a wildfire, the effect of fire on the vegetation and ground surface is not uniform. Instead, there is a mosaic ranging from unburned pockets to areas where the organic material on the soil surface and vegetation is almost completely consumed. This mosaic of fire impact can be present in burned areas across a range of vegetation types and forest structures. The degree of fire’s effect on the vegetation and soil is mapped as soil burn severity (Clark, 2013; Parsons et al., 2010). Soil burn severity is classified as low, moderate, or high reflecting the degree to which fire consumed the vegetation and surface organic matter on the soil and altered near-surface soil characteristics (Parsons et al., 2010). The ground of an area of low soil burn severity area will have charred but still recognizable woody material with most of the understory and canopy vegetation still appearing ‘‘green’’. Parson et al. (2010) notes that areas of moderate soil burn severity will have up to 80 percent of the litter and woody debris on the ground surface consumed leaving a blackened ash. The leaves or needles in the canopy will generally be scorched to a brown color. High soil burn severity areas are areas where fire has consumed many of the woody stems and large woody material on the ground surface; it would also have incinerated organic surface material including fine root mats that bind soil particles. Fire’s effect on the surface organic matter and near-surface soil character is most closely associated with accelerating the rolling and bouncing of rocks down steep, burned hillslopes (Swanson, 1981). Loss of woody stems and consumption of large woody debris on the ground influences rockfall travel

Figure 4. Burned woody plant stems buttressing rocks on the slope above Lumsden Road within the 2013 Rim Fire on the Stanislaus National Forest, Sierra Nevada, California. The slope steepens to the right in this view with the photographer standing on the road. The visibly abundant rock fragments available in the colluvium mantling this slope can potentially be mobilized towards the road during future storm events (Jerome De Graff, U.S. Forest Service).

behavior and distances. This reduction in slope roughness affects whether sufficient momentum is developed to reach a road or facility, and controls the likelihood that rock slides, rolls, or bounces to a certain height. If the source of rockfall after wildfires was limited to those rocks previously buttressed by large woody debris on the ground surface and the stems of existing shrubs and trees, the hazard posed would be short-lived (Figure 4). However, loss of vegetation, consumption of surface organic material, and altering of near-surface soil characteristics also induces dry ravel, a rapid downhill movement of individual particles under the influence of gravity not requiring the presence of water (Swanson, 1981; Florsheim et al., 1991; Gabet, 2003; and Jackson and Roering, 2009). Dry ravel is a post-fire process documented in different parts of the western United States (Florsheim et al., 1991; Cannon and Reneau, 2000; Cannon et al., 2001; and Roering and Gerber, 2005). Movement of fine-grained particles as dry ravel is seen as a primary means for removal of the finegrained matrix in colluvial, glacial, and fluvial deposits mantling steep burned slopes, which undermines support of large rocks on the slope and is a significant trigger for rockfall. Dry ravel deposits form as cones or aprons of material at the angle of repose where natural landscape features like channels or artificial ones like roads provide a flatter slope (Figure 5a and b). To a degree that is not currently quantified, the surface wind generated at the time the

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Figure 5a. A view of rockfall and dry ravel accumulation along the road west of Camp Mather within the area burned during the 2013 Rim Fire. This paved road crosses the upper slopes of the Tuolumne River Canyon. The granitic bedrock underlies the steep slopes above the road (Jerome De Graff, U.S. Forest Service). 5b. A view of rockfall and dry ravel accumulation along a Forest Service road present in a watershed within the area burned by the 2009 Station Fire. The accumulated material is derived from metamorphic bedrock present within this part of the San Gabriel Mountains (Jonathan Schwartz, U.S. Forest Service).

slope burns and wind from the normal weather patterns after the fire contribute to dry ravel (Santi et al., 2013). Lamb et al. (2011) and DiBiase and Lamb (2013) quantified the effect of vegetation in providing the source material for post-fire dry ravel activity on slopes steeper than the angle of repose for the ravel material. Assessment includes modeling the volumetric storage capacity of vegetation ‘‘dams’’ to better compute the wildfire-induced sediment released when they burn. Dry ravel contributes to the accumulated material in channels which can later be mobilized in debris flows during storms during the first few years after a wildfire (Wells, 1987; Jackson and Roering, 2009; and Kean et

al., 2011). Jackson and Roering (2009) documented post-fire ravel deposits formed prior to the first postfire storm events in channels which contained larger rock fragments about 1 m in diameter. Those same storms would also erode additional fine-grained matrix from slope deposits containing rocks and induce more rockfall within the burned area (De Graff and Gallegos, 2012) (Figure 3). In summary, multiple erosional processes interact on slopes where large rocks are present within or rest upon a fine-grained matrix influencing the rate of post-fire rockfall activity. The initial increased rate of rockfall activity reflects loss of woody stems and debris consumed during the wildfire and no longer buttressing rocks on the steep slopes. Across the burned slopes, there is a flux of granular material (dry ravel) contributing to the instability of any large rock fragments or boulders embedded near the surface. Rolling or bouncing rocks can destabilize other rocks present downslope. The rockfall activity from movement of unbuttressed rocks, wind disturbance and dry ravel would all be expected to slow over time (days or weeks) following the wildfire. Subsequently, rockfall activity is expected to accelerate during the initial post-fire storm events because of overland flow eroding the bare ground surface. As overland flow removes accumulated dry ravel and fine-grained matrix material on the burned slopes, some rocks would be undermined and destabilized on the slope surface. Consequently, episodes of rockfall could be induced weeks or months after the wildfire has subsided. The influence of vegetation on dry ravel production (Lamb et al., 2011; DiBiase and Lamb, 2013) reinforces the common understanding that vegetative recovery is the key factor to returning rockfall activity to pre-fire levels. In chaparral-dominated areas, increased postfire rockfall activity associated with storm events may persist for more than one year. On slopes with burned timber stands, falling fire-killed trees serve as a mechanism for initiating rockfall which may persist over longer time scales dependent on tree mortality and decay. REDUCING THE UNCERTAINTY IN IDENTIFYING AT-RISK ROAD SEGMENTS De Graff and Gallegos (2012) point out the challenge posed in determining any potential increased rockfall hazard within a burned area for emergency response. A primary need for this information is to mitigate where greater risk of injury or fatalities or damage to critical facilities caused by post-fire rockfall might exist. Faced with tens to hundreds of kilometers of road within a burned area,

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Figure 6. Map showing the location of the wildfires referred to in either Table 1 or the text. This Google Earth image shows the many mountainous areas within the western states where significant parts of the land are managed by Federal agencies (see Figure 1).

the uncertainty associated with identifying which segments may have a greater rockfall risk is daunting. As mitigation, it is neither practical to close all roads potentially at risk for an extended period nor effective to place hazard warning signs along all roads within the fire perimeter having an assumed greater rockfall risk. An initial effort was made between 2007 and 2013 to identify characteristics useful in identifying road segments at higher risk of wildfire-related rockfall. A geologist experienced in both burned area assessment and landslide processes participated on teams assembled for sixteen wildfires in California during this period. Of the sixteen wildfires, field observations identified seven where significant rockfall occurred on roads within days after the slopes above them burned. The eighth rockfall-affected road in this dataset is the

one previously described by the fire management observer on the Trinity Ridge wildfire (Figure 6). The eight road segments affected by wildfirerelated rockfall varied greatly in their physical character and traffic use. All the roads are paved except for Saddle Springs Road, the Dutch Creek Work Center access road and Lumsden Road. There is a significant amount of traffic on the two State routes with one serving commuters between Mojave Desert communities and Los Angeles and the other accessing Yosemite National Park. Santa Ynez River Road, Onion Valley Road, Saddle Springs Road, and Lumsden road are the only, or the primary, means for reaching high-use recreational facilities or private residences. Several of these roads are important routes for timber harvest and administrative access for national forest management.

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Rockfall Hazard in Burned Areas Table 1. Listing of roads within eight wildfires where rockfall activity caused by wildfire impacts to nearby slopes occurred between 2007 and 2013. In addition to identifying the affected road and wildfire involved, the largest rock displaced onto the road and the physical characteristics of the slope serving as the rockfall source area are described.

Bedrock Type

Slope (%)

Observed After Fire (days)

Moderate

Metasedimentary

1.85/6.1

Santa Ynez River Road

2007

Moderate

Granitic

0.46/1.5

Onion Valley Road

2009

Moderate/high

Granitic

0.40/1.3

2010

Moderate

Metasedimentary

0.34/1.1

CA Hwy 2 - Angeles Crest Highway Saddle Springs Road

2011

High/Moderate

Metasedimentary

0.30/1.0

CA Hwy 140

2012

Moderate

Granitic

1.37/4.5

2013

Moderate/High

Granitic

0.30/1.0

Dutch Creek Work Center road Stump Springs Road

2013

Moderate/High

Metasedimentary

0.79/2.6

Lumsden Road

Fire Name, Location

Year

Rancho, Calif. Coast Range Inyo Complex (Seven Oaks), Eastern Sierra Nevada Station, San Gabriel Mtns Canyon, Piute Mountains Motor, Central Sierra Nevada Trinity Ridge, Idaho Batholith Aspen, Central Sierra Nevada Rim, Central Sierra Nevada

2007

Soil Burn Severity

Data for five basic factors were collected for each road affected by rockfall during post-fire assessment (Table 1). Three factors were physical attributes for the source area of the rockfall: the lithology of bedrock, the slope angle and soil burn severity. In all instances, the rockfall was generated from the colluvium mantling the underlying lithology. The lithology identification was based on published geologic mapping. The slope steepness was measured from 1:24,000-scale topographic maps of the location. The burn severity value was obtained from the map prepared for the wildfire assessment (Clark, 2013). The other two factors collected in the assessment were: the maximum number of days (time span) between burning and the observed or estimated rockfall, and the maximum dimension of the largest rock involved in the rockfall. The time span was determined by reviewing the fire progression map for the day when the source slope was burned and comparing that to the date the rockfall or its deposit was observed. This represents the maximum time between the slope being burned and the rockfall occurring because the specific date of occurrence was not always known. The dimension of the largest rock in each rockfall was measured by a geoscientist/ geologist, except for the Trinity Ridge fire where dimensions were provided by fire management observers. The choice to measure the largest rock present was designed to represent the greatest potential for damage and could be obtained within the limited time available while gathering post-fire emergency assessment data.

Maximum Size Length (m/ft)

Road Affected

Moderate deposition of dry ravel was documented along the road segments affected by rockfall within five of the wildfire areas (Table 1). Dry ravel was not associated with the road segments affected by the rockfall in the Canyon, Motor and Trinity Ridge fires. The maximum size of rocks involved with rockfall on these eight road segments ranged from 1.85â&#x20AC;&#x201C;0.30 m in their largest dimension, and an average of 0.5 m (Table 1). Within the varied geologic settings where the rockfall occurred, both metasedimentary and granitic bedrock was the underlying lithology. All rockfalls were generated from slopes experiencing moderate to high soil burn severity. The slope steepness of the source areas for the rockfalls ranged from 39 to 77 percent with an average of 55 percent. The actual number of days between the slope being burned and rockfall occurrence is only known for the Dutch Creek Work Center rockfall (Trinity Ridge Fire). The other observations for the time of occurrence represent the maximum time between when the slope burned and the rockfall occurred. This time period ranges from 6 to 21 days. AN INITIAL USE OF OBSERVATIONS FROM OTHER FIRES TO PREDICT ROADWAY HAZARDS On August 17, 2013, the Rim Fire started in the Tuolumne River Canyon in the central Sierra Nevada, California (Figure 6). It grew over the following weeks to become the third largest wildfire

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in California since 1932. A total of 104,131 ha (257, 314 ac) burned primarily within the Stanislaus National Forest and Yosemite National Park with 112 structures either damaged or destroyed (Cal Fire, 2013). With the majority of the burned area being within the Stanislaus National Forest, the U.S. Forest Service emergency response team faced assessing rockfall hazard for 748 km of forest system roads. Many of these roads are used by the City and County of San Francisco to operate and maintain hydroelectric facilities associated with Hetch Hetchy Reservoir, by lumber companies to access private timber parcels, and by individuals and organizations to reach private residences or recreational facilities. Additionally, some roads were necessary for administration of the national forest and for public access. Rockfall hazard certainly represented a significant potential geologic hazard to roads within the burned area of the Rim Fire. Several factors contribute to the difficulty of assessing this hazard. First, there was the need to quickly identify the road segments where mitigation was needed. There was only a limited amount of time to institute mitigation measures before public access was restored and winter storms would begin. Also, many private timber operators and other organizations needed to use the road network for their regular operations and to address fire-related and routine maintenance prior to the winter. Second, the length of road needing field assessment was large. Third, fire-damaged trees continued to fall which impeded access to roads and posed a safety hazard to field crews. The initial data collection and characterization of rockfall source areas within a burned landscape permitted a way to prioritize the evaluation of rockfall hazard to the road system (Table 1). A map of the road network on a topographic base was prepared using the Stanislaus National Forestâ&#x20AC;&#x2122;s geographic information system (GIS) data library (public data). The critical factors of 1) moderate or higher burn severity and 2) upslope gradients of 39 percent or greater were combined to identify which road segments in the network were most at risk. By screening for the intersection of these two factors, only 77.4 km of the 748.0 km of roads in the fire area required more detailed examination for increased rockfall potential. Field review was carried out by driving the roads and examining the segments where the GIS exercise had indicated a moderate to high potential for rockfall. Because rockfall activity starts during or immediately after the fire, the primary field evidence was the presence any rock or rocks on the road larger than 0.3 m or a concentration of ten or more rocks smaller than this dimension. Rocks found on the road

were carefully examined to ensure their emplacement occurred post-fire rather than pre-fire. Tracks in the ash on the slope, impact marks on the pavement or road surface, presence or absence of fire-blackened surfaces on the rocks, and similar observations were used make this distinction. This field evidence was conservatively applied to delineate the total length of road segment with potential increased hazards. Segments adjacent to the actual rockfall locations with similar slope and soil burn severity conditions were also included within the hazard areas. Rockfall was found associated with less than 15 percent of the road network identified by the GIS exercise as being potentially at risk from rockfall. A total of 9.7 km of road involving 10 individual segments was verified by field evaluation (Figure 7). On average, the length of identified road segments was 875 meters. The road segments subject to postfire rockfall were on roads needed for year-round access. Consequently, warning signs were posted at the beginning of these segments as a mitigation measure. The organizations responsible for maintaining the affected roads were notified to ensure monitoring and removal of rockfall would occur during the next year. UNCERTAINTY ASSOCIATED WITH THE PERSISTENCE OF POST-FIRE ROCKFALL Determining how long the threat of increased rockfall activity will persist is as important as identifying where that threat exists initially. Mitigation of this post-fire threat to roads can include short and long-term closures which are a burden to the public and the organizations depending on use of those roads (De Graff and Gallegos, 2012). Lacking good data on the persistence of increased rockfall following wildfire makes it difficult to know when the likelihood of rockfall on a road has returned to natural or background levels. Few responsible officials wish to expose the public or their employees to the risk of injury from rockfall by ending closures before the effect of the wildfire on rockfall activity levels has diminished. As noted previously, increased rockfall activity is an immediate response to the fire on the landscape. Anecdotal observations from firefighters and individuals conducting initial assessment for emergency response indicate initially rapid rockfall response slows in the days following initial burning of the slopes serving as the source of the rockfall material (see Figure 2 in De Graff and Gallegos, 2012). The level of rockfall activity can increase for brief periods during subsequent seasonal storm events.

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Figure 7. Map showing the southwestern and central parts of the Rim wildfire with the perimeter of the burned area indicated by the black dashed line. Blue inverted triangles mark the ends of road segments verified during field review as having increased post-fire rockfall hazard. Longer verified road segments are also highlighted by green shading. Warning signs were posted on these ten identified rockfall hazard road segments.

One of the few quantitative studies addressing persistence of increased post-fire rockfall activity took place on the Olympic National Forest in 2006â&#x20AC;&#x201C;2007 (Figure 6). In July 2006, the Bear Gulch II Fire burned the steep slopes above Forest Service Road 24 which parallels the shoreline of Lake Cushman (Badger, 2012). This paved road serves as a popular entrance to Olympic National Park and provides access to a number of privately-owned seasonal residences. The slopes above the road have a history of both rockfall and debris slides. The road was closed to public use during the fire suppression efforts and this closure was extended to permit evaluation of the rockfall hazard. A rockfall inventory was established for the 4 km road segment downslope from the recently burned area. Monitoring was conducted at waypoints established using handheld Global Positioning System (GPS) units. While not including all rocks impacting

the road, thirteen waypoints covered all the locations along this road segment where substantial rockfall activity was present in early fall 2006. Three additional waypoints were established in January 2007 where rockfall accumulation had taken place. At each waypoint, the roadway cross-section was divided into quarters from the inside to the outside lane edge. Rock accumulation in the ditch was not inventoried. During an inventory event, data on the size and number of rocks present in each quarter was gathered. Rocks were measured on their b-axis, the intermediate one perpendicular to the shortest and longest axes. All rocks larger than 0.08 m (0.25 ft) were counted. Each rock was marked with paint to avoid being counted during a later inventory event. Between October 2006 and April 2007, the monitored segment of Forest Road 24 was visited ten times. Inventories were conducted six times on roughly a monthly interval. During this period,

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Figure 8. A view along Forest Service Road 24 within the Bear Gulch II Fire area on the Olympic National Forest near Cushman Lake, Washington. This area is where the waypoint 6–9, known as the ‘‘Bowling Alley’’, is located. The photo was taken in late July during fire suppression activities and shows the results of the rapid rock accumulation that can occur during and immediately after a slope is burned (U.S. Forest Service).

3,463 rocks were inventoried on the road. Of all the inventoried rocks, 3,186 (92%) were less than 0.3 m in diameter. Twenty-five rocks were larger than 0.6 m with four of them being between 1.0 and 1.2 m in diameter. Eight of the sixteen monitored waypoint locations accounted for the vast majority of the rocks counted. With the exception of waypoint 6–9 nicknamed the ‘‘Bowling Alley’’; rockfall activity at the other fifteen waypoints had diminished to minimal levels by February 2007 (Figure 8). Eightyeight percent of the rocks inventoried were found within the inner half of the road. This suggested movement behavior by either rolling or bouncing at a low height for the majority of rocks moving during this period. Because waypoint 6–9 (‘‘Bowling Alley’’) had the greatest number of rocks persisting over the longest period during the monitoring effort, the movement behavior was investigated in more detail at this location. The slope and roadway were examined for impact scars on trees and impact craters at the road. Only scars on trees which exhibited the gouging and splintering characteristic of rocks impact rather than impact by falling trees were collected (Figure 9). Those impact scars also had to expose fresh wood on the burned trunks to indicate the scar was made after the wildfire. Only six scars meeting this criterion were found in a zone between 21 and 61 m upslope from the top of the road cut. With one exception, the impact scars were found on the upslope side of the trees at between 0.5–0.6 m above ground level. The exception was at a height of 1.4 m. Only a few impact craters were found at the road level and those were confined to the roadside ditch slope. The absence of

Figure 9. Large rock buttressed by a tree on the steep slope above Forest Service road 24. The splintered wood (light colored area near contact point between rock and burned tree trunk) of the tree reflects the force of impact by the rock (Bill Shelmerdine, U.S. Forest Service).

impact craters and the low height of the impact scars on trees are interpreted as showing the larger rocks moved primarily by rolling or bouncing at a low height. In order to determine appropriate and cost effective mitigation measures (if any) additional assessment was conducted on rockfall travel behavior. The more detailed assessment consisted of field measuring slope profiles, estimating travel pathway roughness, and modelling predicted rockfall behavior using the Colorado Rockfall Simulation Program (Jones et al., 2000). Modelling was supplemented by observing rockfall behavior during hazard tree removal (cutting and falling) of fire killed trees on the steep slopes above the Bowling Alley. Observations of rockfall during this operation supported the conclusions that: (1) significant rockfall was initiated by impacts from falling fire-killed trees and snags, (2) the origin of many, perhaps most of the rockfall was from colluvium rather than from the adjacent rock faces, and (3) a low-cost, low barrier could be used effectively in this location to eliminate the majority of rocks from reaching the roadway (traffic lanes).

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Rockfall Hazard in Burned Areas

Figure 10. A graph showing the total accumulation of rocks counted at the monitoring sites on Forest Service road 24 during the monitoring events and the accumulation found at the waypoint 6–9 (‘‘Bowling Alley’’) location. The trend over time shows a decreasing number of rocks impacting the Forest Road 24 both in total and at waypoint 6–9. Waypoint 6–9 accounted for a significant proportion of the rocks inventoried and continued to experience rockfall occurrence longer than other waypoint locations.

Figure 10 shows the monthly rock inventory totals for all monitored locations and for the most active location, waypoint 3–9 (‘‘Bowling Alley’’). The trend line for both the total rockfall inventory and that of the most active waypoint location were used to indicate when the closure would no longer be necessary. Data on non-fire related rockfall affecting Forest Service Road 24 prior to the fire would represent a clear threshold for re-opening the road. However, lacking this baseline data for background or non-fire related rockfall activity, the trend line becomes a surrogate for the threshold value. A trend line for seven months of data still represents some uncertainty, but suggested that re-opening of the road might be warranted. Seasonal road closures from October through April were implemented in 2006. There was sufficient Forest Service administrative travel through summer months to observe a lack of additional rockfall activity during that season as expected. The winter closure was again established in October of 2007, and periodic rockfall monitoring was planned. The expectation was that rockfall frequency would be reduced in the second year to a large extent and if this could be documented the closure order could be lifted. However, large rain-on-snow event on December 2–4, 2007 caused substantial damage from rockfall, debris slides, and erosional deposition along Forest Service Road 24 including the road segments within the area affected by the Bear Gulch II Fire. This significant storm event appeared to override the

effect of the fire on rockfall activity affecting Forest Service Road 24. Consequently, the road was reopened for public use after clearing in July 2008. DISCUSSION AND CONCLUSIONS The recognition that increased rockfall activity is associated with wildfires can be found in geologic literature as early as the overview article by Swanson (1981). Unlike debris flows, rockfall was not identified as a geologic hazard requiring specific attention and deserving of detailed research by Swanson (1981) or in later extensive reviews dealing with the geomorphological effects associated with wildfire or efforts to model hydro-geomorphic processes (Gabet and Dunne, 2003; Shakesby and Doerr, 2006; Lamb et al., 2011; Nyman et al., 2013; Moody et al., 2013). The only exception to this trend appears to be Santi et al. (2013) who comment that rockfall, along with debris flows, floods and landslide movement, are linked to wildfires as an agent of landscape change when linked to sufficient rainfall. De Graff and Gallegos (2012) provide the rationale for why a better understanding of the risk posed by increased rockfall after a wildfire is important to disaster response professionals and land managers. The uncertainty associated with predicting where increased rockfall activity will happen, the magnitude of that activity and the time over which any increase will persist is not fully resolved. For example, there is

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a need to better understand dry ravel and related processes to verify a linkage to increased rockfall activity after a wildfire. The data set developed to date is small and can only suggest directions which may reduce uncertainty in predicting location, magnitude and persistence at future wildfire locations. Further reducing uncertainty will require identifying potentially mapable or quantifiable factors for use in predictive efforts. Development of empirical relationships for predicting location, magnitude and persistence of increased post-wildfire rockfall activity will require more data over a larger geographical area. It is expected that some studies would be similar to the one described earlier for Bear Gulch to more fully cover the cycle from the time rocks are mobilized on a burned slope to the point when rockfall activity returns to pre-wildfire levels. Similarly, studies of rockfall after wildfire to collect data on the rock generated and site characteristics would facilitate the identification of empirical relationships suitable for mapping potential hazard areas. The data and applications described earlier in this paper allow us to advance beyond the situation described in De Graff and Gallegos (2012), but only address these overarching questions in terms of risk to roads within a burned area. As noted in De Graff and Gallegos (2012), structures and other stationary features where investment in mitigating measures may be possible require a reliable prediction of the benefit gained. There are also some important issues which are not fully resolved such as how to interpret persistence trends in post-fire rockfall activity to identify reasonable thresholds for permitting road re-opening or utilizing facilities where public safety will be a consideration. The successful advances made in predicting debris flow activity after a wildfire during the last two decades suggests many of these issues and questions about postfire rockfall activity may also be solved. ACKNOWLEDGMENTS This paper grew out of a presentation made at a session on uncertainty in engineering geologic work held during the 2013 AEG annual meeting. The authors appreciate the efforts of Jeffrey Keaton and William Haneberg both in organizing the session and serving as editors for the subsequent papers. The authors also express their appreciation for the many useful comments and suggestions offered on the initial manuscript by Kerry Cato, Paul Santi and an unnamed reviewer. REFERENCES BADGER, T. C., 2012, Maintenance, monitoring, and response. In Turner, A. K. and Schuster, R. L. (Editors), Rockfall-

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NYMAN, P.; SHERIDAN, G. L.; AND LANE, P. N. J., 2013, Hydrogeomorphic response models for burned areas and their applications in land management: Progress Physical Geography, Vol. 37, No. 6, pp. 787–812. PARSONS, A.; ROBICHAUD, P. R.; LEWIS, S. A.; NAPPER, C.; AND CLARK, J. T., 2010, Field Guide for Mapping Post-fire Soil Burn Severity, USDA Forest Service General Technical Report RMRS-GR-243, 49 p. ROERING, J. J. AND GERBER, M., 2005, Fire and the evolution of steep, soil-mantled landscapes: Geology, Vol. 33, No. 5, pp. 349–352. SANTI, P.; CANNON, S.; AND DE GRAFF, J., 2013, Wildfire and Landscape Change, In Shroder, J. F. (Editor), Treatise Geomorphology, Vol. 13, Academic Press, San Diego, pp. 262–287. SHAKESBY, R. A. AND DOERR, S. H., 2006, Wildfire as a hydrological and geomorphological agent. Earth-Science Reviews, Vol. 74, pp. 269–307. STEPHENS, S. L., 2005, Forest fire causes and extent on United States Forest Service lands: International Journal Wildland Fire, Vol. 14, pp. 213–222. SWANSON, F. J., 1981, Fire and geomorphic processes. In Mooney, H. A.; Bonnicksen, T. M.; Christensen, N. L.; Lotan, J. E.; and Reiners, W. A. (Editors), Fire Regime and Ecosystem Properties, pp. 401–421. USDA Forest Service General Technical Report WO-26. TURNER, A. K. AND JAYPRAKASH, G. P., 2012, Introduction, In Turner, A. K. and Schuster, R. L. (Editors), RockfallCharacterization and Control, Transportation Research Board, Washington, D.C., pp. 3–20. VARNES, D. J., 1978, Slope movement types and processes. In Schuster, R. L. and Krizek, R. J. (Editors), Landslide Analysis and Control, Transportation Research Board, Washington, DC, pp. 11–33. WELLS, W. G., 1987, The effects of fire on the generation of debris flows in southern California. Reviews Engineering Geology, Vol. 7, pp. 105–114. WESTERLING, A. L.; HIDALGO, H. G.; CAYAN, D. R.; AND SWETNAM, T. W., 2006, Warming and earlier spring increase U.S. Forest wildfire activity: Science, Vol. 313, pp. 940–943.

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Complex Landslide Triggered in an Eocene Volcanic-Volcaniclastic Succession along Sutherland River, British Columbia, Canada ´ E BLAIS-STEVENS1 ANDRE Geological Survey of Canada, 601 Booth Street, Ottawa, Ontario, K1A 0E8

MARTEN GEERTSEMA BC Ministry of Forests, Lands, and Natural Resource Operations, 1044 5th Avenue, Prince George, British Columbia V2L 5G4

JAMES W. SCHWAB P.O. Box 2525, Smithers, British Columbia V0J 2N0

THEO W. J. VAN ASCH Department of Physical Geography, Utrecht University, P.O. Box 80115, 3508 TC Utrecht, The Netherlands

Key Terms: Northern British Columbia, Landslide, Complex Rock slide-Debris Avalanche, Seismic Signal, Eocene Volcanic and Volcaniclastic Rocks

ABSTRACT On July 13, 2005 a complex 3 Mm3 and 1.5 km long rock slide-debris avalanche occurred near Sutherland River, 40 km west of Fort St. James, British Columbia, Canada. The landslide was initiated in a succession of sub-horizontal competent mafic basalts (Endako Formation) capping weaker felsic volcanic and volcaniclastic rocks (Ootsa Lake Group) of Eocene age. Several landslides have been observed in similar volcanic successions worldwide including in southern British Columbia. Some common characteristics of these landslides are: structurally undisturbed; horizontal to sub-horizontal bedding; curved head scarp; steep joints; debris consists of intact blocks; volcaniclastics containing smectite (expandable clay mineral); fossils and lignite within the volcaniclastics. The Sutherland landslide is one of many large landslides that have occurred in recent years in northern British Columbia. At least eight other large landslides have been triggered in volcanic rocks within the Nechako plateau.

INTRODUCTION On the southwestern flank of the Sutherland River valley, approximately 40 km west of Fort St. James, within Sutherland River Provincial Park, located in an isolated area of north central British Columbia (BC) is the Sutherland complex landslide; a rock slide-debris avalanche (Blais-Stevens et al., 2007; Figure 1). This large 3 Mm3 complex landslide was initiated in Eocene volcanics and volcaniclastics on the Nechako Plateau (Struik et al., 2000). It destroyed an estimated area of 40 ha with an approximate volume of 8,000 m3 of timber. Air photos dated as far back as 1957 indicate the head scarp had experienced on-going deformation. The objectives of the paper are to expand on preliminary results from research by Blais-Stevens et al. (2007) and describe the Sutherland landslide setting and landslide pre-conditions in an attempt to understand the potential triggers, characteristics, material properties, and slope movement. A comparison is made with other large rock slides in northern BC as well as some that have occurred in similar volcanic successions in southern BC and worldwide. Natural gas and oil pipelines are projected to cross the Nechako Plateau’s subdued topography. Thus, identification of potential landslide zones in this type of terrain will help decision-makers in assessing the landslide risk to infrastructure and population. PHYSIOGRAPHIC AND GEOLOGIC SETTING

Corresponding author email: ablais@nrcan.gc.ca.

The Nechako Plateau physiographic region, where the Sutherland landslide is located, is part of the

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Figure 1. Location map showing the Sutherland landslide site (red star) relative to the bedrock geology of the Nechako Plateau (Massey et al., 2005). Black dots indicate locations of weather stations. Fort St. James has both weather and seismic stations. Numbered red dots indicate locations of landslides triggered in Tertiary volcanics: (1) Buck Creek, (2) Dungate Creek, (3) China Nose, (4) Parrot I and (5) II, (6) landslide southeast of Burns Lake, (7) Cheslata Lake, and (8) Atna Lake. The first five are also mentioned in Geertsema et al. (2009). See Table 2 for locations.

Interior System of the Canadian Cordillera (Holland, 1976). During the Pleistocene, the area was exposed to at least two, perhaps several glaciations (Tipper, 1971a, b) resulting in a rolling, hilly topography with elevations ranging from 900â&#x20AC;&#x201C;1,500 m. Wide, subdued valleys, which are partly occupied by large, long lakes separate the higher elevations (Tipper, 1971a, b; Plouffe, 2000). The landslide (Figure 2) is located in one of these wide valleys on the southwestern valley wall of the Sutherland River, which drains northwest into Babine Lake (Figure 1). Bedrock Geology The Nechako Plateau is part of the Intermontane Belt, one of five morphogeological belts of the

Canadian Cordillera. Mainly composed of interbedded volcanic and sedimentary rocks, the Intermontane Belt consists of northwesterly trending oceanic and islandarc terranes (Sutherland Brown et al., 1970; Monger et al., 1972). As such, the bedrock exposed at the head scarp is composed of alternating Eocene volcanic and volcaniclastic sub-horizontal beds (Struik et al., 2000; Struik and MacIntyre, 2001). The volcanic rocks exposed in the upper 15 m of the head scarp are composed of dark mafic, reddish-gray, weathered, aphyric, and vesicular andesitic basalts. Some of the vesicles show partial filling from green siderite mineralization (Barnes and Anderson, 1999). These volcanic rocks belong to the Endako basalt formation (Haskin et al., 1998; Struik et al., 2000). The lower 35 m exposed at the head scarp and lateral scarps are

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of plagioclase, amphiboles, and volcanic glass in breccias (Figures 3a and 3b). These beds contain several sub-units of organic terrestrial material, e.g., leafy mats of deciduous trees and coniferous needles, soil, branches, and charcoal. Presence of water escape structures within some of the volcaniclastic beds indicates subaqueous deposition. These volcanic and volcaniclastic rocks belong to the Ootsa Lake Group (Grainger and Anderson, 1999; Struik et al., 2000). In terms of density and strength, the overlying Endako mafic volcanics are denser and more competent than the underlying Ootsa Lake felsic volcanics and volcaniclastics as observed in a synthesis of landslides triggered in Tertiary basaltic successions (Evans, 1983; 1984). Figure 2. Oblique air-photo of the Sutherland complex landslide looking southwest. Lodgepole pine beetle infestation is shown by abundance of dead (red) trees.

composed of felsic light gray volcaniclastic rocks. These are horizontally-bedded, very poorly sorted and in some cases, very poorly indurated volcanic tuffs and volcaniclastic flows with angular fragments

Surficial Geology The Nechako Plateau has been glaciated at least twice during the Wisconsinan where bedrock was glacially moulded and Tertiary valleys were filled with thick glacial and interglacial sediments. During the last glaciation (Late Wisconsinan), the Nechako Plateau was completely covered by the Cordilleran Ice Sheet

Figure 3a. Partial view of the head scarp looking southwest. Dimensions of the head scarp are approximately 50 m in height where the upper 15 m reddish-gray mafic Endako basalts cap the 35 m white-gray felsic Ootsa Lake volcanics and volcaniclastics. The head scarp width is roughly 150 m. Figure 3b. Close-up of the top of the head scarp showing reddish-gray Endako basalts overlying the Ootsa Lake volcanics and volcaniclastics. White arrow points to the orange abrupt contact (chill margin) between the units.

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Figure 4. Surficial geology map indicating location of Sutherland landslide along the Sutherland River valley (black arrow pointing to slope colluvium). Units on the map are: Cs (slope colluvium), Ch (landslide material), Tb (till blanket), Tv (till veneer), Gh (ice contact deposits), Gt (glaciofluvial terrace), O (organics), Au (undivided alluvial sediments), L (glaciolacustrine blanket), Gv (glaciolacustrine veneer), and R (bedrock). Consecutive v symbols within orange Gh unit indicate esker deposits (Plouffe, 2000).

(Tipper 1971a, b; Plouffe, 2000). Plouffe (2000) provided a detailed description of the glacial history of the area. Ice derived from the Coast Mountains flowed towards the east and later, towards the northeast as it coalesced with ice from the Cariboo Mountains. As climate warmed, the ice sheet disintegrated, and large glacial lakes formed in valleys obstructed by retreating glaciers and sediments. Meltwater channels were eroded in sediments and bedrock where drainage was open. Basal peat radiocarbon ages reveal that by 10,000 BP, the area was ice-free (Plouffe, 2000). The landslide was initiated in an area mapped by Plouffe (2000) as till veneer with slope colluvium deposits (Figure 4). Vegetation and Climate The landslide site is located within the Sub-Boreal spruce biogeoclimatic zone of north-central BC consisting of dominant tree species of spruce, subalpine fir, lodgepole pine, trembling aspen, and paper birch (Meidinger and Pojar, 1991). The predominantly lodgepole pine forest within the Sutherland watershed was heavily infested with mountain pine beetle, at the time the landslide occurred. Trees were still standing, but the apparent red needles indicate that the trees were dead (Figure 2).

The closest weather station to the landslide site, the Babine Lake Pinkut Creek weather station (34 km; Figure 1), recorded for the 1971–2000 period that mean annual precipitation was 491.4 mm where 288.7 mm fell as rain and 202.6 mm fell as snow. Mean annual temperature was 3.3uC ranging from a winter mean of 27.4uC to a summer mean of 13.0uC (Environment Canada, 2006). METHODOLOGY The authors visited the site three times to compile information on landslide characteristics and sample for clay mineralogy analysis and fossil identification. Semi-quantitative clay mineralogy analyses were carried out at the Geological Survey of Canada. Fossil identification was carried out with the help of a paleo-botanist at the Canadian Museum of Nature. A high resolution DEM of the landslide was created by photogrammetry at Natural Resources Canada’s Geomatics Division using ortho-rectified BC air photos taken in 2005 (30BCC05052-006) at 1:15,000 scale. Air photos taken over five decades by the BC Government or by Natural Resources’ National Air Photo Library were examined for evidence of previous slope deformation. The various air photos

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Figure 6. Evidence of a pre-2005 slope failure shown by avalanche deposit located on the northwest lateral scarp and exposed during 2005 failure.

Figure 5a. Close up of head scarp area showing deformation (BC air photo 30BCB90061:154). Red arrow points to bare rock wall. Figure 5b. Oblique view looking southwest of the landslide (pink) superimposed on 1995 pre-landslide air photo (30BCB95111-002) draped over a digital elevation model.

examined were taken in 1957, 1960, 1984, 1990, 1995, and 2005. Seismic station data measurements were analyzed at the Geological Survey of Canada and climate data were acquired from Environment Canada. RESULTS AND DISCUSSION Landslide Preconditions Pre-Landslide Deformation We detected previous deformation on air photos from as far back as 1957—the earliest available. This is discernable by the lack of tree cover just below an older head scarp. Some slope movement appeared to have

occurred along the scarp as shown in air photos taken in 1990 (30BCB90061: 154) and 1995 (30BCB95111002; Figures 5a and 5b, respectively). Plouffe (2000) observed deformation (using 1988 BC air photos) by labeling the landslide area as slope colluvium in and around a till veneer (Figure 4). We also observed evidence of a previous landslide on the northwestern lateral scarp, above the bedrock; a 2–3 m thick debris avalanche deposit is exposed (Figure 6). Hummocky topography that extends beyond the toe of the landslide indicates a previous landslide of a larger magnitude than the 2005 event. Previous signs of deformation were present; however, given the subdued topography compared to other regions of the Cordillera, there were no obvious signs that a large landslide was to occur. Heavy forest cover possibly masked signs such as tension cracks that would only have been discernible on high resolution LiDAR imagery, but not on air photos. Seismic Record We have not determined a single landslide trigger, but seismicity has been ruled out. The closest seismic

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station, located 40 km east of the landslide site (Fort St. James; Figure 1), did not record earthquakes on the day the landslide occurred. Nevertheless, the landslide itself produced a seismic signature. The signal indicated by a high frequency (0.1 sec or 10 Hz) lasted for a period 20–30 seconds. This is considered reasonable for a large landslide (Mulder and Lamontagne, 2005). The seismic record produced by the landslide helped determine the exact timing of the event (13 July, 2005, 0023:34:15 Universal Time). Documentation of seismic signatures related to landslide occurrences is becoming more frequent. The Hope landslide in southern BC was first thought to have been triggered by an earthquake, but further studies revealed that it was the rock avalanche that caused a seismic signature (Weichert et al., 1994). Studies of rock falls in the French Alps have compared and differentiated the types of seismic signatures left by earthquakes and those by rock falls (Deparis et al., 2006). The exact timing of the Todagin landslide event, located in northwest BC, was recorded on October 3, 2006 based on its seismic signature (Sakals et al., 2012). A complex rock slide-debris flow was also recorded at seismic stations located at Mount Meager on August 6 2010 in southwest BC (Guthrie et al., 2012). Similarly, Geertsema (2012) reported that a rock/ice avalanche from Lituya Mountain, Alaska, generated a seismic signature. Furthermore, with specialized seismic instrumentation, not only can the timing of an event be recorded, but also the dynamic processes of a large landslide, such as acceleration changes during the slope failure, as described for the Katani landslide in Japan (Yamada et al., 2013). In the case of Sutherland landslide, determining the exact time of the event helped pinpoint the climatic conditions leading up to it. Meteorological Conditions It is possible that climate may have played a role in triggering the landslide. Recorded data from the weather stations (see Figure 1 for station locations) indicate that the previous winter and spring were warm with temperature records set in both seasons. Moreover, from January to June, there were oscillating freeze thaw cycles. Above normal precipitation (177%) was recorded at the at the Fort St. James weather station (Figure 1) for the months of May (56.6 mm), June (91.2 mm) and July (121.2 mm) in comparison to monthly averages of 31.8 mm, 44.3 mm, and 46.5 mm, respectively (Environment Canada, 2006). In addition, at the beginning of July, a significant amount of precipitation was recorded leading up to the landslide. Satellite and radar weather imagery indicate intense thundershower activity for the area in the days before the event (July

8, 10, and 12; Foord, 2007). Thus, climate may have contributed to triggering the landslide. Landslide Description Overall Morphology The Sutherland landslide occurred in a large bowlshaped basin that shows evidence of a prehistoric landslide and recent slope deformation on a somewhat subdued relief of a large meltwater channel. The high and near-vertical head scarp measures up to 50 m in height (Figure 3a) with a northwest-southeast trend, running parallel to the Sullivan River fault, located on the opposite (eastern) side of Sutherland River (Struik et al., 2000). The southern, lateral scarp is about 900 m long with a sub-horizontal bedding plane dipping roughly parallel to the slope (Figure 2). The landslide involved 2.5–3 Mm3 of rock and soil, descended 270 m in elevation, travelled 1.45 km with a travel angle (fahrbo¨schung) of 11u. The slope failure was initiated as a rock slide traveling for about 550 m, and transformed into a debris avalanche for an additional 900 m, extending its travel distance by 164%. The northern fork of the debris avalanche extended 1.2 km from the main scarp (Figures 2 and 7). Thus, the landslide can be classified as a complex rock slide-debris avalanche (Hungr et al., 2001, 2014), or a rock slide-debris flow (Cruden and Varnes, 1996). Hungr et al. (2001, 2014) distinguish debris flows from debris avalanches, limiting the former to confined channels (channelized flows) while Cruden and Varnes (1996) do not make this distinction. In the rock slide area of the landslide, the upper 550 m, there is a higher concentration of the dark, reddish-gray volcanic (Endako basalts) rubble. This area contains numerous mounds (molard-like features; Cassie et al., 1988) and ridges up to 5 m high of large angular boulders (Figure 8) as well as gently sloping treads separated by steeper, minor scarps. At about 550 m down slope, the transition between rock slide and debris avalanche, the color of the deposit changes to lighter brown due to mixing with the underlying light gray volcanics and volcaniclastics (Ootsa Lake Group) and available unconsolidated sediments (glacial material and colluvium). Furthermore, there is a transition observed in topography of the deposit from the rock slide area to the debrisavalanche area (Figure 7). The debris avalanche zone has a more subdued topography where lobate ridges occur, up to 1.5 m high, with broad zones of ridges separated by lateral shear zones suggesting that both sliding and flowing occurred. It is in the distal portion of the slide that the debris avalanche bifurcated into two lobes. Debris in the southern

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Figure 7. Ortho-rectified air photo (30BCC05052-006; taken in 2005) showing the landslide with elevation contours. Thicker brown lines are 25 m apart and thinner ones are 5 m apart. Red dashed line indicates the transition from rock slide to debris avalanche. The yellow line shows the location of the landslide deposited up and down a 10 m ridge.

lobe travelled over a 10 m high ridge and continued to travel another 250 m before coming to a halt (see yellow line on Figure 7). Main Scarp The main scarp [see Cruden and Varnes, 1996, for terminology] displays several vertical fractures

(Figure 9) with two visible joint sets. One is striking parallel to the regional bedrock trend, northwestsoutheast, and the other perpendicular to it. The jointing is equally obvious in both rock types. Steep vertical jointing at the head scarp is one of the ubiquitous characteristics described in Evans’ (1983, 1984) analysis of the distribution of landslides triggered in Tertiary volcanic successions in southern British Columbia and worldwide. Moreover, fresh slickensides were observed on the surface of the southwest arm of the bowl-shaped head scarp. These display a rotational movement of the ruptured bedrock (Figure 10). This rotational movement is obvious in the underlying light gray Ootsa Lake volcanic and volcaniclastic rocks where there is greater potential for comminution during frictional movement of blocks due to the weaker nature of the bedrock (Figure 10). Properties of Failing Bedrock Ootsa Lake Volcanics and Volcaniclastics

Figure 8. Photograph of mounds almost entirely composed of Endako basaltic rocks deposited in the rock slide portion of the landslide. Yellow arrows point to two of the (molard-like) mounds.

We collected samples at the base of the southeastern wall of the main scarp at approximately 50 m depth (Figure 3a). These consisted of wet, very poorly indurated, light gray volcanics and volcaniclastics of the Ootsa Lake Group. Semi-quantitative

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Blais-Stevens, Geertsema, Schwab, and van Asch Table 1. Laboratory results on very poorly indurated light gray volcaniclastics. tr 5 trace; A 5 Abundance; Qtz 5 Quartz; Pl 5 Plagioclase feldspar; Ill 5Illite; Chl 5 Chlorite; Sm 5Smectite; ML 5Mixed-layer clay mineral. Samples were also analyzed for clay-size fractions of K-feldspar, Kaolinite, calcite and goethite, but results were negative.

Figure 9. Photo showing two sets of steep fractures; one set oriented parallel to the main scarp (red arrow; SSE-NNW) and the other, almost perpendicular (yellow arrow; ENE-WSW), parallel to the lateral scarps. Steep joints are also observed in Figure 3b.

clay mineralogy analyses were carried out using X-ray diffraction laboratory standards at the Geological Survey of Canada. Results revealed an abundance of smectite-chlorite mixed layer clay minerals (with about 20% chlorite layers) or smectite group minerals, including montmorillonite. A sub-sample of wet sandstone (poorly indurated when wet) collected just above coarser volcanic breccia showed a high abundance (.80%) of smectite with traces of plagioclase (Table 1). The presence of expandable clay minerals of this type above the wet volcaniclastics indicates weathering of volcanic ash and/or feldspar and also that the rocks from this unit have likely been altered and weakened. Based on our observations, we

Sample No.

Quartz

Ill/Mica

Chl

BMB05-15A BMB05-15B BMB05-16 BMB05-17

— tr — —

tr tr — tr

Tr Tr — —

tr tr tr tr?

— — — A

A A A —

suggest that this wet zone is likely the main rupture surface, situated at approximately 50 m depth. In addition, groundwater penetration observed in some of the shallower sub-units of the main scarp (Figure 3a) could have contributed to weakening of these layers, and subsequent rupture. Similarly, Evans (1983, 1984) noted a common stratigraphic setting in volcanic suites from the southern interior of BC and across the world: porous volcaniclastics with expandable clay minerals overlain by thick basaltic cap rocks. The rupture surface is often associated with weaker porous volcaniclastic rocks. Organic Layers-Fossils One particular feature detected in the Ootsa Lake volcaniclastics is the presence of beds rich in fossils and lignite (Figure 11a) found at the base of several blocks. The fossils include: sequoia leaves and pine needle imprints (Figure 11b) and alder leaf imprints (Figure 11c). Almost invariably, the wide variety of fossil and lignite layers is located sedimentologically at the base of the blocks. This feature seems to reflect potential zones of weakness within the bedrock and may have contributed to movement. The presence of fossils and lignite was observed at other large landslide sites around the world, such as western Greenland (Evans, 1984). Landslide Movement

Figure 10. Rotational failure movement at the head scarp. Yellow dashed lines indicate the rotational movement deduced from the slickensides on the exposed head scarp surface within the Ootsa Lake Formation (felsic volcanics and volcanicalstics).

The initial rock slide likely involved rotation and sliding, perhaps involving (in part) a joint set and translational sliding along a dipping bedding plane. A seepage zone was concentrated along the bedding in white-gray weaker Ootsa Lake volcanic and volcaniclastic bedrock. Initial rotational movement in volcanics overlying weaker volcaniclastics is also described in Evans (1983). In the initial stage of the landslide, the overlying dark reddish-gray Endako volcanic rocks were transported in a translational movement on top of the light gray rocks with little

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mixing, forming the molard-like mounds (Figure 8). Similar mounds have been observed in other rock slide-debris avalanches (Geertsema et al., 2006a; Catane et al., 2007; Xu et al., 2012). Downslope, with groundwater likely playing a role, the rock slide transformed into a debris avalanche where the reddish-gray volcanics and the lighter gray volcanics and volcaniclastics encountered till and colluvium from previous slope deformation. We interpret that the unconsolidated material was responsible for the mobility in the debris avalanche portion of the landslide. Un-drained loading of the rock slide would have fluidized the material, thus extending the travel distance as it bifurcated into two lobes (van Asch et al., 2004; Xu et al., 2012). Both lobes flowed up and down a 10 m ridge (consisting of previous landslide material and ice-contact deposits) to travel at least another 250 m before stopping. Comparison with Other British Columbia Rock Slides

Figure 11a. Lignite (black) found within the volcaniclastics at the base of a landslide blocks. Shovel handle is 1.5 m long for scale. Figure 11b. Eocene Sequoia branch and pine needles within the Ootsa Lake volcaniclastics. Figure 11c. Leaf imprint of a deciduous tree, cf. Alnus (alder species) mixed within a lignite layer.

Compared with landslides described by Evans (1983, 1984), the Sutherland landslide fits better with the geological features described in the Neogene volcanic successions rather than the Paleogene successions; even though, the Endako basalts and Ootsa Lake volcanics and volcaniclastics are of Eocene age, part of the Paleogene. Some of these features of the Neogene successions that are the same as at Sutherland landslide are: structurally undisturbed with minor warping; horizontal to subhorizontal bedding; arcuate head scarps; steep joints; landside debris consists of intact blocks; basaltic lavas associated with intravolcanic and basal volcaniclastic sediments. The main Paleogene successions consist of basaltic lavas as well as a variety of pyroclastic and sedimentary material, which are structurally disturbed (Evans 1983; see Figure 6 in 1984). In terms of geographic distribution, the Sutherland landslide was triggered in sub-horizontal mafic basalts capping a weaker felsic volcanic and volcaniclastic succession within the Nechako Plateau. Eight other large landslides have been triggered in volcanic successions on the Nechako Plateau. These are: Dungate, Buck Creek, China Nose, Parrot I, and Parrot II (Geertsema et al., 2009), Burns Lake, Cheslata Lake, and Atna Lake (Figure 1 and Table 2). Some of them date to prehistoric time (Geertsema et al., 2009). It is not known whether weaker volcaniclastics underly all of these volcanic successions; however, we suspect that there may be a similar setting as with the volcanic-volcaniclastic successions from Sutherland landslide. Hazard assessment for infrastructure development, such as pipelines and transportation corridors, should include

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Blais-Stevens, Geertsema, Schwab, and van Asch Table 2. Numbers refer to locations on Figure 1. Number on map

Landslide Name

1 2 3 4 5 6 7 8

Buck Creek Dungate China Nose Parrot I Parrot II Burns Lake Cheslata Lake Atna Lake

Coordinates 54u229 54u209 54u239 54u059 54u049 54u119 53u439 54u019

N, N, N, N, N, N, N, N,

126u369 126u349 126u229 126u189 126u179 125u399 125u199 127u429

W W W W W W W W

further detailed analyses of the Nechako Plateau volcanics and underlying volcaniclastics. Some recommendations include: detailed analyses of geotechnical/geophysical properties of the materials, magnitude/frequency of events, and spatial distribution using high resolution imagery (e.g., LiDAR) relative to the regional geology. Assessment of the volcanic and volcaniclastic successions is necessary to identify and avoid potential landslide terrain that may coincide with a transportation corridor across the Nechako Plateau. CONCLUSIONS The Sutherland landslide is a complex rock slidedebris avalanche. It is located about 40 km west of Fort St. James, in Eocene volcanics and volcaniclastics of the Ootsa Lake Formation. The Sutherland landslide possesses a number of characteristics similar to large landslides triggered worldwide in Tertiary basaltic successions. This includes thick horizontal competent basalts overlying weaker layered volcanics and volcaniclastics and a very steep head scarp parallel to major faults and bedrock trend. The rapidly moving landslide involved 3 Mm3 of rock and soil, travelled 1.45 km, and bifurcated in the runout zone. The landslide area encompasses roughly 40 ha. The rupture surface is thought to be a very poorly indurated volcaniclastic deposit of the Ootsa Lake Group, rich in expandable clay minerals, and wet due to abundant water seepage following above average precipitation. Initial landslide movement was rotational at the head scarp shown by presence of rotational slickensides on white-gray volcanic and volcaniclastic rocks in the southern lateral scarp. Historic air photos revealed that deformation was ongoing. Subdued topography and densely forested terrain possibly masked signs that such a large complex landslide might occur. The exact time of the landslide was determined by a seismic signature typical of rock slides and avalanches. Above normal precipitation in the months preceding the event may have been a contributing factor in triggering the

landslide. The prediction of these large rock avalanches and runout could be improved by detailed topographic mapping. Bare earth LiDAR surveys would have revealed the existence and extent of a prehistoric landslide, and possibly, precursor deformation features of the Sutherland landslide. Further studies of past large landslides in volcanic successions within the Nechako Plateau could shed light on the role of weaker underlying volcaniclastics, as documented in southern British Columbia and worldwide. ACKNOWLEDGMENTS The authors wish to thank A. Castagner for drafting Figure 1, formatting text, and ortho-rectifying air photos. F. Salvopol provided the DEM from the post-landslide air photo. Clay mineralogy analyses were carried out by A. Grenier and J. Percival. T. Mulder and M. Lamontagne confirmed the exact timing of the landslide occurrence from seismic records. V. Foord analyzed climate data. Fossils were identified by S. Cumbaa at the Museum of Nature. We wish to thank A. Plouffe at the Geological Survey of Canada and two anonymous critical reviewers for their invaluable comments and suggestions. Part of this research was funded by the Public Safety Geoscience Program at Natural Resources Canada; GSC contribution number: 20130473. REFERENCES BARNES, E. M. AND ANDERSON, R. G., 1999, Bedrock geology of the Uncha Mountain area, northwestern Nechako River map area, central British Columbia; in Current Research 1999-A, Geological Survey of Canada, pp. 129–138. BLAIS-STEVENS, A.; GEERTSEMA, M.; SCHWAB, J. W.; VAN ASCH, T.; AND EGGINGTON, V. N., 2007, The 2005 Sutherland River rock slide-debris avalanche, central British Columbia. In 1st International landslide conference, Vail Colorado AEG Special Publication, No. 23, pp. 677–686. CASSIE, J. W.; VAN GASSEN, W.; AND CRUDEN, D. M., 1988, Laboratory analogue of the formation of molards, cones of rock avalanches: Geology, Vol. 16, pp. 735–738. CATANE, S. G.; CABRIA, H. B.; TOMARONG, C. P., JR.; SATURAY, R. M., JR.; ZARCO, M. A. H.; AND PIOQUINTO, W. C., 2007, Catastrophic rockslide-debris avalanche at St. Bernard, Southern Leyte, Philippines: Landslides, Vol. 4, No. 1, pp. 85–90. CRUDEN, D. M. AND VARNES, D. J., 1996, Landslide types and processes: In: Turner, A. K. and Shuster, R. L. (Editors), Landslides: Investigation and Mitigation: Transportation Research Board, Special Report 247, pp. 36–75. DEPARIS, J. J.; JONGMANS, D.; COTTON, F.; AND THOUVENOT, F., 2006, Analysis of seismic records of rock falls in the French Alps EGU General Assembly, Programs and abstracts, Vienna, Austria, 07894. ENVIRONMENT CANADA, 2006, Climate Data, available at www. climate.weatheroffice.ec.gc.ca EVANS, S. G., 1983, Landslides in layered volcanic successions with particular reference to the tertiary rocks of south central

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Sutherland Rock Slide–Debris Avalanche British Columbia, Unpublished PhD Thesis, University of Alberta, 350 p. EVANS, S. G., 1984, Landslides in Tertiary basaltic successions: Proceedings for The IV International Symposium on landslides, Toronto 1: 503–510. FOORD, V., 2007, personal communication, B.C. Ministry of Forests, Lands, and Natural Resources, Prince George, British Columbia. GEERTSEMA, M., 2012, Initial Observations of the 11 June 2012 Rock/Ice Avalanche, Lituya Mountain, Alaska: The First Meeting of International Conssortium of Landslides Cold Region Landslides Network, Harbin, China. 2012, pp. 49–54. GEERTSEMA, M.; HUNGR, O.; SCHWAB, J. W.; AND EVANS, S. G., 2006, A large rock avalanche – debris flow at Pink Mountain, northeastern British Columbia: Engineering Geology, Vol. 83, pp. 64–75. GEERTSEMA, M.; SCHWAB, J. W.; BLAIS-STEVENS, A.; AND SAKALS, M. E., 2009, Landslides impacting linear infrastructure in west central British Columbia: Natural Hazards, Vol. 48, pp. 59–72. GRAINGER, N. C. AND ANDERSON, R. G., 1999, Geology of the Eocene Ootsa Lake Group in northern Nechako River and southern Fort Fraser map areas, central British Columbia; in Current Research 1999-A; Geological Survey of Canada, pp. 139–148. GUTHRIE, R. H.; FRIELE, P.; ALLSTADT, K.; ROBERTS, N.; EVANS, S. G.; DELANEY, K. B.; ROCHE, D.; CLAGUE, J. J.; AND JAKOB, M., 2012, The August 6 2010 Mount Meager rock slidedebris flow, Coast Mountians, British Columbia: characteristics, dynamics, and implications for hazard and risk assessment: Natural Hazards and Earth System Sciences, Vol. 12, pp. 1277–1294. HASKIN, M.; SNYDER, L. D.; AND ANDERSON, R. G., 1998, Tertiary Endako Group volcanic and sedimentary rocks at four sites in the Nechako River and Fort Fraser map areas, central British Columbia; in Current Research 1998-A; Geological Survey of Canada, pp. 155–164. HOLLAND, S. S., 1976, Landforms of British Columbia: A physiographic outline: British Columbia Department of Mines and Petroleum Resources, Vol. 48, 138 p. HUNGR, O.; EVANS, S. G.; BOVIS, M. J.; AND HUTCHINSON, J. N., 2001, A review of the classification of landslides of the flow type: Environmental and Engineering Geoscience, Vol. VII, pp. 221–238. HUNGR, O.; LEROUIEIL, S.; AND PICARELLI, L., 2014, The Varnes classification of landslide types, an update: Landslides, Vol. 11, pp. 167–194. MASSEY, N. W. D.; MACINTYRE, D. G.; DESJARDINS, P. J.; AND COONEY, R. T., 2005, Digital Geology Map of British Columbia: Whole Province: B.C. Ministry of Energy and Mines, GeoFile 2005-1.

MEIDINGER, D. AND POJAR, J., 1991, Ecosystems of British Columbia: British Columbia Ministry of Forests Special Report Series, Victoria BC, Vol. 6, 330 p. MONGER, J. W. H.; SOUTHER, J. G.; AND GABRIELSE, H., 1972, Evolution of the Canadian Cordillera: a plate tectonic model: American Journal Science, Vol. 272, pp. 577–602. MULDER, T. AND LAMONTAGNE, M., 2005, personal communication, Geological Survey of Canada, Sidney, British Columbia and Ottawa, Ontario. PLOUFFE, A., 2000, Quaternary geology of the Fort Fraser and Manson River map areas, central British Columbia: Geological Survey Canada Bulletin, 554: 92 p. SAKALS, M. E.; GEERTSEMA, M.; SCHWAB, J. W.; AND FOORD, V., 2012, The Todagin Creek landslide of October 3, 2006, Northwest British Columbia, Canada: Landslides, Vol. 9, pp. 107–115. STRUIK, L. C.; FALLAS, K.; HRUDEY, M. G.; AND WHALEN, J. B., 2000, Bedrock geology of the Burns Lake map area, British Columbia at scale 1:100,000: Geological Survey Canada, Open File 3840. STRUIK, L. C. AND MACINTYRE, D. G., 2001, Introduction to the special issue of Canadian Journal of Earth Sciences: The Nechako NATMAP Project of the central Canadian Cordillera: Canadian Journal Earth Sciences, Vol. 38, pp. 486–493. SUTHERLAND BROWN, A.; CATHRO, R. J.; PANTELEYEV, A.; AND NEY, C. S., 1970, Metallogeny of the Canadian Cordillera: Canadian Institute Mining Metallurgy Transactions, Vol. 74, pp. 121–145. TIPPER, H. W., 1971a, Glacial geomorphology and Pleistocene history of central British Columbia: Geological Survey of Canada Bulletin, Vol. 96, 89 p. TIPPER, H. W., 1971b, Multiple glaciation in central British Columbia: Canadian Journal of Earth Sciences, Vol. 8, pp. 743–752. VAN ASCH, T. W. J.; MALET, J.-P.; REMAIˆTRE, A.; MAQUAIRE, O., 2004, Numerical modeling of the run-out of a muddy debris flow. The effect of rheology on velocity and deposit thickness along the run-out track. In Lacerda, Ehrlich, Fontoura and Sayao (Editors), Landslides: Evaluation and stabilization: Taylor and Francis Group, London, pp. 1433–1438. WEICHERT, D.; HORNER, R. B.; AND EVANS, S. G., 1994, Seismic signatures of landslides: the 1990 Brenda Mine collapse and the 1965 Hope rockslides: Bulletin Seismic Society North America, Vol. 84, No. 5, pp. 1523–1532. YAMADA, M.; KUMAGAI, H.; MATSUSHI, Y.; AND MATSUZAWA, T., 2013, Dynamic landslide processes revealed by broadband seismic records: Geophysical Research Letters, Vol. 40, pp. 2998– 3002. XU, Q.; SHANG, Y.; VAN ASCH, T. W. J.; WANG, S.; ZHANG, Z.; AND DONG, X., 2012, Observations from the large, rapid Yigong rock slide-debris avalanche, southeast Tibet. Canadian Geotechnical Journal, Vol. 49, pp. 589–606, doi:10.1139/ T2012-021

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Modeling the Northern Coastline of Yucatan, Mexico, with GENESIS ´ LEZ-HERRERA1 ROGER GONZA Universidad Autońoma de Yucatań, Facultad de Ingenierıá, Av. Industrias no contaminantes s/n, Perife´rico Norte, Me´rida, Yucatań, Me´xico, CP 97000

ALFONSO SOLI´S-PIMENTEL Axis Ingenierı´a S.A. de C.V., Consultants for Maritime and Environmental Engineering, Merida, Yucata´n, Me´xico

CARLOS ZETINA-MOGUEL Universidad Autońoma de Yucatań, Facultad de Ingenierıá, Av. Industrias no contaminantes s/n, Perife´rico Norte, Me´rida, Yucatań, Me´xico, CP 97000

˜ O-TAPIA ISMAEL MARIN CINVESTAV–IPN, Unidad Me´rida, Km 6 Antigua Carretera a Progreso, Me´rida, Yucata´n, Me´xico, CP 97310

Key Terms: Coastal Erosion, GENESIS, Modeling, Simulation, Yucatan

ABSTRACT The sandy coast of the State of Yucatan is subject to natural and anthropogenic disturbances. Coastal erosion is a clear example of these disturbances. Many actions have been proposed and implemented to combat this problem; however, they have been counterproductive. In the present work the evolution of the northern coastline of Yucatan, Mexico was studied. The GENESIS model was applied to assess the effect of groins, breakwaters parallel to the coast and sand nourishment. The model was calibrated with available oceanographic information; a thorough treatment of this information made the calibration consistent. It was found that: a) the obliquity of the incidence wave is the main cause of longitudinal sediment transport; b) the sediment transport produced as a result of the wave height gradient along the coast is negligible; and c) the incident waves on the coast present very low energy and are governed mainly by local wind action. The flexibility provided by the calibration coefficients in GENESIS allowed it to be used on the beaches of Yucatan; the values obtained during this research reproduced in an acceptable manner both the morphological changes observed and the volumetric variability of sediments. For these values and the calibration 1

Corresponding author email: gherrera@uady.mx.

conditions, the model can be applied to analyze the evolution of the beaches in the Yucatan. Furthermore, it reinforces the hypothesis that the groins have accelerated erosion, breakwaters off the coast are a viable alternative to coastal erosion in the study area, and the placement of artificial sand is just a temporary solution. INTRODUCTION The study area corresponds to the coast of Yucatan (Figure 1). From a social and economic point of view, a large number of human activities of great importance are carried out in this area (Ya´n˜ez-Arancibia et al., 2013). The Yucatan Peninsula is a great platform and forms the northern part of the province of the Gulf of Mexico’s coastal plain; it is made up of limestone, dolomite, and evaporites. Sediments were exposed to the surface between the Upper Cretaceous period and the Holocene epoch, with horizontal layers and carbonates gradually deposited, being younger toward the margins of the peninsula (Gondwe et al., 2010; Bauer-Gottwein et al., 2011). It is a geologically recent region formed by contributions of unconsolidated sedimentary organic rock structures. Underlying rock structures of higher consolidation and older biogenic origin exist and are subjected to lengthy processes of erosion and cementation. The unconsolidated material is present as long barrier islands along the coast (Marin˜o-Tapia et al., 2007). These bars appear as broad, sandy beaches. They are primarily constituted of the remains of skeletal and calcareous structures or

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Gonza´lez-Herrera, Solı´s-Pimentel, Zetina-Moguel, and Marin˜o-Tapia

Figure 1. Location of the study area.

marine plants and animals. Toward the interior of the coastal sand bars, large coastal flooding or inundated zones are common as coastal lagoons, estuaries, marshes, etc. Human settlements of the coast of Yucatan are based on the most extensive sand bars. The largest concentration of people on the coast is located in the central region north of Merida, the capital city of the State of Yucatan. In this area shipping activities take place in a harbor protected by a 7km-long breakwater. A secondary harbor, Yucalpeten, also located in this region, underpins most of the fishing fleet along with the Yucatan’s industrial plants, receiving and processing marine products. Moreover, Progreso is located in this same area, which is the largest coastal city of Yucatan. It concentrates a large amount of residential tourism infrastructure in its environs, used on a permanent or temporary basis. All of this human activity takes place between the towns of Uaymitun, to the east of Progreso, and Chuburna, to the west (Meyer-Arendt, 2001). The coastal zone has many other attributes of social and environmental service that have sparked a growing interest in the conservation, protection, or restoration of these disturbed areas. The more

apparent part of the problem in this coastal region of the Yucatan is the loss of beaches as a result of the erosion processes that have been documented for years (SCT-IMT, 2000; UADY, 2000; and Barrera, 2001). As a result of worsening erosion due to strong hurricanes in the last two decades, the disruption of the coastal zone of the Yucatan has aroused great interest. The identification of these problems was accompanied by actions aimed at protecting the coast in such a way that groins and breakwaters were constructed (Alvarez et al., 2007). However, these engineering works covering different levels of technology were built in a disorderly manner for many years. Each structure attempted to protect a very local section, so that these structures often produced unwanted and counterproductive effects. Faced with increases of both the loss of beaches and the deterioration of the coastal zone (GarciaRubio et al., 2012), many strategies for recovery have been explored. In the early stages, these strategies led to evidence of the lack or dispersion of scientific knowledge and oceanographic data from the coasts of the Yucatan on which to base strategies and decisions that are technologically more suitable. During this

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process it became evident that an increased and systematic knowledge was needed, as was information and data on the coast of Yucatan. At the same time it was understood that the use of simulation models as a tool could provide great help in understanding the coastal processes as well as in studying and evaluating different strategies for the recovery of beaches prior to commencement of engineering projects (Appendini et al., 2012). There are some studies using simulation models of coastal processes of the beaches of the Yucatan. One (SCT-IMT, 2000) addressed the assessment of the impact of the construction of a breakwater in front of the port of Chicxulub, Yucatan. At present, there is great interest and there are several projects in this direction; with an integral perspective on the management of the coast, this is a small part of this great collective effort (De Landa, 1985; Martinez and Pare´s, 1998; SCT-IMT, 2000; UADY, 2000; Eua´n and Scout, 2002; Herrera et al., 2002, 2004; Agu¨ayo, 2003; and Zavala et al., 2004). The main objective of this study was to evaluate the effect of the different options for coastal protection (groins, breakwaters parallel to the coast, and sand nourishment) on the evolution of the coastline of Chicxulub, Yucatan, Mexico. Numerical experiments were performed to test the different structures; they provided important information regarding the most convenient schemes to use. METHODS The Generalized Model for Simulating Shoreline Change (GENESIS) was used to simulate the longterm shoreline change. This model was developed by the U.S. Army Coastal Engineering Research Center (Hanson, 1989) and until now has been the most widely used model because of its robustness and reliability (Hanson and Kraus, 1989; Gravens et al., 1991). The method applied comprises two main steps: a) calibration of the model using observations of beach profiles in an area at two different times and under known oceanographic conditions and b) simulation of the evolution of the coastline under the influence of different engineering works, such as the presence of groins, breakwaters, and beach fills. The starting point of the beach transect chosen for this research is located 360 m east of the access road to the port city of Progreso, Yucatan, Mexico (see Figure 1). This area has detailed bi-monthly survey data of 10 beach profiles from October 2002 through July 2004. Waves (measured in Telchac) and wind directions (measured in Rio Lagartos) were also recorded. The time interval during which

these three variables matched was from March to July of 2004. The field data were obtained from a field program conducted by CINVESTAV (Research and Advanced Studies Center) to monitor beaches as part of the project ‘‘Coastal Erosion and Water Quality in the Northeastern coast of the Yucatan Peninsula,’’ financed by the National Council for Science and Technology (CONACYT). The field equipment and instruments used to carry out the profiling were an automatic level (Sokkia B20), tripod, compass, state, and differential GPS (PROMARK2 Survey System). The data were used to estimate the empirical parameters and input files required to run and calibrate the model. Calibration of the model required selecting an area in which both data on coastal profile and oceanographic observations would be available. At this stage, a portion of the coast located adjacent to the port of Progreso, Yucatan, was selected. The lack of oceanographic observations—specific to the study area and during the time period used in the calibration—required an analysis of winds and their relationship with the observations of the characteristics of waves carried out at different depths during the period of time considered for calibration. Given the characteristics of the wave climate being dominated by locally generated sea breezes and the lack of swell waves, the wave angle of approach was approximated with the direction of the wind. A sensitivity analysis of the model was conducted to the oceanographic conditions, particularly the contribution of the waves to the model results. A simulation was run considering a fraction of beach in front of Chicxulub, Yucatan, where spurs have been constructed. Construction of breakwaters has been proposed and projects have been conducted in filling the beach (UADY, 2000). MODEL SETUP The modeling approach with GENESIS, illustrated in Figure 2, requires a specific coordinate system whereby an arbitrary line, parallel to the coastline, represents the X-axis (baseline), and where the Yaxis is perpendicular to the baseline (BL), in a seaward direction. The initial and final positions of the beach coastline were discretized in Dx-size cells distributed along the coast covering the length of the beachfront in question. The origin of the coordinate axis (0, 0) or left lateral border was established for GENESIS in Universal Transverse Mercator (UTM) coordinates. The starting point of the profile 1 was located at (Q16) X 5 223839; Y 5 2356377. The right border was established at (Q16) X 5 225219; Y

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Gonza´lez-Herrera, Solı´s-Pimentel, Zetina-Moguel, and Marin˜o-Tapia

Figure 2. Modeling approach with GENESIS.

5 2356377, UTM coordinates. Ten profiles were positioned within the domain of the model (P1 to P10, see Figure 1). The BL was behind the boardwalk and the coastal properties, aligned to the transect. The starting point of each profile corresponded to the position of buildings (houses or boardwalk). The data to delineate the coastline position between profiles were obtained by interpolating profile points, identifying –0.25 m level (i.e., Zm 5 20.25 meters above mean sea level [masl]). The cubic Hermite polynomial method was applied to interpolate between points of each profile. Coastline position in Figure 3 was determined with data shown in Table 1. The contour line of the boardwalk in Progreso and the line of homes (SEAWALL) were digitized from aerial photography using the TNTmips software. TNTmips is a geospatial analysis system. This line

represents the area in which there can be no more morphological change as a result of the presence of a physical barrier (boardwalk or houses). Comparisons were made of the coastline obtained from surveys conducted in January, March, May, and July 2004. Once the coastline was found to comply with the convention of the coordinate system of GENESIS, a line was drawn with an orientation of 85u with respect to north to represent the BL. The cell size was set to 10 m within the model domain. A Dy value was defined for each cell, representing the distance from the BL to 1) the initial coastline (SHORL file); 2) the final coastline (SHORM file) to be reproduced in the model calibration; and 3) the line considered as the border of morphological change (SEAWL file), which in this case coincides with the wall of the boardwalk and the position of the buildings on the beach.

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Figure 3. Interpolated coastline. July 16, 2004; filled circles where Zm 5 20.25 masl. Open circles represent positions in which Zm was measured.

EFFECTIVE TRANSPORT RATE (QE) It was important to verify that the rates of the sediment transport calculations were reasonable. For this reason, following the flow chart in Figure 2, an estimate of the total transport rate causing morphological changes (QE) in the area was carried out by determining the amount of erosion and accretion between profiles due to the difference in elevations (h) between two consecutive times (t) for a specific profile. As previously mentioned, the beach profiles were interpolated at fixed intervals of Dy 5 10 cm to perform the subtractions. Assuming a profile width of Table 1. Distance to Baseline (BL), where Zm 5 20.25 m (boldface row), was measured on some profiles. Node No.

Distance to BL (m)

Zm (masl)

35.00 40.80 43.90 48.00 58.00

0.80 0.27 20.25 20.85 21.12

3 4 5 6 7

58.93 69.53 72.83 78.93 88.93

1.08 0.21 20.25 20.97 21.10

P10 4 5 6 7 8

102.73 108.73 110.73 115.73 125.73

1.23 0.11 20.25 20.89 21.01

P1 7 8 9 10 11 P5

1 m, the sum of the absolute value of the difference between two surveys represents the volume of mobilized material in m3/m/d (Eq. 1): P ðht2 Þi {ðht1 Þi Dy ð1Þ QE ~m i~1 t2 {t1 where m 5 correction factor for porosity of the material (Komar, 1976); h 5 profile elevation at different times, t1 and t2; Dy 5 distance interval; and t 5 time in days. The calculations were made only for the period between March and July 2004 to compare the results with the estimates produced by the transport model. Table 2 shows the effective rate of transport (which caused morphological changes of the beach profiles) calculated for each profile. Considering that the length of the beach transect studied was 1,380 m, the gross rate of longitudinal transport, calculated from the beach profiles, is Table 2. Effective transport rates calculated for each profile. Profile

QE (m3/m/d)

1 2 3 4 5 6 7 8 9 10 Average

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0.09 0.06 0.08 0.07 0.08 0.09 0.07 0.03 0.06 0.08 0.07

Gonza´lez-Herrera, Solı´s-Pimentel, Zetina-Moguel, and Marin˜o-Tapia

Qg ~ 0:07 m3 =m=d ð1,380 mÞ ~96:68 m3 =d~35,289 m3 =yr In the simulation for the period covering 125 days, the sediment volume was Qg~ 0:07 m3 =m=d ð1,380 mÞð125 dÞ~12,085 m3 EMPIRICAL PARAMETERS (D50, DB, AND DC) To determine the point at which the waves break and to calculate an average value of the slope to be used in the equation of sediment transport, a configuration profile of the beach must be specified. For this purpose, GENESIS uses the concept of equilibrium profile, which Hanson and Kraus (1989) represent by the equation D~Ayb

ð2Þ

where D is the depth of water; y is the distance from the coastline; and A and b are empirical parameters that depend on the grain size (d50). Moore (1982) established a relationship between A and the average grain size (d50) that constitutes the beach. Thus, the method is basically a test match between an equilibrium profile, calculated with different A and b coefficients, and an average of the field profiles measured. The goodness of fit is evaluated by the absolute mean difference between measured and calculated profiles (Inglis, 1996; Marin˜o, 1998). This criterion has to be corroborated by means of visual inspection of the profiles because error cancellations can occur (Hanson and Kraus, 1989). The shape of the average profile and its main parameters (height of the berm, DB; depth of closure, DC; and effective grain size, d50) must be estimated using measured data. For the estimation of these parameters, information from the profiles obtained in the area during the months of March, May, and July 2004 was used. The beach was divided into two sections. Section 1 consists of the profiles P1 to P5 (zone of influence of the pier), and Section 2 corresponds to the P6–P10 profiles. The reason for this separation is due to the different characteristics in the average profiles of each section. The beach profile in front of the boardwalk lacks a dune crest (Figure 4A) and is steeper than the beach profile east of the boardwalk (see Figure 4B). Profiles were tested each month and averaged for each section, making a total of six sections. For

Figure 4. (A). Beach profiles 1 to 5 in front of the boardwalk. (B). Beach profiles 6 to 10 east of the boardwalk.

estimating the height of the berm, all the profiles that were counted (30) were used. The height of the berm is the point of incidence of all profiles on the inland boundary. In Gravens et al. (1991), Hallermeirer (1983) proposed an expression to calculate the boundary at which the sediments are transported; in terms of a wave height, this is DLT ~2:3Ho {10:9

H2o Lo

ð3Þ

where Ho 5 significant wave height in deep waters and Lo 5 wave length in deep waters (1.56 3 TS2). The height of the wave and its associated period (TS) must be given by the average of the highest significant wave for each 12-hour period, per year. The depth of closure was calculated with this approach. Significant wave height and the period of the higher waves,

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occurring over a period of at least 12 hours, were calculated from April 2004 to April 2005. The equilibrium profile calculated using d50 5 0.38 mm better fits the profiles measured in the field; therefore, it was decided to use this value to calibrate the model. The value of DB considered was 1.80 m, which represents most of the individual profiles. To calculate the depth of closure, the values of wave height and maximum observed period were used. The result was 3.20 m, which was adopted as the value of the depth of closure (DC). WAVE AND WIND DATA GENESIS requires an input file called ‘‘WAVES,’’ which contains characteristics of waves (significant height, period, and angle of incidence to the coast). There was no information on the ‘‘angle of incidence to the coast’’ needed for the WAVES file. However, this parameter was calculated with data on wind direction measured in the field. Thus, the input file was integrated with measured Ho, TS, and the estimated angle of incidence to the coast (h) in order to run GENESIS. The Physical Oceanography and Coastal Processes Laboratory (LAPCOF) of CINVESTAV-Me´rida has two stations for measuring waves and currents anchored opposite the port of Telchac, Yucatan. The first and second stations are 5 m and 20 m deep and are approximately 2 km and 27 km away from the coastline, respectively. Each station consists of a Sontek Argonaut XR ADP (Acoustic Doppler Profiler) and a pressure sensor. This study used data from a depth of 5 m collected between March 13 and July 16, 2004. The data consist of a time series of wave height and wave period recorded on an hourly basis. Despite the distance separating Telchac and Progreso (approximately 50 km), the use of these data to feed into the model was considered to be valid because the coast is affected by similar meteorological phenomena and because bathymetric contours are sufficiently uniform (parallel to the coast), so that the refractive processes are similar in Telchac and Progreso. According to the theory, when the depth (h) is equal to or greater than half the wavelength (L), the wave does not present modifications by refractive effects. In this case, the recorded wave periods (T) are between 2 and 3 seconds; the wavelength is L~1:56 T2 ~1:56ð3Þ2 &14 m

ð4Þ

Such waves begin to ‘‘feel the bottom’’ at a depth of L/2 5 7 m; therefore, measurements at 5 m are not as strongly influenced by the ocean floor. From the time series, data for the months of March, April, May,

June, and July were chosen to perform a descriptive statistical analysis of wave conditions. The next step was to extract the time series matching the period from March 13 to July 16, 2004, to create the file ‘‘WAVES.’’ The direction of waves is a parameter of great importance in GENESIS to estimate changes in the coastline. Observations along the coast of Yucatan show that the coastal wind exerts a dominant influence on the generation of waves near the coast. Figure 5 shows clear evidence that the oscillation frequency of the wind data is very similar to that observed on wave height recorded at a depth of 5 m. The swell waves are practically nonexistent. Another proof of this is the spectral peak period, Figure 6, with very low values (2 to 3 seconds), reflecting the dominance of the waves generated by local wind conditions. Based on these observations, it is assumed that along the Yucatan coast, wind direction is a good indication of the approximate wave direction. The information available on wind direction comes from the meteorological station of Rio Lagartos, Yucatan. These are the data closest to the area of study covering the time interval in which the model was calibrated, which is available in digital format. The data collected are time series recorded every hour; they are wind direction (in degrees from north) and speed (m/s). Time series data for the months from March to July were extracted. A monthly analysis was carried out to determine wave direction bands and to identify those that could generate incident waves on the local coastline. The general trend of the coast is approximately 85u with respect to north, so the bands of wind direction that generate incident waves are between 265u and 85u (Figure 7). The directions included in the bands from 0u to 85u (N and NE directions) were set as negative, and records bearing NW (between 265u and 360u) were set as positive (from 0u to 90u, respectively) to adapt them as required by GENESIS. Lastly, to finish integrating the file WAVES, all periods of the records that match the wind direction, regardless of the coastline, were set as negative. With this connotation GENESIS does not consider these time intervals in the calculation of sediment transport and changes in the coastline. When analyzing data for the whole period it was observed that almost all records of waves (99.5 percent) did not exceed the significant wave height of 50 cm; likewise, more than 99 percent had periods between 2 and 4 seconds. The average height and period for the analyzed data were HS 5 0.24 m and TS 5 3.4 seconds, respectively. There was no significant monthly variation in wave heights and periods recorded, except for during March, when

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Figure 5. Wave measurements at 20 m (top) and 5 m (middle) and wind (lower) on the coast.

there was a greater number of occurrences with waves above 0.25 m. During the period of analysis, about 40 percent of all records generated incident waves on the coast, mainly in the incident wave band between 60u and 80u. MODEL CALIBRATION Once all input files were integrated, as described in the preceding paragraphs and as illustrated in Figure 2, the model calibration followed by first varying, in a systematic manner, the calibration parameters within the ranges recommended by Hanson and Kraus (1989) (0.1 , K1 , 1.0 and 0.5 K1 , K2 , 1.5 K1). For each combination of values the calibration/verification (C/V) error was recorded to determine the goodness of fit. The calculated and measured coastlines were visually inspected for matching in addition to comparing the rate of gross sediment transport estimated by the model and the one calculated using the alternative procedure described above. Modifying the values of K1 and K2 within the typical ranges was not sufficient to obtain

satisfactory answers for the model; therefore, it was necessary to test with values outside these ranges and to analyze other variables that could be modified to obtain better answers. GENESIS offers the option of using a ‘‘smoothed’’ bathymetric contour for the internal calculation of wave transformation. The parameter that controls the calculation is called ISMOOTH. It specifies the number of cells averaged to obtain the ‘‘smoothed’’ contour. Table 3 summarizes the output of the calibration process. With these values, the calculated coastline represented the evolution trends with greater accuracy; the error was shown to be acceptable when compared with those obtained in other studies (Marin˜o, 1998). In addition, the average sediment volume calculated by the model corresponds nicely with that calculated from field data (13,721 m3 , 12,085 m3). The best performance of the model was achieved (Figure 8) with K1 5 2.5, K2 5 2.0, ISMOOTH 5 22, to get a C/V error of 3.3 m. Empirical parameters used were d50 5 0.38 mm, DB 5 1.8 m, and DC 5 3.2 m.

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Modeling the Yucatan Coastline, Mexico

Figure 6. Monthly distribution of the wave period.

The sensitivity of the model was also tested to examine the effect of some of the input variables on the quality of the output data. These tests involve 1) varying the sequence of waves to investigate their ability to replicate acceptable changes in the coastline and 2) changing the empirical parameters that define the characteristics of the beach (d50, DB, and DC). RESULTS With the calibrated model, simulation experiments were conducted to model the evolution of the coastline in the presence of coastal protection structures (see Figure 2). This was carried out in a 500-m transect located in the area of Chicxulub, Yucatan, approximately 5 km east of the area chosen for the calibration of the model. For the simulations the initial coast line was set as reported in the study area by UADY (2000), corresponding to July 2000; its position and the SEAWALL line were obtained from measurements by hand-drawing part of the study performed by UADY (2000) on a map (scale 1:10,000). The conditions under which the simulation experiments were run were 1) ‘‘normal’’ wave conditions (‘‘normal’’ conditions are those that do not occur in northern and tropical storms, such as hurricanes); for this reason the same data were used, time series of waves, with which the model was calibrated. 2) Time intervals. The simulation time was the same in which

the model was calibrated; this is from March 13 to July 16, 2004 (125 days). 3) Size of the mesh. The same cell size (Dx 5 10 m), defined in the calibration stage, was used; this was done to obtain a good resolution in the positioning of the structures. 4) Values of empirical and calibration parameters. The same empirical values obtained from field data and the values of K1 and K2, with which the best results were obtained, as described in the model calibration section, were used. Therefore, the simulation experiments were run with the following values: K1 5 2.5, K2 5 2.0, and ISMOOTH 5 22. The simulation period was 125 days, and the spatial intervals were 10-m increments. For the conditions described above, the effects of groins, breakwaters off the coast, and beach fillings were assessed. The specifications of the experiments conducted are listed below. Effect of Groins Groins that existed in the area in July 2000 were located within the model domain. The position and length of the groins were obtained from measurements by hand drawings using a map (scale 1:10,000) from the study performed by UADY (2000). It was assumed that the water depth at the tip of the submerged groins is 0.5 m and that the permeability (ability of these structures to let sand pass through them) is 25 percent; it is also assumed that these structures are not completely waterproof because they

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Gonza´lez-Herrera, Solı´s-Pimentel, Zetina-Moguel, and Marin˜o-Tapia

Figure 7. Wind direction bands schematic affecting the study area. Blue and brown lines represent bathymetry and topography, respectively; the coast line is represented in red between them.

were built based on ‘‘sacks’’ filled with stone or that they are entrenched. It was taken into account that the waves, during the period of simulation, predominantly affect the coast in a NE-SW direction. Thus, because of the angle of incidence is expected that a) the amount of sand that moves from right to left will be greater than that which moves in the opposite direction (the model estimated 991m3 of sand moving to the right [Qr] and 14,312 m3 to the left [Ql]), b) the sediment transport will be less than that it would be without the presence Table 3. Parameters obtained during calibration. K1

C/V

Q (m3)*

ISMOOTH

2.5 4.0 2.0 0.1 0.5

2.0 2.0 3.0 0.05 0.25

3.23 2.35 5.53 3.78 4.08

13,721 21,996 10,920 546 2,730

22 22 11 11 11

*Q computed for a simulation period.

of structures (the model results indicate that the amount of material that enters or leaves the domain [Qg 5 Qr + Ql 5 15,302 m3] is less compared to that calculated during the model calibration stage, in which structures are not included [Qg 5 21,996 m3]), and c) the transported material accumulates on the exposed face of the structures and accentuates the erosive process downstream of these. The model predicts that more sand will be lost than that which could be accumulated along the represented transect (Volumetric change 5 21.21E + 02) and that only significant progresses of the coastline near the right border of the model are present. The illustrated response of the model (Figure 9) shows a remarkable progress from the coastline in the far left of the domain; specifically, in the position of the shaft 440 m along the coast there is an advance of 10 m with respect to the initial line. This, at first sight, would seem excessive, given that the period of simulation is only 125 days and given that the wave energy is relatively low. Boundaries are open so that sediments can enter

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Modeling the Yucatan Coastline, Mexico

Figure 8. Results of the model calibration.

and leave the domain with no effect in the nearby cells. The accumulation of sediment observed in Figure 9 is expected from the groins trapping sediments. Given the above, the result of numerical simulation reinforces the hypothesis that the groins have contributed to the erosion observed in the area of Chicxulub. Effect of Breakwaters The size and location of the breakwaters that were tested were taken from the report submitted by the Ministry of Communications and Transport and the Mexican Institute of Transport (SCT-IMT, 2000). In addition to specifying the size of the structures, the model calls for setting a transmission coefficient (KT) of waves, referring to the wave motion over and through the structures. This coefficient, defined as the ratio between the wave height behind the structure, Hst, and significant incident wave height, HS (Eq. 5), varies over the range 0 # KT # 1, where 0 means that no transmission of waves exists and 1 means that the

wave does not undergo any change as it passes over or through the structure (Hanson and Kraus, 1989): KT ~

Hst Hs

0ƒKT ƒ1

ð5Þ

Given the characteristics (dimensions and materials of construction) of the structures proposed by the SCT and because it is believed that its crest protrudes from the water ,3.00 m above the lower level average low tide, the KT coefficient was fixed at 0.05. This means that the height of the incident wave is reduced by 95.5 percent when passing through the structures. The evolution of the coastline predicted by the model in the presence of these structures is shown in Figure 10. Beach gains of up to 14.50 m are observed (at X 5 380 m) just behind the breakwater located at the far right of the domain; these gains will decrease slightly in the area not protected by the breakwaters (220 m , X , 280 m). The gain of sand is increased in

Figure 9. Simulation results in the presence of groins.

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Gonza´lez-Herrera, Solı´s-Pimentel, Zetina-Moguel, and Marin˜o-Tapia

Figure 10. Simulation results in the presence of breakwaters.

the area behind the breakwater on the left side of the transect (120 m , X , 200 m). On the far left of the domain (0 m , X , 80 m), changes in the coastline calculated by the model are minimal, but there are areas where erosion is predicted (20 m , X , 40 m). The model estimated that 10,700 m3 of material would be gained at the end of the simulation period. Effect of Beach Fillings During May 2003 an artificial nourishment was carried out on the beaches of Progreso, in which between 30 and 50 m3 of material per linear meter of beach were placed (SEMARNAT, 2003). This amount of sand represented a gain in the coastline of approximately 22.50 m. On this basis, during the experiments, the placement of beach fills was simulated at the beginning of the simulation period to represent a gain in the coastline of 20 m along the entire transect represented in the model. The response of the model before the construction of a beach fill, with the quantities of material considered, led to the loss of material placed on the beach at the end of the period of simulation. There was even a decrease in the coastline almost in the entire domain; the volume change calculated was on the order of 3,000 m3 (Figure 11). DISCUSSION The GENESIS model is designed to reproduce the changes in the coastline for long periods of time (months, years, or decades). It is not suitable to simulate morphological changes in short periods of time (hours or days) caused by extreme weather

events (storms or hurricanes). Furthermore, even if the model is calibrated, there are a number of processes represented that are simplified to a great extent. The period of calibration of the model for this study was 125 days (,4 months). All data used in the model were obtained from field measurements. Wave conditions do not show considerable variability, there were no extreme weather events, and what happens on the coast of Yucatan can be accurately represented during much of the year. The method used to survey the beach profiles from which shorelines were obtained, can provide adequate treatment of the data. This includes methods of interpolation, corrections as a result of elevations, addition of details on field observations, etc. With respect to the information on waves, the monitoring stations on the coast of the State of Yucatan are equipped with pressure sensors. They lack information on wave directions; however, wind direction, measured in the coast, was used to infer the direction of the waves. There is sufficient evidence to assume that this is valid for this area because local wind exerts a dominant influence on the characteristics of coastal waves. As demonstrated in Figure 5, the wave and the wind variability at 5-m depth follow the same behavior, showing a direct influence of the diurnal sea breeze and a lack of swell wave presence. The uncertainty in the data collected to calibrate the model is minimal since the information is reliable; it was obtained with suitable instruments following well-established methods. Because of this, it can be considered that in terms of this work, the model application allows for a reliable approximation. Because GENESIS was developed to be applied in beaches constituted of quartz sand and subjected to

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Modeling the Yucatan Coastline, Mexico

Figure 11. Results of the simulation for a beach fill.

energetic waves, the authors of the model stress the importance of treating the coefficients K1 and K2 as site-specific parameters where the model is intended to be implemented (Hanson and Kraus, 1989; Gravens et al., 1991); however, this does not limit its use outside the recommended ranges of K1 and K2 (0.1 , K1 , 1.0 and 0.5 K1 , K2 , 1.5 K1). The values of these coefficients can fall in different ranges as long as the sediment volumes calculated by the model are reasonable and the changes in the coastline are reproduced in an acceptable manner (Gravens, 2006). The values the authors of the model suggest to start the calibration process (K1 5 0.5 and K2 5 0.25) do not provide the necessary sediment volume to produce the morphological changes observed with the conditions of this study. This suggested the use of higher values of K1 and K2 to improve estimates of the gross transport and, consequently, the simulation of the coastline. Sensitivity analysis showed that variations in the value of K1 have a marked influence on the sediment volume that the model estimates and on the final position of the coastline. Modifying the value of K2 does not give very significant changes in the responses of the model. This marked influence of the value of K1 in the variability of volumes of material estimated makes the angle of incidence of the waves the main promoter of the movement of material along the coast. On the other hand, little change in the model responses to changes in the value of K2 indicates that the gradient of wave heights along the coast contributes little to the amount of material that moves. Although the result obtained in the calibration show that the scenario yields a lower error, C/V, of 3.09 and a good fit between the visual response of the model and field observations, it was decided to sacrifice this to adopt K1 and K2 values with which sediment volumes similar to those estimated from the beach profiles are obtained, even if the value of C/V is larger. With these

parameters, K1 5 2.5 and K2 5 2.0, the trend in changes of the coastline is reproduced in an acceptable manner, and the sand volume calculated by the model aligns well with field estimations (QGENESIS 5 13,721 m3 vs QESTIMATED 5 12,085 m3). The fact that the values for the calibration coefficients are higher can be explained based on the consideration that the sands that make up the beaches in the area of study (up to 40â&#x20AC;&#x201C;70 percent of fragmented remains of marine mollusks, algae, corals, etc.) show flat, angular, or sub-angular shapes (Logan et al., 1969) and are less dense than those formed by quartz, and, therefore, the potential for movement is greater. Taking this into account, and recalling that the model was calibrated under conditions of low energy (low wave heights and shorter periods), values of K1 and K2 obtained as best can be considered suitable. Unlike beaches of quartz, with quasispherical grains, the calcareous beaches of Yucatan have a high mobility, even under conditions of really low energy, and this explains the high erosive rates observed in the Yucatan coast. The sensitivity analysis showed that the more the wave heights increase, the optimal calibration coefficients decrease considerably, and the visual fit between the computed coastline and the measured one diminishes. This indicates that the model is very sensitive to variation of this parameter, suggesting that caution should be exercised if the model is to be applied to wave conditions other than that under which it was calibrated. The results suggest that the values of K1 and K2 depend on the wave conditions. Should GENESIS be applied for prolonged times during which wave conditions are variable, it would be necessary to obtain optimal values of K1 and K2 for each of the wave conditions present. The numerical experiments performed to test the coastal protection structures provided important

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information regarding the most convenient schemes to use. The simulation considering the presence of groins reproduces results in a manner that would be expected of such schemes (i.e., accumulation of sand in the ‘‘updrift’’ structures, which reduces material to the beaches ‘‘downdrift’’). This is corroborated by comparing the response of the model with aerial photographs of the area. Furthermore, the model predicts a net sediment loss in the whole domain, which is a clear indication that these structures can contribute to the erosion process, as observed in the area of Chicxulub and in many other regions of the Yucatan coast. On the other hand, model results considering the presence of shore-parallel breakwaters off the coast show that this geometry can help considerably the accreting processes on local beaches. Sand accumulates in the protected part of the beach (behind the breakwaters); this sand accumulation in the coastline decreases slightly in the fair space where the beach is not directly protected by the structures, but the effect is very mild. Using the geometry of these structures, the model predicts that, in general, it will be a net gain of sediment on the beach. Despite the fact that such structures are a viable alternative to protect the coast studied, this specific design could generate several shortcomings due to its size, which include a hazard for leisure navigation and the damaging of the aesthetic value of a tourist-oriented beach (by blocking of the view). Finally, beach nourishment has been used worldwide as a successful beach protection scheme, which is especially useful for sediment-starved beaches, such as those of Yucatan. Nevertheless, this choice appears less encouraging, as the model results suggest that it could be lost rapidly (after just 4 months). Despite what recent beach profile analysis suggests—that this rapid beach loss is overestimated—beach loss after nourishment is undeniable. Measures that could help the nourishment to remain longer could be increasing the length of the beach fill or the volume of sand fed. Combination of beach protection schemes could also be a way to improve the performance of these schemes. CONCLUSIONS The northern coast of the Yucatan peninsula has been suffering the problem of shoreline retreat for several decades, and a series of beach protection schemes have been implemented with limited degrees of success. A shoreline change model, GENESIS, was implemented on the beaches of the region, using a set of field data to define the shorelines, profile characteristics, and wave climate to study the effect on

shoreline stabilization of different beach protection schemes, including groins (the most popular), offshore parallel breakwaters, and beach nourishment. Groins, as shown in practice and as evidence from many regions of the world point out, are a very inefficient scheme that generated sand accumulation only for the first and second updrift structures, but the overall result on the whole beach is widespread erosion, with more sediment being lost than accumulated. Offshore breakwaters have the capability of retaining sand, resulting in a beach width gain of about 5–10 m after a 4-month simulation. Nevertheless, the geometry of the structures needs to be carefully planned so they do not represent a hazard for navigation or an obstacle to the aesthetic value of the coast. Nowadays beach nourishment is one of the preferred options for shore protection, as it renourishes sediment-starved beaches; however, for it to function properly it must be performed for enough lengths along the coast and it must supply an adequate volume. For the region of interest, model results suggest that beach fills could be rapidly lost; therefore, a combination of appropriate beach fills and shore parallel structures seem to offer an adequate beach protection scheme for this region. ACKNOWLEDGMENTS This study was carried out while the second author was granted a scholarship to carry out graduate studies by the National Council of Science and Technology (CONACYT); we are thankful for the support provided by FOMIX–CONACYT, through the projects ‘‘Coastal Erosion and Water Quality in the Northeastern Coast of the Yucatan Peninsula’’ and ‘‘Use Of Simulation Models for the Assessment of Strategies for Physical Recovery of Beaches Based on Coastal Engineering Works on the Coast of Yucatan’’; and the Center for Research and Advanced Studies (CINVESTAV) in Merida, for providing part of the field data used in this work. We are also grateful with the personnel of the FIUADY and the Physical Oceanography and Coastal Processes Laboratory (LAPCOF) at CINVESTAV, Merida Unit, for the facilities granted for the development of this work. REFERENCES AGUÄYO, G. M., 2003, Patrones de Variacioń de la Vegetacioń Acua´tica Sumergida en la Costa de Yucatań como Indicadores de la Calidad del Agua Costera: Master’s Thesis, CINVESTAV-IPN, Unidad Me´rida, Me´xico. ALVAREZ, E.; RUBIO, R.; AND RICALDE, H., 2007, Beach restoration with geotextile tubes as submerged breakwaters in Yucatan, Mexico: Geotextiles Geomembranes, Vol. 25, pp. 233–241.

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Modeling the Yucatan Coastline, Mexico APPENDINI, C. M.; SALLES, P.; MENDOZA, E. T.; LO´PEZ, J.; AND TORRES-FREYERMUTH, A., 2012, Longshore sediment transport on the northern coast of the Yucatan Peninsula: Journal Coastal Research, Vol. 28, No. 6, pp. 1404–1417. BARRERA, C. M., 2001, Deformacioń en la Lıńea de 1 Kilo´metro de Playa Comprendido Entre las Comisarıás de Chelem Puerto y Chuburna´ Puerto: Tesis de Licenciatura, Universidad Autońoma de Yucatań, Me´xico. BAUER-GOTTWEIN, P.; GONDWE, B. R. N.; CHARVET, G.; MARIŃ, L. E.; REBOLLEDO-VIEYRA, M.; AND MEREDIZ-ALONSO, G., 2011, Review: The Yucatań Peninsula karst aquifer, Mexico: Hydrogeology Journal, Vol. 19, pp. 507–524. doi:10.1007/ s10040-010-0699-5 DE LANDA, D., 1985, Relacioń de las Costas de Yucatań, Historia 16: Crońicas de Ame´rica 7. Madrid, Espanã. EUAŃ, A. J. AND SCOUT, W. G., 2002, Promoting integrated coastal management in the Yucatan Peninsula, Mexico: Journal Policy Studies, Vol. 12, pp. 1–16. GARCIA-RUBIO, G.; HUNTLEY, D.; AND RUSSSELL, P., 2012, Assessing shoreline change using satellite-derived shorelines in Progreso, Yucatań, Me´xico, In Lynett, P. and Smith, J. M. (Editors), Coastal Engineering 2012, Proceedings of the 33rd Coastal Engineering. Santander, Spain. Published by the Coastal Engineering Research Council. GONDWE, R. N.; LERER, S.; STINSEN, S.; MARIŃ, L.; REBOLLEDOVIEYRA, M.; MEREDIZ-ALONSO, G.; AND BAUER-GOTTWEIN, P., 2010, Hydrogeology of the southeastern Yucatań Peninsula: New insights from water level measurements, geochemistry, geophysics and remote sensing: Journal Hydrology, Vol. 389, pp. 1–17. doi:10.1016/j.jhydrol.2010.04.044 GRAVENS, M. B.; KRAUS, N. C.; AND HANSON, H., 1991, GENESIS: Generalized Model for Simulating Shoreline Change. Report 2. Workbook and System User’s Manual: No. CERC-TR-8919-2. U.S. Army Corps of Engineers. Coastal Engineering Research Center (CERC), Vicksburg, MS. GRAVENS, M. B., 2006, personal communication. HALLERMEIER, R. J., 1983, Sand transport limits in coastal structure design. In J. Richard Weggel (Editor). Coastal Structures ’83. Conference Proceedings. American Society of Civil Engineers. New York, NY, pp. 703–716. HANSON, H., 1989, GENESIS: A Generalized Shoreline Change Numerical Model: Journal Coastal Research, Vol. 5, No. 1, pp. 1–27. HANSON, H. AND KRAUS, N. C., 1989, GENESIS: Generalized Model for Simulating Shoreline Change, Report 1. Technical Reference: U.S. Army Corps of Engineers. Coastal Engineering Research Center (CERC), Vicksburg, MS. HERRERA, S. J. A.; COMIŃ, S. F.; AND CAPURRO, F. L., 2004, Los usos y abusos de la zona costera en la Penıńsula de Yucatań. In El Manejo Costero en Me´xico, Ed. EPOMEX, Me´xico, pp. 387–396. HERRERA, S. J. A.; MEDINA, G. N.; ZALDIVAR, J. A.; RAMIREZ, J.; AND TREJO, J., 2002, Trophic status in coastal waters of the Yucatan Peninsula (SE, Mexico) using water quality

indicators. In Brevibia, C. A. (Editor), Environmental Problems in Coastal Regions IV: Wit-Press, pp. 351–359. INGLIS, M. L., 1996, A Numerical Model Study Using GENESIS V.3 of a Macrotidal, Shingle Beach after Construction of Extensive Sea Defenses at Sidmouth, Devon: M.S. Thesis, University of Plymouth, U.K. KOMAR, P. D., 1976, Beach Processes and Sedimentation: PrenticeHall, Englewood Cliffs, NJ. LOGAN, B. W.; HARDING, J. L.; AHR, W. M.; WILLIAMS, J. D.; AND SNEAD, R. G., 1969, Carbonate Sediments and Reefs, Yucatan Shelf, Mexico: American Association of Petroleum Geologists, Washington, DC. MARINÕ, T. I., 1998, Morphological Changes on Teignmouth Beach, South Devon, UK: A Comparison between Field Observations and Numerical Model: M.S. Thesis, University of Plymouth, U.K. MARINÕ-TAPIA, I.; RUSSELL, P. E.; O’HARE, T. J.; DAVIDSON, M. A.; AND HUNTLEY, D. A., 2007, Cross-shore sediment transport on natural beaches and their relation to sand bar migration patterns: Part 1. Field observations: Journal Geophysical Research, Vol. 112, p. C03001. doi:10.1029/ 2005JC002893 MARTINEZ, L. B. AND PARE´S, S., 1998, Circulacioń del Golfo de Me´xico Inducida por Mareas, Viento y Corrientes de Yucatań: Ciencias Marinas, Vol. 24, No. 1, pp. 65–93. MEYER-ARENDT, K. J., 2001, Recreational development and shoreline modification along the north coast of Yucatań, Mexico. Tourism Geographies: International Journal Tourism Space, Place Environment, Vol. 3, No. 1, pp. 87–104. doi:10. 1080/14616680010008720 MOORE, B., 1982, Beach Profile Evolution in Response to Changes in Water Level and Wave Height: M.S. Thesis, Department of Civil Engineering, University of Delaware, Newark, DE. SCT-IMT, 2000, Reporte Tećnico Preliminar del Estudio de Dina´mica Costera Para Definir las Obras de Proteccioń Contra la Erosioń de la Playa Localizada entre Huaymitun y Chuburna´, Yuc: Secretarıá de Comunicaciones y Transportes–Instituto Mexicano del Transporte, Mexico. SEMARNAT, 2003, Erosioń Costera, Secretarıá de Medio Ambiente y Recursos Naturales, Me´xico: Electronic document, available at www.semarnat.gob.mx/yucatan/Erosion Costera/ProgramaRecuperacion03.shtm UADY, 2000, Modelo Hidrodina´mico del Arrastre de Arenas y Sedimentos en Chicxulub Puerto, Estado de Yucatań: Universidad Autonoma de Yucatan, Mexico. YAŃ˜EZ-ARANCIBIA, A.; DAY, J. W.; AND REYES, E., 2013, Understanding the coastal ecosystem-based management approach in the Gulf of Mexico. In Brock, J. C.; Barras, J. A.; and Williams, S. J. (Editors), Understanding and predicting change in the coastal ecosystems of the Northern Gulf of Mexico: Journal Coastal Research, Vol. 63, Special Issue, pp. 243–261. ZAVALA, H. J.; GALLEGOS, A.; MOREY, S. L.; AND O’BRIEN, J. J., 2004, Upwelling and circulation on the Western Shelf Gulf of Mexico. In XII International Conference on Physics of Estuarine and Coastal Seas: Merida, Mexico.

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Collection and Application of Outcrop Measurements in Glacial Materials for Geo-Engineering and Hydrogeology along the Vermilion River, East-Central Illinois CHRISTOPHER J. STOHR1 Hydrogeology Section, Illinois State Geological Survey, Prairie Research Institute, University of Illinois Urbana-Champaign, 615 East Peabody Drive, Champaign, IL 61820

ANDREW J. STUMPF BARBARA J. STIFF Quaternary and Engineering Geology Section, Illinois State Geological Survey, Prairie Research Institute, University of Illinois Urbana-Champaign, 615 East Peabody Drive, Champaign, IL 61820

Key Terms: Photogrammetry, Clast Pavement, Buried Channels, Hydrogeology, Outcrop Measurement and Characterization

ABSTRACT Outcrop mapping by close-range photogrammetry was undertaken at three remote sites as part of a study of water resources to improve characterization of glacial sedimentary assemblages and to evaluate the utility of terrestrial remote sensing to supplement routine geologic mapping. Two features were measured from georeferenced stereomodels, clast pavements, and buried channel deposits over a 2-year period along a segment of the Middle Fork of the Vermilion River. Cobble-size clasts, spaced less than 3.28 ft (1 m) apart, form clusters approximately 6.56 to 9.84 ft (2 to 3 m) in length. These clasts compose a semi-continuous pavement between two tills deposited during the Wisconsin Episode. Five of the 26 clusters measured occur within the Tiskilwa Formation. Deposits of coarse- and finegrained sediments, informally assigned to the Glasford Formation lithostratigraphic unit, fill buried channels that provide an important source of groundwater in east-central Illinois for areas that do not receive water from the Mahomet aquifer. Measurements of buried channels included width, maximum sediment thickness, area, and perimeter. Widths equated to more than half of the outcrop length. Aspect ratios (width:thickness) of the channels are consistent with deltaic distributary systems formed in front of retreating ice margins, and 1

Corresponding author email: cstohr@illinois.edu.

systems having varying interconnectivity. Differences in area:perimeter of the buried channels provide a measure of shape that may partially account for the variation in yields from water wells in this area. Consequently, we postulate that yield could be improved through lateral drilling within the channel or by connecting adjacent channels. INTRODUCTION Geologic investigations conducted for groundwater and other subsurface resource analyses rely on borehole logs, geophysical profiles, and descriptions of field outcrop profiles where available (e.g., Culshaw, 2005; Keefer et al., 2011). Measurements of geologic features in two and three dimensions from outcrops, surface mines, quarries, and deep exposures such as for foundations and structural supports provide insights not available from subsurface borings. These borings routinely lack geophysical logs, which are a principal source of lithologic and texture information for sequence stratigraphy and 3D mapping (Dixon-Warren and Stohr, 2003; Stohr, et al., 2004). Observations from outcrops and mine highwalls are increasingly difficult to access because of safety concerns (Lato et al., 2013). Descriptions of outcrops, although providing greater insight into local geology, are commonly reduced to point data on maps (equivalent to boring logs) without retaining information collected in two- (2-D) and three-dimensions (3-D). A remote sensing method can be used to obtain high-resolution 2-D and 3-D information that can improve sediment characterization.

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Remote measuring by terrestrial or close-range photogrammetry using uncalibrated cameras is a well-established, low-cost method of collecting field data (Karara, 1972; Karara and Abdel-Aziz, 1974). The delineation of features by close-range photogrammetric techniques is analogous to the use of vertical stereomodels for geologic mapping with projection stereoplotters (Rabben et al., 1960; Moffitt and Mikhail, 1980; Avery and Berlin, 1985). Remote sensing techniques, such as photography, surveying, close-range photogrammetry, and laser scanning, offer important opportunities to supplement traditional geologic mapping methods by creating a georeferenced 3-D gridded model upon which sedimentary features on an exposed surface can be delineated and measured (Xu et al., 2000; Bellian et al., 2005; Haneberg, 2008; Stohr et al., 2011). Furthermore, with image processing and field observations, additional information or consultation not obtained at the site can be measured in a virtual revisit. These relatively new techniques require sophisticated instruments, software, and expertise not in common geologic practice. Only a few educational institutions teach these methods as part of their field mapping courses (Whitmeyer et al., 2009). Consequently, integration of these methods into the geosciences curriculum has been slow. For this study, two sedimentary features were measured twice at three sites, namely, clast (cobble and boulder) pavements and channel deposits. The former are of interest because of potential damage to excavating equipment and interference in drilling and driving sheet piles (Sauer, 1974). The Illinois State Geological Survey has received inquiries about the character of boulder pavements from consultants performing excavations in the Midwestern states. This study provides the measurements about the continuity of the glacial sedimentary feature. The principal features of interest are buried meltwater channels that are now exposed in glacial sediments interpreted to have been deposited during the deglacial phase of the Illinois Episode glaciation. These channels are filled by coarse-grained sediment that constitutes a local aquifer, and are recognized as an important source of groundwater (Kempton et al., 1981), especially in areas not receiving water from the Mahomet aquifer. Because water wells completed in this aquifer (formed during the Illinois Episode) have low to moderate pumpage rates, information concerning the distribution, character, and dimensions of these buried channels will assist water specialists by improving the maps and models used to develop water management strategies. Improvement of yields from wells can reduce problems during drought and

allow rural residents and farmers to expand commercial and personal use of water resources. GEOLOGY Outcrops analyzed in this study were selected from a 2-mi (3-km) reach of river length of the Middle Fork of the Vermilion River, a National Scenic River that includes a State Fish and Wildlife Area (Figure 1). The outcrops were accessed from three sites, Blue Hole, Higginsville, and Porter Cemetery, informally named for nearby geographic locations or local cultural features. The outcrop at the Blue Hole site is situated along a bank at a right-angle bend in the river. The Higginsville outcrop is located a short distance downstream of the Blue Hole site along a north-to-south linear bank of the river. The outcrop at the Porter Cemetery site is situated at a bend in the river oriented N32uW. The sites were chosen because of their accessibility, the variety of sediments exposed, and the extent of the exposures (Table 1). The three sites are located in a river valley that is incised into subglacial and proglacial deposits of multiple glaciations. The sediments exposed in outcrops at these sites are assigned to two separate glacial events. Deposits of the more recent Wisconsin Episode include the Yorkville Member till and Batestown Member till of the Lemont Formation, and the Tiskilwa Formation till. Underlying these are deposits of the Illinois Episode, including sediment of the upper and lower units of the Vandalia Member of the Glasford Formation (Figure 2). Inset into this diamicton are glacial meltwater channels of the upper unit of the Vandalia Member of the Glasford Formation filled with deposits consisting of horizontally bedded to cross-bedded sand and gravel, or laminated to bedded silt and clay. In some places, the channels are cut deeper and extend into the underlying till of the lower unit of the Vandalia Member of the Glasford Formation. Detailed investigation of these outcrops revealed a notable clast pavement between tills of the Batestown Member and Tiskilwa Formation (Figures 3, 4, and 5). Although these tills have very similar textures, the units can be easily distinguished by their distinctive color and surface weathering. In some places, the contact between them has been obscured by material that has fallen on the slope from above. Clast pavements were delineated at three sites; however, only the pavement at the Higginsville site was entirely accessible. In earlier studies undertaken at the Higginsville site, Voorhees (1996, 1998) excavated and described a clast pavement ‘‘cobble zone’’ in considerable detail. The exact location of the pit and outcrop studied by Voorhees is not known but

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upper portion of the Delavan (Tiskilwa) diamicton’’ (p. 63). The description and distribution of clast pavements are reported by Voorhees (1998) for additional locations along the Middle Fork of the Vermilion River further downstream. Development of clast pavements can vary as a consequence of erosion or as subglacial transport and deposition (Boulton and Hindmarsh, 1987; Hicock, 1991; Ham and Mickelson, 1994; Voorhees, 1996, 1998; Evans et al., 2006). Pavements are typically studied where clast alignments are observed. Although this bias skews their study, it is somewhat unavoidable because of their exposure distribution. METHODS Field Measurements

Figure 1. Surveyed control points for three study sites along the Middle Fork (MF) of the Vermilion River. The base map was developed from color aerial orthophotography recorded on March 19, 2011, for the Illinois Department of Transportation. The inset map shows the Middle Fork study area within Illinois (black square) and the extent of glaciers over the state during the Wisconsin (light tan) and Illinois (dark tan) Episode glaciations. The Illinois portion of the subsurface Mahomet Aquifer is shown in dark blue.

is assumed to be near the site location in this study. During these previous studies, a cubic meter (35.32 cubic feet) of overburden was excavated at the site to expose a 4.27 by 3.28 ft (1.3 by 1.0 m) area ‘‘classified as a Tiskilwa stone bed’’ (Voorhees, 1996, 1998). Voorhees uncovered 63 clasts along the contact between the Batestown Member and Tiskilwa Formation units. The clasts were in relatively close proximity ‘‘with an estimated 15 percent lateral gap (i.e., about 15 cm) within the upper 5 to 10 cm of the

Safety concerns for climbing steep bluffs over deep pools and time constraints limited examination of outcrop features during surveying and stereophotography collection at the outcrops. For example, the clast pavement in the near-vertical bluff at the Blue Hole site was 39 feet (12 m) above the pool at the river bend (Figure 1). Ground and outcrop control points were surveyed, and photographs taken to make georeferenced measurements. The overhanging tree canopy prevented direct satellite surveying to establish ground control at or near the outcrops on the river bank or stream bars. New surveying control was established by satellite surveying in an open prairie adjacent to the sites and a trail cut through bottomland forest. Establishing geospatial control on or near the outcrops was achieved by installing bolts and securing plastic disks that were marked with florescent paint so that they could be viewed in the stereophotographs. Further details regarding the surveying protocols and procedures are provided in Stohr et al. (2011). Seasonal changes in site conditions interfered in recording images of the outcrops. High water levels in the river during the spring and early summer prevented collection of stereophotography at the desired survey locations (i.e., point bars) until the water levels receded. By mid-summer, foliage from overhanging trees, overturned tree trunks, and

Table 1. Length, height, and orientation of outcrops studied along the Middle Fork of the Vermilion River.

Outcrop

Outcrop Length (ft)

Outcrop Height (ft)

Outcrop Orientation

Blue Hole

233.6

53.2

Higginsville

148.6

52.8

N41.72E N45.25W N-S

Tiskilwa Formation Thickness (ft)1 11.5, 13.1, 18.4 7.2, 8.2

Thickness of the Tiskilwa Formation was measured at representative points along the outcrop.

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Figure 2. Lithostratigraphic units of Quaternary-aged sediments in east-central Illinois (modified from Stumpf and Atkinson, 2014). The stratigraphic positions of the clast pavements are delineated by black ovals.

understory brush along the river bank obscured visibility and access, especially on the bank opposite the outcrop. By mid- to late- summer, the site conditions had improved, but the sediment in the outcrops had become dry, obscuring the distinctive moist soil colors used for delineating the stratigraphic units. Comparison of enhanced reconnaissance photography (i.e. linear stretch and contrast adjusted), permitted clast pavements and buried channels noted in the field to be digitized on the stereomodels. Camera stations for stereophotography used for the photogrammetry were surveyed so that intervals were a proportional distance from the camera to the

outcrop and ground control (Aiken et al., 2004). The Porter Cemetery site was divided into upstream and downstream segments for the 2010 data acquisition. A change made in the procedure used for the collection of stereophotography in 2011 enabled both of these segments to be merged into a single stereomodel. Stereophotography was recorded in two separate years (Table 2). Stereomodels Close-range photogrammetry methods for this study are similar to the techniques described by

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Figure 3. Clast pavements and buried channel deposits in an outcrop at the Blue Hole site along the Middle Fork of the Vermilion River. Clasts are circled in blue in the Batestown Member till, in red in the Tiskilwa Formation till, and in green in unnamed sands of the Glasford Formation and the Vandalia Member till. Clasts forming the pavement between the Batestown and Tiskilwa are circled in black. Solid orange, blue, green, and black lines represent formation boundaries. The buried channels within the upper unit of the Vandalia Member of the Glasford Formation are delineated with a stippled pattern, and the dashed lines indicate contacts that are covered or obscured. The white sheets of paper are targets used as controls in the stereomodel.

Haneberg (2008), Poropat (2006), and Stohr et al. (2011). Photographs were taken with a tripodmounted Nikon D80 camera (10.2 megapixels) and a Nikon 28-mm f/2 lens. A custom-made bracket with a clinometer and compass was used to ensure that the camera orientation along three axes was consistent. Creation of 3-D models and mosaics and determination of georeferenced positions, feature delineations, and other measurements were done using CAE SirovisionTM software (version 4.1) developed by the Australian Commonwealth Scientific and Industrial Research Organization (CSIRO) and CAE Mining. CAE Sirovision is a commercial close-range terrestrial digital photogrammetry program created for geological and geotechnical mapping. The software consists of two programs: Siro3D for 3-D model creation and Sirojoint (version 5.0.18.0) for structural mapping and analysis.

Euclidean distances and summary statistics for all measurements were calculated using Microsoft Office ExcelH software (version 2010). Computations of projected channel areas and perimeters were made using ArcMapTM Desktop software (version 10.1.1), a geographic information system and geodatabase management system developed by the Environmental Systems Research Institute (ESRI, Redlands, CA). Hugin freeware software (http://hugin.sourceforge. net/) and Adobe Photoshop (version 12.0.3) were used to make uncontrolled mosaics (i.e., not geometrically corrected) for office use. The location and distribution of individual cobblesized clasts were determined by digitizing on georeferenced stereomodels in Sirovision. Resolution and overlap of individual images in the composite stereomodel required enhancement of the field photographs for consultation to aid in identifying

Figure 4. Clast pavements and buried channel deposits in an outcrop at the Higginsville site along the Middle Fork of the Vermilion River. Clasts are circled in blue in the Batestown Member till, in red in the Tiskilwa Formation till, and in green in Unit 1 of the Glasford Formation and Vandalia Member till. Clasts forming the pavement between the Batestown and Tiskilwa are circled in blue. Solid orange, blue, green, and black lines represent formational boundaries. The buried channels within the upper unit of the Vandalia Member of the Glasford Formation are delineated with a stippled pattern, and the dashed lines indicate contacts that are covered or obscured. Control marks are marked with orange paint. The white sheets of paper are targets used as controls in the stereomodel.

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Figure 5. Clast pavements and buried channel deposits in an outcrop at the Porter Cemetery site along the Middle Fork of the Vermilion River. Clasts are circled in blue in the Batestown Member till, in red in the Tiskilwa Formation till, and in green in the Glasford Formation and Vandalia Member till. Clasts forming the pavement between the Batestown and Tiskilwa are circled in black. Solid orange, blue, green, and black lines represent formational boundaries. The buried channels within the upper unit of the Vandalia Member are delineated with the stippled pattern and horizontal lines. Dashed lines indicate contacts that are covered or obscured. The white sheets of paper are targets used as control in the stereomodel.

individual clasts. The photographs were carefully studied to identify large cobbles. The centroid of the cobble was then digitized on the georeferenced image. RESULTS Buried Channels Meltwater channels were mapped on 3-D models of each of the outcrops studied (Figures 6a, 7a, and 8a). Table 3 and Figure 9 provide the dimensional data for channels measured on the 2011 imagery, including width, maximum thickness, area, and perimeter. Because the available information was insufficient to determine the exact orientation of the channels behind the outcrop face, measurements should be considered approximate. Two channels, 72 ft (22 m) apart when measuring between their centers, were exposed at the Blue Hole site (Figure 6a). Because the outcrop at the Blue Hole site is located at a right-angle bend in the river, we presumed the two channels were part of the same sedimentary feature. The Blue Hole channels are composed of stratified deposits of sand and pebbly sand. Only the southernmost of the Blue Hole channels was accessible.

Table 2. Dates of stereophotography acquisition at three sites along the Middle Fork of the Vermilion River. Year of Photography Blue Hole Higginsville Porter Cemetery 2010 2011

August 31 August 6

August 2 July 26

July 31 July 27

The Higginsville channel is composed of fine to coarse sand with traces of gravel and some silt. It is the only outcrop that is fully accessible. The Porter Cemetery channel is composed of stratified beds of sand, gravelly sand, and laminated silt with clay. Although the southern end of the channel at the Porter Cemetery site is covered by debris, the calculated width of the channel is probably smaller than the actual width. Widths of channels at the three sites averaged 84.1 ft (25.6 m), with a range in width of 22 ft (6.7 m). Sediment thicknesses of the channel fill were similar for three of the channels but were considerably less for the channel mapped at the Higginsville site. This difference in sediment thickness directly influences the calculated aspect ratios, which are nearly twice as large for the channel at the Higginsville site compared with the other channels. An intriguing measurement computed for the channels was the ratio between channel width and overall length of the outcrop (Table 3). The sediments filling the channels constituted nearly one-half the length of an entire outcrop. This relationship applies to the Blue Hole site when the lengths of both channels were summed. The aspect ratios, although moderately variable, are similar for channels in Giblingâ&#x20AC;&#x2122;s (2006) distributaries classification associated with distal alluvial fans and aprons, delta distributaries, or crevasse channel deposits, features that share many hydraulic characteristics, such as channel-body connectedness. Features of crevasse channel deposits are typically eroded only a few meters into the underlying sediment (i.e., by rapid excavation and filling), whereas deltaic

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Figure 6. (a) A stereomodel constructed from stereophotography collected at the Blue Hole site in 2011. Two buried channels are shown (outlined in green and red) that are excavated into fine-grained sediment of the upper unit of the Vandalia Member. (b) A stereomodel constructed from stereophotography collected at the Blue Hole site in 2011. Clast pavements formed in till of the Tiskilwa Formation are shown, with individual clasts located by the pink, green, and red coloring.

distributary deposits can be interconnected (Gibling, 2006). Depending on the degree of interconnectivity between channels, wells constructed in one channel will have varying hydraulic connections with adjacent channel deposits. This could result in a difference in effective capacity of the aquifer. The total area of channel deposits exposed in an outcrop was similar for three of the channels studied but differed for the channel at the Porter Cemetery site, which covers more than twice the area of the others. The range in area differs because of the shape of the channel and the happenstance nature of exposure of the landform. At the Blue Hole and Higginsville sites, the channels have irregular shapes,

whereas at the Porter Cemetery, the channel-fill deposit is lenticular. Irregularly shaped channels would be expected to create greater resistance to fluid flow through the porous medium as the cross-sectional area changes. Channel shape can be characterized by computing the ratio of the area to the perimeter of the deposit, an indicator of its shape that influences flow. For example, the narrow, elongated channel having an irregular shape at the Higginsville site has a relatively small ratio (1.28; Table 3), which would not be expected to support as much flow or allow the transport of as much water as a channel with a thicker fill and more ovulate shape (e.g., channel at the Porter

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Figure 7. (a) A stereomodel constructed from stereophotography collected at the Higginsville site in 2011. A buried channel is shown (outlined in green) that is excavated into fine-grained sediment of the upper unit of the Vandalia Member. (b) A stereomodel constructed from stereophotography collected at the Higginsville site in 2010. Clast pavements formed in till of the Tiskilwa Formation are shown, with individual clasts located by the red, green, blue, pink, and orange coloring. Control marks are marked with orange paint.

Cemetery site, 4.25; Table 3), keeping all the other channel characteristics constant. Accordingly, some of the variability in groundwater yield from aquifers in this region could be attributable to the shape of the buried channels. Considering the range of well yields caused by the variability of channel shape and dimensions, one possible strategy to improve water availability would be to employ horizontal drilling to extend the

screened interval along the length and width of the channel deposit similar to radial wells (Williams, 2008; Fournier, 2005). The installation of multiple collector wells helps to improve yield and capacity. A second strategy would be to extend connections to neighboring channels of the distributary system (Anderson, et al., 2013). Surface and airborne geophysics can provide additional information about the distributary system.

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Figure 8. (a) A stereomodel constructed from stereophotography collected at the Porter Cemetery site in 2011. A buried channel is shown (outlined in red) that is excavated into fine-grained in the upper unit of the Vandalia Member. (b) A stereomodel constructed from stereophotography collected at the Porter Cemetery site in 2011. Clast pavements formed in the Tiskilwa Formation are shown, with individual clasts located by the red, green, and pink coloring.

Clast Pavements Proximity and spacing of the digitized points were used as criteria to support the interpretation that clasts are arranged in clusters or linear associations. Clasts were aggregated into clusters based on distance between the cluster and adjacent clasts. The decision to include clasts in a cluster was based on the geologistsâ&#x20AC;&#x2122; judgment. Spacing between clusters was determined by measuring the distance between the midpoints of adjacent clusters of clasts. Five of the 26 clusters identified are within till of the Tiskilwa Formation, whereas the remaining clusters are along the contact with the overlying Batestown Member till. Euclidean distances of clast spacing, length of cluster, and distance between clusters are shown in Table 4. More than one-half the clasts (33 of 59) were separated from adjacent clasts by less than 3 ft (1 m), and only 5 of 59 clasts were more than 10 ft (3 m) apart.

The linear extent of clusters was quite variable, with lengths ranging from 6.5 to 82 ft (2 to 25 m). Approximately one-half of the clast clusters were less than 6.56 ft (2 m) in length, and fewer than 25% extended over a distance of 16.4 ft (5 m). Approximately one-half of the clusters were less than 33 ft (10 m) apart. For one measurement at the Porter Cemetery site, a distance of 167.7 ft (51.1 m) for cluster spacing was obtained and subsequently considered an outlier because the measurement was made from two stereomodels from 2010 that were not connected. Consequently, this measurement was omitted from further calculations. CONCLUSIONS This study affirms the utility of close-range photogrammetry as an aid to geologic mapping to measure features in three dimensions that are not easily accessible in the field (e.g., clast pavements).

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Stohr, Stumpf, and Stiff Table 3. Dimensions and statistics of buried channels exposed at three sites along the Middle Fork of the Vermilion River.

Location Blue Hole, North Blue Hole, South Higginsville Porter Cemetery Mean Standard error Median Standard deviation Range

Width, ft

Channel Width as % Length of Outcrop

Area, sq ft

Max. Thickness, ft

Perimeter, ft

Aspect Ratio (Width:Thickness)

Area/ Perimeter

75.24 67.28 77.20 116.80 84.13 11.10 76.22 22.20 49.52

32 30 55 43 40 5.76 37.5 11.52 25

526.46 327.41 212.35 1199.71 566.48 220.82 426.94 441.64 987.37

14.02 10.18 5.10 13.94 10.81 2.10 12.06 4.21 8.92

165.97 144.08 165.86 282.20 189.53 31.32 165.91 62.64 138.13

5.37 6.61 15.13 8.38 8.87 2.18 7.49 4.35 9.77

3.17 2.27 1.28 4.25 2.74 0.63 2.72 1.27 2.97

The method is also useful for describing the geometry of sand and gravel aquifers to improve the accuracy of hydrogeologic models. Indirect observations and measurements indicated that clasts in till can form irregularly spaced clusters, creating a semi-continuous clast pavement along the contact of stratigraphic units or as part of intra-unit features. The spacing of these clast clusters may be an important element contributing to understanding of the depositional processes active in their formation at the base of a warm, debris-rich glacier. The geometric parameters of glacial meltwater channel deposits, including measurements of width, sediment thickness, perimeter, and area, were used to calculate the area-to-perimeter and aspect ratios. These parameters provide new information for and

characterize the origin of the buried channels which, in some parts of east-central Illinois, are the sole source of groundwater. In this case, the aspect ratio measurements are consistent with a distributary system of interconnected channels that might form along a retreating ice margin. This result is anecdotally supported by the unexpectedly large area over which the channel deposits outcrop. Shapes of the buried channels can be characterized by their area-to-perimeter ratio. Channels having larger values would be expected to yield larger amounts of water than features having smaller values. This is notable in an area where there is a large variability in the pumping rate from glacial aquifers. Based upon outcrop measurements and inferences drawn from the derived geometric parameters, it is

Figure 9. Dimensions of four channels exposed at the three sites along the Middle Fork of the Vermilion River.

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Outcrop Measurements for Geo-Engineering Table 4. Summary of clast and clast cluster measurements for pavements at three sites along the Middle Fork of the Vermilion River.

Mean Standard Error Median Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Count

Clast Spacing

Cluster Length, ft

Cluster Spacing, ft w/o exception

4.12 0.48 2.87 3.68 13.51 2.13 1.50 16.49 0.54 17.03 59

10.14 1.86 7.14 9.50 90.26 0.63 1.25 31.79 1.05 32.84 26

40.22 5.97 29.28 25.34 642.14 21.54 0.35 76.34 6.93 83.27 18

our opinion that low-yielding wells could be improved by drilling horizontal collector wells. Extending the screened length though lateral drilling within a channel or across multiple channels would increase hydraulic connections and yield and capacity would be improved. ACKNOWLEDGMENTS Funding for this study was provided by the Illinois State Geological Survey and the Illinois American Water Company (Champaign, IL). We thank William (Bill) Dey for assistance in searching for outcrops along the Middle Fork of the Vermilion River and setting global positioning system (GPS) ground control surveys. Undergraduate research assistants Steve Picek and Mary Elizabeth Warner, University of Illinois at Urbana-Champaign and graduate student Lisa Atkinson, University of Waterloo, provided logistical support in field surveying and in gathering stereophotography and terrestrial laser scanning data. The Leica 399 GPS receivers and Optech ILRIS 3-D terrestrial laser scanner were acquired on loan, courtesy of the Aerial Surveys Section of the Illinois Department of Transportation (Springfield, IL), to conduct the surveying and laser scanning. Professor James Best, Department of Geology at the University of Illinois at UrbanaChampaign, loaned the Leica reflectorless total station used to undertake the surveying. Dr. Andrew Phillips (ISGS) provided consultation on stream channel morphology and hydraulic flow. Donald Keefer reviewed and Susan Krusemark (ISGS) edited the manuscript. John Hott, superintendent at the Middle Fork Fish and Wildlife Area, arranged for access to the outcrops. Sirovision software was provided by Terra Source (Reno, NV) and CAE Mining North America (Littleton, CO), including

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