United States
Environmental Protection
Agency
Office of Research and
Development
Washington, D.C. 20460
EPA/600/R-00/042
February 2000
Landscape Indicator
Interface with
Hydrologic Models
Rainfall
Topography
Land use/cover
Soils
Satellite data
Watershed
discretization
Characterization
of hydro logic
elements with
GIS
Hydrology
Model
R eports
Maps
Statistics
Knowledge
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EPA/600/R-00/042
February 2000
Landscape Indicator Interface
with Hydrologic Models
Research Plan
David C. Goodrich
Principal Investigator
Mariano Hernandez
Scott Miller
Bruce Goff
USDA Agricultural Research Service
Southwest Watershed Research Center
2000 East Allen Road
Tucson, AZ 85719
Telephone: (520) 670-6380, ext. 175
Facsimile: (520) 670-5550
e-mail: qoodrich@.tucson.ars.ag.gov
William G. Kepner
Principal Investigator
Bruce Jones, Curt Edmonds
Tim Wade, Don Ebert
Dan Heggem
US Environmental Protection Agency
Office of Research and Development
National Exposure Research Laboratory
P.O. Box93478, Las Vegas, NV89193
Telephone: (702)798-2193
Facsimile: (702) 798-2692
e-mail: kepner.william@.epamail.epa.gov
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Acknowledgements
We gratefully acknowledge Dr. Everett P. Springer, Environmental Science Group, Los
Alamos National Laboratory, Los Alamos, NM; Dr. Ward Brady, Environmental Resources
Program, Arizona State University, Tempe, AZ; Dr. Xiaohui Zhang, Advanced Resource
Technology Group, University of Arizona, Tucson, AZ; and Dr. Ronald Parker, U.S. EPA, Office
of Prevention, Pesticides, and Toxic Substances, Washington, DC for their helpful criticism and
suggestions as reviewers for this Research Proposal.
Notice: The U.S. Environmental Protection Agency (EPA), through its Office of Research and
Development (ORD), partially funded and managed the research described here. It has
been peer reviewed by the EPA and approved for publication. Mention of trade names
or commercial products does not constitute endorsement or recommendation by EPA for
use.
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Table of Contents
Section
Page
Abstract iv
Section 1 Introduction 1
1.1 Goal and Objectives 1
1.2 Problem Statement 1
Section 2 Background 3
2.1 Effects of Land Cover on Ecological and Hydrological Processes 3
2.2 Effects of Aggregation of Landscape Attributes on Watershed Response 4
2.3 Integration of GIS and Remote Sensing in Hydrologic Modeling 4
2.4 Model Selection and Development 7
Section 3 Conceptual Model 9
Section 4 Technical Approach 11
4.1 Description of the Area 11
4.2 Study Design / Methodology 12
4.2.1 Model development 12
4.2.2 Model testing and optimization 15
4.2.3 Model application 17
4.2.4 Model implementation 18
4.3 Data Acquisition 18
4.4 Quality Assurance 19
4.4.1 Rationale for model selection 19
4.4.2 Source data 22
4.4.3 Model application 23
4.4.4 Software development 25
Section 5 Anticipated Results and Products 26
References 27
Appendix 36
Resumes 38
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Abstract
Critical gaps in our understanding of scale effects on hydrological and ecological processes,
biological community factors and their interactions affecting semi-arid ecosystems limit our ability
to scale up from point processes to broader areas of the landscape. The proposed research will
narrow this gap and determine the vulnerability of arid and semi-arid landscapes to a variety of
natural and anthropogenic stressors at multiple scales. A fundamental project task is the
modification of existing watershed models to consider human-induced changes in landscape
pattern. The research utilizes the 40-year record of vegetation, soils, and hydrological data
collected at the Walnut Gulch Experimental Watershed and remotely sensed and ground-based
data to develop process models that relate landscape composition and pattern attributes to the
hydrologic condition of the watershed (water storage and availability, infiltration, surface water
quality; erosion, flood frequency, duration and intensity). The long-term goal of this research is to
provide operational models that relate landscape pattern to watershed condition and can be
extrapolated across multiple scales including subwatershed, watershed, and basin, for a variety of
arid and semi-arid basins.
In the first phase of the research project, two hydrological models were selected for
watershed assessment across multiple scales. The two models were selected based upon the
influence of vegetation characteristics on watershed response. Both models are currently being
applied at the Walnut Gulch Experimental Watershed and at the San Pedro River Basin to
evaluate the effects of land cover on watershed response. Particular research objectives being
investigated in this phase of the project are: (1) evaluation of the effects of misclassification error
of Landsat land use/ land cover imagery on watershed response and, (2) assessment of the
number of subwatershed elements (basin delineation) and averaged land cover information on
hydrologic response as a function of scale. Future research will focus on developing and
implementing a PC landscape/hydrologic modeling tool for ecosystem risk assessment across
multiple scale domains. The modeling tool will accommodate scientific advances in the
quantification of risk assessment via hydrologic process modeling and landscape analysis.
IV
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Section 1
Introduction
In June 1997, the United States Environmental Protection Agency (EPA), National Exposure
Research Laboratory (NERL), Landscape Science Program and the United States Department
of Agriculture, Agricultural Research Service (ARS) entered into an Interagency Agreement for
the purpose of improving ecosystem risk assessment via characterization research, process
modeling, and long-term monitoring studies.
1.1 Goal and Objectives
The primary goal of this project is to develop methods and provide operational hydrologic
modeling tools for determining the vulnerability of arid and semi-arid landscapes to natural and
human induced landscape pattern changes across multiple scale domains.
The specific objectives of this project are to: (1) Develop a sound modeling approach through
calibration and validation on the Walnut Gulch and Upper San Pedro River watersheds; (2)
Assess the impacts of data resolution and misclassification error on watershed response; (3)
Determine model sensitivity to watershed variability and input data; (4) Determine the degree of
complexity required for accurate modeling and assessment at a range of scales; (5) Apply
defensible modeling techniques to a number of basins throughout the semi-arid Southwest; (6)
Assess the impacts of land cover change for a variety of basins with differing topographic,
hydrologic, and land cover pattern characteristics; (7) Develop a desktop computer application for
assessing the hydrologic impacts of land cover change in semi-arid regions; and 8) Publish
methodology and results in peer-reviewed journals.
1.2 Problem Statement
Environmental quality affects our health, our quality of life, and the sustainability of our
economies. Yet pressures from an increasing population coupled with the need for economic
development and an improved standard of living often have multiple negative effects on our
natural resources.
Natural resources of semi-arid regions, such as timely water supplies, fertile soils, vegetation
and wildlife, tend to be scarce, and existing resources are easily damaged by changes in
precipitation pattern and by human action.
Ecosystem management requires a solid understanding of landscape-level ecosystem
processes, and in particular the interaction of geomorphological, hydrological and biological
processes (Stanley, 1995). At present, poor understanding and a lack of information regarding
landscape-scale processes generally hinders assessment of the ecological consequences of
human actions and helps institutionalize land use conflicts (Montgomery et al., 1998). Landform
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analysis can provide an understanding of geomorphological processes that influence hydrological
and ecological processes and systems. Environmental impact analysis protocols developed in
response to environmental legislation generally focus on site-, ownership-, or species-specific
issues at scales inadequate for assessing ecosystem processes and condition (Montgomery et al.,
1995). Hence, the integrated effects of local management decisions can be incompatible with
broader-scale management objectives. Implementing ecosystem management requires a
framework for gathering and interpreting environmental information at a scale and resolution
necessary for addressing the tradeoffs between economic and ecological considerations inherent
to making land management decisions (Slocombe, 1993).
Although a number of initiatives and strategies focus on larger-scales (WFPB, 1992;
FEMAT, 1993; SAT, 1993), there is not yet a consensus on how to implement ecosystem
management (Montgomery et al., 1998). A key element is the development of a practical
operational framework for integrating ecosystem management into land use decision-making.
Watersheds define basic, hydrologically, ecologically and geomorphologically relevant
management units (Chorley, 1969; Likens and Bormann, 1974; Lotspeich, 1980) and watershed
analysis provides a practical analytical framework for spatially explicit, process-oriented scientific
assessment that provides information relevant to guiding management decisions. Watershed
analysis has been adopted to implement ecosystem-oriented management on state and private
(WFPB, 1992; 1993) and federal (FEMAT, 1993) lands in the Pacific Northwest.
This research project will develop methods and provide operational hydrologic modeling tools
under a watershed analysis framework for determining the vulnerability of semi-arid landscapes
to natural and human-induced landscape pattern changes across multiple scale domains.
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Section 2
Background
As populations grow and economic activity increases in the western semi-arid regions of the
United States, there is increasing demand for scarce water resources. This focuses attention on
maximizing the development and protection of renewable water resources. It is therefore
essential to develop modeling techniques that can represent the dominant hydrological processes
and their temporal variability so that vulnerability of semi-arid landscapes to a variety of natural
and anthropogenic stressors at multiple scales can be investigated.
2.1 Effects of Land Cover on Ecological and Hydrological Processes
Many studies have shown that the land uses within a watershed can account for much of the
variability in stream water quality (Omernick, 1987; Hunsaker et al., 1992; Charbonneau and
Kondolf, 1993; Roth et al., 1996). Agriculture on slopes greater than three percent, for example,
increases the risk of erosion (Wischmeier and Smith 1978). A drastic change in vegetation cover,
such as clear cutting in the Pacific Northwest, can produce 90% more runoff than in watersheds
unaltered by human practices (Franklin, 1992). The linkage between intact riparian areas and
water quality is well established (Karr and Schlosser, 1978; Lowrance et al., 1984; 1985). For
example, riparian habitats function as "sponges", greatly reducing nutrient and sediment runoff
into streams (Peterjohn and Correll, 1984).
The percentage and location of natural land cover influences the amount of energy that is
available to move water and materials (Hunsaker and Levine, 1995). Forested watersheds
dissipate energy associated with rainfall, whereas watershed with bare ground and anthropogenic
cover are less able to do so (Franklin, 1992). The percentage of the watershed surface that is
impermeable, due to urban and road surfaces, influences the volume of water that runs and
increases the amount of sediment that can be moved (Arnold and Gibbons, 1996). Watersheds
with highly credible soils tend to have greater potential for soil loss and sediment delivery to
streams than watersheds with non-erodible soils.
Moreover, intense precipitation events may exceed the energy threshold and move large
amounts of sediments across a degraded watershed (Junk et al., 1989; Sparks, 1995). It is during
these events that human-induced landscape changes may manifest their greatest negative impact.
A direct and powerful link exists between vegetation and hydrological processes in semi-arid
environments. Vegetation plays a pivotal role in determining the amount and timing of the runoff,
which ultimately supplies mass and energy for the operation of hydrologic and erosive processes
(Graf, 1988). Most analyses that assess the variability of sediment yield demonstrate that at the
lower end of the precipitation scale (representing semi-arid conditions), small changes in annual
precipitation bring about major changes in vegetation communities and associated sediment yields
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(Figure 1), trailing off at lower values
because of lower runoff totals and at
higher ones because an increasingly
abundant vegetation cover affords better
protection against erosion.
2.2 Effects of Aggregation of Landscape
Attributes on Watershed Response
Recent papers (e.g., Roth et al., 1996;
Weller et al., 1996) suggest that the importance
of landscape features may change in different
environmental settings, or when moving from one
spatial scale to another. Therefore, methods to
analyze and interpret broad spatial scales are
becoming increasingly important for hydrological
and ecological studies. Parameters and processes
important at one scale are frequently not important
or predictive at another scale, and information is
OJ
Ml
OJ
400-,
300-
200 -
100-
sediment yield
1250
D 250 500 750 1000
mean annual precip. (mm)
often lost as spatial data are considered at coarser scales of resolution (Meentemeyer and Box, 1987).
Furthermore, hydrological problems may also require the extrapolation of fine-scale measurement for
the analysis of broad-scale phenomena. Therefore, the development of methods that will preserve
information across scales or quantify the loss of information with changing scales has become a
critical task. Wood et al. (1988) carried out an empirical averaging experiment to assess the impact of
scale. They averaged runoff over small subwatersheds, aggregating the subwatersheds into larger
watersheds, and repeating the averaging process. By plotting the mean runoff against mean
subwatershed area, they noted that the variance decreased until it was rather negligible at a watershed
scale of about 1 km2. That analysis has been repeated for the runoff ratio (Wood, 1994) and
evaporation (Famiglietti and Wood, 1995) using data from Kings Creek, Kansas, which was part
of the FIFE ' 87 experiment. Results from the experiment show that at small scales there is extensive
variability in both runoff and evaporation. This variability appears to be controlled by variability in soils
and topography whose correlation length scales are on the order of 102 - 103m, typical of hillslopes.
At an increased spatial scale, the increased sampling of hillslopes leads to a decrease in the difference
between subwatershed responses. At some scale, the variance between hydrologic response for
watersheds of the same scale should reach a minimum.
2.3 Integration of GIS and Remote Sensing in Hydrologic Modeling
Spatially distributed models of watershed hydrological processes have been developed to
incorporate the spatial patterns of terrain, soils, and vegetation as estimated with the use of
remote sensing and geographic information systems (GIS) (Band et al., 1991; 1993; Famiglietti
and Wood, 1991; 1994; Moore and Grayson, 1991; Moore et al., 1993; Wigmosta et al., 1994;
Star et al., 1997). This approach makes use of various algorithms to extract and represent
watershed structure from digital elevation data. Land surfaces attributes are mapped into the
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watershed structure as estimated directly from remote sensing imagery (e.g., canopy leaf area index),
digital terrain data (slope, aspect, contributing drainage area) or from digitized soil maps, such as soil
texture or hydraulic conductivity assigned by soil series.
Over the past decade numerous approaches have been developed for automated extraction of
watershed structure from grid digital elevation models (e.g., Marks et al., 1984; O'Callaghan and Mark,
1984; Band, 1986; Jenson and Dominque, 1988; Moore et al., 1988; Martz and Garbrecht, 1993;
Garbrecht and Martz, 1993; 1995; Garbrecht et al., 1996). O'Callaghan and Mark (1984) define a digital
elevation model (DEM) as any numerical representation of the elevation of all or part of a planetary
surface, given as a function of geographic location. The most widely used method for the extraction of
stream networks that has emerged is to accumulate the contributing area upslope of each pixel through a
tree or network of cell to cell drainage paths and then prune the tree to a finite extent based on a
threshold drainage area required to define a channel or to seek local morphological evidence in the terrain
model that a channel or valley exists (Band and Moore, 1995).
The techniques used for delineation of the drainage path network by surface routing of drainage
area and local identification of valley forms are ultimately dependent on a topographic signal generated in
a local neighborhood on the DEM. As the approach is used to extract watershed structure with
increasingly lower resolution terrain data, higher frequency topographic information is lost as the larger
sampling dimensions of the grids act as a filter. Therefore, if watershed structural information is used to
drive the hydrological model, the scaling behavior and consistency of the derived stream network with grid
dimension needs to be addressed. One of the primary questions dealing with automated extracted channel
network is that of the appropriate drainage density. Some authors suggest criteria to find this appropriate
scale. For example, Goodrich (1991) found a drainage density of approximately 0.65 to 1.52 x 10'3m for
watersheds greater than 1 hectare was adequate for kinematic runoff modeling in semi-arid regions.
Similarly, La Barbera and Roth (1994) proposed a filtering procedure based on the identification of
threshold value for the quantity ASk, where A is the contributing area, S the stream slope and k = 2. This
procedure consists in the progressive removal from the drainage network of the first order stream which
presents the minimum ASk value; the procedure is iterated up to a given target value for the area drained
by first order streams. Galore et al. (1997) found that above a certain threshold, an increase in resolution
in the spatial description of drainage networks obtained from a DEM cannot be directly linked to an
increase of information. The criterion they used for assessing the amount of information contained in the
drainage was based on the information entropy concept of Shannon (1948).
Land use is an important watershed surface characteristic that affects infiltration, erosion, and
evapotranspiration. Thus, almost any physically based hydrologic model uses some form of land use data
or parameters based on these hydrologic processes (Spanner et al., 1990; 1994; Nemani et al., 1993).
Distributed models, in particular, need specific data on land use and their location within the basin. Some
of the first research for adapting satellite-derived land use data was done by Jackson et al. (1976) with
the US Army Corps of Engineers STORM Model (US Army Corps of Engineers, 1976). However, most
of the work on adapting remote sensing to hydrologic modeling has been with the Soil Conservation
Service (SCS) runoff curve number model (US Department of Agriculture, 1972). The SCS model has
been widely used in hydrology and water resources planning of agricultural areas. The model was
originally developed for predicting runoff volumes from agricultural fields and small watersheds.
However, it has been expanded for subsequent use in a wide variety of conditions at many basin sizes
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including urban and suburban areas. In early work with remotely sensed data, Jackson et al. (1977),
demonstrated that land cover (particularly the percentage of impervious surface) could be used
effectively in the STORM Model (US Army Corps of Engineers, 1976). In a study of the upper Anacostia
River basin in Maryland, Ragan and Jackson (1980) demonstrated that Landsat-derived land use data
could be used for calculating synthetic flood frequency relationships. Results can be erroneous if land use
is mislabeled. A study by the US Army Corps of Engineers (Rango et al., 1983) estimated that any
individual pixel may be incorrectly classified about one-third of the time. However, by aggregating land
use over a significant area, the misclassification of land use can be reduced to about 2% (Engman and
Gurney, 1991).
More recently, vegetation classification studies implementing digital satellite data have utilized
higher spatial, spectral, and radiometric resolution Landsat Thematic Mapper (TM) data with much more
powerful computer hardware and software. These studies have shown that the higher information content
of TM data combined with the improvements in image processing power result in significant
improvements in image processing power resulting in significant enhancement in classification accuracy
for more distinctive classes (Congalton et al., 1998).
A detailed analysis of the effects of the thematic accuracy of land cover is necessary before any
attempt on using the hydrologic modeling tool to determine the vulnerability of semi-arid landscapes to
land cover changes. The accuracy of maps made from remotely sensed data is measured by two types of
criteria (Congalton and Green, 1999): location accuracy and, classification or thematic accuracy. Location
accuracy refers to how precisely map items are located relative to their true location on the ground.
Thematic accuracy refers to the accuracy of the map label in describing a class or condition on the earth.
For example, if the earth's surface was classified as forest, thematic map accuracy procedures will
determine whether or not forest has been accurately labeled forest or inaccurately labeled as another
class, such as water.
The widespread acceptance and use of remotely sensed data has been and will continue to be
dependent on the quality of the map information derived from it. However, map inaccuracies or error can
occur at many steps throughout any remote sensing project. According to Congalton and Green (1999),
the purpose of quantitative accuracy assessment is the identification and measurement of map errors.
Quantitative accuracy assessment involves the comparison of a site on a map against reference
information for the same site. The reference data is assumed to be correct.
The history of accuracy assessment of remotely sensed data is relatively short, beginning around
1975. Researchers, notably Hord and Brooner (1976), van Genderen and Lock (1977), proposed criteria
and techniques for testing map accuracy. In the early 1980s more in-depth studies were conducted and
new techniques proposed (Rosenfield et al., 1982; Congalton et al., 1983; and Aronoff, 1985). Finally,
from the late 1980s up to present time, a great deal of work has been conducted on accuracy assessment.
An important contribution is the error matrix, which compares information from reference sites to
information on the map for a number of sample areas. The matrix is a square array of numbers set out in
rows and columns that express the labels of samples assigned to a particular category in one classification
relative to the labels of samples assigned to a particular category in another classification. One of the
classifications, usually the columns, is assumed to be correct and is termed the reference data. The rows
usually are used to display the map labels or classified data generated from remotely sensed data. Error
matrices are very effective representation of map accuracy, because the individual accuracy of each map
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category are plainly described along with both errors of inclusion (commission errors) and errors of
exclusion (omission errors) present in the map (Congalton and Green, 1999). A commission error occurs
when an area is included in an incorrect category. An omission error occurs when an area is excluded
from the category to which it belongs. In addition to clearly showing errors of omission and commission,
the error matrix can be used to compute overall accuracy.
Soils information derived from a GIS are generally gathered in a similar manner to vegetation,
with the exception that remote sensing often cannot provide critical information about soil properties,
especially if the soil is obscured by a vegetation canopy (Band and Moore, 1995). Substantial progress has
been made in estimating near-surface and profile soil water content with active and passive microwave
sensors and in the estimation of hydraulic properties by model inversion (e.g., Entekhabi et al., 1994).
However, in general, soil spatial information is the least known of the land surface attributes relative to its
well-known spatial variability that has been observed in many studies (Nielsen and Bouma, 1985).
2.4 Model Selection and Development
Hernandez et al. (1998 a) provided an extensive review and evaluation of existing hydrologic
models that might possibly be used in the analysis of landscape effects on watershed response at various
spatial scales. Those models that met certain selection criteria were then examined and described in
greater detail. The authors presented an overview of the availability of required model input data in US
and Mexico. They then discussed the primary hydrologic processes important for multi-scale hydrologic
modeling in the Lower Colorado River basin.
In a subsequent report, Hernandez and Goodrich (1998 b) examined the relationship between
vegetal cover and surface runoff, erosion, and sediment yield from watersheds. The authors conducted a
more detailed examination of likely hydrological models and determined that the SWAT (Arnold et al.,
1994) and KINEROS (Smith et al., 1995) models were the most appropriate for evaluating watershed-
scale and river basin-scale landscape effects, respectively. In order to conduct watershed scale
assessment at the scale of the San Pedro River Basin and the Walnut Gulch Watershed, it is necessary to
allow characterization of a variety of hydrologic process at different spatial and temporal scales. With the
available data, the hydrologic model SWAT appears to be the best model suited for characterizing the
hydrological and erosion processes at the scale of the San Pedro River Basin. The SWAT model offers
flexible watershed configuration, reach routing transmission losses, irrigation and water transfer, lateral
flow, groundwater, and detailed lake water quality components. Four strategies for parameterizing the
subwatersheds in the SWAT model include: a three dimensional grid, two-dimensional hillslope, multiple
one-dimensional, and lumped one-dimensional. The effects of land cover and land use can be incorporated
explicitly in the basin modeling by using the grid, two dimensional, and multiple one-dimensional
configurations. In addition, SWAT operates on a daily time step and more seasonal framework. This
feature allows the simulation of precipitation, snowmelt, evapotranspiration, soil moisture, and infiltration at
a limited complexity level.
KINEROS is suitable for a smaller scale such as the Walnut Gulch Experimental Watershed and
more focused, detailed investigations of runoff an erosion because is a distributed, event-oriented,
physically based model describing the processes of surface runoff and erosion from small agricultural and
urban watersheds. However, the greater complexity of KINEROS also entails greater data requirements.
It has been developed and validated largely in arid and semi-arid setting with explicit treatment of channel
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losses. In addition, KINEROS has a specially developed space-time rainfall interpolator that allows it
accurately treatment of highly variable thunderstorm rainfall. KINEROS infiltration and erosion
parameters are primarily derived through soil characteristics with modifications made for surface cover
conditions. For watersheds larger than 1000 ha, application of a detailed, process based model, such as
KINEROS, may be difficult to justify in the absence of distributed rainfall data, given comparable results
from a simpler model which does not entail the costs associated with detailed basin characterization
required for KINEROS model inputs.
The combination of these two models will allow users to identify hydrological and ecological
problems at the basin scale using the SWAT model, once the problem area has been defined, if enough
data is available, the KINEROS model can be applied to further investigate possible solutions to the
problem.
In a third report, Hernandez et al. (1998 c) tested the response of these two models on a subset
within the Walnut Gulch Experimental Watershed in Tombstone, Arizona. Based on the results, calibration
and validation of the hydrologic models were recommended to improve the reliability of the models as a
function of model input data Furthermore, a sensitivity analysis was advised to support the integration of
The North American Landscape Characterization (NALC) (USGS, 1999b) based land cover class and
the State Soil Geographic (STATSGO) Database (USDA-NRCS, 1994) with the hydrologic simulation
models.
The NALC project is a component of the Multi-Resolution Land Characteristics (MRLC)
Consortium. The MRLC vision is to facilitate the development of a national multi-resolution land cover
database from both coarse (Advanced Very High Resolution Radiometer [AVHRR]) and medium
resolution (Landsat Thematic Mapper [TM]) satellite imagery and field data. The main objective of the
NALC project is to produce standardized remote sensing data sets that consists of three or more
registered Landsat Multi-Spectral Scanner (MSS) images corresponding to the 1990s, 1980s, and 1970s
time periods. On average, a NALC data set consists of one scene from the 1990s and 1980s and two
from the 1970s.
The STATSGO database was designed primarily for regional, multi-state, river basin, multi-county
resource planning, management, and monitoring. This data is not detailed enough to make interpretations
at a county level. Soil maps for STATSGO are compiled by generalizing more detailed soil survey maps.
Where more detailed soil survey maps are not available, data on geology, topography, vegetation, and
climate are assembled, together with Landsat images.
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Section 3
Conceptual Model
The conceptual model
of the integrated landscape/
hydrologic modeling tool is
described in Figure 2. The
conceptual model is designed
within a data base management
system framework which
comprises the following
elements: collection, input &
correction; storage and retrieval
manipulation & analysis; and
output & reporting. Each
element is described in the
following sections.
Rainfall
Topography
Land use/cover
Soils
Satellite data
Landscape
Analysis
Hydrology
Model
Reports
Maps
Statics
Knowledge
Collection, Data Input and Correction
D This module covers all aspects of capturing spatial data from existing maps, field observations, and
sensors (including aerial photography, satellites, and recording instruments) and converting them to a
standard digital form. Once the data have been entered, the data will be checked for errors such as possible
inaccuracies, omissions, and other problems.
Storage and Retrieval
D Building a digital database is costly and time-consuming process and it is essential that the digital
map information is transferred from the magnetic media of the computer to a more permanent storage medium
where it can be safely preserved.
Manipulation and Analysis
D The landscape analysis and the hydrologic modeling will be carried out in this module. The
landscape analysis consists of an error matrix for land cover; a computer program to simulate the spatial
distribution of errors; and a computer program to calculate landscape metrics. The hydrologic modeling
component consists of a computer program to characterize watershed complexity; a user-friendly GIS
interface to parameterize the hydrologic models; and hydrologic models. Results from the landscape
analysis will provide information to the hydrologic model for evaluating the effects of misclassification
error of Landsat land use/cover on watershed response. Furthermore, the effects of drainage network
density and land cover on watershed response will be carried out in this module.
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Output and Presentation
The data output and presentation module is concerned with the way the data are displayed and
how the results of the analyses are reported to the users. Data will be presented in a variety of ways
ranging from the image on the computer screen, through hardcopy output drawn on printer or plotter to
information recorded on magnetic media in digital form.
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Section 4
Technical Approach
This section focuses primarily on describing the tasks to be carried out on the research
proposal. A tentative schedule and milestones for calendar years 1999-2002 is presented in Table 1
(Appendix). This section includes description of the area, study design/methodology, data acquisition,
and quality assurance.
4.1 Description of the Area
Walnut Gulch Experimental Watershed
The Walnut Gulch Experimental
Watershed (WGEW) encompasses
approximately 150 km2 in southeastern
Arizona, USA (Figure 3) surrounding the
historical western town of Tombstone.
Walnut Gulch is a tributary of the
San Pedro River, which originates
in Sonora, Mexico and flows
north into the United States. The
watershed is representative of the
brush and grass covered rangeland found
throughout the semi-arid Southwest and is a
transition zone between the Chihuahuan and
Sonoran Deserts. Elevation of the watershed
ranges from 1,220 m to 1,890 m. Cattle grazin
is the primary land use with mining,
distributed urbanization, wildlife habitat and
recreation making up the remaining uses.
The city of Tombstone is undergoing
relatively fast growth, and the urban
area within the watershed is growing.
For further details on the
description of the Walnut Gulch
Experimental Watershed see
Osborn (1983) and Renard et al.
(1993).
The San Pedro Basin
Figure 3. Locations of the Walnut Gulch Experimental
Watershed and the Upper San Pedro Basin
within the Lower Colorado River Basin.
The Sonoran desert and surrounding vegetation extend from central Sonora, Mexico, up through
southern Arizona, USA, providing an exceptionally diverse ecosystem that is being studied by
scientists in many disciplines on both sides of the border. The San Pedro River Basin (SPRB)
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covers about 12,000 km2 and spans the Mexico-US Border from northern Sonora into southeastern Arizona
(Figure 3). It has particularly interesting characteristics: significant topographic variability (1,200 - 2,900 m)
providing ecological and climatic diversity over distances as short as 20 km; significantly different cross-
border land uses visible from satellite multi-spectral images. Diverse vegetation types include Sonoran and
Chihuahuan desertscrub, grasslands, chaparral and Madrean evergreen woodlands, and high-elevation conifer
forests (McClaran and Brady, 1994).
The San Pedro riparian corridor is a narrow area sustained many times of the year by the regional
aquifer. It has been declared one of the '12 Last Great Places of the Western Hemisphere' by The Nature
Conservancy and the No. 4 most endangered U.S. river by American Rivers (1997). For further details on the
description of the San Pedro River Basin see Goodrich (1994).
4.2 Study Design/Methodology
Pursuant to the primary objective of modeling hydrologic response to land use, the life cycle of this
research project will be divided into several overlapping phases that build in complexity and understanding
towards the development of a robust scientifically defensible application. Throughout the project, user
interface programs will be designed and developed to ease model application. The first phase will be devoted
to model development and application on relatively small, homogeneous areas. Once the fundamental validity
of this modeling approach has been demonstrated, significant model testing and optimization will be used to
determine potential sources of error and the best techniques and tools at a range of basin scales and
complexities. Model application on a variety of basins throughout the semi-arid Southwest will be used to
further refine the approach. Ultimately, the model will be implemented as a unified GIS/hydrologic modeling
tool for desktop applications.
4.2.1 Model development
Compile climatic and hydrologic data for the WGEWand SPRB
On Walnut Gulch, rainfall information will be compiled from the historical 85-raingauge network
data. Runoff data will come from various historical and current gaging structures. For the San Pedro basin,
rainfall data will be retrieved from the National Climatic Data Center database (US Department of Commerce,
1995), and streamflow data at Charleston will be obtained from the USGS database (USGS, 1999a).
Assemble CIS data at high/low resolution for the WGEWand SPRB
Input data is the driving force behind model development and implementation. One of the first steps
in the evolution of this 4-year research project will be the assemblage of these input data layers for three
intensive study watersheds. To date, the emphasis on modeling has been placed at the sub-watershed level to
demonstrate model functionality given the range of input data. During the next phase of research, these
findings will be expanded to include the entire WGEW and the SPRB. As such, we will be acquiring and
error-checking the input data required for model application on the WGEW and the SPRB.
A high-resolution, highly accurate geographic information system (GIS) database has been created
for the WGEW. A detailed soil survey (Breckenfield et al., 1995) was digitized as part of the
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effort for capturing detailed geographic data. This soil survey provides an opportunity for determining the
impact of input data quality and resolution on model efficiency (defined as the degree to which model
prediction is similar to observed data). Towards this end, data will be assembled for model input for both
high and standard resolutions within WGEW, and at the standard resolutions on the SPRB.
Assess input parameters and construct look-up tables for GIS
The hydrologic models that will be used in this research are spatially distributed, and hence require a
large number of input parameters. These parameters describe the physical properties of the model area, and it
is critical that the methods used to determine these values be as consistent, accurate and repeatable as
possible under the constraints of the base input data across a range of applications. Many of the model
parameters, such as those relating to topography and vegetation type, will be determined directly from
available spatial data stored in a GIS (Martz and Garbrecht, 1992; Garbrecht et al., 1996; DiLuzio et al.,
1998). However, a large number of parameters vary widely in space and time, and are not directly
measurable. These parameters must be derived through empirical relationships that relate quantifiable
parameters to those that must be estimated (Brakensiek and Rawls, 1983; Shen and Julien, 1993).
A thorough vetting of the required parameters and the methods used to estimate their values
throughout the study areas is of primary importance to the early phases of this project. Input parameter tables
are available for both KINEROS and SWAT, and we will develop and report on the manner by which each of
the parameters will be determined. Such a report will be critical to future interests towards quality assurance
and control since the measures will be repeatable and clear.
Once the methods used to determine the input parameters have been decided upon, techniques for
implementing their application across the study area will be developed. Many of these procedures have
already been developed and utilized in the preliminary modeling exercise (Hernandez et al., 1998c), but more
research is needed on variable terrain (such as exists on the entire Walnut Gulch watershed and the San Pedro
basin) to validate and finalize these procedures.
The goal of this phase of the research project is to create automated methods for characterizing
modeled basins and parameterizing the hydrologic models. Neither SWAT nor KINEROS may be
characterized as possessing simple input requirements for successful implementation. The complexity of the
models requires complex input, and parameterizing these models at the necessary detail on large basins would
be onerous and beyond the scope of this project in the absence of GIS and automated methods.
Calibrate and validate hydrologic models for the WGEW and SPRB at various model resolutions with
KINEROS and SWAT
The calibration and validation process will be carried out using historical stream flow values along
the San Pedro River at Charleston, Arizona and at the outlet of the WGEW. A statistical analysis of the
historical records will be performed to determine the most appropriate periods for calibration and validation.
A detailed analysis will be carried out to determine the influence of land cover changes on hydrologic
and erosion parameters as part of the calibration process. Furthermore, data from channel reaches and storm
events on Walnut Gulch will be selected to estimate and calibrate transmission losses. That is, the
transmission loss magnitude will be estimated by comparing the measured hydrographs at the upstream and
downstream stations of a channel reach from storm events with all runoff originating above the upper station.
The calibration procedure will be performed with the assistance of a computer-based optimization routine
(PEST; Doherty, 1998) to adjust the parameters until simulated outputs fit the observed data as closely as
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possible. The selection of a criterion of model accuracy is an important aspect of the model calibration
procedure since it provides the basis for adjusting parameter values. The Nash-Sutcliffe (1970) efficiency
coefficient will be used as a criterion of goodness of fit in the calibration process.
There is the potential for introducing uncertainty and error both in calibrating the model parameters
and in obtaining field data with which to compare the simulated output. Therefore, in order to reach some
objective conclusion as to whether or not the model is performing to satisfaction, a validation procedure will
be implemented. As in the calibration phase, the process of validation will involve a comparison of the
observed and simulated outputs. However, in the validation phase, hydrologic parameters will not be adjusted,
and a separate set of observed data will be used to compare total runoff, peak discharge, and suspended
sediment concentration.
Results from the validation procedure illustrate model stability for a range of input data. Instability
may exist due to the presence of either interdependent parameters or a parameter that was inadequately
accounted for during calibration suddenly becoming crucial to model efficiency. If neither of these reasons
for parameter variation can be substantiated, it may be an indication that the model itself can only fit observed
data when optimized, and is therefore inadequate as a predictive tool for a non-calibrated situation (Beck,
1987).
Development of criteria for selecting watersheds based on hydrologic, ecological, and geomorphic
characteristics
Model calibration and validation will be performed for the Walnut Gulch watershed and San Pedro
Basin; investigations performed on these areas will provide the scientific justification for model application at
a range of scales. Methodologies developed on Walnut Gulch and the San Pedro will be extended to a several
(3-4) other basins in the semi-arid Southwest for intensive study.
Selection of the basins for future intensive study will be made on the basis of basin hydrologic,
ecological, and geomorphic characteristics. The Walnut Gulch watershed and San Pedro Basin allow for
investigations into the effects of scale, data error, and land use change on model efficiency and can provide
insight into appropriate applications of this emerging technology. These study areas are located in the
transition zone between the Chihuahuan and Sonoran deserts. However, they do not provide a means for
investigating the impact of differences in basin physiographic characteristics nor basin input processes such
as snowmelt, and hydrologic regime on the model application. Other gauged basins will be selected to
provide spatial variability in model input across a broad range of basin scales.
The set of criteria for selecting the intensive study basins will be finalized within the next year. The
possible selection criteria include basin size, climate, data availability, and hydrologic response. In order to
investigate scaling issues, the selected basins will range in size from 3000-10,000 km2, thereby bracketing the
Upper San Pedro Basin. A range in climate and hydrologic response is desired; therefore, basins will be
selected from different climatic regimes, potentially including the Sonoran desert (runoff caused by winter
and summer rainfall), Colorado Plateau (runoff driven primarily by snowmelt) and Mohave desert (runoff
from winter rains). The selection of basins may be refined by ecological region such as those outlined by
Omernik (1987). Because the approach outlined in this research plan relies on the calibration and validation
of the watershed models, it is a prerequisite that the chosen basins have an abundant data history. Necessary
data include accurate spatial data describing topography, soils, and land cover derived from remote sensing
(NALC, MRLC), and extensive runoff gaging data for the time period in which the land classification took
place.
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In addition, watersheds will be selected based on criteria that identify distinct landscape scale and
form. Three attributes that quantitatively distinguish landscapes are drainage density (D), slope (S), and relief
(H) (Dietrich and Montgomery, 1998). Strahler (1964) combined these attributes to form the geometry
number: G=HD/S. The drainage density, slope, relief, and the geometry number will be calculated for a
number of watersheds of different sizes from available DEMs.
Assess model efficiency relative to data resolution
Spatially distributed data that will serve as input to the hydrologic models will be developed from GIS
map layers. Topographic, soil, and vegetation data will come from the three primary sources: the USGS DEM
(USGS, 1999a) products; the NRCS STATSGO (USDA-NRCS, 1994) soil data; and the NALC (USGS,
1999b) land classification products. These data are served at relatively low levels of resolution (30 m, 30 m,
and 60 m, respectively), and the impact of this resolution on model response needs to be investigated.
An investigation into the role of GIS data resolution will be carried out using the two soil maps at a
range of scales on Walnut Gulch. The hydrologic models KTNEROS and SWAT will be parameterized using
primary and secondary data derived from the soil maps, and the impact of these data on model response will
be determined. The hypothesis put forth for this phase of the research is that the more detailed soil map will
improve model efficiency. However, the question of spatial scale must also be addressed to determine
whether model behavior will be altered at the basin scale, where variability in soil classification tends to be
less hydrologically significant.
4.2.2 Model testing and optimization
Analyze land cover misclassification based on error matrix
One of the most significant sources for model parameterization will be the NALC land classification
data. The NALC data set contains information regarding the spatial distribution and temporal change in
vegetation and land use across the study areas. These data are important because they not only contain
relevant model data (estimates of canopy and ground cover, for examples), but also dynamically affect a host
of hydrologic parameters (e.g., curve number, infiltration). The impact of misclassification of NALC data
must therefore be addressed for better understanding model response and providing for quality assurance and
control.
Land cover accuracy assessment
An accuracy assessment of 1997 Landsat Thematic Mapper (TM) land cover classification of the
Upper San Pedro River Basin is being carried out in cooperation with the University of Arizona, Office of
Arid Lands Studies. The accuracy assessment will produce an array of numbers set out in rows and columns
that express the sample units assigned to a particular category in one classification relative to the number of
samples units assigned to a particular category in another classification.
Determine impact of land cover misclassification errors on model efficiency
The effects of misclassification among inter-class land cover will be evaluated using a simulation
model developed by Wickham et al. (1997). The error simulation model, written using the Arc/Info GRID
module (ESRI, 1994), is based on (1) misclassification calculated from an error matrix, and (2) spatial
autocorrelation in land cover classification error (Congalton, 1988). The model will be used to randomly
introduce error into the San Pedro River Basin land cover map. The simulation model will provide different
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spatial distribution patterns of error. An error matrix supplied by Dr. Stuart Marsh, the University of Arizona,
Office of Arid Lands Studies, will serve as the source data for the simulation model. For each error
distribution pattern, the hydrologic parameters will be altered and the hydrologic simulation models will be run
to evaluate the response of the watershed. Furthermore, errors of commission and omission will be
interpreted with respect to the extent that these errors significantly alter parameter estimation; offsetting
errors may mitigate the impact on model efficiency, while compounding errors may yield unrealistic results.
Sensitivity Analysis
A sensitivity analysis will be performed to examine the response of the hydrologic models as a
function of land cover changes on the WGEW and SPRB. Sensitivity analysis is normally conducted by
assessing the effect on the model output of a fixed percentage change in each model parameter, while holding
all other parameter values constant (McCuen and Snyder, 1986). However, in this case a sensitivity analysis
based on a fixed percentage change for each parameter value may be unrealistic due to the range of variation
that is observed for each parameter in the field (Kirkby et al., 1993). Consequently, an alternative approach
taken will be used employing frequency distributions for each hydrologic parameter. Model parameter
changes will be a function of the parameters' standard deviations. Results from the sensitivity analysis will
provide sound information as to whether land cover maps resolution being used as an input for the hydrologic
models are adequate for multi-scale watershed assessment. Furthermore, a sensitivity analysis within each
class cover will be carried out to examine the response of the hydrologic models as a function of canopy
cover conditions. That is, hydrologic parameter values will be changed to consider canopy cover conditions
such as poor, fair, and excellent for each class.
Create subwatershed maps at varying levels of complexity and assess model efficiency relative to network
complexity for the WGEW and SPRB
Evaluation of the effects of the number of subwatershed elements and the averaging of land cover
information on hydrologic response will be carried out. To address the issue of number of subwatershed
elements necessary for adequate model behavior, the WGEW and SPRB will be divided into several scenarios
with differing complexities. The criterion for delineating the watersheds is based on the critical source area
concept wherein the initiation of channel routing is adjusted. Each of the watershed configurations will be
modeled for runoff and sediment yield. On Walnut Gulch, KTNEROS and SWAT will be used for single
storm and continuous analyses, respectively, while only SWAT will be applied on the San Pedro. Model
efficiency will be determined for the various simulation runs. It is predicted that an inverse relationship
between watershed size and geometric complexity will be found. Determining this relationship is necessary
for the distribution of model implementation since there is a need for a standardized approach to the
determination of watershed configuration and minimum data requirement.
The entropy (Shannon, 1948) concept approach will be employed to assess the amount of
information lost by averaging subwatershed elements. The performance of the hydrologic models is assessed
by computing the entropy information for each watershed configuration and by comparing monthly and
annual runoff simulations with observed data. The value of the entropy information increases with increasing
the number of watershed elements up to a number of elements that no longer captures new information and
the model results are not further improved.
4.2.3 Model application
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Define design storms
A statistical analysis will be performed on measured rainfall records to determine the most likely
spatial and temporal distribution of design storms. For example, peak rainfall timing will vary within different
storm events, as will the loci of intensities. The analysis of measured rainfall hyetographs will show the most
likely rainfall distributions, which will be used to develop the design storms. Design storms will be determined
for the 2, 5, 10, and 25 year return periods.
Calibrate and validate hydrologic model for the intensive study basins
The calibration and validation process will be carried out using historical stream daily values at
several stream gauges along the selected basins. A statistical analysis of rainfall and stream records will be
performed to determine the most appropriate time periods. The calibration and validation of these data will be
carried out employing the same procedure used for the Walnut Gulch watershed and San Pedro basin
described above. However, since only one MRLC scene will be available, the calibration will be carried out
using hydrologic information compiled for one-year period prior to the date the image was acquired.
Similarly, the validation of the hydrologic model will be carried out using hydrologic information for one-year
period after the date the image was acquired.
Model and analyze watershed response as a function of land cover change, spatial distribution of rainfall
and design storms for the WGEWthe SPRB
The measurement of rainfall during a storm event consists of determining the time over which an
increment of rainfall depth occurs at a defined location. Consequently, the measurement of rainfall is a point
measurement of a spatially variable parameter. Meteorological data, such as rainfall intensity, for non-
measurement locations are not defined by measurement processes and must therefore be inferred, thereby
introducing uncertainty and error. Many methods have been developed for the inference of the spatial
distribution of rainfall from measured at a specific location (Luk and Ball, 1997). A spline surface method will
be implemented for characterizing the spatial distribution of rainfall. This method consists of using low-order
polynomials to avoid over-fitting the measurement points by high-order polynomials (Luk and Ball, 1997).
Surfaces generated in this fashion have been found to be a robust spatial interpolation for meteorological data.
Using digital land cover maps, interpolated rainfall depth and design storms, the hydrologic models will be
calibrated and validated for assessing the relative changes of cover on watershed response.
Model and analyze watershed response as a function of land cover change and design storms for intensive
study basins
A similar approach used for modeling the WGEW and the SPRB will be carried out for modeling
intensive study basins.
4.2.4 Model implementation
Create/update GIS programs (AML, Avenue) to automate GIS parameterization for delivering GUI-driven
GIS/hydrologic modeling tool
As the models are integrated with the GIS data a suite of programs will be developed to automate the
parameterization of the hydrologic models. The development of graphical-user-interface (GUI) tools is a
critical step towards implementing the techniques across a range of scales by a variety of clients. The largest
drawbacks to hydrologic modeling at larger scales are the complexity of the input data and expert knowledge
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and proficiency required to initiate the model runs. Without a great deal of complexity, models cannot achieve
the desired accuracy, but this complexity restricts their application by persons who are not intimately familiar
with their operation. A stated goal of this research project is to develop and apply user-friendly programs that
will allow for the rapid and accurate application of SWAT and KINEROS at a range of basin scales given a
minimum of expertise and input data. These tools will be critical for transferring this technology to resource
managers and regional planners who are interested in projecting the impact of land use change on hydrologic
response.
It is proposed that the GUI tools be designed for personal computer (PC) application. While it is
recognized that powerful UNIX-based programs exist, it is probable that users of this technology will be
more familiar with and have access to PCs; hence, to appeal to a wide audience, the programs will be tailored
for the PC environment. Collaborating scientist at the US-EPA NERL location have been developing a PC-
based landscape analysis toolkit (ATtlLA-Analytical Tools Interface for Landscape Assessments) integrating
GIS data with the derivation of landscape indicators in a GUI-driven environment by embedding the spatial
analysis within Arc View (a PC-based GIS). It is proposed that this approach be extended to incorporate the
hydrologic modeling work such that a comprehensive suite of landscape analysis tools including ecologic and
hydrologic models is available to the interested party via a user-friendly interface.
Furthermore, an effort to link SWAT with Arc View has been undertaken by the USDA-ARS,
Blackland Research Center. During the course of this project, collaborative efforts with scientist at the
Blackland Research Center will be pursued, and the integration of KINEROS and SWAT with a PC-based
GIS should be enhanced by existing research. Sections of the programming and GUI development effort will
be developed throughout the life of the project, and intermediate products made available to beta testers, with
the delivery and technology transfer targeted for 2002.
4.3 Data Acquisition
Due to the large scale on which model development and implementation will be based, data
acquisition will play a critical role in the success of this project. The fundamental spatially distributed GIS
data that will serve as input to the hydrologic models are soils, land cover, and topography. It is proposed
that topography be derived from freely available USGS 7.5' digital elevation models (OEMs), that soils
information be derived from USDA-NRCS STATSGO soil polygons (also freely available), and that land
cover come from EPA-NALC and Multi-Resolution Land Characteristics (MRLC) products, to be supplied by
the US-EPA for the proposed research study basins. Distributed climatic data is the other primary model
input supplied by the National Climatic Data Center (NCDC, 1997).
Data will be acquired on an as-needed basis according to the four project phases. The acquisition
and verification of these data will be time-consuming due to the large quantity of data, and as such it will be
collected in advance of each subsection of the project and error-checked prior to use. As part of the joint
collaboration between the National Exposure Research Lab and the Southwest Watershed Research Center,
the EPA will be responsible for providing digital cartographic data (GIS theme layers) to the USDA-ARS for
the proposed study basins.
On Walnut Gulch, rainfall information will be compiled from historical 85-raingauge network data.
Runoff data will come form various historical and current gaging structures. For the San Pedro Basin, rainfall
data will be retrieved from the National Climatic Data Center database (US Department of Commerce, 1995),
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and stream flow data at Charleston will be obtained from the USGS database (USGS, 1999 a).
Input data is the driving force behind model development and implementation. One of the first steps
in the evolution of this long-term research project will be the assemblage input data layers for three study
watersheds. To date, emphasis on modeling has been placed at the subwatershed level to demonstrate model
functionality given the range of input data. During the next phase of the research, these findings will be
expanded to include the entire Walnut Gulch watershed and the larger San Pedro Basin. As such, we will be
acquiring and error-checking the input data required for model application on the Walnut Gulch and San
Pedro watersheds.
4.4 Quality Assurance
Quality assurance for this project will address the following issues: the rationale to select hydrologic
models and how each model captures relevant processes; assessment procedures for identifying and
correcting errors in source data; methods to interpret and analyze results from calibration and validation
processes; procedures to document performance results; and, procedures to document software
development. The following sections will focus on the description of each issue.
4.4.1 Rationale for model selection
In the selection process, strong emphasis was placed on models that were able to characterize
complex watershed representations to explicitly account for spatial variability of soils, rainfall distribution, and
vegetation heterogeneity. The effects of land use and land cover on surface runoff and sediment yield were
also stressed in the model selection criteria. Furthermore, we concentrated on models that characterize
surface runoff and sediment yield producing mechanisms. For analysis of large watersheds, where storage
characteristics plays a key factor on surface runoff, we selected models that account for channel routing and
reservoir storage. Moreover, the governing equations describing the hydrologic and soil erosion processes
were a major factor in selecting the models. That is, we were interested in models with equations based on
fundamental principles of physics or robust empirical methods widely used in computing surface runoff and
sediment yield.
The following discussion provides an overview of the theory and structure of both models and
verification of model results. First, the basic theory and assumptions of the processes are presented. Next,
test results for both models are presented.
The "Soil and Water Assessment Tool" (SWAT) (Arnold et al. 1994) is public domain software
developed and actively supported by the USDA-Agricultural Research Service at the Grassland, Soil and
Water Research Laboratory in Temple, Texas. SWAT is a continuous-time model that operates on a daily
time step. The objective in model development was to predict the impact of management on water, sediment
and agricultural chemical yields in large ungaged basins. To satisfy the objective, the model (a) uses readily
available inputs; (b) is computationally efficient to operate on large basins in a reasonable time; and (c) is
continuous time and capable of simulating long periods for computing the effects of management changes.
The SWAT components can be placed into eight major divisions: hydrology, weather, sedimentation, soil
temperature, crop growth, nutrients, pesticides, and agricultural management. The SWAT model
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characterizes the main hydrologic processes contributing to runoff and sediment yield as follows.
Weather
The weather variables necessary for driving SWAT are precipitation, air temperature, solar radiation,
wind speed, and relative humidity. If daily precipitation and maximum/minimum temperature data are
available, they can be input directly to the model. If not, the SWAT weather generator routine can simulate
daily rainfall and temperature. Solar radiation, wind speed, and relative humidity are always simulated.
Surface runoff
Runoff volume is estimated with a modification of the SCS curve number method (USDA-SCS,
1972). Peak runoff rate estimates are based on a modification of the Rational Formula. The runoff
coefficient is calculated as the ratio of runoff volume to rainfall. The rainfall intensity during the watershed
time of concentration is estimated for each storm as a function of total rainfall using a stochastic technique.
The watershed time of concentration is estimated using the Manning's Formula considering both overland
and channel flow.
Transmission losses
Flow abstractions, or transmission losses, reduce runoff volume as the flood wave travels
downstream. SWAT uses Lane's method described in Chapter 19 of the SCS Hydrology Handbook (USDA-
SCS, 1983) to estimate transmission losses. Channel losses are a function of channel width and length, and
flow duration. Both runoff volume and peak rate are adjusted when transmission losses occur.
Sediment yield
Sediment yield is estimated for each subbasin with the Modified Universal Soil Loss Equation
(MUSLE) (Williams and Brendt, 1977). The model runoff component supplies runoff volume and peak runoff
values required by the MUSLE.
KINEROS, an acronym for KTNematic runoff and EROSion model, has evolved over a number of
years primarily as a research tool (Smith et al., 1995). However some consulting firms have been attracted by
several of its unique features and KINEROS has been used as an engineering tool in the U.S. and abroad.
KINEROS is public domain software developed by the USDA-Agricultural Research Service, and supported
by the Southwest Watershed Research Center in Tucson, Arizona. KINEROS is an event oriented, physically
based model describing the processes of interception, infiltration, surface runoff, and erosion from small
agricultural and urban watersheds. A cascade of planes and channels represents the watershed; and the partial
differential equations describing overland flow, channel flow and erosion, and sediment transport are solved
by finite difference techniques. Spatial variability of rainfall and infiltration, runoff, and erosion parameters
can be accommodated.
KINEROS divides the watershed of interest into an equivalent network composed of runoff surfaces
or planes, intercepting channels, and ponds or detention storages. Each of these is oriented such that the 1-
dimensional flow can be assumed. Runoff surfaces may be composed of a cascade of rectangular surfaces,
which allows the simulation of converging flow areas, or areas of non-uniform slope, hydraulic resistance, or
soils. Hortonian runoff is then simulated for the network of elements, culminating in the production of a
simulated hydrograph at the outlet. The processes simulated will be described in general order of occurrence
in the runoff- erosion generation process.
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Distributed rainfall data
The model requires information in the form of accumulated depth/time pairs, and converts this data
into rainfall rate pulses.
Interception
A total depth of interception can be specified for each runoff element, based on the vegetation or
other surface condition. This amount is taken from the earliest rainfall pulses until the potential interception
depth is filled. The modified rainfall pulse data then becomes input to the soil surface.
Infiltration
The infiltration model used in KINEROS (Smith and Parlange, 1978) is based on an approximate
solution of the basic equation of unsaturated flow.
Overland flow
When the rainfall rate exceeds the infiltration capacity and sufficient water ponds on the surface to
overcome surface tension effects and fill small depressions, overland flow begins. The kinematic wave
equations are a simplification of the Saint Venant equations and do not preserve all the properties of the more
complex equations. Specifically, backwater cannot be simulated. It has been shown that the kinematic wave
formulation is an excellent approximation for most overland flow conditions (Woolhiser and Ligget, 1967;
Morris and Woolhiser, 1980).
Channel routing
Unsteady, free surface flow in channels is also represented by the kinematic approximation to the
equations of unsteady, gradually varied flow. Channels segments can receive uniformly distributed but time-
varying lateral inflow from planes on either or both sides of the channel, or from one or two channels at the
upstream boundary, or from a plane at the upstream boundary. The dimensions of planes are chosen to
completely cover the watershed, so rainfall on the channel is not considered directly.
Eros/on
The model can simulate the movement of eroded soil along with the movement of surface water.
KINEROS accounts separately for erosion caused by raindrop energy and erosion caused by flowing water
and continues the simulation through channel and pond elements.
The computer codes and the underlying assumptions of each model have been thoroughly tested
with one or more studies. The purpose of code verification is to demonstrate that the model represents
accurately the effects of an actual or hypothetical set of processes and forecast one or more possible
outcomes. Examples of code verification are included in the user's manual of both models.
The SWAT model has been validated at two different spatial scales: small watershed and river basin.
At the small scale Arnold et al. (1994) applied the model to a 17.7 km2 watershed in Riesel, Texas within
the Texas Blackland Prairie Land resource area. They reported efficiency coefficients of predicted
annual water yields and sediment yields between 0.70 and 0.80, indicating a reasonable goodness of fit.
The hydrologic response of the Lower Colorado River basin in Texas has been simulated and compared
to measured USGS streamflow data to test the model on a relatively large river basin (9,000 km2). At the
upstream end of the simulated area, measured outflows from Lake Travis (west of Austin) were input to
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the model and flow was routed trough the basin until it reached the Gulf of Mexico at Matagora Bay. The
only measured data available was streamflow data. Sediment and nutrient loadings were not available for
the basin. A comparison of monthly and annual measured and predicted stream flows at Bay City, Texas
show efficiency coefficients of 0.60.
The kinematic overland flow routing component of KINEROS has been thoroughly tested using
data from the Colorado State University Outdoor Rainfall-Runoff Experimental Facility (ORREF) (Smith
et al., 1995). This unique facility allows relatively precise measurement of rainfall and runoff at a scale
comparable to small watersheds, and has been described by Dickenson et al. (1967) and Woolhiser et al.
(1971). Singh (1974) analyzed data from 210 experiments runs with 50 different configurations and found
that the kinematic wave formulation provided a good description of surface runoff from the facility. Kibler
and Woolhiser (1970) have demonstrated that the response of a converging section can be well
approximated by the response of a cascade of rectangular surfaces as used in KINEROS. The
KINEROS model has been applied to several semi-arid watersheds covering a range of basin scales
within the USDA-ARS Walnut Gulch Experimental Watershed, in southeastern Arizona (Smith et al.,
1995).
A detailed analysis describing model selection, model structure, and model assumptions is
provided by Hernandez et al. 1998 b.
4.4.2 Source data
It is critical that the validity of derived information products be tested to provide a reasonable
estimation of confidence for use in ecological/hydrological modeling. Accuracy information is required in
process modeling in order to understand the risk involved in relying on GIS- and remote sensing-based
information products. The type of accuracy assessment required may depend on whether the results are
relative or absolute measurements. In cases where simple information on distance or area is derived from
a single data source, error such as a simple coordinate offset may not be significant. However,
information derived from multiple spatial sources will generally require enforcement of absolute positional-
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accuracy (Star et al, 1997). A similar division applies to thematic accuracy assessment when derived
information products are either interval (relative accuracy) or ratio (absolute accuracy) in nature.
D Contingency matrices will be used to compare database content with samples derived from
ground survey or some other information source in which there is a high degree of confidence.
These matrices provide detailed information on the types and magnitudes of error found in
original data or derived information products. In remote sensing classification, the matrix relates
the class assigned to a pixel in the database with the class determined for the same pixel by
ground survey. In GIS applications, the error matrix may compare the class assigned to an
entire polygon with the class assessed by visiting the polygon in the field. In addition to assessing the
accuracy of attribute measurement within a field, it may also be important to understand the
positional accuracy of features. Statistics for describing the probabilistic position of points are
the root mean square error (RMSE) and the mean square positional error (MSPE). The majority
of image processing and GIS software packages derive the RMSE. In order to track error
accumulation effectively, methods are required to assess the generation of error associated with
specific processes and to keep an accounting of the spatial, temporal, and attribute characteristics
of this accumulating error. Methods for providing a transcript of data processing histories exist in
many commercial remote sensing and GIS packages. An integrated solution to tracking the data
processing flow, called lineage tracing, is described by Lanter (1989). This approach uses a LISP
language shell in which the Arc/Info GIS package (produced by Environmental Systems
Research Institute) is run. The described algorithm allows automated backwards and forwards
reconstruction of intermediate data products between data inputs and information outputs.
4.4.3 Model application
D The following discussion focuses on the calibration and validation of the models,
documentation to code, documentation of statistical analyses, and summary of performance
results.
Measured output Acceptable error
Field Rvstfim I"
The purpose of the calibration is to
establish that the model can reproduce
observed runoff and sediment yield.
During the calibration, a set of values of
the parameters describing the
main hydrologic processes in
the watershed is found that
approximates the observed
runoff and sediment yield
within a pre-established range of error. Figure 4
depicts the procedure that will be carried out in
this research project. The calibration
procedure begins by estimating initial
parameters values of the hydrologic
model. Next, the model is executed
and results are compared to the
observed values from the field Figure 4. Calibration procedure of hydrological models.
system. Based on the error analysis;
that is, if the differences between computed and measured output are within the predefined range
Computed output Unacceptable error
New parameter estimates
23
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of error, the model is considered calibrated, otherwise, parameter values are adjusted and the model is
run again until acceptable results are achieved.
Owing to uncertainties in the calibration, the set of parameter values used in the calibrated
model may not accurately represent observed values. Consequently, the calibrated parameters may not
accurately represent the system under a different set of hydrologic conditions. Model validation helps
establish greater confidence in the calibration. In the validation process, values of hydrologic parameters
determined during calibration are used to simulate a second set of field data. If the calibrated
parameters were changed significantly during the validation, it may not be possible to match the
calibration within a predefined range of error. Therefore, it will be necessary to repeat the calibration and
validation processes until a set of parameter values is identified that produces a good match between
simulated and observed values.
The judgment of when the fit between model and reality is good enough is a subjective one. To
date, there is no standard protocol for evaluating the calibration and validation processes. The
Watershed Management Committee of the Irrigation and Drainage Division of the American Society of
Civil Engineers (ASCE, 1993) authorized a Task Committee to define criteria that can be used to
evaluate hydrologic models. The Task Committee recommended the following goodness-of-fit criteria
for continuous simulation. The deviation of runoff volumes
(1)
where V is the measured yearly or seasonal runoff volume, and V is the model computed yearly or
seasonal runoff volume. The Nash-Sutcliffe coefficient, E, (Nash and Sutcliffe, 1970)
." (Q, • -Qi)2
E = l'*= (2)
." (Q, ' 'W
\=i
where Q; is the measured daily discharge, CV is the computed daily discharge, Q is the average
measured discharge, and n is the number of daily discharge values. The average measured discharge
is determined from the year or period in question. The E value measures how well the daily simulated
and measured flows correspond.
Generally the objectives of single event modeling are the determination of peak flow rates, flow
volumes, and hydrograph shape and timing. The Task Committee recommended for event modeling the
following goodness-of-fit criteria. To evaluate the peak flow rates, a simple percent error in peak (PEP)
is recommended.
Q 'Q
PEP(%)= ps po'ioo (3)
^po
where Qps is the simulated peak flow rate, Qpo is the observed peak flow rate. For volumetric
assessment, a simple comparison using a measure such as the Dv is sufficient. For assessing the
shape of a simulated hydrograph, a simple sum of squared residuals, G, is proposed
24
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G=." \Q0(f)-Qs(f)\2 (4)
1=1
where Q0(t) is the observed flow rate at time t, Qs(t) is the simulated flow rate at time t.
During the modeling study there will be many changes in parameter values and initial conditions
and possibly even in modeling strategy between the initial runs of the model and the final runs. A log will
be kept during the modeling study to chronologically document the changes in input files, the rationale
for the changes, and the effects of the changes on the results.
4.4.4 Software development
The development of the software will be under a quality system framework following ISO 9000-3
software development guidelines (Schuler et al., 1996). That is, a software quality manual will be
prepared which will serve as a formal procedures manual, and externally as evidence for customers
interested in the software development process as subject to quality control by management. The
manual will cover all aspects of software development, including:
1. Organizational overview - description of the product to be delivered and the
overall structure of the organization.
2. Responsibilities - Who is responsible for which activities and how they are
interrelate.
3. Tools - All software development tools that are used in development, which might
include such things as third-party compilers, bug tracking systems, and
configuration management software.
4. Standards - Programming languages used, internal source code format
requirements, user interface guidelines, and so forth.
Basically, the document will reference all quality procedures and activities associated with each
step of the development process - from design specification writing to preparation of user
documentation.
Emphasis will be placed on the development of a quality plan from a verification and validation
stand point, this includes verification of the inputs and outputs of all development phases, criteria for
inputs and outputs, and details of validation (schedules, activities, resources).
The software will be tested and validated at the end of the appropriate development phase to
insure that it meets all specified requirements. Testing will take place at the variety of stages during the
software development life cycle:
1. Unit level - Includes testing one unit or module of the program usually comprised
of anywhere from 50 to 500 lines of code.
2. Integration level - Involves testing the interaction of program units.
3. System level - Includes testing the complete system.
4. Acceptance level - Involves testing the delivered software product to the
customer's requirements specification.
25
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Section 5
Anticipated Results and Products
The specific results and products for this research effort are linked to the activities discussed in
section 4.0 and outlined in Table 1. In general, the anticipated results and products of this research fall
into three main categories:
1. Interim reports, documentation, and model products.
2. Manuscripts submitted to peer-reviewed journals for publication.
3. A final landscape/hydrologic assessment modeling tool for use by EPA, model
documentation, and a final report on the research program.
26
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References
American Rivers, 1997. www.amrivers.org/99endangered.html.
Arnold, C. L. and C. J. Gibbons, 1996. Impervious surface coverage: the emergence of a key
environmental indicator. J. American Planning Assoc. 62:243-258.
Arnold, J. G., J. R. Williams, R. Srinivasan, K. W. King, and R. H. Griggs, 1994. SWAT-Soil Water
Assessment Tool. USDA, Agricultural Research Service, Grassland, Soil and Water Research
Laboratory, Temple, Texas.
Aronoff, S., 1985. The minimum accuracy value as an index of classification accuracy.
Photogrammetric Engineering & Remote Sensing, 51 (1): 99-111.
ASCE, 1993. Criteria for evaluation of watershed models, Journal of Irrigation and Drainage
Engineering, 119:429-443.
Band, L. E., 1986. Topographic partition of watersheds with digital elevation models. Water Resources
Research, 22(1): 15-24.
Band, L. E. and I. D. Moore, 1995. Scale: landscape attributes and geographical information systems,
in: scale issues in hydrological modeling (Eds.) Kalma, J. D. and M. Sivapalan, John Wiley &
Sons.
Band, L. E., D. L. Peterson, R. Nemani, and S. W. Running, 1993. Forest ecosystem processes at the
watershed scale: incorporating hillslope hydrology, Ecol. Modell., 63: 93-126.
Band, L. E., D. L. Peterson, S. W. Running, J. C. Coughlan, R. B. Lammers, J. Dungan, and R.
Nemani, 1991. Forest ecosystem processes at the watershed scale: basis for distributed
simulation, Ecol. Modell., 56: 151-176.
Beck, M. B., 1987. Water quality modeling: A review of the analysis of uncertainty. Water Resources
Research, 23(5), pp. 1393-1441.
Brakensiek, D.L., and W.J. Rawls, 1983. Agricultural management effects on soil water processes Part
II: Green and Ampt parameters for crusting soils. Transactions of the ASCE 26(6): 1747-1752.
Breckenfield, D.J., W.A. Svetlik, and C.E. McGuire, 1995. Soil survey of Walnut Gulch Experimental
Watershed. USDA, Soil Conservation Service Special Report.
27
-------
Galore, C., P. La Barbera, and G. Roth, 1997. On the use of informational entropy in GIS. in: Remote
Sensing and Geographic Information Systems for Design and Operation of Water Resources
Systems, Proceedings of Rabat Symposium, IAHS, Publ. No. 242.
Charbonneau, R., and G.M. Kondolf. 1993. Land use change in California, USA: nonpoint source water
quality impacts. Environ. Man. 17:453-460.
Chorley, R. J., 1969. The drainage basin as the fundamental geomorphic unit. In: Water, Earth and Man,
R. J. Chorley (Ed.), Methuen, London, pp. 77-99.
Congalton, R. M. and K. Green, 1999. Assessing the accuracy of remotely sensed data: principles and
practices. Lewis Publishers, CRC Press, NY.
Congalton, R. G., M. Balogh, C. Bell, K. Green, J. A. Milliken, and R. Ottman, 1998. Mapping and
monitoring agricultural crops and other land cover in the Lower Colorado River Basin.
Photogrammetric Engineering & Remote Sensing, 64(11): 1107-1113.
Congalton, R. M., R. G. Oderwald, and R. Mead, 1983. Assessing Landsat classification accuracy using
discrete multivariate statistical techniques. Photogrammetric Engineering & Remote Sensing,
49(12): 1671-1678.
Congalton, R. M., 1988. Using spatial autocorrelation analysis to explore the errors in maps generated
from remotely sensed data. Photogrammetric Engineering & Remote Sensing, 54(5): 587-592.
Dickinson, W. T., M. E. Holland, and G. L. Smith, 1967. An experimental rainfall-runoff facility. Hydrology
Paper No. 25, Colorado State University, Fort Collins, CO, 81 pp.
Dietrich, W. E. and D. R. Montgomery, 1998. Hillslopes, channels, and landscape scale, in: Scale
dependence and scale invariance in hydrology, (Eds.) Sposito, G., Cambridge University Press,
UK.
DiLuzio, M., R. Srinivasen, and S.L. Neitsch, 1998. SWAT ArcView Interface User's Guide for SWAT
98.1, 1998 Beta Release. Blackland Research Center, Texas Agricultural Experimental Station.
Doherty, J., 1998. PEST: Model Independent Parameter Estimation. Watermark Computing, Australia.
Engman, E. T. and R. J. Gurney, 1991. Remote sensing in hydrology. Chapman and Hall, Van Nostrand
Reinhold, NY.
Entekhabi, D., H. Nakamura, and E. G. Njoku, 1994. Solving the inverse problem for soil moisture and
temperature profiles by sequential assimilation of multifrequency remotely sensed observations.
IEEE Trans. Geosci. Remote Sensing, 32: 438-448.
Environmental Systems Research Institute (ESRI), 1994. Cell-based modeling with Grid. Version 7,
Redlands, CA.
Famiglietti, J. S. and E. F. Wood, 1995. Effects of spatial variability and scale on areal-average
evapotranspiration. Water Resources Research, 31: 699-712.
28
-------
Famiglietti, J. S. and E. F. Wood, 1994. Multi-scale modeling of spatially variable water and energy
balance processes. Water Resources Research, 30: 3061-3078.
Famiglietti, J. S. and E. F. Wood, 1991. Evapotranspiration and runoff for large land areas: land surface
hydrology for atmospheric general circulation models. Surv. Geophys., 12: 179-204.
Forest Ecosystem Management Assessment Team (FEMAT), 1993. Forest ecosystem management:
An ecological, economic, and social assessment. Report of the Forest Ecosystem Management
Assessment Team. U. S. Government Printing Office 1993-793-071, Washington, D.C.
Franklin, J. F. 1992. Scientific basis for new perspectives in forests and streams, pp. 25-72. IN: R. J.
Naiman (ed.) Watershed Management. Springer-Verlag, NY.
Garbrecht, J., P.J. Starks, and L. W. Martz, 1996. New digital landscape parameterization
methodologies. Proceedings of the 1996 AWRA Symposium on CIS and Water Resources,
Sept. 22-26, Ft. Lauderdale, FL.
Garbrecht, J. and L. W. Martz, 1995. TOPAZ: An automated digital landscape analysis tool for
topographic evaluation, drainage identification, watershed segmentation and subcatchment
parameterization; overview. ARS Pub. No. NAWQL 95-1, USDA-ARS, Durant, OK, 16 pp.
Garbrecht, J. and L. W. Martz, 1993. Network and subwatershed parameters extracted form digital
elevation models: The Bills Creek Experience, Water Resources Bulletin, American Water
Resources Association, 29(6): 909-916.
Graf, W. L., 1988. Fluvial Processes in dryland rivers, Springer-Verlag Berlin Heidelberg, Germany.
Goodrich, D. C., 1994. SALSA-MEX: A large scale semi-arid land surface-atmospheric mountain
experiment. Proc. 1994 Int'l. Geoscience and Remote Sensing Symp. (IGARSS '94), Pasadena,
CA, Vol. 1, pp. 190-193.
Goodrich, D. C., 1991. Basin scale and runoff model complexity, University of Arizona, Dep. of
Hydrology and Water Resources Technical Report No. HWR 91-010, 361 p.
Hernandez, M., D. C. Goodrich, S. N. Miller, and C. L. Unkrich, 1998a. Landscape indicator interface
with hydrologic and ecological models, Report No. 1 (June) prepared as part of EPA/IAG Project
No. DW12937916-01-0, USDA-ARS, Southwest Watershed Research Center, Tucson, AZ.
Hernandez, M. and D. C. Goodrich, 1998b. Landscape indicator interface with hydrologic and ecological
models, Report No. 2 (October) prepared as part of EPA/IAG Project No. DW12937916-01-0,
USDA-ARS, Southwest Watershed Research Center, Tucson, AZ.
Hernandez, M., S. N. Miller, D. C. Goodrich, and B. F. Goff, 1998c. Landscape indicator interface with
hydrologic and ecological models, Report No. 3 (November) prepared as part of EPA/IAG Project
No. DW12937916-01-0, USDA-ARS, Southwest Watershed Research Center, Tucson, AZ.
Hord, R. M. and W. Brooner, 1976. Land-use map accuracy criteria. Photogrammetric Engineering &
Remote Sensing, 42(5): 671-677.
29
-------
Hunsaker, C.T., and D.A. Levine. 1995. Hierarchical approaches to the study of water quality in rivers.
Bioscience 45:193-203.
Hunsaker, C.T., D.A. Levine, S.P. Timmins, B.L. Jackson, and R.V. O'Neill. 1992. Landscape
characterization for assessing regional water quality. Pp. 997-1006, in D.H. McKenzie, D.E.
Hyatt, and V.J. McDonald (eds.), Ecological indicators. Elsevier Appl. Sci., New York.
Jackson, T. J., R. M. Pagan, and W. N. Fitch, 1977. Test of landsat-based urban hydrologic modeling.
ASCE J. Water Resources Planning Management Division, 103, No. WR1 Proc. Paper 12950,
pp 141-158.
Jackson, T. J., R. M. Ragan, and R. P. Shubinski, 1976. Flood frequency studies on ungauged urban
watersheds using remotely sensed data. Proc. Natl. Symp. on Urban Hydrology, Hydraulics and
Sediment Control, University of Kentucky, Lexington, KY.
Jenson, S. K. and J. O. Domingue, 1988. Extracting topographic structure from digital elevation data for
geographical information system analysis. Photogrammetric Engineering and Remote Sensing
54(11): 1593-1600.
Junk, W. J., P.B. Bailey, and R.E. Sparks. 1989. The flood pulse concept in river-floodplain systems.
Can. Spec. Publ. Fish. Aquat. Sci. 106:110-127.
Karr, J. R. and I. J. Schlosser. 1978. Water resources and the land-water interface. Science 201:229-
233.
Kibler, D. F. and D. A. Woolhiser, 1970. The kinematic cascade as a hydrologic model, Hydrology
Paper No. 39, Colorado State University, Fort Collins, CO, 27 pp.
Kirkby, M. J., P. S. Naden, T. P. Burt, and D. P. Butcher, 1993. Computer simulation in physical
geography, John Wiley & Sons, Inc. New York.
La Barbera, P. and G. Roth, 1994. Scale properties and scale problems: network morphology and
network identification from digital elevation maps. In: Advances in Distributed Hydrology (Ed.)
Rosso et al., 131-148, Water Resources Publication, Colorado.
Langbein, W. B. and S. A. Schumm, 1958. Yield of sediment in relation to mean annual precipitation.
Trans. Am. Geophys. Union, 39:1076-1084.
Lanter, D., 1989. Techniques and method of spatial database lineage tracking. Ph.D. dissertation,
University of South Carolina.
Likens, G. E. and F. H. Bormann, 1974. Linkages between terrestrial and aquatic ecosystems.
Bioscience 24: 447-456.
Lotspeich, F. B., 1980. Watersheds as the basic ecosystem: This conceptual framework provides a
basis for natural classification system. Water Resources Bulletin, American Water Resources
Association, 16:581-586.
30
-------
Lowrance, R. R., R. Leonard, and J. Sheridan. 1985. Managing riparian ecosystems to control non-point
pollution. Journal of Soil and Water Conservation 40:87-91.
Lowrance, R. R., R.L. Todd, J. Fail, O. Hendrickson, R. Leonard, and L.E. Asmussen. 1984. Riparian
forests as nutrient filters in agricultural watersheds. Bioscience 34:374-377.
Luk, K. C. and J. E. Ball, 1997. Modeling the instantaneous spatial variability of rainfall. International
Congress on Modeling and Simulation, Hobart, Tasmania, pp. 71-76.
Marks, D., J. Dozier, and J. Frew, 1984. Automated basin delineation from digital elevation data.
GeoProcessing, 2:299-311.
Martz, L.W., and J. Garbrecht, 1993. Automated extraction of drainage network and watershed data
from digital elevation models. Water Resources Bulletin, American Water Resources
Association, 29(6):901-908.
Martz, L.W., and J. Garbrecht, 1992. Numerical definition of drainage network and subcatchment areas
from digital elevation models. Computers and Geosciences 18(6): 747-761.
McClaran, M. P. and W. W. Brady, 1994. Arizona's diverse vegetation and contributions to plant
ecology. Rangelands 16:208-217.
McCuen, R. H. and W. M. Snyder, 1986. Hydrologic modeling: Statistical methods and applications.
Prentice-Hall, Englewood Cliffs, NJ.
Meentemeyer, V. and E. O. Box, 1987. Scale effects in landscape studies. In: Landscape heterogeneity
and disturbance, pp. 15-34. (Ed.) M. G. Turner, Springer-Verlag, NY.
Montgomery, D. R., G. E. Grant, and K. Sullivan, 1995. Watershed analysis as a framework for
implementing ecosystem management. Water Resources Bulletin, American Water Resources
Association, 3(3):369-386.
Montgomery, D. R., W. E. Dietrich, and K. Sullivan, 1998. The role of GIS in watershed analysis. In:
Landform Monitoring, Modeling and Analysis, Eds. Lane, S., K. Richards, and J. Chandler, John
Wiley & Sons.
Moore, I. D. and R. B. Grayson, 1991. Terrain based prediction of runoff with vector elevation data.
Water Resources Research, 27: 1177-1191.
Moore, I. D., E. M. O'Loughlin, and G. J. Burch, 1988. A contour-based topographic model for
hydrological and ecological applications. Earth Surface Processes Landforms, 13: 305-320.
Moore, I. D., A. K. Turner, J. P. Wilson, S. K. Jenson, and L. E. Band, 1993. GIS and land surface-
subsurface modeling. In: Geographic Information Systems and Environmental Modeling, (Eds.)
Goodchild, M. F., P. O. Parks, and L. Y. Stayaert, Oxford University Press, Oxford, pp. 196-230.
Morris, E. M. and D. A. Woolhiser, 1980, Unsteady one-dimensional flow over a plane: Partial
equilibrium and recession hydrographs, Water Resources Research, 16(2):355-360.
31
-------
Nash, J. E. and J. V. Sutcliffe, 1970. River flow forecasting through conceptual models. Part I: A
discussion of principles. Journal of Hydrology, 10:282-290.
Nemani, R., L. L. Pierce, S. W. Running, and L. E. Band, 1993. Ecological processes at the watershed
scale: sensitivity to remotely sensed leaf area index estimates. Int. J. Remote Sensing, 14:
2519-2534.
Nielsen, D. R. and J. Bouma, 1985. Soil spatial variability. In: Proceedings of a Workshop of the
International Soil Science Society and the Soil Science Society of America, Pudoc, Wageningen,
243 pp.
O'Callaghan, J. F. and D. M. Mark, 1984. The extraction of drainage networks from digital elevation data.
Computer Vision, Graphics and Image Processing, 28:323-344.
Omernik, J.M. 1987. Ecoregions of the United States: Map at a scale of 1:7,500,000. Suppl. Annals
American Assoc. Geogr. 77(1).
Osborn, H. B., 1983. Precipitation characteristics affecting hydrologic response of southwestern
rangelands. USDA-ARS, Agric. Rev. and Manuals, ARM-W-34, 55p.
Peterjohn, W. T. and D. L. Correll 1984. Nutrient dynamics in an agricultural watershed: Observations
on the role of a riparian forest. Ecology 65:1466-1475.
Ragan, R. M. and T. J. Jackson, 1980. Runoff synthesis using Landsatand SCS model. J. Hydraulic
Division, ASCE 106, (HY5), 667-678.
Rango, A., A. Feldman, T. S. George, III, and R. M. Ragan, 1983. Effective use of Landsat data in
hydrologic models. Water Resources Bulletin, 19, 165-74.
Renard, K. G., L. J. Lane, J. R. Simanton, W. E. Emmerich, J. J. Stone, M. A. Weltz, D. C. Goodrich,
and D. S. Yakowitz, 1993. Agricultural impacts in an arid environment: Walnut Gulch studies.
Hydrological Science and Technology, 9 (1-4): 145-190, American Institute of Hydrology.
Rosenfield, G. H. K. Fitzpatrick-Lins, and H. Ling, 1982. Sampling for thematic map accuracy testing.
Photogrammetric Engineering & Remote Sensing, 48(1): 131-137.
Roth, N.E., J. D. Allan, and D.L. Erickson. 1996. Landscape influences on stream biotic integrity
assessed at multiple scales. Landscape Ecology 11:141-156.
32
-------
Scientific Analysis Team (SAT), 1993. Viability Assessment and Management Considerations for
Species Associated with Late-Successional and Old-Growth Forests of the Pacific Northwest.
USDA Forest Service, U. S. Government Printing Office 1993-791-566, Region 10, 530 pp.
Schuler, A. C., J. Dunlap, and K. L. Schuler, 1996. ISO 9000 - Manufacturing, Software, and Service,
Delmar Publishers, Albany, NY.
Shen, H.W., and P.Y. Julien, 1993. Erosion and sediment transport. Chapter 12 in: Handbook of
Hydrology, D.R. Maidment, ed. McGraw-Hill, New York.
Shannon, C. E., 1948. A mathematical theory of communications. Bell System Technology Journal, 27:
379-423, 623-659.
Singh, V. P., 1974. A nonlinear kinematic wave model of surface runoff, Ph.D. Dissertation, Colorado
State University, Fort Collins, CO., 282 pp.
Slocombe, D. S., 1993. Implementing ecosystem-based management. Bioscience 43:612-622.
Smith, R. E., D. C. Goodrich, D. A. Woolhiser, and C. L. Unkrich, 1995. KINEROS -A kinematic runoff
and erosion model, In: Computer Models of Watershed Hydrology, edited by V. P. Singh, Water
Resources Publications, Highlands Ranch, CO.
Smith, R. E. and J. -Y. Parlange, 1978. A parameter-efficient hydrologic infiltration model, Water
Resources Research, 14(3):533-538.
Spanner, M. A., L. Johnson, J. Miller, R. McCreight, J. Freemantle, J. Runyon, and P. Gong, 1994.
Remote sensing of seasonal leaf area index across the Oregon transect. Ecol. Appl., 4: 258-
271.
Spanner, M. A., L. L. Pierce, D. L. Peterson, and S. W. Running, 1990. Remote sensing of temperate
forest leaf area index: influence of canopy closure, understory vegetation and background
reflectance. Int. J. Remote Sensing, 11: 96-111.
Sparks, R.E. 1995. Need for ecosystem management of large rivers and their floodplains. Bioscience
45:168-182.
33
-------
Stanley, T. R. Jr., 1995. Ecosystem management and the arrogance of humanism. Conservation
Biology, 9, 255-262.
Star, J. L, J. E. Estes, and K. C. McGwire, 1997. Integration of Geographic Information Systems and
Remote Sensing, Cambridge University Press, Cambridge, UK.
Strahler, A. N., 1964. Quantitative geomorphology of drainage basins and channels networks, In:
Handbook of Applied Hydrology, (Ed.) Chow, V. T., pp. 4-39-4-76.
U.S. Army Corps of Engineers, 1976. Urban storm water runoff, STORM. Computer program 723-
L2520, Hydrologic Engineering Center, Davis, CA.
USDA-NRCS, 1994. State Soil Geographic (SATSGO) Data Base. Miscellaneous Publication No. 1492.
U. S. Department of Agriculture, Soil Conservation Service, 1983. National Engineering Handbook,
Hydrology Section 4, Chapter 19, US Government Printing Office, Washington, D.C.
U. S. Department of Agriculture, Soil Conservation Service, 1972. National Engineering Handbook,
Section 4, Hydrology US Government Printing Office, Washington, D.C.
U. S. Department of Commerce, National Oceanic and Atmospheric Administration, National Climatic
Data Center, 1995. Cooperative Summary of the Day, TD3200 - Period of Record through 1993.
USGS, 1999b. Landsat Pathfinder Program (North American Landscape Characterization Project):
http://edcdaac.usgs.gov/pathfinder/pathpage.html
USGS, 1999a. USGS Geographic Data Download:
http://edcwww.cr.usgs.gov/doc/edchome/ndcdb/ndcdb.html
van Genderen, J. L. and B. F. Lock, 1977. Testing land use map accuracy. Photogrammetric
Engineering & Remote Sensing, 43(9): 1135-1137.
Washington Forest Practice Board (WFPB), 1992. Standard methodology for conducting watershed
analysis. Washington Forest Practice Act Board Manual, Version 1.0, 13 pp.
Washington Forest Practice Board (WFPB), 1993. Standard methodology for conducting watershed
analysis. Washington Forest Practice Act Board Manual, Version 2.0, 85 pp.
Weller, M.C., M.C. Watzin, and D. Wang. 1996. Role of wetlands in reducing phosphorus loading to
surface water in eight watersheds in the Lake Champlain Basin. Environ. Man. 20:731-739.
Wickham, J. D., R. V. O'Neill, K. H. Ritters, T. G. Wade, and K. B. Jones, 1997. Sensitivity of selected
landscape pattern metrics to land-cover misclassification and differences in land-cover
composition, Photogrammetric Engineering & Remote Sensing, 63(4): 397-402.
Wigmosta, M. S., L. W. Vail, and D. P. Lettenmier, 1994. A distributed hydrology-vegetation model for
complex terrain. Water Resources Research, 30:1665-1680.
34
-------
Williams, J. R. and H. D. Berndt, 1977. Sediment yield prediction based on watershed hydrology, Trans.
ASAE 20(6): 1100-1104.
Wischmeier, W. H., and D. D. Smith. 1978. Predicting rainfall erosion loss: A guide to conservation
planning. Agricultural handbook 537. U. S. Department of Agriculture, Washington, D.C.
Wood, E. F., 1994. Scaling, soil moisture and evapotranspiration in runoff models. Advances Water
Resources, 17: 25-34.
Wood, E. F., M. Sivapalan, K. Beven, and L. Band, 1988. Effects of spatial variability and scale with
implications to hydrologic modeling. Journal of Hydrology, 102: 29-47.
Woolhiser, D. A. and J. A. Ligget, 1967. Unsteady, one-dimensional flow over a plane-the rising
hydrograph, Water Resources Research, 3(3):753-771.
Woolhiser, D. A., M. E. Holland, G. L. Smith, and R. E. Smith, 1971. Experimental investigation of
converging overland flow, Trans. ASAE, 14(4):684-687.
35
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Appendix
Table 1. Tentative schedule and milestones for fiscal years 1999-2002 beginning in January,
1999, also showing calendar years 1999-2002. Working and completion target dates
indicated by quarter. Symbols used in this chart: WG = Walnut Gulch Experimental
Watershed; SP = San Pedro River Basin.
Calendar Year
Calendar Year Quarter
Fiscal Year
Fiscal Quarter
• Compile climatic/hydrologic
data, WG and SP
• Assemble CIS data at high/low
resolution for WG, SP
• Analyze climatic data, WG, SP
• Assess input parameters;
construct look-up tables for GIS
• Develop multi-year research
plan suitable for peer review
• Submit manuscript for
publication
• Calibrate and validate
hydrologic models, WG, SP;
model at various resolutions
with KINEROS, SWAT
• Submit report
• Submit manuscript for
publication
• Select criteria for choice of
intensive study basins
• Assess model efficiency
relative to data resolution
• Analyze misclassification
errors; dependent on matrix
• Determine impact of
misclassification errors on
model efficiency
• Choose intensive study basins
1999
1 2 3
1999
II III IV
• •
• •
• • • •
• • • •
• • • •
• •
• •
• •
• •
• •
2000
4123
2000
I II III IV
• •
• •
• • • •
• • • •
• • • •
2001
4123
2001
I II III IV
2002
4123
2002
I II III IV
36
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Table 1. Continued
Fiscal Year
Fiscal Quarter
• Sensitivity Analysis
• Create subwatershed maps at
varying levels of complexity,
WG, SP
• Submit report
• Submit manuscript for
publication
• Assess model efficiency
relative to network complexity
• Compile climatic and
hydrologic data, intensive study
basins
• Assemble GIS data for
intensive study basins;
dependent on MRLC
• Define design storms
• Calibrate and validate
hydrologic models, intensive
study basins
• Submit report
• Submit manuscript for
publication
• Model and analyze watershed
response as a function of land
cover change and design
storms, WG, SP
• Model and analyze watershed
response as a function of land
cover change and design
storms, intensive study basins
• Submit report
• Submit manuscript for
publication
• Create/update GIS programs
(AMI, Avenue) to automate
GIS parameterization; deliver
final GUI-driven GIS/ hydrologic
modeling tool
1999
II III IV
2000
I II III IV
• • • •
• • • •
• •
• •
• •
2001
I II III IV
• •
• •
• •
• •
2002
I II III IV
• •
• •
37
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Resume
Name: William G. Kepner
Affiliation:
US Environmental Protection Agency, National Exposure Research Laboratory, P.O. Box 93478, Las Vegas,
Nevada 89193-3478, USA
Education and Certification:
MPA, University of Nevada, Las Vegas (Public Administration), 2000
M.S., Arizona State University (Zoology), 1982
B.S., University of Arizona (Biology), 1975
A.A., Phoenix College (Biology), 1973
Professional Registration:
Certified Wildlife Biologist, The Wildlife Society, 1983
Certified Fisheries Scientist, American Fisheries Society, 1982
Certificate in Business Management, UNLV College of Business and Economics, Department of Management in
cooperation with the American Management Association, 1995.
Selected Experience and Accomplishments:
5/90 - Present Research Ecologist, U.S. Environmental Protection Agency, National Exposure Research
Laboratory, Las Vegas, Nevada.
8/94 - 5/90 Environmental Contaminant Specialist, U.S. Fish and Wildlife Service, Phoenix, Arizona.
5/78 - 7/84 Wildlife Biologist, U.S. Bureau of Land Management, Phoenix, Arizona.
10/77 - 3/78 Research Assistant, Lower Colorado River Basin Research Laboratory, Arizona State University,
Tempe, Arizona.
5/77 - 8/77 Hydrologist, U.S. Forest Service, Apache-Sitgreaves National Forests, Springerville, Arizona.
38
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Selected Publications and Reports:
Chehbouni, A., D.C. Goodrich, S.M. Moran, C.J. Watts, W.G. Kepner, et al. A Preliminary Synthesis of Major
Scientific Results during the Semi-Arid Land-Surface-Atmosphere (SALSA) Program. Special Edition of
Agriculture and Forest Meteorology. Accepted.
Goodrich, D.C., A. Chebouni, B. Goff, B. MacNish, T. Maddock, W.G. Kepner. Preface Paper to the Semi-Arid-
Land-Surface-Atmosphere (SALSA) Program. Special Edition of Agriculture and Forest Meteorology.
Accepted.
Kepner, W.G. and D. T. Heggem. 2000. San Pedro Geodata Browser. U.S. EPA, EPA/600/C-00/002.
Kepner, W.G, C.J. Watts, C. M. Edmonds, J.K. Maingi, S.E. Marsh, and G. Luna. 2000. A Landscape Approach
for Detecting and Evaluating Change in a Semi-arid Environment. Journal of Environmental Monitoring and
Assessment. Vol. 64, No. 1.
Hernandez, M., S.N. Miller, D.C. Goodrich, B. F. Goff, W.G. Kepner, C.M. Edmonds, and KB. Jones. 2000.
Modeling Runoff Response to Land Cover and Rainfall Spatial Variability in Semi-arid Watersheds. Journal of
Environmental Monitoring and Assessment. Vol. 64, No. 1.
Goodrich, D.C., A. Chehbouni, B. Goff, C.J. Watts, W.G. Kepner, et al. 1998. An overview of the 1997 activities
of the semi-arid land-surface-atmosphere (SALSA) program. Proc. Amer. Met. Soc., Special Symposium on
Hydrology, Jan. 11-16, Phoenix, AZ, pp 1-7.
Mouat, D.A., J. Lancaster, T. Wade, J. Wickham, C. Fox, W.G. Kepner, and T. Ball. 1997. Desertification
Evaluated Using an Integrated Environmental Assessment Model. Environmental Monitoring and Assessment
48:139-156.
Riitters, K.H., J.D. Wickham, KB. Jones, W.G. Kepner, and D.J. Chaloud. 1996. A Landscape Atlas of the
Chesapeake Bay Watershed. Tennessee Valley Authority, Norris, TN. 29 pp.
Kepner, W.G, KB. Jones, D.J. Chaloud, J.D. Wickham, K.H. Riitters, and RV. O'Neill. 1995. Mid-Atlantic
Landscape Indicators Project Plan. EPA/620/R-95/003. U.S. Environmental Protection Agency, Office of
Research and Development, Washington, D.C. 38 pp.
Wade, T.G, J.D. Wickham, and W.G. Kepner. 1995. Using GIS and a Graphical User Interface to Model Land
Degradation. Geo Info Systems. Pp. 38-42.
Kepner, W.G. 1995. Environmental Monitoring and Assessment Program: A Landscape Approach to
Environmental Assessment: Application to Neotropical Migratory Bird Issues. Partners in Flight, 4(2).
National Fish and Wildlife Foundation, Washington, D.C.
Breckenridge, R.P., W.G. Kepner, and D.A. Mouat. 1995. A Procedure for Selecting Indicators for Monitoring
Condition of Rangeland Health. International Journal of Environmental Monitoring and Assessment, 36:45-60.
Kepner, W.G. et al. 1994. Environmental Monitoring and Assessment Program, Arid Ecosystems 1992 Pilot
Report. EPA/620/R-94/015. U.S. Environmental Protection Agency, Office of Research and Development,
Washington, D.C. 116 pp.
Kepner, W.G. et al. 1991. Arid Ecosystems Strategic Monitoring Plan. EPA/600/4-91/018. U.S. Environmental
Protection Agency, Office of Research and Development, Washington, D.C. 299 pp.
39
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Radtke, D.B., W.G. Kepner, and R.J. Effertz. 1988. Reconnaissance investigation of water quality, bottom
sediment, and biota associated with irrigation drainage in the Lower Colorado River Valley, Arizona,
California and Nevada, 1986-87. U.S. Geological Survey Water-Resources Investigations Report 88-4002.
Pp. 1-77. Denver, CO.
40
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Resume
Name: David C. Goodrich
Affiliation:
US Department of Agriculture, Agricultural Research Service, Southwest Watershed Research Center, 2000 East
Allen Road, Tucson, Arizona 85719, USA.
Education and Certification:
Ph.D., Hydrology and Water Resources (Engineering), The Univ. of Arizona, 1990
M.S., Civil & Environmental Engineering, Univ. of Wisconsin-Madison, 1982
Cert. Post-Grad. Study, Systems Engineering, Cambridge University, 1981
B.S., Civil & Environmental Engineering, Univ. of Wisconsin-Madison, 1980
Registered Professional Engineer (#27569) in the state of Wisconsin.
Experience and Accomplishments:
1988 - Present Research Hydraulic Engineer, USDA-Agricultural Research Service, Southwest Watershed
Research Center, Tucson, Arizona.
1990 - Present Assistant Adjunct Professor, Dept. of Hydrology and Water Resources, Univ. of Arizona.
1982 -1983 Scientist, Autometric, Inc., Falls Church, Virginia
1978 -1981 Civil Engineer, U.S. Geological Survey, Water Resources Div., Anchorage, Alaska; full time in
the summers.
1976-1978 Student Trainee, U.S. Geological Survey, Water Resources Div., Madison, Wisconsin, full time in
summers and part time during the academic year.
Selected Publications and Reports:
Goodrich, D. C., and Woolhiser, D. A. Catchment Hydrology. U.S. Report on Hydrology to the International
Union of Geodesy and Geophysics 1987-1990, Reviews of Geophysics, Supplement, pp. 202-209, 1991.
Goodrich, D. C., Woolhiser, D. A., and Keefer, T. O. Kinematic routing using finite elements on a Triangular
Irregular Network (TIN). Water Resources Research, 27(6):995-1003, 1991.
41
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Kustas, W. P., Goodrich, D. C., Moran, et al. An interdisciplinary field study of the energy and water fluxes in
the atmosphere-biosphere system over semiarid rangelands: Description and some preliminary results. Bull.
Amer. Meteor. Soc., 12(11): 1683-1706, 1991.
Jackson, T. I, Le Vine, D. M., Griffis, A., Goodrich, D. C., Schmugge, T. I, Swift, C. T., and O'Neill, P. E. Soil
moisture and rainfall estimation over a semiarid environment with the ESTAR microwave radiometer. IEEE
Trans. Geosci. Remote Sensing, 31(4):836-841. 1993.
Kustas, W. P. and Goodrich, D. C. Preface to the special section on MONSOON '90. Water Resources
Research, 30(5): 1211-1225. 1994.
Goodrich, D. C., Schmugge, T. I, Jackson, T. J., Unkrich, C. L., Keefer, T. O., Parry, R, Bach, L. B., and Amer,
S. A. Runoff simulation sensitivity to remotely sensed initial soil water content, Water Resources Research,
30(5): 1393-1405. 1994.
Goodrich, D. C. SALSA-MEX: A Large Scale Semi-Arid Land-Surface-Atmospheric Mountain Experiment.
Proc. 1994 Intern. Geoscience and Remote Sensing Sym. (IGARSS'94), Pasadena, CA, Vol. 1, p. 190-193,
Aug. 8-12, 1994.
Goodrich, D. C., and Simanton, J.R, Preface to the special issue of the J. of Soil and Water Conservation on
"Water Research and Management in Semiarid Environments", J. Soil and Water Cons., 50(5):416-419, 1995.
Goodrich, D. C., Faures, J. M., Woolhiser, D. A., Lane, L. J., and Sorooshian, S. Measurement and analysis of
small-scale convective storm rainfall variability. Journal of Hydrology, 173(1995):283-308. 1995.
Faures, J. M., Goodrich, D. C., Woolhiser, D. A., and Sorooshian, S. Impact of small-scale spatial rainfall
variability on runoff simulation. Journal of Hydrology, 173(1995):309-326. 1995.
Goodrich, D.C., Lane, L.J., Shillito, R.A., Miller, S.N., Syed, K.H. Syed, and Woolhiser, D.A., Linearity of Basin
Response as a Function of Scale in a Semi-Arid Watershed, Water Resources Research, 33(12):2951-2965.
1997.
Goodrich, D.C., A. Chehbouni, B. Goff, et al. An overview of the 1997 activities of the semi-arid land-surface-
atmosphere (SALSA) program. Proc. Amer. Met. Soc., Special Symposium on Hydrology, Jan. 11-16,
Phoenix, AZ, pp 1-7, 1998.
42
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Resume
Name: Curtis M. Edmonds
Affiliation:
US Environmental Protection Agency, National Exposure Research Laboratory, P.O. Box 93478, Las Vegas,
Nevada 89193-3478, USA
Education and Certification:
B.S., University of Nevada, Las Vegas (Mathematics/Computer Science), 1980, Professional Registration
Engineer-In-Training (EIT) Registration - 1977
Experience and Accomplishments:
Electronic Engineer. More than 20 years experience designing and construction of remote sensing
instrumentation for the agency. Primarily responsible for the design of the data acquisition portions of the UV-Dial
system, the fluorosensing system, the two frequency lidar system and the portable lidar system. Currently serving
as agency's Technical Director of North American Land Characterization (NALC) program and Project Officer
of cooperative agreements with the Institute de Geografia, Universidad Nacional Antonoma de Mexico, and the
University of Nevada. Responsible for developing an in-house capability to produce digital products based on
remotely sensing data.
EPA Bronze - UV-Dial Remote Sensing System
Bronze - Microprocessor Development Science and Technology Achievement Award
Selected Publications and Reports:
Edmonds, C.M., A.C. Neale, D.T. Heggem, J.D. Wickham, and K.B. Jones. A Comparison of Landscape
Change Detection Methods. Environmental Monitoring and Assessment. Accepted.
Heggem, D.T., A.C. Neale, C.M. Edmonds, L.A. Bice, R.D. Van Remortel, and K.B. Jones. 1999. An Ecological
Assessment of the Louisiana Tensas River Basin. U.S. Environmental Protection Agency, Washington D.C.,
EPA/600/R-99/016, 123 pp.
Heggem, D.T., C.M. Edmonds, A.C. Neale, L. Bice, and K. Bruce Jones. 1999. Forested Wetland Restoration:
Identifying Potential Sites in Northeast Louisiana. Geo Info Systems, 9 (5): 34-39.
43
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Yfantis, E. A., G. T. Flatman and C. M. Edmonds. 1996. Stochastic Properties of Wavelet Transformations.
Presented at the Eleventh Thematic Conference and Workshop on Geologic Remote Sensing, Las Vegas,
Nevada.
Moosmuller, H., R. J. Alverez II, C. M. Edmonds, R. M. Turner, D. H. Bundy, and J. L. McElroy. 1993. Airborne
Ozone Measurements with the USEPA UV-DIAL. In Optical Remote Sensing of the Atmosphere Technical
Digest, 5, Optical Society of America. Washington, D.C., pp 176-179.
Moosmuller, H., R. J. Alverez II, R. M. Jorgensen (Turner), C. M. Edmonds, D. H. Bundy, D. Diebel, M. P.
Bristow, and J. L. McElroy. 1993. An Airborne Lidar System for Tropospheric Ozone Measurement.
Proceedings of the 86th Annual Meeting of the Air & Waste Management Association, Air & Waste
Management Association, Pittsburgh, PA.
McElroy, J. L., H. Moosmuller, R. M. Jorngensen (Turner), C. M. Edmonds, R. J. Alverez II, D. H. Bundy. 1993.
Airborne UV-DIAL Measurements of Ozone Distributions in Southeastern Michigan. Proceedings of the 86th
Annual Meeting of the Air & Waste Management Association, Air & Waste Management Association,
Pittsburgh, PA.
Edmonds, C. M., and R. M. Turner. 1990. A VMS-Based Distributed System for Realtime Data Collection,
presented at DECUS U.S. Chapter, New Orleans Convention Center.
Bristow, M. P., R. M. Turner, C. M. Edmonds, and D. H. Bundy. 1989. Short and Long Term Memory Effects in
Intensified Array Detectors: Influence on Airborne Laser Fluorosensor Measurements. Applied Optics. Vol.
28. pp. 472-480.
McElroy, M. L., D. H. Bundy, M. P. Bristow, M. Pitchford, C. M. Edmonds, D. Diebel, R. Turner, R.
Viswanathan, and W. H. Hankins. 1988. Remote Sensing for Air Quality at EMSL-LV (EPA). Published in
the Proceedings of the Symposium on Lower Tropospheric Profiling: Needs and Technologies. Boulder,
Colorado.
44
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Resume
Name: K. Bruce Jones
Affiliation:
US Environmental Protection Agency, National Exposure Research Laboratory, P.O. Box 93478, Las Vegas,
Nevada 89193-3478, USA
Education and Certification:
Ph.D., University of Nevada, Las Vegas (Paleoecology), 1995
Experience and Accomplishments:
1992 - Present Project Lead, Landscape Ecology, U.S. EPA, Las Vegas, NV.
1988 -1992 Associate Director, Environmental Monitoring and Assessment Program, U.S. EPA, Las Vegas,
NV.
1985 -1988 Senior Endangered Species Biologist in the U.S. Fish and Wildlife Service's Office of Endangered
Species, Washington, D.C.
1981 -1985 National Wildlife Training Coordinator, U.S. Bureau of Land Management.
1976 -1981 Wildlife Habitat Survey Team Leader, U.S. Bureau of Land Management.
Committees/Task Groups:
1997 - Present Member, International Committee on Environmental Indices, INENCO (Russia).
1992 -1995 Member, GAP Analysis Advisory Group, USA.
1991 -1992 Member, National Forest Health Monitoring Review Committee.
1984 -1987 Member, Office of Technology Assessment Review Committee, In-situ Biological Diversity
Monitoring Technology.
1985 President, The Arizona Chapter of the Wildlife Society.
Selected Publications and Reports:
50+ peer-reviewed publications in biogeography, herpetology, landscape ecology, molecular evolution, and
environmental monitoring.
45
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Resume
Name: Scott Miller
Affiliation:
US Department of Agriculture, Agricultural Research Service, Southwest Watershed Research Center, 2000 East
Allen Road, Tucson, Arizona 85719, USA.
Education and Certification:
University of Arizona, Tucson, AZ. Doctoral candidate, Watershed Management with a minor in Hydrology.
Predicted graduation date, December 2000.
University of Arizona, Tucson, AZ. Master of Science, Watershed Management, December 1995.
Brown University, Providence, RI. Bachelor of Science, Geosciences, May 1991.
Experience and Accomplishments:
9/95 - Present Research Specialist for the USDA-ARS Southwest Watershed Research Center.
8/97 - Present Co-developer of and instructor for WSM 569: Spatial Analysis for Hydrology and Watershed
Management, a graduate-level course at the University of Arizona.
8/95 - 6/97 Teacher at St. Gregory College Preparatory School.
5/93 - 9/95 Research Assistant for the USDA Agricultural Research Service and the University of Arizona
School of Renewable Natural Resources.
1/95 - 5/95 Teaching Assistant, Watershed Management program, University of Arizona.
5/92 - 1/93 Head Brewer for Otter Creek Brewing, Inc. in Middlebury, VT.
9/91 - 3/92 Geologist with GEO Inc., of Golden, CO.
9/90 - 1/91 Teaching Assistant, Department of Geological Sciences in the Brown University Meikeljohn
program.
46
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Selected Publications and Reports:
Goodrich, D.C., L.J. Lane, D.A. Woolhiser, R. Shillito, S.N. Miller, and K.H. Syed, 1997. Linearity of basin
response as a function of scale in a semi-arid ephemeral watershed. Water Resources Research 33(12):
2951-2965.
Miller, S.N., D.P. Guertin, and D.C. Goodrich, 1999. Deriving stream channel morphology using GIS-based
watershed analysis. In: GIS for Watershed Characterization, Analysis and Management. Ann Arbor Press,
Chelsea, MI. To be published: Feb. 1999.
Miller, S.N., M. Hernandez, and L.J. Lane, 1997. GIS applications in the spatial extrapolation of hydrologic data
from experimental watersheds. Proceedings of the Conference on Management of Landscapes Disturbed by
Channel Incision: Stabilization; Rehabilitation; Restoration, May 19-22, 1997, Oxford, MS.
Miller, S.N., D.P. Guertin, and D.C. Goodrich, 1996. Linking GIS and geomorphology field research at Walnut
Gulch. Proceedings of the AWRA's 32nd Annual Conference and Symposium: "GIS and Water Resources",
Sept. 22-26, 1996, Ft. Lauderdale, FL.
Miller, S.N., D.P. Guertin, and D.C. Goodrich, 1996. Investigating stream channel morphology using a geographic
information system. 16th Annual ESRI User Conference Proceedings, May 20-24, 1996, Palm Springs, CA.
Abstracts, Presentations, and Poster Sessions:
Masterson, J., S.N. Miller, and D. Yakowitz, 1996. Software enhancements to the USDA multi-objective decision
support tool. Presentation at the Malama 'Aina 1995: Multiple Objective Decision Making for Land, Water,
and Environmental Management, Honolulu, HI, July 23-28, 1995.
Goodrich, D.C., L.J. Lane, D.A. Woolhiser, R. Shillito, and S.N. Miller, 1996. Linearity of basin response as a
function of scale in a semi-arid watershed. Abstract and poster presentation, Conference and Workshop on
Basin Response, Vienna, Austria, June 17-20, 1996.
Potter, T., D.P. Guertin, and S.N. Miller, 1996. Hydrologic modeling of an urban watershed using GIS and
remote sensing. Oral Presentation; AWRA's 32nd Annual Conference and Symposium: "GIS and Water
Resources", Sept. 22-26, 1996, Ft. Lauderdale, FL.
Hernandez, M., D.C. Goodrich, S.N. Miller, and C.L. Unkrich, 1998. Landscape indicator interface with
hydrologic and ecological models. EPA / IAG Project No. DW12937916-01-0; Report #1, June, 1998.
47
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Resume
Name: Mariano Hernandez
Affiliation:
US Department of Agriculture, Agricultural Research Service, Southwest Watershed Research Center, 2000 East
Allen Road, Tucson, Arizona 85719, USA.
Education and Certification:
Ph.D., Watershed Management, University of Arizona, Tucson, Arizona, 1992
M.S., Hydrology, University of Arizona, Tucson, Arizona, 1985
B.S., Civil Engineering, National Polytechnic Institute, Mexico City, Mexico, 1980
Experience and Accomplishments:
1996 - Present Research Scientist (Faculty), University of Arizona, School of Renewable Natural Resources and
affiliated to the US Department of Agriculture - Agricultural Research Service, Tucson, Arizona.
Present Co-leader for an interdisciplinary research project between the USDA-ARS and the University of
Arizona.
Present Research Scientist for an interdisciplinary research project between the USDA-ARS and the
EPA-NERL
1994 -1995 Hydrologist, US Department of Agriculture - Agricultural Research Service, Tucson, Arizona.
Development and implementation of a mathematical model to study the effects of capillary
membranes in rainfall simulator plots.
1992 -1993 Postdoctoral Research Appointment at the US Department of Agriculture - Agricultural
Research Service, Tucson, Arizona. Participation in the calibration and validation of hydrologic
models in the Multiple Objective Decision Support System for the USDA Water Quality Initiative,
1992-1994.
1986 -1991 Doctoral Research Assistant, US Department of Agriculture - Agricultural Research Service,
Tucson, Arizona. Participation in the development of the hydrologic component in the Water
Erosion Prediction Project (WEPP).
1984 -1985 Graduate (MS) Research Assistant, University of Arizona, Department of Hydrology and Water
Administration, Tucson, Arizona
48
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1983 -1984 Graduate (MS) Research Assistant, University of Arizona, Water Resources Research Center,
Tucson, Arizona.
Selected Publications and Reports:
Hernandez, M., D.C. Goodrich, S.N. Miller, and C.L. Unkrich, 1998. Landscape indicator interface with
hydrologic and ecological models. EPA / IAG Project No. DW12937916-01-0; Report #1, June, 1998.
Hernandez, M. and D.C. Goodrich, 1998. Landscape indicator interface with hydrologic and ecological models.
EPA / IAG Project No. DW12937916-01-0; Report #2, June, 1998.
Hernandez, M., D.C. Goodrich, S.N. Miller, and B.F. Goff, 1998. Landscape indicator interface with hydrologic
and ecological models: demonstration of SWAT and KINEROS model parameterization. EPA / IAG Project
No. DW12937916-01-0; Report #3, November, 1998.
Hernandez, M., P. Heilman, L. J. Lane, J. L. Oropeza-Mota, and H. M. Arias-Rojo, 1998. Use of a DSS for
evaluating land management system effects on tepetate lands in central Mexico, Chapter 48, In: Multiple
Objective Decision Making for Land, Water, and Environmental Management, Edited by S. A. El-Swaify and
D. S. Yakowitz, Lewis Publishers, CRC Press.
Hernandez, M., Lane, L. J., Stone, J. J., Martinez, G J., and Kidwell, M., 1997. Hydrologic Model Performance
Evaluation Applying the Entropy Concept as a Function of Precipitation Network Density, Proceeding of the
International Congress on Modeling and Simulation, Hobart, Tasmania.
Freedman, V. L., Lopes, V. L., and Hernandez, M., 1998. Parameter Identifiability for catchment-scale erosion
modelling: a comparison of optimization algorithms, Journal of Hydrology, Vol. 207, No. 1-2, pp. 83-97.
Lane, L. J., Hernandez, M., Nichols, M., 1997. Processes controlling sediment yield from watersheds as a
functions of spatial scales, Journal of Environmental Modelling & Software, Vol. 12, No. 4, pp. 355-369.
Lane, L. J., M. Hernandez, M. H. Nichols, 1997. Dominant processes controlling sediment yield as a function of
watershed scale, Proceeding of the International Congress on Modelling and Simulation, Hobart, Tasmania.
49
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Resume
Name: Bruce F. Goff
Affiliation:
US Department of Agriculture, Agricultural Research Service, Southwest Watershed Research Center, 2000 East
Allen Road, Tucson, Arizona 85719, USA.
Education and Certification:
PhD degree in Watershed Science, Department of Forest Resources, College of Natural Resources, Utah State
University, USA, 1991.
MS degree in Watershed Management, School of Renewable Natural Resources, University of Arizona, USA,
1985.
BS degree in Renewable Natural Resources (Range Management Major), School of Renewable Natural
Resources, University of Arizona, USA, 1981.
Professional Certification:
Professional Hydrologist. Certified and registered by the American Institute of Hydrology. Certificate Number 96-
H-1138.
Experience and Accomplishments:
1996 - Present Hydrologist and Global Change Program Coordinator, US Department of Agriculture, Agricultural
Research Service, Southwest Watershed Research Center, Tucson, Arizona, USA.
1995 -1996 Environmental Program Manager, The Proteus Corporation, Albuquerque, New Mexico, USA.
1994 Consulting Hydrologist, RPS International, Nairobi, Kenya
1991 -1994 Consulting Hydrologist, Richard Woodroofe and Associates, Addis Ababa, Ethiopia.
1991 Hydrologist, The Sear-Brown Group, Park City, Utah, USA.
1988 -1990 University Lecturer in Watershed Management, Faculty of Forest Resources and Wildlife
Management, Moi University, Eldoret, Kenya.
50
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1985 -1988 Watershed Scientist, Doctoral Research Assistant, Watershed Science Unit, Utah State
University, USA.
1983 -1985 Watershed Scientist, Graduate (MS) Research Assistant, USDA-ARS Southwest Watershed
Research Center, Arizona, USA.
1980 -1982 Range Conservationist, US Forest Service, Apache-Sitgreaves National Forests and Prescott
National Forest, Arizona, USA.
Selected Publications and Reports:
Goff, B.F. and D.C. Goodrich. 1998. Think globally, act locally: Community participation in "SALSA" Global
Change Research. Proceedings of American Meteorological Society, Special Symposium on Hydrology,
Phoenix, Arizona, 11-16 Jan. 1998. pp. 238-243.
Goff, B.F. 1997. Appendix A: synopsis of hydrogeological studies. Environmental assessment, 1995 Base
Realignment and Closure, Fort Huachuca, Arizona. Directorate of Engineering and Housing, US Army
Garrison, Fort Huachuca, Arizona.
Goff, B.F. 1996. Hydrology, water resources and riparian habitat sections. Draft environmental impact statement
(DEIS) for installation master plan, Fort Huachuca, Arizona. Report submitted to US Army-Fort Huachuca,
Arizona, by The Proteus Corporation, Albuquerque, New Mexico, USA.
Goff, B.F. 1994. Regional flood analysis manual (Blue Nile Catchment). Ethiopian Valleys Development Studies
Authority (EVDSA), Institutional Support Project (ISP) Report No. M2/C4/S3/4. Richard Woodroofe and
Associates. Addis Ababa.
Goff, B.F. 1994. Flood frequency analysis manual (Blue Nile Catchment). EVDSA, ISP Report No. M2/C4/S3/5.
Richard Woodroofe and Associates. Addis Ababa.
Goff, B.F. 1994. Sediment monitoring and data processing manual. EVDSA, ISP No. M2/C5/1. Richard
Woodroofe and Associates. Addis Ababa.
Goff, B.F., GC. Bent, and GE. Hart. 1994. Influence of rainfall intensity on the interrill credibility of two
rangeland soils. Transactions of the American Society of Agricultural Engineers 37(5): 1445-1448.
Goff, B.F. 1993. Rainfall analysis methods. EVDSA, ISP Report No. M2/C5/2. Richard Woodroofe and
Associates. Addis Ababa.
Goff, B.F. 1993. Hydrological yearbook publication guidelines. EVDSA, ISP Report No. M2/C4/S1/9. Richard
Woodroofe and Associates. Addis Ababa.
Goff, B.F., GC. Bent, and GE. Hart. 1993. Erosion response of a disturbed sagebrush steppe hillslope. Journal of
Environmental Quality 22(4): 698-709.
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Resume
Name: Daniel T. Heggem
Affiliation:
US Environmental Protection Agency, National Exposure Research Laboratory, P.O. Box 93478, Las Vegas,
Nevada 89193-3478, USA.
Education:
Capital University, B.S. in Biology, 1976
Experience and Accomplishments:
7/87 - Present Environmental Research Scientist: U.S. Environmental Protection Agency, Las Vegas, NV.
3/80 - 7/87 Marine/Aquatic Biologist: U.S. Environmental Protection Agency, Washington, DC.
4/77 - 3/80 Biologist: U.S. Environmental Protection Agency, Washington, DC.
Awards and Honors:
U.S. Environmental Protection Agency Award: Bronze Medal for commendable service for improving the
Agency's methods for safely analyzing toxic substances, May 27, 1981.
U.S. Environmental Protection Agency, Special Achievement Award, in recognition of outstanding contributions
to the Chlorinated Dioxin Work Group by Lee Thomas, Assistant Administrator for Solid Waste and Emergency
Response, September 13, 1984.
U.S. Environmental Protection Agency Gold Medal for Exceptional Service in Recognition of Creative and
Responsive Leadership of the Alaskan Oil Spill Bioremediation Project, Washington, D.C., 1989.
U.S. Environmental Protection Agency Special Achievement Award, for outstanding efforts as the on-site Quality
Assurance Manager to the Bioremediation Project in the aftermath of the oil spill in Prince William Sound, Alaska,
Las Vegas, NV, 1989.
U.S. Environmental Protection Agency Special Achievement Award, in recognition of high quality performance,
U.S. Environmental Protection Agency Headquarters, 1989.
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U.S. Environmental Protection Agency Special Achievement Award, in recognition of high quality from Robert N.
Snelling, Las Vegas, NV, 1989.
U.S. Environmental Protection Agency Special Achievement Award, for consistent, positive leadership in planning
and implementing the Alaska 1990 Bioremediation program under adverse conditions from October, 1989 to
September, 1990.
EMSL-Las Vegas Environmental Protection Agency Award for Outstanding QA Support, 1990.
Selected Publications and Reports:
Heggem, D.T., C.M. Edmonds, A.C. Neale, L. Bice, and K. Bruce Jones. A Landscape Ecology Assessment of
the Tensas River Basin, Mississippi River Delta Region, Gulf of Mexico. Environmental Monitoring and
Assessment. Accepted.
Jones, K.B., D.T. Heggem, T.G. Wade, AC. Neale, D.W. Ebert, M.S. Mash, M.H. Mehaffey, K.A. Hermann,
A. R Selle, S. Augustine, I.A. Goodman, J. Pedersen, D. Bolgrien, J.M. Viger, D. Chiang, Y. Zhong, J.
Baker, and R.D. Van Remortel. Assessing Landscape Condition Relative to Water Resources in the Western
United States: A Strategic Approach. Environmental Monitoring and Assessment. Accepted.
Edmonds, C.M., A.C. Neale, D.T. Heggem, J.D. Wickham, and K.B. Jones. A Comparison of Landscape
Change Detection Methods. Environmental Monitoring and Assessment. Accepted.
Heggem, D.T., A.C. Neale, C.M. Edmonds, L.A. Bice, R.D. Van Remortel, and K.B. Jones. 1999. An Ecological
Assessment of the Louisiana Tensas River Basin. U.S. Environmental Protection Agency, Washington D.C.,
EPA/600/R-99/016, 123 pp.
Heggem, D.T., C.M. Edmonds, A.C. Neale, L. Bice, and K. Bruce Jones. 1999. Forested Wetland Restoration:
Identifying Potential Sites in Northeast Louisiana. Geo Info Systems, 9 (5): 34-39.
Bradford, D.F., S.E. Franson, A.C. Neale, D.T. Heggem, GR Miller, and GE. Canterburry. 1988. Bird Species
Assemblages as Indicators of Biological Integrity in Great Basin Rangeland. Environmental Monitoring and
Assessment, Vol. 49, pp. 1-22.
West, N.E., J.M. Start, D.W. Johnson, M.M. Abrams, J.R Wight, D.T. Heggem, and S.E. Peck. 1994. Effects of
Climate Change on the Edaphic Features of Arid and Semiarid Lands of Western North America. Arid Soil
Research and Rehabilitation, Vol. 8, pp. 307-351.
Heggem, D.T. and J.E. Pollard. 1991. Quality Assurance Sample Design for Large and Small Environmental
Water Quality Surveys: A Look at the Past and Future. Accountability in Research, Vol. 1, pp. 155-167.
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Resume
Name: Timothy G. Wade
Affiliation:
US Environmental Protection Agency, National Exposure Research Laboratory, P.O. Box 93478, Las Vegas,
Nevada 89193-3478, USA.
Education:
Master of Arts in Geography, University of California, Santa Barbara - 1986
Bachelor of Arts with honors in Geography, University of California, Santa Barbara - 1984
Experience and Accomplishments:
9/98 - Present Geographer, U.S. EPA Landscape Ecology Branch, Las Vegas, NV.
12/91 - 9/98 GIS Programmer Analyst, Desert Research Institute, Reno, NV.
7/96 - 9/98 (40% FTE). GIS Manager and Webmaster, Biological Resources Research Center, University of
Nevada, Reno.
1995, 1996 (Spring Semester) Instructor, University of Nevada, Reno.
1/90 - 6/91 Public Service Intern IV, Washoe County Department of Comprehensive Planning, Reno, NV.
Professional Training:
Introduction to Arc/Info
Using Grid with Arc/Info -6.1
Introduction to ArcView
Programming with Avenue
ESRI Authorized ArcView Instructor
Awards and Honors:
UCSB Dean's List for Scholastic Excellence (4 times)
First Place, Poster competition, ESRI User Conference 1990
Third Place, Poster competition, ESRI User Conference 1991
Third Place, AML Tools competition, ESRI User Conference 1995
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Third Place, AML Applications competition, ESRI User Conference 1996
First Place, AML Shortcuts competition, ESRI User Conference 1997
Selected Publications and Reports:
Wade, T.G., Wickham, J.D., and Bradford, D.F. Accuracy Assessment of Road Density Estimates Derived from
USGS Data for Use in Habitat Fragmentation Studies (in press). Photogrammetric Engineering and Remote
Sensing.
Wickham, ID., Jones, K.B., Riitters, K.H., Wade, T.G., and O'Neill, R.V., 1999. Transitions in Forest
Fragmentation: Implications for Restoration Opportunities at Regional Scales. Landscape Ecology, 14:137-
145.
Wade, T.G., Wickham, J.D., Schultz, Bradley W. 1998. Modeling the Potential Spatial Distribution of Beef Cattle
Grazing Using a Geographic Information System. Journal of Arid Environments, 38:325-334.
Wickham, ID., O'Neill, R.V., Riitters, K.H., Wade, T.G, and Jones, K.B. 1997. Sensitivity of Landscape Pattern
Metrics to Land Cover Misclassification and Environmental Condition Gradients. Photogrammetric
Engineering and Remote Sensing. 63(4):397-402.
Wickham, ID., Riitters, K.H., O'Neill, R.V., Jones, K.B., and Wade, T.G. 1996. Landscape 'Contagion' in Raster
and Vector Environments. International Journal of Geographic Information Systems 10(7):891-899.
Wade, T.G. and Wickham, J.D. 1995. Using GIS and a Graphical User Interface to Model Land Degradation.
Geo Info Systems, 5(2): 3 8,42.
Wickham, ID., Wade, T.G, Jones, K.B., O'Neill, R.V., Riitters, K.H. 1995. Diversity of Ecological Complexes in
the Conterminous United States. Vegetatio, 119:91-100.
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Resume
Name: Donald W. Ebert
Affiliation:
US Environmental Protection Agency, National Exposure Research Laboratory, P.O. Box 93478, Las Vegas,
Nevada 89193-3478, USA
Education and Certification:
M.S., University of Nevada - Las Vegas (Biological Sciences), 1993
B.S., University of Cincinnati (Biology), 1984
Experience and Accomplishments:
10/98 - Present Ecologist, U.S. Environmental Protection Agency, National Exposure Research Laboratory,
Environmental Sciences Division, Landscape Ecology Branch, Las Vegas, Nevada.
Principal developer for the Analytical Tools Interface for Landscape Assessments (ATtlLA) extension for
Arc View 3.1 desktop Geographic Information Systems software.
5/93 - 10/98 Research Associate, National Biological Service Cooperative Research Unit, University of
Nevada - Las Vegas, 4505 South Maryland Parkway, Las Vegas, Nevada 89154-4004.
Helped develop methodology for mapping disturbed lands within Lake Mead National Recreation Area using
digital orthophoto quadrangles as primary data source.
Developed a GIS database for the Northern and Eastern Mojave Planning Area (approx. 8 million acres) using
Arc/Info, Arcview, Spatial Analyst, and GRASS. Assembled coverages from a wide variety of sources, including
USGS dems, digs, drgs, doqs and gnis data files, NBS gap analysis vegetation layers, BLM gcdb files, GPS data
files, Landsat and SPOT satellite imagery, analog maps, and text files. Retrieved files from various media,
including the Internet, 8 mm tape, quarter-inch tape, and CDs. Performed data conversions to common formats
and map projections. Developed new coverages from spatial queries and analyses. Wrote data to CD-ROM.
Prepared output maps and metadata.
Provided Arcview 3.0a training courses to the Northern and Eastern Mojave Planning team and to staff from
Death Valley National Park.
Assisted in the development of a park-wide GIS database for Lake Mead National Recreation Area (approx. 1.3
million acres) using GRASS and Arc/Info. Assembled coverages from a wide variety of sources, including USGS
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dems, digs, drgs, and gnis data files, NBS gap analysis vegetation layers, GPS data files, and miscellaneous aerial
photographs and Landsat satellite imagery.
Short Courses:
Programming with Avenue, ESRI, Inc., Redlands, California, 24 hours, December 1998
Introduction to Avenue, ESRI, Inc., Redlands, California, 16 hours, September 1998
Troubleshooting, Maintaining, and Upgrading PCs, CompuMaster Training, Las Vegas, Nevada, 8 hours,
June 1998
UNIX System Administration - Introduction, University of Nevada - Las Vegas, 12 hours, May-June 1996
Selected Publications and Reports:
Effect of Scale on Defining Topographically Suitable Desert Bighorn Habitat. Divine, D. D., D. W. Ebert, C. L.
Douglas. Desert Bighorn Counc. Trans. 40:13-18. 1996.
Papers Presented:
Modification of Cunningham's Habitat Evaluation Model for Desert Bighorn Sheep. 39th Annual Desert Bighorn
Council Meeting, Alpine, Texas, April 1995.
Desert Bighorn Movements and Habitat Use in Relation to the Proposed Black Canyon Bridge Project - Nevada.
36th Annual Desert Bighorn Council Meeting, Bullhead City, Arizona, April 1992.
Computer Operating Systems and Software
Windows NT/95/98
ARC/INFO 7.2
MS ACCESS
STATVIEW
3D Analyst 1.0
GRASS 4.1.5
dBASE III+, IV
MacOS System 8
Spatial Analyst 1.1
GEO-PC 2.01
SURFER
MS-DOS 6.0
ARC VIEW 3.1
Excel 7.0
STATGRAPHICS
SunOS 4.1.3
IDRISI for Windows
FORTRAN 77
SUPERANOVA
ER Mapper 5.5
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