A Paleoflood Assessment of the Greenbrier River in Southeast, West Virginia, USA

A thesis presented to

the faculty of

the College of Arts and Sciences of Ohio University

In partial fulfillment

of the requirements for the degree

Master of Science

Sara A. Thurkettle

May 2019

This thesis titled

A Paleoflood Assessment of the Greenbrier River in Southeast, West Virginia, USA

SARA A. THURKETTLE

has been approved for

the Department of Geological Sciences

and the College of Arts and Sciences by

Gregory S. Springer

Associate Professor of Geological Sciences

Joseph Shields

Interim Dean, College of Arts and Sciences 3

ABSTRACT

THURKETTLE, SARA A., M.S., May 2019, Geological Sciences

A Paleoflood Assessment of the Greenbrier River in Southeast, West Virginia, USA

Director of Thesis: Gregory S. Springer

Slackwater deposits and hydraulic modeling were used to extend the historical record of floods and determine the critical threshold of sediment entrainment in the

Greenbrier River of southeastern West Virginia (SE WV). The southward flowing bedrock river incises Paleozoic sandstones, limestones, and shales. The river has experienced three catastrophic floods since 1985: the 1985, 1996, and 2016 floods, which caused extensive damage in communities alongside the river with the most recent flood destroying roughly 1,200 homes in the Greenbrier River watershed. This study better constrains the frequency of floods that have the potential to cause similar damage, which is a matter of urgent need. Paleostage indicators (PSIs) found in Greenbrier River Cave were used as proxies for extending the historical record and reconstructing recurrence intervals of floods. Radiocarbon dating was used to determine ages of pre-historic slackwater deposits (floods) in the cave. Wolman counts were performed in the river channel to determine stream competence and sediment transport thresholds. Known discharges and recoverable paleostages were used to calibrate channel roughness in a 1- dimensional modeling program, HEC-RAS. Channel roughness (Manning’s n) during large floods was determined to be 0.029. Discharges and clast entrainment velocities were calculated using a Shield’s critical shear stress equation and clast size data, then compared against velocities determined in HEC-RAS. HEC-RAS was used to back- 4 calculate discharges for the pre-historic flood deposits which allowed for the 100-year flood frequency to be re-evaluated. Recurrence intervals were assigned to discharges, calculated from clast size data and critical shear stress equation, using the existing flood frequency analysis. The reliability of using paleostage indicators and HEC-RAS to create flood chronologies is discussed. 5

ACKNOWLEDGMENTS

I would like to acknowledge the Ohio University Geological Sciences Alumni

Association for providing funding for my thesis research through their research grant and summer fellowship. I would also like to acknowledge my field assistant that helped with the collection of field data: Andrew Alder.

TABLE OF CONTENTS

Page

Abstract ...... 3 Acknowledgments...... 5 List of Tables ...... 8 List of Figures ...... 9 Chapter 1: Introduction ...... 10 Chapter 2: Previous Research ...... 12 History of Paleoflood Hydrology...... 12 Hydraulic Models: Uses in Paleoflood Research...... 14 Chapter 3: Study Area ...... 17 Historical Flooding of West Virginia ...... 17 Greenbrier River ...... 20 Regional Climate ...... 26 Chapter 4: Objectives ...... 28 Chapter 5: Methods ...... 29 Field Techniques ...... 29 Data Processing ...... 35 Hydraulic Modeling ...... 35 Estimating Critical q Values and Sediment Transport Velocities ...... 38 Flood Frequency Analysis ...... 39 Chapter 6: Results ...... 41 Grain Size Analysis...... 41 Channel Roughness and Paleodischarge Estimates ...... 43 Silt Line and Slackwater Deposits ...... 50 Flow Competence Analysis ...... 51 Flood Frequency Analysis ...... 52 Chapter 7: Discussion ...... 55 Confidence in Channel Roughness Estimate ...... 55 Slackwater Deposits and Silt Lines: How comparable are results obtained from them? ...... 59 Reliability of the 1996 and 2016 Flood Deposits ...... 60 7

Sediment Transport and Stream Competence ...... 62 Modeled Velocities versus Clast Size-derived Velocities ...... 64 Discharges from Clast Size Analysis versus Previous FFA ...... 66 RI’s of 1985, 1996, and 2016 Based upon 2010 FFA ...... 68 Paleoflood Recurrence Intervals ...... 68 Reconstructed Flood Frequency Analysis ...... 70 Chapter 8: Conclusions ...... 73 References ...... 75 Appendix A: Cross Section Data for HEC-RAS ...... 81 Appendix B: Frequency Curve Data for HEC-SSP ...... 93 Appendix C: Wolman Count Data ...... 94

LIST OF TABLES Page

Table 1 Summary of selected historical floods in West Virginia from 1877 to 2016. .... 18 Table 2 A summary of each cross section location...... 33 Table 3 A list of Manning’s n (channel roughness) values used in the paleoflood literature...... 37 Table 4 A summary of clast sizes measured in the field as well as their equivalent sorting classification...... 43 Table 5 Calculated water surface elevations are presented in columns 1 and 2 correspond to the 1996 and 2016 floods...... 44 Table 6 A summary of discharges (Q) calculated using various channel and bank roughnesses (n)...... 48 Table 7 A summary of discharges calculated using SWD’s from the 1996 and 2016 floods...... 50

Table 8 A summary of the critical discharges (qc) calculated and parameters used to calculate the values...... 51

Table 9 A summary of discharges calculated from qc values and channel width...... 51 Table 10 A summary of sediment-derived velocities calculated using clast sizes and critical discharges...... 52 Table 11 A list of velocities calculated using clast size versus using HEC-RAS...... 65 Table 12 A list of velocities calculated in HEC-RAS using historical flood discharges. 66 Table 13 A summary of discharges used in 2010 FFA and their assigned RIs...... 67 Table 14 A summary of paleoflood discharges from this study estimated in HEC-RAS and their assigned RIs...... 69 Table 15 A comparison of large flood sizes between the 2010 FFA and Scenario 1...... 71 Table 16 A comparison of large flood sizes between scenario 1 and scenario 2...... 72

LIST OF FIGURES

Page

Figure 1. A map of the globe showing locations of paleoflood studies done between the years of 1982 and 2002...... 13 Figure 2. Location map of the Greenbrier River basin in southeastern West Virginia .... 21 Figure 3. Peak streamflow for Greenbrier River at Alderson USGS gaging station between 1896 and 2016 ...... 22 Figure 4. Silt line deposit left by the 1996 flood in Greenbrier River Cave ...... 23 Figure 5. Silt line deposit from the 2016 flood in Greenbrier River Cave ...... 24 Figure 6. Location of Alderson, West Virginia and the Alderson gaging station ...... 25 Figure 7. A map of GRC...... 27 Figure 8. Map showing length of study reach (~1,590 m) and location of Greenbrier River Cave along the reach ...... 30 Figure 9. Location of cross sections along study reach where measurements were taken...... 31 Figure 10. A 3-dimensional representation of the Greenbrier River in HEC-RAS...... 37 Figure 11. Location map of where Wolman counts were collected along the Greenbrier River ...... 42 Figure 12. A cumulative percent finer diagram displaying all three Wolman counts ...... 43 Figure 13. A plot based on the data in Table 5 ...... 45 Figure 14. A plot based on the data in Table 5 ...... 46 Figure 15. A plot based on the data in Table 3 ...... 47 Figure 16. A frequency curve showing a 122-year record for the Greenbrier River...... 53 Figure 17. A frequency curve showing 2148 years of record for the Greenbrier River ... 54

CHAPTER 1: INTRODUCTION

The average number of floods worldwide has increased by 33% over the last two decades; accounting for 47% of all weather-related disasters and impacting ~2.3 billion people globally (CRED, 2015). Despite floods becoming increasingly problematic, there is still a lack of knowledge on how to better predict and prepare for them. Until recently, scientists have relied on historical floods to make flood predictions. Historical floods are events that took place within the gaging record and have observed or measured stages.

This flood prediction method relies solely on the length of the gaging record and most records are less than 150 years (Baker et al., 2002). Paleoflood hydrology is a growing field within the scientific community that has begun to fill in the gaps in the flood records.

Paleofloods are defined as flood events that occurred outside the gaging record which leave behind geological evidence, such as paleostage indicators, that can be used to reconstruct flood magnitudes (Baker et al., 2002). Paleostage indicators (PSIs) are geological evidence of past water surface elevations or stages and can be used to back- calculate discharges of both ancient and historical floods. Slackwater sedimentary deposits (SWDs) are the most common PSIs used to reconstruct minimum flood stages because they are relatively common and have a high preservation potential (Lam et al.,

2017). These indicators form from suspended fine-grained sand and silt that rapidly settles out in low energy zones of rivers such as surfaces within shallow caves, tributary mouths, and meander bends (Kochel and Baker, 1982). Other PSIs include high water- stage marks such as silt lines and tree scars (Springer, 2002). Historical floods can also 11 leave behind paleostage indicators that can be useful for paleofood reconstructions. Many successful historical flood and paleoflood studies have been conducted in bedrock channels, because they have had a stable geometry, which reduces the chance of channel cross sections undergoing significant changes since a PSI was created (Lam et al., 2017).

Traditional flood frequency analysis (FFA) estimates flood recurrence intervals using historical peak discharge values directly measured at stream gaging stations.

However, discharge data obtained from gaging stations only extend as long as gaging records (~100 years), which makes it difficult to assess floods of large magnitudes; the return periods of large floods are usually much longer than the historical record (Kochel and Baker, 1982). When they do occur, these floods have such high energy that they commonly damage gaging stations and compromise instrument readings (Baker et al.,

2002). The uncertainties that accompany the short gaging record led scientists to explore various indirect methods for measuring discharges and to find better ways to constrain the frequency of large floods. Paleoflood hydrology uses geological evidence, combined with modern channel geometry and hydraulic modeling, to estimate paleodischarges and improve flood frequency analysis by extending the gaging record to thousands of years, with the goal of reducing risks associated with large magnitude floods (Webb and Jarrett,

2002).

This study has the rare opportunity to calibrate a hydraulic model using PSI data.

PSI deposits are not always available and their life spans are uncertain. One of the main focuses of this project is to predict the average life spans of various PSI deposits, which is important when attempting to use them as a calibration tool in paleoflood studies. 12

CHAPTER 2: PREVIOUS RESEARCH

History of Paleoflood Hydrology

Tarr (1892) was one of the first studies to identify SWDs, which were found along the Colorado River, and had the idea to use them as a way to reconstruct floods that were necessarily in the gaging record. Several other researchers followed suit and began using PSIs in flood reconstruction research. Baker (1974) studied floods in central and west Texas and noticed that floods deposit sediment in eddies near the mouths of tributaries and that the sediment elevations reflect the minimum stages of these flood.

Kochel and Baker (1982) added that the heights of these flood deposits (SWDs) can be extrapolated to the main stream in order to make a conservative estimate of peak stage.

These early workers established that when using SWDs to estimate paleodischarges, assumptions need to be made such as 1) the top of the deposit corresponds to the flood surface; and 2) no changes occurred to the channel over the time span of SWD accumulation (Kochel and Baker, 1982). Kochel and Baker (1982) also showed that using

SWDs in paleoflood studies is important because they contain organic material that can be dated using radiocarbon techniques. Not only do these deposits provide peak stage estimates but they can also establish age constraints for formative floods (Kochel and

Baker, 1982).

The first flood frequency analysis done using SWDs was a 10,000-year record of the Pecos River in west Texas (Patton and Baker, 1977). It was not until the late

Pleistocene Missoula flood deposits were recognized that paleoflood studies began to take off and scientists were looking towards SWDs to help constrain flood frequency 13 analyses (Costa, 1987; Patton, 1987). Paleoflood studies started becoming more popular across the globe, however, the majority of them were mainly located in arid and semi-arid regions, where PSIs have greater preservation potential (Fig.1) (Baker et al., 2002).

Humid climates tend to have greater bioturbation pedogenesis that leads to PSIs having little chance of survival, which is why these regions have seen less paleoflood studies

(Kite et al., 2002). Springer (2002) describes how caves located in humid regions have an important relationship to surface streams that are nearby. The preservation potential for

SWDs is greater within cave settings than at the surface in humid climates which indicates that it is possible to conduct paleoflood studies in these environments (Springer and Kite, 1997). This project seeks to expand paleoflood studies to a greater range of climates.

Figure 1. A map of the globe showing locations of paleoflood studies done between the years of 1982 and 2002. Figure taken from Baker et al. (2002).

Hydraulic Models: Uses in Paleoflood Research

The advances in computational modeling and other scientific technology over the past few decades provided new ways for scientists to approach paleoflood studies (Baker et al., 2002). PSIs can be used in hydraulic models along with modern channel geometry to estimate paleodischarges and establish flood magnitude-frequency relationships

(Wohl, 2002). These models require the calibration of specific parameters, such as the roughness coefficient (Manning’s n), to successfully reconstruct flood conditions (Kidson et al., 2006). Manning’s n values originally came from back calculations using the empirical Manning equation (Manning, 1891), but can also be estimated using several different semi-empirical equations (e.g. see Table 1 in Kidson et al., 2006). These equations are based on specific channel properties (physical features); their use is dependent on the type of stream channel being studied. In bedrock channels, channel properties of interest include median clast (particle) size, cross sectional area, friction slope, and hydraulic radius. The channel gradient is often substituted for the friction slope because the latter is not readily apparent in PSIs. However, hydraulic radii will differ for each flood. And clast size distributions in bedrock channels can span silt to boulders.

Nonetheless, methods such as the Wolman-count can be used to estimate clast size distributions and determine critical thresholds in bedrock channels (Wolman, 1954).

Additionally, by measuring block sizes in the stream, calculations can be made for the energy and velocity needed to move these blocks (Wolman, 1954).

Studies involving paleodischarge reconstruction have utilized both one- dimensional and multidimensional hydraulic modeling programs. However, one- 15 dimensional models are used more frequently due to the excessive amount of data collection needed to run multidimensional models (Webb and Jarrett, 2002). The most common one-dimensional model used in paleoflood studies is HEC-RAS (e.g. Kidson et al., 2006; Lam et al., 2017; Ruiz-Villanueva et al., 2013; Wang et al., 2014). HEC-RAS is a step-back water model, created by the Army Corp of Engineers, which can use

Manning’s n values and PSIs to iteratively calculate paleodischarges and reconstruct water-surface profiles. However, the model can be adjusted to produce a variety of specified simulations based upon input parameters. For instance, a historical flood can sometimes be used to calibrate a model if the events have well constrained discharges and peak stages (Springer, 2002), but in cases where calibration is not possible PSI elevations can be used to estimate Manning’s n, which can be used to further predict paleodischarges.

A case study conducted by Kidson et al. (2006) in northern Thailand, used an extreme flood event in 2001 as a calibration to compare estimated Manning’s n values against those obtained from known discharges. The purpose of that study was to shed light on the uncertainty that comes with estimating Manning’s n from semi-empirical equations and the compounding effects this has on the FFA. Their results showed that there is an inverse relationship between roughness and discharge. There is also a lack of understanding in the stage-roughness relation during extreme flood events in bedrock channels. Because of this, roughness is often underestimated which in turn leads to overestimations in discharge calculations. They suggest constraining this uncertainty in future studies by utilizing a wider variety data sources (e.g. visual observations, semi- 16 empirical equations, PSI-SWDs) -instead of relying on a single criterion for all cases

(Kidson et al., 2006).

This study uses HEC-RAS to model both historic floods and paleofloods in a bedrock channel. Manning’s n values are both back-calculated and assumed using HEC-

RAS and literature n values. The estimation of Manning’s n values stem from various combinations of parameters as suggested by Kidson et al. (2006): visual observations of

PSI-SWDS, Manning’s equation, semi-empirical equations, and historical flood events with known discharges and stages. The purpose of this is to minimize the uncertainty in the roughness coefficient estimation and, therefore, better constrain the discharge estimates produced from HEC-RAS.

CHAPTER 3: STUDY AREA

Historical Flooding of West Virginia

Flooding is a frequent occurrence throughout West Virginia due to its climate and the mountainous landscape which creates many small basins - the ideal setting for floods.

West Virginia lies within three physiographic provinces: the Appalachian Plateau, Valley and Ridge Province, and Blue Ridge, whose elevations create an orographic effect that enhances precipitation relative to regions to the west and immediately east. Across the region, flooding results from several types of climatic events, such as frontal storm systems in winter and spring, rainfall on snowpack, and extratropical cyclones (Wiley et al., 2000). However, some of the most intense floods have occurred from localized summer thunderstorms (Doll et al., 1963). The sizes, extent, fatalities, and property losses of major historic floods are summarized in Table 1.

Table 1

Summary of selected historical floods in West Virginia from 1877 to 2016. Recurrence Date Area Affected Remarks Interval (years) 1877-88 Potomac and >50 Major floods outside Monongahela River the period of record; basins. areal extent unknown. 1912 Big Sandy Creek and 25 to >50 Largest discharge Tygart Valley River. known on Big Sandy Creek within period of record. 1918 Greenbrier and Cheat >50 Second largest Rivers discharge on Greenbrier River in more than 90 years. 1932 Gauley, Greenbrier, and >50 Storm produced Tygart Valley Rivers. record discharge on Gauley River, Williams River, and headwaters of Greenbrier River. Mar. 9-22, Potomac River basin 25 to >100 Regional; 10-20 1936 and Cheat River. inches of rain in snow- covered northern part of State. 1949 Potomac River basin >50 Flash flooding in South Branch Potomac River basin; 50 homes destroyed. Lives lost, 9; damage, $2.5 million. June 25, 1950 West Fork River, 25 to >50 Locally intense storm Middle Island Creek, produced small-stream and Little Kanawha flooding in north- River. central part of State. Minor damage. Mar. 6-19, Tug Fork, Guyandotte, 25 to 100 Three floods. More 1963 Big Sandy, Little than 5,000 people Kanawha, Cheat, and homeless. Lives lost, Greenbrier River basins. 7; damage, $10 million in 22-county disaster area.

Table 1: Continued Mar. 7, 1967 Kanawha and 25 to >50 About 5 inches of Monongahela River rainfall in 3 days, basins. augmented by snowmelt. Damage, $16 million. Feb. 26, 1972 Buffalo Creek Unknown Dam failure. About 125 people killed or missing; damage, $100 million. Apr. 4-5, 1977 Tug Fork and 25 to >100 Damage, $60 million. Guyandotte River. 1980 Lost and Little >100 Widespread Grave Creeks thunderstorms created small-stream flooding that had recurrence intervals exceeding 100 years. 1984 Tug Fork and 25 to >50 Second major flood in Guyandotte River. southern West Virginia within 10 years. Nov. 4-5, 1985 North-central and 25 to >100 Worst flood in West eastern areas of Virginia history. State. Deaths, 47; damage, $500 million. Flood caused by tropical storm. 1996 Greenbrier River 100 Flooded throughout Watershed entire Greenbrier River Watershed. Flood was due to a large snow melt that took place in late January. 2016 Howards Creek 500 Killed 26 people. tributary of Tributary of Greenbrier Greenbrier River River flooded. Worst of flood was near White Sulphur Springs, WV. Flood caused by summer thunderstorms. Modified from https://md.water.usgs.gov/publications/wsp-2375/wv/

Devastating floods have become much more frequent in the last 30 years across the globe. Southeastern West Virginia, particularly the Greenbrier River watershed, was hit with devastating floods in 1985, 1996, and 2016 (Table 1). However, regional gaging stations have only been maintained for ~100 years, making the recurrence intervals of these “100-year” and “500-year” floods uncertain. Paleoflood studies could potentially extend the flood record, but because surficial PSI’s are not well preserved in humid climates, such as West Virginia (Kite et al., 2002; Springer, 2002), other means for reconstructing paleofloods are needed so that the likelihood of occurrence for these events is better understood. In this study, PSIs from caves are used as a tool for reconstructing paleofloods of higher preservation potential (Springer and Kite, 1997).

Karst and caves are common in West Virginia (Dasher, 2012), which – together with its frequent floods – makes it an excellent region for attempting to use caves to reconstruct paleofloods.

Greenbrier River

This paleoflood study focuses on constraining the frequency of large floods in the

Greenbrier River basin (~3800 km2), of southeastern West Virginia (Fig. 2). The

Greenbrier catchment straddles the boundary between the Appalachian Plateau and

Valley and Ridge provinces and is both rugged and flood-prone. The Greenbrier River is a moderate-gradient (0.002), southward flowing, bedrock river underlain by Paleozoic limestones, sandstones, and shales (Springer et al., 2003), although limestone is the only exposed bedrock in the reach studied. The Greenbrier River is a natural stream, meaning it has not been channelized or dammed. The Greenbrier River watershed encompasses 21 four counties: Greenbrier, Pocahontas, Summers, and Monroe. As of 2008, a total of

~38,400 people resided within the Greenbrier River watershed (WRTC, 2008).

Figure 2. Location map of the Greenbrier River basin in southeastern West Virginia. Figure created using ArcMap 10.5.1.

In the past 33 years, three catastrophic flood events have caused severe damage to communities within the Greenbrier River watershed: the 1985, 1996, and 2016 floods

(Fig. 3). All three floods were caused by different weather phenomena. The 1985 flood occurred in the fall and was initiated by a decaying tropical storm that brought heavy rains to the region (Teets and Young, 1985). The 1996 flood was due to a large snow melt that took place in late January (Kite et al., 2002). This flood left behind a silt line

PSI in Greenbrier River Cave (GRC) (Fig. 4). The 2016 flood was caused by a series of severe summer thunderstorms. According to a report written on June 29th, 2016, by Kevin

Byrne, an AccuWeather.com staff writer, the 2016 flood destroyed ~1,200 homes and 22 killed nearly two dozen people. The 2016 flood also left behind a silt line PSI in

Greenbrier River Cave (Fig. 5). Flood stages and peak discharge values for all three floods were recorded at the USGS-Alderson gaging station, which is in Greenbrier

County ~8.85 km downstream of GRC (Fig.6).

Figure 3. Peak streamflow for Greenbrier River at Alderson USGS gaging station between 1896 and 2016. The three most devastating floods occurred within the last 33 years. Data taken from water.usgs.gov.

Figure 4. Silt line deposit left by the 1996 flood in Greenbrier River Cave. Photo taken by G.S. Springer. 24

Figure 5. Silt line deposit from the 2016 flood in Greenbrier River Cave. Photo taken by G.S. Springer. 25

Figure 6. Location of Alderson, West Virginia and the Alderson gaging station. GRC is just upstream of Alderson on the west side of the river. Figure created using ArcMap 10.5.1.

The gaging record at Alderson contains 122 years of data (water.usgs.gov), which is longer than the average gaging record, but is still problematic when attempting to constrain extreme flood frequencies. The 1985 flood reached a stage of ~7.3 m (23.95 ft.) and a peak discharge of ~2,566 m3 s-1 at the Alderson gage. The 1996 flood reached a stage of ~7.42 m (24.33 ft.) and a peak discharge of ~2650 m3 s-1. The 2016 flood reached a stage of ~6.7 m (22 ft.) and a peak discharge of ~2285 m3 s-1 (water.usgs.gov).

The flood stage warning for the Greenbrier River at Alderson is ~4.3 m (14 ft.). The river is at a major flood stage when the stage reaches ~5.8 m (19 ft.). At this stage, flooding can occur in Alderson.

Regional Climate

The climatic setting of rivers can have a large influence on the long-term preservation of PSIs left behind by floods. The majority of paleoflood studies have taken place in arid and semi-arid regions because PSIs are more likely to survive for long periods of time (Kite et al., 2002; Springer, 2002). Therefore, there is a lack of paleoflood studies in humid climates. Springer (2002) sheds light on the relationships between caves and nearby surface streams and the use of caves in paleoflood studies in humid regions.

While surface flood deposits are not likely to be preserved more than a few years in humid climates, PSIs can be preserved in nearby caves long enough to be used for paleoflood reconstruction. The Greenbrier River is in a humid temperate region that has several nearby cave systems, making it an excellent candidate for paleoflood research. As mentioned earlier, the cave used in this study is GRC. The cave is developed in

Mississippian-age limestone (Springer, 2002) (Fig. 7). GRC does not contain a stream independent of the Greenbrier River and only contains flowing water when flooded by the river.

Figure 7. A map of GRC. The cave is located on the western side of the river and has eight entrances (E). The cave is developed in Mississippian-age limestone. Figure created by G. S. Springer.

CHAPTER 4: OBJECTIVES

The purpose of this study is to extend the gaging record for the Greenbrier River and use paleoflood data as a rare opportunity to calibrate a hydraulic model. This was accomplished by completing the following objectives:

1) Determine channel properties for use in hydraulic models and calculations using

the Manning equation and HEC-RAS.

2) Determine median clast size (d50) of the Greenbrier River in the study reach, as

well as maximum and minimum transported boulder sizes (d16 and d90) .

3) Use historical floods to calibrate Manning’s n values for use with paleoflood

models.

4) Determine discharges for given historic deposits (e.g. silt lines, sediments) and

determine how much sedimentary paleostage indicators underestimate discharge

values.

5) Determine what velocities and discharges are required to move sediment and

boulders in the Greenbrier River.

6) Compare model and sediment-derived velocity estimates to test reproducibility.

7) Compare model estimates of Manning’s n values to estimated values determined

using the Manning’s equation and other semi-empirical equations.

8) Estimate recurrence intervals of 1996 and 2016 floods using both systematic and

nonsystematic data.

9) Project how long 1996 and 2016 flood deposits will survive in Greenbrier River

Cave. 29

CHAPTER 5: METHODS

Field Techniques

A ~1,590 m long reach of the Greenbrier River was surveyed ~9 km upstream of the Alderson gaging station (37°43'27”, 80°38'30", 466.04 m amsl). The reach spans

Greenbrier River Cave where PSI deposits are present (Fig. 8). A Sokkia CX-65 SDR basic total station and survey rod with attached prism were used to survey 10 cross sections (Fig. 9). Each cross section consisted of 20-35 individual measurements. A stationary control point was selected near the cross section, located farthest upstream and was used as a datum. The datum was assigned an arbitrary elevation of 100 meters because no benchmark was readily available to determine its absolute elevation. A leap frog method was then used to align all measurements onto this single datum. This method consists of shooting a new control point farther downstream before moving the total station to a new setup and beginning the next cross section. Measurements recorded are represented as spatial coordinates in the format of northing (N), easting (E), and elevation

(Z). Each set of coordinates is based on its spatial location in relation to the datum, a stationary, temporary reference point or the attached prism. All NEZ vales were subsequently adjusted to use the reference datum (coordinates 0 m, 0 m, 100 m). 30

Figure 8. Map showing length of study reach (~1,590 m) and location of Greenbrier River Cave along the reach. Overall flow is to the west. Figure created using Google Earth Pro.

Figure 9. Location of cross sections along study reach where measurements were taken. Overall flow is westward. Figure created using Google Earth Pro.

Cross section locations were chosen on the basis of whether they were in a riffle or pool. The first cross section was chosen to be in a riffle. Then, continuing downstream, cross section locations were alternated between a riffle and a pool, unless otherwise noted

(Table 2). Measurements for each cross section began on river left end (RLE) and the terrain at each starting location was noted (e.g. floodplain, hillside, bedrock cliff, etc).

Changes in vegetation type and density, substrate (bedrock versus cobbles), and deepest part of channel (thalweg), were also noted when measurements were taken within each cross section. Individual survey points within cross sections were chosen by observing changes in substrate or major topographic breaks. For example, points were chosen where 32 a cobble bar met bare bedrock and at ledge edges. In deeper parts of the channel, the prism rod height (1.6 m) was too short and/or water velocities too great to allow a direct reading, so an ultrasonic depth probe was used instead. The margins of the cross sections were shorter than typical margins used in a hydraulic model because the banks were either bedrock confined, very steep, or completely taken over by vegetation. Therefore, the cross sections appear slightly truncated in HEC-RAS. Additional measurements were taken in riffles upstream of the first cross section and downstream of the last cross section. These were needed to determine the exit and entry gradients of the total channel segment being surveyed. Each cross section was sketched in a field notebook. These sketches included where bedrock exposure within the channel is located because the presence of exposed bedrock in the channel and knowing its location can assist with estimating Manning’s n values for use in HEC-RAS.

Table 2

A summary of each cross-section location. Cross section Riffle or Pool Width (m) Notes XS-10 (upstream) Riffle 51.1 Floodplain has grassy vegetation. XS-9 Pool 38.8 Floodplain is brushy. XS-8 Riffle 41.1 Includes large cobbles bars. XS-7 Riffle 48.3 Floodplain includes heavy vegetation and sand. XS-6 Pool 62.3 All sand and lots of vegetation on floodplain. XS-5 Riffle 62.3 Includes cobble bar, much vegetation, and sand on floodplain. XS-4 Pool 44.4 Sand and gravel on floodplain. XS-3 Riffle 47.7 Lots of vegetation and trees on floodplain. XS-2 Pool 45.9 River-left is brushy slope. XS-1 Riffle 50.2 Grassy floodplain with some (downstream) trees.

SWDs located in Greenbrier River Cave were surveyed using the same reference datum as used while doing the cross sections. Elevations in GRC were measured using a

DistoX2 laser instrument that yields distance, azimuth, and inclination for each shot. The values were then used to calculate elevations and distances relative to the entrance datum, which was itself adjusted to be on the same datum as channel cross sections. Two survey pins with known geospatial coordinates were measured using the total station to verify elevations within GRC. This allows the assumption that elevations of SWDs in

Greenbrier River Cave are minimum water surface elevation in the stream during the corresponding flood. In other words, a straight line can be extended from the SWD in the cave to the opposite side of the cross section. This comes into play during the modeling 34 portion of this study. Six SWD samples in Greenbrier River Cave were collected and prepped for radiocarbon dating and then sent to BetaAnalytic to obtain ages. The material dated here was charcoal.

The Greenbrier River is a bedrock stream. Therefore, various regions of the channel bed have been broken into large boulders, which are interspersed with sandstone boulders being transported by the river. The Wolman Count method was used to collect information about median clast size (d50) atop cobble/boulder bars (Wolman, 1954). d50 is reported after calculating cumulative percent finer and plotted using semi-long graphs.

The calculations and plotting do not use probabilities to estimate percent finer. Clast sizes allowed the minimum competence of the river to be estimated. Competence is a major part of stream character and can assist in determining parameters such as erosion rates and thresholds necessary for sediment transport. The amount of energy and velocity needed to move a particular clast in a stream can be calculated by measuring cobble

(clast) sizes (Janson, 2006). Clast sizes were measured by first choosing a starting location, then taking a step and measuring length of the intermediate (B) axis of the rock that was stepped on, recording the length, then repeating this process until 100 clasts were measured. This process was performed on three different bars. The majority of clasts measured were river-transported sandstone; a very few were limestone or conglomerate.

The Shields equation for critical shear stress when dealing with poorly sorted gravel (Komer, 1987), was used to back calculate the minimum velocity required to move the sediment: 35

0.6 0.4 τc = 0.045(ρs – ρ)g푑50 d (1)

3 where τc is the critical shear stress, ρs is the density of clasts (2,650 kg/m ), ρ is the

3 density of water (1,000 kg/m ), d is the clast diameter, d50 is the median clast size, and g is the acceleration due to gravity. The true density of the formative floodwaters was probably somewhat greater than the value used because of suspended sediments, but the actual value is unknown. The equation was used to better understand the magnitude of floods required to mobilize sediments observed in the study reach.

For each of the three cobble bars, the 10 biggest river-transported sandstone boulders were identified, and all three axes (A, B, C) were measured. The distribution or variation in clast sizes in the river was determined using Excel.

Data Processing

Cross section data were processed in Microsoft Excel. Elevations (Z) were adjusted so that rod height was subtracted from each value. There were 7 total station setups and 6 control points. Once this process was completed for all control points and setups, the data were processed using a VisualBasic cross section processor (written by

G.S. Springer). The cross-section processor prepares the data for HEC-RAS by converting station locations to distance from river-left end.

Hydraulic Modeling

A hypothetical representative river reach was created in HEC-RAS (Fig.10). Once drawn, cross section data were added manually. The locations of left overbank (LOB), right overbank (ROB), and thalweg were specified for each cross section. Steady flow boundary conditions were set to normal depth to begin modeling runs. Normal depth was 36 estimated for the upstream cross section using an entry gradient (S) of 0.004. Normal depth was estimated for cross section farthest downstream using an exit gradient of

0.0026. These values were determined by calculating the entry and exit gradients measured in the field. Calibration for the model was either set to the 1996 known flood discharge or the 2016 known flood discharge. Manning’s n values were varied in HEC-

RAS in order to match the PSI elevations of either the 1996 or 2016 silt lines. The purpose of the calibration was to determine the appropriate channel roughness for the

1996 and 2016 floods based upon known silt line elevations and discharges reported by the U.S. Geological Survey. Several simulations were run, changing Manning’s n values each time, in order to match the water surface elevation in the model to the known silt line elevations. Cross section 6 was used for calibrating the model because it was located directly across from Greenbrier River Cave, where slackwater and silt line elevations are known. Once the model was calibrated to a specific Manning’s n value, the model could be used to match discharge values for the 1996 and 2016 floods using SWD elevations in the cave by holding the Manning’s n value constant and varying the discharge values until the values generated a water surface elevation that matched the elevation of the

SWDs. SWDs give underestimations of discharges because they only represent minimum stages. Using SWDs to predict discharges allowed an estimate to be made of how much that method underestimated actual discharges.

Figure 10. A 3-dimensional representation of the Greenbrier River in HEC-RAS. The black lines represent the channel bottoms. Cross section six was used to calibrate the model.

The channel roughness values determined in this study were also compared to roughness values used in other paleoflood studies in order to evaluate the value chosen for the Greenbrier River. A range of channel center Manning’s n values were chosen from similar studies that were conducted by Greenbaum et al. (2014), O'Connor et al.

(1986), and Ballesteros et al. (2011) (Table 3).

Table 3

A list of Manning’s n (channel roughness) values used in the paleoflood literature. Channel n Value Bank n Value Literature Cited from 0.029 0.055 This project 0.028 0.045 Greenbaum et al. (2014) 0.035 0.035 Greenbaum et al. (2014) 0.035 0.040 O’Connor et al. (1986) 0.040 0.035 Ballesteros et al. (2011) 38

Estimating Critical q Values and Sediment Transport Velocities

Janson (2006) used clast sizes and a modified version of the Shields critical shear stress equation to estimate discharges in a channel. Discharge is related to channel width and, if clast size is known, with addition of several other parameters (see below), a critical unit discharge (qc) can be calculated. This critical unit discharge can be multiplied by the channel width to get an actual discharge for the channel through a particular cross section. A similar technique was used in this study. The purpose of doing this was to have a better understanding of the flood magnitudes needed to transport out-of-place sandstone clasts within the Greenbrier River. This information was used to constrain channel velocities and for comparison those to values generated by HEC-RAS modeling.

Critical unit discharge (qc) gives the discharge per unit channel width (Janson,

2006). A unit width is 1 meter. Once qc is calculated, it can be used to determine the discharge across the entire channel width. Following Janson (2006), two equations are used in this study to calculate qc. They represent a modified version of Shields (1936) equation for critical shear stress when combined:

1.5 (1-x) (c+1.5) c+1 qc = a 푑50 (d/d50) /S (2)

0.5 c+1.5 a = m(8g) ((ρs /ρ – 1) τc*50) (3) where d50 is the median clast size of each cobble bar measured in the field and d is d84, or the 84th percentile clast size for each cobble bar. The constants values are c=0.37 and m=1.14 (Thompson and Campbell, 1979). Other variables in Equation 1 are x and S, where x is the hiding factor, assumed to be 0.90 (Parker, 1990), and S is the channel slope. In this study, the average channel slope was calculated by taking the difference in 39 elevation between cross sections 1 and 10 and then dividing that by the total distance of

3 the reach being studied. In Equation 2, ρs is clast density (2650 kg/m ), ρ is water density

(1000 kg/m3), and g is the acceleration due to gravity. For large cobbles that are poorly sorted, such as in this study, a critical shear stress (τc*50) value of 0.045 is used (Janson,

2006). Equation 2 was used to determine a and then this was plugged into Equation 1 to determine qc for each of the three Wolman Count-derived clast size values.

Once qc values were determined (3 total), these values were then multiplied by the total channel width of the cross section closest to that cobble bar. The result is the discharge needed in order to move clasts at least the size of d50 within the entire channel width. The resulting discharges were used to calculate average velocities in order to understand the behavior of the channel in terms of how the stream is moving these clasts and at what threshold. In HEC-RAS, a fixed discharge and channel roughness were set and the model was run in order to calculate the area for each of the three cross sections where Wolman counts were conducted. The discharge was then divided by the cross- sectional area in order to determine average sediment-moving water velocities.

Flood Frequency Analysis

A program, called HEC-SSP, was used to run a Bulletin 17 flow frequency analysis for systematic (gaged) and non-systematic floods on the Greenbrier River. The purpose was to be able to compare the results between the systematic FFA and an analysis which included both paleofood and systematic data. The ages of paleoflood deposits were converted from BP years to calendar years by using 1950 as the reference point and subtracting the paleoflood dates from 1950. This process led to three values 40 being in negative years. In order for the program to acknowledge these negative values, an arbitrary 1500 years was added to each date for which there are discharges, including the gaging data. This allowed for all values to be positive so the program could read it.

The program required a day and month to be assigned to the discharges so arbitrary dates were assigned.

Once data were uploaded into the program, the Bulletin 17 Editor was used to assign either a 17C EMA or 17B method for computing statistics and confidence limits.

The 17B method allowed for historical data to be entered separately from the systematic data. The paleoflood data were treated as historic data in this program because the program cannot distinguish the difference between these two categories. The rest of the gaged data was systematic. A regional skew and regional skew measured standard error

(MSE) of .110 and .169 were assigned based on values taken from the Wiley and Atkins

(2010)’s FFA of West Virginia. Default confidence limits of 0.05 and 0.95 were used.

Once these data were entered, the program was run and a report was generated.

CHAPTER 6: RESULTS

This study was designed to accomplish several objectives including determining the median clast size in the channel, calculating a channel roughness value, back- calculating paleodischarges, retrieving ages for PSIs in the GRC, and using the summation of these to recompute the flood frequency analysis for the Greenbrier River.

In this section, a summary of results is presented.

Grain Size Analysis

Wolman counts were collected at three locations along the study reach (Fig. 11).

At each location, the intermediate axes of 100 clasts were measured. These values were then plotted versus cumulative percent finer. Median clast sizes (d50) were determined to be 147 mm, 166.5 mm, and 203 mm (Fig. 12).

Clast sizes measured ranged from pebble to boulder. A summarized list of d16, d50, and d84 values for all three locations, as well as the sorting classification for each size, is presented in Table 4. The clasts that were measured in this study were sandstone and conglomerate, with the exception of one limestone clast. However, there are only a few outcrops at higher elevations within the valley and the rest of the exposures are from much farther away. This indicates that the clasts must have been eroded, entrained, and then transported from somewhere upstream to where they now reside. In a later section, the velocities required to move these cobble-sized clasts will be discussed.

It is important to note that even though the exposed bedrock throughout the entire study reach is limestone, there is little that is broken up. Therefore, the limestone is mechanically resistant and not frequently plucked despite the high velocity currents that 42 flow across it frequently. The limestone does have widely spaced jointing present where some erosion occurs. Where the limestone is exposed in the river, it creates a lower channel roughness than if the channel bed consisted of loose clasts, which allows water to flow over smoothly. However, the limestone is chemically non-resistant, so presumably corrosion is an important agent of incision (Springer et al., 2003).

Figure 11. Location map of where Wolman counts were collected along the Greenbrier River. Figure created using Google Earth Pro.

Figure 12. A cumulative percent finer diagram displaying all three Wolman counts. This diagram shows the median clast size values of each Wolman count collected. Note the linear y-axis.

Table 4

A summary of clast sizes measured in the field as well as their equivalent sorting classification. Wolman Count d5 (mm) d16 (mm) d84 (mm) d95 (mm) Sorting Location WC1 42 61 299 446 121 WC2 58 89 266 348 88 WC3 81 120 403 510 136 Note: Sorting was calculated based on Folk (1968) sorting classification equation.

Channel Roughness and Paleodischarge Estimates

Channel roughness was estimated as a result of back calculations using known silt line elevations and discharges for the 1996 and 2016 floods in the Greenbrier River. A 44 range of roughness values used in Greenbaum et al. (2014) were chosen to see which combination of roughness values (bank and channel n) most closely correlated with the known elevations for silt line deposits. The range of n values and corresponding silt line elevations are presented in Table 5.

Table 5

Calculated water surface elevations are presented in columns 1 and 2 correspond to the 1996 and 2016 floods. These values were predicted using hypothetical n values and compared to known silt line elevations. Manning’s n Water surface elevations (m) Water surface elevations (m) values (1996 flood) (2016 flood) 0.015 102.2 101.4 0.018 102.3 101.5 0.020 102.3 101.5 0.025 102.5 101.7 0.027 102.5 101.8 0.028 102.6 101.8 0.029 102.7 101.9 0.030 102.7 102.0 0.031 102.8 102.0 0.032 102.9 102.1 0.033 103.0 102.2 Note: Manning’s n values were taken from Greenbaum et al. (2014). The known silt line elevations are 102.66 m (1996) and 101.79 m (2016).

For this study, a channel roughness value of 0.029 was determined for the 1996 flood (Fig. 13) and 0.028 for the 2016 flood (Fig. 14). The left and right bank roughness values were set at 0.055 for all runs. This number was chosen after carefully comparing it against other values used in the paleoflood literature similar to this one (see Table 3). It seemed that most studies used a value of at least 0.035 or higher for their bank roughness

(Greenbaum et al., 2014; O’Connor et al., 1986; Ballesteros et al., 2011). The slightly 45 higher roughness value of 0.055 was used because the stream has steep banks, extensive tree cover, and several areas where the channel is bedrock confined. Most paleoflood studies cite their roughness values from Chow (1959), which was consulted for this study as well. A high roughness value for the banks results in a lower roughness value for the central channel, which is the case in this study.

1996 Flood Deposit 2.50 Silt line elevation: 102.7 m

2.00 Best-fit n value: 0.029

1.50

1.00 Actual elevation measuredelevation 0.50 -

0.00 0.010 0.015 0.020 0.025 0.030 0.035 Manning's n

Figure 13. A plot based on the data in Table 5 showing the squared difference (to get rid of negative values) between measured silt line elevation and calculated silt line elevation (y-axis) and range of Manning’s n values (x-axis) for the 1996 flood on the Greenbrier River.

2016 Flood Deposit 2.00 Silt line elevation: 101.8 m

Best-fit n value: 0.028

1.50

1.00 Actual elevation measuredelevation

0.50 -

0.00 0.010 0.015 0.020 0.025 0.030 0.035 Manning's n

Figure 14. A plot based on the data in Table 5 showing the squared difference between measured silt line elevation and calculated silt line elevation (y-axis) and range of Manning’s n values (x-axis) for the 2016 flood on the Greenbrier River.

To justify these n values, the larger floods were compared to a smaller flood that occurred along the Greenbrier River. In May of 2001, a small flood with a peak discharge of only 768 m3s-1, left behind a silt line in GRC. This flood was modeled to calculate n values the same way as the 1996 and 2016 floods (Fig. 15). The best-fit n value was determined to be 0.035. This is 20% higher than what was calculated for the 1996 and

2016 floods. This makes sense because lower velocity floods tend to have higher roughness due to the channel bottom still governing the process. Whereas, with larger floods, the roughness in the channel declines as the velocity increases and the banks become completely flooded. 47

2001 Flood Deposit 9 8 7

6 5 4 3 Actual elevation measuredelevation

- 2 1 0 0.01 0.015 0.02 0.025 0.03 0.035 0.04 Manning's n

Figure 15. A plot based on the data in Table 3 showing the squared difference between measured silt line elevation and calculated silt line elevation (y-axis) and range of Manning’s n values (x-axis) for the 2016 flood on the Greenbrier River.

Moving forward, the roughness value of 0.029 was chosen as most representative for the Greenbrier River because it most closely estimated the correct discharge and silt line elevations when modeling. The channel roughness value was compared against other roughness values used in similar paleoflood studies (see Table 3) in order to see if our value was reasonable. The values were compared by using HEC-RAS to calculate discharge values for each specific roughness value for a fixed water surface elevation

(Table 6). This table also lists the radiocarbon ages for the six paleoflood deposits that were dated. So, given a hypothetical n value and fixed silt line elevation, discharge was varied until the predicted and known stages were the same. A few discharges were already known from gaging station records; thus, the purpose of this procedure was see how well the different sets of roughness values could reproduce known discharges. 48

Table 6

A summary of discharges (Q) calculated using various channel and bank roughnesses (n). Type of PSI Elevation Year Channel Bank n Calculated (m) n Q (cms) 1996 SWD (Top) 102.0 1996 0.029 0.055 2338 0.028 0.045 2461 0.035 0.035 2334 0.035 0.040 2273 0.040 0.035 2152 1996 SWD (Bottom) 101.6 1996 0.029 0.055 2142 0.028 0.045 2256 0.035 0.035 2131 0.035 0.040 2077 0.040 0.035 1964 1996 Silt Line 102.7 1996 0.029 0.055 2664 0.028 0.045 2804 0.035 0.035 2668 0.035 0.040 2597 0.040 0.035 2470 2016 Silt Line 101.8 2016 0.029 0.055 2243 0.028 0.045 2362 0.035 0.035 2236 0.035 0.040 2179 0.040 0.035 2062 Station GS15 (2016 101.0 2016 0.029 0.055 1889 SWD) 0.028 0.045 1991 0.035 0.035 1870 0.035 0.040 1824 0.040 0.035 1719 Station SUN5 (2016 101.5 2016 0.029 0.055 2120 SWD) 0.028 0.045 2234 0.035 0.035 2110 0.035 0.040 2056 0.040 0.035 1943 Station BD20 (SWD) 100.2 930 BP 0.029 0.055 1539 0.028 0.045 1621 0.035 0.035 1510 0.035 0.040 1474 0.040 0.035 1384 GRC1 (2001 SWD) 101.5 1250 +/- 40 BP 0.029 0.055 2083 0.028 0.045 2192 49

Table 6: Continued 0.035 0.035 2069 0.035 0.040 2016 0.040 0.035 1904 GRC2 (2001 SWD) 101.5 160 +/- 30 BP 0.029 0.055 2083 0.028 0.045 2192 0.035 0.035 2069 0.035 0.040 2016 0.040 0.035 1904 GRC3 (2001 SWD) 101.5 2060 +/- 50 BP 0.029 0.055 2110 0.028 0.045 2223 0.035 0.035 2098 0.035 0.040 2046 0.040 0.035 1933 GRC4 (2001 SWD) 102.4 2080 +/- 40 BP 0.029 0.055 2511 0.028 0.045 2644 0.035 0.035 2542 0.035 0.040 2480 0.040 0.035 2322 GRC5 (2001 SWD) 102.4 2010 +/- 40 BP 0.029 0.055 2511 0.028 0.045 2644 0.035 0.035 2542 0.035 0.040 2480 0.040 0.035 2322 Note: See Table 3 for reference of literature used for various Manning’s n values.

Other channel roughness values found in literature (see Table 3) either greatly underestimated or overestimated the discharges for the 1996 and 2016 floods (Table 6).

Ballesteros et al. (2011) used a channel n value of 0.040 and bank n value of 0.035, which underestimated the actual discharge for both the 1996 and 2016 floods by ~10%

(see Table 6). O’Connor et al. (1986) used a channel n value of 0.035 and bank n value of

0.040. This resulted in underestimating the actual discharges for both the 1996 and 2016 floods (see Table 6). Greenbaum et al. (2014) also used a channel n value of 0.028 and bank n value of 0.045. Using these values resulted in an overestimation of the actual 50 discharges for both the 1996 and 2016 floods (see Table 6). The conclusion was that the channel roughness value of 0.029, with bank value of 0.055 that were determined in this study serve as a good representation of the actual roughness occurring along the

Greenbrier River based upon the calculated discharges.

Silt Line and Slackwater Deposits

Elevations for both silt lines and SWDs were measured in Greenbrier River Cave for the 1996 and 2016 floods, both with known discharges, in order to see how close

SWD’s predict the actual discharge that is represented by silt lines or measured at the gaging station.

Using the SWD elevation for the 1996 flood resulted in an underestimation of the actual discharge (see Table 7). There were similar results for the 2016 flood. Two different SWDs were measured in the field for the 2016 flood. Both of which underestimated the actual flood discharge (see Table 7).

Table 7

A summary of discharges calculated using SWD’s from the 1996 and 2016 floods. Flood Actual Discharge Percent Actual WSE Minimum Event Discharge using SWD’s Underestimate from Silt Line WSE using (cms) (cms) (%) (m) SWD (m) 1996 2661 2338 12.2 102.65 101.99 2016 2285 1889 17.4 101.80 101.03 2285 2120 7.2 101.80 101.54 Note: WSE stands for water surface elevation. Only top sediment was used for the 1996 flood (See Table 6). Two separate SWDs were measured in the field for the 2016 flood (See Table 6). 51

Flow Competence Analysis

Once d50 values for each cobble bar were determined, these values were then used in a modified Shield’s equation to calculate critical unit discharge (qc). The results of

2 using Shield’s equation gave qc values between 30 and 60 m /s (Table 8). Once qc was determined, these values were multiplied by their channel widths to get the discharge for the entire channel width (Table 9). Center channel width values used here were initially calculated when using the cross-section processor, which was discussed earlier. The product of channel widths and qc values were discharges that ranged between 1,500 and

4,000 cms (Table 9). These discharges represent the flow occurring across a particular cross-sectional area that enables sediment movement.

Table 8

A summary of the critical discharges (qc) calculated and parameters used to calculate the values. Wolman Count d50 (m) d84 (m) Nearest Cross Critical q Location section (m2/s) WC1 0.147 0.299 10 34.9 WC2 0.1665 0.266 8 40.2 WC3 0.203 0.403 5 56.3

Table 9

A summary of discharges calculated from qc values and channel width. Wolman Count Center Channel Width (m) Discharge (cms) Location WC1 51.1 1782 WC2 41.1 1651 WC3 62.3 3511 52

Having these discharges, which were generated using clast sizes, enabled sediment movement thresholds, or critical velocity, to be calculated. This calculation was done simply by taking the discharge and dividing it by the cross-sectional area of that particular location. The cross-sectional area for each cross section was calculated in

HEC-RAS. The results yielded velocities between 3 and 6 m/s, which was the minimum flow rate to entrain the cobble bars (Table 10). The density of water value used in the critical discharge equation was changed from 1000 kg m-3 (clean water) to 1010 kg m-3

(turbid floodwater) to test variability in resulting discharges. The changes in values were less than 3% so the density of clean water was used as suggested in Janson (2006).

Table 10

A summary of sediment-derived velocities calculated using clast sizes and critical discharges. Wolman Count Calculated Q Nearest Cross Cross sectional Velocity Location (cms) section Area (m2) (m/s) WC1 1782 10 500 3.6 WC2 1651 8 290 5.7 WC3 3511 5 708 5.0

Flood Frequency Analysis

The historical record was extended to 2148 years of record, with 122 of those years being the gage data. Six paleoflood events were added to the new FFA. A frequency curve was computed for just the systematic data in order to set a standard for reasonable values in the program (Fig. 16). Based upon the systematic FFA, a 100-year 53 flood has a discharge of 2,437 cms, a 200-year flood has a discharge of 2,708 cms, and a

500-year flood has a discharge of 3,081 cms (see Appendix B for table of values).

Figure 16. A frequency curve showing a 122-year record for the Greenbrier River. This includes the systematic floods through 2017. The y-axis is flow in cfs. The program would not compute flow in meters.

When calculating a FFA that included both the systematic data and paleoflood data, the results were slightly different than the results of just the systematic analysis. A similar frequency curve to the systematic analysis was generated to show the recurrence intervals for a particular size flood based on the addition of the paleofloods (Fig. 17). The 54 discharges for a given recurrence interval were lower than when running just the systematic data in HEC-SSP. For the 100-year flood, a discharge of 2,061 cms was calculated. The 200-year flood had a discharge of 2,261 cms and the 500-year flood had a discharge of 2,531 cms. The discrepancy between the systematic and paleoflood results will be discussed in a later section.

Figure 17. A frequency curve showing 2148 years of record for the Greenbrier River. This includes the systematic floods and paleofloods. The y-axis is flow in cfs. The program would not compute flow in meters.

CHAPTER 7: DISCUSSION

Confidence in Channel Roughness Estimate

It was decided to use values that are within reason of several other studies with successful attempts at predicting channel roughness (see Table 3). A channel roughness value of 0.029 and bank value of 0.055 were chosen for this study to represent the realistic roughness of the Greenbrier River channel. The decision to go with these values was made by modeling historic floods in HEC-RAS and comparing n values to those found in other paleoflood studies in order to see how they matched up. HEC-RAS was used to estimate n values because using a varied flow model for estimating Manning’s n is more appropriate than using Manning’s equation since natural systems do not have perfectly uniform flows, especially in bedrock-confined channels (Richards, 1978).

In HEC-RAS, a range of n values were used to model historic floods with known water surface elevations and discharges. The model would run each time with a different n value until the modeled water surface elevation matched the actual water surface value.

The range of n values were plotted against the squared difference between the modeled water surface elevation and the actual water surface elevation related to the 1996 and

2016 floods (See figures 13 and 14). The idea to do this was taken from a similar study done by Greenbaum et al. (2014), where they found n values that were very similar to this study (Table 3). In order to ensure these n values were representative, assumptions made by other studies were looked at to see whether there was a consistency among all of these and this study, as well as compare n values. 56

Greenbaum et al. (2014) studied a reach of the Colorado River that carves through a bedrock canyon. When calculating discharges in HEC-RAS, they assumed the water surface to be a smooth channel bed and flow to be subcritical. They calibrated HEC-RAS by using known flood discharges, similar to this study. Because of the limited amount of studies that have used selected Manning’s n values for large discharge calculations,

Greenbaum et al. (2014) decided to use a range of n values (0.015 to 0.040 for channel,

0.045 for bank) in order to produce several different hydraulic conditions and select the best fit for their data. Two sets of n values best fit: 0.028 (channel) and 0.045 (banks), and 0.035 (channel and banks). The reasoning behind the second set (n =0.035 for channel and banks) was derived from a study by Magirl et al. (2008). Magirl et al. (2008) summed up all roughness elements throughout the Colorado River and believed this to be the best estimate. It was also consistent appropriate n values in Chow (1959). When running these different values in HEC-RAS, Magirl et al. (2008) determined that the first set of n values (0.028 for channel, 0.045 for banks) was the best fit for the data. Magirl et al. (2008) based this on how well the n values estimated the known discharges in the model using the water surface elevations they measured in the field. Magirl et al. (2008) value of 0.045 for the river banks was also from Chow (1959). The channel n value in this study were similar with Greenbaum et al. (2014) when using the same techniques.

However, based on descriptions of their banks (clean bedrock canyon, steep banks, minor vegetation), it was decided that the bank values in this study should be higher than 0.045.

Some areas were bedrock confined but mostly steep banks with trees, cobbles, and 57 vegetation, so leading the roughness should be higher (see Table 2 for floodplain descriptions).

Ballesteros et al. (2011) estimated floodplain roughness values and discharges using tree-ring PSIs and a one-dimensional hydraulic model called MIKE FLOOD (DHI,

2008). Their study area was located along the Alberche River in the Eastern Sierra of

Gredos. The study area was within a bedrock confined reach. They chose their n values based upon the table created by Chow (1959). They used several channel and floodplain descriptions and provided a minimum and maximum value for each n value taken from

Chow (1959) (see Table 1 in Ballesteros et al., 2011). These selected n values were compared to n values that were listed in the table used by Ballesteros et al. (2011) and most closely described the Greenbrier River. Their main channel value of 0.040 was chosen, which was described to be a clean and straight channel with stones and weeds, and a bank value of 0.035, which was described as short grassy floodplain.

When looking at the resulting discharges from using these values in HEC-RAS, the discharges were greatly underestimated compared to what were generated using n values from other studies (see Table 6). Another issue with most of their n values is that their channel values were higher than their bank values. Typically, the opposite is true.

The banks usually have higher roughness due to vegetation, trees, steepness, etc.

Therefore, after running these values through HEC-RAS and seeing how much they underestimate the flow, it was decided these values were not valid.

The last study that n values were compared to was by O’Connor et al. (1986).

They conducted their paleoflood study in Boulder Creek, Utah, which is characterized by 58 a well-developed pool and riffle pattern, similar to the Greenbrier River. The riffles were made out of basalt boulders, which are very resistant. Their channel was confined by steep walls of sandstone, with sand and heavy vegetation on the banks (O’Connor et al.,

1986). They used HEC-2 to model paleofloods using known water surface elevations and assuming subcritical flow. They assumed channel n values of 0.035 and bank values of

0.040. These were chosen from Chow (1959) and best fit their data. When comparing the discharges calculated using their n values versus the ones in this study, their n values underestimate the actual discharges for the 1996 and 2016 floods and, similarly, underestimated the discharges calculated from PSI deposits in this study (see Table 6).

The channel in this study had a lower roughness value than that of other studies consulted except for Greenbaum et al. (2014), which had only a 0.001 difference in their channel roughness value than ours. The Greenbrier River best fit the description of a mountain stream with no vegetation in the channel, steep banks with trees and brush along banks, and gravel and cobbles along the channel bottom. This falls under category

2a of Chow (1959)’s list of Manning’s n values. A bank value of 0.055 for this study was higher than other studies but is still represented in Chow (1959)’s table as a reasonable value to use in a stream with conditions that matches with the Greenbrier River. The 1996 flood had a discharge of 2,663 cms and the n values estimated a discharge of 2,338 cms

(SWD) and 2,664 cms (silt line). The results from the 2016 flood were similar. An argument can be made that this closeness in estimation indicates that the rest of these discharges, estimated for the PSI deposits, are reliable. 59

Slackwater Deposits and Silt Lines: How comparable are results obtained from them?

PSI’s were measured in Greenbrier River Cave in the form of either silt lines or

SWD’s. Silt lines are interpreted as high-water marks (marking maximum stage) and

SWD’s are minimum stage heights. The elevations listed in Table 6 were measured in the field. The discharges listed in the same table are estimates from HEC-RAS based upon the PSI elevations. For the 1996 and 2016 floods, there are actual discharges measured at the Alderson gaging station. These discharges were compared to the modeled discharges in order to determine the accuracy of using silt lines and SWD’s to calculate paleodischarges.

Table 7 shows the actual discharges for the 1996 and 2016 floods as well as the discharges determined using SWD’s. From this information, it is evident that SWD’s result in close estimations of actual discharges. Silt lines represent peak flood stage which is why, when using these elevations to obtain discharges, they match up to the actual values. The goal was to see how well SWD’s can predict paleodischarges in case they were the only PSI available to use for paleoflood studies. This is very possible since

SWD’s stay preserved the longest. While the SWD’s did underestimate the actual discharges, the values were less than a 20% lower than the actual values when using the n values determined in this study (channel n = 0.029, bank n = 0.055) (Table 7). Accepting the error and using these would still lead to a better flood frequency estimate than what currently stands in the USGS gaging record (water.usgs.gov).

SWD’s can serve as an important use for developing a flood chronology if they are datable and well preserved (O’Connor, 1986). These deposits can contain charcoal, 60 quartz sand, or other organic material left behind from a flood that can be dated using several different dating methods. While carbon-14 dating is the most popular, if organics are unavailable, optically stimulated luminescence (OSL) can be used to date quartz sand clasts. Even if SWD’s don’t represent the absolute peak flood stage, they can still give a time period for the flood which allows for a longer flood record. For instance, in this study SWD’s were dated from potentially six different floods that went back to ~2080 BP

(Table 6). SWD’s might not be the most accurate, but they can provide age constraints to floods and can extend flood frequencies and mitigate future flood damages.

Silt lines are a representation of peak stage and can accurately predict paleodischarges. However, they have relatively short lifespans causing them to be rarely used in paleoflood studies. This topic will be discussed in the next section. This study has been able to show that SWD’s can serve as a good indicator for paleodischarges when silt lines are unavailable. They serve an important purpose of acting as geologic archives, preserving sediments which can later be used to get flood ages.

Reliability of the 1996 and 2016 Flood Deposits

Preservation of PSI’s is the number one concern when conducting a paleoflood study (Baker, 1987). SWD’s tend to settle out in low energy zones of rivers. In channels that favor SWD accumulation during high stages, there is a greater likelihood of those deposits being eroded during frequent low stage floods (Baker, 1987). Silt lines can stay well preserved depending on the climate and biological activity. Since they are high water marks, silt lines are unlikely to be destroyed during low stage floods. 61

Greenbrier River Cave (GRC) only has flowing water during floods. All other times, it is a dry cave. This is important for the long-term preservation of flood deposits.

There is also less biological activity occurring in environments like that of the GRC, which minimizes bioturbation in the deposits. This seems to be mostly true in the cave.

When digging for SWD’S in GRC, nine deposits were located at different elevations.

There was little evidence of the sediments undergoing any bioturbation so they were relatively well preserved. However, two of those deposits were from the 2016 flood, and one could not be dated. The 2016 silt line was still well preserved in the cave as it was the most recent large flood. The 1996 silt line still exists but is very ragged and, at this point, would not serve as a good indicator for flood research. The 1996 silt line was measured by G.S. Springer during a trip to the cave in 2001. In 18 years, it has diminished to the point it is barely recognizable as a silt line. No other signs of older silt line deposits were found. This brings up a few questions: are the flood deposits just not being preserved for as long as they should be in a cave environment? Or is this just due to having few large floods throughout history? These questions are hard to answer unless the cave can be consistently monitored for decades, but a prediction can be made for how long the current flood deposits in the cave will last and then use this as a model for future floods.

After just 18 years, the 1996 silt line is almost gone. If we consider the 2016 silt line to behave similarly, it should be almost gone by 2035. It is harder to say how long the SWD’s will last because some of the deposits are over 2,000 years old (Table 6).

However, when venturing out during this study, it was hard to locate any other SWD’s 62 that were not already collected from previous visits in 2001 and 2017. Nonetheless, the short lifespans of silt lines in GRC shows the value of using SWD’s in paleoflood studies.

Sediment Transport and Stream Competence

Stream competence is defined as the largest particle a stream can move, but in a more general sense it is a measure of a river’s ability to transport sediment based on the sizes of the particles in a channel (Baker and Ritter, 1975). Clast size varies throughout streams but determining the median clast size can tell information about what a stream can typically carry during floods. At higher stages, stream power increases and larger clast sizes are carried or moved along the channel bottom. Frequent flooding can often skew the median clast size to larger values, but these events can give information about the type of hydraulic conditions needed to initiate their movements (Baker and Ritter,

1975).

In the Greenbrier River, the median clast sizes for the three Wolman counts were relatively coarse. The values were all above 100 mm, which is cobble size (Fig. 8), and cobbles require a large amount of energy to move. There are several routes to take to understand stream competence using clast sizes. However, shear stress is the most practical because depth is easier to measure in the field than velocity (Baker and Ritter,

1975). A specific value for critical dimensionless shear stress of 0.045 is used for coarse bedload material (Janson, 2006). This value was used in the critical discharge calculation.

Since velocity is harder to determine in the field, the critical dimensionless shear stress and critical discharge equation can be used to back-calculate velocity values and get a better idea of boulder movement threshold. This topic will be discussed in a later section. 63

The generalized competence of a stream channel is typically represented by the clast size of the largest particles (d90) (Baker and Ritter, 1975). In this study, d84 values were looked at because the critical discharge equation suggested this value be used

(Janson, 2006) (Table 4). Based upon these values, the Greenbrier River can carry sizes greater than 250 mm, which is boulder-sized. The d84 values can be used to determine the velocity and discharge thresholds and relate these to a particular size flood. According to other studies (e.g. Howard, 1980; Jackson and Beschta, 1982), d84 – d90 represent the principal roughness components and are, therefore, most influential on channel gradient and morphology (Jansen, 2006). That being said, floods have a large effect on channel morphology and gradient by being able to carry such large clasts. It was noted in the field that the clasts were well imbricated. This shows that the clasts are acting as one single matrix increasing the threshold for entrainment and transport such that only high velocity events can move them (Hickin, 1995).

There were a few limitations to the sediment transport data collected in the field.

Only three Wolman counts could be completed due to the lack of cobble bars along the study reach. One of the Wolman counts (WC3) had to be done in the water instead of the cobble bar because the bank had been completely taken over by vegetation. During high stages, this vegetation gets completely wiped out. However, it continues to grow back between major floods (G.S. Springer, personal communication, 2019). Doing the

Wolman count in the water means there is a chance that the most representative samples were not taken as opposed to the cobble bar samples taken farther upstream. If there were 64 not time constraints on the project, more cross sections could have been done to offer more areas for Wolman counts and more sediment transport data.

Modeled Velocities versus Clast Size-derived Velocities

HEC-RAS has been a very useful tool in this study. The software has provided results for channel roughness, paleodischarges, and boulder velocities. The accuracy of the model can be determined by comparing the results to field data collected. If they are within acceptable limits, then this shows how reliable HEC-RAS can be in paleoflood studies when certain data is not available to be measured in the field.

The stream velocity during floods is extremely hard to measure. Because of this, it was decided to use clast sizes and Shield’s shear stress equation to back calculate discharges and velocities needed in order to move a particular clast size. Velocities were also obtained in HEC-RAS when running the model under the standard calibration (n =

0.055 bank, 0.029 channel) and using the discharges calculated from the empirical shear stress equation (Table 11). Differences of ~20% are significant but, given the amount of uncertainties made, the values are still within reason for this study. The various assumptions made in this project leaves room for future work to be done in order to better constrain these uncertainties. An important question to ask now is: How do these velocities and discharges relate to big floods and to historical floods?

Table 11

A list of velocities calculated using clast size versus using HEC-RAS. Wolman Count Velocity using Clast Average Velocity Percent Location Size (m/s) using HEC-RAS Difference (%) (m/s) WC1 3.57 4.35 21.9 WC2 5.69 6.88 20.9 WC3 4.96 5.37 8.3 Note: Velocity values determined from clast sizes were used as the true value when calculating percent error.

Sediment-derived velocities can be compared to velocities that HEC-RAS computed for the historical flood discharges in order to see the competence these floods had. The three largest historical floods were used: 1985, 1996, and 2016. Their discharges were used to calibrate the model along with the standard n values from this study. The resulting velocities are presented in Table 12. It appears that the velocities matched relatively well with a few exceptions. The velocities calculated in HEC-RAS using the historical flood data are slightly higher than the velocities obtained from the clast size data. The most recent historical floods were big enough to move the boulders and possibly, these floods also had the competence to carry larger boulders than are supplied to the river or found in this reach.

Table 12

A list of velocities calculated in HEC-RAS using historical flood discharges. Historical Discharge Wolman Velocity in Velocity using Percent Flood (cms) Count HEC-RAS Clast Sizes Difference Location (m/s) (m/s) (%) 1985 2567 WC1 5.0 3.6 39.5 WC2 8.0 5.7 39.7 WC3 4.7 5.0 4.4 1996 2664 WC1 4.1 3.6 14.6 WC2 6.6 5.7 15.7 WC3 4.5 5.0 10.1 2016 2287 WC1 3.9 3.6 8.1 WC2 6.2 5.7 9.5 WC3 4.2 5.0 16.1 Note: Velocity values determined from clast sizes were used as the true value when calculating percent error.

Discharges from Clast Size Analysis versus Previous FFA

The discharges that were calculated using the shear stress equation provide some interesting results. A similar analysis can be done, as to what was done with the boulder- derived velocities, where the discharges obtained from using clast sizes can be compared to the discharges measured at the Alderson gaging station. The difference here is that the discharges from the gaging station represent values that were measured farther downstream from where the Wolman counts were taken. In this case, it is assumed that the discharge stayed relatively constant between the study region and the gaging station.

With these discharges, the goal was to see what size events would have taken place based on the values and how they compare to the historical discharges. In other words, what are the recurrence intervals (RIs) that are equivalent to the discharges determined from clast sizes? 67

A systematic FFA was conducted on the Greenbrier River in 2010 by the USGS.

Discharges were compared to the discharges used in the 2010 FFA and were assigned recurrence intervals (Table 13). For WC1 (XS-10) and WC2 (XS-8), discharges of 1782 and 1651 cms fall between a 10 and 25-year flood. WC3 had interesting results. The discharge calculated was 3511 cms, which is much larger than the 500-year flood discharge determined in the 2010 FFA. This location had a higher d50 and discharge than the other two Wolman counts (see Tables 8 and 10). This brings up some important questions: why does this area in particular require such a large Q? What is happening to increase the clast size values? And what makes it possible to transport that sediment at discharges lower than the calculated Q?

Table 13

A summary of discharges used in 2010 FFA and their assigned RIs. RI 1.1yr 1.5yr 2yr 5yr 10yr 25yr 50yr 100yr 200yr 500yr Q (cms) 578 810 961 1326 1564 1856 2069 2278 2485 2757 Note: The 2010 FFA discharge data was taken from Wiley and Atkins (2010).

Based upon the current FFA (2010), there has not been a flood recorded greater than a 500-year flood, which concludes the sediment clasts at WC3 were transported at a lower discharge. However, the current FFA is based on limited data. Therefore, the reconstructed FFA in this study can potentially justify this discharge and provide a better estimate for recurrence intervals of these discharges. The WC3 location is interesting because it is where the cobble bar was completely taken over by vegetation and the

Wolman count had to be done in the water. It is also located just before a large bend in 68 the river (Fig. 9). The vegetation at this cobble bar gets wiped out during floods and grows back in between. It can be suggested that the flow field is not uniform in this area.

The water takes a shorter path over the (now vegetated) cobble bar and re-circulates immediately upstream of a river bend. The width of downstream-oriented flow is smaller than total channel width because flow is concentrated on the river-right side of the channel, including atop the cobble bar. The clast sizes measured at this location are presumably representative of the narrower forward flow rather than the full channel width used in the calculations; the sediment-derived discharge calculated for WC3 is unreasonable.

To reiterate, the discharge that was estimated is showing that a flood greater than the 500-year flood was needed to move these boulders. However, we know that they had to move at much smaller discharges due to historical and paleoflood records.

RI’s of 1985, 1996, and 2016 Based upon 2010 FFA

Table 13 shows the RI’s and discharges based on the 2010 FFA, which was conducted by Wiley and Atkins (2010). This analysis can be used to see what the RI’s would be for the three largest floods along the Greenbrier and then compare these to the reconstructed FFA done in this study. According to the 2010 FFA, the 1985 flood falls between a 200 and 500-yr RI, the 1996 flood falls between a 200 and 500-yr flood, and the 2016 flood falls between a 100 and 200-yr RI (see Chapter 2 for discharges).

Paleoflood Recurrence Intervals

Discharges calculated in HEC-RAS for the paleofloods can be looked at and assigned a recurrence interval using the 2010 FFA study conducted by Wiley and Atkins 69

(2010). The discharges and assigned recurrence intervals are presented in Table 14. The majority of the paleoflood deposits collected were from small floods that had less than or equal to a 50-yr RI. However, two deposits show evidence of large floods occurring in pre-historic times. The discharges for GRC4 and GRC5 are in between the discharges for the 1985, 1996, and 2016 floods. These paleoflood deposits are showing floods similar in size to the most recent large floods in the Greenbrier. According to the 2010 analysis, both GRC4 and GRC5 have recurrence intervals of ~200 years. Considering the ages of the sediments, this likelihood of occurrence is accurate. However, if we look at the last 33 years and the three catastrophic floods the Greenbrier has endured, it can be concluded that this is not always the case and these events can happen every year based upon probability even though it is only a small chance.

Table 14

A summary of paleoflood discharges from this study estimated in HEC-RAS and their assigned RIs. Paleoflood SWDs Age Minimum RI (years) Discharge (cms) GRC1 1250 +/- 40 BP 2,083 50 GRC2 160 +/- 30 BP 2,083 50 GRC3 2060 +/- 50 BP 2,110 50 GRC4 2080 +/- 40 BP 2,511 200 GRC5 2010 +/- 40 BP 2,322 200 Station BD20 930 BP 1,539 10 Note: RIs and discharges for the 2010 study can be found in Table 13.

The 1985 and 1996 floods had between a 200 to 500-yr RI and the 2016 flood was only between a 100 and 200-yr RI, according to the 2010 FFA. However, all of these 70 floods occurred within 20 years of one another. This observation can draw some interesting questions about why this is happening. Therefore, it is extremely important to consider both systematic and nonsystematic floods when doing a flood frequency analysis so that the record will be longer and can be more precise.

Reconstructed Flood Frequency Analysis

Two flood frequency analyses were completed: Scenario 1) Systematic gaging data and Scenario 2) A combination of the gaging data and paleoflood data. Overall, the record was extended to 2148 years. Both analyses were run to see the difference in recurrence interval estimates between scenario 1 and 2. It is also important to see how the new analyses match up with the 2010 FFA and if the added flood data increased or decreased the size of the 100, 200, and 500-year floods.

When looking at the results of Scenario 1 and the recurrence interval estimates compared to the 2010 FFA, the flood sizes increased (Table 15). It seems that larger sized floods are now occurring at a lower recurrence interval, which makes sense with the number of catastrophic floods that have occurred in the past few decades. One thing to consider here is that scenario 1 is only gaged data. Only 7 years of record was added since the last FFA in 2010 and only one of those years was considered to have a catastrophic flood (2016). Nevertheless, the new analysis shows a more accurate representative of flood frequencies and size. If we look at the largest flood in the record, the 1996 flood (2,664 cms), the 2010 FFA estimates that this size flood is between a 200 and 500-year flood. However, under the new FFA, it is estimated to be between a 100 and 200-year flood. We can also look at the 2016 flood (2,287 cms) to see what the new 71 recurrence interval would be based upon scenario 1. In this scenario, the 2016 flood is estimated to be between a 50 and 100-year flood.

Table 15

A comparison of large flood sizes between the 2010 FFA and Scenario 1. Discharges 10-yr 25-yr 50-yr 100-yr 200-yr 500-yr (cms) 2010 1,564 1,856 2,069 2,278 2,485 2,757 This Study 1,583 1,918 2,174 2,437 2,708 3,080 Note: See Appendix B for a list of frequency curve data computed in HEC-SSP.

When comparing the results between Scenario 1 and 2, there is a large difference between the two. Scenario 2 had the systematic data entered as one unit and then the paleoflood data was added in separately and read as “historic data” in the program.

Before getting the results, the concerns were the uncertainty of if the program was reading the data as if each missing year of non-systematic data as a discharge of zero.

The program needed to read these paleoflood events as random events and not as yearly values with a systematic record of 2148 years. When the program was run, it appeared that the program was reading these paleoflood events correctly. The report showed a systematic record of 122 years and 6 historic events. However, the discharge data for the frequency curve was still lower than expected. Table 16 shows a comparison between scenario 1 and 2 analyses. The frequency curve for scenario 2 shows discharges much lower than what was estimated in scenario 1. Looking at the 1996 flood again, the recurrence interval would be >500 years according to scenario 2. This is unlikely due to 72 the 1985 flood occurring only 11 years earlier and having a similar discharge (2,567 cms). The probability of a 500-year flood to occur in a given year is 0.2%.

Table 16

A comparison of large flood sizes between scenario 1 and scenario 2. Discharges 10-yr 25-yr 50-yr 100-yr 200-yr 500-yr (cms) Scenario 1 1,583 1,918 2,174 2,437 2,708 3,080 Scenario 2 1,411 1,670 1,865 2,061 2,261 2,531

Scenario 1 more accurately represents the likelihood of a 1996 and 1985-sized flood occurring in a given year (0.5 to 1%). Again, looking at the most recent catastrophic flood (2016; 2,287 cms), the recurrence intervals could be estimated based on scenario 2. This scenario estimates the 2016 flood to be between a 200 and 500-year event.

There is uncertainty that still exists with these new predictions. While the program seems to have accurately estimated the frequency curve for scenario 1, it does not seem that way with scenario 2 because the recurrence intervals are now larger. There are other programs that can be used to run a FFA using paleoflood data, such as

PEAKFQ which is a USGS program. The PEAKFQ program had a much bigger learning curve than HEC-SSP did and, therefore, given the time constraints on this project, it was easier to use HEC-SSP. Using paleoflood data in FFA’s is still very new. It was difficult to find research that showed how the paleoflood data was handled in a program such as

HEC-SSP or PEAKFQ. 73

CHAPTER 8: CONCLUSIONS

The purpose of this study was to improve the FFA of the Greenbrier River in order to help minimize future flood risks. Even though this was the overall objective for this study, several other things were looked at in this study such as channel roughness, sediment competence and velocities, accuracy of modeling software, predicting paleodischarges using silt lines and slackwater deposits, and projecting how long current flood deposits will last in the Greenbrier River Cave. Below is a summary of conclusions:

 Median clast sizes (d50) were determined to be 147 mm, 166.5 mm, and 203 mm

(Fig. 10).

 Channel roughness for the Greenbrier River was determined to be 0.029 with

bank roughness values of 0.055. This was determined using HEC-RAS and PSI

deposits and compared against similar paleoflood studies.

 Using historical data to reproduce flood conditions in HEC-RAS was successful.

 Slackwater deposits can confidently be used to back-calculate paleodischarges

and are better preserved for long periods of time over silt lines.

 While the SWD’s did underestimate the actual discharges, they still were less than

20% away from the actual values when using the n values determined in this

study (channel n = 0.029, bank n = 0.055).

 The 2016 silt line will most likely be unrecognizable by 2035 based upon the

condition of the 1996 silt line in Greenbrier River Cave.

 Modeling velocities were within a 22% difference from calculated velocities

using the critical shear stress equation and clast sizes. Compared to historical 74

floods, the velocities in HEC-RAS were less than a 40% difference than the

calculated velocities using the critical shear stress equation and clast sizes.

 Under scenario 1 (systematic FFA), the 1996 flood (2,664 cms) is estimated to be

between a 100 and 200-year flood.

 Under scenario 1 (systematic FFA), the 2016 flood (2,287 cms) is estimated to be

between a 50 and 100-year flood.

 Under scenario 2 (systematic plus paleoflood), the 1996 flood is estimated to be

greater than a 500-year flood.

 Under scenario 2 (systematic plus paleoflood), the 2016 flood is estimated to be

between a 200 and 500-year event.

REFERENCES

Baker, V.R., 1974, Techniques and problems estimating Holocene flood discharges,

Amer. Quat. Assoc., Abstracts of the 3rd Ann. Mtg, p.63, Univ. of Wisconsin,

Madison.

Baker, V. R., and Ritter, D. F., 1975, Competence of rivers to transport coarse bedload

material: Geological Society of America Bulletin, v. 86, no. 7, p. 975-978.

Baker, V. R., 1987, Paleoflood hydrology and extraordinary flood events: Journal of

Hydrology, v. 96, no. 1-4, p. 79-99.

Baker, V. R., Webb, R. H., and House, P. K., 2002, The Scientific and Societal Value of

Paleoflood Hydrology, Ancient Floods, Modern Hazards, American Geophysical

Union, p. 1-19.

Ballesteros, J. A., Bodoque, J. M., Díez-Herrero, A., Sanchez-Silva, M., and Stoffel, M.,

2011, Calibration of floodplain roughness and estimation of flood discharge based

on tree-ring evidence and hydraulic modelling: Journal of Hydrology, v. 403, no.

1-2, p. 103-115.

Blott, S. J., and Pye, K., 2001, GRADISTAT: a clast size distribution and statistics

package for the analysis of unconsolidated sediments: Earth Surface Processes

and Landforms, v. 26, no. 11, p. 1237-1248.

Bohorquez, P., García-García, F., Pérez-Valera, F., and Martínez-Sánchez, C., 2013,

Unsteady two-dimensional paleohydraulic reconstruction of extreme floods over

the last 4000yr in Segura River, southeast Spain: Journal of Hydrology, v. 477,

no. Supplement C, p. 229-239. 76

Center, W. R. a. T., 2008, Total Maximum Daily Loads for Streams

in the Greenbrier River Watershed, West Virginia: Water Resources and TMDL

Center.

Costa, J.E., 1987a, A history of paleoflood hydrology in the United States, 1800-1970, in

History of Hydrology, pp. 49-53, American Geophysical Union, History of

Geophysics, Vol. 3, Washington, DC.

Dasher, G. R., 2012, The Caves and Karst of West Virginia, vol. 19, 264pp: Barrackville,

WV: West Virginia Speleological Survey Bulletin.

Disasters, C. f. R. o. t. E. o., 2015, The Human Costs of Weather-Related Disasters 1994-

2015.

DHI, 2008. MIKEFLOOD. 1D-2D Modelling. User Manual. DHI, 108 pp.

Doll, W. C., Meyer, G., and Archer, R. J., 1963, Water resources of West Virginia:

Charleston: West Virginia Department of Natural Resources, Division of Water

Resources.

Enzel, Y., Ely, L. L., Martinez-Goytre, J., and Vivian, R. G., 1994, Paleofloods and a

dam-failure flood on the Virgin River, Utah and Arizona: Journal of Hydrology,

v. 153, no. 1-4, p. 291-315.

Ferguson, R. I., Sharma, B. P., Hardy, R. J., Hodge, R. A., and Warburton, J., 2017, Flow

resistance and hydraulic geometry in contrasting reaches of a bedrock channel:

Water Resources Research, v. 53, no. 3, p. 2278-2293.

Folk, R. L., 1980, Petrology of sedimentary rocks, Hemphill Publishing Company.

Hickin, E. J., 1995, River geomorphology, John Wiley & Sons. 77

Howard, A. D., 1980, Thresholds in river regimes: Thresholds in geomorphology, p. 227-

258.

Jackson, W. L., and Beschta, R. L., 1982, A model of two‐phase bedload transport in an

Oregon Coast Range stream: Earth surface processes and landforms, v. 7, no. 6, p.

517-527.

Jansen, J. D., 2006, Flood magnitude–frequency and lithologic control on bedrock river

incision in post-orogenic terrain: Geomorphology, v. 82, no. 1-2, p. 39-57.

Kidson, R. L., Richards, K. S., and Carling, P. A., 2006, Hydraulic model calibration for

extreme floods in bedrock-confined channels: case study from northern Thailand:

Hydrological Processes, v. 20, no. 2, p. 329-344.

Kite, J. S., Gebhardt, T. W., and Springer, G. S., 2002, Slackwater deposits as paleostage

indicators in canyon reaches of the central Appalachians: Reevaluation after the

1996 Cheat River Flood: Ancient Floods, Modern Hazards, p. 257-266.

Kochel, R. C., and Baker, V. R., 1982, Paleoflood Hydrology: Science, v. 215, no. 4531,

p. 353.

Komar, P. D., 1987, Selective gravel entrainment and the empirical evaluation of flow

competence: Sedimentology, v. 34, no. 6, p. 1165-1176.

Lam, D., Thompson, C., Croke, J., Sharma, A., and Macklin, M., 2017, Reducing

uncertainty with flood frequency analysis: The contribution of paleoflood and

historical flood information: Water Resources Research, v. 53, no. 3, p. 2312-

2327.

Magirl, C. S., Breedlove, M. J., Webb, R. H., and Griffiths, P. G., 2008, Modeling water- 78

surface elevations and virtual shorelines for the Colorado River in Grand Canyon,

Arizona: U. S. Geological Survey.

Manning, R., 1891, On the flow of water in open channels and pipes: Transactions of the

Institution of Civil Engineers of Ireland, v. 20, p. 161-207.

O'Connor, J. E., Webb, R. H., and Baker, V. R., 1986, Paleohydrology of pool-and-riffle

pattern development: Boulder Creek, Utah: Geological Society of America

Bulletin, v. 97, no. 4, p. 410-420.

Parker, G., 1990, Surface-based bedload transport relation for gravel rivers: Journal of

hydraulic research, v. 28, no. 4, p. 417-436.

Patton, P.C., and V.R. Baker, 1977, Geomorphic response of central Texas stream

channels to catastrophic rainfall and runoff, in Geomorphology of Arid Regions,

Publ. in Geomorph., pp. 189-217, State Univ. New York, Binghamton, NY.

Patton, P.C., 1987, Measuring the rivers of the past: A history of fluvial paleohydrology,

in History of Hydrology, pp. 55-67, American Geophysical Union, History of

Geophysics, No. 3, Washington, DC.

Richards, K. S., 1978, Simulation of flow geometry in a riffle‐pool stream: Earth surface

processes, v. 3, no. 4, p. 345-354.

Ruiz-Villanueva, V., Bodoque, J. M., Díez-Herrero, A., Eguibar, M. A., and Pardo-

Igúzquiza, E., 2013, Reconstruction of a flash flood with large wood transport and

its influence on hazard patterns in an ungauged mountain basin: Hydrological

Processes, v. 27, no. 24, p. 3424-3437.

Shields, A., 1936, Application of similarity principles and turbulence research to bed- 79

load movement.

Springer, G. S., 2002, Caves and their potential use in paleoflood studies: Water Science

and Application, v. 5, p. 329-343.

Springer, G. S., and Kite, J. S., 1997, River-derived slackwater sediments in caves along

Cheat River, West Virginia: Geomorphology, v. 18, no. 2, p. 91-100.

Springer, G. S., Wohl, E. E., Foster, J. A., and Boyer, D. G., 2003, Testing for reach-

scale adjustments of hydraulic variables to soluble and insoluble strata: Buckeye

Creek and Greenbrier River, West Virginia: Geomorphology, v. 56, no. 1, p. 201-

217.

Tarr, R.S., 1892, A hint with respect to the origin of terraces in glaciated regions,

American Journal of Science, 144, 59-61.

Te Chow, V., 1959, Open-channel hydraulics, McGraw-Hill New York.

Teets, B., and Young, S., 1985, Killing Waters: The Great West Virginia Flood of 1985,

Headline Books, Incorporated.

Thompson, S. M., and Campbell, P. L., 1979, Hydraulics of a large channel paved with

boulders: Journal of Hydraulic Research, v. 17, no. 4, p. 341-354.

Wang, L., Huang, C. C., Pang, J., Zha, X., and Zhou, Y., 2014, Paleofloods recorded by

slackwater deposits in the upper reaches of the Hanjiang River valley, middle

Yangtze River basin, China: Journal of Hydrology, v. 519, no. Part A, p. 1249-

1256.

Webb, R. H., and Jarrett, R. D., 2002, One-Dimensional Estimation Techniques for

Discharges of Paleofloods and Historical Floods, Ancient Floods, Modern 80

Hazards, American Geophysical Union, p. 111-125.

Wiley, J. B., and Atkins, J. T., 2010, Estimation of flood-frequency discharges for rural,

unregulated streams in West Virginia, US Department of the Interior, US

Geological Survey.

Wiley, J. B., Atkins, J. T., and Tasker, G. D., 2000, Estimating magnitude and frequency

of peak discharges for rural, unregulated, streams in West Virginia, US

Department of the Interior, US Geological Survey.

Wohl, E., 2002, Modeled Paleoflood Hydraulics as a Tool for Interpreting Bedrock

Channel Morphology, Ancient Floods, Modern Hazards, American Geophysical

Union, p. 345-358.

Wolman, M. G., 1954, A method of sampling coarse river-bed material: Eos,

Transactions American Geophysical Union, v. 35, no. 6, p. 951-956.

APPENDIX A: CROSS SECTION DATA FOR HEC-RAS

Table 1

Raw cross section data used to run cross section processor. XS # N E Z Adjustments: Notes: XS-1-1 -29.365 500.285 320.065 5.12 River-left end (R.L.E) XS-1-2 -12.39 497.815 317.82 5.12 XS-1-3 -0.53 495.665 315.615 5.12 XS-1-4 41.655 494.84 315.515 5.12 XS-1-5 74.255 490.03 314.82 5.12 XS-1-6 85.715 487.065 313.11 5.12 XS-1-7 101.775 485.385 312.4 5.12 XS-1-8 110.545 486.185 311.59 5.12 XS-1-9 118.62 485.21 311.37 5.12 Thalweg XS-1-10 137.11 480.065 312.16 5.12 XS-1-11 145.15 477.205 312.31 5.12 XS-1-12 152.85 479.755 313.82 5.12 XS-1-13 166.3 477.25 315.055 5.12 XS-1-14 186.975 469.265 317.93 5.12 XS-1-15 218.51 458.375 323.185 5.12 XS-2-1 -408.54 38.415 318.97 5.12 R.L.E XS-2-2 -403.24 22.53 318.19 5.12 XS-2-3 -391.39 8.01 316.3 5.12 XS-2-4 -375.08 -17.715 314.42 5.12 XS-2-5 -368.66 -26.435 313.17 5.12 XS-2-6 -364.38 -31.905 313.155 5.12 XS-2-7 -360.79 -36.18 312.25 5.12 XS-2-8 -357.57 -41.585 311.11 5.12 XS-2-9 -355.51 -44.35 311.22 5.12 XS-2-10 -351.81 -45.015 310.64 5.12 XS-2-11 -345.87 -48.155 311.105 5.12 XS-2-12 -342.09 -52.15 311.345 6.22 XS-2-13 -340.78 -57.92 310.475 5.12 XS-2-14 -336.72 -62.165 311.66 6.22 XS-2-15 -319.25 -57.375 311.375 4.3 depth=4.3ft XS-2-16 -310.12 -62.88 311.575 6.22 XS-2-17 -308.27 -91.66 312.12 5.12 XS-2-18 -307.61 -93.035 311.535 6.22 XS-2-19 -306.30 -95.725 311.42 5.12 XS-2-20 -301.52 -97.895 311.82 6.22

XS-2-21 -300.08 -104.14 312.49 6.22 XS-2-22 -297.21 -112.08 309.74 6.22 Thalweg XS-2-23 -287.68 -124.965 314.15 5.12 XS-2-24 -284.4 -130.4 319.785 5.12 XS-2-25 -284.665 -133.225 322.265 5.12 XS-3-1 -534.48 -11.7 321.515 6.22 R.L.E XS-3-2 -531.42 -17.465 320.32 6.22 XS-3-3 -528.27 -23.235 319.98 6.22 XS-3-4 -515.515 -55.1 316.255 5.12 XS-3-5 -511.055 -71.195 314.62 5.12 XS-3-6 -506.26 -82.07 314 5.12 XS-3-7 -505.37 -87.55 312.875 5.12 XS-3-8 -504.385 -92.695 312.055 5.12 XS-3-9 -502.22 -96.98 310.915 5.12 XS-3-10 -498.32 -101.645 311.305 5.12 XS-3-11 -497.455 -106.05 310.875 5.12 Thalweg XS-3-12 -496.12 -108.945 312.095 6.22 XS-3-13 -495.67 -110.61 313.01 5.12 XS-3-14 -493.375 -113.775 312.57 5.12 XS-3-15 -491.945 -116.185 311.52 6.22 XS-3-16 -492.925 -121.27 311.95 6.22 XS-3-17 -487.215 -128.505 312.37 6.22 XS-3-18 -483.575 -132.665 311.38 5.12 XS-3-19 -483.035 -136.505 311.095 5.12 XS-3-20 -480.665 -142.045 312.68 6.22 XS-3-21 -478.59 -148.665 313.05 6.22 XS-3-22 -476.995 -154.315 312.48 6.22 XS-3-23 -468.665 -170.05 311.02 6.22 XS-3-24 -469.38 -173.765 311.515 6.22 XS-3-25 -468.15 -177.075 312.165 6.22 XS-3-26 -466.57 -189.655 311.595 5.12 XS-3-27 -465.07 -197.885 314.065 5.12 XS-3-28 -461.415 -206.635 321.555 6.22 XS-10-1 -1188.3 -181.305 319.475 6.22 R.L.E XS-10-2 -1164.86 -212.815 311.26 5.12 XS-10-3 -1160.75 -223.535 309.985 5.12 XS-10-4 -1158.51 -232.675 308.86 5.12 XS-10-5 -1154.06 -242.815 307.94 5.12 XS-10-6 -1148.75 -255.345 309.805 6.22 XS-10-7 -1139.31 -266.815 309.5 6.22 XS-10-8 -1134.61 -280.91 309.03 6.22

XS-10-9 -1112.26 -279 311.06 2.8 XS-10-10 -1106.54 -288.91 310.415 2.7 XS-10-11 -1103.61 -311.735 308.13 5.12 XS-10-12 -1127.71 -328.97 308.7 6.22 XS-10-13 -1120.4 -335.425 310.09 2.7 XS-10-14 -1113.67 -341.955 310.8 5.12 XS-10-15 -1110.3 -346.825 311.03 5.12 XS-10-16 -1110.18 -351.18 308.525 5.12 XS-10-17 -1110.61 -360.545 310.5 5.12 XS-10-18 -1110.17 -361.68 312.655 5.12 XS-10-19 -1108.66 -365.245 318.125 5.12 XS-4-1 -1571.01 -215.06 326.05 6.22 R.L.E XS-4-2 -1569.91 -225.67 321.545 5.12 XS-4-3 -1572.07 -238.055 319.12 5.12 XS-4-4 -1574.39 -250 316.93 5.12 XS-4-5 -1574.03 -263.465 314.075 5.12 XS-4-6 -1575.36 -272.245 310.4 5.12 XS-4-7 -1573.43 -278.485 308.665 5.12 XS-4-8 -1573.28 -281.155 308.875 5.12 XS-4-9 -1572.8 -294.535 307.755 5.12 XS-4-10 -1573.78 -302.405 307.725 5.12 XS-4-11 -1573.89 -306.185 307.605 5.12 XS-4-12 -1573.3 -311.32 306.865 5.12 XS-4-13 -1573.58 -320.335 306.695 5.12 XS-4-14 -1572.09 -330.97 306.75 5.12 XS-4-15 -1569.47 -342.01 306.59 5.12 XS-4-16 -1568.12 -348.43 308.58 6.22 XS-4-17 -1565.61 -353.86 308.21 6.22 XS-4-18 -1565.95 -355.46 307.35 4.7 depth=4.7 ft XS-4-19 -1580.61 -372.185 305.46 5.9 depth=5.9 ft XS-4-20 -1576.76 -393.285 305.115 6.6 depth= 6.6ft XS-4-21 -1578.36 -407.115 304.155 7.3 depth= 7.3 ft, thalweg XS-4-22 -1566.01 -420.86 306.03 5.7 depth=5.7ft XS-4-23 -1563.79 -429.55 306.205 5.12 XS-4-24 -1561.91 -437.73 308.44 5.12 XS-4-25 -1562.12 -454.1 310.75 5.12 XS-4-26 -1557.46 -467.55 313.42 5.12 XS-5-1 -2203.45 -519.515 312.795 6.22 River -right end (R.R.E) XS-5-2 -2201.08 -501.83 309.04 5.12

XS-5-3 -2196.99 -484 307.9 5.12 XS-5-4 -2197.85 -462.965 306.93 5.12 XS-5-5 -2198.71 -451.73 306.35 5.12 XS-5-6 -2198.82 -440.765 306.205 5.12 XS-5-7 -2198.29 -425.275 305.845 5.12 XS-5-8 -2197.35 -410.75 306.24 5.12 XS-5-9 -2195.16 -398.175 306.11 5.12 XS-5-10 -2193.62 -383.505 306.47 5.12 XS-5-11 -2194.45 -368.035 307.845 5.12 XS-5-12 -2193.72 -359.97 308.21 5.12 Thalweg XS-5-13 -2194.64 -346.595 308.14 5.12 XS-5-14 -2195.28 -326.715 308.24 5.12 XS-5-15 -2197.51 -312.245 308.755 5.12 XS-5-16 -2200.68 -301.555 309.075 5.12 XS-5-17 -2202.47 -297.375 309.82 5.12 XS-5-18 -2204.24 -292.265 311.385 5.12 XS-5-19 -2203.27 -289.445 315.52 5.12 XS-6-1 -2723.21 -701.285 316.135 5.12 R.R.E XS-6-2 -2732.2 -696.575 310.405 5.12 XS-6-3 -2735.43 -693.485 309.705 5.12 XS-6-4 -2742.4 -688.4 308.8 5.12 XS-6-5 -2748.12 -682.45 307.62 5.12 XS-6-6 -2754.52 -678.005 306.335 5.12 XS-6-7 -2761.58 -672.95 305.57 5.12 XS-6-8 -2766.7 -669.395 305.33 5.12 XS-6-9 -2772.77 -662.18 305.235 5.12 XS-6-10 -2774.1 -661.08 305.745 6.22 XS-6-11 -2775.51 -654.215 305.555 4.5 depth=4.5ft XS-6-12 -2780.36 -652.09 306.055 6.22 XS-6-13 -2783.54 -648.215 304.96 4.8 depth=4.8ft XS-6-14 -2790.45 -644.77 305.17 4.7 depth=4.7ft XS-6-15 -2800.02 -648.455 304.535 5.3 depth=5.3ft XS-6-16 -2810.14 -643.845 304.45 5.5 depth=5.5ft XS-6-17 -2818.46 -641.88 304.29 5.5 depth=5.5ft XS-6-18 -2825.21 -628.005 302.39 7.5 depth=7.5ft XS-6-19 -2847.32 -621.815 302.37 7.1 depth=7.1ft,thalweg XS-6-20 -2853.47 -615.125 304.3 5.7 depth=5.7ft XS-6-21 -2865.25 -610.21 304.565 5.1 depth=5.1ft XS-6-22 -2871.03 -604.285 306.385 6.22 XS-6-23 -2876.62 -599.31 306.17 6.22

XS-6-24 -2878.38 -595.54 306.18 5.12 XS-6-25 -2881.49 -593.06 307.585 5.12 XS-6-26 -2883.93 -591.22 308.785 5.12 XS-6-27 -2886.27 -588.86 309.965 5.12 XS-6-28 -2889.95 -587.785 311.74 5.12 XS-6-29 -2892.34 -586.565 313.49 5.12 XS-7-1 -2877.59 -1596.87 311.5 5.12 R.R.E XS-7-2 -2892.84 -1588.88 311.23 5.12 XS-7-3 -2903.64 -1587.74 310.44 5.12 XS-7-4 -2920.3 -1584.54 309.14 5.12 XS-7-5 -2942.7 -1579.11 308.145 5.12 XS-7-6 -2956.26 -1580.02 307.97 5.12 XS-7-7 -2971.26 -1575.26 307.465 5.12 XS-7-8 -2981.27 -1574.66 307.33 5.12 XS-7-9 -2990.7 -1572.93 306.765 5.12 XS-7-10 -2998.01 -1572.31 306.685 5.12 XS-7-11 -3009.8 -1567.32 307.04 5.12 XS-7-12 -3017.66 -1565.73 306.95 5.12 XS-7-13 -3025.39 -1564.4 306.935 5.12 XS-7-14 -3034.8 -1561.83 306.3 5.12 XS-7-15 -3043.2 -1559.75 305.915 5.12 XS-7-16 -3054.47 -1559.89 305.55 5.12 XS-7-17 -3060.69 -1558.28 304.685 5.12 XS-7-18 -3063.11 -1555.64 305.575 5.12 XS-7-19 -3084.65 -1552.88 306.145 5.12 XS-7-20 -3091.23 -1549.1 306.65 5.12 XS-7-21 -3098.03 -1548.52 305.39 5.12 XS-7-22 -3109.51 -1547.97 307.68 5.12 XS-7-23 -3116.19 -1549.42 310.695 5.12 XS-8-1 -3184.73 -2152.28 313.005 6.22 R.L.E XS-8-2 -3178.03 -2152.47 305.61 5.12 XS-8-3 -3164.26 -2155.17 304.915 5.12 XS-8-4 -3152.22 -2158.01 304.78 5.12 XS-8-5 -3134.6 -2162.11 305.82 5.12 XS-8-6 -3121.04 -2165.1 305.24 5.12 XS-8-7 -3108.54 -2166.93 304.41 5.12 XS-8-8 -3098.69 -2168.95 303.175 5.12 XS-8-9 -3086.16 -2170.15 303.065 5.12 XS-8-10 -3067.44 -2171.86 303.055 5.12 XS-8-11 -3054.25 -2170.64 303.865 5.12 XS-8-12 -3035.57 -2170.05 302.675 5.12

XS-8-13 -3020.96 -2171.83 303.3 5.12 XS-8-14 -3010.3 -2172.47 301.945 5.12 XS-8-15 -2995.18 -2174.23 303.125 5.12 XS-8-16 -2986.3 -2175.51 303.71 5.12 XS-8-17 -2970.92 -2177.39 304.445 5.12 XS-8-18 -2957.33 -2179.83 305.675 5.12 XS-8-19 -2955.44 -2179.95 308.255 5.12 XS-8-20 -2926.95 -2182.56 311.65 5.12 XS-9-1 -2939.16 -2464.5 310.52 5.12 R.R.E XS-9-2 -2961.32 -2464.51 306.83 5.12 XS-9-3 -2968.44 -2464.51 305.94 5.12 XS-9-4 -2980.22 -2464.53 305.74 5.12 XS-9-5 -2996.41 -2463.69 304.805 5.12 XS-9-6 -3015.09 -2462.24 304.93 5.12 XS-9-7 -3032.98 -2460.77 304.555 5.12 XS-9-8 -3039.61 -2461.57 303.63 5.12 XS-9-9 -3045.05 -2462.28 303.36 5.12 XS-9-10 -3053.91 -2460.89 303.82 5.12 XS-9-11 -3064.86 -2458.26 303.18 5.12 XS-9-12 -3075.52 -2454.93 304 5.12 XS-9-13 -3087.09 -2454.51 304.385 5.12 XS-9-14 -3102.66 -2453.04 304.065 5.12 XS-9-15 -3117.12 -2451.93 304.29 5.12 XS-9-16 -3128.26 -2450.69 304.285 5.12 XS-9-17 -3142.13 -2449.16 304.67 5.12 XS-9-18 -3144.39 -2448.92 305.22 5.12 XS-9-19 -3160.25 -2448.73 305.41 5.12 XS-9-20 -3182.12 -2448.3 306.31 5.12 XS-9-21 -3194.05 -2453.03 310.165 5.12

Table 2

Python distances used to calculate channel width. Channel Channel XS # Bank L Bank R Thalweg Width Comments Width (ft) (m) XS-10 29.2 196.8 148.4 167.6 51.1 Upstream XS-9 75.9 203.2 187.2 127.3 38.8 XS-8 63.9 198.7 101.3 134.8 41.1 XS-7 38.2 196.6 70.1 158.3 48.3 XS-6 34.7 239.2 191.4 204.5 62.3 XS-5 7.9 212.4 135.8 204.5 62.3 XS-4 27.6 173.2 57.1 145.6 44.4 XS-3 6.3 162.8 56.2 156.6 47.7 XS-2 64.8 215.3 175.6 150.5 45.9 XS-1 49.4 214.1 129.3 164.7 50.2 Downstream

Table 3

Processed cross section data used in HEC-RAS. XS-1 Rounded Station R Z R Z XS-1-1 0 320.065 0 320.07 XS-1-2 17.14922 317.82 17.15 317.82 XS-1-3 29.20168 315.615 29.2 315.62 XS-1-4 70.93387 315.515 70.9 315.52 XS-1-5 103.8795 314.82 103.9 314.82 XS-1-6 115.6735 313.11 115.7 313.11 XS-1-7 131.7888 312.4 131.8 312.4 XS-1-8 140.3027 311.59 140.3 311.59 XS-1-9 148.4272 311.37 148.4 311.37 XS-1-10 167.5162 312.16 167.5 312.16 XS-1-11 175.9205 312.31 175.9 312.31 XS-1-12 183.0876 313.82 183.1 313.82 XS-1-13 196.767 315.055 196.8 315.06 XS-1-14 218.4839 317.93 218.5 317.93 XS-1-15 251.393 323.185 251.4 323.19 XS-2-1 0 318.97 0 318.97 XS-2-2 15.98251 318.19 15.98 318.19 XS-2-3 34.69124 316.3 34.69 316.3 XS-2-4 65.09304 314.42 65.09 314.42 XS-2-5 75.9239 313.17 75.92 313.17 88

XS-2-6 82.86119 313.155 82.86 313.16 XS-2-7 88.43154 312.25 88.43 312.25 XS-2-8 94.69874 311.11 94.7 311.11 XS-2-9 98.14343 311.22 98.14 311.22 XS-2-10 100.8479 310.64 100.85 310.64 XS-2-11 106.8702 311.105 106.87 311.11 XS-2-12 112.3189 311.345 112.32 311.35 XS-2-13 117.7643 310.475 117.76 310.48 XS-2-14 123.5853 311.66 123.59 311.66 XS-2-15 129.9276 311.375 129.93 311.38 XS-2-16 139.7345 311.575 139.73 311.58 XS-2-17 164.1515 312.12 164.15 312.12 XS-2-18 165.6556 311.535 165.66 311.54 XS-2-19 168.6035 311.42 168.6 311.42 XS-2-20 173.1575 311.82 173.16 311.82 XS-2-21 179.0671 312.49 179.07 312.49 XS-2-22 187.1821 309.74 187.18 309.74 XS-2-23 203.2074 314.15 203.21 314.15 XS-2-24 209.2239 319.785 209.22 319.79 XS-2-25 211.6697 322.265 211.67 322.27 XS-3-1 0 321.515 0 321.52 XS-3-2 6.472241 320.32 6.47 320.32 XS-3-3 12.98075 319.98 12.98 319.98 XS-3-4 47.29534 316.255 47.3 316.26 XS-3-5 63.9318 314.62 63.93 314.62 XS-3-6 75.79791 314 75.8 314 XS-3-7 81.24167 312.875 81.24 312.88 XS-3-8 86.40508 312.055 86.41 312.06 XS-3-9 91.17735 310.915 91.18 310.92 XS-3-10 96.91439 311.305 96.91 311.31 XS-3-11 101.3428 310.875 101.34 310.88 XS-3-12 104.5221 312.095 104.52 312.1 XS-3-13 106.2392 313.01 106.24 313.01 XS-3-14 110.0083 312.57 110.01 312.57 XS-3-15 112.7669 311.52 112.77 311.52 XS-3-16 117.1845 311.95 117.18 311.95 XS-3-17 125.9633 312.37 125.96 312.37 XS-3-18 131.1362 311.38 131.14 311.38 XS-3-19 134.9214 311.095 134.92 311.1 XS-3-20 140.9408 312.68 140.94 312.68 XS-3-21 147.8679 313.05 147.87 313.05 XS-3-22 153.7183 312.48 153.72 312.48 XS-3-23 171.3759 311.02 171.38 311.02 XS-3-24 174.6037 311.515 174.6 311.52 89

XS-3-25 178.1348 312.165 178.13 312.17 XS-3-26 190.4691 311.595 190.47 311.6 XS-3-27 198.702 314.065 198.7 314.07 XS-3-28 208.1782 321.555 208.18 321.56 XS-10-1 0 319.475 0 319.48 XS-10-2 38.22933 311.26 38.23 311.26 XS-10-3 49.70183 309.985 49.7 309.99 XS-10-4 58.97941 308.86 58.98 308.86 XS-10-5 70.05078 307.94 70.05 307.94 XS-10-6 83.65908 309.805 83.66 309.81 XS-10-7 97.93758 309.5 97.94 309.5 XS-10-8 112.7397 309.03 112.74 309.03 XS-10-9 119.8651 311.06 119.87 311.06 XS-10-10 129.5454 310.415 129.55 310.42 XS-10-11 149.3292 308.13 149.33 308.13 XS-10-12 159.5848 308.7 159.58 308.7 XS-10-13 168.4109 310.09 168.41 310.09 XS-10-14 177.0773 310.8 177.08 310.8 XS-10-15 182.8874 311.03 182.89 311.03 XS-10-16 186.9296 308.525 186.93 308.53 XS-10-17 195.3528 310.5 195.35 310.5 XS-10-18 196.5692 312.655 196.57 312.66 XS-10-19 200.4406 318.125 200.44 318.13 XS-4-1 0 326.05 0 326.05 XS-4-2 10.65397 321.545 10.65 321.55 XS-4-3 22.90542 319.12 22.91 319.12 XS-4-4 34.70893 316.93 34.71 316.93 XS-4-5 48.17361 314.075 48.17 314.08 XS-4-6 56.86999 310.4 56.87 310.4 XS-4-7 63.20445 308.665 63.2 308.67 XS-4-8 65.87865 308.875 65.88 308.88 XS-4-9 79.26488 307.755 79.26 307.76 XS-4-10 87.07132 307.725 87.07 307.73 XS-4-11 90.83973 307.605 90.84 307.61 XS-4-12 95.99924 306.865 96 306.87 XS-4-13 104.9863 306.695 104.99 306.7 XS-4-14 115.6856 306.75 115.69 306.75 XS-4-15 126.8504 306.59 126.85 306.59 XS-4-16 133.3332 308.58 133.33 308.58 XS-4-17 138.8902 308.21 138.89 308.21 XS-4-18 140.4694 307.35 140.47 307.35 XS-4-19 156.385 305.46 156.39 305.46 XS-4-20 177.661 305.115 177.66 305.12 XS-4-21 191.3854 304.155 191.39 304.16 90

XS-4-22 205.7725 306.03 205.77 306.03 XS-4-23 214.5687 306.205 214.57 306.21 XS-4-24 222.838 308.44 222.84 308.44 XS-4-25 239.1729 310.75 239.17 310.75 XS-4-26 252.8533 313.42 252.85 313.42 XS-5-19 0 315.52 0 315.52 XS-5-18 2.819531 311.385 2.82 311.39 XS-5-17 7.929385 309.82 7.93 309.82 XS-5-16 12.10802 309.075 12.11 309.08 XS-5-15 22.79561 308.755 22.8 308.76 XS-5-14 37.26391 308.24 37.26 308.24 XS-5-13 57.14342 308.14 57.14 308.14 XS-5-12 70.51771 308.21 70.52 308.21 XS-5-11 78.58326 307.845 78.58 307.85 XS-5-10 94.05263 306.47 94.05 306.47 XS-5-9 108.7238 306.11 108.72 306.11 XS-5-8 121.3005 306.24 121.3 306.24 XS-5-7 135.8262 305.845 135.83 305.85 XS-5-6 151.3166 306.205 151.32 306.21 XS-5-5 162.2815 306.35 162.28 306.35 XS-5-4 173.5158 306.93 173.52 306.93 XS-5-3 194.5502 307.9 194.55 307.9 XS-5-2 212.3833 309.04 212.38 309.04 XS-5-1 230.0701 312.795 230.07 312.8 XS-6-29 0 313.49 0 313.49 XS-6-28 2.662762 311.74 2.66 311.74 XS-6-27 6.311709 309.965 6.31 309.97 XS-6-26 9.577163 308.785 9.58 308.79 XS-6-25 12.6252 307.585 12.63 307.59 XS-6-24 16.59111 306.18 16.59 306.18 XS-6-23 20.16393 306.17 20.16 306.17 XS-6-22 27.5828 306.385 27.58 306.39 XS-6-21 35.69633 304.565 35.7 304.57 XS-6-20 48.20426 304.3 48.2 304.3 XS-6-19 57.04928 302.37 57.05 302.37 XS-6-18 78.82185 302.39 78.82 302.39 XS-6-17 92.19255 304.29 92.19 304.29 XS-6-16 100.1811 304.45 100.18 304.45 XS-6-15 111.1481 304.535 111.15 304.54 XS-6-14 116.9954 305.17 117 305.17 XS-6-13 124.6478 304.96 124.65 304.96 XS-6-12 129.4589 306.055 129.46 306.06 XS-6-11 134.6614 305.555 134.66 305.56 XS-6-10 139.686 305.745 139.69 305.75 91

XS-6-9 141.4001 305.235 141.4 305.24 XS-6-8 150.4736 305.33 150.47 305.33 XS-6-7 156.7105 305.57 156.71 305.57 XS-6-6 165.3867 306.335 165.39 306.34 XS-6-5 173.1784 307.62 173.18 307.62 XS-6-4 181.2522 308.8 181.25 308.8 XS-6-3 189.879 309.705 189.88 309.71 XS-6-2 194.2825 310.405 194.28 310.41 XS-6-1 204.3664 316.135 204.37 316.14 XS-7-23 0 310.695 0 310.7 XS-7-22 6.263965 307.68 6.26 307.68 XS-7-21 17.63467 305.39 17.63 305.39 XS-7-20 24.41229 306.65 24.41 306.65 XS-7-19 31.60321 306.145 31.6 306.15 XS-7-18 53.26783 305.575 53.27 305.58 XS-7-17 56.15629 304.685 56.16 304.69 XS-7-16 62.57183 305.55 62.57 305.55 XS-7-15 73.59805 305.915 73.6 305.92 XS-7-14 82.24635 306.3 82.25 306.3 XS-7-13 91.9769 306.935 91.98 306.94 XS-7-12 99.81392 306.95 99.81 306.95 XS-7-11 107.8321 307.04 107.83 307.04 XS-7-10 120.369 306.685 120.37 306.69 XS-7-9 127.6605 306.765 127.66 306.77 XS-7-8 137.2468 307.33 137.25 307.33 XS-7-7 147.1816 307.465 147.18 307.47 XS-7-6 162.8258 307.97 162.83 307.97 XS-7-5 175.943 308.145 175.94 308.15 XS-7-4 198.9719 309.14 198.97 309.14 XS-7-3 215.937 310.44 215.94 310.44 XS-7-2 226.7519 311.23 226.75 311.23 XS-7-1 243.2675 311.5 243.27 311.5 XS-8-1 0 313.005 0 313.01 XS-8-2 6.676406 305.61 6.68 305.61 XS-8-3 20.66739 304.915 20.67 304.92 XS-8-4 32.9571 304.78 32.96 304.78 XS-8-5 50.93952 305.82 50.94 305.82 XS-8-6 64.75139 305.24 64.75 305.24 XS-8-7 77.37953 304.41 77.38 304.41 XS-8-8 87.40291 303.175 87.4 303.18 XS-8-9 99.98179 303.065 99.98 303.07 XS-8-10 118.774 303.055 118.77 303.06 XS-8-11 131.7316 303.865 131.73 303.87 XS-8-12 150.2201 302.675 150.22 302.68 92

XS-8-13 164.9374 303.3 164.94 303.3 XS-8-14 175.5943 301.945 175.59 301.95 XS-8-15 190.822 303.125 190.82 303.13 XS-8-16 199.7907 303.71 199.79 303.71 XS-8-17 215.2794 304.445 215.28 304.45 XS-8-18 229.0613 305.675 229.06 305.68 XS-8-19 230.9579 308.255 230.96 308.26 XS-8-20 259.5579 311.65 259.56 311.65 XS-9-21 0 310.165 0 310.17 XS-9-20 11.71053 306.31 11.71 306.31 XS-9-19 33.57752 305.41 33.58 305.41 XS-9-18 49.42503 305.22 49.43 305.22 XS-9-17 51.69853 304.67 51.7 304.67 XS-9-16 65.62352 304.285 65.62 304.29 XS-9-15 76.80278 304.29 76.8 304.29 XS-9-14 91.30328 304.065 91.3 304.07 XS-9-13 106.9184 304.385 106.92 304.39 XS-9-12 118.5006 304 118.5 304 XS-9-11 129.2945 303.18 129.29 303.18 XS-9-10 140.3517 303.82 140.35 303.82 XS-9-9 149.2652 303.36 149.27 303.36 XS-9-8 154.6678 303.63 154.67 303.63 XS-9-7 161.2553 304.555 161.26 304.56 XS-9-6 179.1983 304.93 179.2 304.93 XS-9-5 197.9196 304.805 197.92 304.81 XS-9-4 214.1279 305.74 214.13 305.74 XS-9-3 225.902 305.94 225.9 305.94 XS-9-2 233.0149 306.83 233.01 306.83 XS-9-1 255.1479 310.52 255.15 310.52

APPENDIX B: FREQUENCY CURVE DATA FOR HEC-SSP

Table 1

Frequency tabular data for systematic floods in the Greenbrier River. Percent Chance Computed Curve Confidence Limits Flow in cfs Exceedence Flow in cfs 0.05 0.95 0.2 108714.3 161690.4 79752.9 0.5 95559.5 130504.5 74549.3 1.0 85998.7 110607.0 70252.8 2.0 76722.0 93398.9 65550.7 4.0 67664.5 74128.9 58396.8 10.0 55853.1 61662.8 51737.6 20.0 46792.4 50414.7 43505.0 50.0 33610.2 36176.9 31276.9 80.0 24385.8 26092.4 22750.5 90.0 20702.6 22265.0 18881.0 95.0 18121.1 20103.0 15959.3 99.0 14184.9 17343.7 11119.5

Table 2

Frequency tabular data for systematic and paleofloods in the Greenbrier River. Percent Chance Computed Curve Confidence Limits Flow in cfs Exceedence Flow in cfs 0.05 0.95 0.2 89325.5 101956.3 80091.7 0.5 79770.3 89952.1 72212.2 1.0 72725.1 81215.7 66337.6 2.0 65796.3 72730.3 60497.3 4.0 58931.4 61803.1 52734.5 10.0 49805.0 53641.8 46704.2 20.0 42643.1 45391.9 40318.8 50.0 31901.9 33565.0 30316.9 80.0 24077.0 25469.0 22613.5 90.0 20856.0 22229.9 19377.2 95.0 18556.6 19930.3 17064.8 99.0 14969.5 16338.7 13480.6

APPENDIX C: WOLMAN COUNT DATA

Table 1

Raw data collected from Wolman counts for clast size analysis. Clast sizes are in mm. WC1 WC2 WC3 447 334 510 225 173 240 242 179 200 212 282 510 190 144 80 86 236 296 134 99 235 218 177 176 184 218 364 284 110 180 392 252 185 211 234 270 561 303 120 220 171 121 238 154 87 107 190 180 56 302 160 229 64 650 209 149 265 74 29 172 298 87 320 90 171 165 42 94 139 34 126 140 79 69 290 61 209 223 118 164 191 185 673 192 43 113 180 55 132 105 155 88 304 115 79 65 462 67 260 105 121 245 76 123 106 165 454 110 127 110 205 598 297 490 61 266 189 101 162 280 120 108 342 149 140 210 95

414 216 122 166 58 164 56 266 74 104 252 305 240 67 172 93 185 264 435 151 144 155 92 200 159 59 104 275 381 109 131 167 174 121 332 141 62 189 340 164 166 88 205 183 129 94 127 160 50 236 125 472 215 202 44 76 120 160 97 204 61 236 370 145 330 88 286 50 111 90 170 115 163 163 490 141 114 223 63 349 181 441 96 160 60 145 418 32 161 275 104 169 236 50 170 131 69 192 176 196 84 454 161 192 205 58 245 538 63 213 400 335 109 144 96 52 350 41 160 439 343 316 180 206 162 61 116 604 293 386 193 182 431 102 403 325 215 283 73 100 254 299 64 189 96

161 182 406 68 306 463 49 99 591 426 219 472 188 163 305 82 168 244 73 251 423 215 273 517 126 136 232 187 131 397

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