Wildfire Impacts on Soil Physical Properties: a 3-Year Assessment for The

Wildfire Impacts on Soil Physical Properties: A 3-year assessment for the

2016 Erskine Fire, CA

Cindy Rodriguez

A Thesis Submitted to the Department of Geological Sciences California State University, Bakersfield In Partial Fulfillment for the Degree of Master of Science in Geology

Spring 2021

Cindy Rodriguez

2021

Wildfire Impacts on Soil Physical Properties: A 3-year assessment for the 2016 Erskine

Fire, CA

By Cindy Rodriguez

This thesis has been accepted on behalf of the Department of Geological Sciences by their supervisory committee:

______Dr. Junhua Guo, Committee Chair

______Dr. William Krugh

______Dr. Eduardo Montoya

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ACKNOWLEDGEMENTS

Thanks to the CSUB Center for Research Excellence in Science and Technology (CREST)

program for fully supporting me financially and making it possible for me to attend school and

conduct this research.

I want to thank my advisor Dr. Guo, for his guidance, support, and patience throughout each

stage of my thesis research. Thank you, Dr. Krugh and Dr. Montoya, for your input and revising

my thesis, and being part of my committee. Thanks to Elizabeth Powers and Sue Holt for

maintaining laboratory supplies, providing door access, and their positive attitudes.

To my friends, thank you for all the happy distractions to rest my mind outside of

my research. Special thanks to Toni Ramirez, to whom I worked closely throughout my time at

CSUB. Thanks for the laughs, advice, and words of encouragement. I would also like to

acknowledge my cat Lexie for her companionship through the writing process.

Most importantly, I would like to thank my parents and sister for their continued

support throughout my academic journey. Their love and emotional support helped me

push through difficult times. This would not be possible without them.

ABSTRACT

The 2016 Erskine Fire burned 48,020 acres in a portion of the southern Sierra Nevada,

creating unburned or very low, low, moderate, and high burn severity patterns that allowed us to

quantify the differences in soil alterations as a function of burn severity. Haake et al. (2020)

investigated the same area and found that soil properties changed according to burn severity. In

order to investigate the potential recovery state for the burned soils, we re-collected soil samples from the same site two years later. The physical properties of soils were explored using liquid limit (LL), plastic limit (PL), X-Ray diffractometry (XRD), total organic carbon content (TOC), grain size analysis, and direct shear tests. Results from this study reveal that low burn severity

(LBS) and moderate burn severity (MBS) soils had the highest LL and PL. High burn severity

(HBS) soils had a very low plasticity index (PI) value due to the low abundance of clay minerals.

These results are different from the previous work done by Haake et al. (2020), which could be attributed to the timing of post-fire soil sampling. Shear results reveal variable results under the normal loads of 0.5 tons per square foot (TSF) and 1.0 TSF; and an increasing trend in shear strength with increasing burn severity under the normal load of 2.0 TSF. HBS, MBS, and very low burn severity (VLBS) soils exhibit cohesion values of zero and 0.2 for LBS soils. Shear strength and cohesion values differ from the results of Haake et al. (2020). Differences in results might be due to modifications in the tests. XRD analysis reveals a lower abundance of clay minerals (smectite and chlorite/kaolinite) in the HBS soils. TOC content was highest in HBS and

MBS soils and lowest in LBS soils. This result differs from the previous study by Haake et al.

(2020), where TOC increased with increasing burn severity. Differences in the TOC may be related to the high vegetation density in the sampling locations during the recovery time. Grain size results of this study show clay-sized particles were less abundant in moderate and high burn

severities. Similarly, grain size results from Haake et al. (2020) revealed that clay-sized particles were most abundant in lower burn severity soils. Less abundant clay- and silt-sized particles in the high burn severity soils could be attributed to high temperature exposure from the fire.

Previous studies exploring the impacts of fire on soil properties have produced variable results, suggesting that soil property alterations due to fire is different from case by case.

TABLE OF CONTENTS ACKNOWLEDGEMENTS ...... iv ABSTRACT ...... v LIST OF FIGURES ...... viii LIST OF TABLES ...... ix INTRODUCTION ...... 1 BACKGROUND ...... 3 Fire Behavior ...... 3 Soil Burn Severity Classification ...... 4 Soil properties, vegetation, and slope stability ...... 6 Post-fire debris flows ...... 9 MATERIALS AND METHODS ...... 11 Study Area ...... 11 Sample Collection and Processing ...... 12 Atterberg Limits ...... 13 Direct Shear Strength ...... 15 Mineralogy ...... 16 Total Organic Carbon ...... 18 Grain Size Distribution ...... 18 RESULTS ...... 20 Atterberg Limits ...... 20 Shear Test...... 21 Mineralogy ...... 21 Total Organic Carbon ...... 24 Grain Size...... 24 DISCUSSION ...... 26 CONCLUSION ...... 32 REFERENCES ...... 34 FIGURES ...... 41 TABLES ...... 67 APPENDICES ...... 78

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LIST OF FIGURES

Figure 1. Fire Behavior Triangle ...... 41 Figure 2. Four States of Soil ...... 42 Figure 3. Geologic Map of the Kern River Valley and surrounding region ...... 43 Figure 4. Soil Map of the Erskine Fire Perimeter ...... 44 Figure 5. Images of the sampling locations ...... 45 Figure 6. Map of the Erskine Fire Perimeter and surrounding region ...... 46 Figure 7. Erskine Fire Burn Severity Map ...... 47 Figure 8. Liquid Limit procedure ...... 48 Figure 9. Plastic Limit procedure ...... 49 Figure 10. Direct Shear machine and shear box assembly ...... 50 Figure 11. Powder Bulk XRD procedure ...... 51 Figure 12. Clay-sized fraction XRD procedure ...... 52 Figure 13. Graph of liquid limit results ...... 53 Figure 14. Graph of plastic limit results ...... 54 Figure 15. Graph of plasticity index results ...... 55 Figure 16. Graph of shear strength results ...... 56 Figure 17. Graph of bulk XRD profiles ...... 57 Figure 18. Graph of untreated clay XRD profiles ...... 58 Figure 19. Graph of glycolated clay XRD profiles ...... 59 Figure 20. Graph of heated clay XRD profiles ...... 60 Figure 21. Graph of total organic carbon results ...... 61 Figure 22. Graph of clay-sized particles results...... 62 Figure 23. Graph of silt-sized particles results ...... 63 Figure 24. Graph of sand-sized particles results ...... 64 Figure 25. Satellite imagery before and after the Erskine Fire ...... 65 Figure 26. Graphs of Atterberg Limits results, this study vs Haake et al. (2020) ...... 66

viii

LIST OF TABLES

Table 1. Sample Locations ...... 67 Table 2. Test Methods...... 68 Table 3. Average Atterberg limit results ...... 69 Table 4. Average Shear Strength results ...... 70 Table 5. Bulk XRD results ...... 71 Table 6. Clay-sized XRD results ...... 72 Table 7. Total Organic Carbon (TOC) results ...... 73 Table 8. Grain Size results ...... 74 Table 9. Total Orgainc Carbon (TOC), Haake et al. (2020) vs. this study ...... 75 Table 10. Atterberg Limit results, Haake et al. (2020) vs. this study ...... 76 Table 11. Shear Strength results, Haake et al. (2020) vs. this study ...... 77

Wildfire Impacts on Soil Physical Properties: A 3-year assessment for the 2016 Erskine Fire, CA

Cindy Rodriguez California State University Bakersfield, Department of Geological Sciences, 9001 Stockdale Highway, Bakersfield, CA. 93311 USA

INTRODUCTION

The frequency, severity, and extent of wildfires have increased in the western United

States; the occurrence of large wildfires has increased by six times since the mid-1980s

(Westerling et al., 2006; Westerling, 2016). The occurrence and duration of the fire seasons have increased due to climate change, accumulated dry vegetation that fuels the fires, and expansion of human settlement in fire-prone regions (Westerling et al., 2006; Westerling, 2016; Williams et al., 2019). The 2018 wildfire season was the worst season recorded; the fire seasons have become longer due to California’s ongoing drought (CalFire, 2018; Williams et al., 2019). The increase in wildfires leads to property losses and escalating firefighting costs. The impacts of wildfires are far beyond the fire itself; wildfires disrupt the environment threatening the landscape and the surrounding communities.

Wildfires impact the landscape by disrupting the physical, chemical, and biological activity in the area, leaving the landscape altered years after the fire has passed and contribute to the formation of a burn scar (Certini, 2005). A burn scar is an area where repeated wildfires have removed the ground surface vegetative cover and altered the physical and chemical properties of soil. Soil properties that are important to slope stability include soil water repellency, soil plasticity, shear strength, total organic carbon (TOC), mineralogy, and grain size distribution.

Previous research suggests that such alterations to the soil’s physical properties can play a significant role in post-fire erosion (DeBano 2000; Parise & Cannon, 2012). The change of these

fundamental soil properties leads to reduced cohesion of the soils on the hillslope, thereby

decreasing the soil shear strength, and ultimately initiating slope failure, as well as flash floods

and debris flow events typically following a wildfire and often triggered by precipitation.

Haake et al. (2020) collected samples and investigated the response of soil change due to

the 2016 Erskine Fire near Lake Isabella, CA. Their results revealed that liquid limit, plastic limit, TOC, and consolidated drained shear strength of soils increased with increasing burn severity. X-Ray diffraction (XRD) analyses revealed that soils with higher burn severities

contain a lower concentration of the clay minerals smectite and chlorite/kaolinite, thus making the soils less cohesive and increasing the risk for erosion. The Haake et al. (2020) study, however, just focused on the soil change following a fire and did not examine the recovery of soil properties over time. In this study, I explore the wildfire impacts on soil physical properties as a function of not only burn severity but also time. I hypothesize that in the timespan of three years the soils that were exposed to high intensity fire conditions have not fully recovered to their pre-fire state. To test this hypothesis, I used various laboratory methods to constrain soil properties including Atterberg limits, shear strength, bulk mineralogy, clay mineralogy, total organic content, and grain size distribution for samples collected in 2019 from the same sites within the 2016 Erskine Fire perimeter analyzed by Haake et al. (2020). The goal of this study is to assess the recovery of post-fire alterations of soil physical properties as a function of burn severity.

BACKGROUND

Fire Behavior

Fire, also known as combustion, is an energy releasing chemical reaction controlled by

three components: a heat source, fuel, and oxygen. Wildfires are influenced by three main factors

that include weather, fuel, and topography (Figure 1; Carle, 2008; NPS, 2017). Weather, such as wind, temperature, precipitation, and humidity can affect the intensity, severity and the size of the wildfire. The direction and speed of a fire are controlled by the wind conditions. High temperatures (above 80°F) during a fire, result in fires that are very intense and severe.

Additionally, at low humidity levels (< 20%), fuel sources become dry and are able to ignite easily, and burn quickly (NPS, 2017). Fuel characteristics, such as moisture content, fuel type, and density determine the degree of flammability during a fire. Dead, woody debris contain very little moisture and often produce intense fires that quickly spread. In addition, fuel type and abundance determine the degree of flammability and the duration of a fire (Carle, 2008; NPS,

2017). Topographic features such as steepness and slope aspect also contribute to the fire behavior. Fire generally spreads faster uphill and in areas that have steeper slopes. Elevation and slope aspect determine how hot and dry the area is; fuel dries earlier on south slopes compared to north-facing slopes (NPS, 2017).

Keeley (2009) proposed that fire intensity is used to describe fire characteristics

(temperature and duration), while burn severity is used to characterize post-fire effects. Fire intensity is a measure of the energy released from the combustion of organic matter during various phases of a fire, while fire severity describes the change in organic matter both above ground and within the soil. Fire intensity does not quantify the direct effects on the soil or vegetation. Fire severity is a product of fireline intensity and the duration of heat, in addition to

the effects on the landscape (Carle, 2008; NPS, 2017). Burn severity is sometimes used

interchangeably with fire severity, which is commonly described as high, moderate, or low. Soil

burn severity is defined as the loss of organic matter in or on the soil surface. Both fire intensity

and burn severity are factors that influence the physical properties of soil.

Soil Burn Severity Classification

Post-fire assessments are conducted by the U.S. Department of Agriculture (USDA) and

the Burned Area Emergency Response (BAER) teams after the occurrence of large wildfires.

These assessments identify the post-fire effects on the landscape and determine whether the fire

has created risky environmental conditions for human safety (Parson et al., 2010). A typical burn

severity assessment for the burned areas consists of a post-fire visual inspection of the physical

conditions such as canopy, understory vegetation, ground cover, litter, and signs of erosion

(Romero et al., 2018). To reflect the fire effects on the soil ground surface and identify potential

areas of concern, a burn severity map is required (Parson et al., 2010). Generally, satellite

imagery and remote sensing techniques are utilized for rapid post-fire assessments. Once initial

remote sensing classification is done, field reconnaissance is completed to verify soil conditions and finalized burn severity maps (Parson et al., 2010).

Parson et al. (2010) provided the field guide techniques for mapping post-fire burn severity and discussed the purpose and use of the map along with classifications of burn severity.

Burn severity is usually categorized into four severity types: very low or unburned, low, moderate, and high. Very low to unburned soils typically have experienced no significant damage from the fire. In the low burn severity, the surface appears to be slightly charred with sparse green vegetation and an unaffected soil structure. In the moderate classification, roughly

80% of the ground cover is consumed, leaving behind a brown to black colored soil and an

unchanged soil structure. In the high burn severity classification, nearly all of the ground cover is

consumed and charred, leaving behind a black or white ash layer. The soil structure is also

destroyed due to the consumption of soil organic matter. However, these burn severity indicators

are just guidelines, and they are not necessarily applicable for all fires and vegetation types

(Parson et al., 2010).

California Wildfires

Much of the wildfires that occurred in the twentieth century are related to fuel buildup. It appears that global climate change has magnified the effects of severe weather patterns, resulting as a major driving force (Carle, 2008). Each year, the fire season becomes longer; individual fires last about 37 days longer due to the accumulation of fuel that is available for fires

(Westerling et al., 2006; Steel et al., 2015). Furthermore, bad forest management practices have intensified the wildfire conditions (Steel et al., 2015). Since the 1940s, forest management and fire policies have changed in the West, and the lack of fire suppression has allowed fuel loads to accumulate (Steel et al., 2015). Instead, it is important to utilize fire suppression because it helps with forest thinning, which can potentially prevent larger and catastrophic fires (Westerling et al., 2006; Steel et al., 2015). In addition to bad forest practices and climate change, the expansion of housing into the wildland-urban interface (WUI) has increased, leading to more wildfires in

these areas, thus endangering the ecosystem and economy (Balch et al., 2017).

California’s pattern of wet winters and dry summers is characteristic of Mediterranean climates (Carle, 2008). A changing climate creates warmer and drier conditions that increase wildfire risk. The 2018 wildfire season was among the worst in California’s history, where the largest and most destructive wildfires occurred in the month of August (CalFire, 2018). The 2018

Camp Fire is a prime example of how dangerous and catastrophic wildfires can be, and this fire

was the deadliest and destructive in California’s history. On November 8, 2018, in Northern

California, the Camp Fire burned over 153,000 acres and nearly wiped out the entire town of

Paradise, CA; leaving thousands of people without homes, jobs, and resources (CalFire, 2018).

Bad air quality and contaminated water are some of the resulting impacts of that wildfire.

Although, wildfires have a large impact on the affected communities, the post-fire secondary effects like flash floods and debris flows can have a greater effect on the ecosystem and the community.

Soil properties, vegetation, and slope stability

Wildfires impact the landscape by changing the ground surface cover and the soil.

Depending on the intensity, severity, and duration of the fire, the changes in the landscape can be short-term, long-term, or permanent (Certini, 2005). Wildfires often produce a burn scar with alterations in the vegetation and soil properties. Fire directly affects soil by altering the physical, chemical, and biological properties of soil; these alterations are dependent on the soil type, the amount of vegetation destroyed, the intensity and severity of the fire (Certini, 2005; Barkley,

2002). Soil properties that are altered by fire, for example, include liquid limit (LL), plastic limit

(PL), shear strength, organic matter, mineralogy, grain size distribution, and water repellency.

Liquid limit, plastic limit, and the plasticity index (PI), together referred to as Atterberg limits, can be used to reveal important soil properties such as soil behavior and shear strength. A soil is able to change into four states with increasing water content that include solid, semi-solid, plastic, and liquid states (Figure 2). The water content at which the soil changes from a liquid to a plastic state is termed the liquid limit. The plastic limit is defined as the water content at which the soil changes from a plastic to a semi-solid state. The plasticity index, the difference between

LL and PL, is useful for characterizing the behavior of fine soils. Several studies have reported

mixed results for identifying a direct correlation between burn severity and changes in the

Atterberg limits (Vacchiano et al., 2014; Deng et al., 2017).

Shear strength tests measure the soil’s ability to resists forces that cause the soil to fail. In

the field, plant roots increase effective cohesion, shear strength, and slope stability. Schmidt et

al. (2001) found that shear strength is influenced by plant roots, where the soil-root composite shear strength (Ssr) is expressed by

' ' sr = cs + cr + (σ – u) tan ϕ (1);

' where cs is the effective cohesion 𝑆𝑆of the soil, cr is the apparent cohesion provided by roots, σ is

the normal stress due to the weight of the soil and water of the sliding mass, u is the soil pore-

water pressure, and ϕ' is the effective internal friction angle of the soil that is unaltered by the

presence of roots. During combustion, roots die and decrease the cohesion, shear strength, and

ultimately lead to slope failure. Heat generated from wildfires decomposes plant roots, reduces

the critical shear strength of soils, making them more susceptible to erosion and increase the risk

for debris flow (Schmidt et al., 2001; Moody et al., 2013).

Organic matter (OM) is found in the upper surface of the soil profile; although it is

concentrated on the soil surface, it also plays an important role in the properties of the underlying

soil horizons (Neary et al., 2005). Total organic carbon (TOC) is the carbon stored in soil organic

matter, where the organic matter enters the soil through the decomposition of plant and animal

residues. Soil texture, vegetation, topography, climate, and fire conditions are factors that affect

the organic carbon (Neary et al., 2005). Several studies have reported that changes in TOC are

influenced by wildfires (Mataix-Solera et al., 2011; Wieting et al., 2017). Mataix-Solera et al.

(2011) reported that decreases in TOC are attributed to the combustion of organic matter.

Decreases in TOC are also linked to decreases in aggregate stability, a factor that influences post-fire soil erosion (Mataix-Solera et al., 2011).

The mineralogical composition of soils potentially influences the physical properties of the soil. Wildfires alter the mineralogical composition of the soil. Most mineralogical alterations due to fire occur in moderate to severe fires where the temperatures reach above 500 °C (Certini,

2005; Mataix-Solera et al., 2011). The clay mineral kaolinite is destroyed at temperatures that range from 500 °C to 700 °C (Certini, 2005; Mataix-Solera et al., 2011). Tan et al. (2004) found that the decomposition of the clay mineral smectite decreased the LL and PL. Thus, the mineralogical composition of soils potentially influences the physical properties of the soil.

During a fire, temperatures in the soil litter and canopy reach between 850°C to 1100°C.

Heat produced by the combustion of soil litter vaporizes organic compounds, that move downward into the soil until they reach the cooler underlying soil layers, where they condense and form a wax-like layer called the water repellant layer (DeBano, 2000; Barkley, 2002). Soil water repellency often occurs in moderate to severe wildfires and is common in several vegetation types. The formation of the water repellant layer in the soil impedes water from entering the soil, this process is called infiltration. The soil’s maximum rate at which water enters the soil, is often reduced in the presence of the water repellant layer. When the infiltration capacity is exceeded, for instance, during a rainstorm event, excess water flows off the surface by the process of overland flow and increasing the risk of erosion and sedimentation rates

(DeBano, 2000; Barkley, 2002; Neary et al., 2005).

In summary, a wildfire’s effect on the vegetation can be dramatic depending on the type of fire and its severity, thereby altering key parameters in the soil properties and in the hydrological cycle (Barkley, 2002; Parise & Cannon, 2012). Vegetation can reinforce the soil

and protect the slopes from erosion, therefore, making it an important parameter in slope

stability. A wildfire, however, can damage the vegetation canopy and can even denude entire

hillslopes, leaving the area bare. Water repellency has an important effect on post-fire erosional

processes such as raindrop splash. Large amounts of soil are moved by the raindrop splash, thus

accelerating sediment transport (DeBano, 2000; Parise & Cannon, 2012; Staley et al., 2017). Fire can also damage the plant root system, potentially impacting effective soil cohesion and decreasing the shear strength of the soil.

Post-fire debris flows

Debris flows are fast moving dense slurries of soil, vegetation, boulders, and anything else caught in their path (USGS, 2017). These flows occur on channels and hillslopes where an abundant supply of loose sediment and excess moisture is available. Fluid and solid material influence the motion of the debris flow. They can travel long distances, growing in size as they move downslope (Iverson et al., 1997; Staley et al., 2017; USGS, 2017).

Flooding and debris flows often occur in the aftermath of wildfires. Alterations in the soil physical properties such as grain size, mineralogical transformations, decreases in shear strength can potentially increase the likelihood of post-fire debris flows. Post-fire debris flows typically occur on steep slopes affected by fires, often triggered by the first intense rainfall event of the storm season (DeGraff et al., 2011; Staley et al., 2017; USGS, 2017). In recently burned areas, a lower threshold of precipitation may initiate debris flows. Cannon et al. (2010) found that a 30- minute peak rainfall intensity greater than 10 mm/hour generated sediment transport. Post-fire debris flows can occur more than two years after the initial fire. California is particularly susceptible to post-fire debris flow events, due to the hot and very dry summers that catalyze wildfires followed by wet winters that contribute to high intensity rainfall events.

One recent example is the Montecito debris flow event that occurred three weeks

following the December 2017 Thomas Fire. The destructive Montecito debris flow event caused

23 fatalities, destroyed and damaged over 400 homes, and shut down Highway 101 for two

weeks (Kean et al., 2019). Debris flows are particularly dangerous post-fire hazards because they often occur with little to no warning. Recently, awareness of potential post-fire debris flows was emphasized by state agencies by conducting soil burn severity assessments, debris flow hazard assessments, and early warning systems to predict a potential debris flow event (Staley et al.,

2017; Kean et al., 2019). However, additional research is needed for post-fire debris flow assessments that consider soil physical properties as a variable in the models.

MATERIALS AND METHODS

Study Area

Mesozoic aged metasedimentary and metavolcanic units underlie the study area (Figure

3; Saleeby, 1993; Saleeby et al., 2008). The Sierra Nevada batholith and the Kings Canyon sequence lie along the western portion of the fire perimeter. The Kings Canyon sequence is characterized by Jurassic-Triassic metamorphic quartzite, marble, and metasedimentary basement remnants. Mesozoic aged metavolcanic rocks and Quaternary alluvium underlie hillslopes in the eastern portion of the study area (Saleeby, 1993; Saleeby et al., 2008: Haake et al., 2020). The soils in the surrounding regions belong to the Stineway-Kiscove complex (Figure

4; USDA, 2017). These soils are derived from the weathered metasedimentary rocks and are classified as very gravelly sandy loam and grades into a gravely loam (USDA, 2017). Soil depth ranges from shallow to very shallow. Soils in the hillslope and mountain slopes are well-drained.

The landscape is dominated by steep mountainous terrain and topographic elevation in this region ranges from 2,595 to 4,995 feet above sea level. The study area is primarily vegetated by sparse shrubland (rubber rabbitbrush and sagebrush) and sparse coniferous vegetation

(California juniper and native grey pine) (Figure 5). Areas affected by the fire include the surrounding comminutes of Mountain Mesa, Squirrel Mountain, and Lake Isabella (Figure 6), in addition to areas of the National Forest Service, Bureau of Land Management, and private property lands.

The Erskine Fire burned an area of the southern Sierra Nevada in eastern Kern County

(Figure 6). The fire ignited on June 23, 2016, due to a powerline in close contact with a pine tree

(KCFD, 2016). Dry conditions, low humidity, and strong winds quickly fueled the small ground fire as it spread onto the hillsides. The Erskine Fire burned over 48,020 acres, approximately 300

homes and outbuildings were destroyed, and 75 buildings were damaged (KCFD, 2016). The fire

claimed the lives of two people and injured three firefighters (KCFD, 2016).

The 2016 Erskine Fire resulted in a mosaic of burn severity patterns, including high

(HBS), moderate (MBS), low (LBS), and very low or unburned (VLBS). Approximately, 21,556

acres of LBS soils comprised the fire perimeter, followed by 20,716 acres (MBS), 5,405 acres

(VLBS), and 343 acres for HBS soils (Figure 7).

Sample Collection and Processing

A burn severity map for the 2016 Erskine Fire perimeter, obtained from the Burn Area

Emergency Response (BAER) team, was used as a guide to identify sample locations across a

spectrum of burn severities. The 2016 Erskine Fire resulted in a mosaic of burn severity patterns,

including high (HBS), moderate (MBS), low (LBS), and very low or unburned (VLBS). A

Garmin eTrex 10 handheld GPS unit was utilized to confirm sampling locations from the Haake

et al. (2020) study to identify any changes to soil properties throughout the two-year period. In

this study, the field reconnaissance was conducted three years after the fire, so the visual

inspection of the canopy was ruled out. Samples were collected at 35° 37' 07.00" N -

118°25'11.80" W, 35° 37' 09.80" N -118°25'15.50" W, 35° 37' 11.80" N -118°25'21.10" W, and

35° 37' 14.30" N -118°25'25.10" W for VLBS, LBS, MBS, and HBS soils, respectively (Table

1). One bulk soil sample (~ 4 kg) was collected from each soil burn severity classification. Each sample was manually sieved to obtain soil particles < 425-μm in size following ASTM standard test methods for liquid limit, plastic limit, and plasticity index of soils and for direct shear test of soils under consolidated drained conditions (ASTM D4318-10 and ASTM D3080-11 respectively). Total organic carbon (TOC) content, grain size distribution, and mineralogy were then examined to further constrain how wildfires affect soil physical and mechanical properties.

Instruments and techniques used in this study include X-ray diffractometer, particle size

analyzer, TOC analysis (LOI method), liquid limit device and direct shear box apparatus. The

specific laboratory methods used in this study are outlined in Table 2 and are slightly modified

from those used by Haake et al. (2020). Atterberg limits tests were conducted on approximately

100 g of previously untested soil and repeated twenty times for each soil sample. Direct shear

tests were conducted at three different normal loads (0.5 TSF, 1.0 TSF, and 2.0 TSF) and

replicated five times for each sample. Bulk XRD analysis was performed three times on each

sample. The clay-sized fraction of each sample was examined using a separate slide for air-dried,

glycolated, and heated treatments. TOC analysis was repeated five times on each soil sample.

Three grain size tests were conducted on each soil sample. The specific laboratory procedures for

each analysis are detailed below.

Lab Methods

Atterberg Limits

The soil samples were air-dried under ambient laboratory conditions and manually sieved

to the size fraction of < 425-μm and the Atterberg limits were determined in accordance with the

ASTM Standard Test Methods for Liquid Limit, Plastic Limit, and Plasticity Index of Soils

(D4318-10) procedure. The one-point method was employed using the Humboldt hand-operated

device to determine the liquid limit (LL). This method consists of adding approximately 25

milliliters (mL) of distilled water into 100 grams of soil until it has the consistency of a paste.

Following the addition of water, a portion of the prepared soil was placed in the brass cup of the

Humboldt hand-operated liquid limit device, forming a flat surface, and a groove was formed to bisect the soil pat (ASTM D4318-10). The LL device was hand-cranked at a rate of two blows per second until the two halves of the soil came in contact at the bottom of the groove at

approximately twenty-five blows (ASTM D4318-10). A slice of the soil was removed with a

spatula, placed in a container, and weighed on an electronic mass scale with a precision of 0.01

grams (Figure 8).

The hand-rolled method was performed for the plastic limit (PL) in accordance with the

ASTM D4318-10 procedure. This method consists of using 5 grams of the prepared soil used in

the LL test. The 5-gram soil mass was rolled into an ellipsoid, then hand rolled into a linear thread of 3.2 mm in diameter or until the soil thread breaks into pieces. The soil sample was then placed in a container and weighed (Figure 9).

These procedures were repeated on twenty soil samples for each burn severity classification. After the tests were performed, the samples were oven dried overnight at 105 °C and reweighed. The liquid limit was determined using the equation (2) determined by the ASTM

D4318-10 standard:

. = (2); 0 121 𝑛𝑛 𝑁𝑁 25 where LL is the one-point liquid limit𝐿𝐿𝐿𝐿 for𝑊𝑊 the ∙given � � trial in percent; N is the number of blows causing the closure of the groove; and is the water content for the given trial. The water 𝑛𝑛 content was determined as specified in 𝑊𝑊the ASTM Test Methods for Laboratory Determination of

water (Moisture) Content of Soil and Rock by Mass (ASTM D2216-19) by using:

( / ) ( / ) = (100%) (3). ( / ) 𝑛𝑛 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑜𝑜𝑜𝑜 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑤𝑤 𝑤𝑤𝑤𝑤𝑤𝑤 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 − 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑜𝑜𝑜𝑜 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑤𝑤 𝑑𝑑𝑑𝑑𝑑𝑑 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑊𝑊 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑜𝑜𝑜𝑜 𝑐𝑐𝑐𝑐𝑛𝑛𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑤𝑤 𝑑𝑑𝑑𝑑𝑑𝑑 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 The plastic limit was determined by averaging the water contents and the plasticity index (PI)

was calculated by the difference between liquid limit and plastic limit:

= (4).

𝑃𝑃𝑃𝑃 𝐿𝐿𝐿𝐿 − 𝑃𝑃𝑃𝑃

Direct Shear Strength

Shear strength analysis was employed using the Gilson Dead-Weight Direct Shear

Machine, following the ASTM Standard Test Method for Direct Shear Test of Soils Under

Consolidated Drained Conditions (ASTM D3080-11). Consolidated drained test conditions, include normal stresses and a moisture environment to represent the soil’s natural state which represent field conditions and the rate of shearing should be slow enough to endure drained conditions (ASTM D3080-11). Consolidation times were chosen on the basis of the Unified Soil

Classification System (USCS), where soils that range from clean sands (SW) to silty sands (SM) should be consolidated for sixty minutes under the test’s set normal load.

Five samples from each burn severity classification were tested using the previously prepared air-dried and sieved soil. Each sample was sheared under the normal loads of 0.5 TSF,

1.0 TSF, and 2.0 TSF, and a separate sample specimen was used for each test on all samples at different normal loads. This test method consists of securing the shear box and placing a moist porous disk at the bottom of the box, followed by a filter paper and approximately 100 grams of the sieved soil. A second filter paper and porous stone was placed above the soil followed by the metallic loading ball (Figure 10). The shear box was placed into the loading compartment and the horizontal loading screws were adjusted to make contact with the shear box. The loading hanger was tightened, and both the vertical and horizontal gages were set up. Following this step, the required weight was placed in the loading hanger, to apply a normal (vertical) stress to the sample. The shear box was filled with DI water during the consolidation time (sixty minutes) and during the duration of the test (ASTM D3080-11). A shearing rate of 0.05 millimeters per second

(mm/s) was performed for each test. Horizontal displacement, vertical displacement, and shear force readings were taken as a function of time and recorded on a spreadsheet every fifteen

seconds. This procedure was conducted at three different normal loads and replicated five times

for all four burn severities.

Under a constant normal force, the shear force is increased until the sample fails by

shearing (maximum shear stress). Shear stress values were recorded every 15-second intervals, and the mean value for maximum shear stress under the three normal loads was graphed, and the best fit line was plotted to identify shear strength parameters. The relationship between normal stress ( ) and shear stress ( ) at failure defines a point on the soil’s failure envelope and allows for determination𝜎𝜎 of the cohesion𝜏𝜏 and friction angle of the soil. Where cohesion was determined by the y-intercept and the angle of internal friction was derived from the slope of the line.

Equation 5 is used to express the shear strength of the soil:

= + (5); where represents the cohesion of𝜏𝜏 the ∁soil, 𝜎𝜎 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡and is the normal stress, and ϕ represents the friction∁ angle. σ

Mineralogy

The bulk powder and clay size fraction samples were prepared for mineralogical analysis via the Malvern PANalytical X-Ray Diffractometer (XRD) instrument. The bulk power method consists of grinding the previously air-dried and sieved soil (<425 μm) into a fine powder using a

Sample Prep SPEX Shatterbox 8530. The powered samples were then transferred into sample holders, packed tightly to create a flat surface, and scanned at a 2θ range from 3° – 40° at 45kV and 40mA (Figure 11).

For clay size fraction preparation, approximately 2 grams of the pre-sieved soil (<425

μm) was placed in a 600 mL beaker and treated with 50 mL of hydrogen peroxide (H2O2, 3%)

for 48 hours to get rid of organic material. The samples were then soaked in 250 mL of calgon

(sodium hexametaphosphate) solution (4 g/1000 mL distilled H2O) for 24 hours. The treated

samples were next placed in a sonicated bath for 5 minutes to promote disaggregation and

deflocculation. The samples were finally washed through a centrifuge two times, the first at 3500

revolutions per minute (rpm) for 20 minutes to remove dissolved chemicals in the solution,

followed by a DI water rinse. The second wash used the application of Stoke’s Law, which

consisted of placing the sample solution in the centrifuge at 500 rpm for 2.6 minutes to isolate

clay-sized (< 2 μm) and sand-sized grains. The solution of grains smaller than 2 μm were used to

create glass slides in accordance to the filter-peel method (Moore and Reynolds, 1989) (Figure

12). Each glass slide of clay aggregates was placed in the XRD instrument and scanned at a 2θ

range from 3° – 80° at 45kV and 40mA. Each slide was then saturated with ethylene glycol in a

closed vapor chamber, heated to 60 °C, overnight and analyzed at the same XRD settings the next day. To distinguish the chlorite and kaolinite clay minerals, the clay aggregate slide was heated at 550 °C for 2 hours and then scanned using the same XRD settings. These procedures and instrument settings were used for all the burn severity classifications.

The XRD data was processed via MacDiff software to determine a baseline, smooth counts, correct peak positions, and calculate peak intensities. To calculate mineral intensities, diffraction patterns were first calibrated using quartz peaks as standards to correct any peak offset (Hubbard & Snyder, 1988). The following mineral peaks and reference intensity ratios

(RIR) values were used to distinguish the minerals in the sample: smectite (14.10 Å) RIR = 3.85, chlorite (12.46 Å) RIR = 0.86, illite (10.00 Å) RIR = 0.4, chlorite/kaolinite (7.17 Å) RIR = 0.53, illite (4.99 Å) RIR = 0.4, quartz (4.25 Å) RIR = 3.41, microcline (3.57 Å) RIR = 0.59, quartz

(3.34 Å) RIR = 3.41, quartz (1.99 Å) RIR = 3.41, plagioclase (3.20 Å) RIR = 2.1 (Bish &

Chipera, 1986; Hillier, 2000; Shen et al., 2012). The relative mineral abundances consisted of

dividing the mineral’s intensity by its respective RIR. Next, the total relative abundance for each mineral was determined by dividing the modified intensity by the total intensity of all the minerals in the sample.

Total Organic Carbon

The loss on ignition (LOI) method (Heiri et al., 2001) was used for all total organic

carbon analyses. Five samples for each burn severity were analyzed. This method consisted of

placing the 2 grams of soil in dry crucibles in the Isotemp oven at 105 °C overnight to ensure

that all moisture was evaporated, and the samples were cooled to 45°C before measuring the

mass of the samples. Note, crucibles used in LOI analysis must be completely dried before

weighing before adding the samples. This is important because any remaining moisture can

cause an overestimation in LOI results. Following the initial mass measurements, the samples

were placed into a muffle furnace at 650 °C for two hours. After the samples were combusted

and cooled at 45°C the samples were reweighed to calculate TOC content. TOC was calculated

using:

= × 100% (6); 𝑀𝑀𝑑𝑑𝑑𝑑 − 𝑀𝑀𝑐𝑐𝑐𝑐 𝑇𝑇𝑇𝑇𝑇𝑇 𝑐𝑐𝑐𝑐 𝑐𝑐 where Mds is the mass of the container and the𝑀𝑀 dry− 𝑀𝑀sample, Mcs is the mass of the container and

the combusted sample, and Mc is the mass of the container. These procedures and calculations

were performed on five samples for each burn severity. The change in mass resulting from LOI

was assumed to be attributed to TOC.

Grain Size Distribution

Grain size analysis was performed on three samples from each burn severity using the

Malvern “Mastersizer” Hydro G 2000 laser particle analyzer. The Mastersizer is used to measure

the size of all grains in the sample ranging from 0.01 μm to 2 mm. The procedure entailed

saturating 1 gram of soil in 10 mL of deionized water for 24 hours, then followed by the addition

of 5 mL of calgon (sodium hexametaphosphate) solution. The solution was then placed in a

sonicated bath for 5 minutes to de-coagulate large aggregates. The samples were then diluted

with deionized water to 1/4th of the original concentration by employing the Splitter Method. The

Splitter Method allows a representative sample to be separated from a bulk sample without changing the original grain size distributions. The diluted sample was poured into the

Mastersizer bath until the obscuration reached 18 to 19 percent (Sperazza et al., 2004). The same procedure was used for three samples for each burn severity, and the results were averaged to calculate percentages of clay, silt, and sand content.

RESULTS

Atterberg Limits

All of the measured Atterberg limits, including soil liquid limit, plastic limit, and

plasticity values are presented in Appendix 1 lists the calculated for all of the analyzed soil

samples. Table 3 illustrates the average liquid and plastic limit values for each burn severity.

The LL for VLBS soils ranged from 23.2 to 26.4%, with an average and standard

deviation of 24.5% and 0.9%, respectively. LBS soils had LL values ranged from 28.9 to 32.3%,

with an average LL of 31.0% and a standard deviation of 1.1%. MBS soils had an average LL of

33.5% and a standard deviation of 1.4% and ranged from 31.8 to 34.6%. The LL for HBS soils

ranged from 26.7 to 31.6%, with an average and standard deviation of 29.2% and 1.3%,

respectively (Figure 13).

Figure 14 illustrates the minimum, maximum, and average PL values for all of the burn severities. The PL for VLBS soils ranged from the minimum value of 17.5% to the maximum value of 21.7%, with an average of 19.3% and a standard deviation of 1.1%. LBS soils had an average PL value of 26.4% with a standard deviation of 0.9% and ranged from 24.4 to 28.7%.

The PL for MBS soils ranged from 26.4 to 33.1%, with an average and standard deviation of

28.5% and 1.5%, respectively. Plastic limit values for HBS soils ranged from 23.9 to 26.9%, with an average of 25.5% and a standard deviation of 0.8%.

Plasticity index for VLBS soils ranged from 4.0 to 6.5 with an average and standard deviation of 5.2 and 0.7, respectively. PI values for LBS soils ranged from 2.1 to 5.9 with an average of 4.6 and a standard deviation of 0.9. MBS soils had an average PI of 4.5, a standard deviation of 1.4, and ranged from 1.0 to 7.2. While the HBS soils ranged from 0.1 to 4.5 and had an average PI of 2.4 and a standard deviation of 1.1 (Figure 15).

Shear Test

Five samples from each burn severity (VLBS, LBS, MBS, and HBS) were tested and

each sample was sheared under three trials of normal loads: 0.5 TSF, 1.0 TSF, and 2.0 TSF.

Under the normal load of 0.5 TSF, VLBS, LBS, MBS, and HBS soils had average peak shear

stress values of 1.8 psi, 2.1 psi, 2.3 psi, and 2.2 psi, respectively. Standard deviation for soils

under the normal load of 0.5 TSF for VLBS, LBS, MBS, and HBS are 0.7, 1.3, 0.9, and 0.8,

respectively. Under the normal load of 1.0 TSF VLBS, LBS, MBS, and HBS soils had average

peak shear stress values of 3.1 psi, 5.2 psi, 4.3 psi, and 4.1 psi, respectively. Standard deviation

for soils under the normal load of 1.0 TSF for VLBS, LBS, MBS, and HBS are 1.3, 0.6, 0.9, and

0.6, respectively. The average peak shear stress for VLBS, LBS, MBS, and HBS under the

normal stress of 2.0 TSF are 7.4 psi, 8.9 psi, 10.5 psi, and 12.4 psi, respectively (Figure 16).

Standard deviation for soils under the normal load of 2.0 TSF for VLBS, LBS, MBS, and HBS

are 5.1, 1.5, 0.9, and 2.9, respectively.

The results in Table 4 reveal the shear strength parameters interpreted from the failure

envelopes and calculated using the Coulomb formula (Equation 5). HBS, MBS, and VLBS soils

had cohesion values below zero, while LBS soils had a very low value of 0.2 psi. The angle of

internal friction values for VLBS, LBS, MBS, and HBS soils are 15.3°, 17.6°, 21.8°, and 26.9°,

respectively.

Mineralogy

Powder X-ray diffraction (XRD) provides a rapid identification of the average bulk composition of a given sample. Bulk powder XRD analysis for the Erskine Fire samples reveal relative abundance (%) of quartz, feldspar (plagioclase and k-feldspar), and clay minerals

(smectite, illite, and chlorite/kaolinite phase), figure 17. The VLBS samples were predominantly

quartz (60.4%) and illite (30.6%), with minor amounts of chlorite/kaolinite (5.3%), plagioclase

(2.9%), and smectite (0.7%). The standard deviations are 4.4%, 3.2%, 0.9%, 0.2%, and 0.2%, respectively. LBS samples had a higher illite abundance (45.1%), followed by quartz (35.9%), plagioclase (11.3%), microcline (5.8%), chlorite/kaolinite (1.75%), and smectite (0.2%). Their corresponding standard deviations are 3.5%, 2.9%, 0.5%, 0.6%, 0.3%, and smectite 0.04%, respectively. The average mineral abundance for the MBS samples consists of illite (47.0%), quartz (37.6%), plagioclase (8.3%), microcline (5.4%), chlorite/kaolinite (1.6%), and smectite

(0.15%). The standard deviations for the minerals in the MBS soils are 5.5% (illite), 4.0%

(quartz), 1.2% (plagioclase), 0.5% (microcline), 0.1% (chlorite/kaolinite), and 0.4% (smectite).

In the HBS samples the clay mineral smectite is absent and the remainder of the mineral composition consists of illite (41.9%), quartz (41.1%), plagioclase (9.0%), microcline (5.4%), and chlorite/kaolinite (2.5%). And their corresponding standard deviations are 2.7%, 1.8%,

2.1%, 0.5%, and 0.3%, respectively. A full compilation of the mineralogical composition in percent for each burn severity is listed in table 5.

Clay minerals play an important role in the physical and mechanical properties of soils.

The X-Ray diffraction patterns recorded for the non-treated, ethylene glycol treated, and heated samples are displayed in figures 18, 19, 20. Table 6 contains a full compilation of relative abundance data for each clay mineral in the soil sample.

One sample for all burn severities was analyzed as air-dried at ambient lab temperatures

(non-treated). The XRD pattern for the VLBS sample displays the minerals smectite, chlorite, illite, chlorite/kaolinite, quartz, and microcline. The predominate clay mineral illite, which encompasses about 48% of the mineral composition has peaks at 8.8 and 17.78 °2θ (Figure 18).

Smectite (peak at 6.30 °2θ) and chlorite (peak at 7.26 °2θ) encompass 2.8% and 12.0%,

respectively. The peak at 12.37 °2θ is characteristic of the mineral chlorite and/or kaolinite

which has a relative abundance of 16.6%, followed by microcline (11.3%), quartz (8.1%), and

plagioclase (1.3%) (Figure 18). In the LBS, MBS, and HBS samples the chlorite peak at 2.27 °2θ

disappeared. The microcline (24.94 °2θ) and chlorite and/or kaolinite (12.37 °2θ) peak intensity

decreased while a peak characteristic of plagioclase appeared at 28.23 °2θ (Figure 18). In the

HBS sample, the smectite diffraction peak intensity and peak shape changed in proportions; in

this sample the smectite composition was the lowest at 0.9% (Table 6).

A peak in the VLBS, LBS, and HBS samples shifted from 6.30 to 5.28 °2θ after

saturating the samples with ethylene glycol (Figure 19), clearly revealing the presence of the

smectite phase. The smectite peak appears more distinguishable and with higher intensity. All of

the other minerals seem to follow the same behavior as the non-treated samples for all of the

burn severities. The primary minerals in the glycolated samples are quartz and illite. Followed by

the secondary minerals smectite, chlorite, microcline, and plagioclase; with the exception of the

VLBS sample where microcline and chlorite have high percentages (Table 6). In the LBS, MBS,

and HBS samples the chlorite peak at 7.26 °2θ disappeared and the relative abundance in the

samples decreased to 0%. Conversely, in the same samples the mineral plagioclase appeared, and

the percentage increased from 0% (VLBS) to 1.6% (LBS and MBS) and 1.9% (HBS) (Figure 19;

Table 6).

The heat treatment at 550 °C caused the disappearance of chlorite peak at 7.26 °2θ that was seen in the VLBS non-treated and glycolated samples (Figure 20). The chlorite/kaolinite peak at 12.37 °2θ and the microcline peak (24.94 °2θ) also disappeared for the VLBS, LBS,

MBS, and HBS samples. The diffraction peak for illite at 8.8 °2θ increased and is higher when compared to the non-treated and glycolated samples. All other minerals behaved in the same in

the same pattern. The mineralogy of the heated samples of all burn severity classifications consisted of illite, smectite, quartz, and plagioclase. Illite encompasses the majority of the composition and ranges from 83.0% to 87.6%, followed by smectite which ranges from 2.6%

(VLBS) to 1.6% (HBS). A full compilation of the mineral composition in percent for each burn severity is listed in Table 6.

Total Organic Carbon

Total organic carbon analysis was conducted on five samples for each soil burn severity classification (Figure 21; Table 7). The average TOC content, in percent by mass for VLBS,

LBS, MBS, and HBS soils are 9.2%, 7.2%, 9.4%, and 9.4%, respectively. Standard deviation for

VLBS, LBS, MBS, and HBS soils are 2.0%, 1.7%, 1.8%, and 1.2%, respectively. TOC content for VLBS soils ranged from 7.5% to 12.5%. A minimum TOC content of 5.3% and maximum content of 8.9% was found for LBS soils. MBS soils had a TOC content that ranged from 6.6% to 11.4%. HBS soils resulted in a minimum TOC content of 8.6% and maximum content of

11.5%.

Grain Size

Grain size (particle size) analysis was determined using the Mastersizer 2000 laser diffractometer. Soil results consists of average % sand, % silt, and % clay particle sizes. The mean percent of clay-sized particles, those less than 3.90 μm, is 27.2% for VLBS, 24.2% for

LBS, 23.2% for MBS, and 23.0% for HBS soils (Figure 22). Standard deviation for clay-sized particles in VLBS, LBS, MBS, and HBS soils are 1.3%, 0.5%, 0.1%, and 0.1%, respectively.

The mean percent of silt-sized particles (3.90 – 62.50 μm), is 50.1% for VLBS, 51.7% for LBS,

50.9% for MBS, and 48.4% for HBS soils (Figure 23). Standard deviation for silt-sized particles in VLBS, LBS, MBS, and HBS soils are 2.2%, 2.1%, 0.7%, and 2.3%, respectively. The mean

percent of sand-sized particles in the samples are 22.7% (VLBS), 24.7% (LBS), 25.0% (MBS), and 27.8% (HBS) (Figure 24). And their corresponding standard deviations are 3.4% (VLBS),

3.0% (LBS), 0.6% (MBS), and 3.0% (HBS). A full compilation of percentages for the particles in the three size ranges are shown in Table 8.

DISCUSSION

Factors Impacting Soil Physical and Mechanical Properties

Soil physical and mechanical properties can be affected by the fundamental properties of

soils and include the mineralogical composition, TOC content, and grain size. The transformation of clay minerals in the high burn severity soils can explain the non-plastic

behavior the soil exhibited during the plastic limit test. Mineralogical analysis reveals the

presence of quartz, feldspars, chlorite/kaolinite, illite, and smectite in the bulk XRD analysis

(Figure 17). Simply based on relative XRD peak intensities, smectite and chlorite/kaolinite are

lower in abundance in soils of higher burn severities (Figure 17, 18). The low abundance of these

clay minerals very likely suggest that temperatures reached over 500 °C, the temperature at

which mineralogical changes occur (Tan et al., 2004; Certini, 2005). Clay minerals were altered

and destroyed due to combustion leading to the low abundance of clays in the soils and thereby causing the non-plastic behavior of the soils. High temperature exposure has been linked to decreases in clay-sized particles, and a corresponding increase in silt- and sand-sized particles, thus making soils less cohesive and more vulnerable to erosion and post-fire debris flows (Parise

& Cannon, 2012; Vacchiano et al., 2014).

TOC in the soils is usually derived from the vegetation covering on the top soils. Soils can be strengthened by the vegetation roots. In addition, TOC can increase the soil cohesion.

Some previous studies have produced variable results in the relationship between TOC content and burn severity. For example, high severity fires might lead to a loss of soil organic matter by the combustion of organic-rich top soil. Mataix-Solera et al. (2011) noted that TOC may increase or decrease with increasing burn severity. However, the Haake et al. (2020) study revealed that

TOC content increased with burn severity (Table 9) and TOC content was highest in the HBS,

MBS soils and lowest in LBS and VLBS soils. Similarly, in this study TOC content was highest

in MBS and HBS soils and lowest in LBS soils. This could be associated with the high

vegetation density before the fire. As shown on the Erskine Fire perimeter satellite imagery,

vegetation density was highest in the MBS and HBS sampling locations (Figure 25). Mataix-

Solera et al. (2011) suggested that increases in organic matter may be attributed to external plant

inputs, such as dry leaves, that contribute to TOC content. TOC results from this study and

Haake et al. (2020) show no statistical difference between them, suggesting that the soil has not

recovered. Thus, more analyses are needed to understand this relationship between burn severity

and TOC content.

Another soil property, grain size composition, can greatly influence the behavior of soils.

Previous studies indicate that grain size composition can be altered by fires in a soil sample. In

this study, VLBS samples had a higher abundance of clay-sized particles (Figure 22; Table 8).

HBS soils had a higher sand-size grain abundance. These results are consistent with other studies who noted that sand-sized particles increase with higher burn severities (DeBano, 2000;

González-Perez et al., 2004; Parise & Cannon, 2012). However, SEM analysis done by Haake et al. (2020) was interpreted to show no evidence of grain fusion in the Erskine Fire samples. But grain size results from Haake et al. (2020) showed that both clay- and silt-sized grain abundance was highest amongst VLBS and LBS soils (Table 9). Clay and silt size averages (%) from Haake et al. (2020) slightly differ from the findings presented in this study. A possible explanation for this might be the differences in procedures or the slight differences in the amount of clay and silt particles. Haake et al. (2020) diluted the sample to 1/8th of the original concentration, while in

this study, the sample was diluted to 1/4th. Obscuration (laser beam analyzer) levels were set to

18-20%; however with the 1/8th solution these levels were not met, meaning that there was not

sufficient sample solution for the laser to measure particle size. So, because the samples were only diluted to 1/4th of the original concentration, it is possible that the sample solution had more clay, silt and sand-sized particles.

Atterberg Limits

The relationship between burn severity and Atterberg limits is not well known, and many studies have produced mixed results. For example, Vacchiano et al. (2014) revealed a decrease in liquid limit and plastic limit with increasing burn severity. The likely reason is that Atterberg limits are influenced by other soil properties, such as organic matter, clay content, and time

(ASTM D4318-10; Vacchiano et al., 2014; Deng et al., 2017). Several studies have reported how the liquid limit and plastic limit are influenced by the presence of organic matter (ASTM D4318-

10; Deng et al., 2017). The standard test method for liquid and plastic limits of soils notes that the LL decreases when organic matter decreases (ASTM D4318-10); similar results were also reported by Deng et al. (2017). The type and amount of clay minerals in a soil can also determine soil plasticity. Smectite minerals are expanding clays with a 2:1 structure, making their water capacity higher. More water can be absorbed by smectite than by other clay minerals like kaolinite and illite. Consequently, soils that contain a higher abundance of smectite minerals typically exhibit higher plasticity. The decreasing trend in LL and PL with increasing fire temperature for clayey soils, therefore, is potentially due to the destruction of the clay mineral smectite in moderate to severe fires.

Results of this study revealed an increasing trend in LL from VLBS to MBS but then decreased for the HBS soils, a similar trend is seen in the PL. Soil plasticity values decreased as burn severity increased. Previous work done by Haake et al. (2020) in the same sampling locations revealed a trend of higher liquid limit and plastic limit values as burn severity increased

(Figure 26 and Table 10). The majority of the HBS soil samples analyzed in the study by Haake

et al. (2020) had PI values of less than or equal to zero; where the plasticity index values for

HBS soils in this study reveal an average PI value of 2.4 and a trend of decreasing PI as burn

severity increased (Table 10). Plasticity index values and XRD analyses are interpreted to reflect

low concentrations of clay-sized particles in the higher burn severities, suggesting that these soils

behave non-plastic.

The timing of post-fire soil sampling may be a factor that influences the LL and PL

values and the reason for the slightly different observations. Haake et al. (2020) collected soil

samples one year after the fire, while in this study, samples were collected from the same

locations three years after the fire. In the timespan of two years, it is possible that the soils have

partially or fully recovered to their pre-fire state. Soils within the area have possibly attributed

new vegetation leading to increases in TOC, more sediment influx attributing to more clay

content, thus making soils more plastic. As addressed in the previous sections, soils with large

clay content remain in the plastic state over a wide range of moisture contents, thus have high PI

values. The opposite is true for silty soils, silty soils have low PI values and are non-plastic

(Coduto et al., 2001).

Shear Testing

The direct shear test determines the consolidated drained shear strength of soil material in direct shear. This test is used to determine the maximum shear stress a soil can withstand prior to failure. The direct shear tests also provide estimates of material properties such as the angle of internal friction and cohesion.

Under the normal stress of 0.5 TSF, MBS soils had the highest peak shear stress followed by HBS, LBS, and VLBS soils. LBS (5.2 ± 0.6 psi) and MBS (4.3 ± 0.9 psi) soils had the highest

peak shear stress under the normal load of 1.0 TSF (Table 4). An increasing trend of shear stress

as burn severity increases is shown under the normal load of 2.0 TSF. In contrast, results from

Haake et al. (2020) reveal that HBS soils exhibit the highest shear stress among all soils and

under all normal loads.

Both studies followed the standard method for a consolidated drained test; however,

Haake et al. (2020) did modify the test by adding only 25 mL of water during the consolidation

and shearing process. The strain rate (0.05 mm/s) in both studies was extremely fast that it

caused failure to occur relatively fast. In the study, the combination of the fast strain rate and the

increase in pore water pressure caused failure to occur at lower values of normal stress and

ultimately decreasing shear strength (Bishop 1966; Bergaya & Lagaly, 2013). These factors also influenced the cohesion of soils, which explains why cohesion values were zero in this study.

Haake et al. (2020) reports cohesion values of 3.4 – 4.6, which can be attributed to the low abundance of water during the test. Previous studies indicate that excess soil moisture reduces soil cohesion, but cohesion values are also influenced by soil type, consolidation history, and drainage conditions (Bergaya & Lagaly, 2013). In this study, the angle of internal friction increased with increasing burn severity, this is attributed to grain size content within the soils.

The angle of internal friction (ϕ) is influenced by the grain size, typically soils with high clay content have a lower value and ϕ would rise with increasing sand content (Vangla & Latha,

2015). Shear strength can be influenced by soil physical properties, such as the Atterberg limits, mineralogy, and the abundance of organic matter (Yong-hong et al., 2005; Vacchiano et al.,

2014; Deng et al., 2017). The amount of vegetation can be directly proportional to the amount of organic matter. Thus, soils with high TOC are likely to have higher shear strength. In both studies, HBS soils had the highest shear strength values, which can be attributed to the high TOC

content. Another possible influence on shear strength results is sample disturbance. Laboratory

shear strength tests can underestimate strength values because soils are no longer in their natural

state (normal stresses and moisture content) but to an unknown degree; thus in situ measurements may be more reliable.

CONCLUSION

Wildfires directly impact the chemical, physical, and biological properties of soil. Haake

et al. (2020) explored the burn severity impacts on soil properties from the 2016 Erskine Fire.

Two years after Haake et al. (2020) study, samples were recollected from four different burn

severities (VLBS, LBS, MBS, and HBS). Results from this study reveal that MBS soils had the

highest LL and PL. HBS soils had a very low PI value of 2.4 and are classified as nonplastic,

meaning that they can only retain small amounts of water, due to the low abundance of clay

minerals. The results are different from the previous work done by Haake et al. (2020), in the

same sampling locations, which revealed a trend of higher liquid limit and plastic limit values as

burn severity increased.

Shear results reveal variable results under the normal loads of 0.5 TSF and 1.0 TSF; and an increasing trend in shear strength with increasing burn severity under the normal load of 2.0

TSF. In contrast, results from Haake et al. (2020) reveal that HBS soils exhibit the highest shear

stress among all soils and under all normal loads. Haake et al. (2020) reports cohesion values of

3.4 – 4.6, whereas in this study HBS, MBS, and VLBS exhibit cohesion values of zero and a

cohesion value of 0.2 for LBS soils. The possible reason for the different results might be the

different test strategies in these two studies, differences in the degree of sample disturbance, etc.

As well, laboratory shear strength tests can underestimate the values but to an unknown degree, thus in situ measurements may be more reliable.

Based on peak intensity, XRD analysis reveals a lower abundance of clay minerals

(smectite and chlorite/kaolinite) in the HBS soils. Smectite and chlorite/kaolinite are lower in abundance in soils of higher burn severities where the low abundance of these clay minerals very likely suggest that temperatures reached over 500 °C. TOC content was highest in MBS soils and

lowest in LBS soils. This result is different from the previous study by Haake et al. (2020).

Changes in the TOC are dependent on a variety of other factors, including the type of fire intensity and soil type. TOC content may be related to the high pre-fire vegetation density in the

sampling locations. Grain size results of this study show clay-sized particles were less abundant in high burn severity soils, similar to results from Haake et al. (2020). Less abundant clay- and silt-sized particles could be attributed to the decomposition of the particles due to intense fire conditions. In summary, soil physical and mechanical properties can be affected by multiple factors, which can explain the variable results from previous studies exploring the impacts of fire on soil properties. As considering applying these factors into the role of triggering debris flows, one must realize there is not a constant paradigm to follow.

REFERENCES

ASTM, D3080-11. (2011). Standard test method for direct shear test of soils under consolidated

drained conditions. ASTM International.

ASTM, D4318-10. (2010). Standard test methods for liquid limit, plastic limit, and plasticity

index of soils. ASTM International.

ASTM, D2216-19. (2019). Standard test methods for laboratory determination of water

(moisture) content of soil and rock by mass. ASTM International.

Balch, J. K., Bradley, B. A., Abatzoglou, J. T., Nagy, R. C., Fusco, E. J., & Mahood, A. L.

(2017). Human-started wildfires expand the fire niche across the United

States. Proceedings of the National Academy of Sciences, 114(11), 2946-2951.

Barkley, Y. C. (2002). After the Burn: Assessing and Managing your Forestland After a

Wildfire. Idaho Forest, Wildlife, and Range Experiment Station, University of Idaho.

Bergaya, F., & Lagaly, G. (2013). Handbook of clay science.

Bish, D. L., & Chipera, S. J. (1986). Mineralogy of drill holes J-13, UE-25A No. 1, and USW G-

1 at Yucca Mountain, Nevada.

Bishop, A. W. (1966). The strength of soils as engineering materials. Geotechnique, 16(2), 91-

130.

Bunte, K., & Abt, S. R. (2001). Sampling surface and subsurface particle-size distributions in

wadable gravel-and cobble-bed streams for analyses in sediment transport, hydraulics,

and streambed monitoring. US Department of Agriculture, Forest Service, Rocky

Mountain Research Station.

CalFire. (2018). Camp Fire. Investigation Report. California Department of Forestry & Fire

Protection. Accessed 2018.

Cannon, S. H., Gartner, J. E., Rupert, M. G., Michael, J. A., Rea, A. H., & Parrett, C. (2010).

Predicting the probability and volume of post wildfire debris flows in the intermountain

western United States. Bulletin, 122(1-2), 127-144.

Carle, D. (2008). Introduction to fire in California. University of California Press.

Certini, G. (2005). Effects of fire on properties of forest soils: a review. Oecologia, 143(1), 1-10.

Coduto, D. P., Kitch, W. A., & Yeung, M. C. R. (2001). Foundation design: principles and

practices.

DeBano, L. F. (2000). The role of fire and soil heating on water repellency in wildland

environments: a review. Journal of hydrology, 231, 195-206.

DeGraff, J. V., Wagner, D. L., Gallegos, A. J., DeRose, M., Shannon, C., & Ellsworth, T.

(2011). The remarkable occurrence of large rainfall-induced debris flows at two different

locations on July 12, 2008, Southern Sierra Nevada, CA, USA. Landslides, 8(3), 343-

353.

Deng, Y., Cai, C., Xia, D., Ding, S., Chen, J., & Wang, T. (2017). Soil Atterberg limits of

different weathering profiles of the collapsing gullies in the hilly granitic region of

southern china. Solid Earth, 8(2), 499-513.

Direct Shear Machines (Dead-Weight). GlobalGilson.com. https://www.globalgilson.com/dead-

weight-directresidual-shear-machine. Assessed 2021.

Direct Shear Test. Geoengineer.org. https://www.geoengineer.org/education/laboratory-

testing/direct-shear-test. Assessed 2021.

González-Pérez, J. A., González-Vila, F. J., Almendros, G., & Knicker, H. (2004). The effect of

fire on soil organic matter—a review. Environment international, 30(6), 855-870.

Guo, J., & Underwood, M. (2009). Data report: refined method for calculating percentages of

kaolinite and chlorite from X-ray diffraction data, with application to the Nankai margin

of southwest Japan.

Haake, S. (2020). Burn Severity and Its Impact on Soil Properties: A Study of the 2016 Erskine

Fire in the Southern Sierra Nevada (MS Thesis, California State University, Bakersfield).

Heiri, O., Lotter, A. F., & Lemcke, G. (2001). Loss on ignition as a method for estimating

organic and carbonate content in sediments: reproducibility and comparability of

results. Journal of paleolimnology, 25(1), 101-110.

Hillier, S. (2000). Accurate quantitative analysis of clay and other minerals in sandstones by

XRD: comparison of a Rietveld and a reference intensity ratio (RIR) method and the

importance of sample preparation. Clay minerals, 35(1), 291-302.

Hubbard, C. R., & Snyder, R. L. (1988). RIR-measurement and use in quantitative XRD. Powder

Diffraction, 3(2), 74-77.

Iverson, R. M., Reid, M. E., & LaHusen, R. G. (1997). Debris-flow mobilization from

landslides. Annual Review of Earth and Planetary Sciences, 25(1), 85-138.

KCFD. (2016). Erskine Fire. Investigation Report. Kern County Fire Department. Accessed

2018.

Kean, J. W., Staley, D. M., Lancaster, J. T., Rengers, F. K., Swanson, B. J., Coe, J. A., ... &

Lindsay, D. N. (2019). Inundation, flow dynamics, and damage in the 9 January 2018

Montecito debris-flow event, California, USA: Opportunities and challenges for post-

wildfire risk assessment. Geosphere, 15(4), 1140-1163.

Keeley, J. E. (2009). Fire intensity, fire severity and burn severity: a brief review and suggested

usage. International journal of wildland fire, 18(1), 116-126.

Mataix-Solera, J., Cerdà, A., Arcenegui, V., Jordán, A., & Zavala, L. M. (2011). Fire effects on

soil aggregation: a review. Earth-Science Reviews, 109(1-2), 44-60.

Moody, J. A., Shakesby, R. A., Robichaud, P. R., Cannon, S. H., & Martin, D. A. (2013).

Current research issues related to post-wildfire runoff and erosion processes. Earth-

Science Reviews, 122, 10-37.

Moore, D. M., & Reynolds Jr, R. C. (1989). X-ray Diffraction and the Identification and

Analysis of Clay Minerals. Oxford University Press (OUP).

Neary, D. G., Ryan, K. C., & DeBano, L. F. (2005). Wildland fire in ecosystems: effects of fire

on soils and water. Gen. Tech. Rep. RMRS-GTR-42-vol. 4. Ogden, UT: US Department

of Agriculture, Forest Service, Rocky Mountain Research Station. 250 p., 42.

NPS. (2017). Wildland Fire Behavior. U.S. National Park Service.

https://www.nps.gov/articles/wildland-fire-behavior.htm

Pant, H. R. (2013). Erosional resistance of cohesive sediments in coastal saltmarshes. LSU

Master's Thesis. 386.

Parise, M., & Cannon, S. H. (2012). Wildfire impacts on the processes that generate debris flows

in burned watersheds. Natural Hazards, 61(1), 217-227.

Parson, A., Robichaud, P. R., Lewis, S. A., Napper, C., & Clark, J. T. (2010). Field guide for

mapping post-fire soil burn severity. Gen. Tech. Rep. RMRS-GTR-243. Fort Collins,

CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station.

49 p., 243.

Romero, O. C., Ebel, B. A., Martin, D. A., Buchan, K. W., & Jornigan, A. D. (2018).

Postwildfire measurement of soil physical and hydraulic properties at selected sampling

sites in the 2011 Las Conchas wildfire burn scar, Jemez Mountains, north-central New

Mexico. Scientific Investigations Report, (2018-5028).

Saleeby, J. B., & Busby, C. (1993). Paleogeographic and tectonic setting of axial and western

metamorphic framework rocks of the southern Sierra Nevada, California.

Saleeby, J. B., Ducea, M. N., Busby, C. J., Nadin, E. S., Wetmore, P. H., Wright, J. E., &

Shervais, J. W. (2008). Chronology of pluton emplacement and regional deformation in

the southern Sierra Nevada batholith, California. SPECIAL PAPERS-GEOLOGICAL

SOCIETY OF AMERICA, 438, 397.

Schmidt, K. M., Roering, J. J., Stock, J. D., Dietrich, W. E., Montgomery, D. R., & Schaub, T.

(2001). The variability of root cohesion as an influence on shallow landslide

susceptibility in the Oregon Coast Range. Canadian Geotechnical Journal, 38(5), 995-

1024.

Shen, S., Zaidi, S. R., Mutairi, B. A., Shehry, A. A., Sitepu, H., Hamoud, S. A., ... & Edhaim, F.

A. (2012). Quantitative XRD bulk and clay mineralogical determination of paleosol

sections of Unayzah and Basal Khuff clastics in Saudi Arabia. Powder Diffraction, 27(2),

126-130.

Sperazza, M., Moore, J. N., & Hendrix, M. S. (2004). High-resolution particle size analysis of

naturally occurring very fine-grained sediment through laser diffractometry. Journal of

Sedimentary Research, 74(5), 736-743.

Staley, D. M., Negri, J. A., Kean, J. W., Laber, J. L., Tillery, A. C., & Youberg, A. M. (2017).

Prediction of spatially explicit rainfall intensity–duration thresholds for post-fire debris-

flow generation in the western United States. Geomorphology, 278, 149-162.

Steel, Z. L., Safford, H. D., & Viers, J. H. (2015). The fire frequency‐severity relationship and

the legacy of fire suppression in California forests. Ecosphere, 6(1), 1-23.

Tan, Ö., Yılmaz, L., & Zaimoğlu, A. S. (2004). Variation of some engineering properties of

clays with heat treatment. Materials Letters, 58(7-8), 1176-1179.

USDA. (2017). Soil Survey of Kern County, Northeastern Part, and Southeastern Part of Tulare

County, California. United States Department of Agriculture. Assessed 2019.

USGS. (2017). Predicting Postfire Debris Flows Saves Lives. United States Geological Survey.

Assessed 2019

Vacchiano, G., Stanchi, S., Marinari, G., Ascoli, D., Zanini, E., & Motta, R. (2014). Fire

severity, residuals and soil legacies affect regeneration of Scots pine in the Southern

Alps. Science of the total environment, 472, 778-788.

Westerling, A. L., Hidalgo, H. G., Cayan, D. R., & Swetnam, T. W. (2006). Warming and earlier

spring increase western US forest wildfire activity. science, 313(5789), 940-943.

Westerling, A. L. (2016). Increasing western US forest wildfire activity: sensitivity to changes in

the timing of spring. Philosophical Transactions of the Royal Society B: Biological

Sciences, 371(1696), 20150178.

Wieting, C., Ebel, B. A., & Singha, K. (2017). Quantifying the effects of wildfire on changes in

soil properties by surface burning of soils from the Boulder Creek Critical Zone

Observatory. Journal of Hydrology: Regional Studies, 13, 43-57.

Williams, A. P., Abatzoglou, J. T., Gershunov, A., Guzman‐Morales, J., Bishop, D. A., Balch, J.

K., & Lettenmaier, D. P. (2019). Observed impacts of anthropogenic climate change on

wildfire in California. Earth's Future, 7(8), 892-910.

Yong-hong, Y., Jian-guo, Z., Jian-hui, Z., Shu-zhen, L., Cheng-hua, W., & Qing-hua, X. (2005).

Impacts of soil moisture content and vegetation on shear strength of unsaturated

soil. Wuhan University Journal of Natural Sciences, 10(4), 682-688.

FIGURES

Figure 1. The fire behavior triangle’s three legs are weather, topography, and fuels. Weather conditions such as wind, temperature, and humidity affect the fire intensity and severity. Topography can help or hinder the spread of fire; fire typically moves more quickly uphill than downhill. A fuel’s composition, moisture level, chemical makeup, and density, determines the degree of flammability. Figure modified from National Park Service, 2017.

Figure 2. The Atterberg limits include the shrinkage limit (SL), plastic limit (PL), and liquid limit (LL). These limits are expressed as percent water content. Depending on the water content, a soil can be described in four states: solid state, semi-solid state, plastic state, and liquid state. Figure from Gilson Company, INC.

Figure 3. Geologic map showing bedrock units in the Southern Sierra Nevada. The sample area lies within the Kings sequence, characterized by Jurassic-Triassic aged metasedimentary rocks. Figure from Haake et al. 2017 and Saleeby et al. 2008.

Figure 4. Soil map for the Erskine Fire perimeter. The soils collected in the sampling locations belong to the Stineway-Kiscove complex. 300 = Stineway-Kiscove complex.

Figure 5. Landscape in the Erskine Fire perimeter taken on June 14, 2019, (a) and (c) overlooking the slopes of the high burn severity (b) dry shrubland vegetation on the very low or unburned burn severity (d) charred tree on the cusp of the moderate and high burn severities.

Figure 6. The Erskine Fire perimeter lies within the Southern Sierra Nevada, near the communities of Lake Isabella, Mountain Mesa, and Squirrel Mountain Valley.

Figure 7. Erskine Fire Burn Severity Map. The Erskine Fire burned nearly 48,020 acres. Inset shows the zoomed in sampling locations for VLBS, LBS, MBS, and HBS. VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Figure 8. Liquid limit (LL) test procedure, (a) grooved soil pat in LL device (b) soil pat after groove was closed (c) a slice of soil approximately the width of the spatula was removed and used as the water content sample.

Figure 9. Plastic limit (PL) test, (a) soil water content above the PL (b) soil water content at PL the point at which the soil crumbles.

Figure 10. (a) The Gilson model Dead-Weight Direct Shear Machine (b) schematic of the cross- sectional view assembly for the shear box apparatus. Figure modified from Gilson Company, INC and GeoEngineer.org.

Figure 11. Powder Bulk sample preparation for X-Ray Diffraction (XRD) analysis, (a) powdered sample after being grinded (b) sample packed tightly in sample holder (c) sample placed in holder prior to being scanned.

Figure 12. Clay Size Fraction sample preparation for X-Ray Diffraction (XRD) analysis, (a) soil in hydrogen peroxide solution to get rid of organic matter (b) samples washed in a centrifuge for 20 minutes (c) sample resuspended by vigorous shaking in sonic cell probe (d) clay particle solution placed in suction cups in accordance to the filter-peel method.

Figure 13. Box and whisker plot showing the liquid limit for all burn severities. VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Figure 14. Box and whisker plot showing the plastic limit for all burn severities. VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Figure 15. Box and whisker plot showing the plasticity index for all burn severities. VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Figure 16. Mohr-Coulomb Failure Envelope derived from direct shear tests with three different normal stresses. The envelope yields shear strength parameters, friction angle and cohesion. VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Figure 17. Powder X-Ray diffractograms for all burn severities, containing smectite (S), illite (I), chlorite/kaolinite (C/K), quartz (Q), microcline (M), and plagioclase (P). VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Figure 18. Clay-sized X-Ray diffractograms of untreated (air-dried) soils samples, encompassing smectite (S), illite (I), mixed-layer chlorite/kaolinite (C/K), quartz (Q), microcline (M), and plagioclase (P). VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Figure 19. Clay-sized X-Ray diffractograms of glycolated soils samples. Smectite peaks in all burn severities shifted to from 6.30 °2θ (untreated samples) to 5.28 °2θ after treatment. Mineral composition consists of smectite (S), illite (I), mixed-layer chlorite/kaolinite (C/K), quartz (Q), microcline (M), and plagioclase (P). VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Figure 20. Clay-sized X-Ray diffractograms of heated soils samples. Smectite peaks are still present in VLBS and LBS samples. Mixed-layer chlorite/kaolinite peaks disappeared in all burn severities, in addition to microcline. Mineral composition consists of smectite (S), illite (I), quartz (Q), and plagioclase (P). VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Figure 21. Box and whisker plot showing total organic carbon (TOC) for all burn severities. VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Figure 22. Box and whisker plot showing mean clay-sized particles for all burn severities. VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Figure 23. Box and whisker plot showing mean silt-sized particles for all burn severities. VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Figure 24. Box and whisker plot showing mean sand-sized particles for all burn severities. VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Figure 25. Satellite imagery from the sampling locations (a) before the Erskine Fire in 2013 and (b) after the 2016 Erskine Fire. Vegetation density was highest in the HBS and MBS locations. VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Figure 26. Box and whisker plot showing the liquid limit, plastic limit, and plasticity index for all burn severities. (a), (b), and (c) results for this study (d), (e), and (f) results from Haake et al. (2020). VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

TABLES

Table 1. Sample locations described in latitude and longitude for all burn severities. VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Burn Severity Latitude Longitude Reference

VLBS 35° 37' 07.00" N - 118°25'11.80" W

LBS 35° 37' 09.80" N - 118°25'15.50" W Haake et al. (2020) MBS 35° 37' 11.80" N - 118°25'21.10" W

HBS 35° 37' 14.30" N - 118°25'25.10" W

Table 2. Test methods, instruments, and test runs used during laboratory tests. BS = Burn Severity. TSF = tons per square foot. *Each Direct Shear Strength test consisted of shearing under the normal loads of 0.5 TSF, 1.0 TSF, and 2.0 TSF. **Clay size fraction was explored using one slide per treatment (air-dried, glycolated, and heated).

Test Runs Test Method Instrument Reference per BS

Atterberg Limits Liquid Limit (LL) One - Point 20 Humboldt hand-operated LL device ASTM D4318-10 Plastic Limit (PL) Hand - Rolled 20 Frosted glass plate ASTM D4318-10

Consolidated 5 Direct Shear Strength Drained Gilson Dead-Weight Direct Shear Machine ASTM D3080-11 *0.5 TSF, 1.0 TSF, 2.0 TSF

Mineralogy Guo and Underwood 3 Bulk Sample Bulk Powder Sample Prep SPEX Shatterbox 8530 Malvern (2011)

Guo and Underwood 1 Clay Size Fraction Filter-Peel Malvern PANalytical X-Ray Diffractometer (2011) **Air-dried, glycolated, heated

Total Organic Carbon Loss on Ignition 5 Isotemp Oven Heiri et al. (2001) Muffle Furnace

Grain Size Distribution Splitter 3 Malvern Mastersizer Hydro G 2000 Sperazza et al. (2014)

Table 3. Average Atterberg Limits for all burn severities. LL = liquid limit. PL = plastic limit. Std. Dev. = standard deviation. PI = plasticity index. VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Burn Severity LL Std. Dev. PL Std. Dev. PI Std. Dev.

VLBS 24.5 0.9 19.3 1.1 5.2 0.7

LBS 31 1.1 26.4 0.9 4.6 0.9

MBS 33.5 1.4 28.4 1.5 4.5 1.4

HBS 29.3 1.3 25.5 0.8 2.5 1.1

Table 4. Average peak shear strength testing results under three different normal forces 6.94 psi (0.5 TSF), 13.89 psi (1.0 TSF), and 27.78 psi (2.0 TSF). Std. Dev. = standard deviation. Cohesion and friction angle derived from linear regression lines. VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Burn Severity Normal Force (psi) Shear Stress (psi) Std. Dev. Cohesion (c') Friction (μ)

6.94 1.8 0.7 VLBS 13.89 3.1 1.3 0 15.3 27.78 7.4 5.1 6.94 2.1 1.3 LBS 13.89 5.2 0.6 0.2 17.6 27.78 8.9 1.5 6.94 2.3 0.9 MBS 13.89 4.3 0.9 0 21.8 27.78 10.5 0.9 6.94 2.2 0.8

HBS 13.89 4.1 0.6 0 26.9 27.78 12.4 2.9

Table 5. Powder X-Ray Diffraction mineral composition (%) in all the soil samples. Avg. = averages. Std. Dev. = standard deviation. VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Chlorite/Kaolinite Quartz Plagioclase Burn Severity Sample No. Smectite (%) Illite (%) Microcline (%) (%) (%) (%) 1 0.93 33.93 6.33 55.7 - 3.11 2 0.64 30.27 5.12 61.2 - 2.77 VLBS 3 0.52 27.6 4.55 64.37 - 2.95 Avg. 0.7 30.6 5.33 60.42 - 2.94 Std. Dev. 0.2 3.2 0.9 4.4 - 0.2 1 0.26 49.12 1.99 32.62 5.13 10.87 2 0.19 42.56 1.77 38.26 6.11 11.11 LBS 3 0.22 43.54 1.48 36.68 6.19 11.89 Avg. 0.22 45.08 1.75 35.86 5.81 11.29 Std. Dev. 0.04 3.5 0.3 2.9 0.6 0.5 1 0.19 52.93 1.53 32.99 4.75 7.62 2 0.13 42.19 1.56 40.63 5.8 9.68 MBS 3 0.12 45.84 1.75 39.06 5.51 7.72 Avg. 0.15 46.99 1.61 37.56 5.35 8.34 Std. Dev. 0.4 5.5 0.1 4 0.5 1.2 1 - 40.15 2.25 43.18 5.95 8.48 2 - 45.07 2.76 39.78 5.14 7.26 HBS 3 - 40.61 2.53 40.47 5.06 11.33 Avg. - 41.94 2.51 41.14 5.38 9.02 Std. Dev. - 2.7 0.3 1.8 0.5 2.1

Table 6. Clay-Sized X-Ray Diffraction mineral composition (%) in all soil samples. Smectite (S), illite (I), chlorite (C), chlorite/kaolinite (C/K), quartz (Q), microcline (M), and plagioclase (P). VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Table 7. Total Organic Carbon data, including averages (%) for all burn severity classifications. Avg. = averages. Std. Dev. = standard deviation. VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Burn Severity Sample No. TOC (%) Avg. TOC (%) Std. Dev.

1 7.53 2 7.61 VLBS 3 9.68 9.24 2.0 4 12.5 5 8.89 1 5.32 2 8.7 LBS 3 7.69 7.19 1.7 4 8.89 5 5.38 1 8.89 2 6.59 MBS 3 10.11 9.39 1.8 4 10 5 11.36 1 8.6

2 8.89

HBS 3 8.89 9.37 1.2

4 11.49

5 8.99

Table 8. Grain size data, including averages (%) and standard deviations (Std. Dev.) for all burn severity classifications. Where less than 3.90 μm particles are clay-sized. Silt size particles include particles that range in 3.90 - 62.50 μm. Particles greater than 62.50 μm are sand-sized grains. VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Avg. Burn Sample Avg. 3.90 - 62.5 Avg. < 3.90 µm Std. Dev. 3.90 - 62.5 Std. Dev. > 62.5 µm Std. Dev. Severity No. < 3.90 µm µm > 62.5 µm µm 1 27.96 50.67 21.37 VLBS 2 25.71 27.22 1.3 47.67 50.06 2.2 26.62 22.72 3.4 3 27.99 51.84 20.16 1 24.16 53.27 22.57 LBS 2 23.51 24.21 0.5 49.3 51.73 2.1 28.14 24.72 3.0 3 23.94 52.63 23.43 1 23.87 50.63 25.5 MBS 2 23.96 23.17 0.1 51.72 50.94 0.7 24.32 25 0.6 3 24.36 50.45 25.19 1 24.42 49.36 26.21

HBS 2 24.06 23.0 0.1 50.12 48.41 2.3 25.82 27.81 3.0 3 22.86 45.73 31.41

Table 9. Average TOC content (%) from Haake et al. (2020) and this study (2019). Std. Dev. = standard deviations. VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Burn Severity Haake et al. (2020) 2019

Avg. Std. Dev. Avg. Std. Dev.

VLBS 8.67 6.2 9.24 2

LBS 10.09 1.9 7.19 1.7

MBS 10.68 1.7 9.39 1.8

HBS 12.0 1.3 9.37 1.2

Table 10. Average LL, PL, and PI from Haake et al. (2020) and this study (2019). Std. Dev. = standard deviations. LL = liquid limit. PL = plastic limit. PI = plasticity index. VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Haake et al. (2020) 2019

Burn LL Std. Dev PL Std. Dev. PI Std. Dev. LL Std. Dev PL Std. Dev. PI Std. Dev. Severity

VLBS 27.5 3.2 23.7 3.3 3.9 1.2 24.5 0.9 19.3 1.1 5.2 0.7

LBS 32.9 4.2 27 3.3 5.7 1.4 31 1.1 26.4 0.9 4.6 0.9

MBS 32.6 3.0 29 3.9 3.8 2 33.5 1.4 28.4 1.5 4.5 1.4

HBS 34.2 10.2 34.6 12.9 -0.6 3.8 29.3 1.3 25.5 0.8 2.5 1.1

Table 11. Average peak shear stress under three different normal loads. In addition to average cohesion and angle of internal friction for all burn severities. Results from Haake et al., 2020 and this study (2019). Std. Dev. = standard deviations. VLBS = very low or unburned burn severity. LBS = low burn severity. MBS = moderate burn severity. HBS = high burn severity.

Haake et al. (2020) 2019 Burn Normal Shear Cohesion Friction angle Shear Cohesion Friction angle Std. Dev. Std. Dev. Severity Force (psi) Stress (psi) (c') (μ) Stress (psi) (c') (μ)

6.94 8.7 0.2 1.8 0.7 VLBS 13.89 14.8 1.0 3.4 38.6 3.1 1.3 0 15.3 27.78 25.4 4.0 7.4 5.1 6.94 8.7 0.5 2.1 1.3 LBS 13.89 14.8 0.9 3.5 38.2 5.2 0.6 0.2 17.6 27.78 25.2 1.9 8.9 1.5 6.94 9.2 1.1 2.3 0.9 MBS 13.89 16.8 0.7 3.3 42.5 4.3 0.9 0 21.8 27.78 28.6 2.5 10.5 0.9 6.94 10.8 1.0 2.2 0.8

HBS 13.89 16.9 1.4 4.6 41.7 4.1 0.6 0 26.9 27.78 29.3 1.6 12.4 2.9

77 APPENDICES

I. Liquid Limit, Plastic Limit, and Plasticity Index Data

Water Water Burn Severity Sample # Trial LL (%) PL (%) PI Content (%) Content (%) 1 23.63 19.11 1 23 19 4 2 23.26 18.89 1 23.23 17.72 2 23 17 6 2 23.41 17.21 1 24.58 18.53 3 25 19 6 2 24.62 19.66 1 23.12 18.01 4 23 18 5 2 23.24 17.43 1 24.29 18.73 5 24 19 5 2 24.37 19.49 1 24.77 18.39 6 25 18 7 2 24.45 17.75 1 25.62 18.57 7 25 18 7 2 24.85 17.91 1 24.74 19.95 8 25 20 5 2 24.67 19.11 1 24.24 18.94 9 24 19 5 2 24.52 19.49 1 24.74 19.73 10 25 20 4 2 24.38 20.47 VLBS 1 24.39 19.49 11 24 20 5 2 23.89 19.71 1 24.34 19.1 12 24 19 5 2 24.24 19.27 1 23.77 19.41 13 24 20 4 2 23.27 19.62 1 25.73 19.59 14 26 20 5 2 25.35 21.4 1 25.15 21.56 15 25 20 5 2 25.26 18.14 1 25.49 21.65 16 25 21 4 2 25.3 20.66 1 27.13 21.99 17 26 22 5 2 25.69 21.44 1 25.53 20.05 18 25 20 5 2 25.36 20.18 1 23.42 18.82 19 24 19 4 2 23.62 19.43 1 23.02 17.8 20 23 18 6 2 23.47 17.52 Average 24 19 5

Water Water Burn Severity Sample # Trial LL (%) PL (%) PI Content (%) Content (%) 1 32.27 19.11 1 31.74 26.36 5 2 31.21 18.89 1 30.02 17.72 2 30.22 27.13 3 2 30.42 17.21 1 32.37 18.53 3 32.31 27.42 4 2 32.25 19.66 1 29.66 18.01 4 29.46 25.78 3 2 29.26 17.43 1 31.9 18.73 5 31.5 26.85 4 2 31.1 19.49 1 31.9 18.39 6 31.35 25.5 5 2 30.8 17.75 1 32.46 18.57 7 32.27 27.5 4 2 32.08 17.91 1 32.19 19.95 8 31.49 26.35 5 2 30.79 19.11 1 29.94 18.94 9 29.51 25.3 4 2 29.07 19.49 1 30.18 19.73 10 30.26 26.47 3 2 30.33 20.47 LBS 1 30.3 19.49 11 29.98 24.98 5 2 29.66 19.71 1 29.16 19.1 12 28.94 26.39 2 2 28.71 19.27 1 30.96 19.41 13 31.02 26.42 4 2 31.07 19.62 1 31.49 19.59 14 31.26 25.79 5 2 31.02 21.4 1 31.08 21.56 15 30.69 27.09 4 2 30.3 18.14 1 31.34 21.65 16 31.39 26.93 4 2 31.44 20.66 1 34.25 21.99 17 33.7 28.71 4 2 33.15 21.44 1 31.51 20.05 18 31.39 25.9 5 2 31.26 20.18 1 30.9 18.82 19 30.72 24.44 6 2 30.54 19.43 1 31.02 17.8 20 30.78 25.8 5 2 30.53 17.52 Average 31 26 4

Water Water Burn Severity Sample # Trial LL (%) PL (%) PI Content (%) Content (%) 1 31.39 29.77 1 31.85 29.25 2 2 32.3 28.72 1 33.78 29.73 2 33.25 29.8 3 2 32.72 29.87 1 33.79 26.25 3 33.35 26.84 6 2 32.9 27.43 1 27.55 26.29 4 29.08 26.62 2 2 30.61 26.95 1 33.23 27.91 5 33.27 27.48 6 2 33.3 27.05 1 34.79 30.76 6 33.97 29.97 4 2 33.14 29.17 1 34.06 28.39 7 33.36 27.83 5 2 32.65 27.26 1 37.42 27.37 8 34.29 33.11 1 2 31.16 38.84 1 32.56 26.62 9 32.39 27.4 5 2 32.22 28.17 1 33.46 29.82 10 33.33 29.76 3 2 33.2 29.69 MBS 1 33.37 28.75 11 33.02 28.69 4 2 32.66 28.62 1 32.84 26.48 12 32.84 27 6 2 32.31 27.52 1 33.33 25.67 13 33.09 26.74 5 2 32.84 27.8 1 34.24 28.76 14 34.33 28.59 5 2 34.41 28.41 1 34.8 30.49 15 34.49 29.76 4 2 34.18 29.02 1 34.23 27.86 16 34.35 27.11 5 2 34.46 26.35 1 36.97 31.63 17 36.42 29.96 6 2 35.87 28.29 1 35.06 28.27 18 34.65 28.46 5 2 34.24 28.64 1 34.66 26.17 19 34.52 26.4 5 2 34.37 26.62 1 33.29 26.71 20 33.46 26.36 7 2 33.62 26.01 Average 33 28 4

Water Water Burn Severity Sample # Trial LL (%) PL (%) PI Content (%) Content (%) 1 26.73 25.93 1 26.68 25.49 1 2 26.62 25.05 1 28.18 24.57 2 28.62 24.57 4 2 29.05 24.56 1 29.34 25.8 3 29.5 25.9 3 2 29.65 25.99 1 29.73 25.03 4 29.61 24.64 5 2 29.48 24.25 1 31.65 27.28 5 30.66 26.83 3 2 29.67 26.38 1 32.36 26.4 6 31.11 26.88 4 2 29.86 27.36 1 30.4 25.68 7 30.31 25.47 4 2 30.22 25.25 1 27.02 25.32 8 27.64 25.92 1 2 28.26 26.51 1 29.63 26.52 9 29.17 26.29 2 2 28.7 26.06 1 28.48 24.22 10 28.2 24.61 3 2 27.92 25 HBS 1 28.4 23.92 11 28 23.88 2 2 27.59 23.84 1 24.44 25.05 12 26.85 25.1 0 2 29.25 25.14 1 29.54 25.72 13 29.16 26.09 1 2 28.77 26.45 1 31.01 25.77 14 30.52 26.34 1 2 30.03 26.91 1 28.94 24.36 15 28.8 24.55 2 2 28.65 24.74 1 29.47 25.92 16 29.2 25.82 2 2 28.92 25.71 1 29.98 25.16 17 28.96 24.43 2 2 27.94 23.7 1 28.67 24.4 18 28.37 24.47 2 2 28.06 24.53 1 30.36 26.18 19 30.47 26.02 2 2 30.58 25.86 1 31.63 25.73 20 31.6 26.33 2 2 31.57 26.92 Average 29 25 2

II. Shear Strength Data

Burn Normal Shear Normal Shear Normal Shear Sample # Severity Stress (psi) Stress (psi) Stress (psi) Stress (psi) Stress (psi) Stress (psi)

1 6.94 0.9 13.89 4.1 27.78 15.6 2 6.94 1.6 13.89 4.8 27.78 9.0 VLBS 3 6.94 2.9 13.89 4.5 27.78 10.7 4 6.94 1.8 13.89 3.4 27.78 15.2 5 6.94 1.6 13.89 3.5 27.78 11.3 1 6.94 1.2 13.89 3.0 27.78 11.6 2 6.94 0.6 13.89 4.2 27.78 9.1 LBS 3 6.94 2.8 13.89 5.5 27.78 10.1 4 6.94 1.9 13.89 4.2 27.78 10.9 5 6.94 3.8 13.89 4.7 27.78 11.0 1 6.94 1.3 13.89 5.5 27.78 7.0 2 6.94 1.5 13.89 4.2 27.78 7.6 MBS 3 6.94 3.0 13.89 5.2 27.78 10.1 4 6.94 3.4 13.89 5.3 27.78 10.2 5 6.94 2.3 13.89 5.8 27.78 9.7 1 6.94 2.1 13.89 1.1 27.78 1.2 2 6.94 1.4 13.89 3.9 27.78 6.2 HBS 3 6.94 1.4 13.89 3.3 27.78 15.2 4 6.94 2.7 13.89 3.0 27.78 8.3 5 6.94 3.2 13.89 4.4 27.78 6.2