Impacts of Impervious Surface Cover on Stream Hydrology and Stream-Reach

Home , Impervious surface

Morphology, Northern Georgia

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 Arts

Benjamin J. Young

June 2010

2 This thesis titled

Impacts of Impervious Surface Cover on Stream Hydrology and Stream-Reach

Morphology, Northern Georgia

BENJAMIN J. YOUNG

has been approved for

the Department of Geography

and the College of Arts and Sciences by

Dorothy Sack

Professor of Georgraphy

Benjamin M. Ogles

Dean, College of Arts and Sciences 3 ABSTRACT

YOUNG, BENJAMIN, J., M.A., June 2010, Geography

Impacts of Impervious Surface Cover on Stream Hydrology and Stream-Reach

Morphology, Northern Georgia (91 pp.)

Director of Thesis: Dorothy Sack

Urban development significantly alters the hydrologic and morphologic

characteristics of stream channels. Previous research cites impervious surface cover as a

primary driving force behind fluvial geomorphic alterations associated with urban

development, yet questions remain about the amount of total impervious area (TIA)

responsible for subsequent geomorphic modifications of stream systems and about the

nature of those modifications. This research compares selected characteristics of 29 rural

and 23 urban streams in northern Georgia to determine significant differences in stream

hydrology and morphology associated with the expansion of impervious surface cover.

Compared to rural (pre-urban) streams, urban streams were found to exhibit greater mean and flood discharges, but no difference in flood stage recurrence interval was found between the two sample groups. Analysis of the urban stream data suggests that a watershed TIA of 14-16% may be an amount of impervious surface cover above which channels become unstable. It is important to establish the level of impervious surface cover associated with alterations to the fluvial system in an attempt to offset the negative fluvial effects of urban development.

Approved: ______

Dorothy Sack

Professor of Geography 4 ACKNOWLEDGMENTS

First and foremost, I would like to thank my advisor, Dr. Dorothy Sack. For the past six years she has been a constant source of knowledge, inspiration, and support during my tenure as a student at Ohio University. I would like to thank her for initially sparking my interest in geography, helping to create my appreciation for the physical processes that shape our world and the landforms that comprise the geographic landscape. I can think of no one more influential in my educational experience than she, and for that, I owe her a tremendous debt of gratitude.

I would like to thank my thesis committee members Dr. James Lein and Dr.

Gaurav Sinha for their time and effort as educators and in assisting me throughout the research process. I would like to thank Dr. Lein for putting up with my incessant and rudimentary questions over the statistical analysis of my research data. Also, I would like to extend my gratitude to Dr. Sinha who was instrumental in my graduate education, helping to form my knowledge of geospatial technologies and the science and philosophy behind them. Dr. Sinha’s brilliance is only matched by his ability as a teacher to make the most intellectually challenging concepts understandable to his students. No educator ever challenged me the way he did, forcing me to think about the fundamental fabric of my mental framework, for which I am extremely grateful.

I would also like to thank all the faculty of Ohio University who impacted me during my undergraduate and graduate career, shaping me into the student and person I am today. Particularly, I wish to thank Dr. Margaret Pearce, whose enthusiasm for cartography instilled in me a fascination and deep appreciation for the beautiful and 5 artistic nature of cartographic representations and Dr. Hugh L. Bloemer, for his passion as an educator, his love of geography, and for demanding the best out of his students.

Also, I would like to thank the Ohio University Department of Geography for providing me with a two-year teaching assistantship that allowed me to make an impact in the lives of undergraduate students by educating and imparting on them my knowledge of and passion for geographic concepts. Lastly, my appreciation goes out to my fellow graduate students for being an educational and emotional support at a time when both became extremely necessary.

For Bobbi. You are forever in my mind and heart.

For God did not give us a spirit of fear, but of power, love, and self-control.

II Timothy 1:7 7 TABLE OF CONTENTS

Page

Abstract ...... 3 Acknowledgments...... 4 List of Tables ...... 9 List of Figures ...... 10 Chapter 1: Introduction ...... 11 Chapter 2: Literature Review ...... 15 2.1 Importance of Imperviousness ...... 16 2.3 Geomorphic Thresholds ...... 20 2.4 Experimental Design ...... 22 Chapter 3: Study Area ...... 24 3.1 Site Selection ...... 24 3.2 Metropolitan Atlanta ...... 29 3.3 Physiographic Provinces ...... 30 Chapter 4: Methodology ...... 32 4.1 Data Sources ...... 34 4.2 Drainage Basin Variables ...... 36 4.2.1 Drainage Area ...... 36 4.2.2 Total Impervious Area ...... 39 4.2.3 Topographic Texture ...... 42 4.3 Stream Morphology Variables ...... 42 4.4 Hydrologic Variables ...... 45 4.5 Possible Sources of Error ...... 47 4.5.1 Drainage Area ...... 48 4.5.2 Total Impervious Area ...... 50 4.5.3 NHD-derived Variables ...... 51 4.5.4 Hydrologic Variables ...... 52 4.6 Statistical Analyses ...... 53 Chapter 5: Results ...... 58 5.1 Pre-Urban versus Urban Watershed Characteristics ...... 58 8 5.2 Associations between Variables ...... 61 5.3 Cluster Analysis ...... 68 Chapter 6: Discussion ...... 76 Chapter 7: Conclusion...... 82 References Cited ...... 86 9 LIST OF TABLES

Page

Table 1: Pre-Urban study site labels and corresponding watershed name ...... 26

Table 2: Urban study site labels and corresponding watershed name ...... 27

Table 3: Research variables ...... 33

Table 4: Types of error defined by Collins and Smith (1994 ...... 48

Table 5: Expected research outcomes for pre-urban versus urban watersheds ...... 54

Table 6: Observations and descriptive statistics obtained for the pre-urban study sites ...... 59

Table 7: Observations and descriptive statistics obtained for urban study sites ...... 60

Table 8: Results of difference of means t-test comparing urban versus pre-urban study sites (bold indicates α < 0.05) ...... 61

Table 9: Spearman's rank correlation coefficients for pre-urban study site variable pairs (bold where α< 0.05) ...... 63

Table 10: Spearman’s rank correlation coefficients for urban study site variable pairs (bold where α< 0.05 ...... 64

Table 11: Final cluster centers ...... 70

Table 12: Low Urban category ...... 71

Table 13: Medium Urban category ...... 71

Table 14: High Urban category ...... 72

Table 15: Results of Kruskal-Wallis test comparing the variables of the three cluster groupings (bold where α< 0.05) ...... 73 10 LIST OF FIGURES

Page

Figure 1: Hjulström's curve of critical velocity (after Groundspeak, 2010) ...... 21

Figure 2: Location of the 52 study sites that comprise the sample for this research. Site labels appearing on the map are keyed to location names in Tables 1 and 2 ...... 25

Figure 3: Distribution of study sites within the physiographic provinces of Georgia ....28

Figure 4: A watershed, as defined for this thesis (or as defined by the NHD) delineated by hand in the GIS ArcMap 9.3...... 38

Figure 5: Raw imperviousness data prior to reclassification ...... 40

Figure 6: Imperviousness data after reclassification ...... 41

Figure 7: Main channels identified by RivEx analysis and manual identification ...... 43

Figure 8: Scatter plot of slope and TIA among urban study sites showing within class groupings...... 66

Figure 9: Scatter plot of drainage density and TIA among urban study sites showing within class groupings...... 67

Figure 10: Scatter plot of normalized mean discharge and TIA among urban study sites showing within class groupings ...... 67

Figure 11: Scatter plot of flood stage recurrence interval and TIA among urban study sites showing within class groupings ...... 68

Figure 12: Results of cluster analysis including the spatial distribution of statistical outliers76 Figure 13: Physiographic provinces of Georgia highlighting the spatial distribution of urban study sites ...... 78

CHAPTER 1: INTRODUCTION

Urban development has profound impacts on the geomorphic adjustment of stream channels. As the populations of metropolitan areas continue to grow, more ground surface is converted to urban land uses, e.g., transportation networks and buildings. Such land-use change alters the hydrologic regime, and subsequent changes in the morphologic characteristics of streams are usually attributed to the increase in impervious surface cover resulting from urbanization. Research concerning the impact of urban development on streams has commonly given the most attention to changes in the stream channel itself, usually focusing on the channel cross-sectional geometry. However, a stream’s channel geometry is largely a product of the conditions of its upland watershed.

Investigations into the geomorphic response of stream channels associated with urban development, therefore, should consider the effects of urbanization on hydrologic properties and morphologic characteristics at the drainage basin as well as the stream- reach scale.

Many studies have attempted to quantify the urban-induced geomorphic alterations of streams (e.g., Leopold, 1968; Morisawa and LaFlure, 1979; Booth, 1991;

Schueler, 1994; Booth and Jackson, 1997; Pizzuto et al., 2000; Hession et al., 2003;

Cianfrani et al., 2006). The majority of documented stream modifications associated with watershed urbanization are attributed to increased surface runoff resulting from the substantial expansion of impervious surface cover. By facilitating surface runoff, impervious surface cover decreases lag time, the time between peak rainfall and peak stream discharge. An increase in impervious surface cover also decreases base flow, 12 which is the direct input of groundwater into the stream channel, by inhibiting infiltration of precipitation into the subsurface. Therefore, the rate at which water enters the stream network is faster, increasing the overall peak streamflow of a given stream in an urbanized watershed compared to a similar non-urbanized watershed. The balance between water and sediment supplied to an urban-area stream is thus altered, in this case increasing the propensity of the urban stream to erode its bed and banks. This tendency toward erosion leads to urban channels being wider, deeper, straighter, and overall larger than non-urban streams (Hammer, 1972; Trimble, 1997; Pizzuto et al., 2000; Hession et al., 2003). Research concerning the hydrologic response to urbanization suggests that urban development also leads to an increase in flood activity, in terms of both flood frequency and magnitude (Leopold, 1968; Morisawa and LaFlure, 1979; Chin and

Gregory, 2001).

Impervious surface cover is often cited as a main characteristic driving changes in stream morphology and hydrology within urban watersheds (Leopold, 1968; Hammer,

1972; Booth, 1991; Schueler, 1994; Horner et al., 1999). Impervious surface cover has proven to be a reliable indicator of urban development because it can be quantified with relative ease (Schueler, 1994; Booth and Jackson, 1997; Paul and Meyer, 2001; Cianfrani et al., 2006; Kang and Marston, 2006). Also, imperviousness has garnered substantial attention among fluvial geomorphologists because its hydrologic impact can be explicitly managed and controlled during land development. There is a need, then, to understand the level of urban development, represented by the extent of impervious surfaces, responsible for significant hydrologic and stream morphology changes so methods can be developed to diminish or avoid the irreparable, adverse fluvial effects of impervious 13 surface cover. Identifying the percentage amount of impervious surface at which measureable geomorphic alterations begin will increase the efficacy of existing stream management practices by allowing stream managers to know exactly at what phase in urban development to implement them. Moreover, effective management and restoration of urban streams require an understanding of geomorphic alterations above the scale of the channel cross section because mitigation and restoration techniques are best implemented and most effective at the watershed and stream-reach scale.

The purpose of this research is to investigate the geomorphic response of stream channels associated with urban development at the watershed and stream-reach scale.

Specifically, this research attempts to identify the threshold at which significant geomorphic adjustments of stream channels begin in an urban landscape. The objectives of this research are to:

(1) assess changes in stream hydrology and stream-reach morphology associated

with urban development, and

(2) identify the level of impervious surface cover associated with significant

changes to stream hydrology and morphology.

The advent of Geographic Information Systems (GIS) and their related technologies has made the task of measuring watershed and stream-reach characteristics easier to perform. This thesis uses remotely sensed data in the context of a GIS, supplemented with hydrologic data from USGS gaging stations, to address the research objectives. A study design substituting rural watersheds for conditions prior to urban development is adopted to create a historical record of the necessary hydrologic data.

Data were used to test hypothesized relationships between hydrologic and stream 14 morphology characteristics versus watershed impervious surface cover statistically to identify the threshold amount of impervious surface cover associated with significant alterations among 52 sites in northern Georgia. This study area was selected based on the rapid expansion of the metropolitan Atlanta area, the consistency of the physical conditions between rural and urban watersheds, and the prevalence of USGS gaging stations.

15 CHAPTER 2: LITERATURE REVIEW

Urban development imposes a variety of fluvial geomorphic changes, both at the watershed and stream-reach scales. Knowledge of basic interactions among fluvial forms and processes is needed in order to understand the response of watersheds and stream systems to urban development. Observation of drainage basins and streams was well underway in the late nineteenth century but it was not until process geomorphology of the

1950’s that researchers began to focus on the causes of and processes behind changes in stream hydrology and morphology. During this time, geomorphic research began to drift away from primarily qualitative description of the natural world and focus on the measurement and quantification of the physical landscape. As the field of process fluvial geomorphology grew during the 1950’s, researchers became increasingly aware of the impact that anthropogenic factors have on channel hydrology and stream morphology.

Research began to broaden from the identification of the natural factors responsible for stream alterations to attempting to understand the intricate relationships between the cultural and natural landscapes.

Perhaps the most evident anthropogenic alteration of the landscape is the conversion of natural land cover to urban land cover. Some fluvial researchers of the

1950’s used process geomorphology to study the effects of land cover transition on stream hydrology and morphology. Influential fluvial geomorphology papers on anthropogenic interference appeared in the mid 1950’s with the publication of the classic volume, Man’s Role in Changing the Face of the Earth (Thomas, 1956). Strahler’s

(1956) contribution to the volume concerns induced fluvial aggradation and erosion, and describes how reduction of infiltration capacity causes increased surface runoff that 16 results in heightened soil erosion, leading to forced stream aggradation. Leopold’s (1956) research on land use and sediment yield suggests that human alterations to the landscape have profound impacts on the volume of sediment delivered to a stream. These landmark studies garnered substantial attention among fluvial geomorphologists creating immediate interest in the power of human manipulation of the landscape on hydrology and stream morphology. Subsequent studies by Leopold and Wolman (1957), Wolman (1967),

Leopold (1968), Hammer (1972), and Morisawa and LaFlure (1979) provide the basis upon which ensuing research concerning the anthropogenic impact on watershed and stream system processes rests.

2.1 IMPORTANCE OF IMPERVIOUSNESS

Impervious surface cover is the most important factor of urban land development on hydrologic and stream morphology processes. Increases in impervious surface cover have been linked to changes in channel cross-sectional dimensions, diminished water quality, increased water temperature, overall degradation of instream habitat, reduced riparian corridor, and greatly diminished biotic diversity (Klein, 1979; Morisawa and

LaFlure, 1979; Jones and Clark, 1987; Galli, 1991; Booth and Reinelt, 1993; May et al.,

1997). Research in fluvial geomorphology has consistently used impervious area of a watershed for establishing the magnitude of urban development (Leopold, 1968;

Hammer, 1972; Booth, 1991; Pizzuto et al., 2000; Cianfrani, 2006).

Hydrologic researchers commonly measure impervious surface cover using one of two indices, total impervious area (TIA) or effective impervious area (EIA). TIA is a measure of the area of a watershed covered by all connected and disconnected surfaces 17 that transport water over land to the drainage system. Connected surfaces (e.g., sewer systems or roads) directly convey surface runoff or precipitation to a stream, while disconnected surfaces (e.g., rooftops with gutters draining to a pervious lawn) have no direct input into the drainage system. EIA only measures the impervious surfaces that directly contribute to the drainage system (connected surfaces). Conceptually, EIA captures the hydrologic significance of impervious surfaces, thus making it the preferred parameter of urban development for use in hydrologic modeling (Booth and Jackson,

1997).

Studies have shown EIA and TIA to be equally accurate indices of urban-induced hydrologic alterations (Sutherland, 1995; Booth et al., 2002; Lee and Heaney, 2003).

However, EIA is rarely used in fluvial research focused on the geomorphic changes associated with urban development, in part, due to the difficulty of determining EIA, which requires substantial financial, human, and time obligations that detract from the main research priorities. Previous research concerned with urban-induced stream alterations have shown TIA to be a reliable indicator of urban development that yields consistent and accurate results (May et al., 1997; Paul and Meyer, 2001; Booth et al.,

2002; Chin, 2006; Cianfrani et al., 2006). For these reasons TIA was selected as the index by which to represent the degree of urban development within drainage basins for this research.

2.2 GEOMORPHIC IMPACTS OF URBANIZATION

Urban watersheds show greater surface runoff than comparable non-urban watersheds, and this is attributed to the differences in impervious surface cover in the two 18 settings (Booth, 1991; Horner et al., 1999). Peak streamflows following storm events increase with urbanization while low flows between storms decrease because less water is able to infiltrate the ground (Leopold, 1968). Less infiltration means less ground-water discharge, that is, base flow, into any given stream channel. This subsurface flow of water to the channel is relatively slow. Urbanization disturbs the pre-urban equilibrium between hydrology and sediment yield and this results in geomorphic alterations of stream channels (Hammer, 1972; Morisawa and LaFlure, 1979; Pizzuto et al., 2000). The volume of sediment supplied to a stream changes as a function of the degree of watershed development, as suggested by Wolman (1967) and upheld by Chin (2006) and Grable and

Harden (2006), occurring in three distinct stages: 1) a stable or equilibrium pre-urban stage; 2) a construction period exposing large areas of bare land; and 3) a final, new urban landscape dominated by impervious surfaces.

During the construction phase of development, large expanses of land are stripped and left bare for extended periods of time inducing widespread erosion of the surrounding landscape. This erosion supplies a greater volume of sediment to a stream than in the pre- urban stage, leading to short-term aggradation. Sediment yield during the construction phase has been shown to be as high as 40,000 times greater than pre-urban rates (Harbor,

1999). Deposition and bed aggradation result in overall channel capacity reduction as channel width and depth decrease. As land development continues, impervious surfaces begin to cover the majority of the landscape, through the construction of roads, buildings, parking lots, and other cultural features. Compared to pre-urban conditions, these impervious surfaces generate a larger amount of surface runoff, which is directed into the 19 sediment-choked stream channels as a greater discharge capable of eroding the recently deposited sediment.

When fully urbanized, a watershed is unable to supply much sediment to its stream channels as the majority of land is now covered by impervious surfaces. In response to the increased water input and decreased sediment input the stream erodes its channel bed and banks. This channel erosion may be gradual or rapid depending on the geologic setting and whether human manipulation of the stream takes place (Booth,

1991). A system experiencing unabated erosion can be stopped by natural or synthetic controls. At some point the incision will reach the underlying bedrock and downcutting will halt naturally. A more common occurrence in urban streams is the implementation of synthetic controls, such as artificial bank armoring, to prevent unabated bed erosion.

Urbanization increases downstream flood activity. Impervious surface cover greatly diminishes the infiltration of water and subsurface base flow, thereby increasing the amount of water transported as surface runoff. With water input to the stream no longer slowed by the process of ground seepage, the time it takes for precipitation to enter any given stream channel (lag time) decreases, resulting in a flashier hydrology

(Morisawa and LaFlure, 1979; Chin and Gregory, 2001). Greater surface runoff combined with the shorter lag time in urban watersheds increases flood frequency and flood magnitude. Research has shown that flood recurrence in urban watersheds can increase by as much as five times while flood volume can be six times higher than a non- urban channel of a similar drainage density (Leopold, 1968). Seaburn’s (1965) research found that flood discharges were at least 250% higher in urban catchments when compared to similar non-urban catchments. Current research across geographic regions 20 supports this concept; annual flood peaks are amplified by 22 to 84% in urban watersheds compared to watersheds covered by more natural land cover (Poff et al., 2006). Booth and Jackson (1997) found that the discharge of the 2-year flood in urban catchments equals the discharge of the 10-year flood in similar forested catchments.

The urban hydrologic regime is comprised of water entering the stream system at a faster rate than was the case for the pre-urban hydrology, thereby increasing mean and peak discharge. Changes to the hydrologic regime, whether through land use change or otherwise, force stream channels to adjust to accommodate the new flow. Increased flood activity is a stream’s natural response to high stream velocity and peak streamflows that exceed original, and construction phase, stream capacity. However, the amount of time needed for a stream to adjust properly to an urban hydrologic regime remains unclear.

Increased flood activity should not continue indefinitely as flooding will force the stream channel to adjust until it is capable of handling higher peak streamflows. At the stream- reach level, channel slope should decrease as the increased elevation is no longer needed to create energy for the system. The modified channel form, then, is a function of the magnitude of urban development and the length of time a watershed has been subject to urban development.

2.3 GEOMORPHIC THRESHOLDS

A geomorphic threshold is a critical level of landform stability that is exceeded by changes in either internal or external forces (Schumm, 1979). The concept of thresholds has been recognized in many fields. To a fluvial geomorphologist, the most common threshold example is likely Hjulström’s (1935) curve showing the flow velocity required 21 to move a piece of sediment of a given size (Figure 1). The curve shows how movement and deposition do not occur until some critical point of water velocity is reached. This thesis research attempts to identify the point at which stream channels become unstable when an external force (land cover) is altered, inducing hydrologic and morphologic changes.

Figure 1. Hjulström's curve of critical velocity (after Groundspeak, 2010).

The identification of the threshold of impervious surface cover responsible for geomorphic alterations has been a topic of research for a number of years. Booth and

Jackson (1997) suggest that channel instability and stream degradation begin when a watershed exceeds 10% TIA. This theory has been supported by the work of Paul and 22 Meyer (2001), Hession et al. (2003), and Kang and Marston (2006), which indicates that this level of impervious cover initiates visible and demonstrable effects on streams and their watersheds. However, the designation of 10% TIA as a significant threshold ignores research which maintains that geomorphic modifications begin at lower and higher levels of impervious cover. Changes in channel dimensions have been shown to begin as early as 2%, 4%, and 6% imperviousness (Dunne and Leopold, 1978; Morisawa and LaFlure,

1979; Booth and Jackson, 1997). Conversely, data have shown watersheds with TIA levels as high as 24% were accompanied by no changes in channel morphology

(Cianfrani et al., 2006). While there is variation among research results as to the exact amount of impervious surface cover responsible for geomorphic modifications, it is agreed upon that a threshold of impervious surface cover does exist.

This research uses the findings of previous studies to determine a percentage of

TIA, in this case 5% TIA, above which it can be safely assumed that sites have been impacted by the presence of impervious surface cover. The designation of a site possessing 5% TIA or lower as rural (pre-urban) is based on the empirical evidence of

Morisawa and LaFlure (1979), Booth and Jackson (1997), Paul and Meyer (2001),

Hession et al. (2003), and Kang and Marston (2006), which all cite significant geomorphic changes at or above, but not below, this percentage of TIA.

2.4 EXPERIMENTAL DESIGN

Little work has been conducted on the fluvial effects of urbanization from a pre- urban landscape to an urban landscape because pre-development data are seldom available. In many locations, urban development precedes the hydrologic data necessary 23 to quantify the extent of urban-induced alterations before, during, and after the urbanization process. For this reason, the space-for-time study design is generally applied to approximate pre-urban conditions (Booth and Jackson, 1997; Pizzuto et al., 2000; Chin and Gregory, 2001; Hession et al., 2003; Cianfrani et al., 2006; Grable and Harden, 2006;

Kang and Marston, 2006). Chin (2006) estimates that more than half of the research into urban-induced changes in stream morphology employs the space-for-time substitution method of study. Previous space-for-time studies have used areas upstream of urbanization as surrogates for pre-urban conditions to provide a basis of comparison for urban streams.

Fluvial researchers have measured hydrologic and geomorphic variables to assess the extent of watershed and stream alteration associated with urbanization. The variables selected for measurement are based on specific research goals. These variables often include TIA, bed slope, segment length, channel width, channel depth, pool and riffle development, cross-sectional area, riparian vegetation, peak flow, low flow, flow duration, flood interval, and flood volume (Hammer, 1972; Grable and Harden, 2006;

Kang and Marston, 2006; Poff et al., 2006). This thesis research is not concerned with effects of urban development below the stream-reach scale, thus factors such as cross- sectional area and pool and riffle development are not included for measurement. This thesis uses the space-for-time study model to assess the hydrologic and stream morphology alterations that occur with urban development and attempts to identify the level of impervious surface cover associated with the observed geomorphic changes.

24 CHAPTER 3: STUDY AREA

3.1 SITE SELECTION

Fifty-two study sites in northern Georgia comprise the study area for this thesis

(Figure 2, Tables 1 and 2). This study area was selected based on the rapid expansion of the metropolitan Atlanta area, the consistency of the physical conditions between rural and urban watersheds, and the prevalence of USGS gaging stations. Two initial criteria had to be satisfied for streams and their respective watersheds to be considered as study locations for this project. The first criterion was that any study stream had to have at least

20 years of hydrologic data in the form of annual discharge and stream peakflow measurements to allow for reliable evaluation of the stream’s typical flow regime. The second condition was that any potential site needed to be located in the Piedmont physiographic province to control for natural variation among climate and geology. The climate and geology of an area are two key factors in determining hydrology and morphology of a stream system, thus it is imperative that these factors be as uniform as possible among study sites. Because only 25 sites met the initial criteria, 27 additional sites located in the adjacent Blue Ridge and Ridge and Valley physiographic regions were selected to increase the project sample size to a total of 52 sites (Figure 3). 25

Figure 2. Location of the 52 study sites that comprise the sample for this research. Site labels appearing on the map are keyed to location names in Tables 1 and 2.

26 Table 1. Pre-urban study site labels and corresponding watershed name. Label Watershed Name P1 FALLING CREEK NEAR JULIETTE, GA P2 KETTLE CREEK NEAR WASHINGTON, GA P3 FAUSETT CREEK NEAR TALKING ROCK, GA P4 HEATH CREEK NEAR ARMUCHEE, GA P5 OCONEE RIVER NEAR PENFIELD, GA P6 LITTLE RIVER NEAR WASHINGTON, GA P7 AMICALOLA CREEK NEAR DAWSONVILLE, GA. P8 TALLULAH RIVER NEAR CLAYTON, GA P9 MURDER CREEK BELOW EATONTON, GA P10 CHATTAHOOCHEE RIVER AT HELEN, GA P11 CARTECAY RIVER NEAR ELLIJAY, GA P12 OCMULGEE RIVER NEAR JACKSON, GA P13 ETOWAH RIVER NEAR DAHLONEGA, GA P14 CHATTOOGA RIVER NEAR CLAYTON, GA P15 COOSAWATTEE RIVER NEAR PINE CHAPEL, GA P16 LITTLE RIVER NEAR EATONTON, GA P17 BROAD RIVER NEAR BELL, GA P18 NOTTELY RIVER NEAR BLAIRSVILLE, GA P19 TALKING ROCK CREEK NEAR HINTON, GA P20 CHESTATEE RIVER NEAR DAHLONEGA, GA P21 COOSAWATTEE RIVER NEAR ELLIJAY, GA P22 BROAD RIVER ABOVE CARLTON, GA P23 HOLLY CREEK NEAR CHATSWORTH, GA P24 SNAKE CREEK NEAR WHITESBURG, GA P25 APALACHEE RIVER NEAR BOSTWICK, GA P26 CHATTOOGA RIVER AT SUMMERVILLE, GA P27 TWO RUN CREEK NEAR KINGSTON, GA P28 NEW RIVER AT GA 100, NEAR CORINTH, GA P29 CEDAR CREEK NEAR CEDARTOWN, GA

Table 2.

Urban study site labels and corresponding watershed name. Label Watershed Name U1 COOSA RIVER NEAR ROME, GA U2 OOSTANAULA RIVER AT RESACA, GA U3 ETOWAH RIVER AT GA 1 LOOP, NEAR ROME, GA U4 MILL CREEK NEAR CRANDALL, GA U5 CHATTAHOOCHEE RIVER NEAR CORNELIA, GA U6 ETOWAH RIVER AT CANTON, GA U7 CONASAUGA RIVER AT GA 286, NEAR ETON, GA U8 OOSTANAULA RIVER NEAR ROME, GA U9 ALCOVY RIVER ABOVE COVINGTON, GA U10 LITTLE TALLAPOOSA RIVER AT US 27, AT CARROLLTON,GA U11 CONASAUGA RIVER AT TILTON, GA U12 LINE CREEK NEAR SENOIA, GA U13 MIDDLE OCONEE RIVER NEAR ATHENS, GA U14 ETOWAH RIVER AT ALLATOONA DAM, ABV CARTERSVILLE,GA U15 FLINT RIVER NEAR GRIFFIN, GA U16 SWEETWATER CREEK NEAR AUSTELL, GA U17 SUWANEE CREEK AT SUWANEE, GA U18 SOUTH RIVER AT KLONDIKE ROAD, NEAR LITHONIA, GA U19 BIG CREEK NEAR ALPHARETTA, GA U20 NICKAJACK CREEK AT US 78/278, NEAR MABLETON, GA U21 SOPE CREEK NEAR MARIETTA, GA U22 CHATTAHOOCHEE RIVER NEAR NORCROSS, GA U23 PEACHTREE CREEK AT ATLANTA, GA

Figure 3. Distribution of study sites within the physiographic provinces of Georgia. 29 3.2 METROPOLITAN ATLANTA

Substantial urban development in the greater Atlanta region over the past 40 years makes this area particularly attractive for research concerned with human alteration of the physical landscape. Unprecedented growth of metropolitan Atlanta began in the late

1970’s and did not slow until 2009. During that time period greater Atlanta established itself as one of the fastest growing metropolitan areas in the country, raising its population from 1.5 million people in 1970 to 5.3 million people today (Atlanta Regional

Commission (ARC), 2009). The addition of 1.1 million people in the last eight years alone makes the Atlanta region the second fastest growing urban area in the country, behind Dallas (ARC, 2009). Although population growth in the metro Atlanta area has recently slowed due to the current economic recession and housing market decline, the region continues to be comparatively strong economically, bolstered by its role as the major transportation hub of the southeastern U.S. Over the next 30 years the number of people who call the greater Atlanta area home is expected to rise by 3 million to a total population of 8.3 million (ARC, 2009). As the area continues to grow so does the need for land to support its burgeoning population.

The highest rates of land development occur on the fringes of existing cities and metropolitan areas as previously non-urbanized areas are converted from natural or rural land uses to support the sprawling urban landscape (Heimlich and Anderson, 2001). In

1970, half of the population of the metro Atlanta area lived in incorporated areas, that is, areas legally zoned as cities. By 2009, that proportion had fallen to 39% (ARC, 2010).

Although population of the area increased dramatically during this time, the majority of growth and subsequent land development occurred on the edges of cities in the form of 30 suburbs. In the last decade alone more than 490,000 housing units have been built in the area (ARC, 2010). Along with housing units come transportation networks, commercial centers, services and recreational areas, and industries.

3.3 PHYSIOGRAPHIC PROVINCES

Metropolitan Atlanta and the majority of the study sites for this thesis research are located in the Piedmont physiographic province. This northeast-southwest trending region is the non-mountainous transitional zone between the Appalachian Mountains to the northwest and the relatively flat Coastal Plain province to the southeast (Figure 3).

The gentle topography and sloping hills of the region stretch over a mosaic of metamorphic and igneous rocks formed during the Precambrian and Paleozoic eras.

Gneiss, schist, mica, and granite are the dominant rock types that create an elevation ranging from 58 meters above sea level (asl) in the south to 880 meters asl in the more rugged areas to the northwest (Griffith et al., 2001). Average annual temperature across the region ranges from 15°C to 20°C, and it receives around 140 cm of yearly rainfall

(Gregory and Calhoun, 2006). The land use history of the Georgia Piedmont is a story of dramatic landscape transition. The area has been converted from forest to row-crop and plantation agriculture, back to woodlands, and is currently experiencing widespread urban and suburban development.

The Blue Ridge physiographic province comprises the northeastern portion of

Georgia and contains 11 study sites for this thesis. The highest and wettest mountains in

Georgia are found in this area over a mix of metamorphic, igneous, and sedimentary geology of late Precambrian age. The rugged terrain ranges from 480 m to 1,410 m asl in 31 elevation through a series of irregular mountains, basins, and ridges of predominantly gneiss, schist, and quartzite rock (Hodler and Schretter, 1986). Temperature and precipitation in this region are products of elevation, with rainfall predominantly the result of orographic uplift, which produces over 200 cm of average annual precipitation on the higher slopes (Griffith et al., 2001). This area has remained forested over the years because the ultisols that dominate the region preclude it from substantial agricultural development. Today, the vegetation comprises a mix of hardwood species and pine species at higher elevations.

Fourteen study sites lie in the Ridge and Valley physiographic province. This small region in the northwestern corner of Georgia rests atop mostly sedimentary bedrock. Also known as the Great Valley of Georgia, this relatively low-lying area is located between the Blue Ridge Mountains to the east and the Appalachian Mountains to the west. The unique topography of neatly compacted ridges and valleys in this area are the remnants of an ancient fold-and-thrust belt formed during the Allegheny orogeny

(Griffith et al., 2001). Elevation consistently ranges from 210 m to 240 m asl in the lowland areas but can reach as high as 480 m asl on the ridge tops (Hodler and Schretter,

1986). Sandstone, shale, and siltstone are the dominant rock types, with limestone responsible for the numerous caves and natural springs found in the area. The Ridge and

Valley region is the driest of the three provinces containing study sites, with an average annual precipitation of 120 cm (Griffith et al., 2001). The irregular topography and the relatively acidic soils of this region create an assortment of present-day land use with developed land, pasture, and forest. 32 CHAPTER 4: METHODOLOGY

Three groups of data are used to assess change in hydrologic behavior and stream morphology accompanying urban development: drainage basin characteristics, stream channel morphologic characteristics, and hydrologic variables. Drainage basin characteristics are used to assess overall hydrologic and morphologic conditions of a watershed because they are indicators of the processes acting within their respective stream systems. Drainage basin characteristics measured include drainage area, total impervious area (TIA), drainage density, and stream frequency. Channel morphologic variables collected are stream slope, stream sinuosity, stream length, and Strahler stream order. Hydrologic variables used in this study include mean discharge, mean flood discharge, mean discharge per unit area, mean flood discharge per unit area, and flood stage recurrence interval. Together these variables are used to assess the change in hydrologic and morphologic characteristics of stream channels and their respective drainage basins in association with urban development (Table 3).

Table 3.

Research variables.

Variable Description

Drainage Basin Area Total area of a drainage basin (km2)

Ratio of impervious surface area to drainage area, Total Impervious Area represented as a percent (%)

Drainage Density Length of streams per unit of drainage area (km/km2)

Stream Frequency Number of streams per unit area (#/km2)

Dimensionless ratio of the vertical drop of a stream Stream Slope per stream length

Stream Sinuosity Dimensionless ratio of stream length to valley length

Stream Length Length of a stream (km)

Ordinal classification of channel based on number of Strahler Stream Order tributaries flowing into a stream

Measure of the average amount of water flowing in a Mean Discharge channel, based on mean annual discharge (m3/sec)

Mean Discharge/Unit Area Mean discharge per km2 of drainage area (m3/sec) Average amount of streamflow during a flood event, Mean Flood Discharge based on mean peak flow data (m3/sec)

Mean Flood Discharge/Unit Mean flood discharge per km2 of drainage area Area (m3/sec) Flood Stage Recurrence Average number of years between flows that equaled Interval or exceeded flood stage (yrs) 34 4.1 DATA SOURCES

This thesis utilizes data from the following sources: USGS gaging stations,

National Oceanic and Atmospheric Administration, National Hydrography Dataset

(NHD), and National Land Cover Database 2001 (NLCD 2001). Data were imported into a Geographic Information System (GIS) for measurement and calculation of drainage basin and geomorphic variables. GIS procedures were conducted using ArcGIS 9.3 software in combination with RivEx network analysis extension software. All data were given a Transverse Mercator Projection and a North American Datum 1983 State Plane

Georgia West FIPS 1002 coordinate system. Data were analyzed statistically to identify and describe significant changes in the hydrologic behavior and stream-reach morphology in association with various amounts of impervious surface cover.

The National Hydrography Dataset is the primary source for the representation, measurement, and calculation of drainage basin characteristics and morphologic variables for all study streams and their respective watersheds in the GIS. The NHD is the umbrella dataset for all surface water features as part of the National Map, containing boundaries, river networks, and significant hydrologic features. The NHD is the product of a multidisciplinary and multi-organization collaborative effort to create a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of water, paths through which water flows, and related entities, available for download to the general public through the United States Geological

Survey’s website (USGS, 2009).

Two NHD feature classes were used for this thesis: 10-digit Hydrologic Unit

Code watershed drainage area shapefiles were used to represent watersheds and NHD 35 flowline networks were used to represent study streams and their respective tributaries.

The Watershed Boundary Dataset (WBD) is the companion dataset to the NHD and contains a comprehensive collection of drainage area boundaries, also known as hydrologic units, for the conterminous United States. NHD flowlines are topologically correct representations of stream networks delineated with adherence to strict procedures and data standards. WBD boundaries and NHD flowline networks are products of consistent and reliable methods. Data standards and methods for the delineation of the

NHD and WBD features are provided in detail later as described by the USGS (2000,

2009b).

RivEx is a vector-based river network processing tool designed to work with the

ESRI ArcMap software suite. Using line features as the input layers, RivEx analyzes topologically correct river networks, identifies main channels, and computes attributes such as Strahler order, Shreve order, sum of all stream lengths, and distance from network mouth. For the purposes of this research, RivEx was used to assign each catchment an identification number for initial quality control purposes, determine

Strahler order, compute sum of all stream lengths and total number of streams, and identify then measure the length of main channels. The first step in this process was to ensure that all sampled NHD river networks are topologically correct, meaning all network lines are complete and connected, flowing in the correct direction. This was accomplished by first using RivEx to assign catchment IDs to a network. Ideally, all lines in a network should have been assigned the same catchment ID. However, this is not always the case due to missing segments, incorrect topologies resulting from lines 36 flowing in the wrong direction, and artificial paths. Once topological complications were remedied using the Editor tool, the network was ready for analysis.

4.2 DRAINAGE BASIN VARIABLES

4.2.1 Drainage Area

A drainage basin is the entire region providing runoff to a main channel and its tributaries. The importance of a drainage basin is hinted at in its synonyms which are used interchangeably throughout this thesis and commonly include catchment and watershed. Size or area is the most fundamental and significant among the various morphologic elements which comprise a drainage basin because size has the most influence on the amount of water yield; other elements are length, shape, and relief. The importance of area is best encapsulated by Anderson’s (1957, p. 922) description of drainage basin area as the “Devil’s variable” because almost every watershed characteristic is correlated with area. As such, the correct definition and accurate delineation of a drainage basin is imperative to ensure the proper understanding of the processes within it.

Polygon shapefiles in the GIS representing the studied drainage basins are products of either extraction from the WBD or manual delineation. A number of study watersheds were below the 10-digit HUC resolution. These watersheds were delineated manually in the GIS using the Editor tool. DEM coverage for the state of Georgia was downloaded from the USGS national viewer and displayed as hill-shaded relief in the

GIS to provide an interpretative background for the delineation of watershed boundaries.

In addition, NHD flowlines were overlain on DEMs while simultaneously displaying 37 neighboring WBD-derived watersheds to ensure that the most accurate boundaries were drawn (Figure 4). However, Figure 4 also demonstrates that WBD and hand-delineated boundaries do not always coincide with the true topographic boundaries of a watershed.

The WBD defines a watershed as the fifth level of the hydrologic unit hierarchy, not as the surface area that provides runoff to a main channel and its tributaries (USGS, 2009b).

Therefore, some drainage basin boundaries used for this research do not match their true topographic boundaries. Drainage area was then calculated in square meters and later converted to square kilometers for analysis. 38

Figure 4. A watershed, as defined for this thesis (or as defined by the NHD) delineated by hand in the GIS ArcMap 9.3. 39 4.2.2 Total Impervious Area

TIA was generated for each studied watershed using National Land Cover

Database 2001 (NLCD 2001) imperviousness data derived from Landsat-7 ETM+ imagery. Landsat imagery has a spatial resolution of 30 meters, which is fairly coarse for the determination of impervious surface cover of an area. In order to more accurately determine impervious surface cover than is allowed using the 30 meter resolution of its primary data source, NCLD 2001 imperviousness uses two types of high spatial- resolution imagery, IKONOS and DOQ, both of which have a one meter spatial resolution, to calculate the subpixel variation of impervious surface cover using regression tree algorithm methods (Yang et al., 2002; Homer et al., 2004). Raw NLCD

2001 imperviousness data were imported into the GIS for reclassification. The raw data displayed in raster format in which each cell has a value ranging from 1 – 100% were reclassified as either impervious or not impervious (Figures 5 and 6). All impervious cells were then extracted to individual watersheds using the Spatial Analyst tool. The area covered by impervious surface was calculated based on the number of impervious cells and cell size, then represented as a percentage of total area of the watershed. 40

Figure 5. Raw imperviousness data prior to reclassification. 41

Figure 6. Imperviousness data after reclassification. 42 4.2.3 Topographic Texture

Topographic texture, or the degree to which a basin is channelized, has long been recognized as a variable of fundamental significance in fluvial geomorphology research

(Horton, 1932; Langbein, 1947; Gregory, 1973). Topographic texture is sensitive to changes in the drainage system and as such provides a link between processes and features at the watershed scale to processes and features operating at the stream channel scale. Two indices of topographic texture were calculated for this research, drainage density and stream frequency, obtained through RivEx analysis. The total length of all streams and the total number of streams in each drainage basin were measured through

RivEx analysis of NHD flowline networks.

4.3 STREAM MORPHOLOGY VARIABLES

In addition to being needed to calculate drainage density, stream length is a fundamental aspect of stream morphology. One of the tasks that RivEx performed for this research was the identification of the main channel in a given network and its subsequent length calculation. However, not all study streams were designated the main channel of its respective network by RivEx. In these cases, individual study stream segments were selected manually then exported as shapefiles. This allowed the calculation of their length based on the sum total of all their line segments (Figure 7).

Figure 7. Main channels identified by RivEx analysis and manual identification.

Stream ordering arose out of the need for a simple index of drainage basin size based on the stream network within it. Many methods have been proposed since the early

1900’s but the limitations of earlier methods were overcome by the ease and simplicity of 44 the method proposed by Strahler in 1952. Stream order is important not only as a classification method for drainage basins, but it also provides an approximate index of the amount of streamflow a channel is capable of holding. It is with this purpose in mind that orders of study streams were included in this thesis. Stream order was determined through RivEx analysis of NHD flowlines that accurately identified study streams as main channels. All remaining stream orders were classified through manual analysis of the river network.

Slope is a powerful component of stream morphology because it controls energy input of a channel in the form of gravity. Slope is controlled by lithology, climate, sediment input, and discharge. Given the potential effect of impervious surface cover on sediment and water yield the inclusion of stream slope was deemed to be essential.

Stream slope, the dimensionless ratio of a stream’s vertical drop over the distance a stream travels, was calculated through measurement of study streams, represented as

NHD flowlines, overlain on DEMs. The Identify tool was used to determine the elevation of both the head of a stream and its end based on DEM information. The distance a stream travelled was obtained using the Measurement tool. To reduce error, distance measurements were conducted three times, using the average distance for the final slope calculation.

Stream sinuosity, the dimensionless ratio of stream length to valley length, is a measure of how much a stream meanders and is often used as an indicator of the development of a stream system. Under ideal conditions a channel will become more sinuous with time as it adjusts to the sediment and water input of its drainage basin. The inclusion of sinuosity in this study is based on its close relationship with discharge and 45 stream slope. Also, stream sinuosity has been generally overlooked in fluvial geomorphology research or calculated only as a by-product of the main investigation.

Although its role as a key geomorphic characteristic is frequently underestimated, sinuosity remains in many studies merely as an example of another urban-induced geomorphic alteration. Sinuosity is also an indicator of stream geometry and channel stability because the process of creating channel meanders requires stream banks to be erodible given the stream’s hydrologic regime. Sinuosity was determined using main channel, NHD flowlines as the input into Hawth’s Analysis Tools for ArcGIS.

Hawth’s Analysis Tools is a popular open-source extension pack designed for use in ArcGIS software because it performs functions that are not available in standard versions of ArcGIS. This thesis utilized the line metric tool to facilitate the measurement of stream sinuosity. The line metric tool uses line feature layers to calculate distance between nodes on a line (stream length) and straight-line distance between nodes (valley length).

4.4 HYDROLOGIC VARIABLES

USGS gaging station data and National Oceanic and Atmospheric Administration

(NOAA) hydrologic data were used to determine mean discharge, mean flood discharge, and flood recurrence interval. Annual discharge and peak streamflow data were downloaded from the USGS National Water Information System (NWIS) while flood stage data were accessed through NOAA’s advanced hydrologic prediction service web page and imported into Microsoft Excel. Mean stream discharge was established for all study streams by averaging the annual discharge for all water years available as reported 46 by the USGS. Annual discharge is determined using average monthly discharge readings of a given stream in a water year, which themselves are determined from daily discharge data collected at USGS gaging stations. Similarly, mean flood discharge was determined by averaging the discharge of all peak flows for available water years. Both mean discharge and mean flood discharge were converted to units of cubic meters per second

(m3/sec). The mean discharge and mean flood discharge of streams were then normalized

to adjust for the influence of drainage area on the water input of a watershed. Mean

discharge and mean flood discharge were both divided by the area of their respective

watersheds to determine their value per unit area.

Geomorphologists define a flood as any flow that spills over a stream’s natural or

artificial banks. Annual peak streamflow does not constitute a flood unless water

overtops the stream banks. Instead, annual peak streamflow is the maximum discharge in

a water year regardless of the water level's position with respect to the channel banks.

Recurrence interval is typically calculated from annual peak streamflow data with

established flood-frequency analysis methods (Gordon et al., 2004). Those analyses,

however, estimate the probability of a given annual peak discharge being equaled or

exceeded rather than the probability of discharges that actually resulted in flooding. For this research, it was imperative to assess the recurrence interval of flood events and not the recurrence interval of a given a peak streamflow in order to accurately compare the frequency of flood events in pre-urban and urban watersheds. Flood stage recurrence interval, therefore, was determined by calculating the number of years a peak flow equaled or exceeded flood stage over the length of the given historical record. 47 A calculated flood recurrence interval is influenced by the number of years of data available for the given stream, with longer periods of record yielding a more accurate depiction of flood activity. The USGS requires at least ten years of data to determine annual peak discharge recurrence intervals. This research required each study site to have at least 20 years of data for sufficient confidence in the accuracy of the flood stage (beyond bank full) recurrence intervals calculated.

4.5 POSSIBLE SOURCES OF ERROR

Error in the geospatial realm is defined as the difference between reality and the representation of reality (Heuvelink, 1998). All geospatial data contain a certain degree of error because all geospatial data are subject to generalization or approximation at some stage of their life spans. Research regarding spatial data quality has largely been concerned with the identification, classification, and management of error due to technical components of GISs and their related technologies. That research has led many to conclude that any spatial output of a GIS should include an assessment of error in the final product (Veregin, 1989, 1996; Heuvelink, 1998; Atkinson and Foody, 2002; Zhang and Goodchild, 2002). Since this research was conducted in the context of a GIS with data designed for use in a GIS, it is imperative to identify and discuss the sources of error in those data. This discussion categorizes the sources of error using a schema put forward by Collins and Smith (1994), which separates types of error according to the phase of the research in which they occur (Table 4).

48 Table 4.

Types of error defined by Collins and Smith (1994).

Phase Reasons for error Inaccuracies in field measurements; inaccurate equipment; incorrect Data Collection recording procedure; errors in analysis of remotely sensed data. Digitizing error; nature of fuzzy natural boundaries; other forms of Data Input data entry.

Data Storage Numerical precision; spatial precision (of raster systems). Data Wrong class intervals; boundary errors; spurious polygons and error Manipulation propagation with overlay operations.

Data Output Scaling; inaccurate output device. Incorrect understanding of the information; incorrect analysis of Data Usage information; incorrect use of data.

4.5.1 Drainage Area

The determination of drainage area is, primarily, prone to error at the data

collection and data input phases because drainage basins are represented by polygon

shapefiles created through extraction from the Watershed Boundary Dataset (WBD) or by

manual delineation methods. WBD boundaries have to meet accuracy standards for

1:24,000 scale maps as described in the National Map Accuracy Standards (NMAS).

These standards require 90% of well-defined features, in this case hydrologic boundaries,

to fall within 40 feet or 12.2 meters of their true geographic position (USGS et al.,

2009b).

WBD boundaries are the products of one of three delineation methods. The first

technique is to draw boundary lines manually based on the interpretation of 1:24,000 49 scale paper topographic maps, typically USGS topographic quadrangles. Lines are then digitized through either vectorization or scanning procedures. The second method involves the delineation of boundaries in a GIS using digital sources as interpretive backgrounds. Digital background sources include digital raster graphics (DRGs), digital orthophoto quadrangles (DOQs), quarter-quadrangles (DOQQs), or DEMs. The third technique is to use spatial modeling software that automatically derives drainage areas from elevation data. The most common of these approaches is a combination of the second and third methods in which watersheds created using automatic modeling procedures are checked against boundaries that were derived by the second technique.

WBD boundaries are subject to a number of error sources, but pertain to that of data input when converting actual terrain features to paper maps, followed by the digitization of paper maps. USGS topographic quadrangles and their digital representations (DRGs) contain error in the data collection, data input, and data manipulation phases. However, because they are held to NMAS for 1:24,000 maps it can be ensured WBD boundaries possess no more than 12.2 meters (40 feet) of positional error.

Manual delineation of some of the watersheds used in this thesis closely follows the second technique for the delineation of WBD boundaries, which utilizes digital interpretative backgrounds. Employing hill-shaded DEMs as the interpretative background does, however, create the possibility of error at the data input stage. The 7.5 minute DEMs used for this project have a spatial resolution of 30 meters so any variation in elevation below that scale is undetectable. Therefore, manually delineated boundaries may not represent the true geographic position of drainage boundaries. The degree to 50 which actual boundaries vary from their digital representations is the potential error which remains unknown as boundaries were not subjected to accuracy assessment.

4.5.2 Total Impervious Area

The calculation of TIA introduces error at the data collection, storage, and manipulation stages, but is likely due primarily to the use of the NLCD 2001 imperviousness data as the basis for its calculation. All remotely sensed data contain error, which is generally the result of inaccurate technologies in combination with incorrect analysis of the imagery (data collection). The classification and mapping of imperviousness for the NLCD 2001 database used Landsat-7 ETM+ imagery as the primary data source. However, NLCD 2001 imperviousness is not constrained by the 30 meter spatial resolution of its primary data source because it also employs two types of high spatial-resolution imagery to calculate subpixel variation of impervious surfaces.

IKONOS and DOQ images, each with a one-meter spatial resolution, were used to supplement Landsat imagery, and regression tree algorithms were used to estimate the amount of impervious surface with an average error of 8.8 to 11.4% (Yang et al., 2002).

Reclassification of NLCD 2001 imperviousness data were necessary to convert the raw data to a usable format, and are therefore prone to additional error at the data manipulation stage. NLCD imperviousness data are stored in raster format as a continuous variable in which each cell has a value ranging from 1 – 100%. In order to calculate TIA, cells were reclassified as either not impervious (0-9%) or impervious (9-

100%). This reclassification of data is a potential source of error because the selection of the class intervals influences the percentages of TIA. Also, impervious surfaces are 51 represented by 30 meter pixels, and as a result, any subpixel variation that does exist is eliminated (data storage).

4.5.3 NHD-derived Variables

Error associated with topographic texture, stream length, stream slope, and stream sinuosity is, for the most part, introduced at the data collection and input level. This is attributed to the use of NHD flowlines as the representation of study streams and their respective networks. NHD flowlines are topologically correct representations of stream networks delineated through adherence to data standards for 1:24,000 scale maps according to the NMAS. NHD flowlines are created through the combination of two data sources: (1) digital line graph 3 data (DLG-3), which are captured from 1:24,000 USGS topographic maps, and (2) reach file version 3 data (RF3), developed by the U.S.

Environmental Protection Agency which provide the starting point for reach delineation, direction of water flow information, and positions of geographic names (USGS, 2000).

As discussed in the delineation of WBD boundaries, a certain amount of approximation and generalization occurs on topographic maps. Specifically important is the information loss that occurs during the digitization of streams into lines. In map view, streams in the real world are seen as one smooth, continuous feature. In a GIS, however, streams are represented by lines as a series of straight segments connected to each other by nodes.

Regardless of the number of segments used to draw a curved shape, the line will never be as smooth as the real feature. The error is the gap between the stream as it exists in the real world and its representation as a line in the GIS. Nonetheless, as with WBD boundaries NHD flowlines are considered reliable representations of study streams and 52 their networks because they all have a horizontal positional accuracy of 12.2 meters in accordance with NMAS for 1:24,000 scale maps.

In addition to data collection and input error, the calculation of stream slope creates error in the data manipulation phase. The elevation of the head and end of a stream was determined using the Identify tool with NHD flowlines overlain on DEMs.

Accuracy in the identification of the start and endpoint of each stream was established by viewing streamlines at a large enough scale to eliminate inconsistencies in the 30 meter spatial resolution of the DEM. However, the exact start and endpoint of a stream remains unknown as NHD flowlines are only representations of actual streams. Also, variation in terrain elevation is undetectable below the 30 meter spatial resolution of the DEMs and, as a result, the elevation readings may not reflect the true elevation of the head and end of a stream.

Similarly, stream sinuosity is subject to error through the representation of streams as lines. The line metric tool in Hawth’s Analysis tools that was employed to determine sinuosity measures the distance between nodes and the straight line distance between nodes. The error in sinuosity calculations is a product of how well the NHD lines represent the true shape of the streams.

4.5.4 Hydrologic Variables

Error associated with the hydrologic variables in this study occurs in the data input and data usage stages. Any study assessing the flow regime of a stream is limited by the amount of data available (data input). These data are then used to make extrapolations as to the long-term flow regime of that stream (data usage). Error, then, is the difference between the calculated flow regime based on limited data and its actual 53 flow regime. As such, the more data that are available, the more accurate the depiction of the stream’s hydrologic regime. This study used annual discharge and peak streamflow as the data sources by which mean discharge, mean flood discharge, and flood stage recurrence interval were calculated. Annual discharge and peak streamflow were used because they contain the most hydrologic information, thus providing the most accurate depiction of streamflow processes.

A peak streamflow does not constitute a flood in the sense that water may not necessarily overtop the stream banks but instead is the highest flow in a water year. Peak streamflows are important in the analysis of stream hydrology because they give an indication of yearly high flow activity of a stream from which a stream’s propensity to flood can be attained. However, the use of peak streamflow data prevents the identification of multiple flood events in a water year because there is only one peak streamflow reading provided, thus making it impossible to discern if multiple flows overtopped a stream’s banks. Though these two data sources provide the best overall depiction of stream hydrology some information loss occurs when the amount of data of which these sources are comprised is reduced to a single number. The information lost through the generalization of data (data usage) is potential error.

4.6 STATISTICAL ANALYSES

Data were entered and derived variables calculated in Microsoft Excel to facilitate descriptive and exploratory statistical analyses. Study sites were categorized as pre-urban or urban based on TIA. Drainage basins with TIA ≤5% were classified as pre-urban; those with TIA >5 % were classified as urban. Descriptive statistics were calculated for 54 all variables. One-tailed comparison of means t-tests (α = 0.05) were carried out to determine the statistical significance of the differences between pre-urban and urban characteristics. Table 5 lists hypothesized outcomes of those between-sample comparisons. Subsequent procedures were conducted using the statistical package software SPSS 17.0.

Table 5.

Expected research outcomes for pre-urban versus urban watersheds.

Variable Hypothesized sample with the larger mean

Drainage Density Urban

Stream Slope Pre-urban

Stream Sinuosity Pre-urban

Mean Discharge Urban

Mean Flood Discharge Urban

Flood Stage Recurrence Interval Pre-urban

Bivariate correlation analysis determined the strength and statistical significance

of associations between the variable pairs. One-tailed Spearman’s rank correlation tests

(α = 0.05) were separately conducted on data from pre-urban and urban sites. Spearman’s

rank correlation is the nonparametric counterpart to the more commonly used Pearson’s r

correlation test. Of the two, Pearson’s r is regarded as the more robust measure of

correlation between two variables, however, the data gathered for this research do not 55 meet a key assumption necessary to utilize this parametric statistical test. Pearson’s r is only appropriate for measuring the degree of relationship between variables which are linearly related (Kachigan, 1986). This research is concerned with the geomorphic characteristics of stream channels at varying levels of impervious surface cover (TIA), and therefore variables have different trending associations depending on the percentage of TIA present in a given drainage basin. It cannot be assumed that the associations between the studied variables are linear and, as such, the nonparametric Spearman’s rank correlation test was used to determine the strength and significance between variable pairs.

Individual class correlation analysis was necessary because the two sets of data were expected to have different responses to TIA values. It is hypothesized for this study that pre-urban sites represent natural drainage systems and, as such, other measured variables should not have significant relationships with TIA. Also, within pre-urban sites, it is expected that drainage area, stream order, stream length, and sinuosity will be positively correlated with each other while stream slope should be negatively correlated with these variables. There is also an expectation that the two indices of topographic texture along with the hydrologic variables, including flood stage recurrence interval, will be positively correlated with each other within pre-urban and urban study sites.

Given the documented influence of impervious surface cover on drainage basin processes, it is expected that the measured variables will have a significant correlation with TIA within urban study sites, except for stream order and stream length because no significant association with TIA and these two morphologic parameters has been established. Positive correlations are expected between TIA and the four discharge 56 variables because it is assumed that the rate of water flowing into a given stream will increase with an increase in TIA. A significant negative association between flood stage recurrence interval and TIA is expected as increasing amounts of impervious surfaces should decrease the time between flood events.

Cluster analysis was used to determine if study sites empirically group according to extent of impervious surfaces, thus supporting the notion that watershed hydrology and stream-reach morphology are associated with TIA to a degree of statistical confidence.

Cluster analysis is a multivariate classification method used to arrange a set of cases into homogenous groups or clusters. The aim of the technique is to establish a set of clusters in which cases are more similar to each other than they are to cases in other clusters. The placement of cases in one cluster or another is based on measuring the dissimilarity between cases. There are a number of ways to measure dissimilarity among cases; either through a similarity measure or distance measure. The method used is dependent on project goals, but in the end the decision is left to the researcher to resolve which method will yield the most parsimonious classifications. Ultimately, there is no single correct classification; there is just the attempt to find the most optimal groupings given project circumstances. For this thesis research, nonhierarchical, also known as k-means clustering, as opposed to hierarchical, clustering was selected based on the scatter plots of the variables and TIA, which suggested four possible distinct groupings within urban sites.

Cluster analysis was conducted under two priorities; to assess whether study sites would group according to levels of urban development, and if so, to identify the percentage of TIA associated with the hydrologic and morphologic characteristics of the 57 various clusters. K-means cluster analysis was performed three times to identify five, four, and then three clusters under the criteria of ten iterations and a convergence of zero.

The analysis was applied multiple times under different conditions to ensure that the most parsimonious groupings were identified. Due to the small number of cases within each grouping and the non-linear relationship between the studied variables, as discussed earlier in regards to Spearman’s rank correlation, the nonparametric Kruskal-Wallis test

(α = 0.05) was used to determine if the differences between the characteristics of the within-sample categories were statistically significant.

58 CHAPTER 5: RESULTS

5.1 PRE-URBAN VERSUS URBAN WATERSHED CHARACTERISTICS

Individual and summary data collected for the sampled pre-urban and urban watersheds are presented in Tables 6 and 7, respectively. Mean values from the two tables show that, as a group, the pre-urban sites have the larger drainage area, drainage density, stream frequency, stream order, stream length, slope, and flood stage recurrence interval, whereas the urban sites have greater values of mean discharge, normalized mean discharge, mean flood discharge, and normalized mean flood discharge. Recall that TIA values were used to classify a stream as pre-urban or urban, therefore mean TIA is also much higher for the urban compared to the pre-urban sites. The mean of the sinuosity measurements, on the other hand, is the same for the two groups.

The t-test was used to determine if the differences in the sample means are statistically significant at the 0.05 level. Results show that the pre-urban data set has significantly larger drainage density, stream frequency, and slope values than the urban data set (Table 8). In other words, the pre-urban study areas are steeper and more intensely channeled (finer topographic texture) than the urban study sites. Table 8 also reveals that TIA, mean discharge, normalized mean discharge, and normalized mean flood discharge are significantly greater in the urban compared to the pre-urban study areas. The two groups do not differ significantly in drainage area, stream order, stream length, sinuosity, mean flood discharge, or flood stage recurrence interval.

Table 6. Observations and descriptive statistics obtained for the pre-urban study sites. Drainage Drainage Stream Stream Mean Mean Mean Flood Mean Flood Area Density Frequency Strahler Length Discharge Discharge/Unit Discharge Discharge/Unit Label TIA (%) (km2) (km/km2) (#/km2) Order (km) Slope Sinuosity (m3/sec) Area (m3/sec) (m3/sec) Area (m3/sec) RI (yrs) P1 0.34 279.18 0.90 0.49 4 38.02 0.00360 1.17 1.67 0.00598 86.09 0.30837 1.47 P2 0.78 553.58 0.77 0.35 4 27.72 0.00382 1.12 0.69 0.00125 60.82 0.10987 4.40 P3 0.81 40.71 0.99 0.47 3 14.75 0.02964 1.26 0.46 0.01130 29.29 0.71940 0.02 P4 0.81 65.60 1.33 0.96 4 18.31 0.00911 1.15 0.67 0.01021 17.92 0.27318 3.86 P5 0.93 408.28 0.81 0.37 4 27.67 0.00016 1.12 33.92 0.08308 421.43 1.03220 1.62 P6 1.08 650.94 0.69 0.27 4 57.28 0.00067 1.40 6.72 0.01032 195.41 0.30020 1.52 P7 1.13 253.21 1.14 0.57 5 49.22 0.02202 1.30 5.67 0.02239 116.25 0.45911 5.67 P8 1.24 460.62 0.88 0.40 4 71.78 0.03004 1.21 5.18 0.01125 91.54 0.19873 4.00 P9 1.36 538.15 0.87 0.51 5 64.03 0.00280 1.22 4.24 0.00788 101.62 0.18883 16.00 P10 1.39 402.23 0.93 0.47 4 48.71 0.01682 1.20 3.61 0.00897 76.58 0.19039 6.20 P11 1.46 351.43 0.94 0.45 4 58.22 0.01799 1.35 8.19 0.02330 138.79 0.39493 4.90 P12 1.86 501.00 0.92 0.51 5 50.83 0.00401 1.09 50.08 0.09996 680.02 1.35733 5.60 P13 2.00 461.41 1.09 0.53 5 78.74 0.02394 1.28 3.28 0.00711 89.39 0.19373 1.39 P14 2.05 738.56 0.92 0.42 5 82.44 0.01534 1.18 18.17 0.02460 251.68 0.34077 2.37 P15 2.14 259.14 1.11 0.53 4 50.33 0.01523 1.15 40.08 0.15466 381.94 1.47387 1.51 P16 2.18 205.48 0.89 0.48 4 39.89 0.00270 1.21 6.24 0.03037 166.64 0.81098 2.22 P17 2.18 602.06 0.86 0.42 4 64.79 0.00259 1.10 49.20 0.08172 678.66 1.12722 1.34 P18 2.41 623.95 0.88 0.46 4 57.21 0.02164 1.16 5.33 0.00854 107.30 0.17197 8.43 P19 2.51 338.52 1.20 0.56 4 59.03 0.00823 1.32 4.90 0.01447 173.53 0.51262 3.38 P20 2.87 252.90 1.05 0.54 4 25.72 0.00324 1.16 10.08 0.03986 217.62 0.86048 8.22 P21 2.89 186.11 1.01 0.45 4 36.10 0.01211 1.07 14.08 0.07566 209.18 1.12398 2.34 P22 3.03 388.95 0.84 0.39 4 46.41 0.00461 1.18 33.28 0.08556 430.55 1.10695 1.41 P23 3.07 301.43 1.13 0.57 4 58.74 0.02343 1.27 3.39 0.01125 123.52 0.40978 1.85 P24 3.48 268.15 0.65 0.23 4 48.07 0.00795 1.13 1.47 0.00548 78.87 0.29413 9.00 P25 3.67 459.23 0.87 0.51 4 66.83 0.00161 1.19 6.60 0.01437 118.34 0.25769 18.00 P26 4.06 487.79 0.93 0.50 5 71.58 0.00176 1.17 9.81 0.02011 264.50 0.54224 1.18 P27 4.39 133.93 0.84 0.43 4 29.58 0.00532 1.81 1.19 0.00888 42.86 0.32001 14.00 P28 5.35 668.32 0.51 0.28 4 42.92 0.00288 1.14 3.99 0.00597 96.88 0.14496 3.75 P29 5.48 426.03 0.90 0.51 5 52.00 0.00665 1.24 4.32 0.01014 157.46 0.36960 2.17 Mean 2.31 389.89 0.93 0.47 4.21 49.55 0.01034 1.22 11.60 0.02976 193.26 0.49569 4.75 Median 2.14 402.23 0.90 0.47 4.00 50.33 0.00665 1.18 5.33 0.01325 123.52 0.30709 3.38 ST Dev 1.35 180.89 0.17 0.13 0.49 17.53 0.00921 0.14 14.66 0.08103 172.15 0.95169 4.56

Table 7. Observations and descriptive statistics obtained for urban study sites. Drainage Drainage Stream Stream Mean Mean Mean Flood Mean Flood Area Density Frequency Strahler Length Discharge Discharge/Unit Discharge Discharge/Unit Label TIA (%) (km2) (km/km2) (#/km2) Order (km) Slope Sinuosity (m3/sec) Area (m3/sec) (m3/sec) Area (m3/sec) RI (yrs) U1 6.10 365.82 1.09 0.60 5 50.56 0.003627 1.09 183.69 0.50213 1105.81 3.02281 1.27 U2 6.38 305.36 1.10 0.55 4 54.16 0.004005 1.07 98.31 0.32195 627.74 2.05577 6.00 U3 7.60 411.45 0.86 0.42 4 41.49 0.008929 1.19 68.02 0.16532 623.74 1.51597 4.00 U4 7.63 109.08 1.12 0.56 3 32.24 0.041357 1.19 0.51 0.00468 20.68 0.18959 1.71 U5 7.69 416.03 0.85 0.36 4 43.32 0.000612 1.16 22.28 0.05355 353.64 0.85002 3.63 U6 7.90 249.78 0.96 0.52 4 56.17 0.004589 1.29 33.99 0.13608 366.92 1.46898 1.67 U7 8.09 262.23 1.52 0.77 4 60.81 0.002804 1.27 12.98 0.04950 301.62 1.15023 1.13 U8 9.43 279.14 1.10 0.56 4 43.08 0.006486 1.17 77.69 0.27832 700.05 2.50788 1.59 U9 11.17 573.35 0.83 0.44 4 68.66 0.003373 1.13 6.86 0.01197 81.33 0.14185 3.27 U10 12.33 452.95 0.59 0.22 4 50.86 0.003844 1.17 3.72 0.00821 81.56 0.18006 14.50 U11 12.89 282.42 0.98 0.49 4 51.93 0.018109 1.25 33.77 0.11957 434.84 1.53967 1.18 U12 13.98 639.10 0.84 0.48 5 59.74 0.002058 1.15 3.63 0.00568 113.62 0.17778 1.52 U13 14.23 434.50 0.78 0.40 4 59.60 0.003743 1.24 14.36 0.03305 212.12 0.48820 4.17 U14 16.08 274.91 0.77 0.33 4 27.55 0.001013 1.13 52.17 0.18977 318.47 1.15844 5.14 U15 24.20 791.49 0.71 0.32 5 77.47 0.001513 1.28 9.57 0.01209 162.03 0.20472 1.40 U16 30.47 683.15 0.57 0.15 5 68.25 0.003823 1.41 15.78 0.02310 133.57 0.19552 1.47 U17 34.17 136.07 0.77 0.36 3 30.10 0.004894 1.45 1.93 0.01418 54.58 0.40112 1.20 U18 36.91 644.17 0.58 0.16 4 66.77 0.003882 1.40 8.78 0.01363 212.34 0.32963 4.50 U19 37.67 268.62 0.87 0.42 4 44.35 0.007409 1.14 3.20 0.01191 67.85 0.25259 1.09 U20 48.65 100.99 0.57 0.21 3 21.16 0.007131 1.13 1.42 0.01406 94.95 0.94015 1.05 U21 53.15 102.94 0.53 0.20 3 18.11 0.007730 1.20 1.38 0.01341 101.40 0.98505 1.48 U22 53.33 365.58 0.76 0.37 4 58.02 0.004878 1.12 61.32 0.16773 454.63 1.24357 2.25 U23 59.39 338.41 0.58 0.20 4 35.89 0.004201 1.34 3.83 0.01132 194.57 0.57495 2.11 Mean 22.58 369.02 0.84 0.40 4.00 48.71 0.00652 1.22 31.27 0.08473 296.44 0.80330 2.93 Median 13.98 338.41 0.83 0.40 4.00 50.86 0.00401 1.19 12.98 0.03836 212.12 0.62681 1.67 ST Dev 17.66 192.18 0.24 0.16 0.60 15.91 0.00839 0.11 43.57 0.22669 265.28 1.38039 2.92 61 Table 8. Results of difference of means t-test comparing urban versus pre-urban study sites (bold indicates α < 0.05).

Variable Sig. (1-tailed) Larger Mean

TIA (%) 0.000 Urban Drainage Area (km2) 0.380 - Drainage Density (km/km2) 0.020 Pre-Urban Stream Frequency (#/km2) 0.019 Pre-Urban

Strahler Order 0.129 - Stream Length (km) 0.479 -

Slope 0.026 Pre-Urban

Sinuosity 0.327 - Mean Discharge (m3/sec) 0.050 Urban Mean Discharge/Unit Area (m3/sec) 0.000 Urban Mean Flood Discharge (m3/sec) 0.135 - Mean Flood Discharge /Unit Area (m3/sec) 0.004 Urban Recurrence Interval (yrs) 0.138 -

5.2 ASSOCIATIONS BETWEEN VARIABLES

Within each sample, Spearman's rank correlation was used to determine if

significant associations exist between variables at the 0.05 level (Tables 9 and 10). As

expected, both samples show a significant positive correlation between drainage basin area and stream length, and between stream length and stream order. Likewise, the two measures of extent of channelization, drainage density and stream frequency, are positively correlated with each other in both groups. It is also not surprising that the four 62 discharge variables are positively correlated with each other within the pre-urban and urban categories.

Among pre-urban locations (Table 9), TIA, which is ≤ 5%, is not significantly correlated with the other variables. In addition to longer streams, larger pre-urban drainage basins are associated with smaller drainage densities, smaller stream frequencies, and smaller normalized flood discharges. Drainage density correlates positively with slope and normalized flood discharge as well as with stream frequency.

Stream slope correlates negatively with mean discharge and mean flood discharge. Pre- urban streams with higher mean discharge tend to be somewhat less sinuous than those with lower mean discharge. Pre-urban streams with higher mean flood discharge tend to flood more frequently than those with lower mean flood discharge. 63

Table 9. Spearman's rank correlation coefficients for pre-urban study site variable pairs (bold where α< 0.05). Drainage Stream Mean Mean Mean Flood Mean Flood Drainage Area Density Frequency Stream Length Discharge Discharge/Unit Discharge Discharge/Unit TIA (%) (km2) (km/km2) (#/km2)Strahler Order (km) Slope Sinuosity (m3/sec) Area (m3/sec) (m3/sec) Area (m3/sec)

Drainage Area (km2) 0.02 Drainage Density 2 (km/km ) -0.12 -0.48

Stream Frequency (#/km2)0.01-0.84 0.83 Strahler Order 0.12 0.40 0.15 0.29

Stream Length (km) 0.22 0.60 0.05 0.11 0.52 Slope -0.14 -0.27 0.56 0.25 -0.04 0.11

Sinuosity 0.00 -0.15 0.22 0.24 0.08 0.29 0.31 Mean Discharge (m3/sec)0.160.27-0.01-0.060.230.28-0.32 -0.32

Mean Discharge/ Unit Area (m3/sec)0.06-0.180.300.170.020.03-0.11-0.19 0.84

Mean Flood Discharge (m3/sec) 0.26 0.27 0.02 0.00 0.27 0.28 -0.38 -0.27 0.94 0.82 Mean Flood Discharge/ Unit Area (m3/sec) 0.12 -0.38 0.31 0.18 -0.04 -0.15 -0.12 -0.20 0.66 0.89 0.73 RI (yrs) 0.08 -0.07 -0.11 0.07 0.00 -0.10 0.09 0.06 -0.24 -0.30 -0.40 -0.44 64

Table 10. Spearman’s rank correlation coefficients for urban study site variable pairs (bold where α< 0.05). Drainage Stream Mean Mean Mean Flood Mean Flood Drainage Area Density Frequency Stream Length Discharge Discharge/Unit Discharge Discharge/Unit TIA (%) (km2) (km/km2) (#/km2)Strahler Order (km) Slope Sinuosity (m3/sec) Area (m3/sec) (m3/sec) Area (m3/sec) Drainage Area (km2) -0.06 Drainage Density 2 (km/km ) -0.77 -0.25 Stream Frequency (#/km2) -0.72 -0.28 0.95 Strahler Order -0.20 0.77 0.06 0.07 Stream Length (km) -0.11 0.73 0.05 0.09 0.68 Slope 0.14 -0.57 0.09 0.09 -0.60 -0.49

Sinuosity 0.28 0.07 -0.28 -0.37 -0.06 0.14 0.12

Mean Discharge (m3/sec) -0.49 0.25 0.39 0.35 0.49 0.22 -0.22 -0.29 Mean Discharge/ Unit Area (m3/sec) -0.39 -0.11 0.35 0.34 0.16 -0.06 -0.06 -0.29 0.89 Mean Flood Discharge (m3/sec) -0.42 0.12 0.37 0.34 0.38 0.10 -0.11 -0.24 0.93 0.87

Mean Flood Discharge/ Unit Area (m3/sec) -0.30 -0.38 0.39 0.38 -0.04 -0.31 0.24 -0.23 0.73 0.88 0.85 RI (yrs) -0.23 0.39 -0.09 -0.17 0.03 0.08 -0.25 -0.18 0.30 0.11 0.22 -0.05 65 The urban watershed data display more significant correlations among the studied variables than the pre-urban do (Table 10). TIA correlates negatively with drainage density, stream frequency, mean discharge, mean discharge per unit area, and mean flood discharge; drainage basins having a greater percentage of impervious surfaces are less intensely channeled and tend to have smaller peak discharges than basins with lower TIA.

In addition to the longer trunk streams and higher stream order, larger urban watersheds are associated with lower slope, smaller normalized flood discharge, and longer flood stage recurrence interval. Drainage density correlates positively not only with stream frequency, but also the four discharge variables. There is a tendency for more intensely channeled urbanized watersheds to have larger mean and flood discharges and less sinuous channels. Streams of higher order are not only longer and associated with larger drainage basins, but also exhibit lower slope and higher mean and mean flood discharges.

In summary, within-sample correlation tests indicate interesting associations exist between some variables in both categories. Among pre-urban sites, it is surprising that higher mean flood discharge is associated with more frequent flooding because flood events of greater magnitude usually occur less often than flood events of smaller magnitude. Within urban sites, the indication that watersheds which have a greater percentage of impervious surfaces are less intensely channeled, tend to have smaller mean and peak discharges, and are accompanied by a modest association with increasing sinuosity is quite unexpected.

When plotted against TIA, the measured urban watershed variables show a similar pattern (Figures 8, 9, 10, and 11). These four graphical representations are examples of a pattern that is seen by all research variables within urban sites when plotted against TIA, 66 that three distinct classes may exist within urban study sites based on TIA values: 5 –

16% TIA, 16 – 37% TIA, and 37 – 59% TIA (Figures 8, 9, 10, and 11). Not considering outliers, cases appear to be homogenous within these classes across research variables.

Therefore, 16% TIA may be a significant threshold of impervious surface cover at which point demonstrable geomorphic adjustments begin. Further statistical analyses are needed to investigate the authenticity of the TIA groupings within the urban study sites and determine whether 16% TIA is a significant threshold of impervious surface cover.

Slope v TIA 0.045 0.040 0.035 0.030 0.025

Slope 0.020 0.015 0.010 0.005 0.000 0 10203040506070 TIA

Figure 8. Scatter plot of slope and TIA among urban study sites showing within class groupings.

67 Drainage Density v TIA 1.6 1.4 1.2 1.0 Density 0.8 0.6

Drainage 0.4 0.2 0.0 0 10203040506070 TIA

Figure 9. Scatter plot of drainage density and TIA among urban study sites showing within class groupings.

Mean Discharge/Unit Area v TIA 0.6

0.5

0.4 (m3/sec) 0.3

0.2

Discharge 0.1

0.0 0 10203040506070 TIA

Figure 10. Scatter plot of normalized mean discharge and TIA among urban study sites showing within class groupings.

68 Recurrence Interval v TIA 16 14 12 10

RI 8 6 4 2 0 0 10203040506070 TIA

Figure 11. Scatter plot of flood stage recurrence interval and TIA among urban study sites showing within class groupings.

5.3 CLUSTER ANALYSIS

Nonhierarchical cluster analysis (k-means) indicates that three distinct classes exist within urban study sites, supporting the notion that significant associations do exist between TIA and other studied variables (Table 11). Cluster 1 was eliminated as a distinct class based on the membership of only one study site, which suggests that the group was created as a result of an outlier in the data. Cluster 2 was renamed Low Urban based on a final cluster center of 14% TIA. Despite two of the nine study sites within the low urban category having TIA values above 14 % the group is considered homogenous based on the close similarity of the other seven TIA values. The two sites with TIA >

14% (U14 and U22) are considered outliers and do not reflect the average behavior of variables within the category. Cluster 3was renamed Medium Urban based on a cluster 69 center of 20% TIA. Cluster 4 was renamed High Urban based on a final cluster center of

40% TIA. While each cluster contains at least one questionable site, groupings are considered homogenous based on their high within-class similarity.

Individual and summary data collected for the low, medium, and high urban watersheds are presented in Tables 12, 13, and 14, respectively. Mean values from the three tables indicate that the medium and high urban sites exhibit less intense channelization, moderate increase in sinuosity, and lower discharge values than the low urban sites, thus supporting the associations found during within-sample correlation. The

Kruskal-Wallis test was used to determine if the differences in variable medians for the clusters are statistically significant at the 0.05 level (Table 15). Results show that significant differences exist between the research variables of the three cluster groupings except for stream sinuosity and flood stage recurrence interval.

70 Table 11.

Final cluster centers. Cluster Low Med High Variable Outlier Urban Urban Urban TIA (%) 6.10 14.38 20.47 40.11 Drainage Area (km2) 365.82 316.32 602.67 176.02 Drainage Density (km/km2) 1.09 0.99 0.70 0.74 Stream Frequency (#/km2) 0.60 0.49 0.31 0.32 Strahler Order 5 4 4 3 Stream Length (km) 50.56 48.50 64.48 30.31 Slope 0.00363 0.00571 0.00318 0.01212 Sinuosity 1.09 1.18 1.25 1.24 Mean Discharge (m3/sec) 183.69 51.17 8.96 2.05 Mean Discharge/Unit Area 0.502 0.165 0.015 0.012 (m3/sec) Mean Flood Discharge 1,105.81 464.63 142.37 89.01 (m3/sec) Mean Flood Discharge/Unit 3.023 1.499 0.245 0.557 Area (m3/sec) RI (yrs) 1.27 2.96 4.40 1.44 71

Table 12. Low Urban category. Mean Flood Drainage Area Drainage Density Stream Frequency Stream Length Mean Discharge Mean Discharge/ Mean Flood Discharge Discharge/ Unit Label TIA (km2) (km/km2) (#/km2)Strahler Order (km) Slope Sinuosity (m 3/sec) Unit Area (m3/Sec) (m3/sec) Area (m3/sec) RI (yrs) U2 6.38 305.36 1.10 0.55 4.00 54.16 0.00401 1.07 98.31 0.32195 627.74 2.05577 6.00 U3 7.60 411.45 0.86 0.42 4.00 41.49 0.00893 1.19 68.02 0.16532 623.74 1.51597 4.00 U5 9.43 279.14 1.10 0.56 4.00 43.08 0.00649 1.17 77.69 0.27832 700.05 2.50788 3.63 U6 7.69 416.04 0.85 0.36 4.00 43.32 0.00061 1.16 22.28 0.05355 353.64 0.85002 1.67 U7 7.90 249.78 0.96 0.52 4.00 56.17 0.00459 1.29 33.99 0.13608 366.92 1.46898 1.13 U8 8.09 262.23 1.52 0.77 4.00 60.81 0.00280 1.27 12.98 0.04950 301.62 1.15023 1.59 U11 12.89 282.42 0.98 0.49 4.00 51.93 0.01811 1.25 33.77 0.11957 434.84 1.53967 1.18 U14 16.08 274.91 0.77 0.33 4.00 27.55 0.00101 1.13 52.17 0.18977 318.47 1.15844 5.14 U22 53.33 365.58 0.76 0.37 4.00 58.02 0.00488 1.12 61.32 0.16773 454.63 1.24357 2.25 Mean 8.57 315.20 1.05 0.52 4.00 50.14 0.00650 1.20 49.58 0.16061 486.94 1.58407 2.74 Median 7.90 282.42 0.98 0.52 4.00 51.93 0.00459 1.19 33.99 0.13608 434.84 1.51597 1.67 St. Dev. 2.10 69.51 0.23 0.13 0.00 7.54 0.00576 0.08 31.84 0.10488 159.78 0.55118 1.85

Table 13. Medium Urban category. Mean Flood Drainage Area Drainage Density Stream Frequency Stream Length Mean Discharge Mean Discharge/ Mean Flood Discharge Discharge/ Unit Label TIA (km2) (km/km2) (#/km2)Strahler Order (km) Slope Sinuosity (m3/sec) Unit Area (m3/Sec) (m3/sec) Area (m3/sec) RI (yrs) U9 11.17 573.35 0.83 0.44 4.00 68.66 0.00337 1.13 6.86 0.01197 81.33 0.14185 3.27 U10 12.33 452.95 0.59 0.22 4.00 50.86 0.00384 1.17 3.72 0.00821 81.56 0.18006 14.50 U12 13.98 639.10 0.84 0.48 5.00 59.74 0.00206 1.15 3.63 0.00568 113.62 0.17778 1.52 U13 14.23 434.50 0.78 0.40 4.00 59.60 0.00374 1.24 14.36 0.03305 212.12 0.48820 4.17 U15 24.20 791.49 0.71 0.32 5.00 77.47 0.00151 1.28 9.57 0.01209 162.03 0.20472 1.40 U16 30.47 683.15 0.57 0.15 5.00 68.25 0.00382 1.41 15.78 0.02310 133.57 0.19552 1.47 U18 36.91 644.17 0.58 0.16 4.00 66.77 0.00388 1.40 8.78 0.01363 212.34 0.32963 4.50 Mean 17.73 595.76 0.72 0.34 4.50 64.10 0.00306 1.23 8.99 0.01568 130.71 0.23135 4.39 Median 14.23 639.10 0.71 0.32 4.00 66.77 0.00374 1.24 8.78 0.01209 133.57 0.19552 3.27 St. Dev. 10.15 126.98 0.12 0.14 0.53 8.54 0.00098 0.12 4.77 0.00951 55.49 0.12231 4.64

Outlier 72

Table 14. High Urban category. Mean Flood Drainage Area Drainage Density Stream Frequency Stream Length Mean Discharge Mean Discharge/ Mean Flood Discharge Discharge/ Unit Label TIA (km2) (km/km2) (#/km2)Strahler Order (km) Slope Sinuosity (m3/sec) Unit Area (m3/Sec) (m3/sec) Area (m3/sec) RI (yrs) U4 7.63 109.08 1.12 0.56 3.00 32.24 0.04136 1.19 0.51 0.00468 20.68 0.18959 1.71 U17 34.17 136.07 0.77 0.36 3.00 30.10 0.00489 1.45 1.93 0.01418 54.58 0.40112 1.20 U19 37.67 268.62 0.87 0.42 4.00 44.35 0.00741 1.14 3.20 0.01191 67.85 0.25259 1.09 U20 48.65 100.99 0.57 0.21 3.00 21.16 0.00713 1.13 1.42 0.01406 94.95 0.94015 1.05 U21 53.15 102.94 0.53 0.20 3.00 18.11 0.00773 1.20 1.38 0.01341 101.40 0.98505 1.48 U23 59.39 338.41 0.58 0.20 4.00 35.89 0.00420 1.34 3.83 0.01132 194.57 0.57495 2.11 Mean 46.60 189.41 0.66 0.28 3.40 29.92 0.00627 1.25 2.35 0.01298 102.67 0.63077 1.44 Median 48.65 136.07 0.58 0.21 3.00 30.10 0.00713 1.20 1.93 0.01341 94.95 0.57495 1.34 St. Dev. 10.55 107.95 0.15 0.10 0.55 10.73 0.00161 0.14 1.11 0.00129 54.85 0.32408 0.41

Outlier 73

Table 15. Results of Kruskal-Wallis test comparing the variables of the three cluster groupings (bold where α< 0.05).

Variable Sig.

TIA (%) .001 Drainage Area (km2) .002 Drainage Density (km/km2) .004 Stream Frequency (#/km2) .018

Strahler Order .009 Stream Length (km) .003

Slope .033

Sinuosity .929 Mean Discharge (m3/sec) .001 Mean Discharge/Unit Area (m3/sec) .002 Mean Flood Discharge (m3/sec) .002 Mean Flood Discharge /Unit Area (m3/sec) .001 Recurrence Interval (yrs) .150

The spatial distribution of the cluster analysis results indicates that the variation

among the physical characteristics, i.e., lithology and climate, among the physiographic

provinces over which the urban study sites are spread may be contributing to some of the

trends identified through within-class correlation analysis (Figure 12). Study sites

classified as Low urban are found in each of the three physiographic provinces and are

located farther away from the metropolitan Atlanta area compared to the sites belonging

to the two other cluster categories. However, five of the seven study sites in the Low 74

urban category are located within the Ridge and Valley physiographic province and therefore may be contributing to the trend of increasing sinuosity values at greater levels of TIA by way of valley confinement resulting from the area’s topography. As expected, study sites belonging to Medium and High urban categories are located in the Piedmont physiographic province, which contains the greater Atlanta area.

The spatial distribution of the cluster analysis outliers indicates factors other than site TIA and proximity to the greater Atlanta area may be influencing the group membership of some study sites. Study site U22 is an outlier of the Low urban category and is located between two High urban sites. This may be attributed its large drainage area creating the higher mean, flood, and normalized discharges more indicative of study sites at higher levels of TIA. Study U4 is the outlier of the High urban category and is located in the Ridge and Valley physiographic province. Based on its low TIA value, its location next to Low urban sites, and the substantial distance between it and the greater

Atlanta area it is unexpected that cluster analysis placed site U4 in the High urban category. However, the geomorphic characteristics of U4 more closely resemble High urban sites due to U4’s small drainage area.

Figure 12. Results of cluster analysis including the spatial distribution of statistical outliers.

CHAPTER 6: DISCUSSION

The difference of means t-tests indicate that the studied urban sites exhibit a lower

slope, are less intensely channeled, and have greater mean discharge, normalized mean

discharge, and normalized mean flood discharge than the studied pre-urban streams

(Table 8). Within-sample correlation of urban study sites found that watersheds with a

greater degree of urbanization (TIA) are less intensely channeled, i.e., have coarser

topographic texture, exhibit smaller mean and flood discharges, and have some tendency

to be slightly more sinuous than channels with smaller TIA (Table 10). The relationship between sinuosity and TIA is inferred from the negative correlation of sinuosity with

stream frequency, which, in turn, is negatively correlated with TIA. The within-sample

relations might be a product of artificial alteration of streams in the most densely

urbanized areas so that flow is generally contained in those channels. Another possibility,

however, is that the enhanced overland flow into those channels, which is known to result

from greater TIA, has expanded channel capacity by erosion, that is, through an increase

in channel depth and/or width. The tendency for greater stream sinuosity in those

watersheds with larger TIA might indicate that those streams are just beginning to

achieve dynamic equilibrium with the urban hydrologic regime, and starting to erode the

channel sides, thereby increasing stream sinuosity. The combination of lower mean and

flood discharges, fewer channels, and slightly greater sinuosity with increasing TIA may

suggest that flooding is on the brink of increasing for those urbanized areas if they are approaching equilibrium. 77

The association between higher sinuosity at greater amounts of TIA indicates an

adjustment to urban hydrology not present in similar studies. As a watershed becomes

more urbanized, water enters the system at a faster rate. In the absence of human

interference, i.e., stream management or dams, the presence of sinuous channels within

urbanized watersheds is a natural geomorphic adjustment, given that urban streams were found to have significantly lower slopes than pre-urban streams. An initial increase in

discharge will lower slope as the elevated slope is no longer needed to create energy for

the system, thus allowing a channel to become more sinuous. This finding is further

supported by the spatial distribution of urban study sites.

The majority of the urban study sites are located in the Ridge and Valley and

Piedmont physiographic provinces, totaling 8 and 14, respectively, with only one site

(U6) in the Blue Ridge province (Figure 13). Urban streams located in the Ridge and

Valley region have lower sinuosity values than urban streams in the Piedmont province, and this is probably due to the area’s different geologic history and topography. Valley confinement resulting from the series of parallel ridges and valleys of the region limits the extent to which streams are allowed to meander. Streams in the Piedmont province are not bound by such topographic constrictions, and therefore evolve more freely as dictated by water and sediment input. Urban streams on the Piedmont were found to be more sinuous than non-urban streams in the same area, supporting the conclusion that the studied urban streams may have reached a dynamic equilibrium and have become more sinuous, or at least as sinuous, as non-urban streams. 78

Figure 13. Physiographic provinces of Georgia highlighting the spatial distribution of urban study sites. 79

The three phases of sedimentation provided by Wolman (1967) indicate that when

a stream is altered from its initial, pre-urban equilibrium it will aggrade during the

construction phase of urban development, and then degrade during the final, urban phase,

once a watershed’s landscape is predominantly covered by impervious surfaces. During

these phases, the dynamic balance between water and sediment input is disrupted, due in

large part to impervious surface cover increasing the rate at which water enters a stream

network and decreasing the sediment yield conveyed to a given stream channel. It is expected that a stream will become straighter, i.e., that sinuosity will decrease as the increased volume of water now present in the channel and lowered sediment supply increases a stream’s ability to erode its bed and banks.

Wolman's (1967) three-phase model of stream response to urbanization, which is supported by previous studies (Chin, 2006; Grable and Harden, 2006), implies that the geomorphic adjustments of stream channels associated with watershed urbanization are static because it ends with the urban landscape and does not consider the geomorphic characteristics of stream systems in watersheds that have been influenced by impervious surface cover for some time. Results of this research, however, suggest that stream systems can eventually adjust to the urban hydrologic regime and find a new dynamic balance between water and sediment input. The urban landscape, dominated by impervious surfaces, will lead to greater stream discharge than was present during the pre-urban phase, and this allows channels to lower their slope because gravity is no longer needed to create energy for the stream system. Once a stream has reached a new 80 dynamic equilibrium, the stream responds to the accompanied low slope by increasing channel sinuosity.

The second objective of this research was the identification of the amount of impervious surface cover associated with the modification of stream hydrology and morphologic characteristics. Visual analysis of the plots of other research variables versus TIA for the urban study sites suggests that geomorphic changes occur at 16% TIA.

Results of cluster analysis also indicate that significant geomorphic changes may begin around this amount of impervious surface cover because 14% TIA is the final cluster center of the low urban category. Based on the results of this research, it can be stated that significant fluvial geomorphic alterations in the studied stream channels occur above

14-16% TIA.

The suggestion that 14-16% TIA is the percentage of impervious surface cover associated with geomorphic alterations of the studied stream channels does not coincide with the majority of previously cited research. Paul and Meyer (2001), Hession et al.

(2003), Cianfrani et al. (2006), and Kang and Marston (2006) report demonstrable changes occurring in stream channels with watershed TIA below 10%. This difference may be due to the previously mentioned studies being predominantly concerned with the adverse effects of urban development on stream ecology and overall aquatic degradation and not specifically focused on geomorphic adjustments. Also, previous research has mostly been conducted in the northeastern and Pacific Northwest regions of the U.S. with different climate and lithology than the study area of this research. The results presented in this research suggest that there may be differing responses to urban development in 81

different geographic regions. However, when compared to previous research focused on

the geomorphic adjustment of stream channels associated with impervious surface cover

the results of this research agree with the findings of Booth and Jackson (1997), which states that stream channels become unstable as watershed impervious surface cover exceeds 10%.

CHAPTER 7: CONCLUSION

Fifty two streams and their respective watersheds with varying levels of TIA were examined in northern Georgia. Twelve research variables were analyzed using scatter plots, Spearman’s rank correlation, and non-hierarchical cluster analysis in an attempt to understand the relationship among and between the studied variables and TIA.

Specifically, the project had two research objectives: to determine changes in stream hydrology and stream-reach morphology associated with urban development and to identify the level of impervious surface associated with the observed geomorphic alterations.

Urban study areas have lower slope, are less intensely channeled (coarser topographic texture), and have greater mean discharge and mean flood discharge values than the studied pre-urban streams. Among the studied urban sites, locations at higher levels of TIA, i.e., greater amounts of impervious surface cover, are less intensely channeled, tend to have smaller mean and flood discharges, and are accompanied by a moderate increase in channel sinuosity. This research indicates significant geomorphic modifications of the studied stream channels in northern Georgia occur at watershed TIA levels above14-16%. These results imply that the amount of time a watershed has been subject to urban development may significantly affect the geomorphic characteristics of stream networks at the drainage basin and stream-reach scales.

The results of this research indicate that the three-phase model of sedimentation and urban development proposed by Wolman (1967) is accurate, yet may be incomplete.

The geomorphic impacts of urban development on stream channels do not cease once a 83

watershed’s landscape has become dominated by impervious surfaces. Streams are

dynamic features that tend to adjust to the water and sediment yield provided by their watershed. A fourth and final, post-urban development phase should be added to

Wolman’s (1967) model and investigated with further research to fully understand the geomorphic characteristics of stream systems that have been subject to the influence of urban development for some time.

The concept of geomorphic thresholds is fundamental when attempting to assess the human impact on the physical landscape. In fluvial geomorphology research, it is critical to understand not only what and how fluvial processes are altered via urbanization, but also to identify specifically when geomorphic modifications occur. The results of this research indicate 14-16% TIA is an important percentage of impervious surface cover, above which the studied stream channels became unstable, resulting in significant hydrologic and morphologic alterations. It would be naïve to say that 14-16%

TIA is a definitive threshold of impervious surface based on this research given that a number of geomorphic variables remain to be investigated among the studied sites.

However, it can be said that a noteworthy amount of significant effects were observed in the hydrologic and morphologic characteristics of the sites involved in this research above 14-16% TIA.

The identification of an amount of TIA above which significant geomorphic changes occur is extremely valuable when considering that the metropolitan Atlanta area is still growing. In the last eight years 1.1 million new people settled in the metro Atlanta area, making it the second fastest growing urban area in the nation (ARC, 2009). Stream 84

management techniques can now be implemented more effectively in newly urbanizing areas that have not yet reached the 14-16% TIA level to offset, or completely avoid, the

negative effects of urban development on stream hydrology and morphology. Also,

additional research is needed outside the field of fluvial geomorphology to verify whether

14-16% TIA is a significant amount of impervious surface cover that will also lead to

adverse effects in stream biology and ecology.

This research was conducted at the watershed and stream-reach scales because

geomorphic processes that occur at these scales give an indication of the conditions that

can be expected at the channel cross-section scale. Considering that urban channels were

found to have higher mean and flood discharges yet have no significant difference in

flood stage recurrence interval when compared to non-urban streams, it can be expected

that an increase in total cross-sectional area is occurring in the studied urban streams.

There is a need, then, for further investigation into the geomorphic adjustments taking

place at the channel cross-section scale to understand how watershed and stream-reach

processes and conditions are affecting smaller-scale stream characteristics. Only when a

thorough understanding of the geomorphic alterations of stream channels and their

watersheds associated with urban development is reached, from the watershed scale to

the channel cross-section scale, will stream mitigation and management techniques reach

their full potential.

In fluvial geomorphology research it is crucial that the index of urban

development is as accurate as possible and impervious surface cover is, without question,

a fundamental aspect of urban development. The results of this research support the view 85

that TIA is a reliable indicator of urban development and can accurately measure urban-

induced hydrologic alterations. However, that does not mean that new indices of urban development should not be produced that will yield a more complete picture of the

hydrologic impact of watershed urbanization. Future research would do well to pursue

the development and application of new, holistic indices of urban development that

combine TIA with other hydrologically significant factors. As the population and the land area of the world’s metropolitan areas increase it will become critical to establish more accurate and reliable methods to evaluate the magnitude of urban development on watershed processes.

In order to minimize the negative human impact on the physical environment it is imperative to appreciate the intricate relationship between the cultural and natural landscape. As the urban areas of the world continue to swell in both population and the amount of land they consume, it will only become more critical to identify the subsequent geomorphic modifications of watersheds and their stream systems and understand how changes in fluvial system affect the people that live in those areas.

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