MODELING THE EFFECTS OF FOREST ROAD DENSITY ON STREAMFLOW IN 

THE BLUE RIDGE MOUNTAINS

by

Salli F. Dymond

Thesis submitted to the faculty of the Virginia Polytechnic Institute and State University in 

partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

in

FORESTRY

W. Michael Aust, Chair

Stephen P. Prisley

Mark H. Eisenbies

April 5, 2010

Blacksburg, Virginia

Keywords: Forest roads, flooding, DHSVM, hydrology
MODELING THE EFFECTS OF FOREST ROAD DENSITY ON STREAMFLOW IN 

THE BLUE RIDGE MOUNTAINS

Salli F. Dymond

ABSTRACT

Forested watersheds have often been managed for flood mitigation. Studies have shown 

that forests have the potential to minimize peak flows during storm events, yet the relationship 

between forests and flooding is inexact. Forest roads, usually found in managed systems, can 

potentially magnify the effects of forest harvesting on water yields. A distributed hydrologic 

model (DHSVM) was calibrated for a 760 ha watershed in the Blue Ridge Mountains of North 

Carolina. The impacts of forest road density were evaluated by running the model using uniform 

input parameters but changing road densities. Road densities tested were 0.5, 1.0, 3.0, 4.3, 6.0 

and 12.0 km km-2

. Results indicate that increases in road density increased average streamflows 

at densities ≥ 4.3 km km-2

. During small storm events, discharge was impacted at densities ≥ 6.0 

km km-2 and streamflows were impacted during large rainfall events ≥ 3.0 km km-2 road 

densities. These findings indicate that forest roads can influence water yields and additional

management efforts may be needed that can slow the water yield from forest roads.
iii

ACKNOWLEDGEMENTS

First and foremost, I would like to thank my major professor, Dr. Mike Aust. His 

unyielding guidance and support throughout the ebbs and flows of this project were invaluable in 

its inevitable completion. The contributions of Dr. Steve Prisley were vital to the success of 

running the model, and many thanks are extended to him for his hard work. I wish to thank Dr. 

Mark Eisenbies for his recommendations, advice, and assistance in getting the model up and 

running. I also wish to thank Rupesh Shrestha, Regis Kopper, Dr. Phil Radtke, Dr. Randy 

Dymond, and Jeremy Stovall for much needed assistance in modeling and statistical analysis. A 

sincere thank you goes to Sue Snow, Tracey Sherman, and Kathie Hollandsworth for procedural 

guidance. 

This project was made possible thanks to grants provided by the United States Forest 

Service Southern Research Station. Thank you to Drs. James Vose and Andrew Dolloff for 

providing such support. Thank you to the Coweeta Long Term Ecological Research Station in 

Otto, North Carolina for providing historical data and to Stephanie Laseter for organizing the 

data.

Lastly, thank you to my friends and family who have provided much needed 

encouragement throughout this process. I would like to acknowledge the extreme patience of my 

friends and colleagues who supported me during the challenges of modeling-based research. 

Thank you to my father, Dr. James Johnson, who is consistently a source of inspiration. A final 

thank you goes to my husband, Ben, for providing unconditional support and love. This 

accomplishment is a direct result of your love and encouragement. 
iv

TABLE OF CONTENTS

CHAPTER 1. INTRODUCTION................................................................................................1

1.1 Background.......................................................................................................................1

1.1.1 Forests, Roads and Floods...........................................................................................1

1.1.2 Flooding in the Blue Ridge Mountains........................................................................5

1.1.3 Using Modeling as a Research Tool............................................................................6

1.2 Objectives .........................................................................................................................7

Literature Cited...........................................................................................................................7

CHAPTER 2. LITERATURE REVIEW....................................................................................13

2.1 A History of Hydrology Research at Coweeta..................................................................13

2.2 Effects of Forest Management on Runoff ........................................................................15

2.2.1 Removal of Vegetation .............................................................................................15

2.2.2 Forest Roads.............................................................................................................19

2.3 Watershed Modeling .......................................................................................................23

2.3.1 Hydrologic Models...................................................................................................23

2.3.2 The Distributed Hydrology Soil Vegetation Model ...................................................25

Literature Cited.........................................................................................................................26

CHAPTER 3. DHSVM INPUTS AND CALCULATIONS .......................................................33

3.1 Coweeta Spatial Data ......................................................................................................33

3.1.1 Elevation ..................................................................................................................33

3.1.2 Watershed.................................................................................................................33

3.1.3 Soil Type ..................................................................................................................34

3.1.4 Soil Depth.................................................................................................................34

3.1.5 Vegetation ................................................................................................................35

3.2 Coweeta Network Files ...................................................................................................36

3.2.1 Stream Networks ......................................................................................................36

3.2.1.1 Stream Class File ...............................................................................................36

3.2.1.2 Stream Map File.................................................................................................37

3.2.1.3 Stream Network File ..........................................................................................38

3.2.2 Road Networks.........................................................................................................39

3.2.2.1 Road Class File ..................................................................................................39
v

3.2.2.2 Road Map File ...................................................................................................41

3.2.2.3 Road Network File .............................................................................................42

3.3 Coweeta Meteorological Data..........................................................................................44

3.4 DHSVM Configuration File ............................................................................................46

3.4.1 Model Constants.......................................................................................................46

3.4.2 Soil Inputs ................................................................................................................48

3.4.3 Vegetation Inputs......................................................................................................54

3.4.4 Model State Files......................................................................................................61

Literature Cited.........................................................................................................................63

CHAPTER 4. MODELING THE EFFECTS OF FOREST ROAD DENSITY ON 

STREAMFLOW IN THE BLUE RIDGE MOUNTAINS..........................................................69

Abstract ....................................................................................................................................69

4.1 Introduction.....................................................................................................................70

4.2 Methods..........................................................................................................................71

4.2.1 Basin Description......................................................................................................72

4.2.2 Model Input ..............................................................................................................72

4.2.3 Model Calibration.....................................................................................................79

4.2.4 Road Density Experiment .........................................................................................80

4.3 Results and Discussion ....................................................................................................85

4.3.1 Calibration and Validation of Shope Fork Catchment................................................85

4.3.2 Road Density Effects................................................................................................88

4.3.3 Peak Flows ...............................................................................................................91

4.4 Summary and Conclusions ..............................................................................................96

Literature Cited.......................................................................................................................100

CHAPTER 5. CONCLUSIONS ..............................................................................................106

5.1 DHSVM Calibration......................................................................................................106

5.2 Effects of Road Density on Streamflow.........................................................................107

5.3 DHSVM........................................................................................................................110

Literature Cited.......................................................................................................................111

APPENDIX A. RUNNING DHSVM ......................................................................................115

APPENDIX B. EXAMPLE INPUT FILES .............................................................................134
vi

List of Figures

Figure 1.1 Pathways of precipitation in a forest. Precipitation can 1) fall directly on the forest 

floor or can interact with the vegetation. If a raindrop falls through the canopy, one of three 

events can occur: 2) it can be intercepted and be evaporated back to the atmosphere; 3) the water 

can be utilized by the tree; or 4) the water can fall back to the floor as throughfall. .....................2

Figure 1.2 Schematic of Horton Overland Flow. In some cases, the intensity of precipitation 

exceeds the rate at which water can infiltrate into the soil............................................................2

Figure 1.3 Schematic of infiltration excess runoff. Alternatively, runoff can occur when 

precipitation cannot infiltrate the soil due to soil saturation or frozen conditions. ........................3

Figure 3.1 Coweeta stream and road networks. Stream networks created by 

CreateStreamNetwork AML, road networks provided by Coweeta............................................43

Figure 4.1 The 760 ha Shope Fork catchment (dark gray) used in the DHSVM evaluations is 

located in the northern portion of the Coweeta Basin (light gray). .............................................73

Figure 4.2 The Final Erosion Factor is based on weighted values of the impact factors, which 

include road position, slope, and class (Modified from Bernard, 2006)......................................82

Figure 4.3 The Final Watershed Road Impact Factor (RIF) is a weighted value for the watershed 

based on the Final Erosion Factor and the length of roads in the watershed (Modified from 

Bernard, 2006)..........................................................................................89

Figure 4.4 Simulated and observed streamflow and measured precipitation of Shope Fork 

catchment during calibration period...........................................................................................86

Figure 4.5 Simulated and observed discharge and measured precipitation for Shope Fork 

catchment during validation period............................................................................................87

Figure 4.6 Shope Fork mean monthly discharge averaged over four-year study period. Road 

density 1=0.5 km km-2; Road density 2 = 1.0 km km-2; Road density 3 = 3.0 km km-2; Road 

density 4 = 4.3 km km-2; Road density 5=6.0 km km-2; Road density 6 = 12.0 km km-2 .............89

Figure 4.7 Shope Fork mean hourly discharge during April 2005 rainfall event. Density 1=0.5 

km km-2

; Density 2 = 1.0 km km-2; Density 3 = 3.0 km km-2; Density 4 = 4.3 km km-2; Density 

5=6.0 km km-2

; Density 6 = 12.0 km km-2 .................................................................................93

Figure 4.8 Shope Fork mean hourly discharge during December 2006 rainfall event. Density 

1=0.5 km km-2

; Density 2 = 1.0 km km-2; Density 3 = 3.0 km km-2; Density 4 = 4.3 km km-2; 

Density 5=6.0 km km-2; Density 6 = 12.0 km km-2 ....................................................................95
vii

List of Tables

Table 3.1 Soil series found in the Shope Fork Catchment. .........................................................35

Table 3.2 DHSVM cover types for the Shope Fork catchment...................................................36

Table 4.1 Soil mapping units in the Shope Fork catchment........................................................75

Table 4.2 DHSVM forest cover types for the Shope Fork Catchment. .......................................75

Table 4.3 Description of DHSVM vegetation input parameters. ................................................76

Table 4.4 Description of DHSVM soil input parameters............................................................77

Table 4.5 DHSVM vegetation inputs for calibration of Shope Fork catchment. .........................78

Table 4.6 DHSVM input soil parameters for Shope Fork calibration. Refer to Table 4.1for a 

description of the soil classes. ...................................................................................................79

Table 4.7 Treatment densities for Shope Fork road density experiment. ....................................81

Table 4.8 Final Watershed Road Impact Factors (RIF) for each treatment in the 

Shope Fork road density experiment..........................................................................................83

Table 4.9 Number of stream crossings per treatment in the Shope Fork catchment. ...................84

Table 4.10 Calibration and validation statistics for Shope Fork catchment.................................85

Table 4.11 Abbreviated Analysis-of-Variance statistics for average monthly streamflow for the 

Shope Fork catchment...............................................................................................................90

Table 4.12 Tukey-Kramer test results for significant difference between treatments for monthly 

and average monthly data..........................................................................................................90

Table 4.13 Percent streamflow deviation of treatments from the Shope Fork catchment as 

modeled with no road coverage. ................................................................................................91

Table 4.14 Description of the large and small storm events which received detailed analysis.....92

Table 4.15 Abbreviated ANOVA for April 2005 rainfall event in the Shope Fork catchment.....93

Table 4.16 Tukey-Kramer test results for significant difference between treatments during April 

2005 rainfall event for the Shope Fork catchment......................................................................94

Table 4.17 Abbreviated ANOVA for December 2006 rainfall event in the Shope Fork catchment.

.................................................................................................................................................95

Table 4.18 Tukey-Kramer test results for significant difference between treatments during 

December 2006 rainfall event....................................................................................................95

Table 4.19 Percent deviation from no road density for each treatment for a large and small storm 

event. ........................................................................................................................................96
viii

Table 4.20 Number of peak hours of flow for each treatment density for all return periods for the 

Shope Fork catchment...............................................................................................................96
1

CHAPTER 1. INTRODUCTION

1.1 Background

1.1.1 Forests, Roads and Floods

In 1905, Gifford Pinchot, the first Chief of the U.S. Forest Service, wrote the following 

statement regarding the influence of forests on floods (Pinchot, 1905):

Rain which falls over a bare slope acts differently. It is not caught by the crowns nor held

by the floor, nor is its flow into the streams hindered by the timber and the fallen waste 

from the trees. It does not sink into the ground more than half as readily as in the forest, 

as experiments have shown. The result is that a great deal of water reaches the streams 

in a short time, which is the reason why floods occur. It is therefore true that forests tend 

to prevent floods.

Pinchot’s interpretation of forest hydrology, conceptualized nearly three decades before Horton’s 

groundbreaking research on infiltration and overland flow, remains prevalent in society. More 

than a century later, land use practices resulting in the removal of vegetation from a site are often

considered culpable for large flood events (McCutcheon, 2006). 

The complexity of forest hydrology and Pinchot’s ensuing ideas are easily understood 

when the concepts are generalized (Figure 1.1). When a raindrop falls on a forested landscape it 

can be intercepted by the vegetative canopy or it can fall through the canopy directly to the litter 

soil. Water on the tree can be evaporated back to the atmosphere, utilized by the tree, fall to the 

ground as throughflow, or flow along stems to the ground as stemflow. Precipitation that reaches 

the soil surface will evaporate, infiltrate into the soil or become overland flow. Runoff will occur 

in one of two situations: 1) the soil is saturated and there is no more room for water in the soil 

profile, or 2) the rate of precipitation exceeds the infiltration capacity and thus water is falling at 

a faster rate than the soil infiltration capacity (Figure 1.2 and Figure 1.3). This runoff is known as 

Horton Overland Flow (Horton, 1933). A bare soil ecosystem will act similarly to a forest except 

the vegetation unit of the cycle can be omitted. In this system, the vegetation cannot intercept 

rainfall, only a small fraction evapotranspiration will occur, roots cannot take up water from the 
2

soil, and the litter layer is not replenished. This leaves more water available to flow directly to a 

stream source and can create larger streamflows during rainfall events (Cornish and Vertessy, 

2001).

Figure 1.1 Pathways of precipitation in a forest. Precipitation can 1) fall directly on the forest 

floor or can interact with the vegetation. If a raindrop falls through the canopy, one of three 

events can occur: 2) it can be intercepted and be evaporated back to the atmosphere; 3) the water 

can be utilized by the tree; or 4) the water can fall back to the floor as throughfall. 

Figure 1.2 Schematic of Horton Overland Flow. In some cases, the intensity of precipitation exceeds the 

rate at which water can infiltrate into the soil.

1

2 3

4

Precipitation

Surface Runoff

Subsurface Flow

Infiltration
3

Figure 1.3 Schematic of infiltration excess runoff. Alternatively, runoff can occur when 

precipitation cannot infiltrate the soil due to soil saturation or frozen conditions. 

The concept that forests play a role in buffering flood impacts is widely accepted. The 

magnitude of this effect, however, is greatly disputed. Research has shown varied responses in 

streamflow following forest harvesting. Guillemette et al. (2005) conducted a literature survey of 

50 international basin studies in which they determined whether or not forest practices impacted 

peak flow. In studies looking at watersheds with the removal of greater than or equal to 50% of 

the total land area or basal area, non-significant increases in peak-flow stream response have 

been found at the Fernow in West Virginia (Reinhart et al., 1963), H.J. Andrews and Deer Creek 

in Oregon (Harr et al., 1975; Harr et al., 1982; Harr, 1986; Thomas and Megahan, 1998), and 

Alto, Texas (Blackburn et al., 1986). In studies looking at similar site treatments, significant 

increases in peak flow response have been found at Beaver Creek, Utah (Brown et al., 1974), 

Coyote Creek and H.J. Andrews, Oregon (Harr and McCorison, 1979; Harr, 1986), Coweeta, 

North Carolina (Douglass and Swank, 1976; Hewlett and Helvey, 1970; Swank et al., 1982), 

Grand Forest, Georgia (Hewlett, 1979), Upper Coastal Plain and Holly Spring, Mississippi 

(Ursic, 1970; Ursic, 1982; Ursic, 1991), Marcell, Minnesota (Verry et al., 1983; Verry, 1986), 

and Hubbard Brook, New Hampshire (Hornbeck, 1973; Hornbeck et al., 1997; Pierce et al., 

1970). Many studies have found that stream discharge returns to pre-harvest conditions within 

five years of logging (Hewlett and Helvey, 1970; Hornbeck et al., 1970; Swank et al., 2001).

Impacts of forest harvesting on the stream hydrograph are important yet transient, since 

re-vegetation can occur under continued forest management. A more static issue that forest 

managers must consider are forest roads, which are often permanent and can affect the 

Precipitation

Surface Runoff

Subsurface Flow
4

surrounding ecosystem. Forest roads can influence the natural pathways of runoff. The nonvegetated road corridors can act like stream channels, intercepting overland flow and re-routing 

it down slope. Compacted road surfaces roads can also reduce infiltration, enabling a larger 

volume of water to channel along the road. The increased volume of water and sediment from 

the road surface can negatively impact the health and vitality of the ecosystem. In a particular 

catchment, a road segment may act as a barrier, corridor, sink, or source for both water and 

sediments (Jones, 2000). All of these functions can alter the stream hydrograph, both in quantity 

and in timing.

The construction of roads in national forests has been very controversial, especially 

during the recent decades. In 1999, Bengston and Fan found that U.S. citizens viewed forest 

roads as favorable, citing recreational and commodity benefits as potential uses. Despite these 

public views, there was increasing public and political pressure to ban construction of forest 

roads on “roadless” areas in the forest service (Bengston and Fan, 1999). The debates continued 

with the enactment of the Roadless Area Conservation Act of 2007, which was designed to 

protect and preserve the socio-ecological benefits of healthy watersheds. Cited benefits included 

the lowering the potential of flooding for downstream communities, conservation of wildlife 

habitat, protection of clean drinking water, and recreational opportunities (Roadless Area 

Conservation Act of 2007). 

The ample environmental concerns over the construction of forest roads include forest 

fragmentation, increased sedimentation and thus decreased water and stream quality, increased 

fire hazards and invasive species dispersal. As of 2007, there were approximately 386,000 miles 

of roads located within the national forest system (Forest Service, 2008). The 2008 fiscal year 

budget for the US Forest Service appropriated over $225,000,000 to the management of the road 

network; which was an 8 percent increase from the 2007 budget (Forest Service, 2008). Due to 

mounting importance and scrutiny of forest roads, it is critical for forest roads to be properly 

designed, installed, and maintained.
5

1.1.2 Flooding in the Blue Ridge Mountains

Flooding is a notable concern, especially when the potential socioeconomic impacts are 

considered. In 2008, three major storms cost the American public over $2.5 billion in damages 

(Federal Emergency Management Association, 2009). As the costs of flood damage put their 

strain on society, the censure for responsibility intensifies. Highly publicized flood events often 

support the public misconception that forest harvesting practices are the sole cause of severe 

flooding (McCutcheon, 2006). In the U.S., this is usually evident in the Pacific Northwest, where 

heavy rains and steep slopes facilitate large floods and landslides (Johnson et al., 1997). 

Anthropogenic versus natural causality of flooding is commonly debated in the region, which has 

a history of both intensive forestry practices and grassroots environmental movements (Cutter, 

2009; Streater, 2009).

The dispute over forestry and flooding is not limited to the Pacific Northwest. In the 

Southern Appalachians, a mountainous region in the southeastern U.S., wide scale harvesting 

dates back to the early 19th century. By 1835, an estimated forty sawmills were running in the 

area that would become West Virginia (Yarnell, 1998). Harvesting in the area has continued, and 

in the fiscal year 2007, 163,500 MBF of timber were harvested from national forests in the 

southeastern U.S. (Forest Service, 2009). Approximately 78% of the area in the Blue Ridge 

Mountains is forested (Taylor and Kurtz, 2008) and society continues to debate the impacts that 

management activities may have on their welfare. For instance, extensive flooding occurred in 

southern West Virginia and Southwestern Virginia in July 2001. National Weather Service 

stations recorded maximum rainfall intensities of 50 mm/hr and damages were estimated to be 

around $150 million (Eisenbies et al., 2007). Following this event, numerous counties and 

citizens filed civil suits against logging and mining companies, insisting that the corporations to 

take responsibility for the floods (Recht, 2007). 

As a result of the flood devastation, West Virginia governor Bob Wise created a Flood 

Investigation Advisory Committee. The team was designed to evaluate the impacts of logging 

and mining on flooding. In 2002 the Flood Advisory Technical Taskforce (FATT) 

recommendations were released. The comprehensive report detailed guidelines for mining and 

logging activities and was aimed at reducing their environmental impact. In regards to forestry, 
6

the report suggested stricter regulation of harvesting practices and firmer enforcement of the 

West Virginia forestry Best Management Practices (Flood Advisory Technical Taskforce, 2002).

1.1.3 Using Modeling as a Research Tool

In recent decades, modeling has become more widely used both in helping to understand 

the components of the hydrologic cycle and in predicting hydrologic responses to potential 

anthropogenic impacts. Every hydrology model is designed for specific simulation goals. The 

first available watershed model, The Stanford Watershed Model (later termed the Hydrologic 

Simulation Program-FORTRAN), was developed in the 1960s as a joint venture between 

Harvard and Stanford Universities and the U.S. Army Corps of Engineers (Bedient et al., 2008). 

Additional watershed models were subsequently developed, including HEC-HMS (Center, 

2009), the Natural Resource Conservation Service’s NRCS TR-55 (Cronshey et al., 1986), 

GAMES (Dickinson et al., 1992) and AGNPS (Mostaghimi et al., 1997). Several models can be 

used for modeling flow in forested watersheds. Some examples include FORWADY (Seely and 

Kimmins, 1997), DRAINMOD (Natural Resources Conservation Service, 1980), CRHM 

(Pomeroy et al., 2007), RHESSys (Tague and Band, 2004), WaSiM-ETH (Schulla and Jasper, 

2007), WEPP (Watershed Erosion Prediction Project 1995), TOPMODEL (Beven et al., 1993) 

and DHSVM (Wigmosta et al., 1994).

Hydrologic watershed models have three major operational considerations that can be 

used to characterize the models. First, they can either be a lumped or distributed model. A 

lumped model considers the physical characteristics of the watershed to be uniform, which 

allows for simplicity when running the model. Distributed models are more complex as they 

account for the watershed as it physically occurs in the landscape. This can create problems with 

data collection, model calibration and verification. Second, a model can examine streamflow 

response over time (continuous) or for one event of interest (event). Finally, a model can be 

physically based, meaning that it has a static initial set of conditions determined by input 

parameters or previous model runs. If not static, the model can be stochastic and the conditions 

are dynamic and follow a probability distribution (Bedient et al., 2008). 
7

1.2 Objectives

This study originated from litigious issues surrounding forest management activities, 

such as harvesting and road construction, and how they might affect flood events. Since many 

hydrologic models now have the power to accommodate to the complexity of forested 

ecosystems, a modeling approach was selected. Specifically, the Distributed Hydrology-SoilVegetation Model was used to predict the impacts of forest road density on stream discharge.

The DHSVM model was chosen because it has better capabilities for modeling different road 

designs and densities. The specific objectives of this research were to:

1) Determine whether or not DHSVM could be calibrated for use in the Southern

Appalachian Mountains; and

2) Assess the impacts of forest road density on stream discharge for

a. Mean monthly streamflow

b. Peak flow in response to flood events

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Raleigh County, WV. Civil Action No. 01-C-797. 31 p. 

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Research. 34: 3393-3403.

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in Northern Mississippi. USDA Forest Service Research Paper SO-54. 15 p.

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Yarnell, S. L. 1998. The Southern Appalachians: A History of the Landscape. USDA Forest

Service General Technical Report SRS-18. 52 p.
13

CHAPTER 2. LITERATURE REVIEW

2.1 A History of Hydrology Research at Coweeta

The USDA Forest Service Coweeta Hydrology Research Station (now the Coweeta Long 

Term Ecological Research (LTER) Station) was established in 1933 with the intent to study 

streamflow and erosion in an ecological context (Douglass and Hoover, 1988). The 2185 ha 

Coweeta basin is located in the Blue Ridge Mountains of western North Carolina. The research 

station has a long history of paired watershed research studies, and some representative examples

are described below. More detailed accounts of watershed research at the Coweeta LTER can be 

found in Douglass and Swank (1972 and 1975) and Swank et al. (1988). 

In 1970, Swank and Helvey looked at the impacts of clear-cut harvesting and vegetative 

regrowth on streamflow in the Coweeta watershed. Two small (16.1 and 43.7 ha) watersheds 

were studied, the smaller of which had an initial clear-cut in 1940. This was followed by natural 

regeneration for 23 years and concluded with a second clear-cut. The larger watershed was clearcut in 1963 and was also allowed to naturally regenerate. In both watersheds, all trees were 

harvested and left on the ground, and soil disturbance was minimal. The results indicated 

increases in water yield following harvesting and lower rates of streamflow response as 

vegetation regrowth occurred. In the watershed containing the two cuttings, increases in stream 

discharge declined at a much faster rate following the second harvest than they did after the first 

cutting. The authors attributed this variation in streamflow response to prolific sprouting and 

rapid canopy development that occurred subsequent to the second harvest (Swank and Helvey, 

1970). In 1981, the study by Swank and Helvey (1970) was expanded. The extended study 

confirmed that the regrowth of vegetation was the primary determinant of water yields. The 

authors also suggested that “percent reduction in basal area is an effective predictor of 

streamflow increase in the first year following treatment.” However, not all streamflow trends 

could be explained through vegetation growth and removal. Precipitation and basin 

characteristics were also considered to be predictors of the irregularities of water yield response 

to treatment (Swift and Swank, 1981). 

A similar study began in 1976 on Coweeta Watershed 7. Access roads were constructed 

(2.95 km total) before clear-cutting and cable yarding of the 59 ha watershed in 1977. Storms 
14

greater than 2 cm in volume were used as an analysis of streamflow response. In the initial year 

after harvest, streamflow increased by 26 cm (28%) over pre-treatment conditions. By the fourth 

year of study, flow was 12 cm (13%) above baseline. Results were consistent with other studies 

and discrepancies in streamflow were considered a function of rapid forest regeneration (Swank 

et al., 1982). The effects of clear-cutting and cable yarding on the storm hydrograph were also 

investigated in the study, and it was found that the watershed had greater responses to smaller 

storm events. The authors suggested that this could have been a function of the linear response 

relationship used in the model. They unsuccessfully attempted to create a non-linear model to 

define the relationship between treatment and streamflow response (Swank et al., 1982). The 

study was updated almost two decades later to further investigate the effects of cable yarding 

(Swank et al., 2001). It was found that increases in streamflow initially decreased at a rate of 5 to 

7 cm year-1 until the 5th year following treatment. When the forest reached a successional age of 

15 years, a significant increase in water yield occurred, followed by a significant decrease. 

Afterward, streamflow leveled off below baseline conditions, although insignificantly. The 

authors attributed the initial increase to stem exclusion and mortality in the stand. The decrease 

in flow was puzzling, but was thought to be a result of species conversion in the forest. The 

longer-term study showed that clear-cutting with cable yarding had little long term effects on 

water yield (Swank et al., 2001). 

Harvesting a watershed is not the only way to impact the water yield in a catchment. In 

1968, Swank and Miner looked at the implications of converting a native hardwood forest to 

white pine. Two Coweeta watersheds were clear-cut and planted with white pine (Pinus strobus)

seedlings in 1956 and 1957. Competing hardwood vegetation was chemically and mechanically 

suppressed on both watersheds. Results indicated an initial increase in streamflow following 

hardwood harvesting, followed by decreases in streamflow additions. After six years of study, 

streamflow rates fell below pre-treatment levels. Streamflow levels continued to drop ten years 

after planting. The authors attributed the decreased water yields to larger evapotranspiration rates 

of evergreen white pines (Pinus strobus) during the deciduous species’ dormant season and to 

increased interception of precipitation of by the pines. They hypothesized that streamflow levels 

would continue to decline in the ensuing years (Swank and Miner, 1968). This hypothesis was 

verified by Swank and Douglass in 1974. The continuation of the study showed that the white 
15

pine (Pinus strobus) covered catchments reduced streamflow until crown closure occurred 

(approximately 16-17 years following planting), after which stream discharge stabilized at a rate 

of 20% less than that expected under hardwood conditions (Swank and Douglass, 1974). Twenty 

years later, water yield from the converted pine watershed was still lower in storm volume, peak 

flow rate, and low flow conditions (Swank and Vose, 1994). 

Not all species conversions result in such long term alterations of streamflows. In 1958, 

merchantable timber from 8.9 ha Coweeta Watershed 6 was removed and replaced with fescue 

grass. Under non-fertilized conditions, the grass had trouble surviving and ground cover was 

essentially bare soil. Under these conditions, streamflow increased. However, when fertilization 

occurred and grass cover was viable, stream discharge was similar to forested conditions. As 

shrubs re-vegetated, flow returned even closer to the original, forested yields (Hibbert, 1969; 

Burt and Swank, 1992). This suggested that grass and shrub cover had similar water uses to 

hardwood forests.

The Coweeta watershed studies have not focused solely on forest management effects on 

water yield. Many investigations have looked at how harvesting may influence the ecology of an 

ecosystem in other fashions. For instance, forest harvesting may increase temperatures in 

streams, adversely affecting stream vegetation and micro- and macro-fauna (Swift, 1982). Many 

studies also suggest that forest management can affect stream chemistry. Swank and Vose (1994) 

showed that streamflow from white pine forests had less calcium, potassium, magnesium, and 

sodium cations than streamflow from hardwood watersheds. Scientists have also been studying 

the impacts of road building and sediment erosion on water quality (Riedel and Vose, 2002; 

Riedel et al., 2007). 

2.2 Effects of Forest Management on Runoff

2.2.1 Removal of Vegetation

The removal of vegetation from a watershed can greatly affect the water budget of the 

catchment, particularly if the removal is permanent. The presence of vegetation primarily affects 

streamflow through evapotranspiration processes and by the interception of precipitation. If 
16

vegetation is removed, the leaf area index (LAI) of a watershed will decline, thus reducing 

evapotranspiration (ET). The decreased ET can lead to increases in soil moisture, especially 

during the growing season, when ET in a forested ecosystem would be most prevalent. 

Heightened soil moisture can lend to increased peak flows and baseflows in the catchment 

(Hewlett and Hibbert, 1963; Ziemer, 1981). 

For decades, scientists have shown that reductions in ET can lead to increases in 

streamflow. In 1966, Hibbert reviewed 39 paired-catchment studies from across the United 

States and concluded that reductions of forest cover led to increased water yield. From the 

studies, it was concluded that for every percent reduction in forest cover, streamflow increased 

by a maximum of 4.5 mm per year. None of the 39 studies reported a decrease in water yield 

following the removal of vegetation. The review also concluded that the increase in water yield 

was greatest in the year following disturbance, that the afforestation of a site decreased water 

yield, and that watershed response to treatment was highly variable (Hibbert, 1966). 

In 1982, Hibbert’s review was updated, with the new analysis comparing 94 catchment 

studies (Bosch and Hewlett, 1982). In no cases did the review detect a reduction in water yield 

following harvest, although the review noted differences in response based upon vegetative 

cover. For example, coniferous forests had a larger influence on water yield than deciduous 

forests. A 10% change in forest cover resulted in an annual water yield change for coniferous 

forests of approximately 40 mm and only 25 mm for deciduous forests (Bosch and Hewlett, 

1982). Jones and Post (2004) further examined the effect of the removal of vegetation type on 

stream discharge. They compared studies from 14 sites in the conifer-dominated forests of the 

Pacific Northwest and the mixed deciduous forests of the East Coast. The authors found that, for 

the first five years following harvest, changes in maximum daily flow were 6 to 8 mm for 

coniferous forests and 2 to 3 mm for deciduous forests (Jones and Post, 2004). 

Studies have concluded that harvesting will increase streamflow for approximately five 

years following forest removal (Hewlett and Helvey, 1970; Hornbeck et al., 1970; Kochenderfer 

et al., 1990; Swank et al., 2001). Swank et al. (2001) analyzed long-term changes in streamflow 

following clear-cutting in a 59 ha catchment in the Coweeta watershed located in the southern 
17

Appalachians. They found that streamflow increased by 28% in the first year following harvest

but decreased in the ensuing years. The study also found that streamflow had returned to baseline 

conditions within five years of the harvest (Swank et al., 2001). The authors attributed the initial 

change in streamflow to be a function of reduced evapotranspiration. As vegetation re-growth 

occurred, the impact of harvesting was lessened (Swank et al., 2001). When a portion of the 

Hubbard Brook Experimental Forest was clear-cut and treated with herbicides, researchers 

noticed an increase in average annual water yield of 40% in the year following harvest

(Hornbeck et al., 1970). Like Swank et al. (2001), the authors attributed the initial change in 

streamflow to be a function of reduced evapotranspiration (Hornbeck et al., 1970). In an analysis 

of watershed response data at the Fernow Experimental Forest, Kochenderfer et al. (1990) found 

that stream discharge increased following patch clear-cuts, clear-cutting, selection cutting, and 

harvesting plus herbicide treatment. In all cases except those including herbicide treatments, 

streamflow returned to initial conditions within five years (Kochenderfer et al., 1990). Hewlett 

and Helvey (1970) conducted a study in which a 108 ac catchment in the Coweeta LTER was 

clear-cut to determine the effects of vegetation removal on storm runoff. The authors found that 

in the year following harvest, stormflow volume increased by 11% and peak discharge increased 

by 7% (Hewlett and Helvey, 1970).

Response of a stream to vegetation removal is dependent upon numerous contributing 

variables, including, but not limited to, climatic conditions and the season. In areas dominated 

by rainfall precipitation, such as the Coweeta LTER, seasonal distribution of water yields is 

varied. In his review of 39 paired catchment studies, Hibbert (1966) found small increases in 

water yield during the growing season, while large increases in water yield occurred throughout 

the year. This did not hold true for all watersheds, and the disagreement between seasonal and 

treatment water yields was attributed to watershed features, such as slope, aspect, and soil depth 

(Hibbert, 1966). Many studies do find that streamflow fluxes are larger during the growing 

season. In two deforested West Virginia watersheds, it was found that streamflow increased by 

254 mm annually, with most of the increases occurring during the late growing season (Patric, 

1973). Hibbert (1966) also found that Fernow Experimental watershed studies noted water yield 

increases during the growing season. These were ascribed to the decrease in evapotranspiration 

resulting from the removed vegetation (Hibbert, 1966). In their review of 14 paired catchment 
18

studies, Jones and Post (2004) noticed that “forest effects on streamflow are strongly seasonal.” 

In deciduous forests, the authors found that absolute and relative changes in streamflow were 

more likely to occur in the warmer growing season months. Exceptions occurred at the Coweeta 

basin, where streamflow response paired Watersheds 13 and 14 were not associated with any 

season and in paired Watersheds 7 and 34, where relative streamflow changes occurred during 

colder months (Jones and Post, 2004). Seasonal streamflow response to vegetative reduction was 

also found in the central Appalachians. Significant increases in growing season peak flow were 

found in comparison to no changes in dormant season flows. Therefore, the authors concluded

that the hydrologic response was a mechanism of the vegetative removal and not one of 

compaction or disturbance of soil during the harvesting process (Kochenderfer et al., 1997).

Hibbert (1966) examined data from Coweeta and the Fernow and suggested that the 

percent reduction in forest cover is not proportional to the first year stream yield increases. This 

implied that other variables, such as climate, slope, and aspect influenced stream discharge. 

Kochenderfer et al. (2007) further suggested this based on analysis of long-term streamflow data 

from the Fernow Experimental Forest. The authors examined data from 7 of the experimental 

watersheds and analyzed streamflow and precipitation records dating as far back as 1951. Using 

a model to determine the most influential factors in predicting peak streamflow, the authors 

found that storm characteristics such as duration, intensity, and volume influenced the variability 

in streamflow, while disturbance history had minimal influence (Kochenderfer et al., 2007). A 

1982 study from Fernow found similar results, suggesting that the depth of rainfall and initial 

streamflow velocity accounted for over 85% of the variability in peak discharge (Brewer et al., 

1982). Sturdevant-Rees et al. (2001) looked at the effect of a large hurricane event on flooding in 

the central Appalachians. The study found that antecedent soil moisture was a significant factor 

in determining peak discharge and that the amount of vegetative cover was of little importance 

(Sturdevant-Rees et al., 2001).

For sites having long term water yield measurements, the data suggest increases in 

harvest area cause larger stream discharges. At the Coweeta and Fraser Experimental Forests, 

early findings found linear relationships between the percent reduction in vegetative cover and 

the magnitude of stream discharge (Hibbert, 1966). In 1971, Patric and Reinhart reported on a 
19

study to compare the effects of deforestation on stream discharge. In West Virginia, the upper 

half of one watershed was deforested while the lower half of a paired catchment was clear-cut. 

Three years later, both watersheds were completely deforested. The researchers noted a 15.2 cm

increase in average water yield from the half deforested watershed and a 25.4 cm increase after 

complete deforestation (Patric and Reinhart, 1971). In a study comparing four different 

harvesting treatment types on stream parameters, Reinhart et al. (1963) concluded that a clear-cut

had the largest impact on stream discharge, followed by diameter limit cut, extensive selection, 

and intensive selection.

 Other studies have suggested that the area harvested is proportional to stream 

discharge. In a review of 95 catchment studies, Stednick (1996) found that different regions of 

the United States had varying harvesting thresholds after which detectable changes in streamflow 

discharge were observed. The threshold was lowest for the Rocky Mountains (15%) and largest 

for the Eastern Coastal Plains and Piedmont (45%). Other thresholds included 20% for the 

Appalachian Mountains and 25% for the Pacific Coast. For the Appalachian Mountain region, 

each 10% increase in area harvested resulted in an increased annual water yield of 28 mm 

(Stednick, 1996). Kochenderfer et al. (1990) summarized the effects of various harvesting 

treatments on streamflow response in the Fernow Experimental Forest. The authors found that 

stream discharge was directly related to the amount of vegetation that was removed. The authors 

also discovered that vegetation suppression in addition to removal created larger and more 

sustained impacts on streamflow (Kochenderfer et al., 1990).

2.2.2 Forest Roads

The issue of forest roads has become increasingly scrutinized. While an important 

component for research, transportation, recreation, and business purposes, forest roads can pose a 

threat to the surrounding ecosystem. Such threats include loss of space, unmanaged recreation, 

spread of invasive species, and spread of wildland fire (Grace and Clinton, 2007). One of the 

most sensitive areas to forest roads, however, is the riparian ecosystem. Forest roads have the 

ability to alter water movement and contribute sediment to streams. Understanding the road 

ecosystem can help forest managers achieve goals while coexisting with the natural environment 

(Gucinski et al., 2001).
20

Forest roads can potentially affect the natural movement and extent of runoff by 

redistributing the water through the road network. A road can affect the hydrology of a forested 

ecosystem through three different processes:

1) Interception of Subsurface Flow: the water table rises above the road cut and 

subsurface flow seeps into the road network and drainage system and is channeled 

down the road;

2) Infiltration Excess Runoff: the compacted road surface decreases the infiltration 

capacity of the soil and the excess water becomes runoff; 

and

3) Overland Flow: the road and drainage system intercepts overland flow and re-routes 

it from the hillslope through the road network.

Scenario three is less likely to occur in a forested ecosystem, as infiltration rates are often 

high for forest floors and overland flow is not a primary mechanism in forest hydrology. This 

continues to hold true during many storm events (Bates, 2000). For sediments, a road segment 

may act as a barrier, corridor, sink, or source for both water and sediments (Jones et al., 2000). 

Numerous studies have attempted to determine the effects of forest harvesting and road 

construction on stream discharge. However, it can be difficult to separate the two effects because 

harvesting and road construction often occur simultaneously. Thus, results are often conflicting 

due to the multitude of ways that roads may interact with the hydrology of a forest. Such factors 

include the location and design of the forest road, associated harvesting operations, watershed 

characteristics, antecedent soil and stream conditions, and storm characteristics (Thomas and 

Megahan, 1998). Ziemer (1981) conducted a study at Caspar Creek, California to investigate the 

impacts of forest roads on peak flow. From 1971 to 1973, 67% of the 424 ha South Fork 

watershed was harvested. The watershed contained approximately 15% impervious surface, 5% 

in roads and 10% in skid trails and landings. Over the study period, the author found that road 

construction resulted in no measured change in storm flow (Ziemer, 1981). A study by Rothacher 

(1970) looked at road and road plus harvesting effects on water yield at the H.J. Andrews 
21

Experimental Forest in western Oregon. The study found similar results to Ziemer (1981) and 

concluded that the construction of forest roads did not significantly increase mean discharge in 

the study watersheds (Rothacher, 1970). 

Additional research agrees with Rothacher (1970) and Ziemer (1981). Wright et al. 

(1990) studied a 424 ha coastal watershed in northern California in which 4.5% of the total area 

consisted of road networks. The authors concluded that road construction did not significantly 

increase total volume, quick-flow volume, peak flows, or lag-times in the study catchments 

(Wright et al., 1990). King and Tennyson (1984) found similar results in a study located in the 

Nezperce National Forest in north-central Idaho. The small, study watersheds (28 ha to 148 ha) 

had road compaction over 1.8 to 4.3% of the total catchment area. Overall, the study found no 

significant streamflow response to the constructed road networks. In one watershed, the 25% 

exceedance flow increased while the 5% exceedance flow decreased in another watershed.

Exceedance flow is the streamflow that is met or exceeded a specified percent of the time (in this 

study, 25% and 5%). The increase in flow was attributed to the road’s midslope location, which 

diverted subsurface flow to the stream as surface flow, with a shorter lag time. The decrease in 

exceedance flow occurred on the watershed that had the highest density of roads (King and 

Tennyson, 1984).

In contrast, studies have found that stream discharge is affected by road construction. 

Harr (1979) states that the collective results of watershed studies imply that roads have positive, 

negative, and neutral impacts on water yields. This was exemplified in a study conducted on 

small watersheds in the Oregon Coast Range where forest roads had no impact on peak 

discharge, except when they occupied greater than 12% of the total area of the watershed. Over 

the short, one year study period, the authors found that roads decreased the average volume of 

quick-flow, increased delayed flow volume, and had inconsistent effects on time to peak and 

overall volume of the storm hydrograph (Harr et al., 1975). Jones (2000) compared ten basins in 

the Western Cascade Range in Oregon to determine peak discharge responses to forest removal, 

re-growth, and road construction. Jones found that roads increased peak discharge in events with 

>1 year return periods by 13 to 36%. It was noted that this was primarily through the interception 

of subsurface flow by the deep road cuts as opposed to the interception and re-routing of 
22

overland flow. The author also suggested that the configuration of roads along a hillslope may 

play an important role in the magnitude of impact on stream discharge (Jones, 2000). 

The investigation of the effects of forest roads on streamflow can be controversial. In 

1996, Jones and Grant conducted a study in a 100 ha watershed in western Oregon. The road 

density in the catchment was 6%, which was well below Harr et al.’s (1975) suggested threshold 

of 12%. Four years following road construction, storm runoff was increased and peak flows were 

20% higher than base conditions, although this was not considered statistically significant. After 

patch clear-cutting on the same watershed, the authors found that water yield increased by 50% 

in the first five years following treatment. In the subsequent twenty years, discharges were still 

25 to 40% greater than pre-treatment conditions. Jones and Grant contributed the increases in 

water yield to the increased interception of subsurface moisture and the modification of flow 

paths by forest roads (Jones and Grant, 1996). The approach used by Jones and Grant was later 

disputed by Thomas and Megahan (1998) for using an incorrect approach to analyze the 

discharge data. The model used by Jones and Grant used percent increases in the differences of 

the logarithms of peak discharges, thereby inflating the magnitude of actual change in water 

yield. Using the model presented by Thomas and Megahan (1998), no treatment effect was 

detected in the same watershed after 10 years following patch clear-cutting. This also suggests 

that water yield is impacted by the removal of vegetation as opposed to changes in flow routing 

due to roads (Thomas and Megahan, 1998).

More recently, the advancement of computer models has allowed the scientific 

community to parse out the effects of forest roads on streamflow. Bowling and Lettenmaier 

(1997) used a Distributed Hydrology Soil Vegetation Model (DHSVM) to simulate the effects of 

forest roads on streamflow in two catchments in western Washington. The drainage areas ranged 

from 2.3 to 2.8 km2 and the road lengths were 10.7 and 11.4 km, respectively. The authors found 

that over the 11 year simulation phase, roads networks increased the ten year return period flood 

by 8 to 10%. With mature vegetation, it was found that forest roads could increase ten-year 

return peak flow rates by as much as 22% (Bowling and Lettenmaier, 1997). The authors 

discovered that roads redistribute water throughout the basin, resulting in drier zones directly 

beneath the road (due to compaction) and saturated zones in areas surrounding culvert drainage 
23

points (Bowling and Lettenmaier, 1997). Cuo et al. (2006) also used DHSVM to study the effects 

of roads on evapotranspiration, soil moisture, depth to water table, and stream discharge on the 

Pang Khum Experimental Watershed in northern Thailand. The 93.7 ha basin had compaction 

from road construction over approximately 1.2% of the area. Based upon model results, the 

authors found that the effects of road networks on the measured variables were small and/or 

negligible. Stream discharge was found to increase with the presence of road networks, but only 

by 3% (Cuo et al., 2006). 

2.3 Watershed Modeling

2.3.1 Hydrologic Models

In recent decades, models have become a prominent research tool in forest hydrology. 

Computing advancements have made it possible to apply hydrologic models specifically to 

forested watersheds. Such tools can be extremely helpful, since paired watershed studies can be 

expensive and labor intensive. The modeling environment gives the researcher a platform to 

simulate multiple scenarios over one watershed. Detailed below are descriptions of some current 

hydrologic models that can be applied to forested landscapes. 

WRENSS

The Water Resources Evaluation of Non-Point Silvicultural Sources (WRENSS) was 

developed by the US EPA in 1980 with the overarching goal of estimating the effects of 

silvicultural practices on water yield and quality (Swanson, 2004). The model is restricted in 

power by the small number of outputs, although this creates simplicity for the user (Beckers et 

al., 2009). 

TOPMODEL

TOPMODEL is a simplistic, topography-based hydrologic model that was developed in 

the 1970’s (Beven, 1997). It is an open source model, which gives the modeler the flexibility to 

adjust the model to the needs of the project. There are two main underlying assumptions required 

for model consistency with the TOPMODEL approach: 1) The water table changes can be 
24

predicted by a uniform subsurface model over a unit area (a), draining at a point; and 2) the 

topographic slope determines the hydraulic gradient of the subsurface zone (Beven, 1997). 

ForWaDy

Forest Water Dynamics, or ForWaDy, can be used in a forested environment to solve a 

water budget under set climatic and vegetative conditions. It was designed for ease of use, so 

data requirements and input parameters are minimal (Seely and Kimmins, 1997). Beckers et al. 

(2009) list it as a “medium-complexity” water balance model. Model advantages include the 

potential linkage with forest growth models, while a major disadvantage of ForWaDy is the lack 

of experimental use (Beckers et al., 2009). Seebacher (2007) used ForWaDy to model a water 

stress index as linked with Western Redcedar growth. The author found the model to be sensitive 

to climatic variables such as solar radiation, precipitation, and temperature (Seebacher, 2007).

WaSim-ETH

The Water Simulation Model-ETH (WaSim-ETH) is a complex distributed hydrologic 

model that is unique in its consideration of snow hydrology, lake and groundwater processes, and 

channel routing (Beckers et al., 2009; Schulla and Jasper, 2007). In simulating the hydrology of a 

mountainous Swiss catchment, Gurtz et al. (2003) found that WaSim-ETH was acceptable in 

replicating in situ hydrologic measurements and in modeling flood events.

RHESSys

The Regional Hydro-Ecological Simulation System (RHESSys) is a distributed hydroecological model that simulates the cycling of water, carbon and nutrients (Tague and Band, 

2004). The intermediary complexity of the model requires climatic, spatial, vegetative, soils, and 

land-use history inputs. Applications of the model include predictions of streamflow and soil 

moisture fluxes under changing land use (Tague and Band, 2004). Beckers et al. (2009) list 

drawbacks of RHESSys to be the level of difficulty for users and the high parameter inputs. 

However, these downfalls can be offset by the ability of the model to function both 

hydrologically and ecologically. 
25

DHSVM

The Distributed Hydrology Vegetation Model (DHSVM) is highly complex, but 

functionally powerful model (Beckers et al., 2009). It is described in more detail in the following 

section.

2.3.2 The Distributed Hydrology Soil Vegetation Model

The Distributed Soil Hydrology Vegetation Model (DHSVM) is a physically based 

model that represents watershed processes at the scale of a digital elevation model (DEM) 

(Wigmosta et al., 2002). The use of distributed models is becoming increasingly important in 

hydrologic predictions because they allow the user to utilize available spatial data for both input 

and testing. DHSVM has been used to analyze the effects of land use change, harvesting, and 

road networks on streamflow in forested, mountainous environments throughout the world 

(Bowling and Lettenmaier, 1997; Bowling et al., 2000; Cuo et al., 2006; Doten and Lettenmaier, 

2004; Doten et al., 2006; VanShaar et al., 2002). It has also been used in hydrologic modeling 

(Haddeland and Lettenmaier, 1995; Kenward and Lettenmaier, 1997; Westrick et al., 2002;

Wigmosta et al., 1994; Wigmosta and Lettenmaier, 1999) and to look at the interactions between 

climate and climate change and hydrology (Arola and Lettenmaier, 1996; Leung and Wigmosta, 

1999; Wigmosta et al., 1995). 

DHSVM works by explicitly calculating the energy and water budgets for a catchment at 

the scale of a grid cell. DHSVM integrates user-defined inputs for meteorological, soil, 

vegetation, and stream and road morphology data to model evapotranspiration, snow influx and 

efflux, and the movement of unsaturated soil moisture, saturated subsurface flow, overland flow, 

and channel flow. This is completed for each grid cell at a specified time step. Outputs of the 

model include but are not limited to water yield, soil moisture, overland flow, and 

evapotranspiration. More detailed information can be found in Wigmosta et al. 1994 and 2002.

Since DHSVM requires a high number of inputs, calibration of the model for parameters 

that cannot be measured is necessary. Bowling and Lettenmaier (1997) used a calibration period 

of 3 years for their study on the effect of forest road systems on forested catchments in the 

Pacific Northwest. When calibrating the model for discharge, the authors had to adjust LAI, 
26

lateral hydraulic conductivity, deep layer soil depth, height of road cuts, and decrease in lateral 

hydraulic conductivity. In general, the model under predicted the base flows and over predicted 

storm peaks (Bowling and Lettenmaier, 1997). 

Cuo et al. (2006) used DHSVM to simulate the effects of road networks on hydrological 

processes in Northern Thailand. During calibration, the authors found that the model adequately 

replicated soil moisture and depth but only accurately simulated streamflow for two of the three 

calibration years. This was attributed to year to year changes in land cover that were not 

reproduced in the model (Cuo et al., 2006). In an early presentation of DHSVM, Wigmosta et al. 

(1994) present model calibration data for a forested basin in Montana. For discharge, the model 

had a daily simulation RMSE of 1.2 mm and a R2 of 0.95. The authors also found that DHSVM

slightly over predicted hydrograph recession for the growing season and under predicted low 

flow during the dormant season (Wigmosta et al., 1994). 

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32

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33

CHAPTER 3. DHSVM INPUTS AND CALCULATIONS

The distributed hydrology-soil-vegetation model (DHSVM) is a highly parameterized 

model that requires extensive inputs. DHSVM was selected for this project due to its ability to 

model road effects on hydrology, however the complexity and non-user friendly nature of the 

program require extensive knowledge of the inputs. Thus, this chapter (along with Appendix A)

is intended to serve as a user manual for future users. The model and basic user guidelines can be 

downloaded from the DHSVM website interface (Land Surface Hydrology Research Group, 

2006). This manual describes steps that were used to run the model specifically for the Coweeta 

watershed in western North Carolina.

3.1 Coweeta Spatial Data

3.1.1 Elevation

DHSVM requires a digital elevation model (DEM) in binary format. The DEM defines

the grid cell scale at which the all hydrologic calculations are modeled. A DEM was obtained for 

the Coweeta study site, which is located in the Prentiss Quadrangle in western North Carolina. 

The data were obtained from the Geo Community, which offers free GIS and geospatial data to 

community members (GeoCommunity, 2007). The 30 m Prentiss DEM was downloaded in 

Spatial Data Transfer Standard (SDTS) format and converted to a raster grid. It was then 

converted into a 2D binary format. A finer resolution of 10 m was desired; however DHSVM 

requires that stream and road dimensions be contained within the grid cell. This was a noted 

constraint of the model. The 10 m resolution also increased model processing time increased 

fourfold. Thus, the 30 m resolution was considered ideal. 

3.1.2 Watershed

The watershed input is a binary file that outlines the study area to be analyzed. The 760 

ha Shope Fork catchment, located in the northern boundary of Coweeta, was manually delineated 

using ESRI ArcMap version 9.2 (Hillier, 2007). This was based on the current Coweeta 

boundary, the 30 m Prentiss DEM, and the internal watershed boundaries. Multiple methods of 

watershed delineation are possible, but it is important to verify that all cells that contribute water 
34

to the catchment are included in the boundary of the watershed. All watershed boundary shape 

files were obtained from the Coweeta Hydrologic Lab. GIS input layers were then clipped to the 

watershed extent for model input. 

3.1.3 Soil Type

Soil input parameters required by DHSVM may be obtained from an appropriate soil type 

map. As with elevation, the map must be in 2D binary format. Soil data for the Coweeta Basin 

were obtained from the Natural Resource Conservation Service (NRCS) Soil Survey Geographic 

(SSURGO) Database (Soil Survey Staff, 2008). SSURGO data were preferred over other data 

sources due to the fine mapping resolution (1:12,000 to 1:63,360). DHSVM usually requires 

coarse resolution soil data that are often aggregated by soil texture. Due to the availability of the 

data, soil types for Shope Fork were mapped and input based on soil series rather than soil 

texture (Table 3.1). 

3.1.4 Soil Depth

DHSVM has been found to be very sensitive to soil depth during its hydrologic 

simulation process (Surfleet, 2008), so a detailed soil depth map was imperative for accuracy of 

the model calibration. A soil depth algorithm is present within the DHSVM pre-processing steps 

(see Appendix A), although the precision of this algorithm is uncertain. Surfleet (2008) found 

that, when compared to other estimations of soil depth, the soil depth model provided by 

DHSVM was the best at predicting field measurements of soil depth. However, this occurred 

when the weighted values and the power exponents for the soil depth variables (elevation, slope, 

and area) were fitted to match the field measurements. Since few field measurements were taken 

for this study, maximum soil depths were taken from the Macon County Soil Survey (Thomas et 

al., 1996). Soil depths in the Shope Fork catchment ranged from 0.43 to 2.03 m. 
35

Table 3.1 Soil series found in the Shope Fork Catchment.

Soil Series Taxonomic Classification

Burton* Coarse-loamy, mixed, frigid Typic Haplumbrepts

Cashiers Coarse-loamy, micaceous, mesic Umbric Dystrocrepts

Chandler Coarse-loamy, micaceous, mesic Typic Dystrochrepts

Chestnut* Coarse-loamy, mixed, mesic Typic Dystrochrepts

Cleveland* Loamy, mixed, mesic Lithic Dystrocrepts

Cowee* Fine-loamy, mixed, mesic Typic Hapludults

Craggey* Loamy, mixed, frigid Lithic Haplumbrepts

Cullasaja* Loamy-skeletal, mixed, mesic Typic Haplumbrepts

Edneyville* Coarse-loamy, mixed, mesic Typic Dystrochrepts

Evard* Fine-loamy, oxidic, mesic Typic Hapludults

Fannin Fine-loamy, micaceous, mesic Typic Hapludults

Plott Coarse-loamy, mixed, mesic Typic Haplumbrepts

Saunook Fine-loamy, mixed, mesic, Humic Hapludults

Trimont Fine-loamy, mixed, mesic, Humic Hapludults

Tuckasegee* Fine-loamy, mixed mesic Typic Haplumbrepts

Wayah Coarse-loamy, mixed, frigid Typic Haplumbrepts

Whiteside* Fine-loamy, mixed, mesic Aquic Hapludults

*Series occurs in a complex with another series.

3.1.5 Vegetation

The cover type map file is similar to the soil map file in that it is linked to the DHSVM 

vegetation input parameters. Land cover data for the Coweeta basin were originally obtained 

from the Coweeta Geographic Network (Coweeta LTER, 2008). Data were in 2001 National 

Land Cover Dataset (NLCD) format. However, these data were deemed too coarse for model 

input. Four cover types (Table 3.2) were delineated based upon elevation and slope aspect

(Bolstad et al., 198; Day et al., 1988). 
36

Table 3.2 DHSVM cover types for the Shope Fork catchment. 

Cover Type Possible Species Present

Northern Hardwoods Acer saccharum, Tilia spp., Betula alleghaniensis,

Aesculus octandra, Acer rubrum

Cove Hardwoods Liriodendron tulipifera, Betula lenta, Magnolia 

spp., Rhododendron spp.

Mixed Deciduous Quercus alba, Quercus rubra, Robinia 

pseuduoaccacia, Carya spp., Acer rubrum

Xeric Oak-Pine Quercus coccinea, Quercus prinus, Oxydendrum 

arboreum, Pinus rigida, Kalmia latifolia

Sources: Day et al., 1988 and Bolstad et al., 1998 

3.2 Coweeta Network Files

DHSVM employs two types of networks: streams and roads. Three input files are needed 

for each network. Arc macro language (AML) is used to assign some of these parameters, the 

rest are user-defined. Coweeta stream and road shape files were downloaded from the LTER 

COGENT data. 

3.2.1 Stream Networks

3.2.1.1 Stream Class File

The class file contains routing information for the network as it relates to each stream 

order. This file is downloaded from the DHSVM website (Land Surface Hydrology Research 

Group, 2006) and edited manually. Coweeta stream class variables were chosen based on field 

measurements and literature (Appendix B). At the sampling site, Shope Fork is a third order 

stream (Figure 3.1). The following variables are needed for the stream class file:

1) Channel ID

The channel ID is a unique identifier for each stream class. It must be a positive integer.
37

2) Hydraulic Width (m)

The hydraulic width is used to calculate the volume of storage in the stream. It 

corresponds to the width of the main stream channel, or the channel at bankfull discharge. 

This is the discharge at which flow is most effective at forming a channel and can be 

above or below the defined stream channel in some cases. The widths of the streams for 

the Coweeta Basin were based on field measurements.

3) Hydraulic Depth (m)

This is the depth of the stream channel. Input depths for the Coweeta Basin were based 

on field measurements.

4) Friction Coefficient

The friction coefficient refers to Manning’s n for the stream channel. This input is not 

used during stream calculations, but DHSVM requires that the field be filled.

5) Maximum Infiltration (m/s)

This input is not actually used during stream routing, but DHSVM requires that the field 

be filled. Zero values will suffice. 

3.2.1.2 Stream Map File

The stream map file links physical stream data (length, depth, width, etc.) to cell 

locations. It is automatically generated using the create stream network AML. Some 

modifications to the AML may be necessary for specific watersheds (Appendix A and Appendix 

B). The file contains the following information:

1) Column Number

This is the column number of the grid cell. It starts in the western portion of the grid.
38

2) Row Number

This is the row number of the grid cell. It starts in the northern portion of the grid. The 

model assigns the NW corner cell the value of 0,0.

3) Channel ID

The channel ID identifies the channel segment and will correspond to a value in the 

stream network file.

4) Length (m)

This is the straight distance length measurement of the channel segment.

5) Depth (m)

The depth refers to the stream bank height.

6) Width (m)

This is the width of the stream channel.

7) Sink Indicator

If the word sink appears, the model assumes that a sink is present. Water in this cell will 

return to the cell rather than being routed through the grid.

3.2.1.3 Stream Network File

The stream network file is similar to the stream class file in that it contains information 

that is common to similar segments of the stream. Like the stream map file, the stream network 

file is automatically generated by the create stream network AML script (Appendix A). The 

AML will output the following parameters:

1) Channel ID

The channel ID is a unique identifier for the channel segment. It will correspond to a 

segment in the stream map file.
39

2) Segment Order

The segment order is a code that tells the model the order of routing calculations. 

Segments that have a lower order are routed first and thus flow into segments of a higher 

order.

3) Segment Slope (m m-1

This is the slope of the channel segment.

)

4) Channel Class ID

This is the class of the stream channel and will refer to a class specified in the stream 

class file.

5) Destination Channel ID

This identifies the segment in which the segment will flow. The basin outlet has a 

destination channel ID of 0.

6) Save Indicator

Routing results can be saved to an output file if the word save appears. 

3.2.2 Road Networks

The input files for road networks are almost identical to stream networks. Nevertheless, 

they are detailed below since there are some slight changes in the files.

3.2.2.1 Road Class File

The class file contains routing information for the network as it relates to each road order. 

This file is downloaded and edited manually. See Appendix B for the Coweeta Road Class file.

The following variables are needed for the road class file:

1) Channel ID

The channel ID is a unique identifier for each road class. It must be a positive integer.
40

2) Hydraulic Width (m)

For roads, the hydraulic width refers to the dimension of the ditch along the road. In this 

case, it is the width of the ditch. The width of the Coweeta road classes were found using 

measurements taken on site.

3) Hydraulic Depth (m)

The hydraulic depth is the depth of the road ditch. Coweeta road network depths were 

based on field measurements.

4) Friction Coefficient

The friction coefficient refers to Manning’s n for the road ditch. Manning’s n is also 

required for the road surface; this is used as part of the mass wasting model. Values were 

found using Table 5.3 in Gordon et al. (2004). Class 1 roads were assumed to be coarse 

gravel while class 3 roads were considered fine gravel. Coweeta class 5 roads are 

typically closed service roads that are covered in vegetation; these were considered young 

trees intergrown with weeds. Lastly, class 7 roads are walkways or trails and these were 

assumed to be earth. Ditches were all assumed to be composed of earth.

5) Maximum Infiltration (m s

-1

This is the maximum infiltration rate through the surface of the road ditch. Values were 

kept constant with those found in the DHSVM tutorial. 

)

6) Road Type

The road type refers to the crown of the road. DHSVM allows three options: crowned, 

insloped, and outsloped. The default value is outsloped. Assigned road types were based 

off of visual observations.

7) Road/Overland Erodibility Coefficients

These are used in the mass wasting model and refer to the detachability of a soil particle 

upon raindrop impact. The road erodibility coefficients were found by using the average 
41

watershed k values in the SSURGO revised universal soil loss equation (RUSLE) report

(Soil Survey Staff, 2008). 

8) d50

d

(m)

50 is the mean particle size diameter for the road surface. This is used in the mass 

wasting model. This was based on visual observation.

9) Surface

This input refers to the surface of the road. DHSVM allows three options: paved, 

unpaved, or dirt. Surface inputs were based on visual observations.

10) Road Width (m)

This is the road width for each class and does not include the corresponding ditch. Like 

the hydraulic width and depth, the road width inputs are based on field measurements.

3.2.2.2 Road Map File

The road map file links physical road data (length, depth, width, etc.) to cell locations. It 

is automatically generated using the create road network AML (Appendix A). The output file 

contains the following information:

1) Column Number

This is the column number of the grid cell. It starts in the western portion of the grid.

2) Row Number

This is the row number of the grid cell. It starts in the northern portion of the grid. The 

model assigns the NW corner cell the value of 0,0.

3) Channel ID

The channel ID identifies the road segment and will correspond to a value in the road 

network file.
42

4) Length (m)

This is the straight line length measurement of the road segment.

5) Depth (m)

The depth refers to the road ditch height.

6) Width (m)

This is the width of the road.

7) Sink Indicator

If the word sink appears, the model assumes that a sink is present. Water in this cell will 

return to the cell rather than being routed through the grid.

3.2.2.3 Road Network File

The road network file is similar to the road class file in that it contains information that is 

common to similar segments. Like the road map file, the road network file is automatically 

generated by the create road network AML script (Appendix A). The AML will output the 

following parameters:

1) Channel ID

The channel ID is a unique identifier for the channel segment. It will correspond to a 

segment in the road map file.

2) Segment Order

The segment order is a code that tells the model the order of routing calculations. 

Segments that have a lower order are routed first and thus flow into segments of a higher 

order.

3) Segment Slope (m m-1

This is the slope of the road segment.

)
43

4) Channel Class ID

This is the class of the road channel and will refer to a class specified in the road class 

file.

5) Destination Channel ID

This identifies the segment in which the segment will flow. The basin outlet has a 

destination channel ID of 0.

6) Save Indicator

Routing results can be saved to an output file if the word save appears. 

Figure 3.1 Coweeta stream and road networks. Stream networks created by CreateStreamNetwork AML, 

road networks provided by Coweeta.
44

3.3 Coweeta Meteorological Data

Meteorological data for the model must be provided and DHSVM allows a variety of 

formats. It will accept ASCII input, MM5, or PRISM data. The model is designed to run on an 

hourly timestep; a daily timestep is considered coarse and is not recommended. Many studies 

have used 3-hour timesteps (Bowling et al., 2000; Doten et al., 2006; LaMarche and Lettenmaier, 

2001). Data were provided by the Coweeta Hydrologic Lab. One climate station was present 

within the boundary, as well as two rain gages. For initial testing, only one climate station was 

used (CS01 and RG06). Input values are used for the entire catchment and are weighted using a 

Cressman approach (Cressman, 1959). Data manipulations are described below.

1) Precipitation (m)

Hourly precipitation data for the watershed were obtained in hundredths of inches and 

were subsequently converted to meters. Missing values were assumed to be zero. There 

were no cases of missing data during a recorded rainfall event.

2) Air Temperature (°C)

Minimum, maximum, and average hourly air temperature in degrees C were given for the 

climate station. Average air temperature values were used.

3) Wind Speed (m s

-1

The average hourly wind speed was obtained for the watershed. Units were already in m 

s

)

-1 and no data manipulation was necessary.

4) Relative Humidity (%)

Similar to the wind speed, the hourly percent relative humidity was acquired for the 

watershed and no data manipulation occurred.

5) Incoming Shortwave Radiation (W m-2

Coweeta records the hourly incoming shortwave radiation in Langley. Converting from 

Langley to Watt hours per square meter required a conversion factor of 11.62. 

)
45

6) Incoming Long-wave Radiation (W m-2

Coweeta does not record incoming long-wave radiation, so this data had to be interpreted 

from the known meteorological data using the following equations (Prata, 1996). It 

should be noted that the subsequent calculation is for clear-sky emissivity and does not 

account for cloudiness, which can greatly affect incoming long-wave radiation. 

)

(Eq. 1) LL ↓ = �(1 − (1 + ξξ)

�−(1.2+3.0ξξ)

1

2�

� σσTT0

4

Where:

LL ↓ = incoming longwave radiation in W m-2

σ = Stephan-Boltzmann constant and is equal to 5.67xx10−8

ξ = precipitable water and can be found by:

(Eq. 2) ξξ = 46.5 ee0

TT0

Where:

e0 = actual vapor pressure in hPa and can be found by:

(Eq. 3) eeoo = RRRR

100 �

eess

 

Where:

RH = relative percent humidity 

es = saturated vapor pressure hPa and can be found by:

(Eq. 4) eess = 6.11 xx 10

7.5TT

237.7+TT

Where:

To = the screen level temperature in K
46

3.4 DHSVM Configuration File

The DHSVM configuration file relays all of the input variables to the model. It contains 

information on the model settings, file path names, soil and vegetation parameters, and output 

conditions. Many of the model variables are detailed below. See Appendix B for a complete 

copy of the Coweeta configuration file.

3.4.1 Model Constants

This section includes model parameters that stay constant throughout time and space 

during the model calculations. 

1) Ground/Snow Roughness (m)

The roughness length refers to the height above the displacement plane (in this case, the 

ground) at which the mean wind speed becomes zero (Glickman, 2000). A lower 

roughness length indicates more interaction between the ground surface and the wind 

profile and thus stronger winds close to the ground. Smoother, snow covered surfaces 

will have a lower roughness length than forested surfaces. Davenport (1960) classified 

effective terrain roughness. The Coweeta vegetative cover was classified as “skimming” 

to “chaotic” while snow roughness was considered “smooth” since the Coweeta snow 

cover would rarely cover enough vegetation to consider it “featureless.”

2) Rain/Snow Threshold (°C)

These values set the minimum temperature at which rainfall can occur and the minimum 

temperature at which snowfall can occur. These temperatures are not exactly at 0°C, as 

one might expect. However, they are dependent upon the surface air temperature, the dew 

point, and the wet bulb temperature (Ahrens, 2002). The threshold values become 

increasingly important and complicated in the Southern and Mid-Appalachian regions, 

where much of the winter precipitation falls in the form of sleet and freezing rain as 

opposed to snow. The rain and snow thresholds were set at -1°C as opposed to 0°C to 

account for these forms of precipitation.
47

3) Snow Water Capacity

The liquid water holding capacity of snow is dependent upon many factors, including the 

regional topography, ice layer characteristics, and snow density (Singh and Singh, 2001). 

The impact of snowpack hydrology is not a large factor in the Coweeta basin, although

some annual snowfall does occur. A rough estimate of 2.5% liquid water holding 

capacity at a snowpack of 0°C is suggested for low snow densities (100 to 350 kg m-3).

4) Reference Height (m)

The reference height is the height at which all meteorological observations are taken. For 

modeling purposes, this must be at least 10 meters above the highest canopy height. The 

highest canopy height was the cove hardwoods (35.5 m), so the reference height was set 

to 46 m.

5) Rain/ Snow LAI Multiplier

DHSVM models the maximum interception storage capacity using an LAI multiplier for 

rain, as found in Dickinson et al. (1991). 

(Eq. 5) IIIIII IIII IIIIII IImmmm = LLLL MMMMMM MM xx LLLL xx FF CC CC rr

LAI at Coweeta averages 6.2 m2m-2 (Monk and Day, 1988) for overstory vegetation and 

maximum interception is approximately 3 mm (Helvey and Patric, 1988). Assuming 90% 

canopy cover, the LAI rain multiplier for Coweeta is 0.00054. DHSVM recognizes no 

distinction between rain and snow when modeling storage interception (Wigmosta et al., 

1994).

6) Minimum Intercepted Snow

The minimum intercepted snow is used in the snowpack model and is not critical to the 

calibration of hydrologic modeling in the southern Appalachian Mountains. Storck 

(2000) found that the minimum amount of intercepted snow that can be melted or 

sublimated off the canopy to be 5 mm. Since this value was observed in the Pacific 
48

Northwest where snowfall is much higher and snow interception is larger, this value was 

arbitrarily pared down to 3 mm.

7) Temperature Lapse Rate (°C/m)

The temperature lapse rate can be set to constant or variable settings. For simplification 

purposes, temperature was assumed to change consistently with elevation. Gaffin et al. 

(2002) conducted a study in the Great Smoky Mountains National Park, which lies just

west of the Coweeta Hydrologic Lab. The park has much higher elevations than the study 

site, but for the scope of this review it was assumed that the temperature lapse rates were 

approximately equal for the two areas. Gaffin et al. (2002) found the temperature lapse 

rates in the Great Smoky Mountains average 3.3 °F 1000 ft-1

, which converted to a lapse 

rate of -0.0018 °C m-1

.

8) Precipitation Lapse Rate (m m-1

The lapse rate for precipitation can be set to constant or variable settings. For 

simplification purposes, precipitation was assumed to change consistently with elevation. 

Swift et al. (1988) found that, in the Coweeta basin, precipitation increases with elevation 

on the east-west axis but the lapse rate is static along the north-south axis. For DHSVM 

purposes, Coweeta was assumed to have a lapse rate of 0.00025 m m

)

-1

. 

3.4.2 Soil Inputs

DHSVM requires numerous soil input parameters for each soil type and within each of 

these soil types, numerous inputs for each horizon (Refer back to Table 3.1 for soil types). All 

parameters are required for each individual soil type. A * indicates that the parameter is required 

for each soil horizon within an individual soil type.

1) Lateral Saturated Hydraulic Conductivity (m s

-1

The saturated hydraulic conductivity is the volume of unit water that will flow through a 

medium (saturated soil in this case) in a given unit of time. Hydraulic conductivity can be 

saturated or unsaturated and can be vertical or horizontal. In a clayey soil, Miwa (1999)

)
49

found that lateral hydraulic conductivity in the upper soil horizon was 6.96 m d-1. 

Hydraulic conductivity will increase as soil particle size increases, so this value was used 

as a base value to determine the lateral saturated hydraulic conductivities for the Coweeta 

soils. 

2) Exponential Decrease

When calculating the rate of saturated subsurface flow within the watershed, the lateral 

hydraulic conductivity for each soil was assumed to decrease exponentially with depth 

(Equation 2.48 in Wigmosta et al., 2002). Beven (1982) found that observed soil 

hydraulic conductivities among various studies yielded in a decay coefficient between 1 

and 9. For Ohio and Pennsylvania, exponential decreases were in the order 4.0. 

3) Maximum Infiltration (m s

-1

The rate at which water moves into the soil surface is known as the infiltration capacity. 

When the infiltration rate for a soil has reached its maximum, no more infiltration can 

occur and surface runoff or ponding will result. The maximum infiltration rate is 

dependent upon many soil characteristics, including texture, structure, and the presence 

of restricting layers. A rough estimate of the soil infiltration rate was found in Table 12.1 

of Hillel (1982).

)

4) Capillary Drive (m)

Capillary suction (ψ) measures the adhesive forces that bind water molecules to soil 

particles and the cohesive forces that bind water particles to each other. Capillary suction 

increases with decreasing water content, since unsaturated water is held under tension. 

Capillary suction is dependent upon the soil moisture, soil texture, and position within the 

soil horizon although generic values can be determined for different soil textures. For 

DHSVM input, Green-Ampt infiltration parameters were used from Table 1.7 in Bedient 

et al. (2008).
50

5) Surface Albedo

The surface albedo is the fraction of incoming radiation that is reflected by that surface. It 

can be as low as 0.1 for dark surfaces and as high as 0.5 for lighter surfaces (Brady and 

Weil, 2004). DHSVM uses albedo when modeling snowpack hydrology. The dry surface

albedos for the Shope Fork soils were found in the SSURGO Wind Erosion and 

Prediction report (Soil Survey Staff, 2008). 

6) Porosity*

Soil porosity is the percent volume of the soil bulk that is not occupied by solid particles. 

The porosity is extremely important in hydrologic calculations as it determines how much 

available pore space the soil has for water. The porosity for each soil was found using the 

following equation from Burger (2008):

(Eq. 6) TT PP PP = 2.65−BB BB DD

2.65

The mean soil bulk density was found in the SSURGO Physical Soil Properties report

(Soil Survey Staff, 2008). For soil complexes, the porosity was weighted based on the 

percent of map unit covered by that particular series. In the case where the map unit 

percents did not add to 100 percent of the area, the missing area was divided equally 

amongst the series in the complex. Map unit percents were found in the SSURGO Wind 

Erosion and Prediction report (Soil Survey Staff, 2008).

7) Pore Size Distribution*

The volume of the various sizes of pores in a soil is known as the pore size distribution 

(Brady and Weil, 2004). Although usually expressed as a percentage of the bulk soil 

volume, it can also be measured using the Brooks-Corey Index, as shown in Equation 7 

(Rawls et al., 1993). DHSVM uses the pore size distribution when modeling the 

percolation of water through the soil column.
51

(Eq. 7) λλ = ee [−0.7842831 + 0.0177544(SS) − 1.062498(φφ) −

0.00005304(SS2) − 0.00273493(CC2) + 1.11134946(φφ2) −

0.03088295(SS)(φφ) + 0.00026587(SS2)(φφ2) − 0.00610522(CC2)(φφ2) −

0.00000235(SS2)(CC) + 0.00798746(CC2)(φφ) − 0.00674491(φφ2)(CC)]

Where:

λ = Brooks-Corey pore-size distribution index

C = percent clay (5 < % < 60)

S = percent sand (5 < % < 70)

Φ= porosity (volume fraction)

The percent clay and percent sand were found in the SSURGO Wind Erosion and 

Prediction report (Soil Survey Staff, 2008). 

8) Bubbling Pressure (m)*

The bubbling pressure refers to the air pressure that is required to force air through a 

porous cup. The bubbling pressure is used when calculating desorption in the model, 

which is part of the movement of unsaturated soil moisture. Bubbling pressure was found 

using the Brooks-Corey Index, as found in Table 5.3.3 in Rawls et al. (1993).

(Eq. 8)

hbb = ee [5.3396738 + 0.1845038(CC) − 2.48394546(φφ) −

0.00213853(CC)2 − 0.04356349(SS)(φφ) − 0.61745089(CC)(φφ) +

0.00143598(SS)2(φφ2) − 0.00855375(CC2)(φφ2) −

0.00001282(SS2)(CC) + 0.00895359(CC2)(φφ) − 0.00072472(SS2)(φφ) +

0.0000054(CC2)(SS) + 0.50028060(φφ2)(CC)]

Where:

hb

C = percent clay (5 < % < 60)

= Brooks-Corey bubbling pressure

S = percent sand (5 < % < 70)

Φ= porosity (volume fraction)
52

The percent clay and percent sand were found in the SSURGO Wind Erosion and 

Prediction report (Soil Survey Staff, 2008). 

9) Field Capacity*

Field capacity is the percent water held at -0.33 bars. This is the water that is available for 

plant uptake and is important when modeling soil moisture, which is consequently the 

basis of many calculations used by the model. Field capacity was found in the SSURGO 

Wind Erosion and Prediction report (Soil Survey Staff, 2008). 

10) Wilting Point*

Wilting point is the percent water in the soil that is held at -15 bars. This is water that is 

unavailable at plants. Like field capacity, the wilting point is also used to calculate soil 

moisture. The wilting point was found in the SSURGO Wind Erosion and Prediction

report (Soil Survey Staff, 2008). 

11) Bulk Density (kg m-3

The soil bulk density is the mass of the soil per unit volume. The bulk density was found 

in the SSURGO Physical Soil Properties report (Soil Survey Staff, 2008). The table gives 

the moist bulk density in g cm

)*

-3

. This was converted to the DHSVM required units, kg 

m-3, by multiplying by 1000. The bulk density was found for each individual soil layer 

within the series and then weighted to get the bulk density for the DHSVM soil type.

12) Vertical Conductivity (m s

-1

The vertical conductivity of the soil is the maximum rate of saturated hydraulic 

conductivity, or k

)*

sat. This value was taken as the maximum value found in the SSURGO 

Wind Erosion and Prediction report (Soil Survey Staff, 2008). The data were converted 

to m s

-1

.

13) Thermal Conductivity (W m-1 K-1

The thermal conductivity of a soil explains the soil’s ability to transmit heat, mainly 

through conduction and is usually defined as the quantity of heat that flows through a unit 

)*
53

area in a unit time under a unit temperature gradient (Jackson and Taylor, 1986). Thermal 

conductivity is extremely variable and is dependent on soil bulk density, particle 

arrangement, temperature, and, most importantly, soil volumetric water. Since thermal 

conductivity is time variable, the thermal conductivity of the solids was found using an 

equation from Bowling and Letttenmaier (1997).

(Eq. 9) TT = SS∗TT qq qq +CC∗TT

SS+CC

Where:

TC = Effective solids thermal conductivity

S = Percent sand fraction

C = Clay fraction

TCquartz = 8.8 W m-1 K-1

TCclay = 2.9 W m

Thermal conductivity of quartz (Hillel, 1982)

-1 K-1 is the thermal conductivity of clay 

The percent sand and clay fractions were found in the SSURGO Wind Erosion and 

Prediction report (Soil Survey Staff, 2008).

14) Thermal Capacity (J m-3 °K-1

The thermal heat capacity of a soil is the change in heat content of a unit bulk volume of 

soil per unit change in temperature. The thermal capacity is dependent upon the soil bulk 

density, the solid phase, and the volumetric water and is thus variable throughout time. 

Most soil constituents have a heat capacity around 2.0 x 10

)*

6 J m-3 °K-1 (Hillel, 1982).

15) Manning’s n*

Manning’s roughness coefficient (n) estimates the roughness of a channel material and is 

not typically used to describe a soil particle or the soil surface. These values were 

assigned based on the soil texture particle size as determined by USDA definitions 

(Figure 5.1.1 in Rawls et al., 1993). Manning’s roughness was then determined for the 

particle size based on equation 12.2.2 from Shen and Julien (1993).
54

(Eq. 10) = (dd50)

1

6�

21.0

Where:

n = Manning’s roughness coefficient for the particle

d50 = particle size diameter in meters

Soil texture for each layer was determined using the SSURGO Wind Erosion and 

Prediction report (Soil Survey Staff, 2008).

3.4.3 Vegetation Inputs

DHSVM requires 17 vegetation input parameters for each cover type and within each of 

these cover types, numerous inputs for each vegetation layer. 

1) Fractional Coverage*

The fractional coverage is essentially the canopy density. The fractional coverage is used 

when calculating the maximum storage interception capacity for the canopy. It is also 

used when calculating longwave and shortwave radiation budgets. Observed data were 

used and were verified using NASA Land Data Assimilation Systems (LDAS) data 

(Rodell, 2009).

2) Trunk Space*

DHSVM requires an input for the fractional proportion that the trunk space exhibits in 

comparison to the entire tree height. It is used when calculating aerodynamic resistance. 

The height of the canopy top was taken to be the height of the tree (see Vegetation Input 

#9) and the height of the canopy bottom was taken to be the height of the trunk. Trunk 

height data were based on best known estimates.

(Eq. 11) FF TT SS = h oooo CC BB (mm)

h oooo CC TT (mm)
55

3) Aerodynamic Attenuation*

The aerodynamic attenuation index is a dimensionless extinction coefficient that is used 

to calculate the exponential wind profile through the overstory canopy (Wigmosta et al., 

2002).The attenuation index is approximately between -0.3 to 3.0 and is dependent upon 

stage of growth and the density and flexibility of the canopy (Cionco, 1972). A mid-range 

value of 1.5 was used for each layer.

4) Radiation Attenuation*

The radiation attenuation input is a dimensionless coefficient that is used to calculate the 

fraction of shortwave radiation transmitted by the overstory using a Beer’s Law 

relationship (Wigmosta et al., 2002). To determine the attenuation coefficient (ks) one 

must think about the shadow that the canopy casts upon the ground surface. In general, 

the leaves in a deciduous forest exhibit a random distribution of elevation angle (β), 

which is essentially the vertical angle of a leaf in relation to the ground. On average, 

deciduous trees also express randomness with respect to azimuth angle (θ), which is the 

leaf angle with respect to direction around the branch. Since both elevation angle and 

azimuth angles are randomly distributed, the Coweeta forest types have a spherical or 

ellipsoidal leaf distribution. This is because, if rearranged, the leaves of the trees can 

exhibit a similar distribution along a sphere as they would in nature (Monteith and 

Unsworth, 2007). It was found that many plants exhibit ellipsoidal distribution 

(Campbell, 1986), which gives the following equation for ks (Campbell, 1986; Monteith 

and Unsworth, 2007):

(Eq. 12) ss(ββ) = �xx 2+ 2ββ�

0.5

xx+1.774(xx+1.182)−0.733

Where:

x = ratio of average projected areas of canopy elements on horizontal and 

vertical surfaces

For a spherical leaf distribution, x = 1 (Monteith and Unsworth, 2007). Assuming an 

elevation angle of 60°, ks = 0.86.
56

5) Maximum Snow Interception Capacity*

Snowfall interception is dependent upon branch strength under stress and the shape of the 

canopy as opposed to leaf shape, which is the dominating factor in rainfall interception. 

Falling snow will build up on a branch until the strength of the branch can no longer bear 

the weight of the snow or because the snow is unstable (usually due to melting). Branches 

can usually bear high snow water equivalent, resulting in larger snowfall interception 

capacities than rainfall interception capacities (Ward and Trimble, 2003). Helvey (1971) 

found that bare hardwoods have a rainfall interception capacity of 0.13 cm. In 

comparison, a fully developed Eastern White Pine (Pinus strobus) canopy has a rainfall 

interception capacity of 0.36 cm (Helvey, 1971) and a Western white pine (Pinus 

monticola) sapling has a snowfall interception capacity of about 0.25 cm (Haupt and 

Jeffers, 1967). Since appropriate snowfall interception capacity data are lacking for 

deciduous vegetation, the bare vegetation values were used to estimate the snow 

interception capacity for the Coweeta cover types.

6) Maximum Release Drip Ratio*

In the Pacific Northwest, many studies have been conducted regarding snowmelt and 

snowpack hydrology. In the Southern Appalachians, runoff from snowmelt is not of 

primary concern. The maximum release drip ratio is the ratio of snow in the canopy that 

falls as snow (mass release) to the snow that melts off of the leaves (meltwater drip). 

Stork et al. (2002) found that, in the Pacific Northwest, a drip ratio of 0.4 was 

appropriate. For Coweeta, it was assumed that most snowfall events will be followed 

with periods of rapid melting and snow will primarily be removed from vegetation 

through meltwater drip, indicating a ratio closer to 0.2. This was assumed true for all 

vegetation types. Mass release will only occur if intercepted snow is greater than the 

specified snow water capacity (Andreadis et al., 2009). 

7) Snow Interception Efficiency*

The snow interception efficiency deals with the effectiveness of the canopy to intercept 

snowfall and prevent it from reaching the ground. It is dependent upon temperature and 

highly dependent upon wind speed. Storck et al. (2002) found that Douglas-fir in a 
57

maritime climate exhibited a snow interception efficiency of 60%. Snow interception 

efficiency is known to be a maximum of 0.6 for dense conifers and decreases with 

canopy cover (Armstrong and Brun, 2008).

8) Impervious Fraction

The impervious fraction is the amount of impervious surface area present within the grid 

cell. All cover types were assumed to have an impervious fraction of zero.

9) Height (m)

DHSVM requires the maximum height for both the overstory and understory vegetation 

layers. 

The cove-hardwoods understory is dominated by Rhododendron spp. which, in the 

mountains of North Carolina, reaches a maximum height of approximately 5 m (Lipp and 

Nilsen, 1997). We can assume that this is in open sunlight and that Rhododendron spp. 

found in the understory will be slightly shorter. The overstory of the cove-hardwoods is 

predominately Liriodendron tulipifera. The approximate height was found using a site 

index curve appropriate for the Southern Appalachians (Beck, 1962). Site index for all of

the cover types was obtained from the Macon County soil survey and approximate stand 

ages were gathered from the Coweeta historical data.

Xeric-oak pine cover types are dominated by Quercus spp. in the overstory. Height was 

derived from a site index curve for upland oaks in the Southern Appalachian Mountains 

(Olson, 1959). Kalmia latifolia is the primary species found in the understory. It can 

reach heights up to 9 m (McNab and Clinton, 2003), although this is unlikely in an 

understory environment. Its minimum height of 2 m was a more appropriate estimate. 

The mixed deciduous hardwoods overstory is dominated by Quercus alba, which is also 

considered an upland oak. Olson’s (1959) site index curve for upland oaks in the 

Southern Appalachians was used to determine approximate overstory height. The height 
58

of the understory, which is dominated by Acer rubrum, was assumed to be around 3.0 m 

(Walters and Yawney, 1990).

Acer saccharum is the prevailing species found in the northern hardwoods forest type. 

These heights were estimated using a site index curve found in Carmean (1978). The 

understory was considered the same as the mixed deciduous cover type.

10) Maximum/Minimum Resistance (s m-1

The stomatal resistance is the measure of a plant’s ability to resist the diffusion of water 

out of the stomates. In other words, it is a measurement of the plant’s ability to control 

transpiration. In a study conducted in New Hampshire and Maine, Federer (1977) found 

that Acer rubrum had a mean stomatal resistance of 3.5 s cm

) 

-1 with a standard error of 

0.19 s cm-1. Maximum and minimum resistance were taken to be three standard errors 

from the mean. It should be noted that Federer’s measurements included cuticle 

resistance, which is not normally part of stomatal resistance measurements. However, 

these values are usually found to be negligible (Federer, 1977). Federer also found that 

Quercus coccinea had a mean stomatal resistance of 3.2 s cm-1 with 0.32 s cm-1 standard 

error and that Quercus alba had a mean stomatal resistance of 3.0 s cm-1 with 0.18 s cm-1

standard error. Acer saccharum was found to have a mean stomatal resistance of 4.5 s 

cm-1 with a standard error of 0.20 s cm-1. McConathy and McLaughlin (1978) conducted 

a study in eastern Tennessee and found that Liriodendron tulipifera expressed a 

maximum stomatal resistance of 3.75 s cm-1 while minimum values were 1.93 s cm-1.

11) Moisture Threshold 

When soil moisture falls below a certain water content, transpiration is restricted. This is 

considered to be 50 to 80% of the soil field capacity (Shuttleworth, 1993). The soil 

moisture threshold was calculated by taking the average field capacity under each 

vegetation type as found in the SSURGO data. Understory layers have less extensive root 

systems and were thus assumed to have lower moisture thresholds than overstory layers. 

The understory moisture threshold was set at 50% of field capacity while the overstory 

moisture threshold was determined to be at 80% soil field capacity.
59

12) Vapor Pressure Deficit (Pa)

At higher vapor pressures, plants require more exertion to carry water from the roots to 

the leaves. At a high enough vapor pressure, stomatal closure will occur. Wigmosta et al. 

(2002) list stomatal closure occurring around 4 kPa for most plants. The stomatal closure 

was used to calculate the overstory and understory canopy resistance. 

13) Rpc (W m-2

R

)

pc is the fraction of shortwave radiation that is photosynthetically active (PAR) for each 

canopy layer. It is the light level where the stomatal resistance is twice that of the 

minimum stomatal resistance (Wigmosta et al., 2002). Rpc is usually between 10-50 W m2

. Precise measurement is not necessary, but an accuracy of ±20% is optimal (Dickinson 

et al., 1991).

14) Root Zone Depths (m)

The root zone depths are essentially the depths of the soil layers, which were found in the 

SSURGO database, under the Physical Soil Properties report (Soil Survey Staff, 2008). 

The root zone depths were an estimated average.

15) Overstory/Understory Root Fraction

DHSVM requires data on the fraction of roots in each root zone for each canopy layer. 

After germination, A. rubrum extend a taproot to a depth of approximately 2 to 5 cm 

below the soil surface, upon which the root turns laterally. Feeder roots are extended 

vertically, primarily in the upper 8 cm of soil. Larger red maple trees will extend woody 

roots to depths of up to 80 cm (Walters and Yawney, 1990). 

A. saccharum roots are “strong, oblique laterals with extensive branching” (Godman et 

al., 1990). More roots are contained in the upper soil layers than in the deeper soil layers. 

L. tulipifera root systems include deep taproots and wide spreading lateral roots (Beck, 

1990), indicating a relatively equal proportion of roots in each soil layer. Q. alba initially

develops a prolific taproot, but this is eventually replaced with a wide spreading fibrous 

root system. In a study in the Black Forest in Massachusetts, the deepest point of 

penetration was 1.2 m and the majority of the main branches were contained in the upper 
60

53 cm of soil (Rogers, 1990). In comparison, Q. coccinea maintains a strong and deep 

taproot (Johnson, 1990).

16) Overstory/Understory Monthly LAI 

Leaf area index, or LAI, is the proportion of vegetative surface area to ground surface 

area. LAI is seasonally variable, so monthly data are necessary for the model. LDAS 

(Rodell, 2009) provides estimated monthly LAI values for general cover types. The 

deciduous broadleaf forest values were found to be reasonable with southern Appalachian 

hardwood LAIs. A typical LAI at Coweeta averages 6.2 (Monk and Day, 1988), which 

was within the range of values provided by LDAS. The LDAS values were used for all of 

the overstory cover types.

In a study on great rhododendron in the Coweeta basin, Hille Ris Lambers and Clark 

(2005) found that LAI ranged from 0 to 4.28. Rhododendron spp. is a broadleaf 

evergreen and it is also assumed to have a constant LAI throughout the year. An average 

LAI of 2.2 was used for the shrub and urban cover types. This value was also used for K. 

latifolia. No data could be found for A. rubrum LAI, however a low value leaf area index 

for a maple-beech-birch forest in the Coweeta basin was 2.78 (Sampson et al., 1998). 

This was used in conjunction with the LDAS values to create monthly understory data for 

both the northern hardwoods and mixed deciduous forests.

17) Overstory/Understory Monthly Albedo

Monthly albedo data could not be found. Generic cover type data were found from LDAS 

(Rodell, 2009). These data were kept constant throughout the course of the year. 

Evergreen broadleaf values were used for both the Rhododendron and Kalmia

understories while closed shrub-land values were used for the Acer understories. All 

overstory values were obtained from the deciduous broadleaf forest data.
61

3.4.4 Model State Files

DHSVM requires a number of model state files, which are files containing data on the 

initial conditions of the watershed. The state files are automatically generated (see Appendix A), 

although one text file (Initial State) should be edited according to watershed conditions. The 

Initial State file contains the following information:

1) Model State Date

This is the timestep that the model should begin to run. The Coweeta data began on 

01/01/2003-03.

2) Rows and Columns

This line contains the number of rows in the binary input files followed by the number of 

columns. The information can be found in the binary files or from the raster grids.

3) Vegetation Layers

The number of vegetation layers should be listed. If all and/or some vegetation types 

have both an overstory and an understory, list 2. If all vegetation types only have an 

understory, put 1.

4) Rain Interception (m)

This is the rain interception for each vegetation layer. The number of inputs should match 

the number of vegetation layers listed in #3. Default DHSVM values were used.

5) Snow Interception (m)

Similar to the rain interception, this is the snow interception for each vegetation layer. 

Default DHSVM values were used.

6) Snow Cover Mask 

Snow cover mask for the watershed. Default DHSVM value was used.
62

7) Last Snowfall (days)

The last snowfall lists the number of days since the last snowfall occurred.

8) Liquid Water Content (m)

Liquid water content of the top layer of snowpack. Default DHSVM value was used.

9) Snowpack Temperature (°C)

Temperature in degrees C of the current ground snowpack. Coweeta had no snowpack, 

and a value of freezing (0°C) was used.

10) Cold Content

Cold content of the snowpack. Default DHSVM value was used.

11) Root Zone Layers

Like the number of vegetation layers, this field lists the number of root zone layers. Put in 

the maximum number of layers in any soil type. Usually 3 is a typical number.

12) Soil Moisture

Volumetric soil moisture for each soil layer plus one extra layer. These values were taken 

from the SSURGO database (Soil Survey Staff, 2008).

13) Soil Temperature (°C)

The average temperature of the soil in degrees C. This was taken to be an average of the 

three root zone temperatures.

14) Root Zone Temperature (°C)

The average temperature of each soil layer in degrees C. These values were found in the 

SSURGO database (Soil Survey Staff, 2008).

15) Ground Heat Storage

Ground heat storage. Default DHSVM values were used.
63

16) Runoff

Runoff for the watershed. Default DHSVM values were used.

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69

CHAPTER 4. MODELING THE EFFECTS OF FOREST ROAD DENSITY ON 

STREAMFLOW IN THE BLUE RIDGE MOUNTAINS

Abstract

Forested watersheds have often been managed for flood mitigation. Studies indicate that 

forests can potentially minimize peak flows during storm events, yet the relationship between 

forests and flooding is complex. Forest roads, usually found in managed systems, can potentially 

magnify the effects of forest harvesting on water yields. A distributed hydrology, soil, vegetation

model (DHSVM) was calibrated for a 760 ha watershed in the Blue Ridge Mountains of North 

Carolina. The impacts of forest road density were modeled using uniform input parameters but 

changing road densities. Road densities tested were 0.5, 1.0, 3.0, 4.3, 6.0 and 12.0 km km-2

. 

Results indicate that increases in road density increased average streamflows at densities ≥ 4.3 

km km-2. During small storm events, discharge was impacted at ≥ 6.0 km km-2 and streamflow 

was impacted during large rainfall events ≥ 3.0 km km-2 road densities. These findings indicate 

that higher forest road densities can influence water yields and management efforts may be 

needed that can reduce the water yield from forest roads. The data also indicate that using the 

minimal road density necessitated by the land use can have positive effects.

Keywords: Forest roads, flooding, DHSVM, hydrology
70

4.1 Introduction

Throughout the Blue Ridge Mountains, the impacts of forest harvesting on downstream 

flooding have been intensely debated. In July 2001, extensive flooding occurred in southern 

West Virginia and Southwestern Virginia. National Weather Service stations recorded maximum 

rainfall intensities of 50 mm hr-1 and subsequent damages were approximately $150 million 

(Eisenbies et al., 2007). County governments and citizens filed civil suits against forest product 

and mining companies, urging the corporations to take responsibility for the floods. Ultimately, 

the courts found the industries to be innocent of any wrongdoing (Recht, 2007). However, as a 

result of the 2001 floods, West Virginia Governor Bob Wise created a Flood Investigation 

Advisory Committee, which was designed to evaluate the impacts of logging and mining on 

flooding. In 2002 the Flood Advisory Technical Taskforce (FATT) recommendations were 

released. The comprehensive report detailed guidelines for mining and logging activities and was 

intended to reduce their environmental impact (Flood Advisory Technical Taskforce, 2002).

The scientific community often accepts the concept that forests play a role in buffering 

flood impacts but the magnitude of the role is disputed. Research has found varied responses in 

streamflow following forest harvesting. Following harvest, stream discharge often increases 

slightly due to decreases in evapotranspiration (Cornish and Vertessy, 2001). Many studies have 

found that discharge returns to pre-harvest conditions within five years of logging (Hewlett and 

Helvey, 1970; Hornbeck et al., 1970; Swank et al., 2001).

Impacts of forest harvesting on the stream hydrograph are important yet transient, since 

re-vegetation of the landscape generally occurs (Hewlett and Helvey, 1970; Kochenderfer et al., 

1990). A more static issue that forest managers must consider are forest roads, which are often 

permanent and can affect the surrounding ecosystem. Forest roads can influence the natural 

pathway of runoff. The non-vegetated road corridors can act like stream channels, intercepting 

overland flow and re-routing it down slope. Compacted road surfaces can also reduce infiltration, 

enabling a larger volume of water to channel along the road (King and Tennyson, 1984). The 

increased volume of water and sediment from the road surface can negatively impact the health 

and vitality of the ecosystem. In a particular catchment, a road segment may act as a barrier, 
71

corridor, sink, or source for both water and sediments (Jones, 2000). All of these functions can 

alter the stream hydrograph, both in quantity and in timing.

It can be difficult to study the effects of forest roads because road construction and 

installation are often concurrent with forest harvesting efforts. However, the development of 

complex and sophisticated hydrologic models has allowed researchers to simulate the watershed 

environment and effectively study the potential impacts of road networks. One such model, the 

Distributed Hydrology-Soil-Vegetation Model (DHSVM) has been used in numerous attempts to 

study various aspects of logging and road building (Bowling and Lettenmaier, 1997; Bowling et 

al., 2000; Cuo et al., 2006; Doten et al., 2006; La Marche and Lettenmaier, 2001; VanShaar et 

al., 2002). 

This study was motivated by litigation surrounding forest management activities, such as 

harvesting and road construction, and how they might affect flood events. Since several current

hydrologic models have the power to handle the complexity of forested ecosystems, a modeling 

approach will be used. Specifically, DHSVM will be used to predict the impacts of forest road 

density on stream discharge. This model was chosen because it has enhanced capabilities for 

modeling different road designs and densities. The specific objectives of this research are to:

1) Determine whether or not DHSVM can be calibrated for use in the Southern Appalachian 

Mountains; and

2) Assess the impacts of forest road density on stream discharge for

a. Mean monthly streamflow

b. Peak flow in response to flood events

4.2 Methods

The effects of different road densities on stream discharge were studied by simulating the 

hydrology of a small watershed (760 ha) in the Blue Ridge Mountains of North Carolina using 

DHSVM. The model was calibrated and validated using four years of historical data from the 

watershed. Road density impacts were studied by comparing the response of stream discharge at 
72

various levels of modeled road densities while keeping all other model parameters consistent 

with the model calibration.

4.2.1 Basin Description

The Coweeta Long Term Ecological Research Station (LTER), originally established in 

1933 as the USDA Forest Service Hydrologic Laboratory, conducts research that examines 

streamflow and erosion in an ecological context (Douglass and Hoover, 1988). The 2185 ha 

LTER is located in Macon County, North Carolina (35°03’N, 83°25’W) and is in the Blue Ridge 

Physiographic Province. Coweeta consists of two adjacent bowl-shaped basins; the Coweeta 

Basin and the Dryman Fork Basin. Due to data availability and instrument location, only the 

northern portion of the Coweeta Basin (the Shope Fork catchment, 760 ha) was used in this study 

(Figure 4.1). The elevations in the mountainous region range from 675 to 1592 meters. 

Sideslopes are variable, but generally range from 50 to 60% (Swank and Crossley, 1988).

Precipitation is primarily in the form of rainfall (90 to 98%) and is most abundant during the 

winter months. Mean annual precipitation is approximately 1800 mm. Average temperatures 

range from winter lows of -4°C to summer highs of 23°C (Swift et al., 1988). Soils are 

predominately Inceptisols and Ultisols and are generally sandy loams (Swank and Crossley, 

1988). In 2009, the Shope Fork catchment had a road density of 4.3 km/km2

. Roads included a 

range of road standards from graveled all weather access roads (Class I) to closed, grassed 

harvest roads (Class III).

4.2.2 Model Input

The Distributed Hydrology-Soil-Vegetation Model (DHSVM) is a physically based 

model that explicitly calculates the energy and water budgets for a catchment at the scale of a 

grid cell. DHSVM integrates user-defined inputs for meteorological, soil, vegetation, and stream 

and road morphology data to model evapotranspiration, snow influx and efflux, and the 

movement of unsaturated soil moisture, saturated subsurface flow, overland flow, and channel 

flow. This is completed for each grid cell at a specified time step. More detailed information can

be found in Wigmosta et al. 1994 and 2002. 
73

Figure 4.1 The 760 ha Shope Fork catchment (dark gray) used in the DHSVM evaluations is located in 

the northern portion of the Coweeta Basin (light gray).

DHSVM requires elevation, soil type, soil depth, and vegetation inputs for the study 

catchment in two dimensional binary format. A 30 m digital elevation model (DEM) was 

obtained for the Coweeta study site, which is located in the Prentiss Quadrangle

(GeoCommunity, 2007). The Shope Fork catchment, located within the northern limits of 

Coweeta, was manually delineated using ArcMap (Hillier, 2007). This was based on the current 

Coweeta boundary, the 30 m Prentiss DEM, and the internal watershed boundaries. All 

watershed boundary shapefiles were obtained from the Coweeta Hydrologic Lab. Soil data for 

the Coweeta Basin were obtained from the Natural Resource Conservation Service (NRCS) Soil 

Survey Geographic (SSURGO) Database (Soil Survey Staff, 2008). SSURGO data were 

preferentially used rather than other data sources due to the fine mapping scale (1:12,000 to 
74

1:63,360). Fourteen soil classes were defined and soil textures ranged from sandy clay loams to 

sandy and gravelly loams (Table 4.1). DHSVM is very sensitive to soil depth during its 

hydrologic simulation process (Surfleet, 2008) and a detailed soil depth map was imperative for 

accurate calibration of the model. A soil depth algorithm is present within the DHSVM preprocessing steps, although the precision of this algorithm is debatable. Surfleet (2008) found that, 

when compared to other estimations of soil depth, the model provided by DHSVM was the best 

at predicting the observed field measurements of soil depth. However, accurate results only 

occurred when the weighted values and the power exponents for the soil depth variables 

(elevation, slope, and area) were fitted to match the field measurements. Since few field 

measurements were available for this study, the soil depth inputs were based on the maximum 

soil depth values found in the Macon County, North Carolina Soil Survey (Thomas et al., 1996).

Cover types were determined using tools found in ArcGIS. Four forest cover types were 

delineated based upon elevation, moisture regime, and aspect (Day et al., 1988) (Table 4.2).

DHSVM employs two types of network files: streams and roads. Arc macro language (AML) 

was used to assign some of the parameters needed for the model, the rest were user-defined. 

Coweeta stream and road shapefiles were downloaded from the LTER COGENT dataset 

(Coweeta LTER, 2008). 

Theoretically, all inputs and parameters to DHSVM can be physically measured. 

However, not all physical data can reasonably be collected at resolutions needed to satisfy model 

requirements. Such was the case with this study, and data were based on available historical 

records and literature values. In some cases, known data were combined with empirical 

equations to determine input values. Vegetation and soil input parameters are extensive, and 

descriptions were found from the DHSVM-supported website (Land Surface Hydrology 

Research Group, 2006) (Table 4.3 through Table 4.6). 
75

Table 4.1 Soil mapping units in the Shope Fork catchment.

Soil 

Class Taxonomic Series Texture

Sub-Groups and 

Great Groups

1 Burton Craggey Rock Outcrop Complex Sandy Loam Humic Dystrudept

2 Cashiers Fine Sandy Loam Typic Dystrudept

3 Chandler Fine Sandy Loam Typic Dystrudept

4 Chestnut Edneyville Complex Fine Sandy Loam Typic Dystrudept

5 Cleveland Chestnut Complex Sandy Loam Lithic Dystrudept

6 Cullasaja Tuckasegee Complex Sandy Clay Loam Humic Dystrudept

7 Edneyville Chestnut Complex Fine Sandy Loam Typic Dystrudept

8 Evard Cowee Fine Sandy Loam Typic Hapludult

9 Fannin Fine Sandy Loam Typic Hapludult

10 Plott Fine Sandy Loam Humic Dystrudept

11 Rock Outcrop Cleveland Complex Loam Lithic Dystrudept

12 Saunook Gravelly Loam Humic Hapludult

13 Tuckasegee Whiteside Complex Fine Sandy Loam Humic Dystrudept

14 Wayah Sandy Loam Humic Dystrudept

Table 4.2 DHSVM forest cover types for the Shope Fork Catchment.

Cover Type Representative Species

Northern Hardwoods Acer saccharum, Tilia spp., Betula alleghaniensis, Aesculus 

octandra, Acer rubrum

Cove Hardwoods Liriodendron tulipifera, Betula lenta, Magnolia spp., 

Rhododendron spp.

Mixed Deciduous Quercus alba, Quercus rubra, Robinia pseuduoaccacia, Carya 

spp., Acer rubrum

Xeric Oak-Pine Quercus coccinea, Quercus prinus, Oxydendrum arboreum, Pinus 

rigida, Kalmia latifolia
76

Table 4.3 Description of DHSVM vegetation input parameters.

Parameter Description Units

Fractional coverage Fractional coverage of the overstory Fraction

Fractional trunk space Fractional proportion that the trunk space 

exhibits in comparison to the entire tree 

height

Fraction

Aerodynamic attenuation 

coefficient for wind through the 

overstory

Dimensionless coefficient used to calculate 

the exponential wind profile through the 

overstory canopy 

n/a

Radiation attenuation coefficient Dimensionless coefficient used to calculate 

the fraction of shortwave radiation 

transmitted by the overstory

n/a

Height Height of the canopy m

Maximum/minimum stomatal 

resistance

Measure of a plant’s ability to resist the 

diffusion of water out of the stomates

s m-1

Soil moisture threshold which 

restricts transpiration

Soil moisture at which plant transpiration no 

longer occurs

Fraction

Vapor pressure deficit threshold 

which causes stomatal closure

Vapor pressure at which stomates begin to 

close

Pa

Rpc Fraction of shortwave radiation that is 

photosynthetically active (PAR)

W m-2
77

Table 4.4 Description of DHSVM soil input parameters.

Parameter Description Units

Vertical/Lateral saturated hydraulic 

conductivity

Volume of water that will flow through the 

soil in a given unit of time

m s-1

Exponential decrease rate of lateral 

saturated hydraulic conductivity

Rate at which lateral saturated hydraulic 

conductivity decreases with depth

n/a

Maximum infiltration rate Rate at which water moves into the soil 

surface

m s-1

Capillary drive Measure of the adhesive forces that bind 

water molecules to soil

m

Soil surface albedo Fraction of incoming radiation reflected by 

the soil surface

m s-1

Manning's n Roughness of the soil particle n/a

Porosity Percent volume of soil not occupied by soil 

particles

Fraction

Pore size distribution index Volume of various sizes of pores in the soil n/a

Bubbling pressure Air pressure required to force air through the 

soil

m

Field capacity Percent water held at -33 bars Fraction

Wilting point Percent water held at -15 bars Fraction

Bulk density Mass of soil per unit volume kg m-3

Soil thermal conductivity Explains soil's ability to transmit heat W m-1 K-1

Soil thermal capacity Change in heat content of a unit bulk volume 

of soil per unit change in temperature

J m-3 K-1
78

Table 4.5 DHSVM vegetation inputs for calibration of Shope Fork catchment.

Vegetation Parameters Range of Values Source

% Canopy Cover 0.80 - 0.85 Rodell, 2009

% Trunk Space 0.60 - 0.76 Calculated

Aerodynamic attenuation coefficient 

for wind through the overstory

1.5 Cionco, 1972

Radiation attenuation coefficient 0.86 Campbell, 1986; 

Monteith and Unsworth, 2007

Height* (m) 16.8 - 24.4 Olson, 1959; Beck, 1962; 

Carmean, 1976; Walters and 

2.0 - 3.0 Yawney, 1990; Lipp and 

Nilsen, 1997; McNab and 

Clinton, 2003

Maximum/minimum stomatal 

resistance*

193 - 490

(s/m) 

Federer, 1977; McConathy 

and McLaughlin, 1978; 

312 - 388

Soil moisture threshold which 

restricts transpiration

0.139 - 0.152 *Shuttleworth, 1980; Soil 

Survey Staff, 2008

0.087 - 0.095

Vapor pressure deficit threshold 

which causes stomatal closure (Pa)

4000 Wigmosta et al., 2002

Fraction of shortwave radiation that is 

photosynthetically active (W/m2

0.3

)

Dickinson et al., 1991

* Inputs required for both overstory and understory layers, respectively
79

Table 4.6 DHSVM input soil parameters for Shope Fork calibration. Refer to Table 4.1for a description of 

the soil classes.

Soil Parameter Range of Values Source(s)

Lateral saturated hydraulic 

conductivity (m/s)

9.13 E-04 - 1.00 E-03 Miwa, 1999

Exponential decrease rate of lateral 

saturated hydraulic conductivity

4.0 Beven, 1982

Maximum infiltration rate (m/s) 3.06 E-06 - 1.39 E-06 Hillel, 1982

Capillary drive (m) 0.06 - 0.22 Bedient et al., 2008

Soil surface albedo 0.09 - 0.30 Soil Survey Staff, 2008

Manning's n 0.01 - 0.10 Shen and Julien, 1993

Porosity* (fraction) 0.42 - 0.58 Burger, 2008

Pore size distribution index 0.272 - 0.386 * Rawls et al., 1993

Bubbling pressure* (m) 0.009 - 0.884 Rawls et al., 1993

Field capacity* (fraction) 0.102 - 0.287 Soil Survey Staff, 2008

Wilting point* (fraction) 0.041 - 0.162 Soil Survey Staff, 2008

Bulk density* (kg/m3 937.5 - 1550.0 ) Soil Survey Staff, 2008

Vertical saturated hydraulic 

conductivity*

9.0 E-06 - 1.1 E-05

(m/s)

Soil Survey Staff, 2008

Soil thermal conductivity*

6.27 - 8.00

(W/m K)

Bowling and 

Lettenmaier, 1997

Soil thermal capacity* (J/m3 2.0 E-06 K) Hillel, 1982

 * Inputs required for all soil layers

4.2.3 Model Calibration

The model was calibrated using an hourly time step for data collected from January 1, 

2003 through October 31, 2007. Model output was compared with historical discharge data from 

Coweeta Weir 8. January 1, 2003 through September 30, 2003 was used for a model “warm-up” 

period to ensure that the model had adequate time to acquire to initial site conditions. Water Year 

2004 (October 1, 2003 through September 30, 2004) was used as a calibration period in which 

parameter adjustments were made. Parameter validation occurred from Water Year 2005 through 

2007 (October 1, 2004 through September 30, 2007). Precipitation for the calibration period 

totaled 1787 mm and had the characteristics of a normal precipitation year. The validation 

included one exceedingly wet year and two comparatively dry years (2195, 1439, and 1469 mm, 

respectively). Changes to input parameters were made in order to minimize differences between 

observed and predicted stream discharge. Alterations were made primarily to the lateral saturated 
80

hydraulic conductivity and exponential decrease input parameters, as studies have shown 

DHSVM to be sensitive to these variables (Bowling and Lettenmaier, 1997; Wigmosta and 

Lettenmaier, 1999). Concurrent with Cuo et al. (2006), it was found that increasing the accuracy 

of peak flow resulted in a decrease of baseflow precision and vice versa.

Assessment of the calibration and validation was determined through analysis of the 

timing of the model hydrograph, volume of the hydrograph, and overall model accuracy. The 

correlation coefficient (R) was used to analyze the accuracy of the predicted hydrograph and 

unity was desired (e.g. a value of 1.0 implies a better fit than 0.5). The average volume error 

between the predicted and observed discharge (ΔV/V) was used to look at DHSVM’s ability to 

accurately simulate peak flows. Overall model accuracy was determined using the correlation 

coefficient (R2). Again, unity in the model accuracy was desired, as this statistics describes the 

proportion of initial variance accounted for by the model. Other studies using DHSVM have 

used model efficiency (ME or E′) to look at the effectiveness of the model (Beckers and Alila,

2004; Cuo et al., 2006, Thyer et al., 2004; Wigmosta and Burges, 1997). Model efficiency is a 

similar statistic to the correlation coefficient in that it is also a measure of variance (Nash and 

Sutcliffe, 1970).

4.2.4 Road Density Experiment

Forest roads densities in managed systems are typically much lower than urban roads, but 

can often be higher than some rural road densities. Forest road densities usually range from one 

to six km km-2 and average around three to four km km-2 (Bowling and Lettenmaier, 1997;

Hawbaker et al., 2005; National Forests in North Carolina: Fiscal Year 2007). In some areas, a 

forest road density of two km km-2 is considered high and can even impact the survival of 

wildlife populations (Jones, 2000; Mech, 1989). In the United States, it is estimated that all state 

maintained roads have an average density of 1.2 km km-2 (Forman, 2000). Meanwhile, urban 

locations have much higher road densities than forested and rural areas. A suburban road density 

in a small town may average ten or twelve km km-2 and areas in very urban metropolises can 

approach 100% impervious cover.
81

A stated objective of this study was to evaluate the effects of forest road density on the 

stream hydrograph. Treatment densities were designed to mimic typical forest road densities in 

order to predict potential impacts of road density on stream discharge and flooding. In 2009, the 

density of the Shope Fork road network was 4.3 km km-2

Table 4.7

. Six road densities between 0.5 and 

12.0 km km-2 were modeled with three unique replications at each density ( ). 

Treatments two through five represent the high and low ranges of forest road densities while 

treatments one and six were included to show the difference between rural and urban road 

densities.

Table 4.7 Treatment densities for Shope Fork road density experiment.

Treatment

Road Density 

(km km-2

Road Length 

) (km)

% Area in

Roads Replications

1 0.5 3.9 0.3 3

2 1.0 7.8 0.5 3

3 3.0 23.5 1.2 3

Control (4) 4.3 * 33.7 1.8 3

5 6.0 47.0 2.6 3

6 12.0 94.0 5.6 3

* Road density of the Coweeta LTER as of 2009.

Since the experiment was conducted in a modeling environment, treatment replications 

were done in the form of different road layouts at each treatment density. Determining the impact 

of road density on stream discharge is difficult because multiple road factors can affect 

watershed processes. Such road impact factors included the spatial location of the road with 

relation to streams, the gradient or slope of the road, the surfacing material of the road, road 

design features (such as insloped, outsloped, and crowned roads), the water control features of 

the road, number of stream crossings, and length of the road. Bernard (2006) developed an 

approach to determine the impact of road networks on sedimentation potential. Road position, 

slope, and class were given weighted values based on potential impacts to the watershed (Figure 

4.2). These effects were then amalgamated into one Final Erosion Factor. A Final Watershed 

Road Impact Factor (RIF) is found by integrating road segment length into the model (Figure 

4.3).
82

Figure 4.2 The Final Erosion Factor is based on weighted values of the impact factors, which include road 

position, slope, and class (Modified from Bernard, 2006).

Figure 4.3 The Final Watershed Road Impact Factor (RIF) is a weighted value for the watershed based on 

the Final Erosion Factor and the length of roads in the watershed (Modified from Bernard, 2006).

For the purposes of this project, the Final Watershed Road Impact Factors (RIF) were 

used as a basic index to control for the confounding effects of road impact factors. The RIF for 

the current Shope Fork catchment was determined using data from Coweeta and ArcGIS 

algorithms. Pre-construction road design was completed in an ArcGIS setting and in a “realistic” 

fashion with an attempt to provide access to the various sub-watersheds within the site. After the 

layout was completed, the road classes were altered in order to keep the RIF for each network 

constant with the control (Table 4.8). The number of intermittent stream crossings was not 

accounted for during the design of the road layouts and as can be assumed that as density 

increases in a watershed, the number of stream crossings will also increase (Table 4.9). 

Road Position

Description Value

Ridge 0.1

Midslope 0.35

Valley 1

Road Slope

Description Value

Low (< 8%) 0.2

Moderate (8 - 12%) 1

High (> 12%) 2.5

Road Class

Description Value

Class I 0.4

Class II 1

Class III 1

Class IV 1

+ + = Final Erosion 

Factor

Final Erosion 

Factor

Segment 1

Segment 2

Segment ...

x = Road Segment

Length (km)

Segment 2

Segment 1

Segment ...

Total Road 

Length (km) /

Average 

Weighted

Road Impact 

Factor
83

Table 4.8 Final Watershed Road Impact Factors (RIF) for each treatment in the 

Shope Fork road density experiment.

Treatment Replication

Road Density

km km

% Area in 

Roads -2 RIF

% Deviation 

From Control

1 1 0.5 0.3 0.630 -2.4

1 2 0.5 0.3 0.630 -2.4

1 3 0.5 0.3 0.741 8.7

2 1 1.0 0.5 0.668 1.4

2 2 1.0 0.5 0.671 1.7

2 3 1.0 0.5 0.636 -1.8

3 1 3.0 1.2 0.667 1.3

3 2 3.0 1.2 0.679 2.5

3 3 3.0 1.2 0.685 3.1

Control (4) 1 * 4.3 1.8 0.654 -

4 2 4.3 1.8 0.665 1.1

4 3 4.3 1.8 0.678 2.4

5 1 6.0 2.6 0.626 -2.8

5 2 6.0 2.6 0.684 3

5 3 6.0 2.6 0.666 1.2

6 1 12.0 5.6 0.635 -1.9

6 2 12.0 5.6 0.623 -3.1

6 3 12.0 5.6 0.622 -3.2

* Road density and layout of the Coweeta LTER as of 2009.
84

Table 4.9 Number of stream crossings per treatment in the Shope Fork catchment.

Treatment Replication

Road Density 

(km km-2

% Area 

) in Roads

# of Stream 

Crossings

# Deviation 

From Control

1 1 0.5 0.3 14 -48

1 2 0.5 0.3 9 -53

1 3 0.5 0.3 8 -54

2 1 1.0 0.5 25 -37

2 2 1.0 0.5 15 -47

2 3 1.0 0.5 22 -40

3 1 3.0 1.2 56 -6

3 2 3.0 1.2 29 -33

3 3 3.0 1.2 41 -21

Control (4) 1 * 4.3 1.8 62 -

4 2 4.3 1.8 42 -20

4 3 4.3 1.8 56 -6

5 1 6.0 2.6 79 17

5 2 6.0 2.6 61 -1

5 3 6.0 2.6 80 18

6 1 12.0 5.6 134 72

6 2 12.0 5.6 116 54

6 3 12.0 5.6 122 60

* Road density and layout of the Coweeta LTER as of 2009.

The impacts of road density were analyzed by comparing monthly average streamflow 

between each treatment. This was done for the entire calibration and validation study period. 

Inspection of the model output indicated that one of the samples (Layout 1, Density 5) suffered 

an unidentifiable error and thus was disregarded from the study. Using Statistical Analysis 

Software (SAS/STAT Software, Version 9.2), the data were determined to be normal via proc 

univariate. A proc mixed statement with compound symmetry was used to model the covariance 

structure of the data. To investigate the relationship between the various treatment densities, a 

Tukey-Kramer analysis was conducted in SAS using the proc mixed statement.
85

The effect of road density on peak flows was also analyzed by looking at the number of 

peak flows that fell within a certain return interval during the study time. This was done on an 

hourly scale to capture every peak flows in the time series. Return intervals for the Shope Fork 

catchment were calculated using a method derived by Douglass (1974). The number of hourly 

peak flows in each recurrence interval was calculated, and relationships between treatment 

densities were again analyzed using Tukey-Kramer methods. 

4.3 Results and Discussion

4.3.1 Calibration and Validation of Shope Fork Catchment

Overall, DHSVM was successful in predicting the general trends of the Shope Fork 

hydrograph (Figure 4.4), although both under- and over-prediction by the model occurred during 

peak flow conditions. ΔV/V, or the average difference between the volume of modeled and 

observed streamflow, was high for the calibration period (Table 4.10). Error was approximately 

one-third of the average baseflow conditions. Model timing measures the ability of the model to 

predict peaks and ebbs in streamflow at the same points in time as the observed data. Analyzed 

using the coefficient of determination, DHSVM matched the Coweeta data almost 70% of the 

time. The R2 was lower than desired as DHSVM predicted less than 50% of the initial variance 

of the original data.

Table 4.10 Calibration and validation statistics for Shope Fork catchment.

WYR

Total Precip. 

(mm) ΔV/V R R2

2004 1787 0.10 0.69 0.48

2005 2195 0.06 0.79 0.63

2006 1439 -0.13 0.88 0.78

2007 1469 0.07 0.89 0.80

Calibration 1787 0.10 0.69 0.48

Validation 1692 0.00 0.85 0.73
86

Figure 4.4 Simulated and observed streamflow and measured precipitation of Shope Fork catchment 

during calibration period.

The validation period was consistent with the calibration in that DHSVM predicted the 

general trends of the hydrograph but had trouble predicting peak flows (Figure 4.5). Although 

water year 2005 is representative of a higher than normal degree of tropical storm and remnant 

activity from the Atlantic region, the average volume error for the year was the lowest of the 

entire testing period (Table 4.10). Over the entire validation period, volume error averaged to be 

null. Timing for the validation period increased relative to the calibration (R = 0.85). The 

decrease in volume error and increase in timing accuracy created improved model efficiency 

during the validation period of 0.73. 

0.00

4.00

8.00

12.00

Oct-03 Dec-03 Feb-04 Apr-04 Jun-04 Aug-04 Oct-04

Precipitation (cm)

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

Oct-03 Dec-03 Feb-04 Apr-04 Jun-04 Aug-04 Oct-04

Discharge (cms)

Simulated

Observed
87

Figure 4.5 Simulated and observed discharge and measured precipitation for Shope Fork catchment 

during validation period.

The lack of precision when calibrating DHSVM for the Shope Fork catchment could be 

attributed to the following factors: (1) the difference in terrain and climate regime between 

typical model research applications and the study site; (2) errors in peak flow and baseflow 

modeling due to the lack of preferential flow pathways in the model; (3) user error while 

preparing the model for use; and (4) error in data input as a result of over-parameterization of the 

model.

DHSVM was initially created for use in steep, mountainous terrains with a winter 

snowpack. The topography of the Coweeta watershed is unique for the Appalachian Mountains, 

and its steep slopes are moderately analogous to DHSVM’s home region of the U.S. Pacific 

Northwest. However, the climate regime is quite different. Coweeta experiences no significant 

0.00

2.00

4.00

6.00

8.00

10.00

Oct-04 Feb-05 Jun-05 Oct-05 Feb-06 Jun-06 Oct-06 Feb-07 Jun-07 Oct-07

Precipitation (cm)

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

Oct-04 Feb-05 Jun-05 Oct-05 Feb-06 Jun-06 Oct-06 Feb-07 Jun-07 Oct-07

Discharge (cms)

Simulated

Observed
88

seasonal snowpack, few rain-on-snow events, and can be affected by serious storm events in the

form of hurricane remnants (as was seen during both the calibration and validation period). 

Much of DHSVM’s efforts go into modeling the snowpack hydrology and there is likelihood that 

winter precipitation was incorrectly modeled. One item of note is the absence of preferential 

flow pathways in DHSVM calculations. Beckers and Alila (2004) found that the addition of a 

preferential flow model in a proprietary adaptation of DHSVM increased model accuracy in a 

British Columbia watershed. A similar adaptation might be applied to the Coweeta Basin, which 

has an extensive amount of porous saprolite (Velbel, 1988). DHSVM is a complicated model and 

requires many pre-processing steps before the model can be used. The numerous steps create 

large potentials for user error. The model also requires a large number of input parameters, some 

of which were complex and hard to define. Highly parameterized models can have tradeoffs, as 

the detail in modeling can and should result in highly accurate results. However, it can be 

difficult to measure and obtain values for each parameter and often values can be incorrect. It is 

also important to consider the possibility of equifinality, which is a downfall of highly 

parameterized models such as DHSVM. Equifinality is the concept that, in modeling, one 

outcome can be achieved through many different pathways. It can be problematic because a 

correct answer may be reached, but the modeling components may be incorrect. One way to 

overcome this ambiguity would be to use GLUE (generalized likelihood uncertainty estimation) 

analysis (Romanowicz and Beven, 2006).

4.3.2 Road Density Effects

Results suggest that increases in road density can create heightened streamflows and, in 

almost all cases, streamflow increases with an increase in road density (Figure 4.6). The few 

exceptions occurred in January and October, where discharge from Density 2 (1.0 km km-2

) was 

marginally higher than streamflow from Density 1 (0.5 km km-2). Over the four year study 

period, the wet seasons were found to occur from June through December (with the exception of 

October). This is likely the result of the large hurricane remnants that moved through the area 

that, by chance, did not occur during the October months. The magnitude of average streamflow 

does not seem entirely dependent upon precipitation, suggesting that factors other than the 

volume of precipitation are important in controlling discharge. The variation in response between 

the treatments does not change with relation to the volume of discharge, nor with the month. 
89

Significance of the effects of road density on stream discharge was confirmed by Analysis-ofVariance (ANOVA) using Type III sum of squares with a significance level of <0.0001 (Table 

4.11). 

Figure 4.6 Shope Fork mean monthly discharge averaged over four-year study period. Road density 1=0.5 

km km-2

; Road density 2 = 1.0 km km-2; Road density 3 = 3.0 km km-2; Road density 4 = 4.3 km km-2; 

Road density 5=6.0 km km-2

; Road density 6 = 12.0 km km-2

DHSVM results indicate a significant change in streamflow at road densities ≥ 4.3 km 

km-2 (Table 4.12). Highly significant differences occurred at the 6.0 km km-2 and 12.0 km km-2

densities. These results suggest that streams have an altered response when forest road densities 

reach 4.3 km km-2 and that it is desirable to keep maximum road densities between 3.0 and 4.3 

km km-2

, and are consistent with the literature. Using DHSVM to model the effects of roads on 

streamflow, Bowling and Lettenmaier (1997) found that road densities of 3.7 and 4.9 km km-2

increased streamflow of ten year floods by 8 and 10%, respectively. While significant 

differences in streamflow were detected at a density of 4.3 km km-2

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

J F M A M J J A S O N D

Mean Monthly Discharge (cms)

Month

Road Density 1

Road Density 2

Road Density 3

Road Density 4

Road Density 5

Road Density 6

in the Coweeta watershed, 

larger changes in average monthly discharge were only seen at higher densities. An increase of 
90

5% in discharge was first detected at a density of 6.0 km km-2 and increases greater than 10% 

were not identified until road density reached 12.0 km km-2 (Table 4.13).

Table 4.11 Abbreviated Analysis-of-Variance statistics for average monthly streamflow for the Shope 

Fork catchment.

Effect DF F-Value p-Value

Density 5 102.28 <.0001

Month 11 177668 <.0001

Density*Month 55 17.76 <.0001

Table 4.12 Tukey-Kramer test results for significant difference between treatments for monthly and 

average monthly data.

Treatment 1 2 3 4 5 6

1 NS NS S S* S*

2 NS NS S S* S*

3 NS NS NS S* S*

4 S S NS S* S*

5 S* S* S* S* S*

6 S* S* S* S* S*

NS: No significant difference between treatments; S: significant difference at α = 0.01; S*: significant 

difference at α < 0.0001
91

Table 4.13 Percent streamflow deviation of treatments from the Shope Fork catchment as modeled with 

no road coverage.

% Deviation from No Roads

Month

Density 1

(0.5 km km-2

Density 2

) (1.0 km km-2

Density 3

) (3.0 km km-2

Density 4

) (4.3 km km-2

Density 5

) (6.0 km km-2

Density 6

) (12.0 km km-2

Jan

)

0.0 -0.1 0.2 1.0 3.6 5.3

Feb 0.0 0.0 0.3 1.4 5.0 7.5

Mar 0.0 0.1 0.5 1.6 5.7 8.9

Apr 0.1 0.1 0.8 2.0 6.2 9.9

May 0.0 0.1 0.6 1.6 6.9 11.0

Jun 0.3 0.4 1.9 3.5 7.4 12.2

Jul 0.1 0.2 0.8 1.7 4.8 7.7

Aug 0.3 0.5 2.2 3.9 9.2 14.8

Sep 0.1 0.1 0.3 1.2 4.7 8.1

Oct 0.0 -0.1 0.0 0.5 3.8 6.5

Nov 0.2 0.2 0.8 1.9 5.0 8.2

Dec 0.0 0.0 0.4 1.4 3.7 5.8

Annual 

Mean 0.1 0.1 0.7 1.8 5.5 8.8

4.3.3 Peak Flows

The relationship between road density and peak flow was investigated by analyzing 

stream response following a large and small rainfall event. A storm was defined as any event 

where no precipitation fell four hours prior to or following the event (Swift et al., 1988). The 

storms were chosen randomly based on maximum rainfall intensity and total precipitation 

volume. The two storm events are detailed in Table 4.14. 

Figure 4.7 illustrates the relationship between road density and stream discharge 

following an April 2005 storm event of 4.83 mm. Data were analyzed one hour prior to rainfall 

and six hours following precipitation cessation. Prior to the storm, baseflow response to the 

larger density road networks was already higher than the lower densities. During this storm, the 
92

larger density networks created heightened peak flows. Statistical analysis of inverse-squared 

transformed data suggested that density, hour, and density by hour interaction effects were all 

statistically significant (

Table 4.15).

Table 4.14 Description of the large and small storm events which received detailed analysis.

Storm 

Size

Date of 

Storm

Duration 

of Event 

(hours)

Maximum 

Intensity 

(mm/hr)

Total 

Rainfall 

(mm)

Small 4/12/2005 5 2.29 4.83

Large 12/31/2006 13 24.13 87.12
93

Figure 4.7 Shope Fork mean hourly discharge during April 2005 rainfall event. Density 1=0.5 

km km-2

; Density 2 = 1.0 km km-2; Density 3 = 3.0 km km-2; Density 4 = 4.3 km km-2; Density 

5=6.0 km km-2; Density 6 = 12.0 km km-2

Table 4.15 Abbreviated ANOVA for April 2005 rainfall event in the Shope Fork catchment.

Effect DF F-Value p-Value

Density 5 51.97 <.0001

Hour 11 3937.34 <.0001

Density*Hour 55 2.57 <.0001

Tukey-Kramer results suggest that there was a highly significant difference in stream 

discharge at densities of 6.0 km km-2 or greater (Table 4.16). This was a slight change from the 

monthly data, as a significant change was detected at 4.3 km km-2. For this small rainfall event, 

there was no significant difference between stream discharge at road densities of 6.0 or 12.0 km 

km-2

. This suggests that increasing the road density from 6.0 to 12.0 km km-2 

0.40

0.42

0.44

0.46

0.48

0.50

0.52

0.54

-1 0 1 2 3 4 5 6 7 8 9 10

Discharge (cms)

Time (hour since start of precipitation)

Road Density 1

Road Density 2

Road Density 3

Road Density 4

Road Density 5

Road Density 6

does not create 

significant differences in water yield during small storm events. In forested watersheds, such as 
94

Coweeta, roads impact the natural hydrology predominately through subsurface interception, as 

overland flow in these systems is usually minimal. These results imply that smaller road 

densities do not intercept enough flow to significantly alter the hydrology of the watershed 

during a small storm. Increasing the road density increases the likelihood that flow will be 

intercepted and thus increases the impact of the roads on the storm hydrograph. 

Table 4.16 Tukey-Kramer test results for significant difference between treatments during April 2005 

rainfall event for the Shope Fork catchment.

Treatment 1 2 3 4 5 6

1 NS NS NS S* S*

2 NS NS NS S* S*

3 NS NS NS S* S*

4 NS NS NS S* S*

5 S* S* S* S* NS

6 S* S* S* S* NS

NS: No significant difference between treatments; S: significant difference at α = 0.05; S*: significant 

difference at α < 0.0001

The larger, December 2006 storm event yielded similar results to the April 2005 storm in 

that increased road density created inflated peak flows (Figure 4.8). At higher densities, the peak 

flows lasted slightly longer, creating a slight lag in the falling limb of the hydrograph. As 

conditions returned to baseflow, density had a lower impact on discharge. Statistical analysis 

shows that road density, time, and density by time interaction were all significant (Table 4.17).

Pair-wise comparison analysis shows much different outcomes than the small storm event. The 

results indicate a highly significant change in stream responses when road density reached 3.0 

km km-2 (Table 4.18). Some studies have found that peak flow response to logging and roads do 

not change significantly during large storm events (Rothacher, 1970; Ziemer, 1981). However, 

the modeled results indicate that small increases in road density can impact the storm hydrograph 

during large events (Table 4.19). This is likely the result of a higher degree of soil saturation, 

resulting in an increase in road interception of subsurface flow. Overland flow is modeled during 

DHSVM simulation, and no overland routing occurred during this storm event.
95

Table 4.17 Abbreviated ANOVA for December 2006 rainfall event in the Shope Fork catchment.

Effect DF F-Value p-Value

Density 5 96.83 <.0001

Hour 25 7997.59 <.0001

Density*Hour 125 9.18 <.0001

Figure 4.8 Shope Fork mean hourly discharge during December 2006 rainfall event. Density 1=0.5 km 

km-2

; Density 2 = 1.0 km km-2; Density 3 = 3.0 km km-2; Density 4 = 4.3 km km-2; Density 5=6.0 km km2

; Density 6 = 12.0 km km-2

Table 4.18 Tukey-Kramer test results for significant difference between treatments during December 

2006 rainfall event.

Treatment 1 2 3 4 5 6

1 NS S* S* S* S*

2 NS S* S* S* S*

3 S* S* NS S* S*

4 S* S* NS S S*

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

-1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Discharge (cms)

Time (hour since start of precipitation)

Road Density 1

Road Density 2

Road Density 3

Road Density 4

Road Density 5

Road Density 6
96

5 S* S* S* S S*

6 S* S* S* S* S*

NS: No significant difference between treatments; S: significant difference at α = 0.05; S*: significant 

difference at α < 0.0001

Table 4.19 Percent deviation from no road density for each treatment for a large and small storm event.

Storm % Deviation from No Roads

Size Density 1 Density 2 Density 3 Density 4 Density 5 Density 6

Small 0.7 0.8 1.1 2.0 3.6 5.5

Large 2.3 3.6 10.4 14.2 19.8 37.5

An increase in the number of hourly peak flows for each return interval was concurrent 

with increases in road density. However, no time by treatment trend was detected. For all return 

intervals, significant increases in the number of peak flows occurred at 3.0 or 4.3 km km-2. These 

densities were not found to be significantly different from one another for all return intervals. 

Table 4.20 Number of peak hours of flow for each treatment density for all return periods for the Shope 

Fork catchment.

# of Peak Flow Hours

Return 

Interval 

(years)

Max 

Discharge 

(cms)

Density 

1 

Density 

2

Density 

3 

Density 

4 

Density 

5 

Density 

6 

2.33 2.17 205 209 229 237 262 303

5 3.00 115 117 125 132 146 172

10 3.81 71 73 78 83 97 118

20 4.82 37 39 49 51 58 74

30 5.44 28 28 35 36 43 55

40 5.91 21 22 26 29 32 46

50 6.39 13 14 20 22 25 36

4.4 Summary and Conclusions

A distributed hydrology model was calibrated for use in forested watersheds in the Blue 

Ridge Mountains. Using historical data from the Coweeta LTER, literature, and calculations, 

calibration and validation of the model was met with moderate success. The calibrated period 
97

(Water Year 2004) had model accuracy (R2) of 0.48 while the three-year validation period 

(Water Years 2005-2007) had a higher overall accuracy of 0.73. In other studies using DHSVM, 

model accuracies (R2

) have been recorded from 0.61 to 0.96 (Beckers and Alila, 2004; Leung et 

al., 1996; Thyer et al., 2004; Whitaker et al., 2003; Wigmosta and Burges, 1997). The high 

accuracy of the validation period suggests that DHSVM has potential for use in the Blue Ridge 

Mountains. 

DHSVM was designed for use in mountainous terrains and has primarily been applied to 

studies in the Pacific Northwest (Beckers and Alila, 2004; Bowling and Lettenmaier, 2001; 

Lamarche and Lettenmaier, 1998), although this is beginning to change (Cuo et al., 2006). The 

Blue Ridge Mountains of are dissimilar from DHSVM’s native study sites in that snowpack and 

snowmelt have negligible contributions to the hydrologic budget. While studies have found high 

model accuracy using DHSVM, it should be understood that these experiments are primarily 

located in mountainous, snowpack-dominated systems. Taking DHSVM out of its natural 

environment and calibrating to an R2 of 0.73 should be considered successful. 

DHSVM requires numerous input parameters and this study was conducted using little to 

no field collected data. Accuracy might be improved with field sampling, and a sensitivity 

analysis could be used to determine what parameters would be worth collecting. DHSVM has 

been shown to be sensitive to the lateral saturated hydraulic conductivity and exponential 

decrease input parameters (Bowling and Lettenmaier, 1997; Wigmosta and Lettenmaier, 1999);

although these variables were altered with no positive outcome. It may be possible that the 

Appalachian Mountains have a different set of sensitive parameters. Accuracy of model 

calibration could also be improved by increasing the time span of the study. This analysis was 

restricted due to data availability at the hourly time step and a coarser time step may be more 

feasible for longer studies. 

The model accuracy found for the Blue Ridge Mountains using literature values suggests 

that DHSVM might have success in other regions with long-term hydrologic records. Data and 

sources presented in this study can be broadened and be applied outside of the Coweeta 

watershed. As of now, DHSVM is used solely as a research tool. Attempts have been made to 
98

create a more user-friendly interface for the model. Such advances, along with readily available 

parameter data, could make DHSVM extremely useful for management of forested watersheds.

Using DHSVM as a controlled environment, we were able to model the impacts of road 

density on stream discharge. This process was complex, as road density is not independent from 

other road features and confounding effects were present. Although we attempted to account for 

confounding effects, we realize that they are a constraint in the study. The modeled results 

suggest that mean monthly stream discharge will be impacted when forested road density reaches 

and surpasses 4.3 km km-2. Seasonal changes in the stream hydrograph have been detected 

following vegetation removal (Hibbert, 1966; Patric, 1973). No monthly effects were noted in 

this study and it is believed that reduction in evapotranspiration due to vegetation losses were not 

an important component of changes in streamflow. This is consistent with the literature, which 

suggests that changes in streamflow in the Appalachian Mountains are detectable after 20% of 

the vegetation has been removed (Stednick, 1996). The largest area covered by roads in this 

study was 5.6%. 

This study found that effects on streamflow began when 1.8% of the watershed area was 

in roads. This fell within the range of values in the literature, which suggest that streamflow may 

be impacted at road densities from 1.8 to 15% (Harr, 1979; Ziemer, 1981; King and Tennyson, 

1984; Wright et al., 1990). In the Shope Fork catchment, significant changes were detected as

road density increased beyond 1.8%, which suggests that road managers should be cautious 

when road density reaches and exceeds this threshold. The effects on streamflow in this 

watershed are thought to be a function of intercepted subsurface flow and increased runoff on the 

road surfaces, both of which have been shown to increase streamflow in forested areas with 

roads (Ziemer, 1981; Jones, 2000).

During a rainfall event producing 4.8 mm of rainfall over 5 hours, modeled output 

showed that stream discharge was impacted at densities ≥ 6.0 km km-2

. For larger rainfall event 

(87.1 mm over 13 hours), streamflow was impacted at road densities ≥ 3.0 km km-2

. The 

difference between the road density thresholds during large and small storms can be attributed to 

differences in infiltration excess runoff from the roads and intercepted subsurface flow. 
99

Precipitation rates are lower during small storms and may not exceed infiltration rates, allowing 

more water to percolate into the soil. The opposite is true for a large storm, where an excess of 

water may build up on road surfaces. Streamflow will be thus impacted at lower densities during 

larger storms, due to increased amounts of runoff from road surfaces. The same principle applies 

to the interception of subsurface flow by road cuts. In the variable source area concept (Hewlett 

and Hibbert, 1963), the area in a watershed that contributes to streamflow is dependent on factors 

such as antecedent moisture conditions and precipitation rates and volume. Smaller storms tend 

to have smaller source areas while larger storms usually have larger contributing areas. For the 

smaller storm, it is possible that many of the roads were not located in the area that was 

contributing to the stream hydrograph. Increasing the area increases the likelihood that roads will 

be present, even at smaller densities. 

The number of hourly peak flows during the study period was also analyzed, and these 

were separated by the return interval of the flow. For all return intervals, streamflow was first 

impacted between 3.0 and 4.3 km km-2

. In contrary to the individual rainfall events, there was no 

noticeable distinction between recurrence interval and the road density threshold. This test, 

however, only looked at the difference in frequency of peak flows and not the difference in 

magnitude. Increasing the road density might not change the possibility of an event occurring, 

but may affect the magnitude of discharge and thus possible damages due to flooding.

Since only one small and one large rainfall event was analyzed for this study, it is 

recommended that these results be further investigated. It is also noted that, while DHSVM 

allows for many road features to be controlled, such as road surface and cut slope, it does not 

allow for water control features. The installation of BMPs such as water bars, culverts, and broad 

based dips can be used as important mitigation tools.

Acknowledgements

The authors would like to acknowledge the funding and support provided by the Coweeta 

Hydrologic Laboratory, part of the USDA Forest Service Southern Research Station. The authors 

also thank Stephanie Laseter of the Coweeta Hydrologic Lab, as well as Rupesh Shrestha, Phil 

Radtke, Jeremy Stovall, and Regis Kopper of Virginia Tech, for their support.
100

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CHAPTER 5. CONCLUSIONS

This project examined the impacts of forest road density on stream discharge in the 

Coweeta watershed. 

5.1 DHSVM Calibration

DHSVM was calibrated for Water Year 2004 (Oct. 1, 2003 to Sep. 30, 2004) and was 

validated for Water Years 2005 through 2007. Calibration was met with moderate success. 

Model accuracy was found to be 0.48 for the calibration period. Model efficiencies for DHSVM 

have been recorded from 0.61 to 0.96 (Beckers and Alila, 2004; Leung et al., 1996; Thyer et al.,

2004; Whitaker et al., 2003; Wigmosta and Burges, 1997). The model accuracy takes into 

account both the volume error and the timing of the hydrograph. Efforts were made to increase 

the accuracy of the Shope Fork model output. DHSVM has been shown to be sensitive to the 

lateral saturated hydraulic conductivity and exponential decrease input parameters (Bowling and 

Lettenmaier, 1997; Wigmosta and Lettenmaier, 1999); these variables were altered with no 

positive outcome.

The validation was more successful and the overall model efficiency for this time period 

was 0.73. A more reliable model would measure model efficiency above 0.85. While this value 

was not attained for this particular study, it is suggested that the model can still be used in the 

region. In order to do so, field measurements should be employed in order produce more reliable 

input parameters. It is also recommended that normal precipitation years be used for model 

calibration and validation, as the above and below average precipitation years used in this 

particular model validation may have hindered success.

DHSVM was designed for use in mountainous terrains and has primarily been applied to 

studies in the Pacific Northwest (Beckers and Alila, 2004; Bowling and Lettenmaier, 2001; 

Lamarche and Lettenmaier, 1998), although this is beginning to change (Cuo et al., 2006). The 

Blue Ridge Mountains of are dissimilar from DHSVM’s native study sites in that snowpack and 

snowmelt have negligible contributions to the hydrologic budget. While studies have found high 

model accuracy using DHSVM, it should be understood that these experiments are primarily 
107

located in mountainous, snowpack-dominated systems. Taking DHSVM out of its natural 

environment and calibrating to an R2 of 0.73 should be considered successful. 

DHSVM is a highly parameterized model, yet a goal of this project was to determine if 

the model could be useful to the average watershed manager. Thus, intensive field studies to 

determine accurate input values were not conducted. Rather, parameters were found using 

readily available data from weather stations, online databases, and literature. This proved to take 

months longer than expected and some values are uncertain estimates. This raises the possibility 

of equifinality in highly parameterized models, which may have been likely during the Coweeta 

calibration process. The model also requires extensive knowledge in GIS and computer 

programming, which is unlikely for many watershed managers. Attempts have been made to 

create a user-friendly interface. Such advancements could create ease for watershed managers 

and potentially move DHSVM from a research model to a management tool.

The high accuracy of the validation period suggests that DHSVM has potential for use in 

the Blue Ridge Mountains. DHSVM requires numerous input parameters and this study was 

conducted using little to no field collected data. Accuracy might be improved with field 

sampling, and a sensitivity analysis could be used to determine what parameters would be worth 

collecting. Accuracy of model calibration could also be improved by increasing the time span of 

the study. This analysis was restricted due to data availability at the hourly time step and a 

coarser time step may be more feasible for longer studies. The model accuracy found for the 

Blue Ridge Mountains using literature values suggests that DHSVM might have success in other 

regions with long-term hydrologic records. Data and sources presented in this study can be 

broadened and be applied outside of the Coweeta watershed.

5.2 Effects of Road Density on Streamflow

The second objective of this project was to explore the relationship between forest road 

density and stream discharge. It was hypothesized that increases in road density would increase 

stream discharge. This is based on three underlying theories:
108

1) roads can intercept subsurface flow when the water table elevation rises above the 

road surface;

2) overland flow can be intercepted by the road network thus increasing peak discharge;

3) the compacted surface of a road can decrease infiltration and create heightened 

infiltration excess runoff.

Using DHSVM as a controlled environment, we were able to model the impacts of road 

density on stream discharge. This process was complex, as road density is not independent from 

other road features and confounding effects were present. These were accounted for as much as 

possible using a method created by Bernard (2006). The method uses weighted values for factors 

such as road slope, topographic position, class, and length to create an overall impact value on 

the watershed. The impact value for each treatment was kept close to constant for each treatment 

and this was assumed to control for outside factors. However, the treatment variation in slope, 

topographic position, and road class were a noted constraint in the study.

The modeled results suggest that mean monthly stream discharge will be impacted when 

forested road density reaches and surpasses 4.3 km km-2

. Seasonal changes in the stream 

hydrograph have been detected following vegetation removal (Hibbert, 1966; Patric, 1973). No 

monthly effects were noted in this study and it is believed that reduction in evapotranspiration 

due to vegetation losses were not an important component of changes in streamflow. This is 

consistent with the literature, which suggests that changes in streamflow in the Appalachian 

Mountains are detectable after 20% of the vegetation has been removed (Stednick, 1996). The 

largest area covered by roads in this study was 5.6%. 

This study found that effects on streamflow began when 1.8% of the watershed area was 

in roads. This fell within the range of values in the literature, which suggest that streamflow may 

be impacted at road densities from 1.8 to 15% (Harr, 1979; Ziemer, 1981; King and Tennyson, 

1984; Wright et al., 1990). In the Shope Fork catchment, significant changes were detected as

road density increased beyond 1.8%, which suggests that road managers should be cautious 

when road density reaches and exceeds this threshold. The effects on streamflow in this 

watershed are thought to be a function of intercepted subsurface flow and increased runoff on the 
109

road surfaces, both of which have been shown to increase streamflow in forested areas with 

roads (Ziemer, 1981; Jones, 2000).

During a rainfall event producing 4.8 mm of rainfall over 5 hours, modeled output 

showed that stream discharge was impacted at densities ≥ 6.0 km km-2. For larger rainfall event 

(87.1 mm over 13 hours), streamflow was impacted at road densities ≥ 3.0 km km-2. The 

difference between the road density thresholds during large and small storms can be attributed to 

differences in infiltration excess runoff from the roads and intercepted subsurface flow. 

Precipitation rates are lower during small storms and may not exceed infiltration rates, allowing 

more water to percolate into the soil. The opposite is true for a large storm, where an excess of 

water may build up on road surfaces. Streamflow will be thus impacted at lower densities during 

larger storms, due to increased amounts of runoff from road surfaces. The same principle applies 

to the interception of subsurface flow by road cuts. In the variable source area concept (Hewlett 

and Hibbert, 1963), the area in a watershed that contributes to streamflow is dependent on factors 

such as antecedent moisture conditions and precipitation rates and volume. Smaller storms tend 

to have smaller source areas while larger storms usually have larger contributing areas. For the 

smaller storm, it is possible that many of the roads were not located in the area that was 

contributing to the stream hydrograph. Increasing the area increases the likelihood that roads will 

be present, even at smaller densities. 

The number of hourly peak flows during the study period was also analyzed, and these 

were separated by the return interval of the flow. For all return intervals, streamflow was first 

impacted between 3.0 and 4.3 km km-2

. In contrary to the individual rainfall events, there was no 

noticeable distinction between recurrence interval and the road density threshold. This test, 

however, only looked at the difference in frequency of peak flows and not the difference in 

magnitude. Increasing the road density might not change the possibility of an event occurring, 

but may affect the magnitude of discharge and thus possible damages due to flooding.

Since only one small and one large rainfall event was analyzed for this study, it is 

recommended that these results be further investigated. It is also noted that, while DHSVM 

allows for many road features to be controlled, such as road surface and cut slope, it does not 
110

allow for water control features. The installation of BMPs such as water bars, culverts, and broad 

based dips can be used as important mitigation tools, as was suggested by Eisenbies et al., 2007.

The modeled results suggest that stream discharge can be impacted when forested road 

density reaches 4.3 km km-2. This is the current road density of the Shope Fork watershed. It is 

possible that streamflow might be currently impacted in the watershed and this should be 

considered. During storm events, it was found that road densities greater than 6.0 km km-2

created significant increases in streamflow during a small event, while stream discharge was 

impacted at a density of 3.0 km km-2 during a large rainfall events. The latter density is extremely 

small for a managed watershed and allows for very little access to the area. Since only one small 

and one large rainfall event was analyzed for this study, it is recommended that these results be 

further investigated. It is also noted that, while DHSVM allows for many road features to be 

controlled, such as road surface and cut slope, it does not consider water control features. The 

installation of BMPs such as water bars, culverts, and broad based dips can be used as important 

mitigation tools

5.3 DHSVM 

The Distributed Hydrology-Soil-Vegetation Model (DHSVM) is a physically based 

model that explicitly calculates the energy and water budgets for a catchment at the scale of a 

grid cell. DHSVM integrates user-defined inputs for meteorological, soil, vegetation, and stream 

and road morphology data to model evapotranspiration, snow influx and efflux, and the 

movement of unsaturated soil moisture, saturated subsurface flow, overland flow, and channel 

flow. This is completed for each grid cell at a specified time step. It is a complex and powerful 

model (Beckers et al., 2009). More detailed information can be found in Wigmosta et al. 1994 

and 2002. 

DHSVM has been used to analyze the effects of land use change, harvesting, and road 

networks on streamflow in forested, mountainous environments throughout the world (Bowling

and Lettenmaier, 1997; Bowling et al., 2000; Cuo et al., 2006; Doten and Lettenmaier, 2004; 

Doten et al., 2006; VanShaar et al., 2002). It has also been used in hydrologic modeling 
111

(Haddeland and Lettenmaier, 1995; Kenward and Lettenmaier, 1997; Westrick et al., 2002;

Wigmosta et al., 1994; Wigmosta and Lettenmaier, 1999) and to look at the interactions between 

climate and climate change and hydrology (Arola and Lettenmaier, 1996; Leung and Wigmosta, 

1999; Wigmosta et al., 1995). 

The distributed hydrology-soil-vegetation model has the potential to be an extremely 

useful research tool, if used properly. The model is extremely powerful in that it has the potential 

to model numerous components of the hydrologic cycle, including overland flow, subsurface 

flow, and flow from culverts. Because the physical model attempts to account for all of the 

natural components of the hydrologic cycle, the inputs are numerous, overwhelming, and 

potentially unnecessary. The ambiguity and specificity of parameters make almost impossible to 

correctly input all variables. Models with high inputs also run the risk of equifinality, which 

occurs when outcomes are correctly predicted but the processes by which the predictions are 

made are incorrect. The data and processing steps provided in this thesis are intended to serves as 

a base for use of DHSVM in the Appalachian Mountains. Much work could be done to create a 

user-friendly platform for the model with readily available input parameters. DHSVM has 

potential for widespread application and could be useful to a variety of land management 

scenarios. Work should be continued to move DHSVM from a scientifically, research based 

model to one that can be beneficial to the broader public.

Literature Cited

Arola, A. and D. Lettenmaier. 1996. Effects of subgrid spatial heterogeneity on GCM-scale land 

surface energy and moisture fluxes. Journal of Climate. 9: 1339-1349.

Beckers, J. and Y. Alila. 2004. A model of rapid preferential hillslope runoff contributions to 

peak flow generation in a temperate rain forest watershed. Water Resources Research.

40.

Beckers, J., B. Smerdon, T. Redding, A. Anderson, R. Pike, and A.T. Werner. 2009. Hydrologic 

models for forest management applications Part 1: Model Selection. Watershed 

Management Bulletin. 13(1): 35-44.
112

Bernard, A. M. 2006. Geospatial Modeling of Forest Road Networks and Their Effects on, 

Stream Macroinvertebrate Communities. M.Sc. Virginia Polytechnic Institute and State 

University. Blacksburg, VA.

Bowling, L. C. and D. P. Lettenmaier. 1997. Evaluation of the Effects of Forest Roads on 

Streamflow in Hard and Ware Creeks, Washington. University of Washington Water 

Resources Series 155. 202 p.

Bowling, L. C., P. Storck, and D.P. Lettenmaier. 2000. Hydrologic effects of logging in western 

Washington, United States. Water Resources Research. 36(11): 3223-3240.

Campbell, G.S. 1986. Extinction coefficients for radiation in plant canopies calculated using an 

ellipsoidal inclination angle distribution. Agricultural and Forest Meteorology. 36: 317-

321.

Carmean, W.H. 1978. Site Index Curves for Northern Hardwoods in Northern Wisconsin and 

Upper Michigan. USDA Forest Service Research Paper NC-160. 16 p.

Cionco, R.M. 1972. A wind profile index for canopy flow. Boundary-Layer Meteorology. 3(2):

255-263.

Cuo, L., T. W. Giambelluca, A.D. Ziegler, and M.A. Nullet. 2006. Use of the distributed 

hydrology soil vegetation model to study road effects on hydrological processes in Pang 

Khum Experimental Watershed, northern Thailand. Forest Ecology and Management.

224: 81-94.

Dickinson, R.E., A. Henderson-Sellers, C. Rosenzweig, and P.J. Sellers. 1991. 

Evapotranspiration models with canopy resistance for use in climate models, a review. 

Agricultural and Forest Meteorology. 54: 373-388.

Doten, C. O. and D. P. Lettenmaier. 2004. Prediction of Sediment Erosion and Transport with 

the Distributed Hydrology-Soil-Vegetation Model. University of Washington Water 

Resources Series 178. 70 p.

Doten, C. O., L. C. Bowling, J.S. Lanini, E.P. Maurer, and D.P. Lettenmaier. 2006. A spatially 

distributed model for the dynamic prediction of sediment erosion and transport in 

mountainous forested watersheds. Water Resources Research. 42: 15.

Eisenbies, M.H., W.M. Aust, J.A. Burger, and M.B. Adams. 2007. Forest operations, extreme 

flooding events, and considerations for hydrologic modeling in the Appalachians-A 

review. Forest Ecology and Management. 242:77-98.
113

Haddeland, I. and D. Lettenmaier. 1995. Hydrologic Modeling of Boreal Forest Ecosystems. 

University of Washington Water Resources Series Technical Report 143. 138 p.

Kenward, T. and D. Lettenmaier. 1997. Assessment of required accuracy of digital elevation data 

for hydrologic modeling. University of Washington Water Resources Research Technical 

Report 153. 131 p.

Leung, L. R., M. S. Wigmosta, S.J. Ghan, D.J. Epstein, and L.W. Vail. 1996. Application of a 

subgrid orographic precipitation/surface hydrology scheme to a mountain watershed. 

Journal of Geophysical Research.101(D8): 12803-12817.

Leung, L. R. and M. S. Wigmosta. 1999. Potential climate change impacts on mountain 

watersheds in the Pacific Northwest. Journal of the American Water Resources 

Association. 35(6): 1463-1471.

Thyer, M., J. Beckers, D. Spittlehouse, Y. Alila, and R. Winkler. 2004. Diagnosing a distributed 

hydrologic model for two high-elevation forested catchments based on detailed standand basin-scale data. Water Resources Research. 40: 20.

VanShaar, J. R., I. Haddeland, and D.P. Lettenmaier. 2002. Effects of land-cover changes on the 

hydrological response of interior Columbia River basin forested catchments. 

Hydrological Process. 16: 2499-2520.

Westrick, K. J., P. Storck, and C.F. Mass. 2002. Description and evaluation of a 

hydrometeorological forecast system for mountainous watersheds. Weather and 

Forecasting. 17: 250-262.

Wigmosta, M. S., L. W. Vail, and D.P. Lettenmaier. 1994. A distributed hydrology-vegetation 

model for complex terrain. Water Resources Research. 30: 1665-1679.

Wigmosta, M. S., L. R. Leung, and E.J. Rykeil. 1995. Regional modelling of climate-terrestrial 

ecosystem interactions. Journal of Biogeography. 1: 453-465.

Wigmosta, M. S. and D. P. Lettenmaier. 1999. A comparison of simplified methods for routing 

topographically driven subsurface flow. Water Resources Research. 35(1): 255-264.

Wigmosta, M. S. and S. J. Burges. 1997. An adaptive modeling and monitoring approach to 

describe the hydrologic behavior of small catchments. Journal of Hydrology. 202: 48-77.

Wright, K. A., K. H. Sendek, R.M. Rice, and R.B. Thomas. 1990. Logging effects on 

streamflow: storm runoff at Casper Creek in Northwestern California. Water Resources 

Research. 26(7): 1657-1667.
114

Ziemer, R.R. 1981. Storm flow response to road building and partial cutting in small streams of 

Northern California. Water Resources Research. 17(4): 907-917.
115

APPENDIX A. RUNNING DHSVM

GIS Raster Grid Processing

DHSVM requires numerous spatial data inputs in the 2D binary format. This section 

describes the processes of obtaining and formatting the data as raster grids. Later steps will 

explain how to convert the data from raster into binary format. All GIS processes are described 

using ESRI ArcGIS version 9.3 techniques (Hillier, 2007). All processes and scripts are 

displayed in italics, names of files and folders are displayed in italics and underlined.

Digital Elevation Model Processing

DHSVM requires a digital elevation model (DEM) in binary format. The DEM will 

define the grid cell scale at which the all hydrologic calculations are modeled. DEM processing 

steps are detailed below. 

1.

Free DEM data for most of the United States can be downloaded from the Geo 

Community website (GeoCommunity, 2007). Only community members may download 

data, but no fee is required to join. Download the desired DEM in either 30 meter or 10 

meter grid cell resolution. 

Download the DEM

2.

The DEM will likely be in Spatial Data Transfer Standard (SDTS) format. SDTS is not 

immediately recognized by ArcMap 9.2, so the format must be changed to a raster grid. 

Use the following steps to convert the DEM.

Convert from SDTS to Raster

1) In ArcCatalog, make the ArcView 8x Tools Toolbar visible.

2) Conversion Tools Toolbar → SDTS Raster to Grid (dem)

Note: Some of the grids will have z units that are in feet, not meters. It is important to

 check the elevation units and convert to meters if necessary.
116

3.

Sinks are usually caused by errors in the DEM and, if left unfilled, will cause disjunctions 

in water routing. Before filling, check the DRG to ensure that the sinks are errors before 

filling them.

Fill in Sinks

Spatial Analyst Toolbox → Hydrology → Fill

4.

If the grid is in not in floating point, it must be converted. This information can be found 

in the DEM properties.

Convert to Floating Point

Spatial Analyst Toolbar → Raster Calculator: float[dem]

5.

DHSVM requires that all grids have the same properties. Take note of the following:

Check DEM Properties

1. Columns and Rows

2. Cell Size

3. Extent

4. Spatial Reference (Coordinate System and Datum)

Watershed Delineation

The watershed input is a binary file that outlines the study area to be analyzed. Often the 

watersheds are hand-delineated from a DEM. A watershed can also be delineated based on the 

Hydrology Toolset in ArcGIS, as detailed in the following steps. DHSVM also requires a mask 

of the watershed, which is described in the ensuing watershed delineation process. 

1.

DHSVM will allow you to input a pre-defined watershed boundary, but this can cause 

mass balance errors. It is recommended that the watershed be delineated using ArcGIS.

Run the Hydrology Toolset

1. Load the DEM (dem

2. Run the hydrology toolset 

)

Spatial Analyst Toolbox → Hydrology → Flow Direction
117

3. To delineate a watershed, a pour point must be defined. This will be the outlet of 

the stream. Create a raster of one cell at the watershed outlet. If you have point 

data:

Conversion Tools Toolbox → To Raster → Point to Raster (weirs

Select the desired raster grid cell

)

Spatial Analyst Toolbar → Raster Calculator: 

4. Delineate the watershed

weirs

Spatial Analyst Toolbox → Hydrology → Watershed (mask)

2.

The watershed grid just created is a rectangle; all of the cells outside of the watershed 

boundary contain no data. Nodata points cause the model to crash when in Linux, so a 

mask must be created where all of the cells outside the watershed contain an integer 

value. This will be the grid that is used when the model runs.

Create the Watershed Mask 

Spatial Analyst Toolbar → Reclassify (mask0)

3.

Check to make sure that the watershed grid properties match those of the DEM and that 

the grid is an unsigned integer. Properties that should be identical include:

Check Raster Properties

1. Columns and Rows

2. Cell Size

3. Extent

4. Spatial Reference (Coordinate System and Datum)

Soil Grid Processing

A soil type map is extremely important for DHSVM since soil input parameters 

correspond with soil types defined within the map. Again, the map must be in 2D binary format. 

Soils data for much of the U.S. can be downloaded from the NRCS SSURGO database (Soil 

Survey Staff, 2008).
118

1.

Coweeta soils data were downloaded from SSURGO (including access files).

Download Soils Data

2.

The data will be downloaded in a vector format and should be converted to raster. When 

converting to raster, be sure to use a field that will allow you to identify the soil series. 

This will be helpful in Step 3.

Convert to Raster Format

Conversion Tools Toolbox → To Raster → Feature to Raster 

3.

DHSVM requires that the values in the raster match those in the configuration file, so all 

of the series need to be given a numerical number. Reclassify based on the configuration 

file.

Reclassify the Soil Series

Spatial Analyst Toolbar → Reclassify (soil_series) 

4.

Check to make sure that the downloaded data boundaries match those of the delineated 

watershed. The Coweeta soils data did not exactly match the boundary for the delineated 

watershed. 204 cells on the boundary had data that were assumed to be similar to their 

neighboring cell(s).

Mask the Soil to the Watershed Boundary

1) Convert NoData cells to a separate grid. With NoData values highlighted:

Spatial Analyst Toolbar → Raster Calculator → [soil_series

2) Subtract the watershed mask (be sure to use the original mask, not the reclassified 

mask) from the NoData grid to get all of the cells in the watershed that have null 

soils values.

]

Spatial Analyst Toolbar → Raster Calculator → [NoData_grd]-[mask

3) Convert this raster to a point file.

]

Conversion Tools Toolbox → From Raster → Raster to Point

4) Add a field to the point file and manually edit the field by highlighting points and 

assigning them soils values based on surrounding cells.
119

5) Convert the point file back to raster

Conversion Tools Toolbox → To Raster → Point to Raster (missing_data

6) Merge the grids

)

Be sure to list the missing data raster first so that it overwrites the soil series grid.

Spatial Analyst Toolbar → Raster Calculator → 

merge([missing_data],[soil_series])

5.

The values in the raster grid should match the fields in the soils section of the 

configuration file. Check to make sure that the soil grid properties match those of the 

watershed mask and DEM and that the grid is an unsigned integer. Properties that should 

be identical include:

Check Raster Properties

1. Columns and Rows

2. Cell Size

3. Extent

4. Spatial Reference (Coordinate System and Datum)

Soil Depth Grid

The create stream network AML does have an algorithm that will generate a soil depth 

map, but this can create problems with rooting depths and vegetation types when running the 

model. Creating a soil depth map based on SSURGO data is somewhat crude, but it eliminates 

these errors.

1.

The SSURGO data or soil survey contains information on the depth of each soil series. 

Reclassify the soil grid based on these soil depths. Set the NoData to an average soil 

depth for the watershed. Since soil depths are floats instead of integers, this is best done 

in raster calculator. You can group your soils based on similar depths.

Reclassify the Soil Type Grid

Spatial Analyst Toolbar → Raster Calculator →con([ soil]= 3| [soil]=6 | 

[soil

Continue this process until all soil series have been assigned a depth.

]=10, 1.43)
120

2.

Merge all of the depth grids together to create one raster of soil depth.

Merge Grids

Spatial Analyst Toolbar → Raster Calculator → Merge([depth1],

[depth2],...[depthn])

3.

Check to make sure that the soil depth grid properties match those of the watershed mask, 

soil grid, and DEM and that the grid is in floating point. Properties that should be 

identical include:

Check Raster Properties

1. Columns and Rows

2. Cell Size

3. Extent

4. Spatial Reference (Coordinate System and Datum)

Vegetation Grid Processing

The cover type map file is similar to the soil map file in that it is linked to the DHSVM 

vegetation input parameters. Land cover data can be downloaded from the National Land Cover 

Dataset (Multi-Resolution Land Characteristic Consortium, 2010). Coweeta data were manually 

defined as detailed below.

1.

The four Coweeta vegetation types are based on three parameters: elevation, topographic 

position, and aspect. The elevation grid is provided by the DEM. The aspect grid can be 

created by the spatial analyst tool.

Define the Vegetation Types

1. The topographic position grid needs to be created based on the DEM and the 

stream raster. Assume that coves are within 200 m of streams and ridges are 

within 200 m of a ridgetop. Anything else (for the purpose of this study) will fall

into the slope class. Create a raster grid of these three topographic positions.

Spatial Analyst Toolbar → Distance → Straight Line
121

2. Define the four vegetation types.

a. Northern Hardwoods: Elevations greater than 1220 meters in cove and 

slope positions. Also ridges greater than 1300 meters.

b. Mixed Deciduous: Northeast to northwest facing slopes and ridges of up 

to 760 meters and slopes of all aspects from 760 to 1220 meters. 

c. Cove Hardwoods: Elevations less than 1220 meters in the coves.

d. Xeric Oak Pine: West to southwest facing ridges up to 1300 meters and 

west to southwest facing slopes up to 760 meters.

2.

While only four vegetation types are present in the watershed, they will have different 

rooting depths and root fractions based on soil depth. 

Mask the Vegetation Grid to the Soil Depth Grid

1. Determine new vegetation types. This needs to be done for every vegetation type 

and soil depth combination. There are multiple ways to do this; raster calculator is 

one of them. 

Spatial Analyst Toolbar → Raster Calculator → con([veg]= 

1 & [soild

2. Merge the vegetation types to create one vegetation grid. Make sure that the 

values for each vegetation type match those found in the configuration file.

]≥ 0.44, 1)

Spatial Analyst Toolbar → Raster Calculator → Merge( [veg1], 

[veg2],...[vegn])

3.

The values in the vegetation raster should match the fields in the corresponding section of 

the configuration file. Check to make sure that the vegetation grid properties match those 

of the watershed mask, soil type and depth grid, and DEM and that the grid is an 

unsigned integer. Properties that should be identical include:

Check the Raster Properties

1. Columns and Rows

2. Cell Size

3. Extent

4. Spatial Reference (Coordinate System and Datum)
122

GIS Vector Grid Processing

DHSVM employs two types of networks: streams and roads. Three files are made from 

these vector networks (See Appendix A) and they require some processing in order to do so.

Create Stream Networks

There are multiple ways that stream networks can be utilized by DHSVM. If stream networks are 

provided, they need to be in coverage format. This fie is then used in the Create Stream Network 

(CSN) AML to output the stream class, network, and map files that will be ultimately used by 

DHSVM. If stream networks are not provided, they can be created by the AML. The following 

process includes steps for both.

1.

DHSVM requires a folder in which all of the spatial files are stored. Name this folder 

(arcinfo) and keep in mind that Linux is case-sensitive.

Create ArcInfo Folder

2.

1) Clip the shapefile to the watershed boundary.

If a Stream Network Shapefile is Provided, Convert to Coverage

2) Convert from shapefile to coverage. Be sure to use arc instead of route.

Conversion Tools Toolbox → To Coverage → Feature Class to Coverage

3) Copy into the arcinfo folder.

3.

The initial input files for creating stream networks are:

Initial Inputs for Running CSN AML

1. DEM- raster grid in meters, sinks must be filled and be in floating point

2. A. Mask- raster grid of watershed

B. Mouth- raster grid (point) of basin outlet

3. A. Soil depth grid

B. Maximum and minimum soil depth in meters. Also need the source area in 

square meters.

4. A. Stream network-coverage file

B. Source area (square meters) for beginning of stream networks

4. 

1) The DEM input is the DEM that we have been working with (

Gather the Initial Input Files

dem). The sinks 

have already been filled, and it should already be in floating point.
123

2) We are using the watershed mask to run the networks (mask

3) We are using the soil depth grid that we created earlier (

)

soild

4) We want the AML to create a stream network for us based on the DEM. To do 

this, a source area must be specified. The best way to find an accurate source area 

is to run a flow accumulation in ArcMap to see what the approximate cell 

accumulation is at stream inception. 

)

Spatial Analyst Toolbar → Hydrology → Flow Direction

Spatial Analyst Toolbar → Hydrology → Flow Accumulation

For Coweeta, most streams start at a source area of 20 cells. For a 30m resolution, 

this is a source area of 18000 m2

5) We are going to provide a stream network for the model. The Coweeta stream 

network was downloaded as a shapefile. This can be clipped to the study 

watershed and converted to a coverage file. To find the source area, run the 

hydrology toolset on the DEM. Then overlay the Coweeta stream network to see 

what the cell flow accumulation is where the streams are first starting. The 

average is around 250 cells, which equals a source area of 25000 m

. The AML will create some non-existent 

streams, this will require subsequent editing. 

2

.

5.

Some parts of the AMLs have been written specifically for the tutorial settings. These should 

be changed to ensure more accurate model outputs.

Make Changes to the CSN AML Scripts

1) Stream Classes (CSN.AML)

The channel classes, hydraulic depths and widths, and effective widths are all 

hardwired into the AML. These should be individualized based on the watershed. 

The AML will group stream segments into classes based on the segment slope 

and the segment mean source area. These groups are then assigned similar 

characteristics, such as hydraulic depth and width and effective width. The classes 

are hardwired into the java code and should be individualized based on the 

watershed. For the initial run, keep these classes as they are.
124

1. Cell Size (AddAat2.java)

The area of the cell (or the grid resolution) is hardwired into the java code. The 

tutorial runs on a 150m grid cell and we are running on a 30m grid cell. 

Original Code:

int area=22500, halfarea = 11250; // CRUDE WAY TO SET CELL SIZE

New Code:

int area=900, halfarea = 450; // CRUDE WAY TO SET CELL SIZE

6.

1) This step is performed in ArcInfo. Assuming that we are using our created soil 

depth grid and created stream network, input the following:

Run CSN

&workspace <workspace location>

&amlpath <location of aml scripts>

&run CSN-EIS <dem> <mask> <soil depth> <stream coverage> 

MOUTH|MASK {minimum source area, meters} {minimum soil depth, 

meters} {maximum soil depth, meters}

Example:

&workspace e:\coweeta\calibration4\arcinfo

&amlpath c:\amlscripts

2) If using a created soil depth grid but no stream network input, use this instead:

&run CSN-EIS cowdem cowmask soild streams MASK 

&run CSN-EIS <dem> <mask> <soil depth> <stream coverage>MASK 

{minimum source area, meters}

3) To have DHSVM create a soil grid but not the stream network, input the 

following (the same is true for cases where a soil depth grid and stream network 

are both needed):

&run CSN-EIS cowdem cowmask soild streams MASK 18000

&run CSN-EIS <dem> <mask> <soil depth> <stream coverage>MASK 

{minimum source area, meters} {minimum soil depth, meters} {maximum 

soil depth, meters}

&run CSN-EIS cowdem cowmask soild streams MASK 18000 0.2 2.5
125

7.

1) Edit unnecessary stream arcs. Since the stream network was created by a 

computer, it will likely input streams that are not actually there. If you know the 

stream location, edit the file by deleting the stream arcs. This needs to be done by 

converting the coverage file to a shapefile and manually editing the shapefile.

Edit the Stream Networks

Conversion Tools Toolbox → To Shapefile → Feature Class to Shapefile

Editor Toolbar → Start Editing

You can also look at the attribute table to see the channel classes that the AML 

has assigned the stream segments. 

2) Update all segment lengths in the attribute table. 

Editor Toolbar → Start Editing → Calculate Geometry

3) Edit stream classes. If you haven’t already done so, update the stream classes in 

the create stream network AML.

4) Convert stream network back to coverage. Be sure to use arc instead of route.

Conversion Tools Toolbox → To Coverage → Feature Class to Coverage

5) Delete and save files. Delete all files created by the AML using arcinfo. Copy the 

new stream coverage file into the arcinfo folder.

8.

1) Re-run the stream network script in ArcInfo (with edited stream classes). This 

time we do not need to specify a source area since we are using our own stream 

network file.

Re-Run

2)

3)

&workspace e:\coweeta\calibration4\arcinfo

4) &run CSN-EIS <dem> <mask> <soil depth> <stream coverage> 

MOUTH|MASK{minimum source area, meters} {minimum soil depth, meters} 

{maximum soil depth, meters}

&amlpath c:\amlscripts

Example:

&run CSN-EIS cowdem cowmask soild streams MASK 
126

Create Road Networks

DHSVM does not require road networks to run, but they can increase the accuracy of the 

model output. A road network must be provided. Culvert files can be either provided or created 

using an AML. Be sure that the fixroads program is properly compiled. This is extremely 

important in ensuring that the roads are simulated correctly. 

1.

1) Clip the shapefile to the watershed boundary.

If a Stream Network Shapefile is Provided, Convert to Coverage

2) DHSVM requires that you manually input classes. Make sure to title the field 

“class.” Do this by creating a new field and then editing the attribute table. Skip 

this step for streams.

Attribute Table → Options → Add Field

Editor Toolbar → Start Editing

3) Convert from shapefile to coverage. Be sure to use arc instead of route.

Conversion Tools Toolbox → To Coverage → Feature Class to Coverage

4) Copy into the arcinfo folder.

2.

The initial input files for creating road networks are:

Initial Inputs for Running CRN AML

1. DEM- raster grid in meters, sinks must be filled and be in floating point

2. Mask- raster grid of watershed

3. Road network-coverage file

4. Culvert coverage file

3.

1) The DEM input is the DEM that we have been working with (

Gather the Initial Input Files

dem

2) We are using the soil depth grid that we created earlier (

). The sinks 

have already been filled, and it should already be in floating point.

soild

3) The road network and culvert coverage files

)
127

4.

Only the create road network AML needs to be edited. The road class widths and depths 

should be edited depending on watershed data.

Make Changes to the CRN AML Scripts

5.

1) The road break AML overlays the culverts into the roads, creating sinks at all 

culvert locations. It runs in ArcInfo.

Run Road Break AML

&workspace 

&amlpath 

e:/coweeta/calibration4/arcinfo

&run roadbreak <dem> <streams> <roads> <culverts>

c:/amlscripts

Example:

2) If using basin edges, run the script again.

&run roadbreak cowdem cowstreams cowroads cowculverts

&run roadbreak <dem> <streams> <roads> <edges>

Example:

&run roadbreak cowdem cowstreams cowroads cowedges

6.

This is done in ArcInfo. Be sure that the fix roads program is correctly compiled.

Run CRN

&workspace f:\coweeta\calibration\arcinfo

&amlpath c:\amlscripts

&run CRN <dem> <soil depth> <road network>

Example:

&run CRN-SFD cowdem soild cowroads

Create Culverts

Culverts should, at a minimum, be located where all roads and streams intersect. If you 

do not have a culvert file, one can be created. Steps one and two describe culvert processing 

using ArcMap and the ArcIntersect AML, respectively.
128

1.

This will not create culverts at basin edges (see step 2). 

Creating Culverts Using ArcMap

1) Convert stream and road networks to raster grids.

Conversion Tools Toolbox → To Raster → Feature Class to Raster

2) Find cells where streams and roads intersect.

Spatial Analyst Toolbar → Raster Calculator → 

con([streams]&[roads],1)

3) Convert raster to point file.

Conversion Tools Toolbox → From Raster → Raster to Point

4) Edit the point file. There will be many points that are not actually located at 

stream crossings. These should be deleted. All other points should be moved to 

the direct location where the stream and road coverage file cross. You can do this 

by snapping, but snapping can be unreliable. The best way is to move them by 

zooming in as far as possible and manually moving the points.

Editor Toolbar → Start Editing

5) Convert the point file to a coverage file to get final culvert file.

Conversion Tools Toolbox → To Coverage → Feature Class to Coverage

6) Copy the culvert coverage file into the arcinfo folder.

2.

Culverts can also be created using the arcintersect AML. This can create culverts at basin 

edges, if desired.

Create Culverts Using ArcIntersect

1) Create a coverage file of the basin. Call this “basinedge

Conversion Tools Toolbox → From Raster → Raster to Polygon

.” To do this, convert the 

raster to a shapefile. Create a new polyline shapefile in arccatalog. Edit the new 

shapefile by snapping to the edges and vertices of the basin polygon. Convert the 

polyline shapefile to a coverage file.

Editor Toolbar → Start Editing

Conversion Tools Toolbox → To Coverage → Feature Class to Coverage
129

2) Create culverts at locations where roads and streams intersect. Do this in arcinfo.

&workspace 

&amlpath 

e:\GIS\30mdata\culverts\

&run arcintersect <roads> <streams> <culverts> 

c:\amlscripts

Example:

3) Create sinks where roads exit the basin using arcintersect.

&run arcintersect cowroads cowstreams culverts 

&run arcintersect <roads> <basinedge> <edges> 

Example:

4) Edit the culvert and edge files per step 1-4. This requires conversion to shapefile 

then back to coverage. Make sure that the coverage file has a defined coordinate 

system.

&run arcintersect cowroads basinedge edges 

5) Copy roads, culverts and edges files into arcinfo folder.

Check Stream and Road Network Output Files

1.

Class files are not automatically generated by the AML. They must be manually edited 

and copied into the arcinfo folder. See the sections on stream and road networks for a 

description of the input fields. 

Class Files

2.

Check to make sure that the network files do not contain any errors. Look for errors such as 

unusually large slope or all zeros in the destination channel ID. 

Network Files

1) Channel ID- unique identifier for channel segment.

2) Segment Order- determines order for routing computations. Segments with a 

lower order are routed first and segments receiving inflow from other segments 

must have a higher order from any of the other segments from which inflow is 

received.

3) Segment Slope (m/m)- slope of arc.

4) Segment Length (m)- length of arc.
130

5) Channel Class ID- identifier for channel class.

6) Destination Channel ID- identifier of the segment to which this segment flows 

(basin outlet is 0).

7) Save Indicator- routing results for this segment will be placed in the output file if 

the keyword SAVE appears in this field.

8) Segment Name- segment name in the output file.

3.

Check to make sure that the network files do not contain any errors. Look for large or 

negative cut bank heights.

Map Files

1) Column Number- grid cell column number.

2) Row Number- grid cell row number.

3) Channel ID- channel segment identifier, which must be one of those specified in 

the file with the stream network.

4) Length (m)- straight line length of the channel segment lying within the cell.

5) Depth (m)- stream bank height.

6) Width (m)- effective stream channel width.

7) Save Indicator- outflow for this segment will be placed in the output file if the 

keyword SINK appears in this field

4.

In order for output to be saved at a particular location, save indicators must be inserted 

into the stream and road network and map files. In the network file, the basin outlet will 

have a destination channel ID of 0. 

Save Indicators

Running DHSVM

1.

1) Master folder should contain the following folders:

Before Beginning

arcinfo: contains all GIS data

configfiles: contains configfiles to initialize DHSVM 

metfiles: meteorlogical station data
131

modelstate: initial state files created by supporting code (initially empty)

output: output directory

programs: supporting programs (check DHSVM website for necessary files)

2) Raster grids should all have the same extent, resolution, and projection. A DEM, 

watershed mask, soil, and vegetation grid are all required. Nodata values will 

cause errors, so be sure to convert all values outside the watershed boundary to an 

integer. This will include creating a second mask file. A soil depth grid can be 

created, but is not necessary. A later script will create one if needed. All grids 

should be in the arcinfo folder.

3) Stream and road class files should be in the arcinfo folder and should be edited 

for the watershed.

4) The configuration file should be completed and be in the configfiles folder.

5) All met station data should be in text format and be in the metfiles folder.

2.

1) Reclassify all grids (DEM, both mask files, soil, and vegetation) to ASCII using 

ArcMap. 

Convert Raster Grids to ASCII (Windows)

Conversion Tools Toolbox → From Raster → Raster to ASCII

Save: filename.dos.txt

2) The grids are now in dos format and need to be converted to UNIX. This needs to 

be done for each ASCII file and can be done using the command prompt. 

>copy filename.dos.txt filename.txt

>dos2unix filename.txt

3) There will be two copies of each file. One in dos format (filename.dos.txt) and 

one in UNIX text format (filename.txt). 

3.

1. Remove headers from all ASCII files (filename.txt). This can be done using 

Kwrite. Make sure that each file does not have any -9999 values.

Convert ASCII Files to Binary (Linux)

2. Open the Linux command prompt to convert from ASCII to binary. You should 

be in the arcinfo folder. Once again, this should be done for each file. You will 
132

need the number of rows and the number of columns for the watershed. This can 

be found from the raster in ArcMap or in the header of the text files. The script 

changes based on the grid data (floating point or integer). Floating point grids use 

float (DEM, soil depth) and integer grids use char (mask, soil, vegetation). 

$ myconvert ascii float filename.txt filename.bin #rows #columns

4.

The initial state files allow the model to restart under initial conditions. Be sure to be in 

the programs folder. Edit this file if necessary.

Create Initial State Files (Linux)

$ MakeModelStateBin InitialState.txt

5.

See Section B.2.1 on creating stream networks

Create Stream Network Inputs (Windows)

6.

See Section B.2.2 on creating road networks

Create Road Network Inputs (Windows)

7.

Follow the same steps in parts 2 and 3 for the soil depth grid. If the grid was not 

generated by the stream network AML, be sure to check whether it is floating point or 

integer before converting from ASCII to binary.

Convert Soil Depth to Binary (Windows and Linux)

8.

1) Open the Linux command prompt and navigate to the programs folder. Make the 

script executable.

Create Initial Channel State Files (Linux)

$ chmod 755 MakeChannelState.scr

2) Make the channel state. The starting depth is in meters and should be changed 

based on the initial conditions in the watershed. The date and time string should 

match that defined in the Initial State text file.

$ ./MakeChannelState.scr ../arcinfo/streamnetwork.dat starting depth 

date/time string
133

Example: ./MakeChannelState.scr ../arcinfo/streamnetwork.dat 0.25 

10.01.1990.03.00.00

9.

From the config files folder:

Run DHSVM (Linux)

$ dhsvm3 config file name

Example: $ dhsvm3 INPUT.rainycr

Literature Cited

GeoCommunity. 2007. GIS Data Depot. GeoCommunity. Available online at: 

http://data.geocomm.com.

Hillier, A., 2007. ArcGIS 9.3 Manual. Environmental Systems Research Institute. Available 

online at: http://works.bepress.com/amy_hillier/17.

Multi-Resolution Land Characteristics Consortium. 2010. National Land Cover Database. U.S. 

Department of the Interior Geological Survey. Available online at www.mlrc.gov/. 

Soil Survey Staff. 2008. U.S. General Soil Map (SSURGO) for Macon County, North Carolina. 

USDA Natural Resources Conservation Service. Available online at 

http://soildatamart.nrcs.usda.gov.
134

APPENDIX B. EXAMPLE INPUT FILES

Shope Fork Configuration File

#############################################################################

#DHSVM INPUT FILE FORMAT

#############################################################################

# OPTIONS SECTION 

##############################################################################

[OPTIONS] # Model Options

Format = BIN # BIN, BYTESWAP or NETCDF

Extent = BASIN # POINT or BASIN

Gradient = WATERTABLE # TOPOGRAPHY or WATERTABLE

Flow Routing = NETWORK # UNIT_HYDROGRAPH or NETWORK

Sensible Heat Flux = FALSE # TRUE or FALSE

Sediment = FALSE # TRUE or FALSE

Sediment Input File = # path for sediment configuration file 

Overland Routing = CONVENTIONAL # CONVENTIONAL or KINEMATIC 

Interpolation = VARCRESS # NEAREST or INVDIST or VARCRESS

MM5 = FALSE # TRUE or FALSE

QPF = FALSE # TRUE or FALSE

PRISM = FALSE # TRUE or FALSE 

PRISM data path = # path for PRISM files

PRISM data extension = # file extension for PRISM files 

Canopy radiation attenuation mode = FIXED # FIXED or VARIABLE

Shading = FALSE # TRUE or FALSE 

Shading data path = # path for shading files 

Shading data extension = # file extension for shading files 

Skyview data path = # path for skyview file; 

# for use with shading only

Snotel = FALSE # TRUE or FALSE

Outside = FALSE # TRUE or FALSE

Rhoverride = FALSE # TRUE or FALSE
135

Precipitation Source = STATION # STATION or RADAR

Wind Source = STATION # STATION or MODEL

Temperature lapse rate = CONSTANT # CONSTANT or VARIABLE

Precipitation lapse rate = CONSTANT # CONSTANT, MAP, or VARIABLE 

Infiltration = STATIC # STATIC or DYNAMIC 

Cressman radius = 10 # in model pixels

Cressman stations = 2 # number of stations

##############################################################################

# MODEL AREA SECTION

##############################################################################

[AREA] # Model area

Coordinate System = UTM # UTM or USER_DEFINED Albers

Extreme North = 3883947.106 # Coordinate for northern edge of grid

Extreme West = 273977.382 # Coordinate for western edge of grid

Center Latitude = 35.06 # Central parallel of basin 

Center Longitude = 83.45 # Central meridian of basin 

Time Zone Meridian = -75 # Time zone meridian for area

Number of Rows = 130 # Number of rows

Number of Columns = 175 # Number of columns

Grid spacing = 30 # Grid resolution in m 

##############################################################################

# TIME SECTION

##############################################################################

[TIME] # Model period cod

Time Step = 1 # Model time step (hours)

# spin up

#Model Start = 01/01/2003-03 # Model start time (MM/DD/YYYY-HH) 

#Model End = 12/31/2003-23 

#Validation

Model Start = 01/01/2003-03 # Model start time (MM/DD/YYYY-HH) 

Model End = 12/31/2007-23 
136

##############################################################################

# CONSTANTS SECTION

##############################################################################

[CONSTANTS] # Model constants

Ground Roughness = 1.5 # Roughness of soil surface (m)

Snow Roughness = 0.03 # Roughness of snow surface (m)

Rain Threshold = -1.0 # Minimum temperature at which rain 

# occurs (C) minor decrease in snow from 0

Snow Threshold = -1.0 # Maximum temperature at which snow 

# occurs (C) 

Snow Water Capacity = 0.025 # Snow liquid water holding capacity 

# (fraction)

Reference Height = 46.0 # Reference height (m)

Rain LAI Multiplier = 0.00054 # LAI Multiplier for rain interception

Snow LAI Multiplier = 0.00054 # LAI Mulitplier for snow interception

Min Intercepted Snow = 0.003 # Intercepted snow that can only be 

# melted (m)

Outside Basin Value = 0 # Value in mask that indicates outside 

# the basin

Temperature Lapse Rate = -0.0018 # Temperature lapse rate (C/m) 

Precipitation Lapse Rate = 0.00025 # Precipitation lapse rate (m/m) 

##############################################################################

# TERRAIN INFORMATION SECTION

##############################################################################

[TERRAIN] # Terrain information

DEM File = /../arcinfo/cowdem.bin # path for DEM file

Basin Mask File = /../arcinfo/cowmask0.bin # path for mask file

##############################################################################

# ROUTING SECTION

##############################################################################
137

[ROUTING] # Routing information. This section is 

 # only relevant if the Extent = BASIN

################ STREAM NETWORK##########################################

# The following three fields are only used if Flow Routing = NETWORK

Stream Map File = /../arcinfo/stream.map.dat # path for stream map file

Stream Network File = /../arcinfo/stream.network.dat # path for stream network file 

Stream Class File = /../arcinfo/stream.class.dat # path for stream class file

################ ROAD NETWORK ############################################

# The following three fields are only used if Flow Routing = NETWORK and there is a road 

#network

#Scenario 1 - all existing roads

Road Map File = /../arcinfo/road.map.dat # path for road map file 

Road Network File = /../arcinfo/road.network.dat # path for road network file 

Road Class File= /../arcinfo/road.class.dat # path for road class file 

################ UNIT HYDROGRAPH #########################################

# The following two fields are only used if Flow Routing = UNIT_HYDROGRAPH

Travel Time File = # path for travel time file

Unit Hydrograph File = # path for unit hydrograph file

##############################################################################

# METEOROLOGY SECTION

##############################################################################

[METEOROLOGY] # Meteorological stations

Number of Stations = 1 # Number of meteorological stations

# The following set of lines is to be repeated for each station, with the one 

# replaced by 2, 3, etc.

Station Name 1 = Coweeta_Exp_Station # Name for station 1

North Coordinate 1 = 3883144.19 # North coordinate of station 1

East Coordinate = 278111.694 # East coordinate of station 1

Elevation 1 = 685.5 # Elevation of station 1 in m

Station File1 = /../metfiles/01 # path for station 1 file 
138

################ MM5 ########################################################

# The following block only needs to be filled out if MM5 = TRUE. In that case this is the 

#ONLY block that needs to be filled out

MM5 Start = # Start of MM5 file (MM/DD/YYYY-HH),

MM5 Rows =

MM5 Cols =

MM5 Extreme North =

MM5 Extreme West = 

MM5 DY =

# MM5 met files

MM5 Temperature File = 

MM5 Humidity File = 

MM5 Wind Speed File= 

MM5 Shortwave File = 

MM5 Longwave File = 

MM5 Pressure File = 

MM5 Precipitation File = 

MM5 Terrain File =

MM5 Temp Lapse File =

# For each soil layer make a key-entry pair as below (n = 1, ..,Number of Soil Layers)

MM5 Soil Temperature File 0 =

MM5 Soil Temperature File 1 = 

MM5 Soil Temperature File 2 = 

############### RADAR ######################################################

# The following block only needs to be filled out if Precipitation Source = RADAR. 

Radar Start =

Radar File =

Radar Extreme North =

Radar Extreme West =

Radar Number of Rows =

Radar Number of Columns = 
139

Radar Grid Spacing =

################ Wind ###################################################

# The following block only needs to be filled out if Wind Source = MODEL

Number of Wind Maps =

Wind File Basename =

Wind Map Met Stations =

################ Precipitation lapse rate ######################################

# The following block only needs to be filled out if Precipitation lapse rate = MAP

Precipitation lapse rate =

##############################################################################

# SOILS INFORMATION SECTION

##############################################################################

[SOILS] # Soil information

Soil Map File = /../arcinfo/cowsoil.bin # minimum depth 0.43 maximum depth 2.03

Soil Depth File = /../arcinfo/soild.bin

Number of Soil Types = 15

################ SOIL 1 #######################################################

Soil Description 1 = BURTON CRAGGEY ROCK

Lateral Conductivity 1 = 1.75e-3

Exponential Decrease 1 = 4.0

Maximum Infiltration 1 = 2.78e-6

Capillary Drive 1 = 0.11

Surface Albedo 1 = 0.09

Mannings n 1 = 0.011

Number of Soil Layers 1 = 3

Porosity 1 = 0.49 0.42 0.42

Pore Size Distribution 1 = 0.403 0.393 0.393

Bubbling Pressure 1 = 0.035 0.056 0.056

Field Capacity 1 = 0.221 0.188 0.188

Wilting Point 1 = 0.193 0.041 0.041

Bulk Density 1 = 1357.5 1550.0 1550.0
140

Vertical Conductivity 1 = 2.63e-5 1.54e-5 1.54e-5

Thermal Conductivity 1 = 7.77 7.92 7.92 

Thermal Capacity 1 = 2.0e6 2.0e6 2.0e6

################ SOIL 2 ######################################################

Soil Description 2 = CASHIERS

Lateral Conductivity 2 = 1.75e-3 

Exponential Decrease 2 = 4.0

Maximum Infiltration 2 = 1.94e-6

Capillary Drive 2 = 0.17

Surface Albedo 2 = 0.16

Mannings n 2 = 0.011

Number of Soil Layers 2 = 3

Porosity 2 = 0.47 0.47 0.47

Pore Size Distribution 2 = 0.375 0.375 0.375

Bubbling Pressure 2 = 0.040 0.031 0.031

Field Capacity 2 = 0.183 0.146 0.138

Wilting Point 2 = 0.125 0.064 0.058

Bulk Density 2 = 1400.0 1400.0 1400.0

Vertical Conductivity 2 = 2.8e-5 2.8e-5 2.8e-5

Thermal Conductivity 2 = 7.88 7.92 7.92

Thermal Capacity 2 = 2.0e6 2.0e6 2.0e6

################ SOIL 3 #######################################################

Soil Description 3 = CHANDLER

Lateral Conductivity 3 = 1.75e-3

Exponential Decrease 3 = 4.0

Maximum Infiltration 3 = 1.94e-6

Capillary Drive 3 = 0.17

Surface Albedo 3 = 0.23

Mannings n 3 = 0.011

Number of Soil Layers 3 = 3

Porosity 3 = 0.47 0.47 0.47
141

Pore Size Distribution 3 = 0.375 0.369 0.369

Bubbling Pressure 3 = 0.040 0.138 0.138

Field Capacity 3 = 0.165 0.202 0.202

Wilting Point 3 = 0.098 0.061 0.061

Bulk Density 3 = 1400.0 1400.0 1400.0

Vertical Conductivity 3 = 2.8e-5 2.8e-5 2.8e-5

Thermal Conductivity 3 = 7.88 7.61 7.61

Thermal Capacity 3 = 2.0e6 2.0e6 2.0e6

################ SOIL 4 #######################################################

Soil Description 4 = CHESTNUT EDNEYVILLE

Lateral Conductivity 4 = 1.75e-3

Exponential Decrease 4 = 4.0 

Maximum Infiltration 4 = 1.94e-6

Capillary Drive 4 = 0.17

Surface Albedo 4 = 0.23

Mannings n 4 = 0.011

Number of Soil Layers 4 = 3

Porosity 4 = 0.44 0.44 0.43

Pore Size Distribution 4 = 0.381 0.362 0.379

Bubbling Pressure 4 = 0.046 0.139 0.038

Field Capacity 4 = 0.191 0.163 0.152

Wilting Point 4 = 0.122 0.092 0.070

Bulk Density 4 = 1486.3 1486.3 1500.0

Vertical Conductivity 4 = 2.8e-5 2.8e-5 2.8e-5

Thermal Conductivity 4 = 7.88 7.53 7.88

Thermal Capacity 4 = 2.0e6 2.0e6 2.0e6

################ SOIL 5 #######################################################

Soil Description 5 = CLEVELAND CHESTNUT

Lateral Conductivity 5 = 1.75e-3

Exponential Decrease 5 = 4.0

Maximum Infiltration 5 = 2.78e-6
142

Capillary Drive 5 = 0.17 

Surface Albedo 5 = 0.19

Mannings n 5 = 0.011

Number of Soil Layers 5 = 3

Porosity 5 = 0.47 0.47 0.47

Pore Size Distribution 5 = 0.367 0.356 0.356

Bubbling Pressure 5 = 0.028 0.167 0.167

Field Capacity 5 = 0.188 0.185 0.185

Wilting Point 5 = 0.113 0.075 0.075

Bulk Density 5 = 1406.3 1406.3 1406.3

Vertical Conductivity 5 = 2.3e-5 2.3e-5 2.3e-5

Thermal Conductivity 5 = 7.86 7.41 7.41

Thermal Capacity 5 = 2.0e6 2.0e6 2.0e6

################ SOIL 6 #######################################################

Soil Description 6 = CULLASAJA TUCKASEGEE

Lateral Conductivity 6 = 4.03e-4

Exponential Decrease 6 = 4.0

Maximum Infiltration 6 = 1.39e-6

Capillary Drive 6 = 0.22

Surface Albedo 6 = 0.16

Mannings n 6 = 0.011

Number of Soil Layers 6 = 3

Porosity 6 = 0.65 0.53 0.50

Pore Size Distribution 6 = 0.292 0.336 0.351

Bubbling Pressure 6 = 0.009 0.099 0.031

Field Capacity 6 = 0.214 0.147 0.102

Wilting Point 6 = 0.144 0.083 0.057

Bulk Density 6 = 937.5 1250.0 1325.0

Vertical Conductivity 6 = 2.8e-5 2.8e-5 2.8e-5

Thermal Conductivity 6 = 7.36 7.35 7.77

Thermal Capacity 6 = 2.0e6 2.0e6 2.0e6
143

################ SOIL 7 #######################################################

Soil Description 7 = EDNEYVILLE CHESTNUT

Lateral Conductivity 7 = 1.75e-3

Exponential Decrease 7 = 4.0

Maximum Infiltration 7 = 1.94e-6

Capillary Drive 7 = 0.17

Surface Albedo 7 = 0.23

Mannings n 7 = 0.010

Number of Soil Layers 7 = 3

Porosity 7 = 0.44 0.44 0.43

Pore Size Distribution 7 = 0.383 0.365 0.379

Bubbling Pressure 7 = 0.050 0.105 0.038

Field Capacity 7 = 0.185 0.160 0.153

Wilting Point 7 = 0.112 0.082 0.070

Bulk Density 7 = 1491.3 1491.3 1500.0

Vertical Conductivity 7 = 2.80e-5 2.80e-5 2.07e-5

Thermal Conductivity 7 = 7.88 7.63 7.88

Thermal Capacity 7 = 2.0e6 2.0e6 2.0e6

################ SOIL 8 #######################################################

Soil Description 8 = EVARD COWEE

Lateral Conductivity 8 = 1.75e-3

Exponential Decrease 8 = 4.0

Maximum Infiltration 8 = 1.94e-6

Capillary Drive 8 = 0.17

Surface Albedo 8 = 0.23

Mannings n 8 = 0.010

Number of Soil Layers 8 = 3

Porosity 8 = 0.46 0.47 0.51

Pore Size Distribution 8 = 0.368 0.272 0.304

Bubbling Pressure 8 = 0.198 0.175 0.033

Field Capacity 8 = 0.198 0.231 0.200
144

Wilting Point 8 = 0.114 0.143 0.116

Bulk Density 8 = 1441.3 1400.0 1300.0

Vertical Conductivity 8 = 2.8e-5 9.0e-6 8.3e-6

Thermal Conductivity 8 = 7.84 6.90 7.35

Thermal Capacity 8 = 2.0e6 2.0e6 2.0e6

################ SOIL 9 #######################################################

Soil Description 9 = FANNIN

Lateral Conductivity 9 = 1.75e-3

Exponential Decrease 9 = 4.0

Maximum Infiltration 9 = 1.94e-6

Capillary Drive 9 = 0.17

Surface Albedo 9 = 0.30

Mannings n 9 = 0.010

Number of Soil Layers 9 = 3

Porosity 9 = 0.47 0.47 0.47

Pore Size Distribution 9 = 0.350 0.283 0.350

Bubbling Pressure 9 = 0.034 0.884 0.174

Field Capacity 9 = 0.212 0.287 0.214

Wilting Point 9 = 0.126 0.152 0.076

Bulk Density 9 = 1400.0 1400.0 1400.0

Vertical Conductivity 9 = 2.8e-5 9.0e-6 9.0e-6

Thermal Conductivity 9 = 7.70 6.27 7.31

Thermal Capacity 9 = 2.0e6 2.0e6 2.0e6

################ SOIL 10 ######################################################

Soil Description 10 = PLOTT

Lateral Conductivity10 = 1.75e-3

Exponential Decrease 10 = 4.0

Maximum Infiltration 10 = 1.94e-6

Capillary Drive 10 = 0.17

Surface Albedo 10 = 0.16

Mannings n 10 = 0.010
145

Number of Soil Layers 10 = 3

Porosity 10 = 0.44 0.47 0.44

Pore Size Distribution 10 = 0.353 0.356 0.385

Bubbling Pressure 10 = 0.014 0.106 0.036

Field Capacity 10 = 0.213 0.156 0.110

Wilting Point 10 = 0.140 0.070 0.045

Bulk Density 10 = 1100.0 1300.0 1400.0

Vertical Conductivity 10 = 2.8e-5 2.8e-5 2.8e-5

Thermal Conductivity 10 = 7.92 7.53 8.00

Thermal Capacity 10 = 2.0e6 2.0e6 2.0e6

################ SOIL 11 ######################################################

Soil Description 11 = ROCK CLEVELAND

Lateral Conductivity 11 = 9.13e-4

Exponential Decrease 11 = 4.0

Maximum Infiltration 11 = 1.67e-6

Capillary Drive 11 = 0.09

Surface Albedo 11 = 0.16

Mannings n 11 = 0.010

Number of Soil Layers 11 = 3

Porosity 11 = 0.49 0.49 0.49

Pore Size Distribution 11 = 0.357 0.357 0.357

Bubbling Pressure 11 = 0.126 0.126 0.126

Field Capacity 11 = 0.185 0.185 0.185

Wilting Point 11 = 0.081 0.081 0.081

Bulk Density 11 = 1350.0 1350.0 1350.0

Vertical Conductivity 11 = 1.12e-5 1.12e-5 1.12e-5

Thermal Conductivity 11 = 7.49 7.49 7.49 

Thermal Capacity 11 = 2.0e6 2.0e6 2.0e6

################ SOIL 12 ######################################################

Soil Description 12 = SAUNOOK

Lateral Conductivity 12 = 8.03e-3
146

Exponential Decrease 12 = 4.0

Maximum Infiltration 12 = 3.06e-6

Capillary Drive 12 = 0.06

Surface Albedo 12 = 0.16

Mannings n 12 = 0.010

Number of Soil Layers 12 = 3

Porosity 12 = 0.58 0.51 0.47

Pore Size Distribution 12 = 0.364 0.272 0.386

Bubbling Pressure 12 = 0.191 0.175 0.038

Field Capacity 12 = 0.227 0.226 0.110

Wilting Point 12 = 0.125 0.118 0.052

Bulk Density 12 = 1475.0 1400.0 1475.0

Vertical Conductivity 12 = 2.8e-5 9.0e-5 2.8e-5

Thermal Conductivity 12 = 7.44 6.90 7.81

Thermal Capacity 12 = 2.0e6 2.0e6 2.0e6

################ SOIL 13 ######################################################

Soil Description 13 = TUCKASEGEE WHITESIDE

Lateral Conductivity 13 = 1.75e-3

Exponential Decrease 13 = 4.0

Maximum Infiltration 13 = 1.94e-6

Capillary Drive 13 = 0.17

Surface Albedo 13 = 0.20

Mannings n 13 = 0.010

Number of Soil Layers 13 = 3

Porosity 13 = 0.54 0.50 0.47

Pore Size Distribution 13 = 0.349 0.305 0.306

Bubbling Pressure 13 = 0.025 0.142 0.079

Field Capacity 13 = 0.221 0.209 0.181

Wilting Point 13 = 0.142 0.129 0.108

Bulk Density 13 = 1212.5 1325.0 1412.5

Vertical Conductivity 13 = 2.80e-5 1.85e-5 2.55e-5
147

Thermal Conductivity 13 = 7.76 7.00 7.26

Thermal Capacity 13 = 2.00e6 2.00e6 2.00e6

################ SOIL 14 ######################################################

Soil Description 14 = WAYAH

Lateral Conductivity 14 = 1.00e-3

Exponential Decrease 14 = 4.0

Maximum Infiltration 14 = 2.78e-6

Capillary Drive 14 = 0.11

Surface Albedo 14 = 0.09

Mannings n 14 = 0.010

Number of Soil Layers 14 = 3

Porosity 14 = 0.58 0.45 0.42

Pore Size Distribution 14 = 0.351 0.381 0.410

Bubbling Pressure 14 = 0.013 0.037 0.054

Field Capacity 14 = 0.235 0.141 0.113

Wilting Point 14 = 0.162 0.065 0.046

Bulk Density 14 = 1100.0 1450.0 1525.0

Vertical Conductivity 14 = 2.8e-5 2.8e-5 2.8e-5

Thermal Conductivity 14 = 7.90 7.92 8.07

Thermal Capacity 14 = 2.0e6 2.0e6 2.0e6

################ SOIL 15 ######################################################

Soil Description 15 = BLANK

Lateral Conductivity 15 = 9.13e-4

Exponential Decrease 15 = 4.0

Maximum Infiltration 15 = 1.67e-6

Capillary Drive 15 = 0.09

Surface Albedo 15 = 0.16

Mannings n 15 = 0.010

Number of Soil Layers 15 = 3

Porosity 15 = 0.49 0.49 0.49

Pore Size Distribution 15 = 0.357 0.357 0.357
148

Bubbling Pressure 15 = 0.126 0.126 0.126

Field Capacity 15 = 0.185 0.185 0.185

Wilting Point 15 = 0.081 0.081 0.081

Bulk Density 15 = 1350.0 1350.0 1350.0

Vertical Conductivity 15 = 1.12e-5 1.12e-5 1.12e-5

Thermal Conductivity 15 = 7.49 7.49 7.49 

Thermal Capacity 15 = 2.0e6 2.0e6 2.0e6

##############################################################################

# VEGETATION INFORMATION SECTION

##############################################################################

[VEGETATION]

Vegetation Map File = /../arcinfo/cowveg.bin

Number of Vegetation Types = 16

################ VEGETATION 1 ##############################################

Vegetation Description 1 = NORTHERN HARDWOOD VERY SHALLOW

Overstory Present 1 = TRUE 

Understory Present 1 = TRUE 

Fractional Coverage 1 = 0.85 

Trunk Space 1 = 0.60 

Aerodynamic Attenuation 1 = 1.5

Radiation Attenuation 1 = 0.86

Hemi Fract Coverage 1 =

Clumping Factor 1 =

Leaf Angle A 1 =

Leaf Angle B 1 =

Scattering Parameter 1 =

Max Snow Int Capacity 1 = 0.005

Mass Release Drip Ratio 1 = 0.2

Snow Interception Eff 1 = 0.3

Impervious Fraction 1 = 0.0

Height 1 = 16.8 3.0
149

Maximum Resistance 1 = 490.0 388.0

Minimum Resistance 1 = 410.0 312.0 

Moisture Threshold 1 = 0.139 0.087 

Vapor Pressure Deficit 1 = 4000.0 4000.0 

Rpc 1 = 0.3 0.3

Number of Root Zones 1 = 3 

Root Zone Depths 1 = 0.10 0.13 0.19

Overstory Root Fraction 1 = 0.50 0.30 0.20 

Understory Root Fraction 1 = 0.60 0.40 0.00 

Overstory Monthly LAI 1 = 0.52 0.52 0.87 2.11 4.51 6.77 7.17 6.51 5.04 2.17 0.87 0.52

Understory Monthly LAI 1 = 0.58 0.63 0.63 0.63 0.92 1.77 2.55 2.55 1.73 0.97 0.73 0.63

Overstory Monthly Alb 1 = 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23

Understory Monthly Alb 1 = 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21

################ VEGETATION 2 ##############################################

Vegetation Description 2 = MIXED DECIDUOUS VERY SHALLOW

Overstory Present 2 = TRUE 

Understory Present 2 = TRUE 

Fractional Coverage 2 = 0.85 

Trunk Space 2 = 0.76 

Aerodynamic Attenuation 2 = 1.5 

Radiation Attenuation 2 = 0.86 

Hemi Fract Coverage 2 =

Clumping Factor 2 =

Leaf Angle A 2 =

Leaf Angle B 2 =

Scattering Parameter 2 =

Max Snow Int Capacity 2 = 0.005 

Mass Release Drip Ratio 2 = 0.2 

Snow Interception Eff 2 = 0.3 

Impervious Fraction 2 = 0.0

Height 2 = 24.4 3.0 
150

Maximum Resistance 2 = 336.0 388.0 

Minimum Resistance 2 = 264.0 312.0 

Moisture Threshold 2 = 0.152 0.095 

Vapor Pressure Deficit 2 = 4000.0 4000.0 

Rpc 2 = 0.3 0.3

Number of Root Zones 2 = 3

Root Zone Depths 2 = 0.10 0.13 0.19

Overstory Root Fraction 2 = 0.40 0.30 0.30 

Understory Root Fraction 2 = 0.60 0.40 0.00

Overstory Monthly LAI 2 = 0.52 0.52 0.87 2.11 4.51 6.77 7.17 6.51 5.04 2.17 0.87 0.52

Understory Monthly LAI 2 = 0.58 0.63 0.63 0.63 0.92 1.77 2.55 2.55 1.73 0.97 0.73 0.63

Overstory Monthly Alb 2 = 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23

Understory Monthly Alb 2 = 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22

################ VEGETATION 3 ##############################################

Vegetation Description 3 = COVE HARDWOODS VERY SHALLOW

Overstory Present 3 = TRUE 

Understory Present 3 = TRUE 

Fractional Coverage 3 = 0.90 

Trunk Space 3 = 0.86 

Aerodynamic Attenuation 3 = 1.5

Radiation Attenuation 3 = 0.86

Hemi Fract Coverage 3 =

Clumping Factor 3 =

Leaf Angle A 3 =

Leaf Angle B 3 =

Scattering Parameter 3 =

Max Snow Int Capacity 3 = 0.005

Mass Release Drip Ratio 3 = 0.2

Snow Interception Eff 3 = 0.3

Impervious Fraction 3 = 0.0

Height 3 = 35.4 3.0
151

Maximum Resistance 3 = 375.0 388.0

Minimum Resistance 3 = 193.0 312.0 

Moisture Threshold 3 = 0.109 0.068 

Vapor Pressure Deficit 3 = 4000.0 4000.0 

Rpc 3 = 0.3 0.3

Number of Root Zones 3 = 3 

Root Zone Depths 3 = 0.10 0.13 0.19

Overstory Root Fraction 3 = 0.40 0.30 0.30 

Understory Root Fraction 3 = 0.60 0.40 0.00 

Overstory Monthly LAI 3 = 0.52 0.52 0.87 2.11 4.51 6.77 7.17 6.51 5.04 2.17 0.87 0.52

Understory Monthly LAI 3 = 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20

Overstory Monthly Alb 3 = 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23

Understory Monthly Alb 3 = 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21

################ VEGETATION 4 ##############################################

Vegetation Description 4 = XERIC OAK PINE VERY SHALLOW

Overstory Present 4 = TRUE 

Understory Present 4 = TRUE 

Fractional Coverage 4 = 0.80 

Trunk Space 4 = 0.74 

Aerodynamic Attenuation 4 = 1.5

Radiation Attenuation 4 = 0.86

Hemi Fract Coverage 4 =

Clumping Factor 4 =

Leaf Angle A 4 =

Leaf Angle B 4 =

Scattering Parameter 4 =

Max Snow Int Capacity 4 = 0.005

Mass Release Drip Ratio 4 = 0.2

Snow Interception Eff 4 = 0.4

Impervious Fraction 4 = 0.0

Height 4 = 16.8 2.0
152

Maximum Resistance 4 = 384.0 388.0

Minimum Resistance 4 = 256.0 312.0 

Moisture Threshold 4 = 0.151 0.094 

Vapor Pressure Deficit 4 = 4000.0 4000.0 

Rpc 4 = 0.3 0.3

Number of Root Zones 4 = 3 

Root Zone Depths 4 = 0.10 0.13 0.19

Overstory Root Fraction 4 = 0.40 0.30 0.30 

Understory Root Fraction 4 = 0.60 0.40 0.00 

Overstory Monthly LAI 4 = 0.52 0.52 0.87 2.11 4.51 6.77 7.17 6.51 5.04 2.17 0.87 0.52

Understory Monthly LAI 4 = 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20

Overstory Monthly Alb 4 = 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23

Understory Monthly Alb 4 = 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22

################ VEGETATION 5 ##############################################

Vegetation Description 5 = NORTHERN HARDWOOD SHALLOW

Overstory Present 5 = TRUE 

Understory Present 5 = TRUE 

Fractional Coverage 5 = 0.85 

Trunk Space 5 = 0.60 

Aerodynamic Attenuation 5 = 1.5

Radiation Attenuation 5 = 0.86

Hemi Fract Coverage 5 =

Clumping Factor 5 =

Leaf Angle A 5 =

Leaf Angle B 5 =

Scattering Parameter 5 =

Max Snow Int Capacity 5 = 0.005

Mass Release Drip Ratio 5 = 0.2

Snow Interception Eff 5 = 0.3

Impervious Fraction 5 = 0.0

Height 5 = 16.8 3.0
153

Maximum Resistance 5 = 490.0 388.0

Minimum Resistance 5 = 410.0 312.0 

Moisture Threshold 5 = 0.139 0.087 

Vapor Pressure Deficit5 = 4000.0 4000.0 

Rpc 5 = 0.3 0.3

Number of Root Zones 5 = 3 

Root Zone Depths 5 = 0.18 0.28 0.44

Overstory Root Fraction 5 = 0.60 0.30 0.10 

Understory Root Fraction 5 = 0.70 0.30 0.00 

Overstory Monthly LAI 5 = 0.52 0.52 0.87 2.11 4.51 6.77 7.17 6.51 5.04 2.17 0.87 0.52

Understory Monthly LAI 5 = 0.58 0.63 0.63 0.63 0.92 1.77 2.55 2.55 1.73 0.97 0.73 0.63

Overstory Monthly Alb 5 = 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23

Understory Monthly Alb 5 = 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21

################ VEGETATION 6 ##############################################

Vegetation Description 6 = NORTHERN HARDWOOD

Overstory Present 6 = TRUE 

Understory Present 6 = TRUE 

Fractional Coverage 6 = 0.85 

Trunk Space 6 = 0.60 

Aerodynamic Attenuation 6 = 1.5

Radiation Attenuation 6 = 0.86

Hemi Fract Coverage 6 =

Clumping Factor 6 =

Leaf Angle A 6 =

Leaf Angle B 6 =

Scattering Parameter 6 =

Max Snow Int Capacity 6 = 0.005

Mass Release Drip Ratio 6 = 0.2

Snow Interception Eff 6 = 0.3

Impervious Fraction 6 = 0.0

Height 6 = 16.8 3.0
154

Maximum Resistance 6 = 490.0 388.0

Minimum Resistance 6 = 410.0 312.0 

Moisture Threshold 6 = 0.139 0.087 

Vapor Pressure Deficit 6 = 4000.0 4000.0 

Rpc 6 = 0.3 0.3

Number of Root Zones 6 = 3 

Root Zone Depths 6 = 0.25 0.38 0.58

Overstory Root Fraction 6 = 0.60 0.20 0.20 

Understory Root Fraction 6 = 0.70 0.30 0.00 

Overstory Monthly LAI 6 = 0.52 0.52 0.87 2.11 4.51 6.77 7.17 6.51 5.04 2.17 0.87 0.52

Understory Monthly LAI 6 = 0.58 0.63 0.63 0.63 0.92 1.77 2.55 2.55 1.73 0.97 0.73 0.63

Overstory Monthly Alb 6 = 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23

Understory Monthly Alb 6 = 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21

################ VEGETATION 7 ##############################################

Vegetation Description 7 = MIXED DECIDUOUS

Overstory Present 7 = TRUE 

Understory Present 7 = TRUE 

Fractional Coverage 7 = 0.85 

Trunk Space 7 = 0.76 

Aerodynamic Attenuation 7 = 1.5 

Radiation Attenuation 7 = 0.86 

Hemi Fract Coverage 7 =

Clumping Factor 7 =

Leaf Angle A 7 =

Leaf Angle B 7 =

Scattering Parameter 7 =

Max Snow Int Capacity 7 = 0.005 

Mass Release Drip Ratio 7 = 0.2 

Snow Interception Eff 7 = 0.3 

Impervious Fraction 7 = 0.0

Height 7 = 24.4 3.0 
155

Maximum Resistance 7 = 375.0 388.0

Minimum Resistance 7 = 193.0 312.0

Moisture Threshold 7 = 0.152 0.095 

Vapor Pressure Deficit 7 = 4000.0 4000.0 

Rpc 7 = 0.3 0.3

Number of Root Zones 7 = 3

Root Zone Depths 7 = 0.25 0.38 0.58

Overstory Root Fraction 7 = 0.40 0.40 0.20 

Understory Root Fraction 7 = 0.70 0.30 0.00

Overstory Monthly LAI 7 = 0.52 0.52 0.87 2.11 4.51 6.77 7.17 6.51 5.04 2.17 0.87 0.52

Understory Monthly LAI 7 = 0.58 0.63 0.63 0.63 0.92 1.77 2.55 2.55 1.73 0.97 0.73 0.63

Overstory Monthly Alb 7 = 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23

Understory Monthly Alb 7 = 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22

################ VEGETATION 8 ##############################################

Vegetation Description 8 = XERIC OAK PINE

Overstory Present 8 = TRUE 

Understory Present 8 = TRUE 

Fractional Coverage 8 = 0.80 

Trunk Space 8 = 0.74 

Aerodynamic Attenuation 8 = 1.5

Radiation Attenuation 8 = 0.86

Hemi Fract Coverage 8 =

Clumping Factor 8 =

Leaf Angle A 8 =

Leaf Angle B 8 =

Scattering Parameter 8 =

Max Snow Int Capacity 8 = 0.005

Mass Release Drip Ratio 8 = 0.2

Snow Interception Eff 8 = 0.4

Impervious Fraction 8 = 0.0

Height 8 = 16.8 2.0
156

Maximum Resistance 8 = 384.0 388.0

Minimum Resistance 8 = 256.0 312.0

Moisture Threshold 8 = 0.151 0.094 

Vapor Pressure Deficit 8 = 4000.0 4000.0 

Rpc 8 = 0.3 0.3

Number of Root Zones 8 = 3 

Root Zone Depths 8 = 0.25 0.38 0.58

Overstory Root Fraction 8 = 0.40 0.30 0.30 

Understory Root Fraction 8 = 0.60 0.40 0.00 

Overstory Monthly LAI 8 = 0.52 0.52 0.87 2.11 4.51 6.77 7.17 6.51 5.04 2.17 0.87 0.52

Understory Monthly LAI 8 = 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20

Overstory Monthly Alb 8 = 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23

Understory Monthly Alb 8 = 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22

################ VEGETATION 9 ##############################################

Vegetation Description 9 = NORTHERN HARDWOOD DEEP

Overstory Present 9 = TRUE 

Understory Present 9 = TRUE 

Fractional Coverage 9 = 0.85 

Trunk Space 9 = 0.60 

Aerodynamic Attenuation 9 = 1.5

Radiation Attenuation 9 = 0.86

Hemi Fract Coverage 9 =

Clumping Factor 9 =

Leaf Angle A 9 =

Leaf Angle B 9 =

Scattering Parameter 9 =

Max Snow Int Capacity 9 = 0.005

Mass Release Drip Ratio 9 = 0.2

Snow Interception Eff 9 = 0.3

Impervious Fraction 9 = 0.0

Height 9 = 16.8 3.0
157

Maximum Resistance 9 = 490.0 388.0

Minimum Resistance 9 = 410.0 312.0 

Moisture Threshold 9 = 0.139 0.087 

Vapor Pressure Deficit 9 = 4000.0 4000.0 

Rpc 9 = 0.3 0.3

Number of Root Zones 9 = 3 

Root Zone Depths 9 = 0.35 0.58 0.79

Overstory Root Fraction 9 = 0.60 0.20 0.20 

Understory Root Fraction 9 = 0.90 0.10 0.00 

Overstory Monthly LAI 9 = 0.52 0.52 0.87 2.11 4.51 6.77 7.17 6.51 5.04 2.17 0.87 0.52

Understory Monthly LAI 9 = 0.58 0.63 0.63 0.63 0.92 1.77 2.55 2.55 1.73 0.97 0.73 0.63

Overstory Monthly Alb 9 = 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23

Understory Monthly Alb 9 = 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21

################ VEGETATION 10 #############################################

Vegetation Description 10 = MIXED DECIDUOUS DEEP

Overstory Present 10 = TRUE 

Understory Present 10 = TRUE 

Fractional Coverage 10 = 0.85 

Trunk Space 10 = 0.76 

Aerodynamic Attenuation 10 = 1.5 

Radiation Attenuation10 = 0.86 

Hemi Fract Coverage 10 =

Clumping Factor 10 =

Leaf Angle A 10 =

Leaf Angle B 10 =

Scattering Parameter 10 =

Max Snow Int Capacity 10 = 0.005 

Mass Release Drip Ratio 10 = 0.2 

Snow Interception Eff 10 = 0.3 

Impervious Fraction10 = 0.0

Height 10 = 24.4 3.0 
158

Maximum Resistance 10 = 336.0 388.0 

Minimum Resistance 10 = 264.0 312.0 

Moisture Threshold 10 = 0.152 0.095 

Vapor Pressure Deficit 10 = 4000.0 4000.0 

Rpc 10 = 0.3 0.3

Number of Root Zones 10 = 3

Root Zone Depths 10 = 0.35 0.58 0.79

Overstory Root Fraction 10 = 0.45 0.45 0.10 

Understory Root Fraction 10 = 0.90 0.10 0.00

Overstory Monthly LAI10 = 0.52 0.52 0.87 2.11 4.51 6.77 7.17 6.51 5.04 2.17 0.87 0.52

Understory Monthly LAI10 = 0.58 0.63 0.63 0.63 0.92 1.77 2.55 2.55 1.73 0.97 0.73 0.63

Overstory Monthly Alb10 = 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23

Understory Monthly Alb 10 = 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22

################ VEGETATION 11 #############################################

Vegetation Description 11 = COVE HARDWOODS DEEP

Overstory Present 11 = TRUE 

Understory Present 11 = TRUE 

Fractional Coverage 11 = 0.90 

Trunk Space 11 = 0.86 

Aerodynamic Attenuation 11 = 1.5

Radiation Attenuation 11 = 0.86

Hemi Fract Coverage 11 =

Clumping Factor 11 =

Leaf Angle A 11 =

Leaf Angle B 11 =

Scattering Parameter 11 =

Max Snow Int Capacity 11 = 0.005

Mass Release Drip Ratio 11 = 0.2

Snow Interception Eff 11 = 0.3

Impervious Fraction 11 = 0.0

Height 11 = 35.4 3.0
159

Maximum Resistance 11 = 375.0 388.0

Minimum Resistance 11 = 193.0 312.0

Moisture Threshold 11 = 0.109 0.068 

Vapor Pressure Deficit 11 = 4000.0 4000.0 

Rpc 11 = 0.3 0.3

Number of Root Zones 11 = 3 

Root Zone Depths 11 = 0.35 0.58 0.79

Overstory Root Fraction 11 = 0.40 0.30 0.30 

Understory Root Fraction 11 = 0.60 0.40 0.00 

Overstory Monthly LAI 11 = 0.52 0.52 0.87 2.11 4.51 6.77 7.17 6.51 5.04 2.17 0.87 0.52

Understory Monthly LAI 11 = 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20

Overstory Monthly Alb 11 = 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23

Understory Monthly Alb 11 = 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21

################ VEGETATION 12 #############################################

Vegetation Description 12 = XERIC OAK PINE DEEP

Overstory Present 12 = TRUE 

Understory Present 12 = TRUE 

Fractional Coverage 12 = 0.80 

Trunk Space 12 = 0.74 

Aerodynamic Attenuation 12 = 1.5

Radiation Attenuation 12 = 0.86

Hemi Fract Coverage 12 =

Clumping Factor12 =

Leaf Angle A12 =

Leaf Angle B12 =

Scattering Parameter 12 =

Max Snow Int Capacity12 = 0.005

Mass Release Drip Ratio 12 = 0.2

Snow Interception Eff12 = 0.4

Impervious Fraction12 = 0.0

Height 12 = 16.8 2.0
160

Maximum Resistance 12 = 384.0 388.0

Minimum Resistance 12 = 256.0 312.0

Moisture Threshold 12 = 0.151 0.094 

Vapor Pressure Deficit 12 = 4000.0 4000.0 

Rpc 12 = 0.3 0.3

Number of Root Zones 12 = 3 

Root Zone Depths 12 = 0.35 0.58 0.79

Overstory Root Fraction 12 = 0.40 0.30 0.30 

Understory Root Fraction 12 = 0.60 0.40 0.00 

Overstory Monthly LAI 12 = 0.52 0.52 0.87 2.11 4.51 6.77 7.17 6.51 5.04 2.17 0.87 0.52

Understory Monthly LAI 12 = 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20

Overstory Monthly Alb 12 = 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23

Understory Monthly Alb 12 = 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22

################ VEGETATION 13 ############################################

Vegetation Description 13 = NORTHERN HARDWOOD VERY DEEP

Overstory Present 13 = TRUE 

Understory Present 13 = TRUE 

Fractional Coverage 13 = 0.85 

Trunk Space 13 = 0.60 

Aerodynamic Attenuation 13 = 1.5

Radiation Attenuation 13 = 0.86

Hemi Fract Coverage 13 =

Clumping Factor 13 =

Leaf Angle A 13 =

Leaf Angle B 13 =

Scattering Parameter 13 =

Max Snow Int Capacity 13 = 0.005

Mass Release Drip Ratio 13 = 0.2

Snow Interception Eff 13 = 0.3

Impervious Fraction 13 = 0.0

Height 13 = 16.8 3.0
161

Maximum Resistance 13 = 490.0 388.0

Minimum Resistance 13 = 410.0 312.0 

Moisture Threshold 13 = 0.139 0.087 

Vapor Pressure Deficit 13 = 4000.0 4000.0 

Rpc 13 = 0.3 0.3

Number of Root Zones 13 = 3 

Root Zone Depths 13 = 0.20 0.52 1.30

Overstory Root Fraction 13 = 0.60 0.20 0.20 

Understory Root Fraction 13 = 0.70 0.30 0.00 

Overstory Monthly LAI 13 = 0.52 0.52 0.87 2.11 4.51 6.77 7.17 6.51 5.04 2.17 0.87 0.52

Understory Monthly LAI 13 = 0.58 0.63 0.63 0.63 0.92 1.77 2.55 2.55 1.73 0.97 0.73 0.63

Overstory Monthly Alb 13 = 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23

Understory Monthly Alb 13 = 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21

################ VEGETATION 14 #############################################

Vegetation Description 14 = MIXED DECIDUOUS VERY DEEP

Overstory Present 14 = TRUE 

Understory Present 14 = TRUE 

Fractional Coverage 14 = 0.85 

Trunk Space 14 = 0.76 

Aerodynamic Attenuation 14 = 1.5 

Radiation Attenuation 14 = 0.86 

Hemi Fract Coverage 14 =

Clumping Factor 14 =

Leaf Angle A 14 =

Leaf Angle B 14 =

Scattering Parameter 14 =

Max Snow Int Capacity 14 = 0.005 

Mass Release Drip Ratio 14 = 0.2 

Snow Interception Eff14 = 0.3 

Impervious Fraction 14 = 0.0

Height 14 = 24.4 3.0 
162

Maximum Resistance 14 = 336.0 388.0 

Minimum Resistance 14 = 264.0 312.0 

Moisture Threshold 14 = 0.152 0.095 

Vapor Pressure Deficit 14 = 4000.0 4000.0 

Rpc 14 = 0.3 0.3

Number of Root Zones 14 = 3

Root Zone Depths 14 = 0.20 0.52 1.30

Overstory Root Fraction 14 = 0.50 0.45 0.05 

Understory Root Fraction 14 = 0.70 0.30 0.00

Overstory Monthly LAI 14 = 0.52 0.52 0.87 2.11 4.51 6.77 7.17 6.51 5.04 2.17 0.87 0.52

Understory Monthly LAI 14 = 0.58 0.63 0.63 0.63 0.92 1.77 2.55 2.55 1.73 0.97 0.73 0.63

Overstory Monthly Alb 14 = 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23

Understory Monthly Alb 14 = 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22

################ VEGETATION 15 #############################################

Vegetation Description 15 = COVE HARDWOOD VERY DEEP

Overstory Present 15 = TRUE 

Understory Present 15 = TRUE 

Fractional Coverage 15 = 0.90 

Trunk Space 15 = 0.86 

Aerodynamic Attenuation 15 = 1.5

Radiation Attenuation 15 = 0.86

Hemi Fract Coverage 15 =

Clumping Factor 15 =

Leaf Angle A 15 =

Leaf Angle B 15 =

Scattering Parameter 15 =

Max Snow Int Capacity 15 = 0.005

Mass Release Drip Ratio 15 = 0.2

Snow Interception Eff 15 = 0.3

Impervious Fraction 15 = 0.0

Height 15 = 35.4 3.0
163

Maximum Resistance 15 = 375.0 388.0

Minimum Resistance 15 = 193.0 312.0 

Moisture Threshold 15 = 0.109 0.068 

Vapor Pressure Deficit 15 = 4000.0 4000.0 

Rpc 15 = 0.3 0.3

Number of Root Zones 15 = 3 

Root Zone Depths 15 = 0.20 0.52 1.30

Overstory Root Fraction 15 = 0.40 0.30 0.30 

Understory Root Fraction 15 = 0.60 0.40 0.00 

Overstory Monthly LAI 15 = 0.52 0.52 0.87 2.11 4.51 6.77 7.17 6.51 5.04 2.17 0.87 0.52

Understory Monthly LAI 15 = 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20

Overstory Monthly Alb 15 = 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23

Understory Monthly Alb 15 = 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21

################ VEGETATION 16 #############################################

Vegetation Description16 = XERIC OAK PINE VERY DEEP

Overstory Present 16 = TRUE 

Understory Present 16 = TRUE 

Fractional Coverage 16 = 0.80 

Trunk Space 16 = 0.74 

Aerodynamic Attenuation 16 = 1.5

Radiation Attenuation 16 = 0.86

Hemi Fract Coverage 16 =

Clumping Factor 16 =

Leaf Angle A 16 =

Leaf Angle B 16 =

Scattering Parameter 16 =

Max Snow Int Capacity16 = 0.005

Mass Release Drip Ratio 16 = 0.2

Snow Interception Eff 16 = 0.4

Impervious Fraction 16 = 0.0

Height 16 = 16.8 2.0
164

Maximum Resistance 16 = 384.0 388.0

Minimum Resistance 16 = 256.0 312.0

Moisture Threshold 16 = 0.151 0.094 

Vapor Pressure Deficit 16 = 4000.0 4000.0 

Rpc 16 = 0.3 0.3

Number of Root Zones 16 = 3 

Root Zone Depths 16 = 0.20 0.52 1.30

Overstory Root Fraction 16 = 0.40 0.30 0.30 

Understory Root Fraction 16 = 0.60 0.40 0.00 

Overstory Monthly LAI 16 = 0.52 0.52 0.87 2.11 4.51 6.77 7.17 6.51 5.04 2.17 0.87 0.52

Understory Monthly LAI 16 = 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20 2.20

Overstory Monthly Alb 16 = 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23

Understory Monthly Alb 16 = 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22

##############################################################################

# MODEL OUTPUT SECTION

##############################################################################

[OUTPUT] # Information what to output when

Output Directory = /../output/

Initial State Directory = /../modelstate/

################ PIXEL DUMPS ###############################################

Number of Output Pixels = 0

# For each pixel make a key-entry pair as indicated below, varying the

# number for the output pixel (1, .. , Number of Output Pixel)

North Coordinate 1 = 

East Coordinate 1 = 

Name 1 = 

################ MODEL STATE ############################################### 

Number of Model States = 0 # Number of model states to dump

# For each model state make a key-entry pair as indicated below, varying the

# number for the model state dump (1, .. , Number of Model States)

State Date 1 = # Time for model state dump
165

################ MODEL MAPS ###############################################

Number of Map Variables = 0 # Number of different variables for

 # which you want to output maps

# For each of the variables make a block like the one that follows, varying

# the number of the variable (n = 1, .. , Number of Map Variables)

Map Variable 1 = # ID of the variable to output

Map Layer 1 = # If the variable exists for a number

 # of layers, specify the layers here

 # with the top layer = 1

Number of Maps 1 = # Number of maps you would like to 

 # output for this variable

Map Date 1 1 =

################ MODEL IMAGES #############################################

Number of Image Variables = 0 # Number of variables for which you 

 # would like to output images

# For each of the variables make a block like the one that follows, varying

# the number of the variable (n = 1, .. , Number of Image Variables)

Image Variable 1 = # ID of the variable to output

Image Layer 1 = # If the variable exists for a number

# of layers, specify the layers here

# with the top layer = 1

Image Start 1 = # First timestep for which to output

 # an image

Image End 1 = # Last timestep for which to output

 # an image

Image Interval 1 = # Time interval between images (hours)

Image Upper Limit 1 = # All values in the output equal to or

 # greater than this limit will be set to 255

Image Lower Limit1 = # All values in the output equal to or

 # smaller than this limit will be set to 0
166

################ GRAPHIC IMAGES ############################################

Number of Graphics = 0 # Number of variables for which you 

 # would like to output images

Graphics ID 1 = # ID of the variable to output

# WARNING Use soil mositure layers with caution, to minimize calculations during

# redraw DHSVM does not check to make sure that the assigned soil layer exists

##############################################################################

# END OF INPUT FILE

##############################################################################
167

Class Files

Stream Class File

#Class Width Depth n Inf

1 1.5 1 0.1 0

2 2.4 1.2 0.1 0

3 1 0.3 0.1 0

4 2 0.5 0.1 0

5 2 1.5 0.1 0

6 5 4 0.1 0

7 7.5 7 0.1 0

8 3.5 0.8 0.1 0

9 6 1 0.1 0

Road Class File

#Class Width Depth n Inf

Road 

Type Erod Erod_ol d50 n surface width

1 0.50 0.50 0.02 0.01 CROWN 500 0.26 1.0 0.028 gravel 6.00

2 0.50 0.50 0.03 0.01 CROWN 600 0.35 9.0 9.000 paved 7.93

3 0.25 0.25 0.02 0.01 OUTSLP 300 0.26 0.1 0.024 dirt 4.00

4 0.25 0.25 0.03 0.01 INSLP 300 0.35 0.1 0.015 dirt 3.93

5 0.25 0.25 0.02 0.01 OUTSLP 300 0.26 0.1 0.030 dirt 4.00

6 0.25 0.25 0.03 0.01 OUTSLP 300 0.35 0.1 0.015 dirt 2.93

7 0.15 0.15 0.02 0.01 CROWN 300 0.26 0.1 0.020 dirt 2.00

8 0.25 0.25 0.03 0.01 CROWN 300 0.35 0.1 0.015 dirt 3.93

9 0.25 0.25 0.03 0.01 CROWN 200 0.26 0.5 0.015 dirt 3.93
168

Map Files

Stream Map File (page 1 of 19 shown)

###### This file has been automatically generated #####

###### EDIT WITH CARE!!! #####

# Generated: July 12, 2009 4:44 PM

# Command: ROADMAPFILE OUTCOVER STREAM.MAP.DAT EFFWIDTH EFFDEPTH

# Workspace: e:\coweeta\calibration4\arcinfo

# Segment Cut/Bank Cut Segment

# Col Row ID Length Height Width Aspect SINK?

# (m) (m) (m) (d) (optional)

#

 123 13 1 14.6551 1.9285 2.0000 329.0000

 124 13 1 2.9292 1.9285 2.0000 329.0000

 124 14 1 34.9258 1.9285 2.0000 329.0000

 124 15 1 20.4084 1.9285 2.0000 329.0000

 125 15 1 15.5381 1.9285 2.0000 324.0000

 125 16 1 31.5073 1.9285 2.0000 318.0000

 126 16 1 8.6657 1.9285 2.0000 318.0000

 126 17 1 38.5777 1.9285 2.0000 322.0000

 127 18 1 35.6961 1.9285 2.0000 326.0000

 127 19 1 19.9562 1.9285 2.0000 328.0000

 128 19 1 13.9715 1.9285 2.0000 340.0000

 107 20 5 7.7160 1.9285 2.0000 319.0000

 91 20 6 23.5922 1.7627 2.0000 296.0000

 108 20 5 20.6702 1.9285 2.0000 319.0000

 128 20 1 30.7705 1.9285 2.0000 350.0000

 91 21 6 7.0817 1.7627 2.0000 302.0000

 108 21 5 27.0441 1.9285 2.0000 323.0000

 92 21 6 36.3809 1.7627 2.0000 318.0000

 109 21 5 10.1751 1.9285 2.0000 327.0000

 128 21 1 30.0065 1.9285 2.0000 1.0000

 92 22 6 16.0706 1.7627 2.0000 337.0000

 93 22 6 16.7829 1.7627 2.0000 337.0000

 109 22 5 34.4940 1.9285 2.0000 330.0000

 128 22 1 30.0078 1.9285 2.0000 1.0000

 109 23 5 14.9308 1.9285 2.0000 330.0000

 110 23 5 19.8887 1.9285 2.0000 329.0000

 128 23 1 30.1624 1.9285 2.0000 1.0000

 93 23 6 41.6605 1.7627 2.0000 323.0000

 94 24 6 30.5085 1.7627 2.0000 278.0000

 95 24 6 30.4263 1.7627 2.0000 279.0000

 96 24 6 34.1578 1.7627 2.0000 307.0000

 128 24 1 30.3869 1.9285 2.0000 351.0000

 110 24 5 35.9527 1.9285 2.0000 327.0000

 96 25 6 5.1139 1.7627 2.0000 318.0000

 97 25 6 33.3964 1.7627 2.0000 322.0000

 111 25 5 34.4792 1.9285 2.0000 330.0000

 128 25 1 30.7391 1.9285 2.0000 348.0000

 97 26 6 17.0596 1.7627 2.0000 327.0000

 111 26 5 28.7117 1.9285 2.0000 333.0000

 98 26 6 18.8154 1.7627 2.0000 327.0000

 112 26 5 4.9282 1.9285 2.0000 333.0000

 128 26 1 31.4474 1.9285 2.0000 345.0000

 98 27 6 29.1268 1.7627 2.0000 358.0000

 112 27 5 33.2028 1.9285 2.0000 335.0000
169

Road Map File (page 1 of 31 shown)

###### This file has been automatically generated ########### 

EDIT WITH CARE!!! #####

# Generated: October 20, 2009 12:24 PM

# Command: ROADMAPFILE OUTCOVER TEMP.ROAD.MAP EFFWIDTH CUTDEPTH

# Workspace: f:\coweeta\density\layout1\l1d4\arcinfo

# Segment Cut/Bank Cut Segment

# Col Row ID Length Height Width Aspect SINK?

# (m) (m) (m) (d) (optional)

#

120 11 256 10.1447 0.5085 4.0000 276.0000

120 11 264 3.7645 0.7987 4.0000 215.0000 SINK

121 11 256 31.0568 0.7969 4.0000 285.0000

127 11 339 30.0198 0.3207 4.0000 89.0000

126 11 339 2.5856 0.3303 4.0000 90.0000 SINK

128 11 339 30.0525 0.2663 4.0000 90.0000

122 11 256 12.0637 0.7162 4.0000 285.0000 SINK

126 11 340 27.5189 0.3971 4.0000 86.0000

129 11 339 10.3822 0.2570 4.0000 106.0000

125 11 340 18.6542 0.2736 4.0000 84.0000 SINK

122 11 255 18.4868 0.4056 4.0000 277.0000 SINK

125 11 253 11.5250 0.2555 4.0000 84.0000

124 11 253 30.0740 0.2537 4.0000 86.0000

123 11 253 3.4072 0.2537 4.0000 86.0000 SINK

123 11 255 26.8335 0.2789 4.0000 278.0000

120 11 388 14.3106 0.3252 4.0000 340.0000

129 11 254 21.4707 0.2674 4.0000 291.0000

130 12 254 34.4212 0.3783 4.0000 312.0000 SINK

130 12 338 6.8443 0.3404 4.0000 132.0000

120 12 388 30.7592 0.2739 4.0000 3.0000

131 12 338 4.2343 0.2987 4.0000 133.0000

120 13 388 6.8332 0.2609 4.0000 34.0000 SINK

120 13 341 13.8854 0.7963 4.0000 245.0000 SINK

131 13 338 30.2049 0.3345 4.0000 113.0000 SINK

132 13 338 12.3346 0.4491 4.0000 100.0000

119 13 341 30.2417 0.9985 4.0000 263.0000

118 13 341 6.2240 1.0948 4.0000 262.0000

118 13 252 24.0304 0.5582 4.0000 263.0000 SINK

117 13 252 23.1870 0.4972 4.0000 267.0000

132 13 387 20.8102 0.3410 4.0000 332.0000

132 14 387 31.3166 0.3564 4.0000 342.0000 SINK

134 14 336 20.2454 0.2838 4.0000 96.0000

133 14 387 6.6117 0.2500 4.0000 286.0000

133 14 336 15.4477 0.3160 4.0000 82.0000

133 15 336 8.3519 0.2810 4.0000 90.0000 SINK

109 15 251 16.5961 0.2916 4.0000 238.0000 SINK

134 15 386 19.6046 0.3036 4.0000 326.0000 SINK

108 15 251 4.8051 0.2500 4.0000 243.0000

106 15 250 11.7550 0.9842 4.0000 333.0000

108 15 249 27.3457 0.2656 4.0000 253.0000 SINK

107 15 249 28.2688 0.3185 4.0000 266.0000

107 15 250 5.2400 0.9206 4.0000 340.0000 SINK

107 15 248 1.8160 0.4400 4.0000 261.0000

106 15 248 30.0942 0.4464 4.0000 267.0000

105 15 248 15.3182 0.3424 4.0000 263.0000
170

Network Files

Stream Network File (page 1 of 2 shown)

 1 1 0.25768 647.31626 4 7

 2 1 0.31969 207.13632 3 7

 3 1 0.32537 199.90082 3 25

 4 1 0.30003 331.49430 3 25

 5 1 0.23582 805.28637 4 17

 6 1 0.20089 1104.41145 4 17

 7 2 0.14451 657.47999 8 13

 8 1 0.64909 196.91922 3 15

 9 1 0.67707 144.27326 3 15

 10 13 0.00660 71.05152 9 13

 11 1 0.16422 464.00583 4 10

 12 1 0.27969 449.17942 4 14

 13 14 0.01557 210.46318 9 14

 14 15 0.06440 76.26553 9 -1 SAVE "SHOPE FORK"

 15 2 0.34807 232.84001 4 22

 16 1 0.49711 315.66472 3 22

 17 2 0.18763 465.36847 8 19

 18 12 0.05226 370.21288 9 10

 19 11 0.06342 134.86069 9 18

 20 1 0.39043 396.69564 3 28

 21 1 0.29876 676.97426 4 27

 22 3 0.18722 419.79591 4 27

 23 10 0.06628 434.53916 8 19

 24 1 0.32680 615.26849 3 23

 25 2 0.23499 661.30772 4 29

 26 7 0.06278 74.33549 8 29

 27 4 0.12715 199.16404 8 26

 28 9 0.10523 156.72024 8 23

 29 8 0.14003 445.85022 8 28

 30 5 0.07930 173.59322 8 18

 31 1 0.36688 323.50796 3 34

 32 6 0.12485 558.29894 8 26

 33 1 0.35493 945.43790 4 32

 34 2 0.29556 527.31824 4 36

 35 5 0.18937 78.69696 8 32

 36 3 0.17789 430.45972 8 35

 37 1 0.46177 458.17215 4 34

 38 1 0.27882 344.71952 3 30

 39 4 0.18432 574.27406 8 30

 40 4 0.16568 221.63043 8 35

 41 1 0.36104 107.93788 3 43

 42 3 0.12313 571.51288 4 39

 43 2 0.17952 295.99287 4 42

 44 2 0.23318 538.74566 8 36

 45 1 0.52476 314.60999 3 44

 46 2 0.15306 64.45001 4 42

 47 1 0.18932 100.88954 3 46

 48 1 0.33778 114.62122 3 43

 49 1 0.30880 157.47930 2 46

 50 3 0.19738 445.34883 8 40

 51 1 0.39590 427.73518 4 44

 52 2 0.31050 187.59652 4 50
171

Road Network File (page 1 of 7 shown)

 1 1 0.08872 27.63280 7 0

 2 1 0.28485 22.68496 5 0

 3 1 0.11443 148.47075 7 0

 4 1 0.03932 57.94310 1 0

 5 1 0.06476 50.73960 7 0

 6 1 0.30692 11.31425 7 0

 7 1 0.13197 3.05069 7 0

 8 1 0.05007 25.90982 3 0

 9 1 0.17708 76.35820 1 0

 10 1 0.04395 42.97772 5 0

 11 1 0.08797 42.49444 3 0

 12 1 0.16570 38.68707 3 0

 13 1 0.24967 50.90720 3 0

 14 1 0.15248 78.29739 7 0

 15 1 0.17776 56.59436 5 0

 16 1 0.06667 63.76398 7 0

 17 1 0.02697 46.41499 3 0

 18 1 0.10435 142.38448 3 0

 19 1 0.19893 427.29435 7 0

 20 1 0.03553 47.48942 7 0

 21 1 0.10926 65.82020 5 0

 22 1 0.06939 113.50407 5 0

 23 1 0.33235 57.91962 7 0

 24 1 0.14244 361.92204 3 0

 25 1 0.16479 291.97568 7 0

 26 1 0.01350 62.61656 7 0

 27 1 0.06064 54.64989 7 0

 28 1 0.00432 61.79592 7 0

 29 1 0.19388 2.72813 7 0

 30 1 0.17996 51.43987 7 0

 31 1 0.13149 84.81630 7 0

 32 1 0.08788 75.04872 1 0

 33 1 0.25603 37.17635 7 0

 34 1 0.21120 181.28082 7 0

 35 1 0.11400 63.16022 7 0

 36 1 0.15030 51.74957 7 0

 37 1 0.11068 226.82203 7 0

 38 1 0.13202 48.49419 7 0

 39 1 0.11745 289.06658 7 0

 40 1 0.09768 52.27735 3 0

 41 1 0.29326 52.47758 7 0

 42 1 0.04734 55.52155 7 0

 43 1 0.19090 0.51859 7 0

 44 1 0.17881 57.18247 7 0

 45 1 0.07965 53.76257 5 0

 46 1 0.15653 54.04236 7 0

 47 1 0.17548 52.83736 5 0

 48 1 0.15788 37.77828 7 0

 49 1 0.02688 174.45061 3 0

 50 1 0.06233 57.97162 7 0

 51 1 0.24227 101.48568 7 0

 52 1 0.30213 21.52539 7 0

 53 1 0.06455 108.56444 7 0

 54 1 0.05740 61.07908 5 0