EPA/600/R-09/116 | September 2009| www.epa.gov/athens

 

 Modeling the Impacts of Hydromodification on Water 

Quantity and Quality

 
ii

 

EPA/600/R-09/116 

 September 2009 

Modeling the Impacts of Hydromodification on Water 

Quantity and Quality 

Yusuf M. Mohamoud, Ph.D., P.E. 

U.S. EPA, Office of Research and Development 

National Exposure Research Laboratory 

Ecosystems Research Division 

Athens, Georgia 

Anne C. Sigleo, Ph.D. (Retired) 

U.S. EPA, Office of Research and Development 

National Health and Environmental Effects Laboratory 

Western Ecology Division 

Newport, Oregon 

Rajbir S. Parmar, Ph.D.

U.S. EPA, Office of Research and Development 

National Exposure Research Laboratory 

Ecosystems Research Division 

Athens, Georgia 

Ecosystems Research Division, Athens, GA 30605 

National Exposure Research Laboratory 

Office of Research and Development
iii

NOTICE 

This document has been reviewed in accordance with the U.S Environmental Protection 

Agency Policy and Approved for Publication. Mention of trade names or commercial 

products does not constitute an endorsement or recommendation for use. 
iv

ACKNOWLEDGEMENT 

We are grateful to Ben Scales and Lloyd VanGordon of the Oregon Water Resources 

Department for providing streamflow data from the Chitwood gaging station, to K. 

Ramage for the Nashville weather data, to the Dynamac team for help with sample 

collections. We are also grateful to Bob Swank, Caroline Stevens, Michael Cyterski, and 

Roger Burke for reviewing Part 2 of this report and Jim Carleton for reviewing the entire 

report. 
v

Table of Contents 

List of Figures vii

List of Tables viii

PART 1. METHOD DEVELOPMENT 1

MODELING THE IMPACTS OF HYDROMODIFICATION ON WATER QUANTITY AND 

QUALITY 1

1 INTRODUCTION 3

2 HYDROMODIFICATION: SOURCE OF INTEGRATED STRESSORS 7

3 HYDROMODIFICATION CATEGORIES AND MODELING CHALLENGES 9

3.1 Urbanization 9

3.2 Water Withdrawals and Interbasin Transfers 10

3.3 Channel Modification and Streambank Erosion 11

3.4 Flow Regulation by Dams and Impoundments 12

3.5 Climate Change 13

4 THE NEED FOR HYDROMODIFICATION MODELING FRAMEWORK 15

4.1 Essential Components of a Hydromodification Modeling Framework 18

4.2 BASINS Modeling Framework: A Background 20

4.3 Hydromodification Decision-making Example 24

4.4 Linking Integrated Stressors to Aquatic Ecosystem Impacts 27

4.5 Model Application Example 27

PART 2: APPLICATION EXAMPLE 29

5. FORECASTING URBANIZATION IMPACTS ON WATER QUANTITY AND QUALITY: 

CASE STUDY OF THE YAQUINA WATERSHED, OREGON, USA 29

5.1 Introduction 31

5.1.2 Watershed Description 34

5.2 Methods 38

5.2.1 Model Setup and Input Data 39

5.2.2 Model Calibration and Validation 41

5.2.3 Model Performance Evaluation Criteria 42

5.2.4 Future Build-out Scenario Development 43

5.2.5 Forecasting Flow, Nitrate, and TSS Alterations 45

5.2.6. Linking Stressors and to Impacts 46

5.3 Results and Discussion 48

5.3.1 Model Calibration and Validation Results 48
vi

5.3.2 Forecasting Stressor Levels for the Build-out Scenarios 54

5.4 Determining Ecologically Relevant Urbanization-Induced Stressor Indicators 60

6 CONCLUSION 65

7 REFERENCES 67
vii

List of Figures 

Figure 1. Top 10 sources of impairment in assessed rivers and streams in the United 

States (Source: USEPA, 2009) ........................................................................................... 8 

Figure 2. Hydromodification activities and watershed management questions that 

resource managers must answer before hydromodification projects are implemented. ... 17 

Figure 3. Components of a hydromodification modeling framework .............................. 19 

Figure 4. Matching framework modeling capabilities with hydromodification drivers, 

integrated stressors, and integrated effects. ...................................................................... 22 

Figure 5.. Decision-making framework for watersheds receiving hydromodification 

projects.............................................................................................................................. 26 

Figure 6. Location of the study watershed........................................................................ 35 

Figure 7. Distribution of long-term monthly precipitation and temperature for Yaquina 

Watershed ......................................................................................................................... 36 

Figure 8. Model calibration results: streamflow (a and b), TSS (c), and nitrate (d) 

calibrations........................................................................................................................ 53 

Figure 9. Hydrologic changes simulated for baseline, scenario15, scenario45, and 

scenario85: (a) daily streamflows, (b) annual water balances (c) flow duration curves at 

low flow conditions, and (d) flow duration curves at high flow conditions..................... 56 

Figure 10. HSPF simulated streamflow, TSS, and nitrate concentrations for baseline, 

scenario15, scenario45, and scenario85............................................................................ 59 

Figure 11. HSPF simulated 30-year mean water temperature (a), water surface width (b), 

and flow velocity (c) for baseline, scenario15, scenario45, and scenario85..................... 64 
viii

List of Tables 

Table 1. Baseline and hypothetical future land use scenarios .......................................... 37 

Table 2. Model calibration and validation results............................................................. 49 

Table 3. List of parameter values adjusted during calibration.......................................... 50 

Table 4. Indicators of hydrologic alteration (IHA) calculated from streamflow simulated 

for the baseline and the three build-out scenarios for a 30-year period............................ 61 
1

PART 1. METHOD DEVELOPMENT 

MODELING THE IMPACTS OF HYDROMODIFICATION ON 

WATER QUANTITY AND QUALITY 

Abstract: Hydromodification activities are driven by human population growth and 

resource extraction and consumption including urbanization, agriculture, forestry, 

mining, water withdrawal, climate change, and flow regulation by dams and 

impoundments. These anthropogenic activities alter natural flow regimes and lead to 

reduced downstream water quantity and degraded water quality. Recently, USEPA and 

states recognized hydromodification as a stressor and a leading source of water quality 

impairment in streams and rivers. Hydromodification-induced stressors include chemical 

pollutants, pathogens, nutrients, suspended solids, and flow and habitat alteration. The 

diverse and interacting nature of hydromodification-induced stressors has made Total 

Maximum Daily Load (TMDL) development for impaired streams and rivers a major 

regulatory challenge. Because hydromodification integrates stressors that have combined, 

cumulative, and synergistic effects on water quantity and quality, TMDL modeling 

approaches are not well-suited for simulating the impacts of hydromodification. 

Modeling integrated stressors requires the development and application of predictive 

models and innovative modeling approaches, such as the Better Assessment Science 

Integrating Point and Nonpoint Sources (BASINS) modeling framework. Although 

BASINS has been in use for the past 10 years, there has been limited modeling guidance 

on its applications for complex environmental problems, such as modeling impacts of 

hydromodification on water quantity and quality. This report consists of two parts: Part 1 

presents the development of a BASINS-based methodology that is applicable to modeling 
2

hydromodification. Part 2 is a case study of how the proposed modeling approach can 

forecast the impacts of urbanization on water quantity and quality. 
3

1 INTRODUCTION 

Rivers and river-fed lakes are valuable natural resources providing about 61 percent 

of the nation’s drinking water as well as serving riverine habitats to an estimated 40 

percent of the fish species and about half of the birds in North America (Whiting, 2002). 

In addition, rivers store flood waters for groundwater recharge and provide recreational 

amenities, such as boating, fishing, and swimming (Whiting, 2002) along with 

navigation, irrigation, power generation, and waste load transport and assimilation (Poff 

et al., 1997). These valuable ecosystem services are threatened by hydromodification 

projects such as land resources development for agriculture, energy, mining, forestry, 

transportation, and residential housing; and water resources development for irrigation, 

municipal water supply, and flood control. For instance, urban development and efforts to 

use rivers for transportation, water supply, flood control, irrigation, and power generation 

often alter flow regimes thus threatening the sustainability of the ecosystem services that 

rivers and river-fed lakes provide (Poff et al., 1997). 

Poff et al. (1997) emphasized the importance of managing the impacts of 

anthropogenic watershed disturbances and urged scientists to develop management 

protocols that accommodate economic uses while protecting ecosystem functions. 

Naiman et al. (2002) stated that forecasting the impacts of changing water regimes is a 

fundamental challenge for the scientific community. A way to address these challenges 

and achieve sustainable management of land and water resources at the watershed level is 

to develop integrative modeling approaches that consider stressors as a system with 

positive and negative feedback loops, synergies, and interferences (Zimmerman et al. 

2009). 
4

A number of factors have delayed the development of integrative modeling 

approaches and their application to complex hydromodification problems. First, crossdisciplinary, professional boundaries, and different views among hydrologists, engineers, 

planners, ecologists, and biologists make it difficult to apply a holistic approach to 

evaluating the impacts of hydromodification. Second, watershed boundaries, which are 

basic environmental management units, do not usually coincide with local government 

boundaries where hydromodification decisions are made. Third, water quantity and water 

quality are mostly regulated separately by various federal and state agencies, even though 

water quantity strongly influences water quality. Fourth, land use planning, which has 

strong influence on water quantity and quality, is regulated at the city or county boundary 

level even though the impacts of land use change on water quantity and quality transcend 

local boundaries. 

Recently, a number of studies have used integrative modeling approaches to assess 

water allocation options (Letcher et al, 2004), develop hydrologic, agronomic, and 

economic models for river basin management (Cai et. al. 2003), develop multi-objective 

evolutionary algorithms for managing ecosystem services (Bekele and Nicklow, 2005), 

and integrate water allocation and water quality models (Azevedo et al. 2000). Clearly, 

managing the impacts of hydromodification on water quantity, habitat, and water quality 

requires modeling approaches that forecast the stressor levels associated with alternative 

future scenarios (Mohamoud, 2008). Two recent federal government reports on water 

research and data across federal agencies have emphasized the need for comprehensive 

watershed management approaches (NRC, 2004; General Accounting Office, 2004). In 
5

this report, integrative modeling approaches are defined as those capable of simulating 

multiple stressors and their impacts on water quantity and quality.

The dominant hydromodification–induced stressors include flow alteration, water 

quality degradation, and habitat alteration. At present, EPA's traditional water-quality 

criteria and standards program do not address the effects of habitat alteration and flow 

regulation on aquatic life (Jackson and Davis, 1994). Flow alteration due to 

hydromodification alters water quantity which strongly influences water quality, yet 

regulatory programs usually do not examine how water quantity changes affect water 

quality. An area with strong relevance to water quantity and quality is the TMDL 

program because pollutant load calculations are flow-dependent and are calculated as the 

product of concentration and streamflow. Flow alterations due to hydromodification may 

introduce errors in TMDL allocation estimates because relationships of water quantity 

and quality are not usually examined when developing TMDL plans for impaired water 

bodies for watersheds impacted by hydromodification. 

The proposed modeling approach will allow resource managers to answer some key 

resource development and management questions that have strong influence on 

sustainable management of land and water resources. For example, how can managers 

balance water availability and demand when watershed conditions are continually 

changing? How can managers jointly forecast flow alteration and water quality 

degradation due to hydromodification? How can resource managers identify allowable 

levels of hydromodification using “what-if” scenarios? What ecological risks are 

associated with different hydromodification categories and levels? What management 

options are available to resource managers to mitigate the impacts of hydromodification? 
6

Answering these questions would require innovative modeling approaches that can 

forecast hydromodification-induced stressors, such as flow alteration, water quality 

degradation, and habitat alteration; and evaluate their impacts on the health of aquatic 

ecosystems. Given the complex and the interacting nature of hydromodification-induced 

stressors, a lack of holistic or integrative modeling approaches has been the reason for 

our inability to manage land use, water quantity and quality, and health of aquatic 

ecosystems jointly and sustainably. The objective of this study is to show how BASINS 

can be used to simulate and forecast the impacts of hydromodification on water quantity 

and quality and discuss ways to assess the cumulative impacts that hydromodificationinduced stressors may have on the health of aquatic ecosystems. 
7

2 HYDROMODIFICATION: SOURCE OF INTEGRATED 

STRESSORS 

Hydromodification describes land and water resources development activities that are 

driven by human population growth and resource consumption. These activities often 

produce direct or indirect changes to water quantity and quality. USEPA (1993) defines 

hydromodification as the “alteration of the hydrologic characteristics of coastal and noncoastal waters, which in turn could cause degradation of water resources.” According to 

USEPA (2007), hydromodification consists of channelization and channel modification, 

construction of dams and impoundments, and streambank and shoreline erosion. In the 

literature, hydromodification has also been narrowly defined as hydrograph modification. 

USEPA (2007) presents hydromodification as a leading source of water quality 

impairment for streams, lakes, estuaries, aquifers, and other water bodies in the United 

States. The National Water Quality Inventory Report to Congress (2004) that was 

released in 2009 identified agricultural nonpoint source (NPS) pollution as the primary 

(48%) water quality impairment of assessed streams and rivers followed by 

hydromodification (20%), and habitat alteration (14%) (USEPA, 2009). Figure 1 shows 

the top ten sources of impairment in assessed rivers and streams in the United States. 

They are closely linked to human activities that alter the physical structure or the natural 

function of a water body. Water quality degradations caused by hydromodification 

include increased sedimentation, higher water temperature, lower dissolved oxygen, 

degradation of aquatic habitat structure, and loss of fish and other aquatic populations. 
8

Figure 1. Top 10 sources of impairment in assessed rivers and streams in the United States (Source: 

USEPA, 2009) 

Adding to USEPA’s narrow definition of hydromodification, we define 

hydromodification more broadly to include urbanization, climate change, water 

withdrawals, and inter-basin transfers. Our intention is to use the term for a wide range of 

anthropogenic watershed disturbances that alter natural flow regimes and degrade water 

quality. Addressing the impacts of integrated stressors is more effective than addressing 

stressors individually one at a time because integrated stressors have integrated effects 

that are not independent. Furthermore, many watersheds are impaired by integrated 

stressors and resource managers are unable to identify the cause of impairment. Despite 

being a major source of impairment in assessed water bodies, modeling approaches that 

consider the impacts of hydromodification on water quantity and quality are not 

available. The following sections present selected hydromodification categories and 

introduce our proposed approach to modeling each category. Note that water quantity, 

flow, streamflow, and water availability are used interchangeably throughout this report.
9

3 HYDROMODIFICATION CATEGORIES AND MODELING 

CHALLENGES 

3.1 Urbanization 

As the total impervious area in a watershed increases, peak flow rates and flow 

volumes increase (Arnold and Gibbons, 1996; Tang et al., 2005) and baseflow and 

groundwater recharge decrease (Rose and Peters, 2001). Such alterations of natural flow 

regimes affect the distribution of surface water and baseflow components of streamflow. 

Hydrologic imbalances caused by urbanization have serious consequences for water 

availability. In many parts of the world, incidence of water supply shortages due to land 

use change have been reported for communities in water-rich areas (Okun, 2002). In the 

United States, frequent, severe droughts and urbanization-induced water shortages have 

been observed in some parts of New England, in the southeast (Atlanta), and in areas in 

the west coast (Seattle and Portland) (Sehlke, 2004). 

Urbanization not only alters natural flow regimes, it also degrades water quality. For 

sub-basins in east of Melbourne, Australia, Hatt et al. (2004) found that water quality 

loads were correlated with imperviousness and drainage connections. Other studies have 

linked urbanization and associated imperviousness to increased sediment, bacteria, and 

nutrient loads (Schueler, 1995; Gove et al., 2001; Mallin et al. 2001 

Our approach to forecasting the impacts of urbanization on water quantity and quality 

is presented in Part 2 of this report. 

 
10

3.2 Water Withdrawals and Interbasin Transfers 

Water withdrawn directly from rivers and streams alters the natural flow regime. 

Withdrawals and interbasin transfers are water management options that allow managers 

to balance water demand and water availability by issuing water withdrawal permits. 

However, without knowing the available water levels under future development 

scenarios, issuing water withdrawal permits would not balance water demand and water 

availability. It may, however, lead to violation of downstream environmental flow targets 

and minimum flows required by the Endangered Species Act (ESA). Managing the 

effects of water withdrawals and interbasin transfers not only requires the use of 

hydrologic and water quality models to forecast scenario-specific water quantity and 

quality changes, but also water allocation models to ensure that sufficient quantity and 

quality of water is available to all water users. In general, water allocations are simulated 

with state-specified models that have no hydrologic and water quality simulation 

capabilities. These models include the Water Rights Analysis Package (WARP) used in 

Texas (Wurbs, 2005) and MODSIM: River Basins Management Decision Support 

System used in Colorado (Labadie, 2005). 

Our approach to modeling the impacts of water withdrawals on streamflow uses 

HSPF to simulate water quantity and quality projections for alternative future scenarios. 

Under each scenario, the effects of water withdrawal levels on downstream flow targets 

and water quality impaired streams are evaluated, and the scenario that most closely 

matches water availability with water demand and minimizes the overall impacts of 

hydromodification is selected. 
11

3.3 Channel Modification and Streambank Erosion 

Streams and rivers, which are important habitats to many aquatic organisms, are 

affected by channel modification. Channel modifications include direct channel 

operations such as dredging, widening, and straightening, or indirect modifications 

caused by flow alteration. Today, many streams and rivers are degraded by 

hydromodification-induced stressors such as flow alteration, unsanitary discharge, and 

channelization projects (Leblanc et al.,1997). A number of investigators have examined 

the ecological impacts of hydromodification by assessing the biotic integrity of streams 

using multi-metric indices, such as the indices of biotic integrity (IBI) (Karr 1991; 

Fitzpatrick et al. (2004) or bioindicator approaches (Adams, 2005). To link urbanizationinduced stressors to stream habitat degradation, a number of investigators have examined 

relationships between total impervious area (TIA) and biological integrity (Morse et al., 

2003; Booth et al. 2004). Other investigators have related hydrologic metrics (estimated 

from simulated or observed streamflow data) to biological integrity (Claussen and Biggs, 

1997; Booth et al., 2004; Konrad et al., 2005). For example, Richter et al. (1996) 

identified flow magnitude, frequency, duration, timing, and rate of change as ecologically 

relevant hydrological indicators. 

Our approach to modeling channel modification and streambank erosion consists of 

three steps. First, modelers use HSPF to simulate changes in water quantity and quality 

due to hydromodification. Second, modelers develop hydrologic indicators (e.g., Q2) 

from simulated long-term streamflow time series. In general, Q2 is defined as the flow 

that corresponds with the two-year recurrence interval. Third, modelers evaluate the 

impacts of simulated stressors such as increased flow velocity, shear stress, and 
12

streambed and streambank erosion on stream habitat quality. Specifically, our approach 

to modeling the impacts of channel modification on stream habitat is to use simulated 

hydrologic, hydraulic, and water quality metrics that link hydromodification-induced 

stressors to stream habitat quality. In addition, hydromodification impacts on stream 

habitat can be evaluated by linking HSPF to a habitat suitability model such as 

PHABSIM (Milhous et al. 1984). 

3.4 Flow Regulation by Dams and Impoundments 

Trends in urbanization, population growth, and increased water demand and usage 

have led to extensive damming of rivers and streams. In the United States, more than 

85% of the inland waterways are now artificially controlled (NRC, 1992), including 

nearly 1 million km of rivers that are affected by impoundments (Echeverria et al. 1989). 

Dams and impoundments control flooding, generate electric power, and provide 

irrigation, navigation, recreation, and municipal water needs, but in some cases, their 

benefits to society are outweighed by their adverse environmental impacts. Dams and 

impoundments cause flow alteration, and inundate wetlands and riparian areas. They also 

tend to reduce or eliminate downstream flooding, block fish migration routes, increase 

turbidity and sedimentation during construction, and retain sediment after construction. 

Flow regulation by dams and impoundments profoundly alters natural flow regimes, 

which results in degraded river ecosystems (Ward and Stanford, 1995; Ligon et al. 1995; 

Power et al., 1996). Fishery managers have long argued that maintaining the predevelopment natural flow regime is essential to the composition and structure of native 

riverine ecosystems and associated biodiversity (Richter et al. 2000). To mitigate the 

ecological impacts of flow regulation, several mitigation measures have been proposed. 
13

These measures include: establishing minimum flow releases (Colby, 1990), offering 

controlled flushing flows (Collier et al 1997), or maintaining natural flow regimes to flow 

levels observed before the regulation project (Stanford et al., 1996; Poff et al. 1997). 

Our approach to modeling the impacts of flow regulation on water quantity and 

quality is to set up a flow regulation scenario by placing a hypothetical dam and 

impoundment at different locations in the watershed. The first step is to build an HSPF 

hydraulic function table known as the FTable with the desired elevation-area-storagedischarge relationships to represent the dam and the impoundment. To evaluate the effect 

of flow regulation on natural flow regimes, modelers can compare pre-regulation and 

post-regulation observed and simulated streamflows for each future scenario. Based on 

these comparisons, they select scenarios that maintain the pre-development hydrologic 

condition of the watershed. Resource managers can also examine levels of stressors 

associated with different flow regulation scenarios to select a scenario that minimizes the 

integrated effects of flow regulation on water quantity, quality, and the health of aquatic 

ecosystems. 

3.5 Climate Change 

General circulation models (GCMs) are used to make future climate change 

projections that account for increasing levels of CO2 in the atmosphere. Mean global 

surface temperature is expected to increase in the range of 1.5 to 5.8 ο

C by 2100 

(Houghton et al., 2001). For the United States, mean temperature and precipitation 

projections are about 4.5 ο

c increase in temperature and 7.5 % increase in precipitation. In 

general, GCM model projections are used to estimate changes in precipitation and 

temperature. Four commonly used GCM models are: The Goddard Institute of Space 
14

Studies (GISS), Geophysical Fluid Dynamics Laboratory (GFDL), the United Kingdom 

Meteorological Office (UKMO), and the Oregon State University (OSU) models. Climate 

change projections vary across regions, but most regions are projected to have increased 

frequency of intense storms, increased soil erosion and sedimentation, and increased sealevel. Consequently, climate change can be expected to have serious effects on water 

quantity, water quality, and the health of aquatic ecosystems. 

Our approach to modeling climate change impacts on water quantity and quality is to 

use GCM temperature and precipitation projections. Model users can select scenarios that 

are similar to mean GCM projections for the United States or scenarios that differ from 

mean GCM projections. For example, a modeler could select a scenario that has 10% 

increase in annual precipitation, with a 10% increase in the frequency of high 

precipitation events, and increased return frequency of storms of particular magnitudes. 

For temperature increases, the modeler could select a corresponding scenario with two 

degree increases during the cool season months, and four degree increases during the 

warm months. 

Note that climate change is only one driver of hydromodification, and it can be 

addressed separately or concurrently with urbanization, flow regulation, and channel 

modification. For more discussion on generating climate change scenarios for the HSPF 

model in BASINS, interested readers should refer to the Climate Assessment Tool (CAT) 

manual, which can be accessed at 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=203460. 
15

4 THE NEED FOR HYDROMODIFICATION MODELING 

FRAMEWORK 

The proposed hydromodification modeling approach makes the watershed the unit of 

management and regulatory focus. This is based on the concept that every land area 

belongs to a watershed and that the integrated effects of all hydromodification activities 

that occur in a watershed have a measurable impact at the watershed outlet or at some 

downstream point of interest. Watershed-based approaches also address the complexities 

in modeling hydromodification impacts and allow resource managers to forecast 

scenario-specific hydromodification stressor levels, evaluate their impacts on water 

quantity and quality, and develop management plans that mitigate the impacts of 

hydromodification-induced stressors. The proposed approach is applicable to restoration 

efforts, but is more suitable for protecting least developed watersheds or pristine 

watersheds that require judicious management and development protocols. To guide 

management decisions that protect or restore watersheds, we present some key 

hydromodification related questions that resource managers must answer before 

hydromodification project plans are implemented in a watershed (Figure 2). 

The watershed-based concept presented in Figure 2 resembles the TMDL process in 

the sense that TMDLs are intended to reduce pollutant loads to levels that can be 

assimilated by a water body in order to meet EPA’s water quality standards. In general, 

the TMDL process has a very limited scope because it addresses water quality only, and 

not water quantity. In addition, TMDLs often target a single stressor and a single water 

body. Unlike TMDL approaches, the approach described in this report assesses a 

watershed’s capacity to assimilate not only pollutant loads, but also all 
16

hydromodification-related stresses. Managing hydromodification impacts on water 

quantity and quality at the watershed level has many benefits but it requires cooperation 

among stakeholders. Cooperation among stakeholders is essential because watershed 

boundaries often cross multiple jurisdictions. The New Jersey Department of 

Environmental Protection (NJDEP) recognized that pollutant loads, water withdrawals, 

and land use required new approaches that could not be addressed by regulatory 

programs alone (EPA, 2004). Regulatory programs such as TMDLs have limited scope 

and cannot be used to address integrated stressors such as flow alteration, habitat 

alteration, and water quality degradation. 

 The proposed approach was developed in recognition of the need for comprehensive 

watershed management approaches that link economic development to ecosystem 

sustainability. For instance, using the proposed modeling approach, resource managers 

can identify where to institute land preservation, and where to place hydromodification 

projects in a watershed (Figure 2). 
17

Figure 2. Hydromodification activities and watershed management questions that resource managers 

must answer before hydromodification projects are implemented. 

Dam and Impoundment 

Water Withdrawal 

Channel 

Modification

Urbanization 

 

What levels of flow alteration and water 

quality degradation or loss of ecosystem 

services due to hydromodification can be 

assimilated by a watershed? 

Can land use planners, resource managers, and 

regulatory staff agree on allowable stressor levels before 

implementing hydromodification project plans in a 

watershed? 

What management 

options are 

available to 

mitigate the 

impacts of 

hydromodification 

projects at the 

watershed scale? 
18

4.1 Essential Components of a Hydromodification Modeling Framework 

Figure 3 illustrates a conceptual hydromodification modeling framework that consists 

of four components. The first component characterizes present and future 

hydromodification levels using “what-if” scenarios that have different percentages of 

urban land and percent impervious cover, different distributions of land use across a 

watershed, number and location of dams and water impoundments, and water withdrawal 

and inter-basin transfer amounts. 

The second component identifies and quantifies scenario-specific levels of 

hydromodification-induced stressors, i.e. flow and habitat alterations and water quality 

degradation, and assesses how integrated stressors may affect the health of aquatic 

ecosystems. 

The third component identifies and selects best management practices (BMPs) that 

mitigate the impacts of hydromodification on water quantity and quality. Mitigating the 

impacts of hydromodification requires a watershed-scale hydromodification management 

plan that may include of BMPs for controlling runoff at the source (i.e. those that 

enhance infiltration). Local governments in coastal California and other western states 

developed hydromodification management plans to control nonpoint source pollution 

from urban watersheds. 

The fourth component uses adaptive management to iteratively evaluate how model 

simulation results agree with data obtained after future scenarios are implemented in the 

watershed. By continuously updating the model and comparing scenario-specific model 

forecasts with post-implementation observed data, resource managers can determine 
19

when stressor levels reach unsustainable levels and modelers can validate model 

simulations and minimize predictive uncertainties.. 

Figure 3. Components of a hydromodification modeling framework 

4. Match model simulation 

results and postdevelopment observed data 

(adaptive management) 

3. Manage the impacts of 

hydromodification-induced stressors 

(hydromodifcation management plan) 

2.Quantify hydromodificationinduced stressors 

1. Characterize present 

condition (baseline) and project 

future scenarios 
20

4.2 BASINS Modeling Framework: A Background 

The Clean Water Act was established to restore and maintain the chemical, physical, 

and biological integrity of the nation’s waters. In the 1970s and 1980s, EPA successfully 

regulated point source pollution through the National Pollutant Discharge Elimination 

System (NPDES) permit program. However, managing nonpoint source pollution from 

terrestrial ecosystems has proven to be more difficult. To manage this problem 

efficiently, EPA adopted an approach that makes the watershed the unit of regulatory 

focus (USEPA, 1998; Whittemore and Beebe, 2000). Adoption of the watershed 

approach as the management and regulatory unit has created a need for watershed models 

and modeling approaches. Today, watershed models are widely used to manage nonpoint 

source pollution, particularly through the development of TMDL plans for water quality 

impaired water bodies. To manage land and water resources in a sustainable manner, 

resource managers need modeling approaches that also forecast the impact of 

hydromodification on water quantity and quality. In 2007, EPA released a guidance 

document on managing nonpoint source pollution caused by hydromodification (USEPA, 

2007). This document listed a number of models applicable to hydromodification 

modeling, but did not present specific hydromodification modeling guidance. HSPF and 

AQUATOX, which are part of the BASINS modeling framework, were among the 

models listed. 

In 1996, USEPA released BASINS ver. 1.0 modeling and decision support system 

(EPA, 1996a). BASINS integrates Geographic Information System (GIS) tools, national 

databases (elevation, hydrography, meteorological, land use, and soil), assessment tools 

(target, assess, and data mining), data management and graphing programs (WDMUtil 
21

and GenScen), models (HSPF, SWAT, PLOAD, and AQUATOX), and analysis tools 

including the Climate Analysis Tool (CAT). 

Figure 4 matches BASINS’ modeling capabilities with hydromodification drivers, 

stressors, and impacts. As shown in Figure 4, using GIS tools and databases, BASINS 

provides access to information about soils, topography, and land use and land cover of a 

watershed. In addition, BASINS provides information on hydromodification projects 

already present in the watershed and their distribution in the landscape. Resource 

managers can use BASINS to identify priority areas for preservation and development. 

As stated earlier, hydromodification activities are driven by population growth and the 

accompanying need for resource extraction and consumption. The watershed 

management goal is to minimize the impacts of economic development projects on water 

quantity and quality by reducing hydromodification-induced stressors to levels that can 

be assimilated by the watershed or mitigated through BMPs. Here, we briefly discuss 

some of the models available in BASINS that apply to modeling hydromodification. 
22

BASINS Modeling Framework HSPF AQUATOX 

(Databases, GIS tools, and Models) (CAT and BMPs) 

 

Figure 4. Matching framework modeling capabilities with hydromodification drivers, integrated 

stressors, and integrated effects. 

HSPF is EPA’s premier watershed hydrology and pollutant transport model 

(Whittemore and Beebe, 2000) and is the core watershed model in BASINS. HSPF 

simulates hydrologic processes and water quality for a range of types of user-defined 

scenarios. Because of its extensive water quality simulation capabilities, HSPF is 

frequently used for TMDL plan development for impaired water bodies. In addition to 

water quality and hydrologic process simulations, HSPF also serves as a water allocation 

model. It has been used to assess the effects of land use change on streamflow (Brun and 

Band, 2000; McColl and Agett, 2007), effects of water withdrawal on streamflow 

(Zariello and Reis, 2000), and effects of climate change on water quantity (Middlekoop et 

al. 2000; Goncu and Albek, 2009). Furthermore, HSPF has been used for developing 

hydrological and biological indicators of flow alteration in the Puget Sound low land 

streams (Cassin et al. 2005). Although HSPF is the core model, BASINS has other 

models and tools that are applicable to modeling hydromodification. Models and tools 

that are relevant to the objectives of this study are AQUATOX, climate analysis tool 

(CAT), and HSPF’s BMP Toolkit. Whittemore and Beebe (2000) reviewed BASINS and 

Hydromodification 

(Drivers) 

Channel modification 

Climate change 

Resource extraction 

Dam and impoundment 

Urbanization 

Water withdrawal 

Integrated Stressors 

(Consequences) 

Flow alteration 

Habitat alteration 

(streambank erosion) 

Water quality degradation 

Integrated Effects 

(Impacts) 

Maintain healthy ecosystems to 

achieve ecological sustainability 
23

noted that its success would depend on efforts to share technical experiences and 

solutions to problems. 

The BASINS Climate Analysis Tool generates climate change scenarios for HSPF. 

CAT users need information on climate change projections for the region of interest 

within the United States. Based on Global Circulation Models (GCMs) projections of the 

region of interest, CAT users can develop climate change scenarios by changing 

temperature, precipitation, and evapotarnspiration data. Note that CAT is not available in 

early versions of BASINS, but is available in BASINS 4. 

Representing best management practices and simulating their effectiveness in 

controlling nonpoint source pollution is a desirable feature in regulatory models. In 

general, models employed for TMDL plan development are not well-suited for plan 

implementation because many models lack BMP representation and simulation 

capabilities; Whittemore and Beebe (2002) emphasized these aspects of the HSPF model. 

According to Endreny (2002), HSPF has no explicit simulation of storm sewer networks 

and lacks the capability to simulate common water quality BMPs. A recently developed 

HSPF BMP Toolkit represents some types of BMPs, and it can be used to evaluate their 

effectiveness in controlling runoff and pollutant loads. HSPF BMP related limitations 

have been addressed and HSPF is now one of the few publicly available watershed 

models with capabilities to represent and simulate vegetative, storage, and infiltration 

BMPs. Currently, the BMP Toolkit is a web-based tool and is not part of BASINS, but 

modelers can easily work with the BMP toolkit to modify their model input data. . 

AQUATOX is an ecological effects model that can be used to evaluate past, present, 

and future direct and indirect effects from various stressors, including nutrients, organic 
24

wastes, sediments, toxic organic chemicals, flow and water temperature in aquatic 

ecosystems (Park et al. 2008). AQUATOX uses HSPF output as input, including results 

of simulations that include hydromodification-influenced stressors. Based on the 

magnitudes of stressors generated by HSPF in different scenarios, AQUATOX can 

simulate how hydromodification-induced stressors may affect biota in aquatic 

ecosystems. 

4.3 Hydromodification Decision-making Example 

Figure 5 presents a flow chart describing how to apply the BASINS modeling 

framework to watersheds that are likely to experience hydromodification. As an example, 

we selected two hydromodification categories: urbanization and flow regulation by dams 

and impoundments. For each, we present a list of hydromodification-induced stressors 

and scenarios. As shown in Figure 5, the first step is to characterize the current 

hydromodification levels, then determine if additional hydromodification projects are 

planned for the watershed. First, modelers use HSPF to simulate streamflow and water 

quality; modelers then calibrate and validate the model under present conditions. After 

successful calibration and validation, model users then simulate flow and water quality 

for the future scenarios. Comparisons of flow and water quality for prehydromodification (current watershed condition) and post-hydromodification determines 

flow alteration and water quality degradation levels associated with each scenario. 

 In the second step, after simulating flow and water quality for baseline and future 

scenarios, model users set up HSPF as a water allocation model to allocate and track how 

different hydromodification categories and scenarios affect water use and availability. 

HSPF uses a schematic network of nodes and links to track changes in inflows, storages, 
25

and outflows over time. To minimize flow alteration impacts, resource managers may 

seek to identify scenarios that closely match pre-hydromodification water availability 

levels. 

In the third step, model users simulate how alternative future may alter stream 

habitats and water quality. Models in this case are used to evaluate how simulated 

changes due to hydromodification are likely to affect existing TMDL allocations and/or 

the health of aquatic ecosystems. As an example, modelers can employ the AQUATOX 

model to explore whole-system stressor-response relationships. Modelers can also extract 

hydrologic and water quality indicators or metrics from simulated streamflow and water 

quality data, then use these metrics to explore system stressor-response relationships. 

Based on simulated scenario comparisons, resource managers can select 

hydromodification management plans that minimize undesirable impacts on aquatic 

resources. Finally, to address model predictive uncertainties, resource managers may 

employ an adaptive management approach to compare and update model simulations 

based upon data collected after initiation of hydromodification projects. 
26

 Yes No No 

 

 No 

Figure 5. Decision-making framework for watersheds receiving hydromodification projects

Develop a baseline model for the watershe

Are there any planned hydromodification projects? 

Construction of Dams and Impoundments 

Future build-out scenarios

• Set hypothetical 

“what-if” build-out scenarios with different total impervious area (TIA) levels 

Stressor forecasting (HSPF Model)

• Forecast flow alteration Forecast water availability 

• Forecast water quality degradation Forecast water demand 

• Forecast habitat alteration 

Hydromodification Management Plan

Simulate HSPF with/without BMPs, select suitable best management practices (BMPs) using HSPF BMP Toolkit 

Management Plan EvaluationAre flow alterations and water quality degradation controlled? Are water availability and demand balanced? Are ecosystem services maintained at prepoject levels?

What type of hydromodification projects are planned for the watershed? 

Characterize the current hydromodification level of the watershed 

Future scenarios

Build a new dam/reservoir Test different water withdrawal levels Test different reservoir operations and water allocation options

Adaptive Management 

Select different scenadecision-making pro

Link HSPF to AQUATOX Model 

Assess the ecological risks associated with alternative future scenarios 

Urbanization 
 

27

4.4 Linking Integrated Stressors to Aquatic Ecosystem Impacts 

As stated earlier, AQUATOX is an ecological risk assessment model, which is part of 

the BASINS modeling framework. AQUATOX simulates the effects of multiple 

simultaneous stressors that may include nutrients, organic toxicants, temperature, 

suspended sediment, and flow, on the health of aquatic ecosystems. Potential applications 

of the AQUATOX model include estimation of fish recovery after pollutant loads are 

reduced, ecosystem responses to invasive species, effects of mitigation measures, and 

changes in ecosystem services. By forecasting hydromodification-induced stressors using 

HSPF as the load-generating model, and assessing the resulting ecological impacts of the 

simulated stressors with AQUATOX, resource managers can evaluate the biological 

ramifications of hydromodification projects before the projects are implemented. 

Although linking integrated stressors to aquatic ecosystem impacts is beyond the scope of 

the current study, our suggested approach to establishing stressor-response relationships 

is to link HSPF to the AQUATOX model. 

4.5 Model Application Example 

In Part 2 of this report, we present a case study demonstrating how BASINS can be 

used to forecast urbanization-induced stressors, namely flow alteration and water quality 

degradation under various urban development scenarios. We include in this example a 

brief discussion of how urbanization-induced stressors affect stream channel erosion. We 

also attempt to link urbanization-induced stressors to impacts on the health of aquatic 

ecosystems using hydrological indicators or metrics. As a test watershed, we selected a 
 

28

medium-sized headwater watershed, which is a tributary of the Yaquina Watershed in 

Oregon, USA. 
 

29

PART 2: APPLICATION EXAMPLE 

5. FORECASTING URBANIZATION IMPACTS ON WATER 

QUANTITY AND QUALITY: CASE STUDY OF THE YAQUINA 

WATERSHED, OREGON, USA 

Abstract: Protecting ecosystem services provided by headwater watersheds increasingly 

is becoming an important land and water management objective. To allow resource 

managers to minimize future watershed degradation and eliminate the need for 

restoration, we present a method to forecast potential impacts of urbanization before 

urban development plans are implemented in a watershed. The method establishes both a 

baseline that represents the pre-development condition, and build-out scenarios that 

represent future development. In this report, we arbitrarily selected three future build-out 

scenarios that represent 15, 45, and 85 percent total impervious area (TIA), where 

impervious cover was used as a landscape indicator to represent the effects of 

development. We employed the Hydrological Simulation Program – FORTRAN (HSPF) 

watershed model to simulate streamflow, total suspended solids (TSS), and nitrate in the 

baseline scenario and for the three build-out scenarios. Comparisons of simulated results 

in the baseline scenario to those forecasted for the three build-out scenarios show 

increased peak flows and decreased baseflows for the build-out scenarios. Nitrate 

simulation results show increased nitrate concentrations for the three build-out scenarios. 

Suspended sediment concentrations were forecasted to increase with increasing TIA from 

15 to 45 percent, but the 85 percent TIA scenario resulted in lower TSS concentrations. 

Our forecasts also indicate that mean channel wetted width, flow depth, and flow velocity 

decrease with increasing percent TIA. The proposed method serves as an exploratory 
 

30

approach in which resource managers and land use planners forecast urbanizationinduced stressors before urban development plans are implemented in the watershed. The 

impacts of these stressors on aquatic ecosystems and services can then be simulated. 
 

31

5.1 Introduction 

For nearby receiving water bodies, urbanization has been found to lead to altered 

hydrologic regimes, deteriorated water quality, losses of habitat and biodiversity, beach 

closings, fishery declines, and fish consumption advisories (Nixon, 1995; Richardson, 

1997; Rabalais et al., 1996; Boesch at al., 2001; Elofson et al., 2003; Niemi et al., 2004). 

Many of the adverse ecological impacts of urbanization are closely linked to increases in 

impervious area (Paul and Meyer, 2001; Allan, 2004). The percent of total impervious 

area (TIA) is that portion of a watershed area covered by built surfaces such as paved 

roads, parking lots, sidewalks, driveways, and rooftops. Percent TIA in a watershed has 

been used as a gross indicator of urbanization and associated impacts on streamflow (Ng 

and Marsalek, 1989), water quality (Schueler, 1994; Arnold and Gibbons, 1996; Conway, 

2007), and the health of aquatic ecosystems (Klein, 1979; Morse et al., 2003). 

 In general, as the percent TIA increases, the fraction of precipitation that is able to 

infiltrate the soil and recharge groundwater decreases, and the fraction that becomes 

overland runoff increases (Schueler, 1994). Without major mitigation measures, 

urbanization causes reduced baseflow and declining water tables, and increased flood 

flow magnitude and frequency (Arnold and Gibbons, 1996; Paul and Meyer, 2001), with 

resulting changes in stream channel morphology and in-stream suspended sediment from 

the increased scouring, and consequent stream habitat degradation (Booth, 1990; Paul 

and Meyer, 2001). 

Urbanization also causes increased nutrient (Creed and Band, 1998; Wernick et al., 1998; 

Donner et al., 2004), suspended sediment (Nelson and Booth, 2002; Wotling and 

Bouvier, 2002) and other pollutant inputs (e.g. metals, pesticides, road salts) from 
 

32

terrestrial areas to receiving water bodies, along with increased water temperature (Niemi 

et al., 2004). Such urbanization-induced changes cause stresses to aquatic organisms that 

impair the overall health of aquatic ecosystems. Increased nitrate export to coastal waters 

leads to estuarine eutrophication, with changes in biotic community structure and 

diversity (Turner and Rabalais, 1991; Vitousek et al., 1997; Boesch et al., 2001). 

Compton et al. (2003) found positive correlation between nitrate concentrations and 

broadleaf cover dominated by red alder (alnus rubra) in forested watersheds in the 

Oregon Coast Range. Increased sediment inputs cause reduced light penetration and may 

directly impede the reproductive processes of aquatic organisms (Wotling and Bouvier, 

2002; Nelson and Booth, 2002). 

Land use planners and decision-makers need guidance on how to forecast the impact 

of urban development on hydrologic processes (e.g., increased flood frequency and loss 

of groundwater recharge), on nutrient and sediment concentrations, and on ecological 

integrity (e.g., loss of biodiversity and wildlife habitat). Much of what is known about the 

effects of urbanization-induced stressors on aquatic ecosystems was obtained through 

field observations (Morse et al., 2003; Konrad and Booth, 2005). Monitoring urban 

streams alone cannot, however capture the full impact of urbanization because impacts 

observed in urban streams are often moderated by existing best management practices 

(BMPs). In addition, monitoring urban streams usually does not account for impacts that 

have occurred during construction but before mitigation measures were fully 

implemented. Stream monitoring is a reactive approach that offers limited insights to land 

use planners and decision-makers. The ability to forecast future conditions reliably in 
 

33

urban streams before implementation of development plans obviously would be more 

helpful. 

Many investigators acknowledge the interdependence of structure and function of 

stream ecosystems and natural flow variability (Poff and Allan, 1995, Power et al., 1996, 

Richards et al., 1997). For example, flow alterations often result in changes to the 

ecological organization of aquatic and riparian systems that lead to changes in physiology 

and behavior of individuals, populations, community composition, and food web 

structure (Poff et al., 1997; Bunn and Arthington, 2002; Poff et al., 2006). Building on 

such established relationships, hydrologic metrics with strong ecological relevance have 

been developed for natural flows (Poff and Ward, 1989; Poff et al., 1997) and for altered 

flows (Richter et al., 1996; Olden and Poff, 2003). However, approaches that link 

urbanization-induced stressors explicitly to specific ecological effects, such as loss of 

aquatic life or physical habitat, are lacking. 

A proactive approach is needed for forecasting urbanization-induced stressor levels 

and establishing links between them and the resulting condition of aquatic ecosystems. 

An alternative to monitoring streams to assess the ecological impacts of urbanization is to 

forecast urbanization impacts with hypothetical build-out scenarios or actual 

development plans. Using hypothetical build-out scenarios, resource managers employ 

comprehensive watershed models to forecast urbanization-induced stressor levels before 

implementation of urban development plans and any associated mitigation measures in a 

watershed. We propose herein using the Hydrological Simulation Program - FORTRAN 

(HSPF) (Bicknell et al., 2001) to simulate streamflow, nitrate, TSS, and channel 

morphology changes. 
 

34

The objective of this study is to present a modeling approach to forecast potential 

impacts of alternative urban development scenarios by using watershed total percent 

impervious area as an indicator of urban development impacts on hydrology, flow width, 

flow depth, flow velocity, nitrate concentration, and TSS concentration. 

5.1.2 Watershed Description 

The study watershed is a headwater tributary located upstream of the United States 

Geological Survey’s gaging station near Chitwood, Oregon (44o

 39' 29" N, 123o 50' 15" 

W) (Figure 6). The study watershed is characterized by wet winters, relatively dry 

summers, and mild temperatures that are typical of the Coastal Range areas of Oregon. 

The long-term average annual precipitation for Newport is 1767 mm. Precipitation 

rainfall comes from moist air masses from the Pacific Ocean. A large portion of the 

precipitation occurs in November, December, and January. Conversely, the warmest and 

driest months are July, August, and September (Figure 7). 
 

35

Figure 6. Location of the study watershed. 
 

36

0

50

100

150

200

250

300

350

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

Precipitation (mm)

0

2

4

6

8

10

12

14

16

Temperature ( o C)

Average Monthly Precipitation

Average Monthly Temperature

Figure 7. Distribution of long-term monthly precipitation and temperature for Yaquina 

Watershed 

The watershed has a drainage area of 184 km2

. Presently, the study watershed is 97 

percent covered by evergreen forest, about two percent covered by agricultural land, and 

about one percent covered by medium density urban residential land (Table 1). The 

dominant land cover is coniferous forest because the original complex forest has been 

replaced by single species silviculture and opportunistic pioneer species. In riparian areas 

along streams, disturbed sites are frequently occupied by pioneer broad-leaf trees (e.g., 

red alder) (Ohmann and Gregory, 2002). 

The dominant soil of the study watershed is Bohannon soil series found on the 

western slopes of the Oregon Coast Range areas. The soils of the watershed are generally 
 

37

well drained with poorly developed horizons (Ohmann and Gregory, 2002 ). Geological 

formations of the watershed contain massively to thinly-bedded tuffaceous siltstones, 

sandstones, basalt breccias, and augite-rich tuff that provide a constant source of silica 

and suspended sediment (Snavely and Wagner, 1963). 

Table 1. Baseline and hypothetical future land use scenarios 

_______________________________________________________________________ 

Watershed Area (Hectares) by Land use Category Impervious Area at 

Build-out (%)

Scenarios Forest (ha) Agriculture (ha) Urban (ha) 

__________________________________________________________________ 

Baseline 18057 261 25 --- 

Scenario15 12946 261 5136 15 

Scenario45 7443 261 10639 45 

Scenario85 106 261 17976 85 

________________________________________________________________________ 
 

38

5.2 Methods 

The method proposed to forecast urbanization-induced stressors consists of the following 

steps. More detailed discussion of each step is given in subsequent parts of the methods 

section. 

Step 1: Characterize the current land use, hydrology, and water quality of the watershed 

(baseline analysis and characterization). 

Step 2: Simulate flow, TSS, and nitrate under baseline conditions. 

Step 3: Calibrate and validate flow, TSS concentration, and nitrate concentration 

simulated by the model for the baseline condition using observed flow, TSS, and nitrate 

data. 

Step 4: Project future development scenarios using actual land use plans if available or 

hypothetical build-out scenarios if plans are not available for the watershed. 

Step 5: Simulate flow, TSS, and nitrate for selected future build-out scenarios using the 

model parameters calibrated under the baseline condition. 

Step 6: Develop urbanization-induced stressor indicators from simulated flow, TSS, and 

nitrate data for each scenario. To establish cause and effect relationships between 

forecasted urbanization-induced stressors and their ecological impacts, we used 

hydrological alteration indicators. For each scenario, we estimated 7Q10 (7-day average 

low-flow with a recurrence interval of 10 years), bankfull flow, baseflow index (total 

volume of base flow divided by the total volume of runoff for a period) (Wahl and Wahl, 

1995), and indicators of hydrological alteration (IHA) from scenario-specific simulated 

streamflow. Indicators of hydrologic alteration (IHA) captures five flow characteristics 
 

39

that are related to biological integrity: magnitude, duration, frequency, timing, and rate of 

change (Richter et al., 1996). Other flow-related forecasted physical habitat indicators 

include shear stress, depth, and flow velocity. In addition, we estimated nitrate and TSS 

concentrations for each scenario. Calculating indicators from forecasted streamflow, TSS, 

and nitrate offers to land use planners an exploratory approach where potential ecological 

impacts of urbanization can be predicted before urban development plans are 

implemented in a watershed. 

Step 7: Establish linkages between urbanization-induced stressors and ecological impacts 

(hydrologic, hydrologic, and water quality indicators) 

8. Link HSPF to AQUATOX model and assess the ecological impacts of build-out 

scenarios 

5.2.1 Model Setup and Input Data 

HSPF is a calibrated-parameter model which uses observed data for its calibration 

and validation. For this study, observed streamflow, TSS, and nitrate data were obtained 

at the Chitwood gaging station (USGS 14306030). Daily streamflow data from 1972 to 

1991 were obtained from a USGS website (USGS, 2007), supplemented by data from the 

Oregon Water Resources Department that is available at 

http://www.wrd.state.or.us/OWRD/SW/streamflow_midco.shtml. Daily observed 

streamflow averages 7.1 m3

/sec, although it can vary from 0.06 m3/sec during late 

summer low flow conditions, to 186 m3

/sec at peak flow conditions in late fall and winter 

(October to March). 

Nitrate data were collected weekly from December 1999 through December 2001, and 

monthly from January 1999 to December 2002, at the USGS stream gaging station near 
 

40

Chitwood. Water samples collected for dissolved nutrient measurements were filtered 

(Cole Parmer 142 mm nylon filter 0.45 μm membranes) and frozen for later analysis by 

the Marine Science Institute (MSI), University of California, Santa Barbara, CA using a 

Lachat QwickChem 8000 Autoanalyzer for simultaneous determination of nitrite and 

nitrate + nitrite. Analytical information, including blank procedures, sample replicate 

results, and other quality assurance details are available at www.msi.ucsb.edu/Analab. 

Because nitrite generally comprised less than 2% of the nitrogen species at these sites, we 

treated nitrate + nitrite as nitrate. Daily TSS was obtained from a USGS website (USGS, 

2006); it was available for the period from October 1, 1972 through September 30, 1974. 

The Hydrologic Simulation Program – FORTRAN (HSPF), part of U.S.EPA’s Better 

Assessment Science Integrating Point and Nonpoint Sources (BASINS) (USEPA, 2001), 

was selected to simulate streamflow, TSS, and nitrate for a baseline or reference scenario 

and build-out scenarios. HSPF is a comprehensive, conceptual, continuous simulation 

model that simulates flow and water quality constituents originating from pervious and 

impervious land surfaces, in streams and well-mixed impoundments (Bicknell, et al., 

2001). HSPF uses the PERLND and the IMPLND application modules to represent 

processes that occur on pervious and impervious land surfaces, respectively, and it uses 

the RCHRES module to represent processes that occur in water bodies. HSPF uses 

BASINS to extract soil, land cover, and geomorphological data and parameter values 

from geographic information databases, such as the Digital Elevation Model (DEM), 

State Soil Geographic (STATSGO) database, and National Land Cover Data (NLCD) 

databases, using the BASINS geographic information systems (GIS) analysis tools 

(USEPA, 2001). The study watershed was delineated into eight subwatersheds; each 
 

41

subwatershed has its own land use, soil, and topography and is considered a 

homogeneous hydrological response unit. 

The model uses meteorological data such as precipitation, air temperature, potential 

evapotranspiration, solar radiation, wind speed, dew point temperature, and cloud cover. 

Our climate data were obtained from the Hatfield Marine Science Center in Newport, 

Oregon (www.weather.hmsc.orst.edu), a weather station in Nashville, Oregon, and 

BASINS databases. Potential evapotranspiration was estimated from meteorological data 

using the Hamon (1961) method. 

5.2.2 Model Calibration and Validation 

After necessary input data were prepared, the model was calibrated using historical 

streamflow, TSS, and nitrate data corresponding to the baseline. The parameter values 

that gave the best calibration results for streamflow, TSS, and nitrate were retained. We 

used those parameter values to forecast streamflow, nitrate, and TSS concentrations for 

the three build-out scenarios. The hydrological calibration periods were 1973 through 

1977 and 2000 through 2001; the nitrate calibration period was from October 2001 

through September 2002; and the TSS calibration period was from October 1973 through 

September 1974. We used one year for TSS calibration and the other for TSS validation. 

Because both TSS and nitrate are affected by the hydrological calibration performance, 

we performed two hydrologic calibrations. One coincided with the period when observed 

TSS data were available and the other coincided with the period when observed nitrate 

data were available. After successful hydrological, TSS, and nitrate calibrations, the 

model was validated using observed streamflow data from periods that were not used for 
 

42

model calibration. We employed HSPF calibrations using observed and simulated daily 

data, but we also did some monthly and annual calibration comparisons for the baseline 

and the build-out scenarios. 

5.2.3 Model Performance Evaluation Criteria 

There are no widely accepted quantitative HSPF model calibration criteria to 

determine if the model predictions are acceptable; there are, however, some generally 

accepted HSPF calibration and validation guidelines. For most HSPF streamflow 

calibrations, acceptable model calibration performance is achieved when the correlation 

coefficient between monthly simulated and observed streamflow is greater than 0.85 

(Donigian, 2002). Because sediment and nitrate calibrations are affected by the 

hydrological calibration performance, the TSS and nitrate calibration criteria are far less 

stringent than the hydrologic calibration criteria. Donigian (2002) states that HSPF 

annual and monthly hydrology, sediment, and nutrient simulations are considered “good” 

when the percentage differences between simulated and observed data are between 10 

and 15 for flow, between 20 and 30 for sediments, and between 15 and 25 for nutrients. 

HSPF calibration and validation procedures consist of matching simulated 

streamflow, nitrate, and TSS concentrations with observed data. We used goodness-of-fit 

measures such as coefficient of determination (R2

), root mean square error (RMSE), and 

coefficient of model-fit efficiency (E) (Nash and Sutcliffe 1970) to evaluate the model’s 

performance in predicting observed streamflow, TSS, and nitrate. The coefficient of 

determination is written as: 
 

43

R2

 = { }2

2 0.5 __

1

0.5

1

2

1

[( ) ] [ ( ) ]

( )( )

sim

N

i

sim

N

i

obs obs

N

i

sim sim obs obs

Q Q Q Q

Q Q Q Q

− −

− −

∑ ∑

∑

= =

= (1)

where Qobs is the observed streamflow, Qsim is the corresponding simulated streamflow, 

and Qobs and Qsim are average observed and average simulated streamflow, respectively, 

and N is the number of data points used in the average calculation. The coefficient of 

model fit efficiency can be written as: 

E = 

∑

∑

=

=

−

−

− N

i

sim obs

N

i

obs sim

Q Q

Q Q

1

2 __

1

2

( )

( )

1 (2) .0

where E is the Nash-Sutcliffe coefficient and other terms are as defined in (1). The root 

mean square error (RMSE) is written as: 

RMSE = 2 0.5

1

[ ( ) ] sim

N

i

∑ Qobs − Q =

 (3) 

where Qobs and Qsim are defined as in (1) and N is the number of data points used for the 

comparison period. 

5.2.4 Future Build-out Scenario Development 

The sequential nature of urban development projects introduces uncertainties in land 

use projections, making it difficult to project changing land use across an entire 

watershed. Land use change models that use economic and social drivers can project land 

use change. These include the California Urban Futures Model (Landis, 1995) and the 

Land Use Evolution and Impact Assessment Model (LEAM) (Deal, 2001). The LEAM 
 

44

model forecasts specific land use change projections using watershed-specific drivers, 

such as economic, population, social, geography, transport, and open space. 

Our approach can use information from a land use change model, but since land use 

change information is not usually available at the watershed scale, we present a simple, 

generic method that uses hypothetical build-out scenarios. In areas where information on 

future land use change is available from existing zoning and master plans, watershed 

managers can use actual land use plans instead of hypothetical scenarios. This allows 

resource managers to set an allowable TIA level that corresponds to a watershed’s 

unknown future build-out. It assumes that a watershed will be developed eventually and 

will have a threshold TIA at the end (build-out condition). Build-out is defined as an 

estimate of the amount and location of potential development for an area. It is used by 

land use planners who evaluate the potential impacts of urban development using buildout analysis. Generally, build-out analysis, as currently used by land use planners, does 

not employ watershed-scale models. The proposed method allows land use planners to set 

different impervious area levels for build-out scenarios, and simulate how different 

scenarios alter hydrology and water quality. Although there is no limit on the number of 

build-out scenarios selected and impacts simulated, in this study we limited our analysis 

to three build-out scenarios that correspond with 15, 45, and 85% total impervious levels. 

Table 1 lists the area covered by each land use category and the percentages of TIA 

used for the baseline (i.e., pre-development) and hypothetical future build-out scenarios. 

The three build-out scenarios correspond to low density residential (scenario15), high 

density residential (scenario45), and commercial development (scenario85). Selection of 

15, 45, and 85 percent TIA levels is arbitrary and these levels primarily demonstrate the 
 

45

model’s sensitivity to different levels of imperviousness. Scenario85 (in particular) was 

derived only for comparative purposes since only a few urban watersheds ever reach such 

high TIA levels. Impervious area may not reach 85% because some areas of the 

watershed are unbuildable due to soil and slope characteristics or are zoned as committed 

open spaces. The Ballona Watershed in Los Angles, CA is one of the few watersheds 

with impervious cover that reaches 85%. Land use planners should select percent TIA 

levels that realistically reflect their future land use plans so they can set threshold TIA 

limits for a watershed. 

For each scenario, we kept a minimum of13 percent of the urban land area as 

committed open spaces to include land occupied by detention basins, parks and other 

recreational areas, wetlands, and riparian buffer zones. When developing scenarios with 

this approach, watersheds can be subdivided, and different TIA levels can be assigned to 

different subwatersheds. Questions to answer are what will be the total allowable 

impervious levels at build-out for a watershed and what stressor levels are associated 

with different build-out scenarios. 

5.2.5 Forecasting Flow, Nitrate, and TSS Alterations 

Modeling streamflow, nitrate, and TSS alterations under the three build-out scenarios 

makes certain assumptions. For example, calibrated model parameter values obtained 

from the baseline condition for each land use category were unchanged when modeling 

the altered scenarios. In addition, to forecast streamflow, habitat, and water quality 

conditions for the future scenarios, we converted existing pervious forest land to 

impervious urban land -- but retained the historical precipitation inputs for all simulations 

since it is not possible to forecast future precipitation. The baseline and build-out scenario 
 

46

streamflow, TSS, and nitrate were all simulated for a 30-year (1973 to 2002) period. The 

30-year streamflow, TSS, and nitrate projections for the three build-out scenarios reflect 

the hydrologic and water quality degradations that would have been observed in the 

watershed through 2002 if the watershed had reached 15, 45, or 85 percent urban TIA at 

build-out in 1973. 

As already noted, the 30-year simulations assumed that future precipitation and 

climate would be similar to those observed over the previous 30 years. In addition, the 

HSPF model treated all impervious areas as connected even though, in reality, not all of 

them are. This latter assumption can lead to runoff-water and contaminant input overprediction because it ignores infiltration that occurs when runoff from an impervious area 

passes over an adjacent pervious area. 

5.2.6. Linking Stressors and to Impacts 

To project the ecological impacts of urbanization, indicators of urbanizationinduced stressors were derived from forecasted flow data. For each scenario, we derived 

indicators from long-term simulated flow, TSS, and nutrient data. For example, indicators 

of flow alterations include the indicators of hydrological alterations (IHA) (Richter et al., 

1996), baseflow index, bankfull flow (Q2), and 7Q10. We estimated the baseflow index 

using a recursive digital filter (Eckhardt, 2005); specifically, we examined indicators that 

are closely related to peak flow and baseflow changes under the three build-out scenarios. 

These indicators can be used to establish cause and effect relationships between 

urbanization-induced stressors and changes in abundance, diversity, and fitness of aquatic 

communities. To assess the ecological impacts of simulated urbanization-induced 
 

47

stressors, one can link HSPF to AQUATOX or to the Physical Habitat Simulation model 

(PHABSIM), a major component of the Instream Flow Incremental Methodology (IFIM) 

(Milhous et. al., 1984). Linking HSPF to aquatic ecosystem models and simulating 

ecological impacts of urbanization-induced stressors with AQUATOX AND PHABSIM 

is beyond the scope of this study. 
 

48

5.3 Results and Discussion 

5.3.1 Model Calibration and Validation Results 

 

Hydrologic Calibration and Validation. Statistical and graphical visualization 

calibration tools available in BASINS were used to calibrate and validate HSPF for the 

baseline scenario. Table 2 shows goodness-of-fit measures for the calibrations. For the 

two hydrologic calibration periods, the results were within acceptable calibration range: 

daily calibrations had coefficients of determination (R2) of 0.8 and 0.87, and monthly 

calibrations had R2 values of 0.91 and 0.95. Validation performance was slightly lower 

than that for the calibration. For the two hydrologic validation periods, the R2 values were 

0.72 and 0.82 for the daily validations, and 0.83 and 0.89 for the monthly calibrations. 

The RMSE and Nash-Sutcliffe Efficiencies (E) values are also provided in Table 2. As 

shown in Figures 3a and 3b, simulated streamflow compared well to observed daily 

streamflow for both hydrologic calibration periods. All the parameters adjusted as part of 

the hydrologic, TSS, and nitrogen calibrations are listed in Table 3. 
 

49

Table 2. Model calibration and validation results 

________________________________________________________________________ 

R2 RMSE E R2 RMSE E 

_______________________________ ____________________________________ 

Hydrologic Calibration and Validation

________________________________________________________________________ 

Calibration Period(1973 to 1977) Validation Period (1977 to 1979) 

Daily 0.80 5.8 0.77 0.72 6.4 0.71 

Monthly 0.95 2.2 0.94 0.89 3.0 0.87 

Calibration Period (2000 to 2001) Validation Period (1999 to 2000) 

Daily 0.87 3.6 0.82 0.69 7.8 0.62 

Monthly 0.91 4.1 0.81 0.83 3.5 0.79 

 TSS Calibration and Validation

Calibration Period (1973 to 1974) Validation Period (1972 to 1973) 

Daily 0.52 39.36 0.46 0.56 19.1 0.44 

Monthly 0.89 11.78 0.82 0.78 7.7 0.59 

________________________________________________________________________ 

 Nitrate Calibration and Validation

Calibration Period (2000 to 2001) Validation Period (1999 to 2000) 

Daily 0.75 ---- ---- 0.39 ---- ---

_______________________________________________________________________ 
50

Table 3. List of parameter values adjusted during calibration _____________________________________________________________________________________________________ Parameter Description Calibrated value _____________________________________________________________________________________________________  Hydrology 

LZSN Lower Zone Nominal Storage (cm) 20.32 

 UZSN Upper Zone nominal Storage (cm) 2.36  INFILT Infiltration parameter (cm/hr) 0.17  INTFW Interflow inflow parameter 6.50  IRC Interflow Recession Parameter (per day) 0.70  AGWRC Daily recession constant of groundwater flow (per day) 0.98  DEEPFR Fraction of groundwater inflow (inactive) 0.00  BASETP Fraction of E-T from baseflow 0.00  AGWETP Fraction of E-T from active groundwater 0.00  LZETP Lower zone E-T parameter 0.45  CEPSC Interception storage capacity (cm) 0.51 Sediment Simulation 

KRER Coefficient-detachment equation 1.14 JRER Exponent-detachment equation 1.00 KSER Coefficient-detached washoff equation 3.00 

JSER Exponent-detached washoff equation 5.80 KGER Coefficient-matrix scour equation 5.80 JGER Exponent-matrix scour equation 5.20 KSAND Coefficient-sandload power function 0.01 EXPSND Exponent-sandload power function 3.90 W (cm ⁄ s) Fall velocity in still water 0.14 

M (kg ⁄m2 day) Erodibility coefficient 1.05 

TAUCD (kg ⁄m2) Critical bed shear for deposition 2.44 

TAUCS (kg ⁄m2) Critical bed shear for scour 6.35 

Nitrogen Simulation 

KTAM20 (per hour) Nitrification rates of ammonia 0.015 KTO220 (per hour) Nitrification rates of ammonia and nitrite 0.002 TCNIT (per hour) Temperature correction coefficient for nitrification 1.50 KNO320 (per hour) Nitrate denitrification rate at 20 degrees C 0.02 TCDEN (none) Temperature correction coefficients for denitrification 1.00 DENOXT (mg/l) Dissolved oxygen concentration threshold for denitrification 0.50 

___________________________________________________________________________________________________
 

51

Sediment Calibration and Validation. Suspended sediment concentration calibration 

and validation performances generally are impacted negatively by the lack of available 

long-term historical data with appropriate temporal resolution. In this study, only two 

years of daily TSS concentration data were available for model calibration and validation. 

As shown in Table 2, the coefficients of determination (R2

) for the sediment calibration 

were 0.52 for the daily and 0.89 for the monthly simulations. Sediment validations had 

coefficients of determination (R2

) of 0.56 for the daily and 0.78 for the monthly 

simulations. Graphical comparisons of the observed versus simulated TSS concentrations 

indicated that the model slightly under-predicted the higher TSS values, but overall 

sediment simulations generally followed the runoff hydrograph trend (Figure 8c). Despite 

under-predicting the higher TSS values, overall model calibration and validation 

performances for TSS concentrations were acceptable. 

Nitrate Calibration and Validation. Nitrate concentration simulation performance 

depends on the quality and the quantity of observed data. Observed concentration data 

had many missing data points, which created model calibration problems. To address this 

problem, we compared observed and simulated nitrate only on days when observed 

nitrate concentrations data were available. Despite using only limited observed versus 

simulated comparisons, our nitrate concentration calibrations had coefficients of 

determination (R2

) of 0.75 for the daily simulations (Table 2). The nitrate validations had 

lower coefficients of determination of 0.39 for daily simulations (Table 2). Lack of 

continuous observed daily nitrate data at daily (or shorter) time intervals may have 

contributed to the lower model validation performance. Both observed and simulated 
 

52

nitrate concentrations closely followed the seasonal hydrologic trend, except during the 

first winter storm, suggesting that nitrate export from terrestrial areas is strongly 

influenced by nitrate build-up and storm wash-out mechanisms (Figure 8d). 
53

0

50

100150200250300350400450500

11/30/1973 12/14/1973 12/28/1973 1/11/1974 1/25/1974 2/8/1974 2/22/1974 3/8/1974 3/22/1974 4/5/1974

Day

Total Suspended Sediments (mg/l) Predicted Observed 0.1 0.6 1.1 1.6 2.1 2.6 3.1 3.6 4.1 4.6

10/1/2000 12/1/2000 2/1/2001 4/1/2001 6/1/2001 8/1/2001 10/1/2001 12/1/2001 2/1/2

Day

Nitrate (mg/l)

PredictedObserved

0

20406080

100120140

10/1/2000 12/1/2000 2/1/2001 4/1/2001 6/1/2001 8/1/2001 10/1/2001 12/1/2001 2/1/2002 4/

Day

Streamflow (m

3/sec)

Rainfall (mm) Observed Predicted

0

20406080

100120140160180200

12/30/1973 1/13/1974 1/27/1974 2/10/1974 2/24/1974 3/10/1974 3/24/19744/7/1974 4/21/1974

Day

Streamflow (m

3

/sec)

020406080100120140160180200220240

Rainfall (m) Rainfall Observed Sim ulated

Figure 8. Model calibration results: streamflow (a and b), TSS (c), and nitrate (d) calibrations. 

(a) 

(b) 

(d) (c) 
 

54

5.3.2 Forecasting Stressor Levels for the Build-out Scenarios 

Forecasting Flow Alterations. Flow alterations for build-out future scenarios show 

significant changes as a function of rising percent of TIA levels (Figure 9). To document 

these scenario-specific changes, we compared: daily streamflows simulated for the 

baseline scenario to streamflows forecasted for scenario15, scenario45, and scenario85 

(Figure 9a); simulated annual water balances for all scenarios (Figure 9b); simulated flow 

duration curves at low flow conditions, i.e., for flows with greater than 95% exceedances 

(Figure 9c); and simulated flow duration curves at high flow conditions, i.e., for flows 

with less than 5% exceedances (Figure 9d). Comparisons of the flow duration curves 

show that low flows get smaller with increased percent TIA (Figure 9c) and high flows 

get larger (Figure 9d). Comparisons of the annual average water balance components 

under the baseline versus the build-out future scenarios for a 30-year simulation period, 

using the same historical precipitation data, showed increased surface runoff and 

decreased interflow, baseflow, and evapotranspiration for scenario15, scenario45, and 

scenario85 (Figure 9b). 

Peak Flow. The simulation results clearly show increased flow flashiness associated with 

increases in percent TIA levels (Figures 4a and 4d); specifically, low intensity storm 

events usually did not generate runoff for the baseline and scenario15, but did so for 

scenario45 and scenario85. Thus, the baseline and scenario15 were characterized by 

smooth runoff hydrographs, whereas scenario45 and 85 exhibited high peak flow rates 

(Figure 9a). The data suggest the existence of a threshold percent TIA above which peak 

flows greatly increase with increased percent TIA level. In addition, comparisons of flow 
 

55

duration curves with exceedances less than 5% show small peak flow differences 

between the baseline scenario and scenario15, but relatively large peak flow differences 

among the three build-out future scenarios (Figure 9d). 

Baseflow and Groundwater Recharge. To examine the effect of increased 

imperviousness on baseflow and groundwater recharge, we calculated baseflow index 

(BFI) from observed streamflow (historical condition), streamflow simulated for the 

baseline condition, and streamflow forecasted for the three future build-out scenarios. 

Baseflow indices from the observed and simulated data for the baseline scenario were 

0.62 and 0.64, respectively, whereas indices forecasted for scenario15, scenario45, 

scenario85 were 0.59, 0.46, and 0.28, respectively. This decreasing BFI trend indicates 

how baseflow and groundwater recharge decrease when TIA levels increase. 

To further examine the relationship between increased imperviousness, baseflow 

and groundwater recharge further, we compared flow duration curves for flows with 

exceedance probability of greater than 95% for the baseline scenario and for the three 

build-out scenarios over 30-years (Figure 9c). These comparisons reveal little difference 

between the baseline condition and scenario15 simulations, but show drastic baseflow 

reductions for the scenario45 and scenario85 simulations (Figure 9c). In this report, we 

also calculated 7Q10 flow values from streamflow simulated for the baseline, scenario15, 

scenario45, and scenario85 as 0.16, 0.15, 0.10, and 0.03 m3

/sec, respectively.
56

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

95

95

96

96

96

96

97

97

97

97

98

98

98

98

99

99

99

100

100

Percent of Time Flow Equaled or Exceeded

Streamflow (m

3/sec)

Baseline Scenario15Scenario45 Scenario85

0

200400600800

1000120014001600

Annual Water Balance Components (mm) Baseline Scenario15 Scen

Surface runoff (mm) Interflow runoff (mm) Baseflow runoff (mm) Simulated Evapotranspiration  Water Balance Error (mm) 050 100 150 200 250

10/15/1985

10/29/1985

11/12/1985

11/26/1985

12/10/1985

12/24/1985

1/7/1986

1/21/1986

2/4/1986

2/18/1986

3/4/1986

3/18/1986

4/1/1986

4/15/1986

4/29/1986

Day

Daily Streamflow (m

3

/sec)01

23456

Precipitation (mm) Precipitation (mm) Baseline Scenraio15 Scenario45 Scenario85

0

200400600800

1000120014001600

Annual Water Balance Components (mm) Baseline Scenario15 Scen

Surface runoff (mm) Interflow runoff (mm) Baseflow runoff (mm) Simulated Evapotranspiration  Water Balance Error (mm) 050 100 150 200 250

10/15/1985

10/29/1985

11/12/1985

11/26/1985

12/10/1985

12/24/1985

1/7/1986

1/21/1986

2/4/1986

2/18/1986

3/4/1986

3/18/1986

4/1/1986

4/15/1986

4/29/1986

Day

Daily Streamflow (m

3

/sec)01

23456

Precipitation (mm) Precipitation (mm) Baseline Scenraio15 Scenario45 Scenario85

Figure 9. Hydrologic changes simulated for baseline

and scenario85: (a) daily streamflows, (b) annual water balances (c) flow duration curves at low flow conditions, and (d) flow duflow conditions 

0

50

100150200250300350400

0.01

0.33

0.65

0.97

1.29

1.61

1.93

2.25

2.56

2.88

3.20

Percent of Time Flow Equaled 

Streamflow (m

3/sec)Baseline 

Scenario45 

(a) 
 

57

Forecasting TSS Concentrations. Suspended sediment concentrations forecasted for 

scenario15, scenario45, and scenario85 over a 30-year simulation period did not yield a 

continuously increasing function with percent TIA level. Indeed, our scenario projections 

suggest that sediment concentrations are controlled by available sediment supply and by 

transport capacity. Specifically, during some elevated flow periods, scenario45 

projections had higher TSS concentrations than scenario85, even though scenario85 had 

higher simulated flows, thus higher sediment transport capacity (Figure 9b). One 

explanation is that there is a TIA threshold value beyond which sediment loads 

transported from land surfaces may not increase with increased impervious area. This 

implies that when sediment supply is available, scenario85 would have produced the 

highest simulated TSS concentrations because it has the greatest sediment transport 

capacity. However, as the landscape becomes increasingly impervious, sediment supply 

becomes limited and increases in sediment transport capacity do not translate directly into 

increased sediment export. 

An analysis of forecasted daily TSS concentrations for the build-out scenarios 

indicates a threshold percent TIA value somewhere between 45 and 85 TIA levels where 

increased percent TIA does not translate to increased TSS concentrations. In this report, 

we forecasted 30-year average annual sediment loads of 146, 211, 452, and 241 (kg/hayr) for the baseline, scenario15, scenario45, and scenario85, respectively. 

Forecasting Nitrate Concentrations. For the build-out scenarios, the model forecasted 

higher nitrate concentrations at low flows and lower concentrations at elevated flows 

(Figure 9a). This is because nitrate simulations are strongly influenced by flow 
 

58

alterations, particularly baseflow reduction and increase in surface runoff. Conversely, at 

baseline condition, higher nitrate concentrations were observed during elevated flow 

conditions and lower concentrations at low flow conditions, suggesting that, at baseline, 

nitrate accumulates in the upper soil layers during dry periods and is washed out during 

wet periods (Figure 9). This observed nitrate build-up - wash-out cycle under the baseline 

scenario was severely disrupted by urbanization, which alters the dominant runoff 

generation mechanisms and flow pathways. 

Model results simulated for the build-out scenarios show increased nitrate 

concentrations during dry periods and lower concentrations at elevated flow periods. 

Such interdependence between flow alteration and nitrate concentrations might also 

impact soil denitrification processes. As stated previously, our nitrate projections for the 

three build-out scenarios produced nitrate concentrations in the baseflow that increased 

with increased percent TIA level. Even ignoring other nitrogen sources, such as lawn 

fertilizers and atmospheric deposition, our results indicate that flow alteration alone can 

increase nitrate concentrations that may cause eutrophication threats to coastal waters. In 

summary, our forecasted 30-year average annual nitrate loads were 13, 14, 16, and 23 kgNO3-N/ha-yr for the baseline, scenario15, scenario45, and scenario85, respectively. 
 

59

0

0.5

1

1.5

2

2.5

3

3.5

3/1/1985

3/8/1985

3/15/1985

3/22/1985

3/29/1985

4/5/1985

4/12/1985

4/19/1985

4/26/1985

Day

Nitrate (mg/l)

Baseline Scenario15 Scenario45 Scenario85

0

50

100

150

200

250

300

3/1/19853/8/1985

3/15/19853/22/19853/29/1985

4/5/1985

4/12/19854/19/19854/26/1985

Day

TSS (mg/l)

Baseline Scenario15 Scenario45 Scenario85

0

10

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30

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50

60

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3/1/1985

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3/29/1985

4/5/1985

4/12/1985

4/19/1985

4/26/1985

Day

Streamflow (m 3/sec)

Baseline Scenario15 Scenario45 Scenario85

Figure 10. HSPF simulated streamflow, TSS, and nitrate concentrations for baseline, 

scenario15, scenario45, and scenario85 

(a) 

(b) 

(c) 
 

60

5.4 Determining Ecologically Relevant Urbanization-Induced Stressor 

Indicators 

Hydrologic Indicators. Flow alterations have an adverse impact on biological 

integrity, but quantifying that impact has been a challenge. Richter et al. (1996) present 

indicators of hydrologic alteration (IHA) calculated from observed historical streamflow 

data. In this study, we estimated IHAs from simulated streamflow for the baseline and for 

the build-out scenarios (Table 4). Simulated magnitude of average monthly flows vary 

with the month of year. For some months, the monthly flows decrease with increase in 

TIA levels whereas for other months monthly flows decrease with increase in TIA levels. 

As the TIA levels increased, model results show a decrease in magnitude and duration of 

annual extreme flows for build-out scenarios. Conversely, model results show increased 

frequency and duration of average high and low flow pulses as well as increased average 

rate and frequency of flow changes (Table 4). Simulated IHAs show that urbanization 

does not alter the timing of average annual extreme flows. 

Determining IHA values for build-out scenarios with different percent TIA levels, 

and establishing quantitative links between urbanization-induced stressors and their 

ecological impacts, are essential to urban ecosystem protection and restoration planning. 

Because the amount of water available in a stream defines the suitability of a habitat to 

aquatic organisms, flow alteration, especially low flows, creates unfavorable conditions 

for native species (Poff et al., 2006). To ensure that sufficient water is available for 

aquatic organisms, some states set 7Q10 flow values as an in-stream flow requirement 

that must not be violated when issuing water withdrawal permits for irrigation and 

municipal water supply. 
 

61

Table 4. Indicators of hydrologic alteration (IHA) calculated from streamflow simulated for the 

baseline and the three build-out scenarios for a 30-year period. 

_______________________________________________________________________ 

IHA Parameter Group Baseline Scenario15 Scenario45 Scenario85 

________________________________________________________________________ 

Group #1: Magnitude of average monthly flows (m3/sec) 

October 0.47 0.51 0.41 0.19 

November 4.29 6.16 6.93 5.49 

December 11.35 11.04 11.20 6.12 

January 9.91 10.54 10.16 5.40 

February 9.12 9.88 8.00 3.70 

March 8.45 8.52 8.48 5.75 

April 6.20 6.13 4.95 2.35 

May 3.36 3.27 2.14 0.76 

June 1.98 1.85 1.27 0.40 

July 1.14 0.98 0.64 0.19 

August 0.67 0.58 0.38 0.11 

September 0.47 0.46 0.29 0.09 

Group #2: Magnitude and duration of annual extreme flows (m3

/sec) 

1-day minimum 0.32 0.27 0.18 0.05 

3-day minimum 0.32 0.28 0.18 0.05 

7-day minimum 0.33 0.31 0.20 0.06 

30-day minimum 0.42 0.47 0.37 0.13 

90-day minimum 0.76 0.90 0.98 0.87 

1-day maximum 67.60 84.61 117.06 172.90 

3-day maximum 50.60 57.54 73.07 94.16 

7-day maximum 39.00 41.70 47.59 60.54 

30-day maximum 21.65 22.59 24.86 29.65 

90-day maximum 15.11 5.81 17.90 21.03 

Days with zero flow 0.00 0.00 0.00 0.00 

Base flow index 0.05 0.043 0.02 0.01 

Group #3:Timing of annual extreme flows (Julian Day) 

Date of minimum 277.00 277.00 275.00 275.00 

Date of maximum 347.00 338.00 338.00 338.00 

Group #4: Frequency and duration of average high and low pulses 

Low pulse count 1.00 8.00 9.00 8.00 

Low pulse duration 62.25 5.75 5.00 5.50 

High pulse count 8.50 14.00 27.50 35.00 

High pulse duration 8.50 3.00 2.00 2.00 

Low Pulse Threshold 30.65 32.30 22.20 7.50 

High Pulse Threshold 288.00 307.00 327.00 310.00 

Group #5: Rate and frequency of flow changes 

Rise rate 30.75 98.75 239.10 445.00 

Fall rate -2.53 -6.45 -6.42 -5.35 

Number of reversals 64.00 111.50 119.00 121.00 
 

62

Hydraulic and water quality indicators. High peak flow rates associated with high 

percent TIA levels cause high flow velocities and associated shear stresses that move 

sediments (Poff et al., 2006), and displace benthic invertebrates (Poff and Ward, 1991), 

and small fish (Harvey, 1987). High shear stresses also increase the depth of scour of bed 

sediments to induce higher mortality of benthic invertebrates (Palmer et al., 1992; 

Townsend et al., 1997). To forecast the risks of increased stream channel erosion, we 

used Q2, often referred to as the bankfull flow or channel forming flow, which 

corresponds to a 2-year return period. Wolman and Miller (1960) reported that flows with 

1 to 5 year return periods are important to stream geomorphology because these flows 

move sufficient amounts of sediment. To forecast the impacts of urbanization on 

streambank erosion, we examine how different build-out scenarios alter the frequency of 

bankfull or channel forming flows (Q2). Using Q2 as an indicator of increased channel 

erosion, we forecasted the number of times the baseline scenario Q2 flow value was 

exceeded by Q2 flows forecasted for the three build-out scenarios. We found 22, 91, and 

274 days with flows greater than the baseline scenario Q2 for the 30-year projections for 

scenario15, scenario45, and scenario85, respectively. 

Establishing stressor-response relationships between changes in shear stress and Q2

values under future build-out scenarios and their impacts on aquatic organisms is key to 

developing biologically relevant indicators. Although some stressor indicators may have 

biological relevance, additional field research is needed to establish cause-and-effect 

relationships between stressor indicators and ecological responses. 

The HSPF model simulates many physical habitat variables closely related to channel 

hydraulic geometry. To establish links between forecasted habitat variables and the health 
 

63

of aquatic ecosystems, we propose the use of habitat models such as the Physical Habitat 

Simulation model (PHABSIM), a major component of the Instream Flow Incremental 

Methodology (IFIM) (Milhous et. al., 1984), and ecological models such as AQUATOX. 

We suggest importing HSPF forecasted habitat variables and stressors, such as scenariospecific simulated hydraulic, temperature, nutrient, and contaminant data, as inputs into 

the PHABSIM and AQUATOX models to compute the impact these stressors have on the 

health of aquatic ecosystems. 

Comparisons of simulated flow velocity for the baseline scenario to velocities 

forecasted for the three build-out scenarios indicate significant baseflow velocity 

reductions with increasing percent TIA level (Figure 10). For instance, scenario85, which 

has the highest percent TIA, had the lowest baseflow velocity, followed by scenario45 

and scenario15. The baseline scenario, with the lowest percent TIA, had the highest 

baseflow velocity. As presented earlier, the flow velocities fluctuated between baseflow 

and peak flows within each scenario. However, these fluctuations increased with increase 

in percent TIA; such that peak flows and baseflows had an inverse order relationship to 

percent TIA; that is, scenario85 had the highest peak flows followed in order by 

scenario45, scenario15, and baseline. 
 

64

0

2

4

6

8

10

12

14

16

18

3/1/1985

3/8/1985

3/15/1985

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Day

30-Year Mean Water Temperature ( oC)

Scenario85 Scenario45 Scenario15 Baseline

10

15

20

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30

35

3/1/1985

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30-Year Mean Water Surface Width (m)

Baseline Scenario15 Scenario45 Scenario85

0

0.2

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1

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3/1/19853/8/1985

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4/5/1985

4/12/19854/19/19854/26/1985

Day

30-Year Mean Flow Velocity (m/sec)

Baseline Scenario15 Scenario45 Scenario85

Figure 11. HSPF simulated 30-year mean water temperature (a), water surface width 

(b), and flow velocity (c) for baseline, scenario15, scenario45, and scenario85.

(a)

(c)

(b)
 65

65

6 CONCLUSION 

Controlling nonpoint source pollution requires the use of integrative modeling 

approaches that simulate the interactions among integrated stressors. However, current 

models and modeling approaches cannot represent integrated stressors and many 

available models do not match the complexities of the system that is modeled. Only a 

very few watershed models can represent the various hydromodification types and 

scenarios and can simulate scenario-specific flow alterations and water quality 

degradation. In this report, we present a modeling approach that is suitable for assessing 

how hydromodification projects alter natural flow regimes and degrade water quality. 

The proposed BASINS-based modeling framework enables resource managers to select 

alternative future scenarios, simulate scenario-specific stressors, and select scenarios that 

minimize the impacts of hydromodification on water quantity and quality. 

Urbanization has been closely linked to the degradation of aquatic ecosystems. 

Methods to quantify urbanization-induced stressors from future development scenarios 

have not been generally available. In this study, we present an application of a method to 

forecast urbanization-induced stressors and discuss ways to develop indicators that link 

stressors to their ecological impacts. The proposed approach will enable land use 

planners and resource managers to forecast urbanization-induced stressors from 

forecasted streamflow and water quality data. Forecasted urbanization-induced stressors 

include flow alteration (e.g., altered peak flows and baseflows), water quality degradation 

(e.g., increased nitrate and TSS concentrations), and geomorphic or habitat alterations 

(e.g., changes in flow depth, wetted surface width, and flow velocity). 
 66

66

The ultimate utility of the proposed forecasting approach depends on successfully 

developing ecological response indicators that capture the responses of aquatic 

ecosystems to the stressors of concern. Ecological response indicators can be derived 

possibly from data on changes in native species richness or on the composition and 

functional organization of fish and invertebrates. 

Future research should focus on developing ecological response indicators that 

quantitatively reflect aquatic biota responses to forecasted stressors. Specifically, such 

research should assess the effects of single or integrated stressors on the abundance, 

diversity, and fitness of aquatic communities. Future modeling in this area ideally will 

also simulate how different best management practices moderate the ecological effects of 

urbanization to restore important ecological services and functions. 
 67

67

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