Advancing Water Resources Research and Management
Watershed-Scale Nonpoint Source Pollution Modeling and
Decision Support System Based on a Model-GIS-RDBMS Linkage
Jaewan Yoon
(1)
JavaScript/Form/Table version also available
Table of Contents
Abstract
Introduction
Study Watershed
Geograpphic Information System (GIS)
Agricultural Nonpoint Source Pollution (AGNPS) Model
Direct Linkage between Models and GIS
Methodology
Critical Area Assessment and BMP Scenario Simulation
Summary and Conclusions
References
Abstract
Methods were developed for directly linking the distributed
parameter model, AGNPS with a Geographic Information System (GIS)
and a relational database management system (RDBMS) to
investigate a nonpoint source pollution problem.
AGNPS (Agricultural Nonpoint Source model) is an event-based
model that simulates runoff and the transport of sediment
and pollutants from mainly agricultural watersheds.
Distributed parameter models such as AGNPS are often
applied to large problem domains. Linking such models to
GIS and database management system can facilitate better
data storage, manipulation and analysis than conventional
methods. In this study, rather than manually implementing
AGNPS, extracted data are integrated in an automatic fashion
through a direct linking between the AGNPS model engine and GIS.
This direct linkage results in a powerful, up-to-date tool
that would be capable of monitoring and instantaneously
visualizing the transport of any pollutant that AGNPS can
simulate. Thereby, it reduces the time required to analyze
the numerical output from AGNPS, and enables users to
identify critical areas of nonpoint source pollution (NPS)
and furthermore, to perform various "what if" scenarios to
support the decision making processes such as Best Management
Practices (BMP) for the watershed. A case study was performed
on a watershed of 98.1 km2 (=24240 acres) in the upper
segment of the Sand Hill River Subbasin in Minnesota by
using this linkage implementation. Simulated results showed
that the optimal BMP scenario achieved an average reduction
of about 26% from current nonpoint source pollutant levels
on soluble and sediment-attached nitrogen and phosphorous.
Especially, this optimal BMP scenario was most effective
in controlling the erosion and sediment yield. As a result,
a maximum 50% reduction of sediment yield was observed.
KEYWORDS:
Water Quality, Watershed-scale, NPS, GIS, Linkage.
Introduction
Nonpoint source pollution (NPS) is a type of the pollution
generated from diffused sources in the public and private
domain. The Environmental Protection Agency (EPA) defines
the nonpoint source pollution as pollution originating from
urban runoff, construction, hydrologic modification,
silviculture, mining, agriculture, irrigation return flows,
solid waste disposal, atmospheric deposition, stream bank
erosion, and individual sewage disposal (Corbitt, 1990).
More than fifty percent of the pollution entering the
nation’s waters comes from nonpoint sources (Tyler, 1992),
and it is responsible for almost two-thirds of the pollution
that prevents achievement of water quality standards (
Alm, 1990).
Nonpoint Source Pollution management is highly dependent
on hydrologic simulation models. Evaluating alternative management
strategies through experiments and a limited amount of field
measurements is not feasible, and a modeling study is often
the only viable means of providing input to management decisions.
The hydrologic system was commonly simplified in the past as a
"lumped model." Under this simplification, the
spatial distribution of parameters lost their real meaning
in hydrologic modeling. In contrast to this simplification, a
distributed parameter model maintains the spatial distribution
of the parameters. Therefore, the application of distributed
parameter models is of practical necessity especially in case of
the nonpoint source pollution management. The major disadvantage
of distributed parameter models are the large amounts of
time required for assembling and manipulating the input data sets.
The distributed nonpoint source pollution models used to
study pollutant transport and erosion easily generate towering
amounts of data for analysis in even a small watershed.
On a large non-homogeneous watershed, a complete simulation and
analysis can be very time consuming.
A logical step in improving the quality of the
hydrologic modeling would be the integration of spatially
distributed parameter models with practical data management
scheme such as the geographic information system (GIS) and
database management system (DBMS). This integration of
distributed hydrologic models-GIS-DBMS can be defined as
a tool to collect, manage, analyze, simulate and display
spatially varying information (
Parker, 1990). This paper
describes direct AGNPS-GIS-DBMS linkage to: (1) effectively
pinpoint critical areas where resources are threatened,
(2) give the necessary information instantaneously based
on BMP scenario simulations for further remediation and
conservation efforts, (3) provide quality information to
decision-makers cost effectively, and (4) help impartially
distribute incentives and regulations used for water quality
management.
Table of Contents
Study Watershed
The Sand Hill River Watershed District of the
North Central Minnesota had proposed building a dam
which will create a permanent pool of 5 km2 (=1,217
acres) near the northwestern Minnesota town of Winger
for flood protection on the main stem of the Red River
of the North basin. The feasibility of the dam project
has been controversial since it was first proposed
more than 10 years ago due to concerns regarding
low annual average flow rate and water quality problems.
No definite solutions or alternatives for alleviating
these problems have been investigated (
Polk County
Water Planning Task Force, 1990). This paper will
investigate the application of a distributed parameter
hydrologic model, AGNPS (Agricultural Nonpoint Source
Pollution Model) and GIS-DBMS direct linkage to the
upper segment area of 98.1 km2 (=24,240 acres) which
has an outlet at the proposed dam site.
The upper segment has a complex topographic
pattern which consists of moraines, potholes,
small lakes, wetlands and marshes that affects
its hydrologic characteristics substantially as shown in Figure 1.
Figure 1. Topographic Patterns of the Study Watershed
(12.2Kbytes, 645x386 pixels)
For the analysis of such complex topographic features,
a spatially distributed model such as AGNPS is more
suitable than a conventional lumped model. Also nonpoint
source pollution management on such complex topography is
difficult to plan and highly dependent on simulation models
because nonpoint source pollution is site specific.
Evaluating alternative management plans through field
experiments is usually not feasible, and thus simulation
study is often the only practicable means of providing
input to management decisions. In this sense, AGNPS is
an ideal distributed parameter model for the study area
to pinpoint "critical" areas of sediment and nutrient
production in a watershed, and to estimate the potential
benefits of implementing Best Management Practices (BMP)
to alleviate current water quality problems.
Table of Contents
Geographic Information System (GIS)
The initial attempts to apply computer technology
to the reduction of the substantial spatial data
handling problems were associated with military purposes
and produced useful results only after the massive
application of computing resources. This was a
function not only of the computer technology in the
late '50s and early '60s, but also an early demonstration
of the special problems encountered in digital spatial
data handling (Dangermond, 1987;
Parent and Church, 1987).
Early digital mapping systems in the 1960's were mainly
automated map drafting systems. Their application was to
produce conventional maps with the use of a computer that
became more interactive with the introduction of software
and hardware systems. In the 1970's database management
systems (DBMS) conjugated with the improvements in
computers' software and hardware capabilities had a
great influence on the management and analysis of digital
geographic information. In the seventies the term GIS
became well established, and its application and areas
of research expanded beyond the military use and spread
to planning and management of natural resources, land
records, utilities, etc.
A GIS is an assembly of knowledgeable users
and computer hardware and software designed to collect,
manage, analyze and display spatially referenced data
(Nystrom et al., 1986;
Petersen et al., 1987; Eash, 1994),
wildlife (Giles, 1990), and stormwater management
(Holbert et al., 1990) among many others.
The State of Virginia has implemented a system,
VirGIS (Flagg et al., 1990;
Shanholtz et al., 1990)
to develop spatially represented databases. Spatially
referenced data, and the use of NPS (non-point source)
models allow the identification, prioritization, and
targeting of nonpoint source pollution problems. For
example, using GIS-based data on topography, precipitation,
land cover, soils, runoff, rainfall erosivity, sediment
production, land use, livestock density and animal
loading indices, an Agricultural Pollution Potential
Index (APPI) was developed for each of 104 watersheds
in the State of Pennsylvania. The development of these
indices allowed prioritizing watersheds to deal with
problems concerning nitrogen, phosphorous and loss of
sediment (Hamlett et al., 1990).
Table of Contents
Agricultural Nonpoint Source Pollution
(
AGNPS) Model
Nonpoint source pollution from agricultural
and urban areas affects both surface and subsurface
water quality, and accounts for more than one-half of
the biological oxygen demand (BOD) and most of the
suspended solids, phosphorus, nitrogen and toxic
substances entering waterways.
The AGNPS model was developed by the U.S.
Department of Agriculture, Agricultural Research
Service in cooperation with the Minnesota Pollution
Control Agency (MPCA) and the Soil Conservation Service
(Young et al., 1987,
1995) for the analysis of large
agricultural watersheds ranging in size between 500
and 23,000 acres. The model was originally developed
(1) to analyze and provide estimates of runoff with
primary emphasis upon sediment and nutrients transport,
from agricultural watersheds, and (2) to compare the
effects of various conservation alternatives upon
implementation on the management practices of the
watershed. Though AGNPS was developed for the State
of Minnesota, several investigations in various-sized
agricultural watersheds throughout many regions of
United States have been reported (Bingner et al.,
1989; Lee et al., 1990; Yoon and Disrud, 1994).
AGNPS is a cell-based distributed parameter
hydrologic model which requires 22 categories of
information such as landuse, surface condition,
channel data, fertilization, etc. for each cell,
and is capable of generating both estimates of quantity
and quality of runoff from the watershed for a
given storm event (Young et al., 1987, 1995). Cell
segmentation of 2.5-, 10-, and 40-acre parcels in
the study watershed is shown in Figure 2. Analysis
of pollutant loads from feedlots, investigations
into the effects of implementing various conservation
practices including impoundment terraces, and the ability
to output water quality characteristics at intermediate
points throughout the watershed network are all within
the model's capabilities. The erosion component in
the AGNPS model assumes overland erosion, channel
erosion and deposition in impoundments. The model
uses the modified universal soil loss equation (USLE)
by Wischmeier and Smith (1978). Field topography
is the major factor in determining the type of flow,
i.e. overland, overland-channel, overland-channel-impoundment,
etc.
Figure 2. Cell Segmentation of 2.5-, 10-, and 40-acre Parcels in the Study Watershed
(18.4Kbytes, 645x503 pixels)
The model uses distributed parameter inputs and
operates on a cell basis. The distributed parameter
approach of the model is most appropriate to preserve
the spatial characteristics of the watershed and to
obtain more accurate results. By itself, AGNPS is
data-intensive model. In general, as the area or the
resolution of the watershed increases, data requirements
also increase proportionally. But with tools such as
a geographic information system and a database management
system, AGNPS has an advantage over other lumped models.
Several distributed parameter hydrologic models have
been developed in recent years based on this quantized
grid-cell concept. Models that are structured in this
way include Areal Nonpoint Source Watershed Environment
Response Simulation model (ANSWERS) (
Beasley and Huggins, 1982),
the very detailed Systeme Hydrologique Europeen model (SHE)
(Abbott et al., 1986), and the Water Erosion Prediction
Project model (WEPP)
(Foster et al., 1987).
Various output options are available with the
AGNPS. Primary output includes estimates of runoff
volume, peak flow rate at the watershed outlet, area-weighted
erosion for both upland and channel areas. Also given
are estimates of the sediment delivery ratio, sediment
enrichment ratio, mean sediment concentration, and total
sediment yield for each of five sediment particle size classes.
Also available is a nutrient analysis, which includes N, P
and chemical oxygen demand (COD) mass per unit area for both
soluble and sediment absorbed nutrients, and N, P and COD
concentrations in the runoff. Output parameters can be
requested for a specific cell or for all cells if desired.
Table of Contents
Direct Linkage Between Model and GIS
There have been several attempts to integrate GIS
with existing hydrologic models for the watershed modeling
and simulation. In their attempt to integrate GIS with a
finite element storm hydrograph model (FESHM),
Wolfe and Neale (1988)
had to manually process the data to meet the
FESHM's data format requirement. Improvement in handling
similar procedures was presented by
Ross et al. (1990) through
external data handling utilities for a hydrologic simulation model.
Ozbilgin et al. (1991)
used GIS to generate hydroconductivites
from predefined cross-sections of the San Fernando Valley,
California for a three-dimensional groundwater model
(MODFLOW). Panuska et al. (1991), integrated two
terrain-enhancer programs, TAPES-C and TAPES-G (
Moore, 1988),
into AGNPS to estimate the flow path parameters.
These hydrologic modelings were performed with
the extracted data from the GIS to execute available models.
In most cases, either the data manipulations were done
manually before running models, or all necessary data
manipulation schemes such as transformation of data formats
and extraction of usable data for existing models had to
be provided by specially written software (
Fisher, 1989;
Piwowar and LeDrew, 1990).
These procedural redundancies
have created bottleneck phenomena and even hindered the
ideal integration of GIS and water resources tools. Also,
most of the literature admit that many linking problems
still exist when integrating models with a vast amount of
spatial data in unorganized formats. Problems remain with
the search for the optimal schema for spatial data handling,
especially where adequate transformations of data within a
GIS environment in order to feed the models directly are
absolutely necessary. Methods suggested to improve the
efficiency of the linkage between GIS and models usually
include implementing various database management strategies
or interfaces within the environment for these problems
(Wood et al., 1988;
Cline et al., 1989).
In summary, the main focus in the GIS usage with
existing models has been one-directional data extraction
to the model rather than direct data exchange to and from
the model. It is imperative to develop data handling
procedures to prepare inputs to the model, and to effect
changes in watersheds, and procedures to analyze and
display corresponding model results within the GIS environment.
This bilateral linkage approach through effective use of
DBMS will contribute to better hydrologic modeling and
water resources management.
In this study, methods were developed for
directly linking the distributed parameter model,
AGNPS with the vector-based GIS, Geo/SQL (
Generation
5 Technology, 1990) and the relational database management
system (RDBMS), ORACLE in terms of pre and postprocessing
of data, in applying the GIS to the ongoing water resources
planning and decision making process in the study area.
This direct linkage results in a powerful, up-to-date
tool that would be capable of monitoring and
instantaneously visualizing the transport of any
pollutant that AGNPS can simulate. Thus, the linkage
is truly bilateral rather than the conventional one-way
data extraction setup for input preparation. The advantage
of linkage is that because the distributed parameter
model such as AGNPS is often applied to large problem
domains, a linkage to GIS and RDBMS may be a more appropriate
tool for data storage, manipulation and analysis than a
collection of many input and output files.
Visualization of the spatially varying data and
time-dependent data such as runoff or sediment yield at
the outlet will greatly enhance the ability of conservation
managers to make further analyses and to make proper decisions.
Furthermore, the graphical display may provide an
indication of problems due to erosion and pollutant
movement on a watershed and help pinpoint critical
locations for further study and/or control action
(Yoon and Padmanabhan, 1995).
In a broad sense, graphical
representation of a watershed under consideration and the
simulation results from a pollutant movement and erosion
prediction model would facilitate the researcher's and
user's quick understanding of processes that produce the
erosion and pollutant movement (
Srinivasan and Engel, 1991).
Although this study emphasizes the methodology
to the upper segment of the Sandhill River subbasin in
Minnesota, the methodology is applicable to the investigations
and analysis of watersheds in general.
Table of Contents
Methodology
The basic data layers for the study area were
developed by digitizing the relevant maps, including
USGS-10 foot interval contour quadrangle maps of 1:24000
scale and SCS Soil maps. The spatial land cover/land use
data were compiled from LANDSAT thematic maps as shown in
Figure 3. Prepared data were inserted into ORACLE database
as separate characteristic data groups so that the maximum
flexibility can be assured when various scenarios are applied later.
Then data from the database and digital base layers were
georeferenced to create a spatial database of study area.
Sets of programmed SQL (structured query language) of the
ORACLE database management system were implemented to extract
necessary information in a usable data format whenever a
specific area is selected in GIS for AGNPS executions.
After each AGNPS execution, an output file
containing estimates of runoff volume, peak flowrate
at the watershed outlet, area-weighted erosion for both
upland and channel areas, sediment delivery ratio,
sediment enrichment ratio, mean sediment concentration,
and total sediment yield for each of five sediment
particle size classes (clay, silt, small and large aggregates,
sand), and a nutrient analysis, which includes N, P and
COD mass per unit area for both soluble and sediment
absorbed nutrients, and N, P and COD concentrations in
the runoff are created and loaded into temporary spatial
database tables. This transition of original data with
output results will maintain the valid georeferences
between data tables in the DBMS and graphic entities in GIS.
As a result, graphical representation in GIS facilitates
a more effective and efficient way of interpreting AGNPS'
distributed parameter model results and in making decisions
than dealing with a plethora of numerical output. By
using predefined SQL sets programmed for the study, a
new input data for AGNPS can be constructed based on
user definable scenario by specifying desired input
parameters and degrees of change for each parameter
based on previous results.
Figure 3. Spatial Database Compilation
(13.3Kbytes, 558x432 pixels)
Table of Contents
Critical Area Assessment and BMP Scenario Simulation
By using AGNPS-GIS-RDBMS linking methodology,
assessment of nonpoint sources from the study watershed
was performed using a 25-year, 24-hour storm event.
The SCS synthetic rainfall type II distribution was
applied to simulate rainfall distribution. Critical
areas of high volume of flow, erosion and sediment,
and nutrient yields were assessed within the watershed.
Sensitivity analysis of AGNPS model parameters
as shown in Table 1 was performed to construct "what if,"
or alternative management scenarios to improve current
water quality problems by using Model-GIS-RDBMS linkage.
Input parameter matrix for the sensitivity analysis is
shown in Figure 4. Results from the sensitivity analysis
show that seven AGNPS parameters out of twenty-two were
relatively effective in controlling nonpoint source pollutants
from this particular watershed. They were SCS curve number,
Manning's roughness coefficient, cover and management
factor (C-factor), land slope, channel sideslope,
practice factor (P-factor) and fertilization availability
factor in descending order of significance.
Table 1. Twelve Input Parameters used in the Sensitivity Analysis
Parameter Description Nature of Variable
===========================================================
RA Storm rainfall lumped
-----------------------------------------------------------
EI Storm Energy-Intensity lumped
value
-----------------------------------------------------------
CS Channel slope spatially varying
-----------------------------------------------------------
CSS Channel sideslope spatially varying
-----------------------------------------------------------
MA Manning's roughness spatially varying
coefficient
-----------------------------------------------------------
KF Soil erodibility spatially varying
factor
-----------------------------------------------------------
CF Cover and management spatially varying
factor
-----------------------------------------------------------
PF Practice factor spatially varying
-----------------------------------------------------------
FA Fertilization spatially varying
availability factor
-----------------------------------------------------------
LS land slope spatially varying
-----------------------------------------------------------
SL Field slope length spatially varying
-----------------------------------------------------------
SCS SCS curve number spatially varying
-----------------------------------------------------------
Figure 4. Input Parameter Matrix for the Sensitivity Analysis
(7 Kbytes, 367x341 pixels)
Based on the sensitivity analysis, parameters
were prioritized to minimize nonpoint source pollutants.
After seven simulations by using AGNPS-GIS-RDBMS linkage,
a best management practice (BMP) scenario was formulated
and then simulated to evaluate potential improvements on
current water quality problems (Figure 5). Simulated
results showed that the BMP scenario achieved an average
reduction of about 26% from current nonpoint source
pollutant levels. Especially, the final BMP scenario No.
7 was most effective in controlling the erosion and
sediment yield and a maximum 50% reduction of sediment
yield was observed. Various rainfall frequencies
including 1-, 2-, 5-, 10-, 25- and 100-year return
periods were also investigated to determine the effect
of various return periods on the current watershed response.
Figure 5. Bilateral Linkage of Model-GIS-RDBMS for "What-If" Scenarios
(10.1Kbytes, 588x321 pixels)
The direct AGNPS-GIS-RDBMS linkage developed
in this study is a powerful tool that would be capable
of monitoring the pollutant and sediment transports
within the watershed. In addition, this system will
be capable of effectively pinpointing areas of concern.
The overall long range benefit will be easier
information management for storing, retrieving,
analyzing, updating and maintaining of various
watershed related data, and more effective and
flexible methods for water resources planning and
management. Furthermore, the GIS would be
advantageous when study areas are large, analysis
and modeling are performed repeatedly, or if alternative
"what if" landuse/landcover scenarios are explored.
Further diversified modeling and simulation activities
can be done based on the established database.
Table of Contents
Summary and Conclusions
Methods were developed for directly linking
the distributed parameter model, AGNPS with a GIS and
a relational database management system (RDBMS) to
investigate a nonpoint source pollution problem.
AGNPS is an event-based model that simulates runoff
and the transport of sediment and pollutants from
mainly agricultural watersheds. The model operates
on a cell basis so that the spatial variation in parameters
of each cell can be accounted for in the analysis
throughout the entire watershed. Distributed
parameter models such as AGNPS are often applied to
large problem domains. Linking such models to GIS
and relational database management system can facilitate
better data storage, manipulation and analysis than
conventional methods. In this study, a vector-based
GIS and a relational database management system are
implemented to create and manipulate the watershed
database for AGNPS. Rather than manually implementing
AGNPS, extracted data are integrated in an automatic
fashion through a direct linking between the AGNPS
model engine and GIS. This direct linkage results in
a powerful, up-to-date tool that would be capable of
monitoring and instantaneously visualizing the transport
of any pollutant that AGNPS can simulate. Thus, the
linkage is truly bilateral rather than the conventional
one-way data extraction setup for input preparation.
Thereby, it reduces the time required to analyze the
numerical output from AGNPS, and enables users to
assess critical areas of nonpoint source (NPS) pollution
and furthermore, to perform various "what if" scenarios
to develop best management practices (BMP) for the watershed.
Rather than limiting AGNPS' capabilities just to the critical
area assessment, this "what-if" scenario manipulation can
be used to generate various AGNPS simulation input
scenarios that contain a single parameter or multiple
parameter variations of user specified AGNPS parameters.
Provision is made to check each scenario parameters'
logical upper and lower boundary conditions so that
generated AGNPS scenarios inputs will be realistic.
A case study was performed on a watershed of
98.1 km2 (=24,240 acres) in the upper segment of the
Sand Hill River Subbasin in Minnesota by using this
linkage implementation. Simulated results showed
that the optimal BMP scenario achieved an average
reduction of about 26% from current nonpoint source
pollutant levels on soluble and sediment-attached
nitrogen and phosphorous (Figure 6). Especially,
this optimal BMP scenario was most effective in
controlling the erosion and sediment yield. As a
result, a maximum 50% reduction of sediment yield
was observed. For example, visual comparisons
between before and after the optimized BMP scenario
implementation are shown in Figures 7 and 8 for
soluble nitrogen loading rate and nitrogen concentration
at each cell level.
Although this study emphasizes the
methodology to the upper segment of the Sand Hill
River Subbasin in Minnesota, the methodology is
applicable to the future investigations and analysis
of watersheds in general.
Figure 6. Magnitude of Reductions (%) made by the Final BMP Scenario to Base Values at Watershed Outlet
(15.9Kbytes, 588x384 pixels)
Figure 7. Soluble Nitrogen Reduction Based one Optimal BMP Scenario
(12.1Kbytes, 613x402 pixels)
Figure 8. Nitrogen Concentration Reduction Based one Optimal BMP Scenario
(11.7Kbytes, 631x415 pixels)
Table of Contents
References
- Abbott, M. B., Bathurst, J. C., Cinge, J. A., O'Connell, P. E., and Hasmussen, J., 1986.
- An Introduction to the European Hydrological
System-Systeme Hydrologique Europeen, 'SHE,' 2: Structure
of a Physically-based, Distributed Modeling System,
Journal of Hydrology, 87:61-77.
- Alm, A.L., 1990.
- Nonpoint Sources of Pollution, Environmental Sciences and Technology, 24(7):967.
- Beasley, D.B. and Huggins, L.F., 1982.
- ANSWERS (Areal Nonpoint Source Watershed Environment
Response Simulation): User's Manual, U.S. Environmental
Protection Agency, Chicago, Illinois.
- Bingner, R. L., Murphree, C. E., and Mutchler, C. K., 1989.
- Comparison of sediment yield models on watersheds
in Mississippi, Transactions of the ASAE, 32(2):529-534.
- Cline, T. J., Molinas, A. and Julien, P. Y., 1989.
- An Auto-CAD-Based Watershed Information System for the
Hydrologic Model HEC-1, Water Resources Bulletin, AWRA, Bethesda,
Maryland, 25(3):641-652.
- Corbitt, R.A., 1990.
- Standard Handbook of Environmental Engineering,
McGraw-Hill, New York, New York, pp. 7.50-7.57.
- Dangermond, J., 1987.
- Introduction to Geographic Information Systems
Technology, Proceedings in NCGA's Mapping and Geographic
Information Systems, November 9-12, San Diego, CA, pp. 32- 40.
- Eash, D.A., 1994.
- A Geographic Information System Procedures
to Quantify Drainage-Basin Characterisitcs,
Water Resource Bulletin, 30(1):1-8.
- Fisher, G. T., 1989.
- Geographic Information System/Watershed Model
Interface, Proceedings in the 1989 National Conference
on Hydraulic Engineering, ASCE, Hydraulics
Division, New Orleans, Louisiana, pp. 851-856.
- Flagg, J. M., W. C. Hession and V. O. Shanholts, 1990.
- GIS and Water Quality Models as State Level Nonpoint
Source Pollution Control Management Tools, Proceedings
in Application of Geographic Information Systems,
Simulation Models, and Knowledge-Based Systems
for Land Use Management, Blacksburg, VA.
- Foster, G. R., Lane, L. J., Schertz, D. L., Nordin, J. O., and Wingate, G. D., 1987.
- User Requirements: USDA-Water Erosion Prediction Project (WEPP),
American Society of Agricultural Engineers,
ASAE Paper No. 87-2539, St. Joseph, MI.
- Generation 5 Technology, 1990.
- Geo/SQL Geographic Information System, User's Guide, Westminster, CO.
- Giles, R. H. Jr., 1990.
- A Computer-aided Prescription System for Wildlife-Management
and Related Areas, Proceedings in Application of
Geographic Information Systems, Simulation Models, and
Knowledge-Based Systems for Land Use Management,
Blacksburg, VA, pp. 11-22.
- Hamlett, J. M., Petersen, G. W., Russo, J., Miller, D. A., Baumer, G. M.and Day, R. L., 1990.
- GIS-Based Watershed Rankings for Nonpoint Pollution
in Pennsylvania, ASAE Paper No. 90-2619, ASAE, St. Joseph, MI.
- Holbert, S. B., 1990.
- Development of a Geographic Information System
Based Hydrologic Model for Stormwater
Management and Land Use Planning, Proceedings
in Application of Geographic Information Systems,
Simulation Models, and Knowledge- Based Systems for
Land Use Management, Blacksburg, VA, pp. 197-210.
- Lee, T. C. and Zhang, G. T., 1989.
- Developments of Geographic Information Systems
Technology, Journal of Survey of Engineering, 115(3):304-323.
- Moore, I. D., 1988.
- A Contour-based terrain analysis program for
the environmental sciences (TAPES), Transaction of American
Geophysical Union, 69(16):345.
- Nystrom, D. A., Wright, B. E., Prisloe, M. P. Jr. and Batten, L. G., 1986.
- U.S. Geological Survey, Connecticut
Geographic Information Systems Project, Technical papers,
ACSM-ASPRS Annual Convention, Washington,
DC, pp. 210-219.
- Ozbilgin, M. M., Chieh, S. H., Swanson, W. R., Shuter, K. A., Hoye, W. W. and Blevins, M. L., 1991.
- Management of a Groundwater Basin using an
Integrated Numerical Model and a Geographic
Information System, Proceedings in the 18th Annual
Conference and Symposium, ASCE, Water Resources Planning &
Management Division, New Orleans, Louisiana, pp. 80-85.
- Panuska, J. C., Moore, I. D. and Kramer, L. A., 1991.
- Terrain Analysis: Integration into Agricultural
nonpoint source (AGNPS) pollution model, Journal
of Soil and Water Conservation, pp. 59-64.
- Parent, P. and Church, R., 1987.
- Evolution of Geographic Information Systems
as Decision Making Tools, Proceedings in GIS '87,
American Society for Photogrammetry and Remote
Sensing (ASPRS) and American Congress on Surveying
and Mapping (ACSM), Falls Church, Virginia, pp. 63-70.
- Parker, H.D. 1990.
- Introduction, The GIS Source Book,
GIS World, Inc., Ft. Colins, Colorado, pp. 1-5.
- Petersen, G. W., Miller, D. A., Day, R. L., Sasowsky, K. C. and Evans, B. M., 1987.
- An Introduction to Geographic
Information Systems and Their Role
in Soil and Hydrologic Studies, Paper No. 8252,
Journal Services of the
Pennsylvania Agricultural Experiment Station.
- Piwowar, J. M. and LeDrew, E. F., 1990.
- Integrating Spatial Data. A User's Perspective,
Photogrammetric Engineering and Remote Sensing, 56(11):1497-1502.
- Polk County Comprehensive Local Water Planning Task Force, 1990.
- Polk County Comprehensive Water Plan.
- Ross, M. A., Fielland, C. F. and Tara, P. D., 1990.
- An Integrated GIS/Hydrologic Model for Phosphate
Mining Reclamation Design, Proceedings in the
26th Annual Conference of American Water
Resources Association, Denver, Colorado, pp. 31-39.
- Shanholtz, P. D., Flagg, J. M., Metz, C. D., Desal, C. J. and Kleen, J. W., 1990.
- Information Support Systems Laboratory (and VirGIS)-Overview,
Proceedings in Application of Geographic Information Systems,
Simulation Models, and Knowledge-Based Systems
for Land Use Management, Blacksburg, VA.
- Srinivasan, R. and Engel, B. A., 1991.
- GIS: A Tool for Visualization and Analyzation,
American Society of Agricultural Engineers,
ASAE Paper No. 91-7574, St. Joseph, MI.
- Tyler, E.L. 1992.
- Re-Authorization of the Clean Water Act,
The Universities Council on Water Resources, Water
Resources Update, Spring 1992, Issue No. 88, pp. 7-15.
- Wischmeier, W. H. and Smith, D. D., 1978.
- Predicting Rainfall Erosion Losses, Agricultural
Handbook, no. 537, U.S. Department of Agriculture, Washington, D.C.
- Wolfe, M. L. and Neale, C. M. U., 1988.
- Input Data Development for a Distributed
Parameter Hydrologic Model (FESHM), Proceedings
in Modeling Agricultural, Forest and
Rangeland Hydrology, International Symposium,
ASAE, Chicago, Illinois, pp. 462-463.
- Wood, E. F., Sivapalan, M., Beven, K. and Band, L., 1988.
- Effects of Spatial Variability and Scale with
Implications to Hydrologic Modeling, Journal of Hydrology, 102:29-47.
- Yoon, J. and Disrud, L.A. 1994.
- Fates of Nonpoint Source Pollutants based on
Cascade Routing Model
Formulation, Proceedings in National Symposium
on Water Quality, 30th Annual AWRA Conference
November, 6-10, 1994, Chicago, IL.
- Yoon, J. and Padmanabhan, G. 1995.
- Multiobjective Agricultural Nonpoint Source
Pollution Management on Water Quality, Proceedings in
Soil Conservation and Water Quality Symposium,
the Minnesota Academy of Science (MNAS), April 27~28, 1995, Morris, MN.
- Young, R. A., Onstad, C. A., Bosch, D. D., and Anderson, W. P., 1987.
- AGNPS, Agricultural Nonpoint Source Pollution Model;
A Large Watershed Analysis Tool, Conservation Research Report 35,
Agricultural Research Service, U.S. Department of Agriculture, Washington, D.C.
- Young, R. A., Onstad, C. A., Bosch, D. D., and Anderson, W. P., 1995.
- AGNPS User's Guide, Version 5.00, 1995, Agricultural
Research Service, U.S. Department of Agriculture, Morris, MN.
Table of Contents
Author
Jaewan Yoon
Department of Civil and Environmental Engineering
KDH 135
Old Dominion University
Norfolk, VA 23529-0241
yoon@cee.odu.edu
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