CALIFORNIA STATE UNIVERSITY, NORTHRIDGE Post-Fire

CALIFORNIA STATE UNIVERSITY, NORTHRIDGE

Post-Fire Hillslope Erosion using an Unmanned Aerial Vehicle (UAV) with On-Ground Sediment Analysis

A Thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Geographic Information Science

Nicholas Hager

May 2018

The thesis of Nicholas Hager is approved by:

______Dr. Soheil Boroushaki Date

______Dr. Mario Giraldo Date

______Dr. Amalie Orme, Chair Date

California State University, Northridge

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Acknowledgements

This thesis would not have been possible without the help of the following people:

Dr. Amalie Jo Orme, thesis chair, for teaching field methods, editing, and providing insight and instruction.

Dr. Soheil Boroushaki, committee member, for reviewing, commenting and for providing guidance through the department’s master’s program.

Dr. Mario Giraldo, committee member, for reviewing and commenting.

Dr. Julie Laity, committee member, for countless hours of proof reading, editing, phone calls, and discussion on the topic.

The students in Dr. Orme's Methods in Geomorphology class for their contributions in the field and the laboratory.

Kelsha Anderson, USDA Forest Service, Angeles National Forest for making the appropriate connections and granting permits and access to the study site, supplying vegetation data, and providing insight.

Marianne Jara who provided unlimited support and who accompanied me on many outings to collect data and share in adventures.

Table of Contents

Copyright ...... ii Signature Page ...... iii Acknowledgements ...... iv List of Tables………………………………………………………………………………………………………………………………..……vii List of Figures ...... viii Abstract ...... ix 1.0 Introduction and Objectives of the Study ...... 1 2.0 Scientific Background ...... 4 2.1 Introduction ...... 4 Chaparral and Fire ...... 7 Rain Splash, Sheet Erosion, and Dry Ravel ...... 10 Unmanned Aerial Vehicles (UAVs) Studies in Geomorphic Change ...... 12 Minimizing Error in Photogrammetry...... 15 3.0 Description of Study Area ...... 17 Introduction ...... 17 Climate: Precipitation, Wind, and Insolation ...... 19 3.2.3 Solar Insolation and Aspect ...... 20 Hydrology...... 24 Slope ...... 25 Vegetation ...... 25 4.0 Methods...... 27 Introduction ...... 27 Field and Laboratory Techniques ...... 30 4.2.1 Plot Installation ...... 30 4.2.2 Maintenance and Measurement of Sediment Traps ...... 33 Laboratory Techniques ...... 34 4.3.1 Water Content Determination ...... 34 4.3.2 Particle Size Distribution ...... 35 Unmanned Aerial Vehicle Techniques ...... 35 Photogrammetry Techniques ...... 37 4.5.1 Introduction ...... 37 4.5.2 Photogrammetry Process ...... 37 4.5.3 Photogrammetry Parameters ...... 38 Geographic Information Systems ...... 40 4.6.1 Maps, Data Collection and Organization ...... 40 4.6.2 Model Analysis ...... 40 5.0 Results ...... 45 Introduction ...... 45 Sediment Deposition with Respect to Precipitation, Vegetation Regrowth, and Slope ...... 45 Particle Size Distribution of Sediment Deposits ...... 51 5.3.1 Sorting ...... 51 5.3.2 Skewness ...... 52 5.3.3 Graphic Mean ...... 53 UAV Volumetric Displacement ...... 56 v

6.0 Discussion ...... 59 Introduction ...... 59 The Role of Moisture and Vegetation ...... 61 6.2.1 Moisture Retention ...... 61 6.2.2 Vegetation ...... 62 UAV Methods and Volumetric Displacement ...... 64 6.3.1 UAV Methods ...... 64 6.3.2 Photogrammetry Parameters ...... 66 6.3.3 Volumetric Displacement ...... 67 7.0 Conclusion ...... 72 Bibliography ...... 73 Appendix A ...... 80 Appendix B ...... 81 Appendix C ...... 84 Appendix D ...... 105 Appendix E ...... 106 Appendix F...... 106

List of Tables

Table 4-1: Plot Dimensions ...... 31 Table 4-2: Silt Fence Capacities ...... 32 Table 5-1: Percent Moisture per Plot ...... 46 Table 5-2: Erosion Weight per Plot ...... 46 Table 5-3: Precipitation Events of Study Period...... 47 Table 5-4: Sediment Yeild with Respect to Vegetation Growth ...... 50 Table 5-5: Sediment Yield with Respect to Slope Angle Classes ...... 49 Table 5-6: Sorting Classes using Phi Scale...... 51

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List of Figures

Figure 2-1 Statewide Average Temperature Increase...... 4 Figure 2-2: Charred Remnants of Chaparral ...... 9 Figure 2-3 Rills and Gully of Maple Canyon ...... 11 Figure 2-4: Aerial Survey Target ...... 13 Figure 3-1: Location of the Study Site ...... 18 Figure 3-2: Aspect Map ...... 21 Figure 3-3: Hydrolic/ Geologic Map ...... 23 Figure 3-4: San Gabriel Gravelly Sand ...... 24 Figure 3-5: Slope percentage in study area ...... 25 Figure 3-6: Wild Cucumber ...... 26 Figure 3-7: Scrub Oak ...... 26 Figure 4-1: Slope Map ...... 28 Figure 4-2: Erosion Plot Set Up ...... 33 Figure 4-3: Erosion Plot Installation ...... 34 Figure 4-4: Photograph Coverage ...... 36 Figure 4-5:Total Ground Control Error (Units are in centimeters) ...... 38 Figure 4-6: DEM Dataset without Trees ...... 42 Figure 5-1: Percent Moisture per Plot with Precipitation ...... 46 Figure 5-2: Sediment Distribution ...... 53 Figure 5-3: Graphic Mean ...... 54 Figure 5-4: Particle Size Distribution by Plot Number ...... 55 Figure 5-5: Particle Size Distribution by Date...... 55 Figure 5-6: Erosion Map ...... 57 Figure 6-1: Ash Layer ...... 60 Figure 6-2: Hydrophobic Layer ...... 60 Figure 6-3: Erosion Pedestals ...... 63 Figure 6-4: Sheetwash Erosion ...... 63 Figure 6-5: Small Talus Cone ...... 64 Figure 6-6: Aerial Vegetation Growth Comparison ...... 69 Figure 6-7: Local Scale Vegetation Growth Comparison ...... 70

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Abstract

Post-Fire Hillslope Erosion using an Unmanned Aerial Vehicle (UAV) with On-Ground Sediment Analysis

By Nicholas Hager

Master of Science in Geographic Information Science

Hillslopes are susceptible to accelerated erosion following fire. Vegetation loss encourages slope destabilization during and following rain events, with resultant soil erosion. Between July 22, 2016 and August 9, 2016, the Sand Fire burned nearly 168 km2

(64 mi2) southeast of Santa Clarita increasing soil erosion and deposition in the nearby

Pacoima Reservoir. This thesis focused on erosion within Maple Canyon, part of the larger Pacoima watershed that ultimately drains into the Pacoima reservoir. Data collection used a combination of sediment traps to monitor changing soil properties and aerial imaging techniques to measure overall hillslope sediment loss. Sediment deposition was observed using fifteen sediment traps set across the project site. The resultant data were used to explore sediment erosion rates and patterns on individual hillslopes across various slope angles and aspects. Sediment samples were collected at each of the fifteen soil traps in the study period of four months and analyzed for moisture and particle attributes including sorting, skewness, and graphic mean. Lastly, topographic change was monitored and used as a surrogate for calculating the volume of eroded sediment on the hillsides of Maple Canyon using a technique called Structure from Motion (SfM), to

ix create 3-dimensional models from an Unmanned Aerial Vehicle (UAV). There was a substantial increase in sediment erosion on hillslopes greater than 30 o and, as expected rates of soil loss were much higher before vegetation regeneration. It was documented also, that owing to the high temperature of the fire, the voluminous production of ash and development of a hydrophobic layer decreased the infiltration capacity of the weathered bedrock, thus encouraging accelerated surface runoff. It was calculated that 26,472 cubic meters (440,444.5 ft3) of sediment loss occurred within Maple Canyon between the dates of December 13, 2016 and April 6th, 2017. The research documents erosion rates and processes shortly after a high intensity chaparral fire using traditional sediment capture techniques. When coupled with the use of a UAV platform together with SfM and GIS, topographic change and the calculation of erosion from post-fire hillslopes proves valuable to help predict catchment and reservoir capacities.

1 Introduction and Objectives of the Study

From July 22nd to August 3rd, 2016, the Sand Fire burned nearly 168 km2 (65 mi2) of the

San Gabriel Mountains in the Angeles National Forest, including 75% of Pacoima

Creek’s 75 km2 (29 mi2) watershed. Previously, the Station Fire (2009) and Sayre Fire

(2008) also seared portions of the watershed. The fire ignited outside the city of Santa

Clarita at the intersection of Soledad Canyon and Sand Canyon Road on July 22nd, 2016 and, over the course of 13 days, spread to the western edge of the Angeles National

Forest. At the time of the fire, which began in the afternoon, temperatures were 43°C

(110°F) and winds were 24 km/h (15 mph), with gusts up to 40 km/h (25 mph). The loss of vegetation and the development of hydrophobic soils exposed hillslopes to storm events ultimately, reducing foliage interception of raindrop impact, decreasing infiltration, increasing surface runoff and, as a result, increasing hillslope instability

(Chuvieco 2005, Cerdá 2008, Ferreira 2008). The eroded sediment was transported downslope and deposited into watershed channels that subsequently became choked with debris that was unable to be transported by subsequent rainfall events.

During high runoff events, large amounts of debris from Pacoima Creek and other neighboring small watersheds drained into Pacoima Reservoir—a 4,658,861 m3 (3,777- acre foot) water resource for the City of Los Angeles. In 2017, the Los Angeles County

Flood Control District (LCFCD), working with the Angeles National Forest (ANF), removed excess sediment from Pacoima Reservoir to increase storage capacity and reduce the risk of dam failure and overtopping of water sediment. The efforts to keep pace with reservoir sedimentation have been ongoing following fire and high rainfall events. One of the challenges agencies face is to plan for the rate and volume of post-fire sediment transport and reservoir sedimentation. It is difficult to estimate how much debris could potentially be eroded from burned hillslopes. To address this challenge, this study addresses the nature of sediment yield from a portion of Pacoima Creek’s watershed using high-resolution aerial imagery obtained from an Unmanned Aerial

Vehicle (UAV) to calculate hillslope topographic change as a surrogate for sediment volume yield. Supporting the UAV imagery, on-the-ground sediment traps were installed on hillslopes in the same part of Pacoima Creek’s watershed from where the imagery was obtained. The sediment caught in the traps was weighed and the volume was calculated to estimate actual yield. These results were compared to those from the imaged hillslopes.

It was recognized that post-fire hillslope erosion and sediment deposition into channels and, ultimately, in this case, the Pacoima reservoir, could vary with (1) the timing and amount of rainfall on exposed hillslopes, (2) wind events that may amplify dry ravel or gravity-impelled sediment movement down hillslopes, (3) the intensity of the fire with regard to the development of a water-resistant (hydrophobic) topsoil that enhances surface runoff, and (4) the survival of root systems on burned hillslopes that may provide some subsurface stability and support re-growth of vegetation. To investigate the relationship between these variables and sediment yield from a sub-basin within Pacoima watershed, this study identified three objectives:

 Determination of soil moisture after precipitation events, and weight of sediment

erosion and deposition in 15 erosion traps in the four month study period.

 Determine sediment yield on hillslopes with respect to slope steepness, aspect,

and vegetation re-growth.

 Determine the density and distribution of hillslope rill and gully development and

topographic change as surrogates for sediment yield

An additional goal of the project was to assess the suitability and effectiveness of

Unmanned Aerial Vehicle (UAV) repeat imagery to determine the nature and volume of soil erosion.

To accomplish these objectives, three small basins within the Sand Fire perimeter were selected for study based on the presence of burned hillslopes with varying steepness and aspect. These three sub-basins were located in the upper reaches of Maple Canyon and drain directly into the larger Pacoima Creek that flows immediately into the Pacoima

Reservoir. The study began on November 23rd, 2016 123 days after the fire started. Data collection began on December 5th, 2016, 135 days after the fire began. Data was collected until April 15th, 2017, 266 days after the fire start. The project lasted for a little over four months during the 2016 – 2017 winter.

2 Scientific Background

2.1 Introduction

Southern California climate is characterized by mild winters, hot summers, and seasonal rainfall, usually during the winter months. Over the last several decades, the frequency of wildfires in Southern California forests have nearly doubled owing to several factors that include a regional temperature increase of ~1°C (1.9° F) since the beginning of the twentieth century and a fire season that has expanded two months longer over the past two decades (Rulli 2007, Sawyer and Safford 2014, Goodman 2017). In general,

Southern California has seen an increase in temperature, causing local mountains to have a decrease in snow pack but a slight increase in annual precipitation (Rulli 2007, Sawyer and Safford 2014). This interannual variability has important implications for fire management because vegetation regimes such as forests and other vegetation dominated by woody plants, tend to migrate to higher elevations where cooler temperatures are more

Figure 2-1 Statewide average temperature increase since the close of the 19th century to present time.

4 suitable while woody vegetation in lower elevations become drier and more susceptible to fire. Average annual temperature range in the study area is 10oC to 33oC (50oF to

91oF) (NOAA 2018). However, average temperatures in southern California, like those statewide, demonstrate a trend towards warming (Figure 2-1).

The significance of this trend may factor into increased frequency of wildfire in the region. Rulli (2007) suggests that this phenomenon may be related, in part, to a regional temperature increase of 1.9° F (~1°C) which could, in effect, extend the “dry” season thereby setting the stage for more frequent fires. The frequency of fire in the Southern

California chaparral environment at low- to mid-elevations has increased over the last few decades (Riera 2007, Sawyer and Safford 2014). In 2016, 5,762 fires were reported and over 59,639 ha (147,373 acres) were burned (Cal Fire 2017).

While there may be precipitation during the October – December period, the bulk of the rainfall occurs between January – March. While average annual rainfall in the study area is 355-406 mm (~14- 16 in.), individual storms may deliver up to 25 mm (1 in.) or more per hour. High intensity events like this amplify the effect of precipitation on steep, recently denuded hillslopes. By contrast, there may be periods of six or more months without rainfall and short-term droughts of five to ten years are not uncommon (Rundel

2005).

Southern California has one of the most variable precipitation regimes of all the

Mediterranean climate zones and also one of the most severe summer droughts (Cowling et al. 2005). A 20-year study shows an increased interseasonal variation of precipitation supporting PRISM results that overall precipitation, streamflow and snowpack have declined for watersheds that feed into the Los Angeles basin (Sawyer and Safford 2014).

On hillslopes denuded by fire, critical root systems that normally limit soil erosion are lost, increasing the production of ravel—the downslope movement of particles under gravity. Particle sizes may range from fines (clays and silts) to coarser fractions (gravel, cobbles, and boulders). Ravel may contribute significantly to erosion of the ground surface with unvegetated hillslopes (Krammes 1963, Dunne 1996, Lamb 2011). In general, erosion is accelerated where there is an absence of biomass, especially in a chaparral environment, where reduced duff and litter can play a large role in slope stabilization (Moody and Martin 2001, Meyer 2002, Cannon and Gartner 2005). For instance, when foliage is removed from a hillslope, branches and leaves are not able to slow the impact of raindrops thereby limiting drop dispersal—a critical process for percolation of moisture into the soil and substrate. In fact, the removal of a canopy alone can increase precipitation reaching the ground by ~40% (Dunne and Leopold 1978).

Furthermore, when fire is the primary cause of vegetation loss, ash can work its way into voids in the sediment, diminishing the surface permeability. These two processes of removing vegetation and decreasing soil permeability can contribute to increased surface runoff. Increased runoff may encourage accelerated sheet flow, shallow channel flow, and, ultimately, disruption in water storage and channel habitat. Large fires and

6 subsequent sheet erosion events occur every year in the foothills of the steep San Gabriel

Mountains and, in the process, fill sediment basins and reservoirs costing the surrounding municipalities and federal government millions of dollars annually each winter.

The methods used to address the objectives of this research had to take into account several factors to be effective measures and predictors of erosion rates. When using

UAV-based and ground data, it is important to understand not only the amount of erosion that occurred at the study site but why and how erosion differs from one location to another. Environmental factors such as rain intensity, temperature, fire intensity, slope and vegetation regrowth all contribute to describing local scale deposition patterns and sediment particle distribution.

2.2 Chaparral and Fire

The characteristic vegetation association of the foothills of Southern California and the

Transverse Ranges is chaparral. On slopes, chaparral can grow to be nearly impenetrable thickets of small (ground cover and plants < 1m in height) to large shrubs (> 1 m) and arboreal species (>2 m in height). The study site is covered by lower montane chaparral.

Lower montane chaparral is found below 1,525 m (5,000 ft) elevation, on much drier, shallower soil where woodland environments cannot survive. The root systems are adapted to drought conditions and typically have a combination of a deep taproot with many, smaller, shallow roots that spread across the topsoil for nutrients (Moreno 1994,

Rundel 2005, NPS 2017).

Chaparral can become very dry between rainfall events. The woody structure and waxy cuticles of leaves, and the presence of volatile oils in the leaves and leaf litter help wildfires to spread very quickly. Though the understory of such herbaceous perennials in a chaparral environment may burn quickly, it can contribute to fires that have a high rate of spread. This is especially true if crown fires develop, thereby igniting neighboring plants and trees and encouraging the dispersal of burning embers along air currents developed in a fire. With temperatures up to 648°C (1,200° F), these events are some of the hottest fires of any natural environment in the world (Green 1970, DeBano 1979,

Rundel 2005). Further enhancing the intensity of the fire environment is the history of conflagrations that create a mosaic of different plant species grouping together. Owing to this pattern, chaparral fires vary in intensity over time or geographic area (DeBano 1979,

Rundel 2005). For whatever the intensity of the fire, these events leave in their wake a landscape characterized by the charred remnants of shrubs and trees, and a layer of ash

(Figure 2-2). While much research has been conducted on the role of ash and its contribution to water repellant layers (hydrophobic soil), it is generally assumed that hydrophobicity can decrease initial infiltration rates of rainfall and thereby increase sheet erosion immediately following fire (DeBano 1979).

Figure 2-2: Charred remnants of chaparral and the ash layer, plot #10, December 4th, 2016.

Fire plays a fundamental role in maintaining a healthy chaparral ecosystem and it is recognized that vegetation and geomorphic dynamics significantly and reciprocally affect each other (Rundel 2005, Collins 2004, Cerdá 2008). In erosional studies, vegetation is known to offer rainfall interception, which reduces impact of water velocity on soil (rain splash) and allows for infiltration (Woo et al. 1997; Wainwright et al. 2000; Collins

2004). Litter and duff absorb and store water, heightening a drainage’s hydraulic capacity and reducing the probability of surface runoff generation and erosion. (Collins 2004,

Pannkuk 2003). Through the root zone, vegetation offers cohesion properties helping to keep erosion at a minimum. The root zone also helps to lower soil moisture through the process of transpiration (Viles 1990, Collins 2004) lightening the weight of soil and effects of gravity towards mass wasting.

2.3 Rain Splash, Sheet Erosion, and Dry Ravel

Rain splash takes place when rain drops dislodge particles from the ground, weakening the structure of topsoil. Evidence of this particular activity of erosion would be in the formation of small erodible pedestals capped by pebbles or twigs (Reid 1996, Rulli

2007). This is because the larger pebble or twig acts as a shield against the force of the raindrop while the finer soil beneath it erodes away, creating a pedestal. The effect of rain splash may vary depending on the amount of rainfall interception. Effects that rain splash has on hydrophobic soils have been studied by Terry and Shakesby (1993) who used high speed photography to document the process raindrops had on erosion. Their study suggests that when rain drops land on bare sandy loam soil surfaces, particle detachment is more prevalent on hydrophobic soil as opposed to wettable, hydrophilic soil. Terry and

Shakesby discussed that the median velocity and distance travelled by particles were much higher in hydrophobic soils than hydrophilic resulting in a higher net erosion rate.

Similar results have been found in studies by Meyer (1980) and Ahn et al. (2012). The effect of rain splash was likely to dislocate soil particles at much higher rates on the burned hillslopes of Maple Canyon because of the presence of hydrophobic layer formed after the Sand Fire.

Sheet erosion or overland flow is the movement of a thin layer of sediment, soil, and water that occurs in response to either ground saturation (field capacity) or immediate runoff from a hydrophobic surface. According to Reid and Dunne (1996), overland flow is common on compacted surfaces such as roads, trails, and on surfaces with low

10 infiltration rates such post-fire hillslopes. Overland flow can be measured by velocity meters distributed over hillslopes and adjacent areas. As overland flow intensifies, rills and gullies may develop and provide conduits for sediment and water transport (Figure 2-

3).

Figure 2-3 Shallow rills and a larger gully in Maple Canyon

Dunne et al. (1981) has suggested an ordinal classification to identify the levels of sheetwash erosion and the point of which rills and gullies develop. In his classification, 0

= no sign of sheetwash and 6 = fully developed rills and gullies. Dunne’s classification of

5 (hillslope dissected by rills and small gullies with definite margins and an intervening surface that is fairly smooth and gently sloping but shows general signs of intense wash) and 6 (surface is intricately dissected by gullies) describe well the post-fire hillslopes of the study site.

Also contributing to post-fire hillslope erosion is the downslope movement of dry sediment or ravel. Dry ravel is the process of loose sediment rolling, bouncing and sliding until depositing upon an angle less that the angle of repose (or the internal friction angle of sediment) (Lamb et al.). Dry ravel will often form depositional cones at the bottom of hillslopes. Lamb et al. 2011 suggest that this process occurs prior to wetting and following the drying out of the hillslope. Studies done on ravel erosion and sediment accumulation in the San Gabriel Mountains show that following the first year after a fire, ravel can contribute 8mm/yr, (Lamb 2011). Bennett (1982) described 95% of net sediment accumulation within San Gabriel catchments over a 2-year period was collected within the first 8 months following a fire and can mostly be attributed to dry ravel.

According to Lamb (2011), vegetation acts like a dam and sequesters sediment on hillslopes. After fire, the loss of plant cover on slopes exceeding the ~30o allows ravel to become an important source of sediment in channels.

2.4 Unmanned Aerial Vehicles (UAVs) Studies in Geomorphic Change

Digital terrain modeling (DTM) has emerged in the last decade as a way to visualize geomorphic change in river drainages, sediment transfer, erosion, and sediment budgets

(Forkuo 2008). DTM is one of the process products from the acquisition of data from

Unmanned Aerial Vehicles (UAVs) and has demonstrated its utility in the analysis of geomorphic change. Using Structure from Motion (SfM), a process that creates digital elevation models (DEMs), data can be used in Geographic Information systems (GIS) to display the spatial distribution of key data. This technique is quick, inexpensive, and helps create accurate three-dimensional measurements.

The acquisition of accurate elevation values as a means of quantifying hillslope erosion rates is essential to a successful DTM and usually consists of a blend of ground surveys and airborne photogrammetry. Data from ground surveys are usually collected with GPS or land surveying equipment such as a Total Station. Imagery is collected by fixed wing or quad-copter style UAV platforms and can offer sub-centimeter accuracy. SfM data are dependent on a well-distributed pattern of ground control points that have accurate elevation and location information.

Figure 2-4: Aerial survey target (courtesy Wheaton, 2013)

SfM requires the researcher to paint or lay down several targets across the study site. For example, these targets can be 0.5 m2 (~18 in2.) piece of rubber pond liner painted to be recognizable from the air (Figure 2-4). These targets are used in the imagery processing stage to “stitch” the photos to a coordinate system, allowing spatial data to be created for the DEM model.

A case study in the proglacial zones of Skalafellsjokull, Iceland provides insight into typical survey methods for SfM modeling. In order to identify and measure push moraines at the toe of Skalafellsjokull Glacier, Hackey and Clayton (2015) laid out 15 targets across the study site and used a Leica GPS unit that was deployed in real time kinematic mode (RTK) to produce highly accurate coordinates of the center of the targets. The coordinates were able to achieve ±0.01-meter and ±0.05-meter accuracy in the horizontal and vertical axis respectively. After coordinates were taken from the ground targets, the UAV was deployed and imagery collected.

Despite the advantages of using this method, SfM is subject to many errors. One very common error is known as the “doming” effect, that is, when the DEMs seem to fold up or down in elevation at the edges. One possible cause for this effect is in the camera calibration and can be minimized by including slightly oblique images, up to 20 degrees from the horizontal. This will offset the “doming” effect found on many SfM-based

DEMs. Images must have a significant overlap for the model to create an accurate mosaic of the environment. James (2014) suggests that including oblique images and an overlap of 60% or more will suffice for the most accurate mosaics.

The flight altitude of imagery collection is also important. Associated with James’s study,

Henri Eisenbeiß (2009) and Siebert (2012) suggest that models created at higher altitudes are useful for collecting data for larger study areas but can also produce elevation error in

DEMs thereby diminishing the accuracy of the ground sampling point (target). In other words, the higher altitude the UAV is collecting data, the pixel resolution deteriorates. 14

2.5 Minimizing Error in Photogrammetry

When developing DEMs derived from SfM there will be error associated with point clouds—data points “in space” produced by the scanning function of the UAV. There are certain parameters to follow when minimizing error in a point cloud environment.

AgiSoft Photoscan is a software that develops 3D models using 2D photographs by mosaicking the images together. To expand on this process, it is important to understand how elevation data are derived. When the UAV is taking photos in the air, the on-board

GPS unit is attaching altitude data to the properties of each photograph. The software then uses the focal length of the camera and determines the ground elevation of the photo.

Using elevational data collected by the UAV and the focal length of the camera, several hundred points are portrayed in the form of a point cloud. From this point cloud,

Photoscan suggests adjusting geometry enhancing parameters to statistically minimize error. The smaller the error, the more reliable the model. The range of the parameters to adjust differ slightly from research done by Mallison (2017) and the USGS (2017). The parameters that are primary factors of error in a point cloud are: reprojection error, reconstruction uncertainty, image count, and projection accuracy.

1.) Reprojection error refers to localization of a point within the point cloud. When the images are mosaicked together, point associated with the photos are sometimes placed in the wrong location. This causes false locations when creating a mesh. Mallison (2017) suggests having a reprojection error range of 1 while the USGS article (2017) target of

15 below 10% of all points within the point cloud, allowing for a larger localization error ratio.

2.) The parameter range for reconstruction uncertainty also differs. Reconstruction uncertainty refers to the accuracy of photo placement in the first step of mosaicking photos taken from the UAV. The USGS (2017) agrees with Mallison (2017), that one should strive for 10 (a unitless measurement within the AgiSoft software) but highly encourages the reconstruction uncertainty to be less than 50 (a unitless measurement).

Suggestions done by both researchers advise to “optimize” twice. Each optimization will recreate the point cloud and reset the reconstruction uncertainty values.

3.) Image count is the average amount of image of overlap within the study area. Neither article refers to image count, but it is generally understood that “two photos are likely to be located with poor accuracy” (AgiSoft Photoscan, 2016) and three or more photos are desirable.

4.) Projection accuracy refers to errors associated with scale of the photo. For instance, when a photo is taken from a higher altitude above ground level, it will have more accuracy errors than a photo taken closer to the object in question. Mallison (2017) suggests an error of 7 to 10, while the USGS study urges a more fitted accuracy of 2 to 3.

The USGS also indicates that a one-time optimization should suffice.

3 Description of Study Area

3.1 Introduction

The study site, Maple Canyon (0.14 km2 /0.054 mi2), is located in the western San

Gabriel Mountains in the Angeles National Forest (Figure 3-1). Water and sediment from

Maple Canyon drain into Pacoima Creek 1,500 m (4921 ft) upstream from Pacoima

Reservoir. The main channel of Maple Canyon is intermittent while the smaller tributaries are ephemeral. Topographically, the watershed is bounded by steep hillslopes.

Maple Canyon is oriented northeast and has an elevation range from 600 m (1,800 ft) to

1,080 m (3,240 ft), with moderate (20°-39°) to steep (40°-90°) slopes.

Figure 3-1: Location of the study site, extent of the Sand Fire, and the Pacoima Watershed within Los Angeles County, California.

3.2 Climate: Precipitation, Wind, and Insolation

3.2.1 Precipitation

The climate of southern California is characterized by cool, moist winters, and warm, dry summers. Average annual rainfall based on long-term records (1890 – present) near the study area is 35-41 cm (~14- 16 in), with the heaviest precipitation occurring January –

March (NOAA 2018). Individual storms may deliver up to 2.5 cm (1 in.) or more per hour. There may be years with rainfall > 60-80 cm (1969) and years with rainfall < 15 cm (2015). Additionally, there may be periods of six or more months without rainfall and short-term drought (five to ten years) (Rundel 2005). Snow at the study site is rare, but the soil may occasionally freeze.

From November 2016 to May 2017, the study site received 63.5 cm (25 in.) of precipitation (California Department of Water Resources, Station CP9). In December

2016, 6.3 cm (2.47 in.) of rain fell during the highest intensity rain event: 0.9 cm/hr (0.35 in./hr.) for the period. This occurred 136 days after the fire and produced significant erosion of sediment. Appendix D and E summarize precipitation and wind events in detail for the study period.

3.2.2 Wind

Wind is a major contributor to erosion rates after a fire (Ravi 2007; Sankey 2009). Even months after the Sand Fire, wind caused particulates (PM 2.5) to be lifted into the atmosphere and transported as far east as Sparks NV 730 km (454 mi). However, no direct measurements of the effect of wind on erosion rates were recorded for the Maple

Canyon.

3.2.3 Solar Insolation and Aspect

Insolation factors into erosion owing to the degree of drying of soil. In the northern hemisphere, south-facing (adret) slopes receive higher insolation throughout the year than other aspects and are thus drier (Gordon and White, 1994). East and west-facing slopes tend to receive approximately the same amount of insolation, but west-facing slopes have higher evapotranspiration rates than east facing slopes. North-facing (ubac) slopes are more mesic as they receive the least amount of solar insolation.

The study site was divided into a range of aspects using quadrants representing north-

(315°-45°), east- (45°-135°), south- (135°-225°), and west- (225°-315°) facing hillsides, with north-facing slopes represented 30.93% of the study area, east-facing slopes

15.16%, south-facing slopes 2.2%, and west-facing slopes represented the remaining

30.49% of slopes. Plots were distributed between aspects to capture the characteristics of the study site.

Figure 3-2: Aspect Map

3.3 Geology

Structurally, Maple Canyon is bounded by the Buck Canyon and Lopez Canyon faults along its southern and western perimeters, and by the San Gabriel fault along the northern margin of the Pacoima Creek watershed (U.S. Geological Survey 2006). The study site is underlain dominantly by quartz monzonite-granodiorite with more limited areas of gneiss in the watershed. Located in a tectonically active area, with fault movement contributing to crushing and exposing bedrock, there is significant weathering and production of ample volumes of rock fragments leading to high sediment yield from the watershed

(Dibblee, 1991) (Figure 3.3).

Typically, the hillslopes of the study area are covered with colluvium (unconsolidated weathered bedrock), and shallow, poorly developed soil. Colluvium thickness ranges from 0-61 cm (0-24 in) and is composed of fine sand to pebble size materials and is classified as sandy gravel or gravelly sand (Table 5-5). Known as the San Gabriel

Gravelly Sand (Holmes 1902), this sediment is very permeable with little to no calcrete

(caliche) development. Quartz diorite, a gray, medium grained igneous rock characterized by plagioclase feldspar, biotite, and hornblende make up the ground surface (Figure 3.4).

Figure 3-3: Hydrolic/ Geologic Map

Maple Canyon study site and its proximity to Pacoima Reservior, fault zones, ephemeral streams and surface deposits.

Figure 3-4: San Gabriel Gravelly Sand Core sample at the study site with varying particle size distribution at approximately 30 cm (12 in.) in depth (yellow mechanical pencil length = 15 cm).

3.4 Hydrology

Maple Canyon is a 0.14 km² (0.054 mi²) sub-watershed that contributes to Pacoima

Creek. Channels in Maple Canyon are ephemeral to intermittent creeks. In the upper reaches, there are three smaller sub drainages (Figure 3-2) within the study site boundary.

At the lower elevations of the study are, the drainages are bounded by steep canyon walls. Snow occurring in the study site is rare, but the soil may occasionally freeze, as observed on January 26th 2017, when soil behind the silt fence of Plots 2, 6, and 12 was frozen two centimeters below ground surface.

3.5 Slope

Slope angle is a critical factor in understanding erosion rates. Over 60% of the hillslopes in the San Gabriel Mountains and in the study area in particular are >30o. According to

Lamb (2011), “…the greatest ravel fluxes are observed at bases of hillslopes that are steeper than ~30°, with an abrupt increase at about that angle.”

Mean Slope -26°SD - 15° Figure 3-5: Slope percentage in study area

3.6 Vegetation

Before the Sand Fire, Maple Canyon was clothed in lower montane chaparral, which included thick stands of Scrub Oak (Quercus berberidifolia) and California Lilac (genus:

Ceanothus). Oaks and Big Cone Douglas Fir (Pseudotsuga macrocarpa) grew in the steep drainages and canyons. Manzanita (Arctostaphylos manzanita), wild cucumber

(Marah gilensis), and annual grasses and forbs were found between stands of hardwoods and on exposed ridges as well as along the sides of Kagel Truck Trail road (U. S. Forest

Service 2011).

Figure 3-6: Wild Cucumber Figure 3-7: Scrub Oak Wild Cucumber (Marah gilensis) was Dispite the intense heat generated the first plant to regenerate after the by the Sand Fire, scrub oak fire between between March 17th and (Quercus berberidifolia) began to March 30th. regenerate at the base of chard tree trunks on February 16th, 2017.

4 Methods

4.1 Introduction

To calculate the volume of sediment yield as a measure of hillslope erosion, two methods were developed: (1) sediment traps to collect debris yield from selected hillslopes and (2) two UAV flights to collect ground elevation data change after precipitation events.

The determination of the sediment plot locations, was accomplished through the identification of suitable slopes for the installation of the plots. Before setting up erosion plots in the field, it was important to have a plan or general idea of plot locations as the plots should be set up at an equal distance from each other, so as to not over focus on one area. To do this, data, in the form of shapefiles, were created or collected to identify slope, aspect, geologic and soil data, hydrographic data, vegetation data, and boundary information. These shapefiles were overlain and used to create areas of high interest to set erosion plots. The high interest spots were downloaded into a Garmin GPS and brought to the field to help locate in the real world. Many of the shapefiles used were converted into feature classes in later analysis.

Using the slope tool in GIS, representative slopes were divided into groups and assigned as locations for plots on the ground. To distribute the plots evenly across slope angles, the study site was broken down into groups of 0 - 10°, 11 - 20°, 21 - 30°, 31 - 30, and <40° slopes. An equal number of plots were then distributed between slope values to capture the characteristics of the study site.

Figure 4-1: Slope Map

After the erosion plots were set in the field, a waypoint was created with a Trimble Geo

XH and returned to the laboratory where it was uploaded into the GIS. The shapefile associated with the erosion plots helped to organize data and contained attribute data such

28 as the name of the plot, final plot dimensions, slope, aspect, coordinates, elevation, vegetation type, and percent of vegetation coverage.

No sediment samples were collected in areas with slopes >40° because these areas were too steep to set sediment traps as well as being well over the angle of repose. However, owing to the nature of data capture with the UAV, slopes exceeding 40° were included in the final volumetric calculation in section 5.4. Slope values of individual plots can be found in Table 4-1, Plot Descriptions.

4.2 Unmanned Aerial Vehicle tasks

A Phantom 4 Pro equipped with a 4K RGB high definition camera was utilized to capture hillslope and channel changes during the study period. The UAV acquired images that were processed using Structure from Motion (SfM) to develop Digital Elevation Models

(DEMs) of the study site. Two DEMs were developed, one at the beginning of the study period (December 13th, 2016) and one at the close of the period (April 6th, 2017). The

DEMs used a Geographic Information System (GIS) to calculate differences in elevation values between the periods to give the overall volume of erosion within the project study site.

4.3 Field and Laboratory Techniques

4.3.1 Plot Installations

There were 15 plots set up in the form of erosion sediment traps (Table 4-1). The traps adhered to the USGS and USDA guidelines (Robichaud 2002), which required a trench to be dug at the top of the plot area to detour additional sediment from entering into the plot area (Figure 4-1). Plot installation took place between November 16th and December 5th,

2016. The average dimensions of the plots were 1.5 m (5 ft) by 4.6 m (15 ft) making the average erosion area 7 m² (75 ft²) (Appendix A). The slope of each plot determined the length of the sediment trap according to USDA testing for silt fence storage capacity configurations (Table 4-2).

Table 4.1: Plot Dimensions The table above gives the descriptions of fifteen plots placed throughout the study site. Vegetation percent cover is based off observations at the end of the study period.

Table 4-2: Silt Fence Capacities

Table 4-2 portrays standard dimensions for constructing sediment traps on various hillside slope angles. The standards were developed by the United State Department of Agriculture and United States Forest Service (Robichaud 2002).

At the bottom end of each plot, a silt fence measuring 1.5 m (5 ft) across was installed and held in place by wooden stakes placed at 0.3 m (1 ft) intervals. The silt fence was attached to the wooden stakes via wood staples. The silt fence was a 0.5 m (1.5 ft) tall and draped upslope another 0.5 meters, although some plots differed in this measurement due to varying terrain. The installation of silt fencing created a “pocket” for sediment to be trapped in. The upslope end of the silt fence was buried to help keep the silt fence from slipping down the hill slope (Figure 4-2). The plots were designed to “catch” surface soil erosion during rain events but still allow water to seep through to avoid

“overspill”. At the top of the plots, a 15 – 20 cm (6 – 8 in.) deep trench was dug to keep rolling rocks from entering the plot area (Figure 4-1). Plots 8, 9, 13, and 14 did not have a trench dug in the top of the plot. This was because these plots were close enough to a

32 ridge that the ridge prohibited rock and additional sediment from falling into the sediment trap.

Figure 4-2: Erosion Plot Set Up A trench at the top of the sediment trap helps prohibit upslope pebbles and cobbles from entering in to the sediment trap area (Robichaud 2002).

4.3.2 Maintenance and Measurement of Sediment Traps

Periodic cleanout of the erosion plots is essential for maintenance and measurement.

During field visits, traps were inspected for tears and holes. If a tear or hole was found, an appropriate length of silt fence and silicon caulking was used for patching.

Measurements required shoveling out the sediment collected by the sediment trap and placing it in a container for weighing. The container most frequently used was a five- gallon bucket. The weight of the bucket was subtracted from the sample weight prior to water content determination. After sediment was placed into the bucket, it was weighed using a hanging scale with a 45kg (100-lbs) capacity and a precision of 0.005kg (0.01 lb).

After weighing the sediment, samples were collected and placed into a sealable plastic bag to conduct water content determination in the laboratory. The rest of the sediment 33

was deposited downslope of the silt fencing. A brush was used to clean off any residual dust from the sediment trap.

Figure 4-3: Erosion Plot Installation The above figure shows the standardized methods of erosion plot installation approved by the United State Department of Agriculture and United States Forest Service (Robichaud 2002).

4.4 Laboratory Techniques

4.4.1 Water Content Determination

Using the soil samples collected from the field, the water content was determined and subtracted from the field measurements. The sample was weighed before drying. From the original soil sample, 30 grams of soil were selected and placed evenly onto a tin plate

34 and placed into the Arizona Instrument LLC, Computrac Max 4000XL, set to 90° C The analyzer recorded moisture lost within a time frame specific to how much water was present in the sample. Percent moisture and before and after weights were recorded. The percent water moisture content was then subtracted from its corresponding plot weight that was measured in the field.

4.4.2 Particle Size Distribution

To further explore soil characteristics, each soil sample was processed through a series of sieves. A mechanical sieve shaker was employed for a period of ten minutes, according to the American Standards for Testing Materials (ASTM). The soil remaining in each sieve after this time period was then weighed and recorded. The sieve sizes numbers used were as follows: 5/8, 1/3, 5, 10, 20, 40, 100, 200, Pan. Size units according to millimeter can be found in Appendix F. A graphical mean (frequency distribution), sorting (phi), and skewness were calculated for each soil sample (Appendix F).

4.5 Unmanned Aerial Vehicle Techniques

An Unmanned Aerial Vehicle (UAV) was used to calculate differences in ground elevation over the data collection period of the project. The GPS equipped UAV was flown over the study site and collected thousands of images. Imaging was taken in a grid- pattern following the slopes and elevation of the hillsides (Figure 4-3). The UAV was automated to take a photo every two seconds while in flight. For safety of any persons as well as the aircraft, a spotter accompanied the pilot to observe for low flying aircraft from

35 the County of Los Angeles Fire Station at Camp 9 and Angeles National Forest Fire

Station at Bear Divide.

Figure 4-4: Photograph Coverage

The legend above describes the amount of photographs taken at the study site. The of the dark blue represents nine or more photographs taken in a given area and red represents only one. The better the coverage the better quality of data is collected. The data is used to create a DEM. Flying in a grid-like pattern is optimal.

4.6 Photogrammetry Techniques

4.6.1 Introduction

Once the images were taken in the field, they were downloaded into a computer in the

Geomatics and Aerial Environmental Research Group Laboratory at California State

University Northridge. Using AgiSoft Photoscan software, images were mosaicked together into a single large image The UAV’s on-board GPS and in-field ground truthing techniques were utilized to create a digital elevation model (DEM). AgiSoft software was used for the photogrammetry process to produce three orthophotos and three digital elevation models.

4.6.2 Photogrammetry Process

In order for the final image to be of the highest quality, each photo was inspected to predetermined standards of clarity and angle. Photos that did not meet the predetermined standards were discarded for reasons of fuzziness, blurring, and the inclusion of any portion of the photo containing the sky or distant background. The photos were placed into AgiSoft and then, using the UAV’s GPS coordinates, aligned with each other. The coordinate system assigned to the model while it was being processed was the default unprojected geographic coordinate system WGS 1984. The dataset was later projected to

NAD83 UTM Zone 11N for analysis in ArcMap. After the photos georeferenced to their correct locations using an automated process, a dense point cloud was generated. Each model had between 700,000 to 1,000,000 points associated with its point cloud, data points “in space” produced by the software when triangulating elevation of objects in the

37 photograph. Several parameters (described below in 4.6.3) where used to “scrub” the dense point cloud. Afterwards, a mesh model was produced. The mesh proved useful for identifying large errors within the model and determining continuity in the workflow.

Finally, mosaics created from the “stitched” photographs taken by the UAV were created.

The DEMs were also created in this final step. The products from this process were three statistical reports (Appendix C), three orthophotos and three DEMs with varying XY and

Z values (Figure 4-4). The orthophotos and DEMs were exported in a NAD83 UTM

Zone 11N coordinate system for later analysis.

Figure 4-5:Total Ground Control Error (Units are in centimeters)

There were three models created, the model names are correlated to the date which the data was collected. Errors were calculated as Root Mean Square of all control points in the given model. Errors associated with individual control points are listed in Appendix C.

4.6.3 Photogrammetry Parameters

When the dense point cloud was first created there was error associated with renegade points or points that capture data that do not exist in the real world. This happens because the software does not know where to place a data point when the primary data

(photographs taken by the UAV) is either out of focus, was captured in bad lighting, there was an awkward photo angle, or if there was not enough overlap between photos. These renegade points will warp the model if not removed. Depending on the accuracy of the

38 control points and the removal of “dirty” photos, there were anywhere between 50,000 –

300,000 points that may be removed from a model’s dense point cloud. In order to keep from user bias, to minimize error, and maintain the model’s integrity, several statistical parameters were set when cleaning the dense point cloud, they are: reprojection error, reconstruction uncertainty, image count, and projection accuracy. All three models adhered to the same quality control standards.

1.) High reprojection error is usually cause for concern and was minimized by

adjusting the software’s gradual selection option to an error that is found 3 – 5 (a

unitless measurement).

2.) Reconstruction uncertainty can create noise within the model’s dense point cloud

and can ultimately create large error in the end. High reconstruction uncertainty is

typical for photographs and out of the four listed parameters adjusted for, this one

had to undergo the most adjustment and several sets of image optimization before

standards fell into the quality control standard setting 30 – 50 (a unitless

measurements).

3.) The setting for reconstruction error was set between image count correlates to

how much coverage any given area was subjected too. The photo count was very

good on all models and did not need to be adjusted for.

4.) Projection accuracy refers to a poor resolution of the photograph and its capability

of being able to produce accurate control points. These can be photos that were

taken from a high altitude or just very oblique angles. The setting used to adjust

for error was 7 – 10 (unitless measurements).

4.7 Geographic Information Systems

4.7.1 Maps, Data Collection and Organization

GIS was utilized to create both field maps and publishable maps. Field maps were a valuable resource when negotiating locations in the field site and for updating erosion plot areas and taking notes. Analysis for the DEM raster datasets were analyzed with Arc

GIS 10.3.

4.7.2 Model Analysis

Analysis in ArcMap was conducted with data collected from the UAV and created in

AgiSoft. After data processing was completed and a DEM was created, the elevation data were brought into ArcMap. The DEMs took the form of a raster dataset containing elevation data. Before starting the analysis, the values had to be prepped or “cleaned up”.

First, the three DEM datasets were resampled into a 14 cm (5.5 in.) cell size. The cells were also “snapped” together so the resampled cells align to the original cells. Finally, the DEMs were clipped to the same extent, a polygon feature class outlining the boundary of the study site.

In the analysis, the difference of the model’s elevation values were derived and a final volume of sediment transfer was calculated. Although, three DEM datasets were created, only two were used in the final volumetric calculation for this study. These were the first and last models created on December 13th, 2016 and April 6th, 2017 respectively. The

40 first step was to create a detection of difference raster dataset by using the minus tool in

Arc map. It should be noted that in the model, negative values are areas of growth

(deposition or growth of foliage) and positives values are areas of erosion. The raster output of the detection of difference tool was used as a visual aid when clipping out foliage data which potentially could cause extraneous results if not factored into the final calculation. The foliage, mainly trees between the heights of 3 - 9 m (10 – 29.7 ft), were clipped out of the model by using the raster calculator. Any values below 3 meters were set to null. This provided all values that needed to be identified as excess foliage. The raster calculator equation is shown below:

SetNull([Raster Layer]<=3, [Raster Layer])

To distinguish values of foliage, a method of checking the orthophoto against values greater than three meters was developed. A feature class was created to locate all trees within a drainage area. All values within the detection of difference raster dataset that were larger than three meters and resided within the drainage feature class was determined to be a tree. Raster calculator was used once again to null the located cells that contained trees. The equation used is shown below:

SetNull (~(IsNull( [Erase Raster Layer] )), [Raster Layer] )

This equation provided a raster dataset with values above 3 m that are not in the channel, in other words, all the values representing trees in the study site. This layer was clipped from the original resampled DEMs of each model. Finally, two DEM datasets without trees were produced and were used to calculate volume of erosion.

Figure 4-6: DEM Dataset without Trees

Areas that represent trees are represented as white blotches (no data). These areas usually resided near drainages and skewed erosion calculations. Trees within the study site that were 3m or taller were “clipped” from the model. This process was done to the two models that were used to calculate volume of erosion.

To calculate volumetric difference between the two DEMs, the Cut Fill tool was used in

ArcMap. This tool creates a polygonal feature class with segregated cut and fill values. In other words, one is able to easily see values that have gone down in elevation, rose up in elevation or elevation that has not changed. Once this feature class was created, a selection by attributes was used to select all values above or equal to zero. These are the values represent a rise in elevation. Field calculator was used to null all these values so that all that are left are values of erosion, or values with a drop in elevation. In the attribute table, a summary of the remaining values was used to view the amount of erosion in the study site between the winter months of December 13th, 2016 to April 6th,

2017. This provided a volume of sediment that would have been washed away during the winter’s storms.

4.8 Sediment Weight Calculation

To enable comparison of UAV volumetric measurement to the measurement of sediment traps placed on the ground, the density of San Gabriel Gravelly Sand must be identified.

The final step to the project will be to compare real data to the digital model created by the UAV and GIS workflow. To calculate sediment weight from the volume the following equation will be used:

W = P x V where, P is density and V is volume

The study site is composed of San Gabriel Gravelly Sand which is comprised of about half gravel and half sand. It is known that density will differ from plot to plot and the calculation will be approximating the density of San Gabriel Gravelly Sand using the

43 density of two measurements for sand with gravel. The density of dry gravel with natural sand is 1.92 g/cm3 and the density of dry sand with gravel is 1.65 g/cm3 (Greenwood

2018). The mean of the two density measurements (1.785 g/cm3 or 1,920 kg/m3) will be used in the final calculation.

5 Results

5.1 Introduction

The results section is presented in three parts to address the objectives of the thesis:

 Determination of the rate and volume of sediment erosion and deposition with

respect to precipitation amount, intensity, and time elapsed since the Sand Fire

(Section 5.2).

 Determination of sediment yield on hillslopes with respect to slope steepness,

aspect, and vegetation re-growth (Section 5.3).

 Determination of the density and distribution of hillslope rill and gully

development and topographic change as surrogates for sediment yield (Section

5.4)

5.2 Sediment Yield: Precipitation and time elapsed since the Sand Fire

Sediment samples were collected from each plot and tested for water content to help calculate the actual weight of the sediment captured. Extraction of moisture from the sediment using the Computrac XL4000 yielded the following results: Moisture content was highest in the middle of the study period (December 21, 2016 – January 26, 2017) and lowest in the beginning and end of the study period (Table 5-1). Precipitation data also correlate with moisture values in this time period (Figure 5-1) with an R2 value of

0.79 and a P-value < 0.05 (Figure 5-1). Precipitation occurred on December 16, 2016

(Appendix D), five days before water content analysis; on January 11, 2017, three days prior to water content analysis; and on January 20, 2017, six days prior to water content analysis.

Table 5-1: Percent Moisture per Plot The light blue cells are the lowest values of a plot and orange cells are the highest values. Cells with missing values have no data due to human error or a sediment trap ripped during a storm.

Figure 5-1: Percent Moisture per Plot with Precipitation There is a significant positive relationship between the amount of precipitation and the amount of moisture in the collected sediment, r2 = 0.79, p<0.05

Table 5-2: Erosion Weight per Plot Erosion per plot was determined by subtracting water content values from the weight collected from each sediment trap. The light blue cells are low values and orange cells are high values.

Table 5-3: Precipitation Events of Study Period

The lowest erosion rates occurred on March, 2017, with 86% of the lowest measurements

227 days after the fire (3/17/2017 – 3/30/2017). Eight of 15 plots had the highest erosion occurrence on 1/3/2017 (Table 5-2). This time period had only one rain event, with a rate of 0.46 cm/hr (0.18 in./hr). The total rainfall of this event was 4.5 cm (1.77 in.), not the highest rate of rainfall per rain event, and fourth largest rain event in the study period.

5.3 Sediment yield on hillslopes with respect to slope steepness, aspect, and vegetation re-growth

It is generally assumed that the steeper the hillslope the more erosion occurs with respect to the amount of colluvium that will move downslope. Loose, unconsolidated sediment on steep hills may be the largest contributors to hillslope in this part of the San Gabriel

Mountains. At the study site, plots placed on slopes greater than 30° show exceptionally more erosion than plots with less steep slopes. Gravity has a more significant effect to propelling sediment downward on steeper slopes (Table 5-4). Another factor affecting the rates of erosion within the study site was vegetation proliferation. After a fire, vegetation initially recolonizes with herbaceous species as well as crown-sprouting chaparral species. Table 5 – 5 divides the amount of growth per plot into low, medium and high slope categories to help describe the role of vegetation in hillslope stability and sediment production.

Highest erosion accumulation

Plot 13 had the highest cumulative total erosion at 196 kg (434.2 lbs.) of sediment. This is 24% more erosion than any other plot while also having one of the steepest plot areas

(34°) and the least amount of vegetation growth over the study period. Plot 2 had the second highest cumulative total erosion with 150.8 kg (332.4 lbs.). The slope value at this location was 35° and an estimated 36% of the plot was covered with new vegetation growth at the end of the study period. Plot 5 had the third highest cumulative total erosion with 138.3 kg (304.9 lbs.) of sediment. The slope here was 29°, the median value, and vegetation coverage was estimated to 35% at the end of the study

Table 5-4: Sediment yield with respect to slope angle classes. The steeper the slope the more erosion occurred within each plot. There was a signifacant change after the angle of repose (~30°).

Table 5 -5: Sediment Yeild with Respect to Vegetation Growth

Lowest erosion accumulation

The lowest total accumulation of erosion for any one plot was in Plot 7, with 30.8 kg

(67.9 lbs.) Plot 7 also had the lowest gradient slope of 13° and the highest vegetation growth within the plot area of 85% coverage. The second and third lowest total erosion occurred in Plots 14 and 15, with cumulative totals of 36 kg (79.4 lbs.) and 55.0 kg

(121.3 lbs.) respectively. Plot 14 had a slope of 30° and an end of study vegetation growth coverage of 2%. Plot 15 had a slope of 27° and an end of study vegetation growth coverage of 5%.

5.4 Particle Size Distribution of Sediment Deposits

5.4.1 Sorting

Sorting refers to the distribution of different size classes found in a sediment sample. This characteristic of sediment distributions offers insight into the nature of processes of transportation and deposition. Plots had a range between poorly-to-very poorly sorted soil characteristics except for Plot 2 on 12/21/17 which was moderately sorted (Appendix

F).

Table 5-6: Sorting Classes using Phi Scale

The table above describes the levels of soil sorting using Folk and Ward phi scale (Van Reeuwijk 2002).

The average value of particle sorting for the fifteen plots was 2.04 phi, indicating poorly- sorted material. The most sorted value was 0.84 phi at Plot 2 on 12/21/2016. The most poorly sorted value was 3.23 phi at Plot 8 on 3/17/2017.

Values between 0.84 and 3.23 phi, are moderately-sorted to very-poorly-sorted, and are typical of the study site. Colluvium found in dry ravel also can have an effect on sorting values..

Values between 0.84 and 3.23 phi, are moderately sorted to very poorly sorted, and are characteristic the study site’s top soil. Colluvium found in dry ravel can also have an effect on sorting values. Different roundness and sphericity textures such as quartz, feldspar, hornblendes and micas are common in the soil and help determine sorting values.

5.4.2 Skewness

Skewness refers to the range of particle size distribution in a sample. The average skewness number for all plots is 0.32 or strongly-fine-skewed with a range between 3.49 and -0.33. This suggests an elongated tail of fine sediment, although the soil displayed less than five percent silt and about one percent clay.

Figure 5-2: Sediment Distribution The above graph describes the average distribution breakdown of sediment classes in percentages. Notice the elongated tail giving a positive skewness towards fine particles. Detailed breakdown of sediment distribution can be found in Appendix F.

5.4.3 Graphic Mean

The graphic mean represents the average size of particle. The graphic mean of the study site is 0.19 with a range of 3.01(fine sand) and -2.67 (pebble), (Table 5-3). This places the average sediment size as coarse-grained sand. The sieve screens ranged from -4 phi

(16 mm) to 3.75 phi (0.0743 mm). The range representing fine sand, 3.75 phi (0.0743 mm) and below was not tested owing to time constraints, but on average represented about five percent silt and less than one percent clay.

Figure 5-3: Graphic Mean The graphic mean represents the average particle size.

Figure 5-5: Particle Size Distribution by Date.

Figure 5-4: Particle Size Distribution by Plot Number

There was not a significant change in particle size distribution at the study site during the study period (Figures 5-4 and 5-5). The particle size distribution, represented by geographic area (plot) and by time (study period) did not demonstrate a significant change. However, discovering that the particle size distribution remained the same during the study period allows for a volume to weight conversion to be made without significant changes to an equation (calculated in Section 5.5).

5.5 UAV Volumetric Displacement

There were two DEMs created, one from December 13th, 2016 and one from April 6th,

2017. In order to calculate volume loss, the DEMs were differenced using functionalities of ArcMap. Difference calculated between the two DEMs shows up as either net gain, unchanged, or net loss (Figure 5-4). Net loss is erosion from runoff or blown away by wind. These areas are found around gullies and in the lower canyons where cubic meters of deposited ash and sediment would have been washed away during the winter storms.

Interestingly, the larger red area between Plots 8 and 15 is on a ridge. This seemingly eroded area may be explained from multiple high wind events that occurred during the study period. There was no area at the study site that was unchanged. Areas of net gain seem to have accumulated in places of low slopes angles, such as at Plots 6, 7 and 12.

There is a flat area where the hill slope begins to level that surrounds Plot 12. This may explain much of the large blue area in that region. The white areas in the figure are where thickets of trees are found. Wind events stripped the blackened trees of leaves and small branches thereby causing large error. The “tree zones” were categorized by any

56 topographic change of more than 3 meters and were clipped from the data before the volumetric calculation took place.

Figure 5-6: Erosion Map Areas in white are places that have been clipped out of the analysis calculation owing to errors introduced by tree remnants.

Using the Cut-Fill tool in Arc Map, calculations for net loss were 26,472.82 m3

(934,878.80 ft3) in the five month period. Using the equation below:

W = V x P where, V is volume and P is the density of San Gabriel Gravelly Sand

(1,920 kg/m3, described in Section 4.8).

The weight of sediment eroded in the study period is 50,827,814 kg (50,828 metric tonnes).

6 Discussion

6.1 Introduction

As weather, temperature, vegetation change so does the magnitude and rate of hillslope erosion. Maple Canyon revealed that in the six-month study period three types of soil erosion were identified: water, wind, and dry ravel erosion. The driving processes for these types of erosion are discussed below.

As stated by Reid and Dunne (1996), sheet erosion and overland flow are common on compacted non-permeable surfaces, processes that appear to be active in Maple Canyon, which is dominated by permeable San Gabriel Gravelly Sand (Holmes 1902). How did these processes, typically occurring on a non-permeable surface, take place on permeable sediment? The San Gabriel Gravelly Sand became temporally non-permeable owing to the development of an ash layer following the Sand Fire. The high temperatures of the

Sand Fire vaporized organic material several centimeters below the soil and covered the surface with large amounts of ash (Figure 6.1). The ash which was made up of vaporized organic material, filled the voids of the Gravelly Sand and created a hydrophobic layer and a reduction of infiltration (Figure 6.2). This deduction was supported by similar conclusions made by Ferreira (2007). During the course of the study, it was observed that high winds (Appendix E) blew much of the ash into the air and away from the study site.

Sheet erosion and overland flow dominated in the first rain event, but as the season moved on, the hydrophobic layer began to diminish and another type of erosion began to occur namely rilling. High rain intensity (Appendix D), and the process of rain splash, led

59 to the occurrence of rills. Similar to the findings of Rulli (2007), the dislodgement of particles may be important. The rills could have been initially created in areas of low fire intensity where the temperature of the fire would have been less, causing less vaporization of organic material and thus a weaker hydrophobic layer. Here the processes of rain splash could “work away” at a less compacted surfaces.

Figure 6-1: Ash Layer Figure 6-2: Hydrophobic Layer High temperatures of the Sand Fire When soil is hydrophobic water collects vaporized organic material several on top of the soil surface and resists centimeters below the soil and covered infiltration. This increases water the surface with large amounts of ash. repellency and surface flow. Much of this ash was blown away prior to winter rain storms.

During this time, there was a spike of sediment captured by the sediment traps. The spike occurred between the periods of 1/3/2017 and 3/3/2017 (Table 5-2). Rill formation led to the dislodgment of larger pebbles and cobbles collected in the sediment catchments.

With diminishing rainfall during the study period, dry ravel seemed to be the primary method of soil erosion. The sediment traps collected less and less sediment as the amount of precipitation decreased. The first woody plants where observed to start sprouting around the base of trees on February 16th, 2017, but vegetation proliferation was noted to start between March 17th and March 30th. There was little to no sediment accruement in the sediment traps after vegetation proliferation. This is thought to be results of root cohesion within the topsoil (Viles 1990, Collins 2004).

6.2 The Role of Moisture and Vegetation

6.2.1 Moisture Retention

Soil at the study site is characteristic of San Gabriel Gravelly Sand and described as permeable by Holmes (1902). However, observations suggest that moisture retention can be significant to erosion even in permeable Gravelly Sand. For example, in Table 5-1, the sample taken in Plot 5 on 12/21/16 contained over 20% water even after six days after a rain event. This can be partly attributed to a water retention increase during lower temperatures in winter months, but soil characteristics of the sample may provide another answer. Higher soil retention may be partly caused by erosional events caused by precipitation. Figure 5-1 shows that the dates when water retention was higher coincides

61 during times of greater precipitation intensity. Precipitation intensity has been observed to wash down topsoil containing finer particles of silt and clay. It is likely that water retention is conserved in areas with high amounts of silt and clay, areas like sediment catchments or naturally occurring swales. Data show that there was a higher water content found in sediment catchments with a higher percent of silt and clay.

6.2.2 Vegetation

Without vegetation, rain splash contacts postfire surfaces at maximum velocity and increases sediment particle dislodgment. The formation of erosional “pedestals”, capped by pebbles as described by Reid (1996) and Rulli (2007), are formed by this increased particle dislodgment and were found at the study site (Figure 6-3). Vegetation loss also contributed to an increase to overland flow and sheetwash erosion (Figure 6-4). As described by Ferreira (2007) and Rulli (2007) the formation of a hydrophobic layer can cause an increase in overland flow and an influx of sediment to rise into the water column producing sheetwash erosion. Much of the fine sand and clay particles collected in the plots are thought to be from this process. Without vegetation dams, ravel is known to increase dramatically and cause the highest amount of erosion following a fire (Lamb

2011). During the course of the project, talus cones would form (Figure 6-5) and then be washed away in storm events, contributing to the overall sediment deposition in Pacoima

Reservoir.

Figure 6-3: Erosion Pedestals Figure 6-4: Sheetwash Erosion Soil pedestals capped by pebbles are a sign Sheetwash erodion of a hillside after the of intense erosion took place shortly first storm of the 2016-2017 winter season. before Janurary 26th, 2017, Dark mud, ash and sand freely flowed over 177 days after the fire. a starkly contrasted dirt road near the study site, having no vegetation to impede it’s path.

Figure 6-5: A small talus cone formed after a winter storm event, soon to be washed away by the next rainfall event adding to the overall deposition of Pacoima Reservoir.

6.3 UAV Methods and Volumetric Displacement

6.3.1 UAV methods

Control points are very important to creating a model that is going to determine the elevation difference of a terrain in order to calculate erosion loss. It establishes the real elevation on the ground and allows for the model to build off those elevations by interpolating data. Creating control points are also the only real way to create reliable quality controls, as you can check if the “real world” elevations outside are truly 64 represented in the model. Placing a material on the ground like Hackey and Clayton’s

(2015) 18-inch square piece of linoleum is a common and trusted way to collect control points. However, because the length of this project was expanded many months longer than Hackey and Clayton’s project in Iceland, a different approach was used. Building off of Hackey and Clayton’s method of targets that could be seen from the air, I used posts that were integrated into the sediment traps. These posts gave me the advantage of using what was already at hand but the temporal advantage that I could use them for months without the worry that rain or wind would move them during the duration of the project.

Another method that differed from Hackey and Clayton project was the hardware used to collect elevation data from the ground level. Hackey and Clayton used an RTK GPS unit that was highly accurate while this project used a hand-held Trimble GXH. The hand- held GPS unit would not have any advantage over the RTK unit in terms of accuracy, but it does have one. The hand-held unit is much smaller and easily carry through tough terrain.

There are many similarities between the methods used by the project and those described by James (2014). To create the most accurate mosaic, this project flew in a grid formation

(Figure 4-3). This allowed for the most complete capture of the project site while allowing for a systematic way of achieving a 60% overlap in coverage. While in flight, many photos were taken at oblique angles. According to James this would help to prevent a doming effect that plagues the workflow at later stages of the workflow when creating

DEMs. Although some doming was observed, it seemed to occur much less than other studies.

6.3.2 Photogrammetry parameters

The USGS article (2017) gave the most insight for photogrammetric error mitigation techniques followed by Mallison (2017). As stated before, there were four parameters to adjust when mitigating error, they are: Reprojection Error, Reconstruction Uncertainty,

Image Count, and Projection Accuracy. Although the adjustments used this project were based on the literature, there was a lot of personal experimentation until the best representation was discovered.

When adjusting for reprojection error, the model adjusted until unit of measurement was

3-5 (unitless measurement). This is much closer to what Mallison suggests, which mentions that the reprojection error should strive to equal 1. The best metric for the models concerning reconstruction uncertainty was aligned to both Mallison and the

USGS, which both stated that a model should not have the parameters range more than 50

(unitless measurement). The models were able to adjust for a parameter 30 – 50 (unitless measurement) and were recalibrated twice, following suggestions by both studies. This allowed the photos used in the mosaicking process to become much more accurate in terms of placement and overlapping. Methods used of a grid-like flight pattern made it so the image count fell in the recommended range of above three and did not need to be adjusted. This can be seen in Appendix C, Figure 1. Most of the image overlap is between 4 and 9 photos taken for any given location on the study site. The last parameter deals with projection accuracy. Although the model strived to adhere to the USGS recommendations, a much more fitted projection accuracy of 2-3 (unitless measurement),

66 the model was only able to have a projection accuracy 7-10 (unitless measurement). This was because the lower the projection accuracy the less data would be present in the model. In other words, the choice was between having more information that was less accurate or less information that was more accurate.

Ultimately, the choices made in the parameter adjustments led the models to have very good precision and accuracy. The average x-value of 7.65 cm and y-value of 6.72 cm are found in Appendix C, Table 3, control points. Although, error is usually not acceptable, values found in the combined horizontal error of the x and y values are very good. The horizontal error describes how well the two models overlap. Vertical error is much more important and is usually harder to derive. The average z-value error is 9.12 cm, which is very good. The vertical error determines the error between whether a given cell value is considered erosion loss or erosion gained.

6.3.3 Volumetric Displacement

This project used a technique called Structure from Motion (SfM), utilizing a hand-held

Trimble GPS, a Phantom UAV, Agisoft and ArcMap software to calculate a volume of sediment that is supposed to give an idea of how much erosion occurred within the study period. Although SfM is a method of 3-dimensional analysis, there are errors to overcome during each step of the process. The following discussion focuses on exploring

67 some possible explanations for unexpected results found in the volumetric displacement calculations.

Vegetation regrowth might be the largest variable causing skewed results in the volumetric calculation portion of the project. The erosion map portrayed in Figure 5-5 portrays areas of net gain (elevation loss), areas of net loss (elevation rise), and areas that were not used in the volumetric calculation (labeled as trees). During the analysis, it was detected that there were overly exaggerated areas of soil loss. This was discovered to be areas of trees that had lost biomass. The elevation loss, up to 9 meters (29.7 ft.), was due to mostly scrub oaks (Quercus berberidifolia) and Big Cone Douglas Fir (Pseudotsuga macrocarpa) that grew in the drainages of Maple Canyon. In the field, biomass lost from the trees was in the form of leaves and branches. To mitigate the error associated with such a large elevation change and maintain integrity associated with the erosion calculation, those values were removed from the calculation. By removing the data from the equation, some erosion data would be lost. To preserve as much erosional related elevation change possible, a threshold of elevation change was created. Any elevation loss of ~3 m (~10 ft.) would be removed from the calculation. This threshold gave the best height representation of the trees in Maple Canyon and thus the volumetric displacement would be maintained.

Another, smaller issue, regarding volumetric displacement and vegetation growth involves the smaller grasses and wild cucumber (Marah gilensis) species that grew at ground level. Some of the areas of vegetation growth are shown from a bird’s eye perspective in Figure 6-6 and at ground level in Figure 6-7. Observation of vegetation growth began in early February at the base of burned trees but did not begin growing in

Figure 6-6: Aerial Vegetation Growth Comparison The top images (A) were taken in February while their counterpart images at the bottom (B) were taken in April. Notice the vegetation recolonization is much more apparent in the bottom photos.

large open areas until early April to mid-April. The UAV flights took place between

December 13th, 2016 and April 6th, 2017, coinciding with the earlier stages of vegetation

69 growth. Vegetation regeneration is depicted in the Erosion Map (Figure 5.6) and is surely a contributor to areas of net gain. Although this ground level vegetation regrowth is part of the calculation, there is really no way to tell how much is caught in the final volumetric calculation.

Figure 6-7: Local scale vegetation growth comparison

Plot number 9: (Left) depicted on December 4th, 2016 prior to winter storms and vegetation growth and (Right) on March 23rd, 2017 during vegetation growth

The last observation that could have caused erroneous results in the erosion calculation is wind. Although, wind effects on erosion are highly known, it is much harder to measure.

During the study period, observations detected large amounts of fine particles and ash being blown off the hillsides of Maple Canyon. Although this erosion would show up in the erosion map as a general lowering in topography, aeolian erosion would not 70 necessarily be deposited in the Pacoima Reservoir and is a variable that would need to be accounted for.

7 Conclusion

1. Soil moisture of porous San Gabriel Gravelly Sand can retain moisture over long

periods of time. After the hydrophobic layer diminishes, water retention increases

in high clay and silty soils found in swales helping to produce a regeneration stage

for vegetation.

2. At a local scale, sheet erosion can give way to ravel erosion as the hydrophobic

layer diminishes over time. This project describes soil particle properties of Maple

Canyon after a high intensity fire and examines the change in their sorting,

skewness and graphic mean values during a winter season with respect to

precipitation and wind events. Erosion rates have been shown to be higher right

after a fire and before vegetation proliferation occurs. Once vegetation growth

returns to an area, erosion dramatically declines. This project was administered

during an above average precipitation year and thus the final erosion amount

calculated within the study area might be proportionally higher than in a year

when average precipitation occurs.

3. It has been shown that using a UAV platform together with SfM and GIS can be

used to depict topographic change and calculate erosion from post-fire hillslopes.

Further testing and comparison of the methodology to other models may provide

additional insight to boost volumetric displacement accuracy. Overall, the

application of SfM methods to post fire erosion processes in the San Gabriel

Mountains can be a useful tool to help predict catchment and reservoir capacities. 72

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Appendix A

Plot Description

Appendix B

Water Content

Appendix C

Report for Photogrammetry Modeling 121316

Ground Control Points

Report for Photogrammetry Modeling 020917

Ground Control Points

Report for Photogrammetry Modeling 040617

Ground Control Points

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Appendix D

Precipitation Events

Precipitation events during the collection period collected at Camp 9 weather station, located ~3.500 km (~2 mi) north of the study site at an elevation of 1218 m (3998 ft) above sea level. Precipitation data were collected from November 2016 to April 2017. A precipitation event is period of rainfall intensity at or above >3.81 mm (0.15in) /hr.

(California Department of Water Resources, 2017)

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Appendix E

Wind Events

Wind events at Camp 9 weather station, located ~3500 meters (~2 miles) north of the study site at an elevation of 1218 meters (3998 feet) above sea level. Weather data was collected from 11/2016 to 4/2017. A wind event was defined in this study when winds exceed 32 mph (51.50/ kph) and is not during a precipitation event (>0.15in/hr intensity). To determine a wind event, wind data from the study period was analyzed for the largest natural break. The largest natural break was 60 percent. The natural break occurred at 32 mph (51.50/ kph). This was the speed at which wind was considered the primary factor of erosion.

(California Department of Water Resources, 2017)

106

Appendix F

Particle Size Distribution Statistics

The particle size distribution statistics were analyzed with GRADISTAT, a program designed to calculate and classify a range of outputs including graphic mean, sorting, and skewness of sedimentary soils data. Statistics produced from this study are meant to describe general characteristics of the soil at the study site during the study period. As such, smaller sediment distribution was not explored in detail and thus will show with uniform results within the statistical tables. The statistical tables are listed by plot number for ease of communicating temporal differences of erosion (e.i. plot 1, plot 2, plot 3,…plot 15). The classification technique used was Folk and Ward graphical and descriptive classification. The program runs with Microsoft Excel spreadsheet package.

(Blott and Pye, 2001).

107

SAMPLE STATISTICS

1 ANALYST AND DATE: 1, 12/5/2016 2, 12/21/2016 3, 1/3/2017 4, 1/14/2017 5, 1/26/2017 SIEVING ERROR: 0.2% 0.4% 0.7% 0.2% -0.2% Bimodal, Moderately Bimodal, Very Poorly SAMPLE TYPE: Bimodal, Poorly Sorted Unimodal, Poorly Sorted Unimodal, Very Poorly Sorted Sorted Sorted Slightly Gravelly Muddy TEXTURAL GROUP: Sandy Gravel Muddy Sandy Gravel Gravelly Sand Muddy Sandy Gravel Sand Slightly Very Fine Very Coarse Silty Very Fine Gravelly Very Very Coarse Silty Sandy SEDIMENT NAME: Sandy Medium Gravel Gravelly Medium Silty Sandy Medium Gravel Coarse Sand Medium Gravel Medium Sand FOLK AND MEAN -0.655 -1.313 0.346 1.562 0.282 (xa ) : WARD METHOD SORTING 1.781 0.841 2.081 1.950 2.497 (s I ) : (f) SKEWNESS 0.646 3.495 -0.043 0.145 0.365 (SkI ) : KURTOSIS 0.339 11.66 0.885 1.330 0.462 (KG ): FOLK AND MEAN: Very Coarse Sand Very Fine Gravel Coarse Sand Medium Sand Coarse Sand WARD METHOD SORTING: Poorly Sorted Moderately Sorted Very Poorly Sorted Poorly Sorted Very Poorly Sorted (Description) SKEWNESS: Very Fine Skewed Very Fine Skewed Symmetrical Fine Skewed Very Fine Skewed KURTOSIS: Very Platykurtic Extremely Leptokurtic Platykurtic Leptokurtic Very Platykurtic % GRAVEL: 50.2% 77.1% 26.8% 3.7% 33.8% % SAND: 47.5% 20.3% 70.4% 84.8% 55.5% % MUD: 2.3% 2.5% 2.8% 11.5% 10.7% % V COARSE 0.0% 0.0% 0.0% 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 29.6% 66.8% 6.2% 0.0% 26.2% % FINE GRAVEL: 9.1% 6.1% 7.2% 0.5% 0.8% % V FINE GRAVEL: 11.5% 4.3% 13.3% 3.2% 6.8% % V COARSE SAND: 13.0% 5.2% 17.2% 15.0% 13.2% % COARSE SAND: 10.8% 4.7% 14.9% 18.3% 12.8% % MEDIUM SAND: 11.3% 4.2% 16.9% 23.5% 11.7% % FINE SAND: 9.7% 3.8% 15.3% 20.8% 10.7% % V FINE SAND: 2.8% 2.4% 6.2% 7.1% 7.1% % V COARSE SILT: 0.4% 0.4% 0.5% 1.9% 1.8% % COARSE SILT: 0.4% 0.4% 0.5% 1.9% 1.8% % MEDIUM SILT: 0.4% 0.4% 0.5% 1.9% 1.8% % FINE SILT: 0.4% 0.4% 0.5% 1.9% 1.8% % V FINE SILT: 0.4% 0.4% 0.5% 1.9% 1.8% % CLAY: 0.4% 0.4% 0.5% 1.9% 1.8%

108

SAMPLE STATISTICS

ANALYST AND DATE: 6, 2/16/2017 7, 3/3/2017 8, 3/17/2017 SIEVING ERROR: 0.6% -1.5% 0.6% Bimodal, Very Poorly Bimodal, Very Poorly SAMPLE TYPE: Trimodal, Poorly Sorted Sorted Sorted TEXTURAL GROUP: Muddy Sandy Gravel Gravelly Sand Gravelly Muddy Sand

Coarse Silty Sandy Medium Gravelly Medium Gravelly Very SEDIMENT NAME: Medium Gravel Medium Sand Coarse Silty Fine Sand FOLK AND MEAN (x ) : -0.271 0.463 1.123 a WARD METHOD SORTING (s I ) : 2.951 1.953 3.124

(f) SKEWNESS (SkI ) : 0.216 -0.071 -0.165 KURTOSIS (K ): 0.763 0.903 1.035 G FOLK AND MEAN: Very Coarse Sand Coarse Sand Medium Sand WARD METHOD SORTING: Very Poorly Sorted Poorly Sorted Very Poorly Sorted (Description) SKEWNESS: Fine Skewed Symmetrical Coarse Skewed KURTOSIS: Platykurtic Mesokurtic Mesokurtic % GRAVEL: 43.9% 21.9% 23.4% % SAND: 46.6% 74.8% 60.5% % MUD: 9.4% 3.3% 16.1% % V COARSE 0.0% 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 22.0% 12.2% 10.2% % FINE GRAVEL: 12.9% 0.2% 8.7% % V FINE GRAVEL: 9.1% 9.5% 4.5% % V COARSE SAND: 9.7% 18.6% 7.0% % COARSE SAND: 9.9% 15.9% 9.4% % MEDIUM SAND: 10.7% 18.9% 14.1% % FINE SAND: 10.1% 16.6% 16.0% % V FINE SAND: 6.2% 4.8% 14.1% % V COARSE SILT: 1.6% 0.6% 2.7% % COARSE SILT: 1.6% 0.6% 2.7% % MEDIUM SILT: 1.6% 0.6% 2.7% % FINE SILT: 1.6% 0.6% 2.7% % V FINE SILT: 1.6% 0.6% 2.7% % CLAY: 1.6% 0.6% 2.7%

109

SAMPLE STATISTICS

2 ANALYST AND DATE: 1, 12/5/2016 2, 12/21/2016 4, 1/14/2017 5, 1/26/2017 6, 2/16/2017 SIEVING ERROR: -1.9% -0.7% -0.1% 0.5% 1.0% Unimodal, Very Poorly SAMPLE TYPE: Unimodal, Poorly Sorted Unimodal, Poorly Sorted Unimodal, Poorly Sorted Unimodal, Poorly Sorted Sorted TEXTURAL GROUP: Sandy Gravel Sandy Gravel Gravelly Sand Gravelly Sand Gravelly Muddy Sand Very Fine Gravelly Very Fine Gravelly Very Fine Gravelly Very SEDIMENT NAME: Sandy Medium Gravel Sandy Medium Gravel Medium Silty Medium Coarse Sand Coarse Sand Sand FOLK AND MEAN (xa ) : -0.797 -0.833 0.885 0.843 1.015

WARD METHOD SORTING (s I ) : 1.746 1.224 1.463 1.724 2.624 (f) SKEWNESS (SkI ) : 0.532 1.542 0.081 0.130 -0.025 KURTOSIS (KG ): 0.440 0.435 0.914 0.965 1.230 FOLK AND MEAN: Very Coarse Sand Very Coarse Sand Coarse Sand Coarse Sand Medium Sand WARD METHOD SORTING: Poorly Sorted Poorly Sorted Poorly Sorted Poorly Sorted Very Poorly Sorted (Description) SKEWNESS: Very Fine Skewed Very Fine Skewed Symmetrical Fine Skewed Symmetrical KURTOSIS: Very Platykurtic Very Platykurtic Mesokurtic Mesokurtic Leptokurtic % GRAVEL: 48.4% 59.3% 6.7% 11.1% 19.4% % SAND: 48.6% 39.4% 90.5% 83.7% 70.2% % MUD: 3.0% 1.3% 2.8% 5.2% 10.4% % V COARSE 0.0% 0.0% 0.0% 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 26.1% 43.2% 0.1% 1.5% 6.9% % FINE GRAVEL: 7.2% 6.7% 0.9% 1.1% 3.7% % V FINE GRAVEL: 15.2% 9.3% 5.6% 8.6% 8.9% % V COARSE SAND: 17.5% 12.8% 22.2% 23.0% 14.2% % COARSE SAND: 13.2% 9.8% 25.8% 21.5% 14.5% % MEDIUM SAND: 8.7% 8.3% 21.4% 18.1% 16.7% % FINE SAND: 6.6% 6.7% 16.2% 14.8% 15.8% % V FINE SAND: 2.6% 1.9% 5.0% 6.1% 9.0% % V COARSE SILT: 0.5% 0.2% 0.5% 0.9% 1.7% % COARSE SILT: 0.5% 0.2% 0.5% 0.9% 1.7% % MEDIUM SILT: 0.5% 0.2% 0.5% 0.9% 1.7% % FINE SILT: 0.5% 0.2% 0.5% 0.9% 1.7% % V FINE SILT: 0.5% 0.2% 0.5% 0.9% 1.7% % CLAY: 0.5% 0.2% 0.5% 0.9% 1.7%

110

SAMPLE STATISTICS

ANALYST AND DATE: 7, 3/3/2017 8, 3/17/2017 9, 3/30/2017 SIEVING ERROR: 1.7% 0.4% 2.5% Trimodal, Very Poorly Bimodal, Very Poorly SAMPLE TYPE: Unimodal, Poorly Sorted Sorted Sorted TEXTURAL GROUP: Gravelly Sand Gravelly Muddy Sand Gravelly Sand Very Fine Gravelly Very Very Fine Gravelly Very Fine Gravelly SEDIMENT NAME: Coarse Silty Very Coarse Sand Medium Sand Coarse Sand FOLK AND MEAN 0.129 0.732 0.548 (xa ) : WARD METHOD SORTING 1.168 2.552 2.056 (s I ) : (f) SKEWNESS -0.297 -0.001 -0.314 (SkI ) : KURTOSIS 1.124 1.131 0.867 (KG ): FOLK AND MEAN: Coarse Sand Coarse Sand Coarse Sand WARD METHOD SORTING: Poorly Sorted Very Poorly Sorted Very Poorly Sorted (Description) SKEWNESS: Coarse Skewed Symmetrical Very Coarse Skewed KURTOSIS: Leptokurtic Leptokurtic Platykurtic % GRAVEL: 16.7% 22.1% 24.9% % SAND: 83.0% 69.6% 72.1% % MUD: 0.3% 8.3% 3.0% % V COARSE 0.0% 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 2.2% 7.9% 6.2% % FINE GRAVEL: 3.7% 5.6% 7.9% % V FINE GRAVEL: 10.7% 8.6% 10.7% % V COARSE SAND: 22.2% 15.9% 12.8% % COARSE SAND: 41.1% 14.4% 12.0% % MEDIUM SAND: 15.4% 15.7% 22.7% % FINE SAND: 3.7% 15.0% 20.9% % V FINE SAND: 0.6% 8.6% 3.7% % V COARSE SILT: 0.0% 1.4% 0.5% % COARSE SILT: 0.0% 1.4% 0.5% % MEDIUM SILT: 0.0% 1.4% 0.5% % FINE SILT: 0.0% 1.4% 0.5% % V FINE SILT: 0.0% 1.4% 0.5% % CLAY: 0.0% 1.4% 0.5%

111

SAMPLE STATISTICS

3 ANALYST AND DATE: 1, 12/5/2016 2, 12/21/2016 3, 1/3/2017 4, 1/14/2017 5, 1/26/2017 SIEVING ERROR: -2.7% 1.2% 0.6% 0.1% -0.8% Trimodal, Very Poorly Unimodal, Very Poorly Unimodal, Very Poorly SAMPLE TYPE: Unimodal, Poorly Sorted Unimodal, Poorly Sorted Sorted Sorted Sorted Slightly Gravelly Muddy TEXTURAL GROUP: Sandy Gravel Sandy Gravel Sandy Gravel Gravelly Muddy Sand Sand Slightly Very Fine Medium Gravelly Coarse SEDIMENT NAME: Sandy Very Fine Gravel Sandy Medium Gravel Sandy Medium Gravel Gravelly Coarse Silty Silty Medium Sand Medium Sand FOLK AND MEAN (x ) : -0.455 -1.277 0.004 1.663 0.550 a WARD METHOD SORTING (s I ) : 1.963 1.176 2.753 2.035 2.469

(f) SKEWNESS (SkI ) : 0.042 0.299 0.116 0.130 0.023 KURTOSIS (K ): 0.768 0.302 0.817 1.157 0.934 G FOLK AND MEAN: Very Coarse Sand Very Fine Gravel Coarse Sand Medium Sand Coarse Sand WARD METHOD SORTING: Poorly Sorted Poorly Sorted Very Poorly Sorted Very Poorly Sorted Very Poorly Sorted (Description) SKEWNESS: Symmetrical Fine Skewed Fine Skewed Fine Skewed Symmetrical KURTOSIS: Platykurtic Very Platykurtic Platykurtic Leptokurtic Mesokurtic % GRAVEL: 38.7% 47.3% 38.0% 4.1% 25.8% % SAND: 60.2% 52.2% 55.9% 83.8% 66.1% % MUD: 1.0% 0.5% 6.1% 12.1% 8.1% % V COARSE 0.0% 0.0% 0.0% 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 13.1% 24.7% 15.1% 0.0% 12.4% % FINE GRAVEL: 9.9% 8.7% 9.0% 0.4% 4.1% % V FINE GRAVEL: 15.7% 13.9% 13.9% 3.7% 9.2% % V COARSE SAND: 19.9% 29.6% 14.2% 15.2% 12.8% % COARSE SAND: 15.1% 16.4% 11.0% 17.1% 14.2% % MEDIUM SAND: 13.2% 4.1% 9.5% 20.2% 16.2% % FINE SAND: 10.3% 1.3% 10.5% 19.5% 15.0% % V FINE SAND: 1.7% 0.8% 10.8% 11.8% 7.8% % V COARSE SILT: 0.2% 0.1% 1.0% 2.0% 1.3% % COARSE SILT: 0.2% 0.1% 1.0% 2.0% 1.3% % MEDIUM SILT: 0.2% 0.1% 1.0% 2.0% 1.3% % FINE SILT: 0.2% 0.1% 1.0% 2.0% 1.3% % V FINE SILT: 0.2% 0.1% 1.0% 2.0% 1.3% % CLAY: 0.2% 0.1% 1.0% 2.0% 1.3%

112

SAMPLE STATISTICS

ANALYST AND DATE: 6, 2/16/2017 7, 3/3/2017 8, 3/17/2017 9, 3/30/2017 SIEVING ERROR: 1.1% 0.2% 0.6% 3.1% Unimodal, Moderately Unimodal, Very Poorly Unimodal, Very Poorly Unimodal, Very Poorly SAMPLE TYPE: Sorted Sorted Sorted Sorted TEXTURAL GROUP: Gravelly Sand Gravelly Sand Gravelly Muddy Sand Gravelly Muddy Sand Very Fine Gravelly Very Very Fine Gravelly Very Very Fine Gravelly Very Fine Gravelly Very SEDIMENT NAME: Coarse Silty Coarse Coarse Sand Coarse Sand Coarse Silty Fine Sand Sand FOLK AND MEAN -0.090 0.826 1.760 1.295 (xa ) : WARD METHOD SORTING 0.943 2.231 2.672 2.232 (s I ) : (f) SKEWNESS 0.123 0.029 -0.038 0.105 (SkI ) : KURTOSIS 1.164 1.124 1.183 1.160 (KG ): FOLK AND MEAN: Very Coarse Sand Coarse Sand Medium Sand Medium Sand WARD METHOD SORTING: Moderately Sorted Very Poorly Sorted Very Poorly Sorted Very Poorly Sorted (Description) SKEWNESS: Fine Skewed Symmetrical Symmetrical Fine Skewed KURTOSIS: Leptokurtic Leptokurtic Leptokurtic Leptokurtic % GRAVEL: 12.2% 18.1% 14.4% 12.6% % SAND: 87.6% 74.8% 68.5% 77.3% % MUD: 0.2% 7.1% 17.0% 10.2% % V COARSE 0.0% 0.0% 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 0.2% 3.4% 3.1% 0.4% % FINE GRAVEL: 2.1% 6.7% 4.4% 4.2% % V FINE GRAVEL: 10.0% 7.9% 6.9% 8.0% % V COARSE SAND: 46.5% 16.5% 9.5% 13.6% % COARSE SAND: 29.1% 18.1% 11.3% 18.5% % MEDIUM SAND: 8.5% 17.4% 15.7% 17.9% % FINE SAND: 2.8% 15.2% 17.3% 16.3% % V FINE SAND: 0.5% 7.7% 14.7% 10.9% % V COARSE SILT: 0.0% 1.2% 2.8% 1.7% % COARSE SILT: 0.0% 1.2% 2.8% 1.7% % MEDIUM SILT: 0.0% 1.2% 2.8% 1.7% % FINE SILT: 0.0% 1.2% 2.8% 1.7% % V FINE SILT: 0.0% 1.2% 2.8% 1.7% % CLAY: 0.0% 1.2% 2.8% 1.7%

113

SAMPLE STATISTICS

4 ANALYST AND DATE: 3, 1/3/2017 4, 1/14/2017 6, 2/16/2017 7, 3/3/2017 8, 3/17/2017 SIEVING ERROR: 0.5% 0.8% -0.6% 1.8% 1.0% Unimodal, Very Poorly Unimodal, Very Poorly Bimodal, Very Poorly Unimodal, Very Poorly SAMPLE TYPE: Trimodal, Poorly Sorted Sorted Sorted Sorted Sorted Slightly Gravelly Muddy TEXTURAL GROUP: Muddy Sandy Gravel Gravelly Muddy Sand Sandy Gravel Muddy Sandy Gravel Sand Very Fine Gravelly Slightly Very Fine Coarse Silty Sandy Coarse Silty Sandy SEDIMENT NAME: Coarse Silty Very Sandy Medium Gravel Gravelly Very Coarse Medium Gravel Medium Gravel Coarse Sand Silty Very Fine Sand FOLK AND MEAN (x ) : -0.064 1.121 -0.769 -0.689 3.003 a WARD METHOD SORTING (s I ) : 2.561 2.206 1.441 2.025 2.429

(f) SKEWNESS (SkI ) : 0.260 0.183 1.807 0.669 0.116 KURTOSIS (K ): 0.827 1.097 0.500 0.455 1.166 G FOLK AND MEAN: Very Coarse Sand Medium Sand Very Coarse Sand Very Coarse Sand Very Fine Sand WARD METHOD SORTING: Very Poorly Sorted Very Poorly Sorted Poorly Sorted Very Poorly Sorted Very Poorly Sorted (Description) SKEWNESS: Fine Skewed Fine Skewed Very Fine Skewed Very Fine Skewed Fine Skewed KURTOSIS: Platykurtic Mesokurtic Very Platykurtic Very Platykurtic Leptokurtic % GRAVEL: 36.7% 13.6% 60.3% 51.4% 3.1% % SAND: 55.7% 76.6% 36.1% 43.2% 70.3% % MUD: 7.5% 9.8% 3.6% 5.4% 26.6% % V COARSE 0.0% 0.0% 0.0% 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 14.8% 0.3% 47.1% 34.0% 0.0% % FINE GRAVEL: 7.6% 3.2% 6.4% 5.9% 0.4% % V FINE GRAVEL: 14.3% 10.1% 6.9% 11.5% 2.6% % V COARSE SAND: 16.3% 18.4% 8.4% 13.5% 6.7% % COARSE SAND: 12.6% 18.2% 8.3% 10.9% 10.2% % MEDIUM SAND: 10.2% 16.4% 8.7% 8.0% 15.7% % FINE SAND: 9.6% 14.7% 7.6% 6.7% 18.8% % V FINE SAND: 7.0% 8.9% 3.1% 3.9% 18.9% % V COARSE SILT: 1.3% 1.6% 0.6% 0.9% 4.4% % COARSE SILT: 1.3% 1.6% 0.6% 0.9% 4.4% % MEDIUM SILT: 1.3% 1.6% 0.6% 0.9% 4.4% % FINE SILT: 1.3% 1.6% 0.6% 0.9% 4.4% % V FINE SILT: 1.3% 1.6% 0.6% 0.9% 4.4% % CLAY: 1.3% 1.6% 0.6% 0.9% 4.4%

114

SAMPLE STATISTICS

ANALYST AND DATE: 9, 3/30/2017 SIEVING ERROR: 0.3% Bimodal, Very Poorly SAMPLE TYPE: Sorted TEXTURAL GROUP: Sandy Gravel

SEDIMENT NAME: Sandy Medium Gravel

FOLK AND MEAN 0.031 (xa ) : WARD METHOD SORTING 2.387 (s I ) : (f) SKEWNESS -0.112 (SkI ) : KURTOSIS 0.922 (KG ): FOLK AND MEAN: Coarse Sand WARD METHOD SORTING: Very Poorly Sorted (Description) SKEWNESS: Coarse Skewed KURTOSIS: Mesokurtic % GRAVEL: 30.3% % SAND: 65.8% % MUD: 3.9% % V COARSE 0.0% GRAVEL: % COARSE GRAVEL: 0.0% % MEDIUM GRAVEL: 13.5% % FINE GRAVEL: 7.2% % V FINE GRAVEL: 9.6% % V COARSE SAND: 15.2% % COARSE SAND: 19.2% % MEDIUM SAND: 13.9% % FINE SAND: 10.9% % V FINE SAND: 6.6% % V COARSE SILT: 0.7% % COARSE SILT: 0.7% % MEDIUM SILT: 0.7% % FINE SILT: 0.7% % V FINE SILT: 0.7% % CLAY: 0.7%

115

SAMPLE STATISTICS

5 ANALYST AND DATE: 1, 12/5/2016 2, 12/21/2016 3, 1/3/2017 4, 1/14/2017 5, 1/26/2017 SIEVING ERROR: 12.4% 1.7% 0.9% 0.3% 1.1% Unimodal, Very Poorly Unimodal, Very Poorly SAMPLE TYPE: Trimodal, Poorly Sorted Bimodal, Poorly Sorted Unimodal, Poorly Sorted Sorted Sorted TEXTURAL GROUP: Sandy Gravel Sandy Gravel Muddy Sandy Gravel Gravelly Sand Gravelly Sand Very Coarse Silty Very Fine Gravelly Very Fine Gravelly SEDIMENT NAME: Sandy Medium Gravel Sandy Medium Gravel Sandy Very Fine Gravel Medium Sand Medium Sand

FOLK AND MEAN (xa ) : -0.845 -1.039 0.002 1.180 1.261

WARD METHOD SORTING (s I ) : 1.353 1.933 2.827 1.581 2.120 (f) SKEWNESS (Sk ) : 1.895 0.601 0.147 -0.212 0.037 I KURTOSIS (KG ): 0.477 0.399 0.959 1.057 1.127 FOLK AND MEAN: Very Coarse Sand Very Fine Gravel Coarse Sand Medium Sand Medium Sand WARD METHOD SORTING: Poorly Sorted Poorly Sorted Very Poorly Sorted Poorly Sorted Very Poorly Sorted (Description) SKEWNESS: Very Fine Skewed Very Fine Skewed Fine Skewed Coarse Skewed Symmetrical KURTOSIS: Very Platykurtic Very Platykurtic Mesokurtic Mesokurtic Leptokurtic % GRAVEL: 61.0% 52.7% 38.2% 9.2% 12.2% % SAND: 36.3% 45.2% 53.6% 86.1% 79.1% % MUD: 2.8% 2.2% 8.2% 4.7% 8.7% % V COARSE 0.0% 0.0% 0.0% 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 47.5% 33.0% 13.1% 0.2% 0.3% % FINE GRAVEL: 6.1% 9.3% 11.4% 2.5% 3.5% % V FINE GRAVEL: 7.4% 10.4% 13.7% 6.5% 8.4% % V COARSE SAND: 8.8% 12.6% 13.9% 13.3% 15.3% % COARSE SAND: 8.5% 12.2% 11.8% 16.7% 16.2% % MEDIUM SAND: 8.8% 9.4% 10.7% 27.3% 19.6% % FINE SAND: 7.5% 7.5% 10.1% 24.2% 18.5% % V FINE SAND: 2.6% 3.4% 7.0% 4.5% 9.5% % V COARSE SILT: 0.5% 0.4% 1.4% 0.8% 1.4% % COARSE SILT: 0.5% 0.4% 1.4% 0.8% 1.4% % MEDIUM SILT: 0.5% 0.4% 1.4% 0.8% 1.4% % FINE SILT: 0.5% 0.4% 1.4% 0.8% 1.4% % V FINE SILT: 0.5% 0.4% 1.4% 0.8% 1.4% % CLAY: 0.5% 0.4% 1.4% 0.8% 1.4%

116

SAMPLE STATISTICS

ANALYST AND DATE: 6, 2/16/2017 7, 3/3/2017 8, 3/17/2017 9, 3/30/2017 SIEVING ERROR: 0.3% 0.0% 1.0% 0.9% Bimodal, Very Poorly Unimodal, Very Poorly Bimodal, Very Poorly Bimodal, Very Poorly SAMPLE TYPE: Sorted Sorted Sorted Sorted TEXTURAL GROUP: Gravelly Sand Sandy Gravel Muddy Sandy Gravel Sandy Gravel Fine Gravelly Medium Very Coarse Silty SEDIMENT NAME: Sandy Medium Gravel Sandy Medium Gravel Sand Sandy Medium Gravel FOLK AND MEAN 0.858 -0.102 0.839 -0.379 (xa ) : WARD METHOD SORTING 2.380 2.476 3.232 2.261 (s I ) : (f) SKEWNESS -0.162 0.118 -0.144 0.075 (SkI ) : KURTOSIS 1.068 0.761 0.641 0.468 (KG ): FOLK AND MEAN: Coarse Sand Very Coarse Sand Coarse Sand Very Coarse Sand WARD METHOD SORTING: Very Poorly Sorted Very Poorly Sorted Very Poorly Sorted Very Poorly Sorted (Description) SKEWNESS: Coarse Skewed Fine Skewed Coarse Skewed Symmetrical KURTOSIS: Mesokurtic Platykurtic Very Platykurtic Very Platykurtic % GRAVEL: 21.8% 35.4% 30.0% 39.7% % SAND: 70.8% 58.5% 53.2% 56.9% % MUD: 7.4% 6.1% 16.8% 3.4% % V COARSE 0.0% 0.0% 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 3.5% 15.9% 20.6% 20.1% % FINE GRAVEL: 9.7% 7.9% 6.3% 9.1% % V FINE GRAVEL: 8.5% 11.5% 3.2% 10.6% % V COARSE SAND: 9.8% 14.1% 4.1% 9.5% % COARSE SAND: 13.4% 14.0% 6.3% 11.0% % MEDIUM SAND: 20.4% 12.7% 11.6% 16.5% % FINE SAND: 19.3% 11.3% 15.0% 15.1% % V FINE SAND: 8.0% 6.5% 16.1% 4.8% % V COARSE SILT: 1.2% 1.0% 2.8% 0.6% % COARSE SILT: 1.2% 1.0% 2.8% 0.6% % MEDIUM SILT: 1.2% 1.0% 2.8% 0.6% % FINE SILT: 1.2% 1.0% 2.8% 0.6% % V FINE SILT: 1.2% 1.0% 2.8% 0.6% % CLAY: 1.2% 1.0% 2.8% 0.6%

117

SAMPLE STATISTICS

6 ANALYST AND DATE: 1, 12/5/2016 2, 12/21/2016 3, 1/3/2017 4, 1/14/2017 5, 1/26/2017 SIEVING ERROR: 0.8% 1.5% 0.1% 0.3% -0.5% Unimodal, Very Poorly Unimodal, Very Poorly Unimodal, Very Poorly SAMPLE TYPE: Unimodal, Poorly Sorted Unimodal, Poorly Sorted Sorted Sorted Sorted TEXTURAL GROUP: Gravelly Sand Gravelly Sand Gravelly Sand Gravelly Sand Gravelly Sand Very Fine Gravelly Very Fine Gravelly Very Fine Gravelly Very Very Fine Gravelly Very Fine Gravelly Very SEDIMENT NAME: Medium Sand Coarse Sand Coarse Sand Coarse Sand Coarse Sand

FOLK AND MEAN (xa ) : 1.140 1.048 0.294 1.016 0.166

WARD METHOD SORTING (s I ) : 2.121 1.558 2.015 1.650 2.301 (f) SKEWNESS (Sk ) : 0.028 0.050 0.127 0.112 0.102 I KURTOSIS (KG ): 1.056 0.990 0.904 0.934 1.130 FOLK AND MEAN: Medium Sand Medium Sand Coarse Sand Medium Sand Coarse Sand WARD METHOD SORTING: Very Poorly Sorted Poorly Sorted Very Poorly Sorted Poorly Sorted Very Poorly Sorted (Description) SKEWNESS: Symmetrical Symmetrical Fine Skewed Fine Skewed Fine Skewed KURTOSIS: Mesokurtic Mesokurtic Mesokurtic Mesokurtic Leptokurtic % GRAVEL: 14.4% 7.0% 27.8% 7.4% 28.9% % SAND: 78.3% 88.7% 68.2% 87.5% 65.4% % MUD: 7.3% 4.3% 4.0% 5.1% 5.6% % V COARSE 0.0% 0.0% 0.0% 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 0.9% 1.1% 2.9% 0.3% 7.8% % FINE GRAVEL: 4.8% 1.4% 8.1% 1.3% 7.4% % V FINE GRAVEL: 8.7% 4.4% 16.9% 5.8% 13.7% % V COARSE SAND: 15.6% 17.4% 20.2% 21.1% 20.9% % COARSE SAND: 16.7% 25.7% 17.2% 23.4% 17.3% % MEDIUM SAND: 18.3% 21.7% 13.1% 19.3% 12.1% % FINE SAND: 17.3% 16.8% 11.1% 15.8% 9.8% % V FINE SAND: 10.4% 7.1% 6.6% 7.9% 5.3% % V COARSE SILT: 1.2% 0.7% 0.7% 0.9% 0.9% % COARSE SILT: 1.2% 0.7% 0.7% 0.9% 0.9% % MEDIUM SILT: 1.2% 0.7% 0.7% 0.9% 0.9% % FINE SILT: 1.2% 0.7% 0.7% 0.9% 0.9% % V FINE SILT: 1.2% 0.7% 0.7% 0.9% 0.9% % CLAY: 1.2% 0.7% 0.7% 0.9% 0.9%

118

SAMPLE STATISTICS

ANALYST AND DATE: 6, 2/16/2017 7, 3/3/2017 8, 3/17/2017 9, 3/30/2017 SIEVING ERROR: 0.5% 0.2% 0.5% 0.4% Unimodal, Very Poorly Bimodal, Very Poorly SAMPLE TYPE: Unimodal, Poorly Sorted Unimodal, Poorly Sorted Sorted Sorted TEXTURAL GROUP: Gravelly Sand Sandy Gravel Gravelly Sand Sandy Gravel Very Fine Gravelly Very Fine Gravelly SEDIMENT NAME: Sandy Medium Gravel Sandy Very Fine Gravel Coarse Sand Coarse Sand FOLK AND MEAN 0.510 -0.760 0.697 -0.325 (xa ) : WARD METHOD SORTING 2.171 1.141 1.977 2.251 (s I ) : (f) SKEWNESS 0.064 1.672 -0.034 0.041 (SkI ) : KURTOSIS 1.053 0.576 0.988 0.798 (KG ): FOLK AND MEAN: Coarse Sand Very Coarse Sand Coarse Sand Very Coarse Sand WARD METHOD SORTING: Very Poorly Sorted Poorly Sorted Poorly Sorted Very Poorly Sorted (Description) SKEWNESS: Symmetrical Very Fine Skewed Symmetrical Symmetrical KURTOSIS: Mesokurtic Very Platykurtic Mesokurtic Platykurtic % GRAVEL: 22.4% 62.1% 18.3% 40.6% % SAND: 71.6% 36.2% 76.9% 57.1% % MUD: 6.0% 1.7% 4.8% 2.4% % V COARSE 0.0% 0.0% 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 2.7% 45.0% 4.2% 12.3% % FINE GRAVEL: 8.9% 4.7% 5.0% 13.6% % V FINE GRAVEL: 10.7% 12.4% 9.0% 14.6% % V COARSE SAND: 18.4% 11.8% 18.4% 14.9% % COARSE SAND: 18.6% 9.1% 19.3% 13.6% % MEDIUM SAND: 15.5% 6.9% 17.2% 13.3% % FINE SAND: 12.8% 5.7% 14.6% 11.3% % V FINE SAND: 6.3% 2.7% 7.3% 4.0% % V COARSE SILT: 1.0% 0.3% 0.8% 0.4% % COARSE SILT: 1.0% 0.3% 0.8% 0.4% % MEDIUM SILT: 1.0% 0.3% 0.8% 0.4% % FINE SILT: 1.0% 0.3% 0.8% 0.4% % V FINE SILT: 1.0% 0.3% 0.8% 0.4% % CLAY: 1.0% 0.3% 0.8% 0.4%

119

SAMPLE STATISTICS

7 ANALYST AND DATE: 1, 12/5/2016 2, 12/21/2016 3, 1/3/2017 5, 1/26/2017 7, 3/3/2017 SIEVING ERROR: -0.3% 3.8% 0.3% 2.1% -1.2% Trimodal, Very Poorly Unimodal, Very Poorly Unimodal, Very Poorly SAMPLE TYPE: Unimodal, Poorly Sorted Unimodal, Poorly Sorted Sorted Sorted Sorted TEXTURAL GROUP: Muddy Sandy Gravel Sandy Gravel Gravelly Sand Gravelly Sand Muddy Sandy Gravel Coarse Silty Sandy Medium Gravelly Coarse Medium Gravelly Very Fine Silty Sandy SEDIMENT NAME: Sandy Very Fine Gravel Medium Gravel Sand Coarse Sand Medium Gravel

FOLK AND MEAN (xa ) : -0.574 -0.679 0.569 0.227 -1.402

WARD METHOD SORTING (s I ) : 2.002 1.516 2.173 1.978 2.141 (f) SKEWNESS (Sk ) : 1.488 -0.168 0.034 0.142 0.709 I KURTOSIS (KG ): 0.600 0.923 0.955 0.887 0.565 FOLK AND MEAN: Very Coarse Sand Very Coarse Sand Coarse Sand Coarse Sand Very Fine Gravel WARD METHOD SORTING: Very Poorly Sorted Poorly Sorted Very Poorly Sorted Poorly Sorted Very Poorly Sorted (Description) SKEWNESS: Very Fine Skewed Coarse Skewed Symmetrical Fine Skewed Very Fine Skewed KURTOSIS: Very Platykurtic Mesokurtic Mesokurtic Platykurtic Very Platykurtic % GRAVEL: 58.6% 38.6% 21.7% 23.2% 61.4% % SAND: 33.8% 60.9% 72.1% 71.7% 34.1% % MUD: 7.6% 0.6% 6.3% 5.1% 4.5% % V COARSE 0.0% 0.0% 0.0% 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 47.0% 9.4% 12.3% 13.0% 33.8% % FINE GRAVEL: 6.2% 9.6% 2.2% 2.8% 14.4% % V FINE GRAVEL: 5.4% 19.6% 7.2% 7.4% 13.1% % V COARSE SAND: 8.0% 25.1% 14.4% 21.7% 10.7% % COARSE SAND: 7.2% 24.8% 19.7% 18.6% 8.3% % MEDIUM SAND: 6.4% 7.9% 16.8% 14.7% 6.0% % FINE SAND: 6.4% 2.0% 13.8% 11.8% 5.3% % V FINE SAND: 5.8% 1.1% 7.5% 4.9% 3.7% % V COARSE SILT: 1.3% 0.1% 1.0% 0.8% 0.8% % COARSE SILT: 1.3% 0.1% 1.0% 0.8% 0.8% % MEDIUM SILT: 1.3% 0.1% 1.0% 0.8% 0.8% % FINE SILT: 1.3% 0.1% 1.0% 0.8% 0.8% % V FINE SILT: 1.3% 0.1% 1.0% 0.8% 0.8% % CLAY: 1.3% 0.1% 1.0% 0.8% 0.8%

120

SAMPLE STATISTICS

ANALYST AND DATE: 9, 3/30/2017 SIEVING ERROR: 0.2% SAMPLE TYPE: Bimodal, Poorly Sorted TEXTURAL GROUP: Sandy Gravel SEDIMENT NAME: Sandy Medium Gravel

FOLK AND MEAN -2.067 (xa ) : WARD METHOD SORTING 1.726 (s I ) : (f) SKEWNESS 1.067 (SkI ) : KURTOSIS 0.614 (KG ): FOLK AND MEAN: Fine Gravel WARD METHOD SORTING: Poorly Sorted (Description) SKEWNESS: Very Fine Skewed KURTOSIS: Very Platykurtic % GRAVEL: 74.7% % SAND: 23.9% % MUD: 1.4% % V COARSE 0.0% GRAVEL: % COARSE GRAVEL: 0.0% % MEDIUM GRAVEL: 45.7% % FINE GRAVEL: 19.7% % V FINE GRAVEL: 9.2% % V COARSE SAND: 6.4% % COARSE SAND: 5.3% % MEDIUM SAND: 5.3% % FINE SAND: 4.8% % V FINE SAND: 2.1% % V COARSE SILT: 0.2% % COARSE SILT: 0.2% % MEDIUM SILT: 0.2% % FINE SILT: 0.2% % V FINE SILT: 0.2% % CLAY: 0.2%

121

SAMPLE STATISTICS

8 ANALYST AND DATE: 1, 12/5/2016 2, 12/21/2016 3, 1/3/2017 4, 1/14/2017 5, 1/26/2017 SIEVING ERROR: 0.6% 1.0% 0.3% 0.3% -0.5% Bimodal, Very Poorly Bimodal, Very Poorly SAMPLE TYPE: Bimodal, Poorly Sorted Unimodal, Poorly Sorted Unimodal, Poorly Sorted Sorted Sorted TEXTURAL GROUP: Sandy Gravel Gravelly Sand Gravelly Sand Gravelly Sand Sandy Gravel Medium Gravelly Coarse Very Fine Gravelly Very Very Fine Gravelly SEDIMENT NAME: Sandy Medium Gravel Sandy Medium Gravel Sand Coarse Sand Medium Sand

FOLK AND MEAN (xa ) : -1.277 -0.219 0.388 1.019 -0.258 WARD METHOD SORTING (s ) : 1.933 1.668 2.195 1.405 2.368 I (f) SKEWNESS (SkI ) : 0.517 -0.290 -0.035 -0.327 0.082

KURTOSIS (KG ): 0.586 1.237 0.856 0.816 0.633 FOLK AND MEAN: Very Fine Gravel Very Coarse Sand Coarse Sand Medium Sand Very Coarse Sand WARD METHOD SORTING: Poorly Sorted Poorly Sorted Very Poorly Sorted Poorly Sorted Very Poorly Sorted (Description) SKEWNESS: Very Fine Skewed Coarse Skewed Symmetrical Very Coarse Skewed Symmetrical KURTOSIS: Very Platykurtic Leptokurtic Platykurtic Platykurtic Very Platykurtic % GRAVEL: 60.0% 24.9% 27.9% 9.7% 36.5% % SAND: 38.0% 73.7% 67.3% 88.3% 58.4% % MUD: 2.0% 1.4% 4.8% 2.0% 5.1% % V COARSE 0.0% 0.0% 0.0% 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 25.1% 11.6% 6.3% 0.2% 18.2% % FINE GRAVEL: 16.9% 4.8% 8.7% 2.1% 8.2% % V FINE GRAVEL: 18.0% 8.5% 12.9% 7.4% 10.0% % V COARSE SAND: 12.8% 18.7% 15.7% 16.9% 13.4% % COARSE SAND: 8.5% 35.3% 14.1% 17.1% 13.8% % MEDIUM SAND: 8.0% 13.0% 15.6% 28.5% 13.7% % FINE SAND: 6.8% 3.7% 14.6% 24.5% 12.0% % V FINE SAND: 1.8% 3.1% 7.3% 1.3% 5.4% % V COARSE SILT: 0.3% 0.2% 0.8% 0.3% 0.9% % COARSE SILT: 0.3% 0.2% 0.8% 0.3% 0.9% % MEDIUM SILT: 0.3% 0.2% 0.8% 0.3% 0.9% % FINE SILT: 0.3% 0.2% 0.8% 0.3% 0.9% % V FINE SILT: 0.3% 0.2% 0.8% 0.3% 0.9% % CLAY: 0.3% 0.2% 0.8% 0.3% 0.9%

122

SAMPLE STATISTICS

ANALYST AND DATE: 6, 2/16/2017 7, 3/3/2017 8, 3/17/2017 9, 3/30/2017 SIEVING ERROR: 0.6% 0.2% 1.0% 0.3% Bimodal, Very Poorly Bimodal, Very Poorly Bimodal, Very Poorly SAMPLE TYPE: Bimodal, Poorly Sorted Sorted Sorted Sorted TEXTURAL GROUP: Muddy Sandy Gravel Sandy Gravel Gravelly Muddy Sand Sandy Gravel Very Fine Gravelly Very Coarse Silty Sandy SEDIMENT NAME: Sandy Medium Gravel Coarse Silty Very Sandy Very Fine Gravel Medium Gravel Coarse Sand FOLK AND MEAN -0.271 -0.308 0.923 -0.037 (xa ) : WARD METHOD SORTING 2.951 1.937 2.321 2.299 (s I ) : (f) SKEWNESS 0.216 0.635 0.097 0.025 (SkI ) : KURTOSIS 0.763 0.376 1.016 0.789 (KG ): FOLK AND MEAN: Very Coarse Sand Very Coarse Sand Coarse Sand Very Coarse Sand WARD METHOD SORTING: Very Poorly Sorted Poorly Sorted Very Poorly Sorted Very Poorly Sorted (Description) SKEWNESS: Fine Skewed Very Fine Skewed Symmetrical Symmetrical KURTOSIS: Platykurtic Very Platykurtic Mesokurtic Platykurtic % GRAVEL: 43.9% 46.9% 20.2% 38.0% % SAND: 46.6% 47.9% 71.1% 57.6% % MUD: 9.4% 5.2% 8.6% 4.4% % V COARSE 0.0% 0.0% 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 22.0% 30.5% 0.5% 9.5% % FINE GRAVEL: 12.9% 4.3% 6.2% 12.3% % V FINE GRAVEL: 9.1% 12.2% 13.5% 16.2% % V COARSE SAND: 9.7% 12.6% 15.9% 12.6% % COARSE SAND: 9.9% 11.2% 15.1% 10.4% % MEDIUM SAND: 10.7% 10.1% 15.8% 15.8% % FINE SAND: 10.1% 9.0% 15.1% 14.6% % V FINE SAND: 6.2% 5.0% 9.3% 4.2% % V COARSE SILT: 1.6% 0.9% 1.4% 0.7% % COARSE SILT: 1.6% 0.9% 1.4% 0.7% % MEDIUM SILT: 1.6% 0.9% 1.4% 0.7% % FINE SILT: 1.6% 0.9% 1.4% 0.7% % V FINE SILT: 1.6% 0.9% 1.4% 0.7% % CLAY: 1.6% 0.9% 1.4% 0.7%

123

SAMPLE STATISTICS

9 ANALYST AND DATE: 1, 12/5/2016 2, 12/21/2016 3, 1/3/2017 5, 1/26/2017 6, 2/16/2017 SIEVING ERROR: 0.7% 2.1% 0.2% -0.9% 0.5% Unimodal, Very Poorly Bimodal, Very Poorly Unimodal, Very Poorly SAMPLE TYPE: Unimodal, Poorly Sorted Unimodal, Poorly Sorted Sorted Sorted Sorted TEXTURAL GROUP: Gravelly Sand Sandy Gravel Muddy Sandy Gravel Sandy Gravel Gravelly Sand Very Fine Gravelly Fine Silty Sandy Very Fine Gravelly Fine SEDIMENT NAME: Sandy Medium Gravel Sandy Medium Gravel Medium Sand Medium Gravel Sand

FOLK AND MEAN (xa ) : 1.375 -1.057 -0.512 -1.031 1.323

WARD METHOD SORTING (s I ) : 2.073 1.092 2.438 1.452 2.373 (f) SKEWNESS (Sk ) : -0.090 2.315 0.379 2.684 -0.186 I KURTOSIS (KG ): 1.199 0.475 0.727 0.516 1.229 FOLK AND MEAN: Medium Sand Very Fine Gravel Very Coarse Sand Very Fine Gravel Medium Sand WARD METHOD SORTING: Very Poorly Sorted Poorly Sorted Very Poorly Sorted Poorly Sorted Very Poorly Sorted (Description) SKEWNESS: Symmetrical Very Fine Skewed Very Fine Skewed Very Fine Skewed Coarse Skewed KURTOSIS: Leptokurtic Very Platykurtic Platykurtic Very Platykurtic Leptokurtic % GRAVEL: 12.1% 58.2% 47.7% 59.6% 16.4% % SAND: 80.3% 40.4% 46.2% 36.5% 75.6% % MUD: 7.6% 1.4% 6.1% 3.9% 8.0% % V COARSE 0.0% 0.0% 0.0% 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 1.5% 49.3% 17.6% 53.1% 4.8% % FINE GRAVEL: 3.6% 3.6% 13.5% 2.5% 5.1% % V FINE GRAVEL: 7.0% 5.3% 16.6% 4.0% 6.5% % V COARSE SAND: 11.6% 8.2% 13.2% 6.6% 8.6% % COARSE SAND: 13.6% 17.1% 10.0% 7.4% 11.4% % MEDIUM SAND: 22.2% 8.2% 9.2% 9.3% 20.3% % FINE SAND: 22.1% 4.1% 8.6% 8.8% 21.6% % V FINE SAND: 10.8% 2.7% 5.2% 4.5% 13.6% % V COARSE SILT: 1.3% 0.2% 1.0% 0.7% 1.3% % COARSE SILT: 1.3% 0.2% 1.0% 0.7% 1.3% % MEDIUM SILT: 1.3% 0.2% 1.0% 0.7% 1.3% % FINE SILT: 1.3% 0.2% 1.0% 0.7% 1.3% % V FINE SILT: 1.3% 0.2% 1.0% 0.7% 1.3% % CLAY: 1.3% 0.2% 1.0% 0.7% 1.3%

124

SAMPLE STATISTICS

ANALYST AND DATE: 7, 3/3/2017 9, 3/30/2017 SIEVING ERROR: 0.1% 0.4% Bimodal, Very Poorly SAMPLE TYPE: Bimodal, Poorly Sorted Sorted TEXTURAL GROUP: Sandy Gravel Gravelly Sand Medium Gravelly Fine SEDIMENT NAME: Sandy Medium Gravel Sand FOLK AND MEAN -0.102 1.410 (xa ) : WARD METHOD SORTING 2.150 1.979 (s I ) : (f) SKEWNESS 0.053 -0.290 (SkI ) : KURTOSIS 0.408 1.425 (KG ): FOLK AND MEAN: Very Coarse Sand Medium Sand WARD METHOD SORTING: Very Poorly Sorted Poorly Sorted (Description) SKEWNESS: Symmetrical Coarse Skewed KURTOSIS: Very Platykurtic Leptokurtic % GRAVEL: 34.4% 12.1% % SAND: 61.4% 82.4% % MUD: 4.3% 5.5% % V COARSE 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% % MEDIUM GRAVEL: 23.9% 4.9% % FINE GRAVEL: 3.7% 2.9% % V FINE GRAVEL: 6.8% 4.3% % V COARSE SAND: 10.5% 8.1% % COARSE SAND: 12.9% 11.6% % MEDIUM SAND: 17.0% 26.1% % FINE SAND: 15.4% 26.2% % V FINE SAND: 5.6% 10.5% % V COARSE SILT: 0.7% 0.9% % COARSE SILT: 0.7% 0.9% % MEDIUM SILT: 0.7% 0.9% % FINE SILT: 0.7% 0.9% % V FINE SILT: 0.7% 0.9% % CLAY: 0.7% 0.9%

125

SAMPLE STATISTICS

10 ANALYST AND DATE: 1, 12/5/2016 2, 12/21/2016 3, 1/3/2017 4, 1/14/2017 5, 1/26/2017 SIEVING ERROR: 0.2% 0.0% 0.3% -0.1% 0.8% Trimodal, Very Poorly Trimodal, Very Poorly Bimodal, Very Poorly SAMPLE TYPE: Bimodal, Poorly Sorted Sorted Sorted Unimodal, Poorly Sorted Sorted TEXTURAL GROUP: Sandy Gravel Sandy Gravel Sandy Gravel Gravelly Sand Gravelly Sand Very Fine Gravelly Very Fine Gravelly SEDIMENT NAME: Sandy Medium Gravel Sandy Medium Gravel Sandy Medium Gravel Coarse Sand Medium Sand

FOLK AND MEAN (xa ) : -1.308 -1.203 -0.462 0.942 0.477

WARD METHOD SORTING (s I ) : 1.165 2.146 2.511 1.494 2.220 (f) SKEWNESS (Sk ) : 2.467 0.399 0.216 0.057 0.030 I KURTOSIS (KG ): 0.564 0.553 0.527 0.900 0.898 FOLK AND MEAN: Very Fine Gravel Very Fine Gravel Very Coarse Sand Coarse Sand Coarse Sand WARD METHOD SORTING: Poorly Sorted Very Poorly Sorted Very Poorly Sorted Poorly Sorted Very Poorly Sorted (Description) SKEWNESS: Very Fine Skewed Very Fine Skewed Fine Skewed Symmetrical Symmetrical KURTOSIS: Very Platykurtic Very Platykurtic Very Platykurtic Mesokurtic Platykurtic % GRAVEL: 70.9% 56.2% 38.4% 6.6% 27.0% % SAND: 27.6% 42.1% 55.6% 89.7% 67.2% % MUD: 1.5% 1.7% 6.0% 3.7% 5.8% % V COARSE GRAVEL: 0.0% 0.0% 0.0% 0.0% 0.0% % COARSE GRAVEL: 0.0% 0.0% 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 54.6% 32.8% 23.4% 0.1% 2.6% % FINE GRAVEL: 9.4% 12.2% 6.2% 0.6% 10.5% % V FINE GRAVEL: 6.9% 11.2% 8.8% 5.9% 13.9% % V COARSE SAND: 6.8% 12.0% 13.4% 21.7% 15.4% % COARSE SAND: 6.0% 9.8% 11.9% 23.6% 14.1% % MEDIUM SAND: 7.0% 9.6% 12.6% 22.0% 15.8% % FINE SAND: 6.2% 8.1% 11.7% 17.5% 14.7% % V FINE SAND: 1.7% 2.5% 6.0% 4.9% 7.2% % V COARSE SILT: 0.3% 0.3% 1.0% 0.6% 1.0% % COARSE SILT: 0.3% 0.3% 1.0% 0.6% 1.0% % MEDIUM SILT: 0.3% 0.3% 1.0% 0.6% 1.0% % FINE SILT: 0.3% 0.3% 1.0% 0.6% 1.0% % V FINE SILT: 0.3% 0.3% 1.0% 0.6% 1.0% % CLAY: 0.3% 0.3% 1.0% 0.6% 1.0%

126

SAMPLE STATISTICS

ANALYST AND DATE: 7, 3/3/2017 8, 3/17/2017 9, 3/30/2017 SIEVING ERROR: 0.2% 0.4% 0.4% Unimodal, Very Poorly Bimodal, Very Poorly SAMPLE TYPE: Unimodal, Poorly Sorted Sorted Sorted TEXTURAL GROUP: Sandy Gravel Gravelly Sand Sandy Gravel Very Fine Gravelly SEDIMENT NAME: Sandy Medium Gravel Medium Sand Sandy Medium Gravel FOLK AND MEAN -0.365 1.213 -0.352 (xa ) : WARD METHOD SORTING 1.928 2.063 2.034 (s I ) : (f) SKEWNESS 0.545 -0.101 0.109 (SkI ) : KURTOSIS 0.349 1.290 0.472 (KG ): FOLK AND MEAN: Very Coarse Sand Medium Sand Very Coarse Sand WARD METHOD SORTING: Poorly Sorted Very Poorly Sorted Very Poorly Sorted (Description) SKEWNESS: Very Fine Skewed Coarse Skewed Fine Skewed KURTOSIS: Very Platykurtic Leptokurtic Very Platykurtic % GRAVEL: 46.2% 12.5% 40.7% % SAND: 49.9% 79.4% 57.2% % MUD: 4.0% 8.1% 2.1% % V COARSE GRAVEL: 0.0% 0.0% 0.0% % COARSE GRAVEL: 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 29.1% 1.7% 17.1% % FINE GRAVEL: 8.1% 4.3% 10.2% % V FINE GRAVEL: 9.0% 6.5% 13.4% % V COARSE SAND: 11.2% 12.6% 11.0% % COARSE SAND: 11.5% 14.7% 10.3% % MEDIUM SAND: 11.8% 23.6% 17.5% % FINE SAND: 10.5% 21.8% 15.8% % V FINE SAND: 4.9% 6.8% 2.6% % V COARSE SILT: 0.7% 1.3% 0.3% % COARSE SILT: 0.7% 1.3% 0.3% % MEDIUM SILT: 0.7% 1.3% 0.3% % FINE SILT: 0.7% 1.3% 0.3% % V FINE SILT: 0.7% 1.3% 0.3% % CLAY: 0.7% 1.3% 0.3%

127

SAMPLE STATISTICS

ANALYST AND DATE: 9, 3/30/2017 SIEVING ERROR: -1.2% SAMPLE TYPE: Bimodal, Poorly Sorted TEXTURAL GROUP: Sandy Gravel SEDIMENT NAME: Sandy Medium Gravel

FOLK AND MEAN -0.572 (xa ) : WARD METHOD SORTING 1.589 (s I ) : (f) SKEWNESS 1.024 (SkI ) : KURTOSIS 0.302 (KG ): FOLK AND MEAN: Very Coarse Sand WARD METHOD SORTING: Poorly Sorted (Description) SKEWNESS: Very Fine Skewed KURTOSIS: Very Platykurtic % GRAVEL: 55.2% % SAND: 42.6% % MUD: 2.2% % V COARSE 0.0% GRAVEL: % COARSE GRAVEL: 0.0% % MEDIUM GRAVEL: 29.8% % FINE GRAVEL: 13.8% % V FINE GRAVEL: 11.5% % V COARSE SAND: 7.7% % COARSE SAND: 8.9% % MEDIUM SAND: 12.6% % FINE SAND: 11.0% % V FINE SAND: 2.3% % V COARSE SILT: 0.4% % COARSE SILT: 0.4% % MEDIUM SILT: 0.4% % FINE SILT: 0.4% % V FINE SILT: 0.4% % CLAY: 0.4%

128

SAMPLE STATISTICS

12 ANALYST AND DATE: 1, 12/5/2016 3, 1/3/2017 4, 1/14/2017 5, 1/26/2017 6, 2/16/2017 SIEVING ERROR: 0.2% 0.2% 0.1% -0.1% 0.4% Trimodal, Very Poorly Bimodal, Very Poorly Bimodal, Very Poorly SAMPLE TYPE: Bimodal, Poorly Sorted Bimodal, Poorly Sorted Sorted Sorted Sorted TEXTURAL GROUP: Gravelly Sand Sandy Gravel Gravelly Sand Gravelly Sand Gravelly Muddy Sand Very Fine Gravelly Very Fine Gravelly Very Medium Gravelly Medium Gravelly Coarse SEDIMENT NAME: Sandy Medium Gravel Medium Sand Coarse Sand Medium Sand Silty Medium Sand

FOLK AND MEAN (xa ) : 1.180 -0.239 0.823 0.415 0.255 WARD METHOD SORTING (s ) : 1.925 2.638 1.558 2.660 2.854 I (f) SKEWNESS (SkI ) : 0.060 0.149 0.046 -0.086 -0.071

KURTOSIS (KG ): 1.034 0.838 0.847 1.003 0.885 FOLK AND MEAN: Medium Sand Very Coarse Sand Coarse Sand Coarse Sand Coarse Sand WARD METHOD SORTING: Poorly Sorted Very Poorly Sorted Poorly Sorted Very Poorly Sorted Very Poorly Sorted (Description) SKEWNESS: Symmetrical Fine Skewed Symmetrical Symmetrical Symmetrical KURTOSIS: Mesokurtic Platykurtic Platykurtic Mesokurtic Platykurtic % GRAVEL: 9.8% 42.6% 9.8% 26.6% 28.4% % SAND: 83.3% 51.9% 87.3% 66.6% 63.5% % MUD: 6.9% 5.5% 3.0% 6.9% 8.1% % V COARSE 0.0% 0.0% 0.0% 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 0.1% 14.8% 0.6% 12.2% 16.8% % FINE GRAVEL: 1.7% 14.4% 0.9% 6.7% 4.7% % V FINE GRAVEL: 8.0% 13.5% 8.2% 7.6% 6.9% % V COARSE SAND: 18.9% 14.4% 25.2% 13.0% 11.8% % COARSE SAND: 17.6% 11.2% 18.5% 14.7% 12.9% % MEDIUM SAND: 19.7% 9.4% 20.8% 15.6% 14.9% % FINE SAND: 18.2% 9.3% 18.1% 14.6% 14.6% % V FINE SAND: 9.0% 7.6% 4.6% 8.7% 9.4% % V COARSE SILT: 1.2% 0.9% 0.5% 1.1% 1.3% % COARSE SILT: 1.2% 0.9% 0.5% 1.1% 1.3% % MEDIUM SILT: 1.2% 0.9% 0.5% 1.1% 1.3% % FINE SILT: 1.2% 0.9% 0.5% 1.1% 1.3% % V FINE SILT: 1.2% 0.9% 0.5% 1.1% 1.3% % CLAY: 1.2% 0.9% 0.5% 1.1% 1.3%

129

SAMPLE STATISTICS

ANALYST AND DATE: 7, 3/3/2017 8, 3/17/2017 9, 3/30/2017 SIEVING ERROR: -0.7% 0.2% 0.0% Unimodal, Very Poorly SAMPLE TYPE: Bimodal, Poorly Sorted Bimodal, Poorly Sorted Sorted TEXTURAL GROUP: Sandy Gravel Gravelly Muddy Sand Sandy Gravel Very Fine Gravelly SEDIMENT NAME: Sandy Medium Gravel Coarse Silty Very Sandy Medium Gravel Coarse Sand FOLK AND MEAN -0.435 1.074 -0.410 (xa ) : WARD METHOD SORTING 1.488 2.188 1.806 (s I ) : (f) SKEWNESS 1.287 0.190 0.659 (SkI ) : KURTOSIS 0.539 1.138 0.364 (KG ): FOLK AND MEAN: Very Coarse Sand Medium Sand Very Coarse Sand WARD METHOD SORTING: Poorly Sorted Very Poorly Sorted Poorly Sorted (Description) SKEWNESS: Very Fine Skewed Fine Skewed Very Fine Skewed KURTOSIS: Very Platykurtic Leptokurtic Very Platykurtic % GRAVEL: 56.1% 13.0% 47.9% % SAND: 39.7% 77.3% 48.6% % MUD: 4.2% 9.7% 3.5% % V COARSE 0.0% 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 43.6% 1.8% 26.7% % FINE GRAVEL: 2.6% 2.8% 10.3% % V FINE GRAVEL: 9.9% 8.4% 11.0% % V COARSE SAND: 12.3% 19.7% 13.7% % COARSE SAND: 9.1% 18.7% 13.2% % MEDIUM SAND: 7.4% 16.6% 8.6% % FINE SAND: 6.7% 14.5% 7.2% % V FINE SAND: 4.2% 7.8% 6.0% % V COARSE SILT: 0.7% 1.6% 0.6% % COARSE SILT: 0.7% 1.6% 0.6% % MEDIUM SILT: 0.7% 1.6% 0.6% % FINE SILT: 0.7% 1.6% 0.6% % V FINE SILT: 0.7% 1.6% 0.6% % CLAY: 0.7% 1.6% 0.6%

130

SAMPLE STATISTICS

13 ANALYST AND DATE: 1, 12/5/2016 2, 12/21/2016 3, 1/3/2017 4, 1/14/2017 5, 1/26/2017 SIEVING ERROR: 0.2% 0.3% 0.4% 0.4% 0.1% Unimodal, Very Poorly Bimodal, Very Poorly Bimodal, Very Poorly SAMPLE TYPE: Unimodal, Poorly Sorted Unimodal, Poorly Sorted Sorted Sorted Sorted TEXTURAL GROUP: Sandy Gravel Sandy Gravel Sandy Gravel Gravelly Muddy Sand Gravelly Sand Medium Gravelly Very Very Fine Gravelly Very SEDIMENT NAME: Sandy Medium Gravel Sandy Medium Gravel Sandy Very Fine Gravel Coarse Silty Very Coarse Sand Coarse Sand FOLK AND MEAN (xa ) : -1.412 -0.433 0.368 -0.097 0.741

WARD METHOD SORTING (s I ) : 1.447 2.049 2.171 2.696 1.752

(f) SKEWNESS (SkI ) : 0.709 0.103 0.049 0.161 0.101

KURTOSIS (KG ): 0.439 0.661 0.918 0.927 0.897 FOLK AND MEAN: Very Fine Gravel Very Coarse Sand Coarse Sand Very Coarse Sand Coarse Sand WARD METHOD SORTING: Poorly Sorted Very Poorly Sorted Very Poorly Sorted Very Poorly Sorted Poorly Sorted (Description) SKEWNESS: Very Fine Skewed Fine Skewed Symmetrical Fine Skewed Fine Skewed KURTOSIS: Very Platykurtic Very Platykurtic Mesokurtic Mesokurtic Platykurtic % GRAVEL: 60.8% 31.4% 36.0% 25.5% 15.3% % SAND: 37.7% 66.1% 58.2% 65.3% 80.1% % MUD: 1.5% 2.6% 5.7% 9.2% 4.6% % V COARSE 0.0% 0.0% 0.0% 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 27.2% 17.0% 4.4% 18.0% 1.0% % FINE GRAVEL: 13.6% 5.6% 5.2% 1.9% 3.8% % V FINE GRAVEL: 20.0% 8.7% 26.4% 5.6% 10.5% % V COARSE SAND: 16.5% 19.8% 9.2% 18.8% 22.5% % COARSE SAND: 10.9% 19.4% 16.3% 16.3% 19.4% % MEDIUM SAND: 5.5% 13.1% 15.5% 13.0% 17.1% % FINE SAND: 3.6% 9.6% 12.3% 11.0% 14.6% % V FINE SAND: 1.2% 4.2% 4.8% 6.1% 6.5% % V COARSE SILT: 0.3% 0.4% 1.0% 1.5% 0.8% % COARSE SILT: 0.3% 0.4% 1.0% 1.5% 0.8% % MEDIUM SILT: 0.3% 0.4% 1.0% 1.5% 0.8% % FINE SILT: 0.3% 0.4% 1.0% 1.5% 0.8% % V FINE SILT: 0.3% 0.4% 1.0% 1.5% 0.8% % CLAY: 0.3% 0.4% 1.0% 1.5% 0.8%

131

SAMPLE STATISTICS

ANALYST AND DATE: 6, 2/16/2017 7, 3/3/2017 8, 3/17/2017 9, 3/30/2017 SIEVING ERROR: 0.4% -0.4% 0.3% 0.2% Unimodal, Very Poorly Bimodal, Very Poorly Bimodal, Very Poorly SAMPLE TYPE: Unimodal, Poorly Sorted Sorted Sorted Sorted TEXTURAL GROUP: Sandy Gravel Sandy Gravel Muddy Sandy Gravel Sandy Gravel Very Coarse Silty SEDIMENT NAME: Sandy Very Fine Gravel Sandy Medium Gravel Sandy Medium Gravel Sandy Medium Gravel FOLK AND MEAN -0.340 -1.295 -0.141 -1.014 (xa ) : WARD METHOD SORTING 2.033 2.069 2.851 1.401 (s I ) : (f) SKEWNESS 0.051 0.552 0.235 0.963 (SkI ) : KURTOSIS 0.836 0.503 0.600 0.338 (KG ): FOLK AND MEAN: Very Coarse Sand Very Fine Gravel Very Coarse Sand Very Fine Gravel WARD METHOD SORTING: Very Poorly Sorted Very Poorly Sorted Very Poorly Sorted Poorly Sorted (Description) SKEWNESS: Symmetrical Very Fine Skewed Fine Skewed Very Fine Skewed KURTOSIS: Platykurtic Very Platykurtic Very Platykurtic Very Platykurtic % GRAVEL: 40.5% 55.5% 40.2% 58.9% % SAND: 57.3% 40.9% 50.0% 39.7% % MUD: 2.2% 3.6% 9.9% 1.4% % V COARSE 0.0% 0.0% 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 8.2% 28.6% 22.6% 32.5% % FINE GRAVEL: 14.5% 12.6% 9.0% 10.6% % V FINE GRAVEL: 17.9% 14.3% 8.6% 15.8% % V COARSE SAND: 17.2% 13.7% 9.1% 14.0% % COARSE SAND: 14.6% 10.7% 9.7% 10.7% % MEDIUM SAND: 13.1% 7.7% 11.6% 7.5% % FINE SAND: 10.3% 6.1% 11.6% 5.7% % V FINE SAND: 2.1% 2.7% 8.0% 1.9% % V COARSE SILT: 0.4% 0.6% 1.6% 0.2% % COARSE SILT: 0.4% 0.6% 1.6% 0.2% % MEDIUM SILT: 0.4% 0.6% 1.6% 0.2% % FINE SILT: 0.4% 0.6% 1.6% 0.2% % V FINE SILT: 0.4% 0.6% 1.6% 0.2% % CLAY: 0.4% 0.6% 1.6% 0.2%

132

SAMPLE STATISTICS

14 ANALYST AND DATE: 3, 1/3/2017 4, 1/14/2017 5, 1/26/2017 6, 2/16/2017 7, 3/3/2017 SIEVING ERROR: 0.2% 0.2% 0.0% 2.0% 0.0% Unimodal, Very Poorly SAMPLE TYPE: Unimodal, Poorly Sorted Unimodal, Poorly Sorted Unimodal, Poorly Sorted Unimodal, Poorly Sorted Sorted TEXTURAL GROUP: Gravelly Sand Slightly Gravelly Sand Gravelly Sand Gravelly Sand Gravelly Sand Slightly Very Fine Very Fine Gravelly Very Fine Gravelly Fine Very Fine Gravelly Very Fine Gravelly SEDIMENT NAME: Gravelly Very Coarse Medium Sand Sand Medium Sand Medium Sand Sand

FOLK AND MEAN (xa ) : 1.309 0.837 1.733 1.311 1.170 WARD METHOD SORTING (s ) : 1.830 1.354 1.888 2.210 1.749 I (f) SKEWNESS (SkI ) : -0.138 0.121 -0.016 -0.108 -0.300

KURTOSIS (KG ): 1.206 0.801 1.372 1.245 1.187 FOLK AND MEAN: Medium Sand Coarse Sand Medium Sand Medium Sand Medium Sand WARD METHOD SORTING: Poorly Sorted Poorly Sorted Poorly Sorted Very Poorly Sorted Poorly Sorted (Description) SKEWNESS: Coarse Skewed Fine Skewed Symmetrical Coarse Skewed Coarse Skewed KURTOSIS: Leptokurtic Platykurtic Leptokurtic Leptokurtic Leptokurtic % GRAVEL: 9.6% 4.5% 6.6% 14.0% 12.5% % SAND: 83.9% 93.4% 84.9% 78.1% 83.2% % MUD: 6.5% 2.1% 8.5% 7.9% 4.3% % V COARSE 0.0% 0.0% 0.0% 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 0.3% 0.0% 0.5% 3.3% 3.4% % FINE GRAVEL: 3.1% 0.3% 2.2% 3.9% 2.7% % V FINE GRAVEL: 6.3% 4.2% 4.0% 6.8% 6.5% % V COARSE SAND: 12.7% 26.8% 9.1% 11.0% 10.0% % COARSE SAND: 13.8% 24.3% 14.2% 14.1% 15.3% % MEDIUM SAND: 25.4% 21.6% 24.2% 21.3% 26.7% % FINE SAND: 24.3% 16.9% 24.4% 21.0% 24.6% % V FINE SAND: 7.7% 3.8% 12.9% 10.8% 6.6% % V COARSE SILT: 1.1% 0.3% 1.4% 1.3% 0.7% % COARSE SILT: 1.1% 0.3% 1.4% 1.3% 0.7% % MEDIUM SILT: 1.1% 0.3% 1.4% 1.3% 0.7% % FINE SILT: 1.1% 0.3% 1.4% 1.3% 0.7% % V FINE SILT: 1.1% 0.3% 1.4% 1.3% 0.7% % CLAY: 1.1% 0.3% 1.4% 1.3% 0.7%

133

SAMPLE STATISTICS

ANALYST AND DATE: 8, 3/17/2017 9, 3/30/2017 SIEVING ERROR: 0.5% 0.1% Unimodal, Very Poorly Bimodal, Very Poorly SAMPLE TYPE: Sorted Sorted TEXTURAL GROUP: Gravelly Sand Gravelly Sand

Very Fine Gravelly Fine Gravelly Medium SEDIMENT NAME: Medium Sand Sand FOLK AND MEAN 1.114 1.058 (xa ) : WARD METHOD SORTING 2.029 2.070 (s I ) : (f) SKEWNESS -0.129 -0.207 (SkI ) : KURTOSIS 1.063 1.031 (KG ): FOLK AND MEAN: Medium Sand Medium Sand WARD METHOD SORTING: Very Poorly Sorted Very Poorly Sorted (Description) SKEWNESS: Coarse Skewed Coarse Skewed KURTOSIS: Mesokurtic Mesokurtic % GRAVEL: 15.5% 17.6% % SAND: 78.4% 77.0% % MUD: 6.1% 5.4% % V COARSE 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% % MEDIUM GRAVEL: 0.6% 0.8% % FINE GRAVEL: 6.4% 9.0% % V FINE GRAVEL: 8.6% 7.9% % V COARSE SAND: 12.5% 9.1% % COARSE SAND: 15.1% 15.3% % MEDIUM SAND: 21.4% 21.6% % FINE SAND: 20.3% 20.6% % V FINE SAND: 9.0% 10.3% % V COARSE SILT: 1.0% 0.9% % COARSE SILT: 1.0% 0.9% % MEDIUM SILT: 1.0% 0.9% % FINE SILT: 1.0% 0.9% % V FINE SILT: 1.0% 0.9% % CLAY: 1.0% 0.9%

134

SAMPLE STATISTICS

15 ANALYST AND DATE: 3, 1/3/2017 4, 1/14/2017 5, 1/26/2017 6, 2/16/2017 7, 3/3/2017 SIEVING ERROR: 0.0% 0.4% 0.1% 0.7% 0.4% Unimodal, Very Poorly Unimodal, Very Poorly Unimodal, Very Poorly Unimodal, Very Poorly SAMPLE TYPE: Bimodal, Poorly Sorted Sorted Sorted Sorted Sorted TEXTURAL GROUP: Gravelly Sand Gravelly Muddy Sand Gravelly Muddy Sand Sandy Gravel Sandy Gravel Very Fine Gravelly Very Very Fine Gravelly Very Fine Gravelly Very SEDIMENT NAME: Coarse Silty Very Coarse Silty Very Sandy Very Fine Gravel Sandy Very Fine Gravel Coarse Sand Coarse Sand Coarse Sand

FOLK AND MEAN (xa ) : 0.270 1.419 0.992 -0.415 -0.496 WARD METHOD SORTING (s ) : 1.918 2.300 2.289 2.283 2.223 I (f) SKEWNESS (SkI ) : 0.080 0.172 0.120 0.261 0.267 KURTOSIS (K ): 0.816 1.154 0.993 0.870 0.746 G ( Sk ) FOLK AND MEAN: Coarse Sandf Medium Sand Coarse Sand Very Coarse Sand Very Coarse Sand WARD METHOD SORTING: Poorly Sorted Very Poorly Sorted Very Poorly Sorted Very Poorly Sorted Very Poorly Sorted (Description) SKEWNESS: Symmetrical Fine Skewed Fine Skewed Fine Skewed Fine Skewed KURTOSIS: Platykurtic Leptokurtic Mesokurtic Platykurtic Platykurtic % GRAVEL: 28.6% 9.6% 18.4% 47.4% 43.7% % SAND: 68.2% 76.9% 73.2% 48.2% 51.4% % MUD: 3.3% 13.6% 8.4% 4.4% 4.9% % V COARSE 0.0% 0.0% 0.0% 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% 0.0% 0.0% 0.0% % MEDIUM GRAVEL: 0.8% 1.7% 2.0% 9.2% 16.5% % FINE GRAVEL: 9.6% 1.5% 3.8% 18.2% 8.8% % V FINE GRAVEL: 18.1% 6.4% 12.5% 19.9% 18.3% % V COARSE SAND: 18.3% 17.9% 17.1% 14.6% 15.6% % COARSE SAND: 14.6% 17.3% 15.8% 11.0% 12.6% % MEDIUM SAND: 16.6% 17.1% 14.9% 9.2% 10.4% % FINE SAND: 14.6% 15.6% 14.5% 8.4% 8.8% % V FINE SAND: 4.1% 9.0% 11.0% 5.1% 4.0% % V COARSE SILT: 0.5% 2.3% 1.4% 0.7% 0.8% % COARSE SILT: 0.5% 2.3% 1.4% 0.7% 0.8% % MEDIUM SILT: 0.5% 2.3% 1.4% 0.7% 0.8% % FINE SILT: 0.5% 2.3% 1.4% 0.7% 0.8% % V FINE SILT: 0.5% 2.3% 1.4% 0.7% 0.8% % CLAY: 0.5% 2.3% 1.4% 0.7% 0.8%

135

SAMPLE STATISTICS

ANALYST AND DATE: 8, 3/17/2017 9, 3/30/2017 SIEVING ERROR: 0.5% 0.3% Bimodal, Very Poorly Unimodal, Very Poorly SAMPLE TYPE: Sorted Sorted TEXTURAL GROUP: Gravelly Muddy Sand Sandy Gravel Very Fine Gravelly Very SEDIMENT NAME: Coarse Silty Very Sandy Very Fine Gravel Coarse Sand FOLK AND MEAN 0.918 -0.558 (xa ) : WARD METHOD SORTING 2.250 2.216 (s I ) : (f) SKEWNESS 0.106 0.220 (SkI ) : KURTOSIS 1.055 0.920 (KG ): FOLK AND MEAN: Coarse Sand Very Coarse Sand WARD METHOD SORTING: Very Poorly Sorted Very Poorly Sorted (Description) SKEWNESS: Fine Skewed Fine Skewed KURTOSIS: Mesokurtic Mesokurtic % GRAVEL: 18.3% 48.4% % SAND: 73.4% 48.9% % MUD: 8.3% 2.7% % V COARSE 0.0% 0.0% GRAVEL: % COARSE GRAVEL: 0.0% 0.0% % MEDIUM GRAVEL: 1.2% 11.8% % FINE GRAVEL: 5.3% 14.9% % V FINE GRAVEL: 11.8% 21.7% % V COARSE SAND: 17.8% 13.7% % COARSE SAND: 15.6% 13.0% % MEDIUM SAND: 16.4% 9.5% % FINE SAND: 15.3% 7.9% % V FINE SAND: 8.4% 4.9% % V COARSE SILT: 1.4% 0.4% % COARSE SILT: 1.4% 0.4% % MEDIUM SILT: 1.4% 0.4% % FINE SILT: 1.4% 0.4% % V FINE SILT: 1.4% 0.4% % CLAY: 1.4% 0.4%

136