QUANTIFYING the EFFECTS of FREEZE-THAW PROCESSES on RIVERBANK EROSION in the WHITE CLAY CREEK WATERSHED, PA by Zachary Cannon A

QUANTIFYING THE EFFECTS OF FREEZE-THAW PROCESSES ON

RIVERBANK EROSION IN THE WHITE CLAY CREEK WATERSHED, PA

Zachary Cannon

A thesis submitted to the Faculty of the University of Delaware in partial fulfillment of the requirements for the degree of Master of Science in Geology

Summer 2019

QUANTIFYING THE EFFECTS OF FREEZE-THAW PROCESSES ON

RIVERBANK EROSION IN THE WHITE CLAY CREEK WATERSHED, PA

Zachary Cannon

Approved: ______Michael A. O’Neal, Ph.D. Professor in charge of thesis on behalf of the Advisory Committee

Approved: ______Neil Sturchio, Ph.D. Chair of the Department of Earth Sciences

Approved: ______Estella Atekwana, Ph.D. Dean of the College of Earth, Ocean, and Environment

Approved: ______Douglas J. Doren, Ph.D. Interim Vice Provost for Graduate and Professional Education and Dean of the Graduate College ACKNOWLEDGMENTS

I would first like to thank my thesis advisor Dr. Michael O’Neal for his extensive guidance and assistance throughout my two years at the University of Delaware. I am also incredibly appreciative for the endless support from Dr. Claire O’Neal. Additionally, the insights and suggestions from committee members Dr.

James Pizzuto and Dr. Brian Hanson were greatly valued in the development of this project. The large volume of data collected for this project would not have been possible without the willingness of Troy Saltiel and Josh Edwards to lend a hand with numerous, cold days of fieldwork in the White Clay Creek. I must also recognize the Geology faculty, staff, and especially my fellow graduate students at the University of Delaware. I am tremendously grateful for the friendships that have been crafted during this experience. Finally, I must express my profound thankfulness to my parents for the unconditional love, kindness, and encouragement that they have always provided to help me reach this goal.

iii TABLE OF CONTENTS

LIST OF TABLES ...... vi LIST OF FIGURES ...... viii ABSTRACT ...... xiii

Chapter

1 INTRODUCTION ...... 1

2 BACKGROUND ...... 4

3 STUDY AREA ...... 7

4 METHODS ...... 9

Near-Surface Soil and Air Temperature ...... 9 Bank Profile Soil Temperature ...... 10 Soil Water Content ...... 10 Erosion Pins ...... 11 Image Collection ...... 12 Site Maintenance ...... 13 Digital Elevation Model Generation ...... 14 Difference Model Generation ...... 15 Aerial Image Analysis ...... 17 Climate Data Processing ...... 18

5 RESULTS ...... 20

Near-Surface Soil Temperature ...... 20 Bank Profile Soil Temperature ...... 21 Soil Water Content ...... 21 Erosion Pins ...... 22 Photogrammetry Models ...... 22 Yearly Total Bank Retreat ...... 25

6 DISCUSSION ...... 26

FIGURES ...... 31 TABLES ...... 73

iv REFERENCES ...... 86

v LIST OF TABLES

Table 1 Coordinates (Universal Transverse Mercator, North American Datum 1983) of 10-cm-deep soil temperature probes at field sites ...... 73

Table 2 Length of exposure measured in centimeters for each of the 6 erosion pins at Site 1. The pins are labelled as upper and lower positions at 50 cm and 100 cm from the top of the bank profile, respectively. Measurements were collected simultaneously with photogrammetry surveys ...... 74

Table 3 Length of exposure measured in centimeters for each of the 6 erosion pins at Site 2. The pins are labelled as upper and lower positions at 50 cm and 100 cm from the top of the bank profile, respectively. Measurements were collected simultaneously with photogrammetry surveys ...... 75

Table 4 Duration and magnitude of freeze-thaw cycles recorded by near- surface soil temperature sensors from Table 1. Note that only sensors that recorded a daily average temperature below 0°C were included in this table. Data collection began on 23 November 2017 for Site 1, and 2 December 2018 for Site 2, and 17 October 2017 for Site 3 ...... 76

Table 5 Duration and magnitude of freeze-thaw cycles recorded by bank-wall sensors. Data collection began on 18 January 2018 for Site 1 and 2 March 2018 for Site 2 ...... 77

Table 6 Number of images collected in the field, and the total number of topographic points produced (PointsT) and the number of points used for model comparisons (PointsA) for each CRDP survey at Site 1 ...... 78

Table 7 Number of images collected in the field, and the total number of topographic points produced (PointsT) and the number of points used for model comparisons (PointsA) for each CRDP survey at Site 2 ...... 79

Table 8 Summary statistics for DEMs of difference (DODs) from successive CRDP surveys at Site 1. Values in the negative direction represent erosion, while values in the positive direction represent expansion ...... 80

vi Table 9 Summary statistics for DEMs of difference (DODs) from beginning to end of CRDP survey datasets at Site 1. Values in the negative direction represent erosion, while values in the positive direction represent expansion. Note that each site has 2 beginning surveys and 2 end surveys because of positional adjustments to erosion pins, used as ground control points, made on 25 January 2019 ...... 81

Table 10 Summary statistics for DEMs of difference (DODs) from successive CRDP surveys at Site 2. Values in the negative direction represent erosion, while values in the positive direction represent expansion ...... 82

Table 11 Summary statistics for DEMs of difference (DODs) from beginning to end of CRDP survey datasets at Site 2. Values in the negative direction represent erosion, while values in the positive direction represent expansion. Note that each site has 2 beginning surveys and 2 end surveys because of positional adjustments to erosion pins, used as ground control points, made on 25 January 2019 ...... 83

Table 12 Total amount of change observed for Sites 1 and 2, collected during the winter of 2018 to 2019. Values in the negative direction represent erosion, while values in the positive direction represent expansion ...... 84

Table 13 Results from geospatial analysis of 1-ft aerial imagery collected by DVRPC in 2010 and 2015, and 1-m LIDAR DEM collected by DCNR PAMAP in 2008. Elevation data was extracted to mask an erosion polygon traced on channel margins at each site. The surface volume was then calculated with reference to the lowest point in the DEM, assumed to be the bottom of the bank ...... 85

vii LIST OF FIGURES

Figure 1 A map of the White Clay Creek study sites (red dots) used in this thesis ...... 31

Figure 2 Graphs depicting temperature time series (in °C) at each site using daily averages from suspended air temperature loggers during the winter of 2017 to 2018. The gray area represents the daily mean, the red line represents the daily maximum, the blue line is the daily minimum ...... 32

Figure 3 Graphs depicting temperature time series (in °C) at each site using daily averages from suspended air temperature loggers during the winter of 2018 to 2019. The gray area represents the daily mean, the red line represents the daily maximum, the blue line is the daily minimum ...... 33

Figure 4 Image depicting needle, segregated, and pore-space ice transporting sediment away from the bank profile when the water-saturated riverbank sediments freeze. Layering present due to layering within soil profile. Collected from Site 2 on 5 December 2018 ...... 34

Figure 5 Image depicting needle ice structures that extend 2 cm to 3 cm away from the riverbank surface before breaking off. Collected from Site 2 on 22 January 2019 ...... 35

Figure 6 Image depicting needle ice structures that extend 2 cm to 3 cm away from the riverbank surface before breaking off. Collected from Site 1 on 8 December 2018 ...... 36

Figure 7 Image depicting riverbank material covered in solid ice. Collected from Site 2 on 23 January 2019 ...... 37

Figure 8 Graphs depicting temperature time series (in °C) at each site using daily averages from near-surface soil temperature probes during the winter of 2017 to 2018. The gray area represents the daily mean, the red line represents the daily maximum, the blue line is the daily minimum ...... 38

viii Figure 9 Graphs depicting temperature time series (in °C) at each site using daily averages from near-surface soil temperature probes during the winter of 2018 to 2019. The gray area represents the daily mean, the red line represents the daily maximum, the blue line is the daily minimum...... 39

Figure 10 Graphs depicting temperature time series (in °C) at Site 1 using daily averages from bank profile thermistors during the winter of 2018 to 2019. The gray area represents the daily mean, the red line represents the daily maximum, the blue line is the daily minimum ...... 40

Figure 11 Graphs depicting temperature time series (in °C) at Site 2 using daily averages from bank profile thermistors during the winter of 2018 to 2019. The gray area represents the daily mean, the red line represents the daily maximum, the blue line is the daily minimum ...... 41

Figure 12 Graphs depicting the volume of water per volume of sediment measured by sensors buried at Site 1. The light blue line represents data from the sensor buried at 15 cm and the dark blue line represents data from the sensor buried at 50 cm ...... 42

Figure 13 Graphs depicting the volume of water per volume of sediment measured by sensors buried at Site 2. The light blue line represents data from the sensor buried at 10 cm and the dark blue line represents data from the sensor buried at 50 cm ...... 43

Figure 14 Image of exposed length of one 61-cm-long erosion pin, which also serves as a ground control point for image alignment and DEM development. Image collected from Site 2 on 23 January 2019 ...... 44

Figure 15 Image of circular photogrammetry targets and scale bars used to increase the accuracy of image alignment and apply a spatial reference to each digital elevation model. Collected from Site 2 on 22 January 2019 ...... 44

Figure 16 Example of CRDP survey setup at Site 2 on 8 December 2018 with frozen soil structures apparent ...... 45

Figure 17 Example of image collection process at Site 1 on 11 March 2019 ...... 47

Figure 18 Left and right subsets of the digital elevation models at Site 1: (a) 8Dec18 DEM, (b) 18Dec18 DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 48

ix Figure 19 Left and right subsets of the digital elevation models at Site 1: (a) 18Dec18 DEM, (b) 9Jan19 DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 49

Figure 20 Left and right subsets of the digital elevation models at Site 1: (a) 9Jan19 DEM, (b) 22Jan19 DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 50

Figure 21 Left and right subsets of the digital elevation models at Site 1: (a) 22Jan19 DEM, (b) 25Jan19 DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 51

Figure 22 Left and right subsets of the digital elevation models at Site 1: (a) 25Jan19 DEM, (b) 4Feb19 DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 52

Figure 23 Left and right subsets of the digital elevation models at Site 1: (a) 4Feb19 DEM, (b) 18Feb19 DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 53

Figure 24 Left and right subsets of the digital elevation models at Site 1: (a) 18Feb19 DEM, (b) 11Mar19 DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 54

Figure 25 Left and right subsets of the digital elevation models at Site 1: (a) 11Mar19 DEM, (b) 26Mar19 DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 55

Figure 26 Left and right subsets of the digital elevation models at Site 2: (a) 18Nov18 DEM, (b) 8Dec18 DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 56

Figure 27 Left and right subsets of the digital elevation models at Site 2: (a) 8Dec18 DEM, (b) 18Dec18 DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 57

x Figure 28 Left and right subsets of the digital elevation models at Site 2: (a) 18Dec18 DEM, (b) 6Jan19 DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 58

Figure 29 Left and right subsets of the digital elevation models at Site 2: (a) 6Jan19 DEM, (b) 22Jan19 DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 59

Figure 30 Left and right subsets of the digital elevation models at Site 2: (a) 22Jan19 DEM, (b) 25Jan19 DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 60

Figure 31 Left and right subsets of the digital elevation models at Site 2: (a) 25Jan19 DEM, (b) 4Feb19 DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 61

Figure 32 Left and right subsets of the digital elevation models at Site 2: (a) 4Feb19 DEM, (b) 18Feb19 DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 62

Figure 33 Left and right subsets of the digital elevation models at Site 2: (a) 18Feb19 DEM, (b) 11Mar19 DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 63

Figure 34 Left and right subsets of the digital elevation models at Site 2: (a) 11Mar19 DEM, (b) 26Mar19 DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 64

Figure 35 Left and right subsets of the digital elevation models at Site 1: (a) first 11Mar19 DEM, (b) second 11Mar19DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 65

Figure 36 Left and right subsets of the digital elevation models at Site 1: (a) 8Dec18 DEM, (b) 25Jan19 DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 66

xi Figure 37 Left and right subsets of the digital elevation models at Site 1: (a) 4Feb19 DEM, (b) 26Mar19 DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 67

Figure 38 Left and right subsets of the digital elevation models at Site 2: (a) 18Nov18 DEM, (b) 25Jan19 DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 68

Figure 39 Left and right subsets of the digital elevation models at Site 2: (a) 4Feb19 DEM, (b) 26Mar19 DEM, (c) resultant DOD, and (d) distribution of calculated 2.5-D volume change per 1 mm x 1 mm cells depicted in DOD above ...... 69

Figure 40 Aerial image of Site 1 from 2010 with overlain bank erosion polygon masked with LIDAR-derived elevation data. Erosion polygon, outlined in red, represents the amount of bank material eroded between 2010 and 2015. Datum NAD 1983 2011 StatePlane Pennsylvania South FIPS 3702 Ft US ...... 70

Figure 41 Aerial image of Site 2 from 2010 with overlain bank erosion polygon masked with LIDAR-derived elevation data. Erosion polygon, outlined in red, represents the amount of bank material eroded between 2010 and 2015. Datum NAD 1983 2011 StatePlane Pennsylvania South FIPS 3702 Ft US ...... 71

Figure 42 Graphs depicting the stream stage (ft) and total daily rainfall (in) between 1 November 2018 and 31 March 2019. The blue line represents stage measured at the Middle Branch White Clay Creek gage in Avondale, PA. The Yellow line represents the total daily rainfall measured at the weather station in West Grove, PA...... 72

xii ABSTRACT

Subaerial erosion of riverbank sediments in the Mid-Atlantic region of the eastern United States is of particular concern with regard to sediment supply and water quality. One process heretofore understudied in this region that may play a role in erosion is the freezing and thawing of riverbank sediments, a process that occurs repeatedly as a consequence of the region’s temperate winters. Over the last two years, observations of needle, segregated, and pore-space ice on riverbanks of the White Clay Creek suggest that the region’s silty and sandy soils are prone to riverbank erosion and sediment entrainment related to soil freeze-thaw processes. Prior studies have acknowledged that soil freeze-thaw likely contributes to riverbank erosion but without direct quantification. To better understand the climatic conditions driving these processes and to attempt to quantify their effect on sediment erosion, an array of techniques were employed, including measurement of near-surface temperature and soil water content, erosion pins, and close-range digital photogrammetry surveying to monitor change over days to weeks for two vertical riverbanks. Through the winters of 2017 to 2019, periods of rapid freezing followed by significant thawing of water- saturated banks were repeatedly observed. The applied photogrammetry surveying techniques allowed for topographic models with sufficient resolution to quantify centimeter-scale retreat and expansion over the course of freeze-thaw events, fluxes which were validated by traditional erosion pin surveying measurements. These observations help to quantify the heretofore underestimated importance of soil freeze- thaw processes to an overall sediment budget. Additionally, the techniques presented

xiii herein lay the groundwork for further study to examine the contribution of freeze-thaw cycling to overall bank erosion rates for the White Clay Creek.

xiv Chapter 1

INTRODUCTION

In the White Clay Creek watershed of Delaware and Pennsylvania, recent research projects have focused on better understanding the pathways of subaerial and fluvial erosion of silty and sandy riverbanks typical of this coastal-plain setting. These projects have been driven largely by the need to quantify the persistent, high sediment loads in the water column that affect water quality both along the river system and throughout the Delaware Bay (e.g., McCarthy, 2018; Cribb, 2017; Kauffman et al., 2011). In the majority of this work, the magnitude of sediment entrained is related to the forces of water acting on the channel wall or floodplain as part of both normal flow or event-scale processes that weaken or undermine the bank face. However, the spatial and temporal scales of such observations may underrepresent difficult to monitor erosive processes such as those caused by freeze-thaw events. Freeze-thaw processes in temperate climates are known to generate needle, segregated, and pore-space ice that may increase bank erodibility by reducing soil strength via repetitious, small changes in temperature and moisture (Wolman, 1959; Lawler, 1988). This process is well studied in many regions and has been shown to be capable of significant riverbank erosion in some settings (Ferrick, Gatto, & Grant, 2005); however, the effects of soil freeze-thaw is not well-characterized on riverbanks in temperate climates. Researchers have acknowledged the likely contribution of freeze-thaw cycles to erosion in temperate climates (e.g., Wolman, 1959; Pizzuto & O’Neal, 2009). The impact of such events has not been directly quantified, though

1 previously estimated (Pizzuto, 2009), possibly because they are short-lived and challenging to directly observe (Wynn & Mostaghimi, 2006; Lawler, 1988). Thus, the erosive effect of freeze-thaw processes on riverbanks remains poorly constrained and may be underestimated in temperate regions like the Mid-Atlantic USA. A few recent studies that have focused on the White Clay Creek have suggested that freeze-thaw phenomena may play an important role in subaerial riverbank erosion and sediment entrainment (McCarthy, 2017; Inamdar, Johnson, & Rowland, 2018). Preliminary data from the White Clay Creek watershed, which initiated this thesis, observed that the riverbanks of the study area indeed saturate, freeze, and thaw multiple times during the typical winters. The processes of frost heaving and thaw weakening leaves soils temporarily weaker after thawing than before freezing (Chamberlain, 1981; Gatto, 1995). The weakening of near-surface soils by freeze-thaw cycling is therefore relevant to the shallow nature of riverbank erosion (Ferrick, Gato, & Grant, 2005). Thus, any study that is able to add to our understanding of the impact of freeze-thaw cycling on bank erosion has broad relevance to environmental analysis and restoration, fortification projects, as well as land management. This thesis seeks to quantify erosion related to the freezing and thawing of riverbanks via directly observing, monitoring, and quantifying freeze-thaw processes at three locales in the White Clay Creek watershed, Pennsylvania (Figure 1). To reach this goal, data were collected regularly regarding the near-surface temperatures, soil water content, and detailed topography along two vertical riverbanks. The topographic data rely on close-range digital photogrammetry (CRDP) to monitor the two riverbank sections over time and across events, a technique that has not been applied in this

2 setting previously. The final goal of this study is to classify and describe freeze-thaw events, to accurately measure centimeter-scale erosion of vertical riverbanks, and to correlate model differences and erosion pin exposure with observed freeze-thaw processes.

3 Chapter 2

BACKGROUND

The erosion of riverbanks is generally characterized by interactions between hydraulic forces acting on the lower channel wall and gravitational forces acting on the bank itself (Gatto, 1995; Lawler, 1988). These forces cause the failure of in situ soils, sediments, and rocks, which settle on the bed or are carried downstream. Depending on the morphological evolution of the stream, channel walls may become more or less stable over time. In calculating bank-erosion models, erodibility coefficients for soils are determined based on the following parameters: bank soil structures, cohesion, angle of internal friction, and bulk density. However, each of these properties vary seasonally because of frost effects and are therefore misrepresented if a model is static (Ferrick, Gatto, & Grant, 2005). This variation, when unaccounted for, increases the already-high error involved in bank erosion predictability models. Other factors that affect bank stability include root density, root depth, and herbaceous cover of riparian vegetation. The properties of woody and herbaceous vegetation play an additionally complex role in bank retreat (Pizzuto & Meckelnburg, 1989; Allmendinger et al., 2005; Pizzuto, O’Neal, & Stotts, 2010; Stotts et al., 2014). Sediments that are more loosely consolidated are more likely to experience failure due to gravity or physical weathering due to wind, precipitation, stream flow, or even meltwater flow. In terms of freezing and thawing, sediments that have a higher soil water content (i.e., high sediment porosity and moisture availability) and/or relatively high

4 thermal conductivity have a larger capacity for expansion and thus erosion (Oztas & Fayetorbay, 2003; Gatto, 1995). Freeze-thaw events occur when the air temperature sinks below the freezing point long enough for the water retained in sediment pore spaces to freeze and expand. Upon thawing, erosion may be caused by both the presence of vacant pore space as well as the lateral movement of liquid water toward the bank face (Ferrick & Gatto, 2005). Soils that are exposed to solar heating and nighttime cooling experience a larger diurnal soil temperature range, which contributes to the occurrence of freeze-thaw cycling (Wynn & Mostaghimi, 2006). However, soils that remain frozen for one or more days may experience greater amounts of ice penetration and soil expansion. Therefore, these events may be more significant in terms of the magnitude of associated erosion. Both the magnitude and duration of soil freeze periods may be useful in investigating the erosive effect of freeze-thaw. In the Mid-Atlantic region of the United States, air temperatures begin to reach freezing in November and continue to do so through March (Figures 2 and 3). Between November and March, the region typically experiences air temperatures well- above and -below the freezing point on a daily or weekly basis (Delaware Environmental Observing System, 2019). These are conditions expected to cause the frequent freezing and thawing of soils. Given the pore-water content of floodplain sediments in the Mid-Atlantic coastal plains, such wet soils may begin freezing and expanding overnight or even throughout the day when air temperatures are below 0°C. Once soil freezes, overhanging sediment becomes vulnerable to thawing and falling into the creek or down the bank profile. This effect is expected to be most pronounced in places that have this type of temperature variability combined with perennial

5 moisture on the local and regional scale. These circumstances enable the frequent growth of needle ice, popcorn textures, and other frozen soil structures on silty riverbanks (Figures 4 to 7). Field methods required to capture the spatial and temporal scale of freeze-thaw processes necessitate rapid and repeated field reconnaissance with topographic measurement techniques that can be applied with high fidelity. Previous studies have indicated that terrestrial laser scanning (TLS) and CRDP remote sensing workflows provide the accuracy and spatial resolution capable of capturing centimeter-scale riverbank erosion and deposition (e.g., O’Neal & Pizzuto, 2011; Nasermoaddeli & Pasche, 2008; Cribb, 2017). Each method provides a framework for building accurate topographic models of specific areas of interest. While TLS does this by measuring the return time of a pulsed laser, CRDP applies simple geometric calculations to pixels on adjacent digital photographs. This calculation within CRDP processing determines the distance to each point of the image from the camera after using correlation to determine which pixels of different images represent the same location. Researchers and professionals have traditionally executed both methods from an unmanned aerial vehicle, which surveys the land surface from above. For observing the channel wall, TLS has been used to successfully monitor changes in bank morphology in prior studies, yet it is costly to acquire, time consuming, and data acquisition may be hindered by local topographic occlusion (e.g., O’Neal & Pizzuto, 2011; Javernick, Brasington, & Caruso, 2014). Alternatively, CRDP surveys of vertical soil surfaces are less expensive and easier to implement but remain a topic of ongoing development in research communities. Therefore, this study also aims to validate photogrammetry as a viable method for surveying vertical riverbanks efficiently and accurately.

6 Chapter 3

STUDY AREA

The White Clay Creek watershed spans 279.2 km2 of Pennsylvania, Delaware, and Maryland (Narvaez & Homsey, 2016). The three sites selected for this project lie along the Middle Branch of the creek in Chester County, Pennsylvania (Figure 1). The upstream-most field site, located within the Goodwin Preserve, is protected and managed by the Franklin Township of Pennsylvania. The other two field sites are protected and managed by the State of Pennsylvania within the White Clay Creek Preserve. Each site was selected for having a well-exposed, east-facing, and actively- eroding vertical cut bank with minimal tree root armor. This study focuses on east-facing cut banks under the assumption that they experience the most rapid change in temperature throughout the day. Given that the ground surface generally approaches its lowest temperature between 3 a.m. and 4 a.m. EST, east-facing banks warm more rapidly than the general area due to direct sunlight in the morning. Frozen banks that experience shade in the morning should warm more gradually, which may preclude or underrepresent the erosive potential of freeze-thaw processes. Data collection began on 17 October 2017. Field data was last retrieved on 5 April 2019 and all loggers were left in place to continue data collection for future research. Site 1 is located within the Goodwin Preserve, 2 miles south of West Grove, Pennsylvania. This site is surveyed via CRDP and is equipped with erosion pins, soil water content probes, near-surface temperature probes, and an air temperature logger

7 which record data at 15 minute intervals. The Goodwin Preserve is an active stream restoration site with trees planted as riparian vegetation buffer zones. This segment of the White Clay Creek is an alluvial stream reach that flows through a large floodplain. Bed material is comprised of very fine sands and silts to cobbles and boulders. The bank profile has moderate tree root armor in addition to thin roots from riparian vegetation. Site 2 lies within the northern part of the White Clay Creek Preserve of Pennsylvania near New Peltier Road. This field site contains the same distribution of temperature and soil water content data loggers as Site 1 and is also routinely surveyed using CRDP and measurements of erosion pin exposure. The bank at this monitoring site is composed primarily of well-sorted fine sands and silts. At this section, there is mild tree root armor, as well as thin roots from riparian vegetation at the top of the bank. Additional features include oxidized iron mottles 1 mm to 5 mm in diameter and laminar organic deposits of 1 cm thickness. Site 3 is located immediately upstream from the stream gaging station in Stricklersville, PA. This site is not surveyed for erosion due to difficultly in accessing a viable, non-vegetated eroding bank. However, data loggers were installed and used to collect additional air and soil temperature data beginning on 17 October 2017.

8 Chapter 4

METHODS

Near-Surface Soil and Air Temperature

Sets of three Onset HOBOware Pendant temperature probes were deployed in the soil at Site 1 on 23 November 2017, Site 2 on 2 December 2017, and Site 3 on 17 October 2017 (Figures 8 and 9). Each was buried with 25 m lateral spacing parallel to the stream and set to record near-surface temperatures at 15 minute intervals. An additional temperature probe was deployed within a PVC tube and attached to a tree to monitor local air temperatures at ~ 2m above the ground surface at all three field sites. Each surface logger was labelled, placed in nested polyethylene bags for improved water resistance, and packed into the bottom of a 10 cm deep hole. The 10 cm depth prevents sensors from being directly affected by morning frost and direct sunlight but is shallow enough to allow them to experience the daily flux in near-surface temperatures. All ground sensors were buried 1 m from the bank edge in order to log temperatures associated with the exposed bank face while minimizing risk of loss due to bank collapse and significant bank retreat. A labelled wooden stake, driven into the soil immediately north of a buried sensor, marks the location of each. Field photographs and GPS measurements were also recorded for each of the 9 stakes to facilitate easy locating (Table 1). The most recent date of data retrieval was 3 April 2019 at approximately 9 a.m., but each data logger remains deployed for potential future research.

9 Bank Profile Soil Temperature Sets of four temperature probes were deployed down the bank profile at Site 1 on 18 January 2018 and Site 2 on 2 March 2018 (Figures 10 and 11). A screwdriver was used to puncture a 0.5 cm diameter by 10 cm deep hole into the bank profile at distances 10 cm, 20 cm, 50 cm, and 75 cm from the top of the bank surface. At Site 2, these probes were placed at 10 cm, 20 cm, 50 cm, and 70 cm from the top of the bank. These probes recorded the vertical temperature profiles along the face of each riverbank. Each temperature probe’s wire extends up through a thin groove in the soil, cut by a bow saw, to a HOBOware base station installed above the ground surface. Each weather-resistant, battery-operated data logging station was mounted by zip ties atop a steel fence post, which was set inside a 45 cm deep by 25 cm diameter hole filled with concrete. Loose temperature probe wires were secured by ground-cover anchoring pins to prevent them from getting pulled out of the bank wall by passing debris, animals, or hikers. Like the ground temperature probes, these bank wall probes recorded data every 15 minutes. At this 15 minute interval, each logger recorded 96 temperatures each day.

Soil Water Content

HOBO Soil Moisture Smart Sensors (S-SMD-M005) were deployed at Sites 1 and 2 on 3 December 2018 (Figures 12 and 13). They measured soil water content at 15 cm and 50 cm depth at Site 1 and 10 cm and 50 cm depth at Site 2. Like the temperature loggers, the soil water content sensors recorded data at 15 minute intervals and this information is accessed via a smart sensor-compatible HOBO data logging station at each field site. This technology operates at ± 0.033 m3/m3 (volume of water per volume of sediment) accuracy for temperatures 0°C to 50°C in mineral

10 soils. While they remain functional outside that range, soil water content data collected at extreme temperatures is recorded outside of the sensor’s limits for accuracy. These sensors do not require any regular maintenance aside from making sure that the HOBO data loggers remain water resistant and have sufficient battery power.

Erosion Pins Sets of six pieces of rebar, with lengths of 61 cm and diameters of 1.27 cm, were driven into the riverbank profile at Sites 1 and 2 (Figure 14). At each location, three bars were installed at 50 cm and 100 cm distances from the top of the bank profile. Each bar was hammered into the profile approximately 40 cm deep with horizontal spacing between each pair of approximately 1.5 m. Before each photogrammetric survey, each length of exposed rebar was measured with measuring tape and recorded as an erosion pin distance (Tables 2 and 3). The six erosion pins at each site provided ground-truth values for erosion which could be compared to the average change calculated between the photogrammetric models. The tip of each bar was spray-painted white to increase its visibility in serving as a benchmark for aligning each pair of digital elevation models (DEMs) produced by the photogrammetry software (Figure 14). On 25 January 2019, each bar was again hammered into the bank face to prevent the loss of field equipment. At Site 1, five of six were hammered into the wall so that 10 cm was exposed, while the upper, right- most bar was only reinserted to 23 cm because going deeper would overly decrease visibility under a tree root that was also providing stability. All six bars at Site 2 were returned to 10 cm positions on 25 January 2019.

11 Image Collection Each photogrammetric survey intended to accurately record the position of the entire soil profile to millimeter accuracy of each pixel. To monitor erosion, the relative positions of sediment across the scene should change over time while everything else in the scene should remain the same between surveys. Therefore, each bank face was prepared by gently clipping away exposed roots, grass, and other light vegetation extending out from the surface before each survey. Leaves and fallen debris were also removed from the scene. Larger structures like thick (>1 cm) roots were left in situ to preserve the health of the riparian vegetation in the scene. Large objects are much less likely to move over time, whereas thinner, potentially waterlogged, overhanging lengths of vegetation may move with the wind. Efforts were made to avoid movement within the scene, as it can create visual disturbances that may impair geometric calculations involved in image alignment. After clearing the surface, an arrangement of circular photogrammetry targets, rulers, and scale cards were deployed around the 0.75 m2 area of focus on each eroding bank (Figures 15 and 16). Circular targets like these are coded to be automatically detected in each set of images and aid in the alignment process. In creating these, 10 optical targets were printed on waterproof paper, glued to a plexiglass sheet, and cut into square tiles, with holes punched outside the targets for anchoring. Targets were anchored into the soil with a steel nail to prevent movement during image collection. The rulers and scale cards, annotated with geometric shapes as well as length data, were used to apply a spatial reference to the resulting dense cloud. The optical targets and rulers were deployed around the perimeter of the sampled area with approximate spacing of 20 cm. Scale cards were placed halfway between the upper set of erosion pins as well as halfway between the lower set of

12 erosion pins. This placement was a compromise between maximizing the accuracy of the spatial reference and slightly reducing the coverage within the scene. From October 2018 to March 2019, Sites 1 and 2 were surveyed via CRDP on a roughly biweekly basis. Additional surveys were conducted during expected freeze periods and thaw periods, as well as for model validation and for correcting mistakes made in the process of survey preparation. During each survey, a Nikon D3200 camera with a 35mm lens (a standard length lens for the APS-C sensor size of this camera) was used to collect a minimum of 50 photos in at each site per survey (Figure 17). At a distance of 2.25 m from the bank face, topographic variations of 0.5 m remain in focus with the camera lens used. Spacing between photographs was approximately 0.25 m horizontally and 0.15 m vertically. Vertical spacing was managed by adjusting the height of a monopod that was attached to the bottom of the camera. The horizontal spacing between images was maintained by dropping a 3 m long metal chain with marked positions into the stream, parallel to the bank. The camera’s monopod was then placed atop a series of yellow-painted rings emplaced along the chain at 0.25 m distances.

Site Maintenance Routine maintenance of each field site occurred during each visit. The primary focus of site maintenance was to verify that all sensors remained properly installed and continuously recording data with sufficient battery levels. As the bank profile eroded over time, the temperature probes in the bank face became shallower than their original 10 cm burial depth and were returned to depth using a screwdriver. The batteries for temperature sensors buried in the bank face last >2 years, so those were not replaced during the study. However, the batteries in the pendant temperature

13 loggers buried along each cut bank last up to 1.5 years. These were replaced once during this study. Each battery was checked on data retrieval days, which occurred approximately semi-monthly, concurrently with the downloading of data. Routine data retrieval also aided in keeping track of the location of each buried sensor. All field sites had thick ground cover vegetation from late-spring through mid- fall in 2017 and 2018. Small amounts of vegetation needed to be removed to download sensor data in the summer. Vegetation was cut above the soil surface, and only when necessary, to reduce potential effects on the stability of the sediment and the ecosystem.

Digital Elevation Model Generation The process of generating 3-D topographic point data from overlapping images relies on image analysis software. Several structure-from-motion software packages have been developed in the past decade that can successfully produce high-resolution DEMs from images collected with digital cameras. Each package executes the three principal stages of point cloud construction: image alignment, registration and scaling, and the subsequent building of dense 3-D point clouds. This study used Agisoft Metashape for image processing (Agisoft, 2018). The workflow for image analysis begins with alignment. By analyzing the geometric relationships of the imaged surface and individual camera locations, subsequent images are matched to obtain surface elevation at the image overlap (Bertin & Friedrich, 2016). Our image alignment process utilized the approximate 75% overlap between sets of at least 50 adjacent images to identify a sparse cloud of 3-D points shared by at least 2 images. Up to 21 additional images were collected to ensure that changes in lighting or adjustments to camera positioning did not

14 compromise the analysis. All images collected in the same survey were included in image processing. Following alignment is the manual registration of control points shared between images. Circular photogrammetry targets that weren’t automatically detected were added manually by registering a control point at the center of each target. Points were also manually added at both ends of each ruler and scale card with accuracy of 0.001 m and error of <0.001 m. These additional points enabled the software to establish a definitive scale for each area of the DEM. Once accurately scaled, a dense point cloud was constructed within the image analysis software. Using parallax calculations based on angular distances from each of the camera locations, the image analysis software performs autocorrelation among every pixel of the provided images to create 3-D points in space (Agisoft, 2018; Javernick, Brasington, & Caruso, 2014). This produced a digital elevation model for each CRDP survey.

Difference Model Generation CloudCompare, an open-source 3-D point cloud processing software, was used to align and analyze the differences between two DEMs from subsequent surveys at the same field site (CloudCompare, 2018). The result of this process is a DEM of difference (DOD). Before alignment, each model was cropped down to the 0.75 m2 area of focus, just outside the four lengths of rebar in each bounding corner. Overhanging portions of sediment and vegetation were also segmented out of the scene when it was possible to do so without removing data of interest. Next, pairs of the trimmed DEMs were aligned to one another by selecting control points in the center of the white-tipped length of rebar located in each corner. Cloud-cloud distance

15 was then measured as the absolute distance between individual points of the reference DEM and the aligned DEM succeeding it. Note that the coordinate system for each DEM is defined by 3 cartesian coordinates so that the z component is orthogonal to the surface of the bank. By splitting x, y, and z components of the cloud-cloud distance calculation, it was possible to isolate the expansion and contraction of sediment movement in the z-direction. Lastly, this technique applies a scalar field to the successive DEM based on the amount of change inward or outward from the face of the bank for visual comparative analysis of the result. 2.5-D volumes were calculated within CloudCompare, as well. Each survey’s DEM was segmented into left and right sections in order to exclude the scale cards placed within the center and edges of the scene. Grid cells for the volume calculation were set to 1 mm ´ 1 mm. This process measured change as the maximum distance between each cell of the overlaid DEMs and was repeated for left and right sections of each pair of subsequent surveys. This tool was set to ignore empty cells in the calculation, as well as to use the maximum height for cells with sub-millimeter variations. Point data for each pair of gridded volume rasters were combined to produce a histogram for each pair based on the frequency of particular cell heights within a given range. Using the cell height data, the following statistical parameters were calculated: mean, standard deviation, maximum value, minimum value. This method was applied not only to datasets from sequential survey dates, but also the beginning and end dates of data collection for comparing the cumulative sediment flux throughout the study period. Due to the erosion pin adjustment that occurred on 25 January 2019 following image collection, each site is effectively associated with two independent CRDP

16 datasets with a gap of 10 days in between. Therefore, the two datasets were unable to be compared directly. However, the average, maximum, and minimum sediment flux for each site throughout the first length of time and second length of time were combined to estimate the cumulative erosion that occurred during the winter of 2018 to 2019.

Aerial Image Analysis DEM measurements from this study were compared to 30 cm (1 ft) resolution aerial imagery collected in 2010 and 2015 by the Delaware Valley Regional Planning Commission (DVRPC) and obtained from the Pennsylvania Spatial Data Access (PASDA) website (US Geological Survey, 2016). Stream channel margins were traced along the full length of the eroding bank at Site 1 and Site 2 for both dates and then combined to delineate the area of bank material lost between 2010 and 2015. From this, the average amount of retreat that occurred along each bank was calculated. These erosion polygons were then intersected with a 1 m light detection and ranging (LIDAR) DEM that was collected in 2008 by the Department of Conservation and Natural Resources (DCNR) PAMAP Program and obtained from PASDA (US Geological Survey, 2016). Cells of elevation data were then used to calculate the 3D volume of each bank erosion polygon. The lowest elevation in the clipped dataset, which is interpreted as the elevation of the stream, was set as the plane of reference from which volume was calculated (De Rose & Basher, 2011). The data obtained from PASDA was spatially referenced to the NAD 1983 2011 StatePlane Pennsylvania South FIPS 3702 Ft US coordinate system.

17 Climate Data Processing Correlating observed sediment fluxes with freeze-thaw processes required statistical classification of freeze-thaw cycling within the collected temperature time- series. From the 15 minute temperature series, we calculated daily average, daily maximum, and daily minimum temperature for each probe for freeze-thaw event classification (Figures 8 to 11). Freeze thaw event classification required at least one soil temperature series for each location, averaged daily, to have reached 0.1°C or less. Not all temperature loggers reach the freezing point at the same time due to factors such as varying amounts of shade from vegetation, different conductive properties of soil types, as well as the inherent error associated with individual data loggers (Guo et al., 2018; Wynn & Mostaghimi, 2006). A more localized type of freeze was classified as a minor freeze-thaw event, whereas periods during which temperature sensors reached or dipped below freezing for an extended period of time were major events. During these major freeze-thaw events, soil temperatures could be below freezing for longer than one week. Because the duration and magnitude of freeze-thaw events may vary greatly, the effect of one episode can be quite different than another. For the purposes of this study, daily average soil temperatures were used to distinguish significant events.

Daily average temperature was calculated for each temperature series and plotted along with daily maximum and daily minimum temperatures. Days during which the average surface temperatures are at or below zero are considered frozen with confidence. When the daily average temperature fails to rise above 0°C, the average temperatures trend further below zero. This constitutes a freeze-thaw event of larger magnitude due to the greater penetration depth of ice.

18 Field observations and preliminary analysis of temperature probe data from the bank profile and upper ground surface indicated that soil freeze and thaw occur over a broad range of different rates and times. Occasionally during late-morning fieldwork, the bank profile was observed to be fully thawed at times when the ground along the top of the bank was still frozen at the 10 cm temperature-probe depths. Therefore, quantifying the number of freeze-thaw events was largely based on the daily averages of the probes in the vertical profile, which show more variability than sensors buried along the top of the bank and better represent the soil properties and processes involved at the interface of water, soil, and air (Figures 10 and 11).

19 Chapter 5

RESULTS

Near-Surface Soil Temperature

During the first winter of monitoring, near-surface soil temperature probes recorded several freeze-thaw events from November 2017 to March 2018 (Figure 8). Since 23 November 2017, ground temperature data show at least four multi-day freeze-thaw events between 29 December 2017 and 11 February 2018. Based on data collected every 15 minutes, each of the high-amplitude transitions spanned a 2 to 4 day period and were equally spaced apart from each other, with the exception of the stable, low-fluctuation period between 1 January 2018 and 11 February 2018 (Figure 8). Following the last major freeze-thaw event, shorter freeze-thaw events were observed with near-daily periodicity between 17 February 2018 and 26 March 2018. No observable freeze-thaw events occurred after 28 March 2018 due to the arrival of springtime temperatures as high as 25°C during the day and 7°C at night. These temperature data are consistent across all three field sites, an expected outcome given their close proximity. Based on daily average temperature, the ground surface at Site 1 underwent at least three significant freeze-thaw events per year ranging 1 to 13 days in length (Table 4). Near-surface temperature probes at Site 2 experienced at least five significant freeze-thaw events ranging from 3 to 14 days in the winter of 2017 to 2018 and one 5-day-long freeze-thaw event in the winter of 2018 to 2019. During the winter of 2017 to 2018 at Site 3, ground surface temperature probes recorded at least four

20 freeze-thaw events ranging 2 to 14 days in length and two freeze-thaw events in the winter of 2018 to 2019 ranging 3 to 7 days (Figures 8 and 9).

Bank Profile Soil Temperature The temperature probes that were installed in the riverbank profile collected data over the full winter spanning 1 November 2018 to 31 March 2019 (Figures 10 and 11). These data exhibit notable variation for the vertical profile at Site 2 (Figure 11). Freeze and thaw amplitudes are most pronounced for the 10 cm probe, which experienced the lowest and highest temperatures in the profile over time. Temperature curves for the 20 cm and 50 cm probes are more smoothed out and muted, with the 20 cm probe showing the lowest amplitudes of the four. Temperature probes in the bank profile at Sites 1 and 2 reflect some insulation against freeze-thaw events during each winter as compared to the temperature fluctuations observed in the surface temperature probe datasets (see Table 5). There were two freeze-thaw events of 3 and 13 days at Site 1 during the winter of 2017 to 2018 and one weeklong freeze-thaw event in the winter of 2018 to 2019. Bank profile thermistors were deployed at Site 2 on 20 March 2018, after which the sediment did not freeze. In the winter of 2018 to 2019, these sensors recorded three freeze-thaw events ranging 2 to 7 days long.

Soil Water Content

Data from soil water content probes indicate that the bank material at Site 1 and Site 2 remained wet throughout the study period (Figures 12 and 13). Between December 2018 and March 2019, the bank soil water content at Site 1 ranged from 0.230 to 0.400 cubic meters of water per cubic meter of silty and sandy loam at 15 cm

21 depth. At 50 cm depth, these values ranged from 0.221 to 0.529 m3/m3. High values of the deeper probe occurred among four high-amplitude and two low-amplitude peaks. Maxima of the shallower probe occurred twelve times within that time period (Figure 12). At Site 2, the 10 cm deep soil water content probe measured values ranging from 0.303 to 0.450 cubic meters of water per cubic meter of silty and sandy loam between December 2018 and March 2019. The 50-cm-deep probe at this site recorded values between 0.306 and 0.519 m3/m3. This range occurred over twelve low-amplitude maxima, while that of the deeper probe occurred over four high-amplitude and two low-amplitude peaks in soil water content (Figure 13).

Erosion Pins Erosion pin exposure lengths were collected concurrently with photogrammetric surveying as of 5 December 2018 and are displayed in Table 2 and Table 3. Over the 3 months spanning 5 December 2018 and 26 March 2019, bank retreat at Site 1 averaged 11.35 cm, with maximum change of 26.3 cm and minimum change of -0.5 cm (Table 2). Negative values indicate that exposure lengths became shorter (i.e., an inflation or swelling of the bank due to frost expansion). At Site 2, the average bank retreat was 17.25 cm, with maximum and minimum retreat amounts of 25.6 cm and 10.5 cm, respectively (Table 3).

Photogrammetry Models

A total of 20 image collection surveys were completed through the study period beginning 18 November 2018 and ending 26 March 2019. Each survey collected an average of 57 images; however, only 36 images were collected during the 18 November 2019 survey at Site 2, after which a minimum of 50 images were

22 collected (Tables 6 and 7). From these images, a total of 20 digital elevations models were produced, each composed of 29.7 to 51.7 million 3-D topographic points. Only 2.3 to 7.1 million points were required to calculate digital elevation models of difference and 2.5-D volume comparisons. Eight DODs for Site 1 (Figures 18-25) and nine DODs for Site 2 (Figures 26-34) were developed with a maximum of 0.006 m error. Each DOD was compared to each sequential survey beginning on 18 November 2018 and ending 26 March 2019. The number of sequential models for Site 1 is one fewer than Site 2 because the 18 November 2018 survey for that location resulted in significant occlusion of the bank surface due to improper setup, such that the data were unusable. Sources of error involved with CRDP are typically related to occluded terrain and uncertainty in the positions of ground control points. Thus, photogrammetric modelling is based on maximizing geometric accuracies rather than scale accuracies. The errors of the models presented herein and their resolution are dependent on scale bar inputs from the surveys. Scale bars were input as control points with 0.001 m accuracy and < 0.001 m error. Additional sources of error in CRDP come from nonlinear distortions involved in the data processing phase (Javernick, Brasington, & Caruso, 2014), as well as uncorrectable distortions associated with the camera’s lens (Chandler & Jack, 2005). We intentionally limited our image collection to a small area and used a high-resolution camera to reduce error due to this type of distortion. To ensure high fidelity between CRDP models, we performed a control set of repeated surveys at Site 1 on 11 March 2019 (i.e., imaging the same bank twice in the same day using our experimental CRDP surveying techniques herein described) and generated a DOD to compare volumes (Figure 35). The results of this repeated survey

23 showed a mean difference of -3.50E-05 m and a total eroded volume of -0.003 m3 (Table 8). These values indicate no significant change within the scene and underscore the ability of our CRDP workflow to produce high-resolution models for the study. Histograms of gridded volume outputs display the distribution of cell values within each difference model, which are 1 mm by 1 mm (Figures 18-39). Between surveys at Site 1, measured maximum retreat between points were as large as -22.2 cm and as small as -3.9 cm, with mean changes ranging from -2.6 cm to +1.4 cm (negative values indicating erosion of material from the bank; positive values indicating added bank volume or expansion; Table 8; Figures 18-25). Total change measured over the first half of the study period, 8 December 2018 to 25 January 2019, at Site 1 ranged from +15.2 cm of expansion to -22.5 cm of erosion, with an average change of -5.6 cm across 678,623 cells (Table 9; Figure 36). From the 4 February 2019 to 26 March 2019 dataset, Site 1 experienced changes ranging +8.4 cm to -17.1 cm with an average change of -4.8 cm across 690,204 cells (Figure 37). Between surveys at Site 2, maximum retreat between points was as large as -27.2 cm and as low as -4.9 cm, with mean changes ranging from -5.9 cm of erosion to +1.5 cm of expansion (Table 10; Figures 26-34). Total change measured over the first half of the study period, 18 November 2018 to 25 January 2019, ranged from -2.2 cm to -20.9 cm of retreat with an average change of -11.1 cm over 712,587 cells (Table 11; Figure 38). From the 4 February 2019 to 26 March 2019 dataset, Site 2 experienced changes ranging from -0.08 cm to -31.8 cm of retreat with an average change of -11.4 cm across 760,136 cells (Figure 39). The cumulative average bank retreat was estimated to be -13.0 cm over 98 days at Site 1, with individual topographic differences ranging from -39.6 cm to +23.6

24 cm, by adding the maximum, minimum, and average change for both datasets at each site (Table 12). Total volumetric change at Site 2 between 18 November 2018 and 26 March 2019 ranged from -3.0 cm to -52.7 cm per cell, with a cumulative average retreat of -22.5 cm over 119 days. These results indicate a 73% greater amount of erosion at Site 2 compared to Site 1.

Yearly Total Bank Retreat The results of the GIS analysis, used to approximate historical bank erosion rates at Site 1 and 2, are listed in Table 13 and mapped in Figures 40 and Figure 41. Averaged along the full length of the bank, the results suggest that as much of 25.6 cm/yr of bank retreat occurred at Site 1 and 68.6 cm/yr at Site 2. These measurements and field observations verify that the bank at Site 2 is retreating more rapidly than that at Site 1, as observed in the surveys. Using the lowest elevation within the erosion polygon mask in the GIS analysis, assumed to be the interface between the steam and the bottom of the riverbank, the volume of eroded sediment was estimated to be 13.01 m3 at Site 1 and 73.16 m3 at Site 2. When adjusted for the length of the traced polygon, the average sediment flux along the stream, per year, is 0.05 m3/m downstream at Site 1 and 0.03 m3/m downstream at Site 2. These results suggest that the riverbank at Site 1 is retreating more rapidly in terms of lateral channel margin movement, but that the taller bank of Site 2 is contributing larger volumes of sediment to the stream.

25 Chapter 6

DISCUSSION

Throughout this study, observations and field data indicate that repeated freezing and thawing occurs at the surface and bank face of the study reach each winter and that this process is having a noticeable effect on bank morphology. A variety of freeze-thaw phenomena were observed on the bank itself, including textural and physical changes of sediment structures that would have been difficult to interpret without repeated observations and surveys. This is especially true for the lifting and segregation of the surface sediment grains by needle and frost-flower ice, fracture and failure, as well as the physical expansion of the entire bank. We initially expected that freeze-thaw processes at the bank scale would cause soil expansion and contraction that would drive sediment movement more-or-less uniformly over the bank itself. Instead of sediment flowing coherently down the bank, the most significant volumes of erosion occurred within isolated pockets of less than 0.5 m2. This isolated erosion may be caused by the presence of underlying structural weaknesses due to potential root systems, variation in groundwater flow paths, coarse sedimentary inclusions, or other currently unobserved factors. Soil expansion, on the other hand, occurred quasi-uniformly over the surface of the bank profile. In all observed freeze-thaw events, frozen soil structures consistently grew 2 cm to 3 cm normal to the channel wall. The near-surface and bank soil temperature data indicated at least 3 significant freeze-thaw cycles occurred during the winter of 2018 to 2019 (Tables 4 and 5).

26 Recorded temperatures among individual sensors are variable in terms of duration and minimum temperature, likely due to differences in shade, depth, and the sensitivity and error of each device. In general, these data are useful for being able to correlate a specific survey date with frozen or thawed conditions. In some cases, a ‘flattened’ temperature curve during cold periods may indicate the insulating effect of snow cover above some sensors (a zero-curtain effect). Prior to and following a period of snow cover, recorded ground temperatures are consistent with data for air temperature variation published by Delaware Environmental Observing System (2019) as well as that collected from the hanging data logger at each site. The cumulative DOD data suggest that small-scale, repeated freezing and thawing of bank sediments results in background sediment fluxes of approximately ±2 cm per event. Large-scale erosive events, like those recorded in the 22 January 2019 to 25 January 2019 and 4 February 2019 to 18 February 2019 surveys, are likely triggered by the entropic interplay between underlying structural weaknesses, hydrological variables like stream stage and rainfall, and the magnitude of a given freeze event. The magnitude and duration of subzero sediment temperatures may cause porewater of sediment deeper in the bank profile to freeze and exert more outward stress on the frozen sediment column (Ferrick, Gatto, & Grant, 2005). Failure, facilitated by an initial point or plane of weakness, may then evolve into the sheet-like erosion as observed on the 25 January 2019 survey of Site 1 and Site 2. Note that difference models from successive surveys show only snapshots of erosion and deposition and that future research may capture these events more clearly if monitored on a daily to hourly basis.

27 The sediment flux observed within CRDP models is consistent with that of the steady, yet variable, exposure lengths we measured via erosion pins. Erosion on the bank generally lacks a spatial pattern, with some sections not eroding at all. For example, the total change of -0.5 cm experienced by Pin E at Site 1 is likely due to its protected position just below the overhanging top of the bank as well as tree root armor above and to the right of it. Changes in erosion pin lengths, when averaged, were similar to values determined from back-to-back CRDP surveys, such as the 6 cm average retreat between 22 January 2019 and 25 January 2019. In many instances where the point-effect of erosion or deposition observed with CRDP or erosion-pin data appears quite large, the cumulative effect in terms of total erosion or deposition on the bank appears insignificant due to sediment simply moving downward to the base of the profile. The rates of bank retreat estimated in the GIS analysis generally agree with the observations of the DEM-based difference models and the exposure data from the erosion pins. Averaged along the full length of the bank, the GIS-based (aerial image) analysis estimates up to 25.6 cm/yr of retreat at Site 1 and 68.6 cm/yr at Site 2. Field observations verify that the bank at Site 2 is retreating more rapidly than that at Site 1, as do our CRDP results. While this value for Site 2 may seem like an overestimation, the cumulative mean retreats of 13.0 cm at Site 1 and 22.5 cm at Site 2 are proportionally similar to the full-year change identified in the GIS-based analysis, yet lower than expected at Site 2. For freeze-thaw processes to be a relatively large driver of bank erosion, we would expect that the proportion of winter-term erosion would be larger than 50% of the total yearly erosion. However, confidence in the CRDP model differences and erosion pin datasets remains high, while we recognize that results from

28 the GIS analysis are much coarser and may have a margin of error that likely intersects the amount of annual erosion observed in the field. Sources of error in the GIS can largely stem from the resolution of data obtained and errors produced in the alignment and registration of the aerial imagery (Rowland et al., 2016). Tracing vegetated channel margins across aerial image cells with ground sample distances of 30 cm produces uncertainty in the estimation of actual bank position. Additionally, bank evolution does not occur uniformly from the beginning of a cut bank to the end and the erosion polygons drawn in the GIS encapsulate the 85 m of the bank at Site 1 and 154 m of the bank at Site 2. The associated variance in retreat distances along each bank is more complicated than the scope of this study and results from variables such as bank geometry, stream velocity, and the distribution of riparian vegetation and forest (e.g., Allmendinger et al., 2005; McCarthy, 2018). In conclusion, the goals of this study were twofold: exploring the use of CRDP to accurately measure sediment fluxes across repeated, frequent surveys; correlating erosion measurements with soil freeze-thaw processes. The highly accurate 3-D models produced allowed for the gross characterization of erosion over time, but the problem of relating these changes to our observations of freeze-thaw cycling remains difficult. While our erosion models indict the process of ice expanding and lifting sediments as a likely cause for erosion, the period of freeze and thaw captured between surveys overlaps with some periods of increased river flow (Figure 42). It remains difficult to separate flow-related erosion from that of freeze-thaw due to the perennial precipitation and/or snow melt throughout the study region. However, the timing and magnitude of elevated stage data from stream gage sources do not correlate strongly with the magnitudes of erosion measured between freeze-thaw surveys to

29 overshadow the contribution of ice to the process (US Geological Survey 2019). Although the data herein do not isolate ice as a sole erosional factor between surveys, the data display evidence of a significant contribution from freezing and thawing in the overall erosion that occurs in this region. These observations are promising in terms of a first assessment of the impacts of ice on riverbank erosion on the White Clay Creek and lay a groundwork for future research on the topic.

30 FIGURES

Figure 1 A map of the White Clay Creek study sites (red dots) used in this thesis.

31 Air Temperatures from 1Dec17 to 31Mar18

30 20 10 0 Site 2 -10 C)

° -20 -30 40

Temperature ( Temperature 0 Site 3 -20

-40

Dec17 Jan18 Feb18 Mar18 Date

32 Air Temperatures from 1Nov18 to 31Mar19

40 20 0 Site 1 -20 -40

C) 40 ° 20

0 Site 2

-20 Temperature ( Temperature 30 20 10 0 Site 3 -10 -20

Nov18 Dec18 Jan19 Feb19 Mar19

Date

Figure 5 Image depicting needle ice structures that extend 2 cm to 3 cm away from the riverbank surface before breaking off. Collected from Site 2 on 22 January 2019.

Figure 6 Image depicting needle ice structures that extend 2 cm to 3 cm away from the riverbank surface before breaking off. Collected from Site 1 on 8 December 2018.

Figure 7 Image depicting riverbank material covered in solid ice. Collected from Site 2 on 23 January 2019.

37 Near-Surface Temperatures from 1Dec17 to 31Mar18

15 10 5 0 Site 1 -5 30 C) ° 20 10 0 Site 2 -10 Temperature ( Temperature 20 15 10 5 0 Site 3 -5 -10 Dec17 Jan18 Feb18 Mar18 Date

38 Near-Surface Temperatures from 1Nov18 to 31Mar19 20 15 10 5 0 Site 1 -5 20 C) ° 15 10 5 0 Site 2

Temperature ( Temperature -5 20 15 10 5 0 Site 3 -5 Nov18 Dec18 Jan19 Feb19 Mar19 Date

Figure 9 Graphs depicting temperature time series (in °C) at each site using daily averages from near-surface soil temperature probes during the winter of 2018 to 2019. The gray area represents the daily mean, the red line represents the daily maximum, the blue line is the daily minimum.

39 Bank Soil Temperatures from 1Nov18 to 31Mar19 at Site 1 20 10 0 10 cm -10 -20 20

10 C)

° 0 20 cm

-10 20

10 Temperature ( Temperature 0 50 cm -10 20 15 10 5 0 75 cm -5 Nov18 Dec18 Jan19 Feb19 Mar19 Date

40 Bank Soil Temperatures from 1Nov18 to 31Mar19 at Site 2 20

0 10 cm

-10 20

C) 0 20 cm °

-10 20 15 10

Temperature ( Temperature 5 0 50 cm -5 15 10 5 0 70 cm -5

Nov18 Dec18 Jan19 Feb19 Mar19 Date

41 Soil Water Content from 1Nov18 to 31Mar19 at Site 1

0.6 ) 3

/m 0.5 3

0.4 15 cm 0.3 50 cm 0.2

0.1

Soil Water Content (m Content Water Soil 0 Dec18 Jan19 Feb 19 Mar19

Date

42 Soil Water Content from 1Nov18 to 31Mar19 at Site 2 ) 3 0.6 /m 3 0.5

0.4 10 cm 0.3 50 cm

0.2

0.1 Soil Water Content (m Content Water Soil 0

Dec18 Jan19 Feb 19 Mar19 Apr19

Date

Figure 14 Image of exposed length of one 61-cm-long erosion pin, which also serves as a ground control point for image alignment and DEM development. Image collected from Site 2 on 23 January 2019.

Figure 16 Example of CRDP survey setup at Site 2 on 8 December 2018 with frozen soil structures apparent.

Figure 17 Example of image collection process at Site 1 on 11 March 2019.

47 CRDP Survey from 8Dec18 to 18Dec18 at WCC Site 1

a m