SCIENCE
| MASHews

UISING GIS TO MODEL NONPOINT SOURCE POLLUTION IN
ANAGRICULTURAL WATERSHED IN SOUTHEAST MICHIGAN
John Patrick Fay
Z
A thesis submitted in partial fulfillment of the
requirements for the degree of Master of Science
School of Natural Resources and Environment
University of Michigan
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Thesis committee:
Professor David Allan, Chair
Assistant Professor Donna Erickson
Associate Professor Terry Brown
Table of Contents
List of Tables
List of Figures
Abstract
Introduction
Building the Model
Study Area
The AGNPS Model
Integrating GIS with the AGNPS Model
Using GIS to Build the AGNPS Model
Using the Model: A Case Study
Evaluating the Impacts of Land Use Change
Basin Wide Impacts
Spatial Variation in Land Use Change
Summary/Conclusions
Discussion
References
Appendices
Data Acquisition and Conversion: Building the Master Database
Generating AGNPS Input Variables: The Subwatershed Database
Generating Land-Use Expansion Scenarios
ERDAS model (GISMO) scripts contained in AGNPS.MLB library
ERDAS scripts contained in the AGNPS.DSL descriptor library
ASCII file used to insert soils descriptor information to soils map
The MASTER.AUD batch file
The SUB1.AUD batch file
The SUB2. AUD batch file
The SUB3.AUD batch file
ANSI-C source code for CELLID.EXE
ANSI-C source code for ERD2AGN.EXE
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1.
2.
3.
List of Tables
AGNPS Input Variables
AGNPS Output Variables
Derivation Sources for Recoded AGNPS Variables
List of Figures
. The Saline River Basin, Southeastern Michigan
. Surficial Geology of the Saline River Basin
. Soil Textures of the Saline River Basin
. Land Use of the Saline River Basin
. Diagram of Land-Use Expansion Buffering Method
. Rates of Change in Basin Land Use under Different Land Use Scenarios
. Land Use/Cover under Different Expansion Scenarios
. Relationships between Land Use Change and Nonpoint Source Pollution Loading
. Diagram of Nutrient Outflow Modeled under Impermeable and Permeable Surfaces
. Basin-wide Runoff Volume under Different Land Use Scenarios
. Basin-wide Soil Loss under Different Land Use Scenarios
. Basin-wide Soluble Nitrogen Concentration under Different Land Use Scenarios
. Basin-wide Soluble Phosphorus Concentration under Different Land Use Scenarios
. Saline River Sub-basins Modeled to Examine Spatial Variation in NPS Delivery
. Rates of Change in NPS Pollution Generated within Sub-Basins of the Saline River
. Soil Erosivity ('K') Factors of the Saline River Basin
. Digital Elevation Map of the Saline River Basin
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ABSTRACT
Distributed parameter hydrologic models offer many advantages over
lumped parameter models in tracking and controlling nonpoint Source
pollutants, but they are constrained by the excessive data input and
processing they require. However, rapidly emerging technology in the form
of geographic information systems (GIS) and the increasing availability of
digital spatial databases are greatly reducing these constraints and expanding
the range of applications of these models. This study describes the coupling
of a distributed parameter agricultural nonpoint source pollution model
(AGNPS) with a GIS (ERDAS) to evaluate the impact of nonpoint source
pollution on water quality in a mid-sized (334 km") agricultural watershed in
southeast Michigan. ERDAS is used to integrate spatial data sets from
multiple sources into a single GIS database from which parameters needed to
run the AGNPS model can be generated. The AGNPS model estimates the
amounts, origin and distribution of sediments and nutrients across the
watershed in response to a storm of a specified magnitude. By integrating the
GIS and by using existing statewide and national spatial databases, the
AGNPS model is easily employed to run iterative simulations of hypothetical
landscape scenarios. These simulations indicate the agriculturalization of
land and the loss of forested cover significantly alter sediment and nutrient
budgets across the watershed. The expansion of urban land cover appeared
to have the greatest overall impact on runoff and nutrient fluxes, while
agricultural expansion generated the largest increases in sediment
concentrations and sediment yields. Forest expansion dramatically reduced
the generation and delivery of all forms of nonpoint source pollution
modeled. This integration of GIS with distributed parameter landscape
modeling has considerable potential to aid in land-use planning and in
identifying locations most vulnerable to storm-driven runoff and erosion.
Introduction
The control of industrial effluents, municipal sewage discharges, and
other point sources of water pollution has greatly improved the quality of
our aquatic resources over the last several years, yet water quality still falls
below acceptable standards in many regions of the United States (USEPA
1984). In many of these cases, pollution originating from diffuse sources, such
as sediment eroded from cropland and excess nutrients accumulated in storm
runoff, continues to degrade the lakes, rivers, and streams into which they
flow (Clark et al. 1985, Waters 1995, Chesters and Shierow 1985).
Acknowledging the threat that these nonpoint source pollutants pose to
aquatic resources, scientists and environmental managers have since begun to
look beyond instream processes and more at how surrounding valley and
landscape characteristics and processes might influence water quality and
ecological integrity of streams (Hynes 1975, Hunsaker and Levine 1995).
Many believe that this more holistic approach to the study of aquatic systems
will not only provide a better mechanism for controlling nonpoint source
pollution, but also lead to a more complete and thorough understanding of
stream ecosystems (Minshall 1988, Johnson et al. 1995).
This holistic approach, however, will require a new set of tools and
methods for stream research. If we are to look at streams from a landscape
perspective, we must first develop the means to characterize differences
among landscapes and then determine how these differences manifest
themselves in the lotic environment. More precisely, we need to devise a set
of technologies that enables us to better understand how the spatial
arrangement and interaction of various landscape features can affect the
biology and ecology of stream systems. These technologies must be capable
of storing and relating data collected over broad spatial scales and with
varying levels of accuracy. Moreover, if they are to be successfully integrated
into the scientific and management communities, these technologies must be
easy to use, cost-effective, and flexible enough to tackle a variety of different
research questions.
Although meeting these diverse needs will be no small undertaking,
rapid improvements in desktop computer systems and information
technologies should significantly speed progress towards this goal. In
particular, the capabilities afforded by geographic information systems (GIS)
should be especially useful in the development of such tools. Briefly, GIS is
an organized collection of computer hardware and software designed to
collect, store, manipulate, retrieve, analyze and display spatially referenced
data (ESRI 1995). Its automation of many operations on spatial data is well
suited to meet the requirements of landscape-scale studies of stream systems.
GIS, however, is a relatively young technology; we are only beginning to
realize its wide range of capabilities (Burrough 1986). Consequently, if we are
to fully realize its potential, we must continue to pursue new and innovative
uses of GIS. That is the underlying purpose of this research.
In this thesis, I demonstrate how GIS can be used to develop an easy-to-
build and easy-to-use, yet powerful tool capable of examining landscape
influences on aquatic systems. Specifically, I show how GIS can be integrated
with a hydrologic model to simulate the effects of landscape-scale alterations
on the generation and movement of nonpoint source (NPS) pollutants (runoff,
sediments and nutrients) across a typical mid-sized watershed in the lower
Great Lakes region. I begin by describing the steps needed to build the
model, including a description of the study area, the hydrologic model
chosen, and how coupling GIS with a NPS model makes this approach much
more powerful and easy to use. I then use this integrated model to compare
the terrestrial and aquatic impacts of three hypothetical scenarios of
landscape alteration, specifically, increases in the extent of urban land, of
agricultural land, and of forested land. The results of these scenarios are used
to evaluate the effectiveness of the model, both in terms of accuracy and
overall utility. Finally, using the capabilities and findings of this model as a
basis for evaluation, I comment on the overall usefulness of GIS and this
approach to the study of streams from a landscape perspective. In particular,
I discuss how such technology can be improved and what direction future
research along these lines should take.
Building the Model
Study Area
The Saline River basin, a 334 km (129 mi.) watershed draining portions
of Washtenaw and Monroe counties in Southeastern Michigan (Figure 1), was
chosen to represent a typical rural landscape in the lower Great Lakes area.
The upland portion (the northern two-thirds) of the watershed is
characterized by gently rolling till plains and ground moraines mixed with
broad, flat outwash channels and gravel plains (Figure 2). Soils in this part of
the basin are mostly well-drained sandy loams with some very sandy soils
(Figure 3). Pockets of muck and peat soils are also scattered across small
depressions in the hilly uplands. The lower portion of the watershed crosses
a glacial lake plain of extremely low relief. Soils in this portion are
predominantly poorly-drained loams and clay loams developed on top of
former lake-deposited sediments. The total range of elevation in the Saline
watershed extends from 314 m (1060 ft) in the northeastern uplands, to 192 m
(630 ft) at the river's confluence with the River Raisin, a tributary of Lake Erie.
The climate of the Saline River area is characterized by warm summers
and cold winters, with extremes buffered by the surrounding Great Lakes.
The average annual temperature is 9°C (48°F) with an average summer high of
22°C (72°F) and a winter low of -6°C (22°F). The growing season averages 180
days, with the first freeze usually arriving around October 21, and the last
freeze on April 23. Precipitation is distributed evenly throughout the year,
but is slightly higher in summer than in winter. May has the highest monthly
average precipitation with 76 mm (3.0"), and January has the lowest with 51
mm (2.0"). Brief showers usually occur every three days in summer months,
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Geology
[T] Lacustrine Clay & Siſt
T Lacustrine Sand & Gravel
Tº Glacial Outwash
Fine-Textured Till
T End Moraines of Fine Till
End Moraines of Medium Till
Figure 2. Surficial Geology of the Saline
River basin (Farrand 1996).



Texture
Sand
Sandy loam
* Loam
Clay
Muck
Urban
- Water
Figure 3. Soil textures of the Saline
River basin (MIRIS 1993).



but are often highly localized. The heaviest 1-day rainfall for the period of
1944 to 1990 was 94 mm (3.71") in December, 1965 (NOAA, 1990).
The landscape of the Saline River watershed, once covered in wetlands
and hardwoods forests (Roth 1994), is now dominated by agricultural usages
(Figure 4). Two-thirds of the land area in the basin is intensively cropped,
with a mixture of corn (50%), small grains (25%), pasture (13%), and soybeans
(12%). An estimated 350 farms operate within the basin, supporting
approximately 9500 head of livestock (Johengen et al. 1991). The potential soil
erosion and nutrient pollution from these operations pose serious threats to
water quality in the watershed. The basin has already been identified as
having the highest areal phosphorous loading rate in southeast Michigan
(Johengen et al. 1991), and other water quality studies also have indicated the
Saline River habitat to be significantly degraded (Allan et al. 1996; Smith et al.
1981).
More recently, the Saline's rural landscape has undergone significant
urbanization and suburbanization. Growth in Ann Arbor to the north,
metropolitan Detroit to the northeast, and in the towns of Saline and Milan
from within are causing more and more land to be converted to residential,
commercial, and industrial land uses. The population of Saline alone has
grown over 38% in the period of 1970 to 1990 (US Census). Preliminary
studies have shown that agricultural land area is declining, while forested
area actually is increasing, although not nearly as rapidly as urban land
(Erickson 1995).
Overall, the size, topographic variation, and degree of human influence
on the Saline River watershed make it an ideal subject for a nonpoint
pollution study. The landscape changes and NPS pollution problems
occurring here are characteristic of many of the rural watersheds in the lower
Agricultural
Forested
Non-Forested
Urban
- Water
Wetland
Figure 4. Land Use/Land Cover of the Saline
River Basin (MIRIS 1985).
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Great Lakes region. Consequently, the knowledge gained from this study
should prove helpful for other watersheds in the area.
The AGNPS Model
Hydrologic models are of great value in stream research. Lotic systems
are complex, and hydrologic models offer a means to integrate the numerous
interacting factors that shape these systems. A number of hydrologic models
have been developed over the past several years, and as more is understood
about the physical processes that affect hydrologic systems, these models
become more and more complex. The hydrologic model used in this study,
the Agricultural Nonpoint Source (AGNPS), is one of the more sophisticated
and consequently more accurate models available today (Gordon and
Simpson 1990).
The AGNPS model (version 2.65), a FORTRAN-based program that
runs on a 386 or higher PC, was developed in the early 1980's by the
Agricultural Research Service in cooperation with the Minnesota Pollution
Control Agency and the Natural Resources Conservation Service (then Soil
Conservation District) to simulate runoff, sediment, and nutrient transport
arising from storm events in agricultural watersheds (Young et al. 1987). It has
been applied extensively to midwestern conditions and has been found to
give reasonably accurate estimates of soil erosion, sediment yield, and
nutrient transport in landscapes similar to the Saline River watershed
(Gordon and Simpson 1990; Tim and Jolly 1994; Prato et al. 1989).
AGNPS uses a distributed parameter approach, subdividing the
watershed into a gridwork of uniformly sized cells and handling all inputs
and calculations at the cell level. Cells can range in size from 0.4 ha to 16 ha,
with smaller cells more accurately reflecting spatial variations in the
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landscape, but requiring greater input and Computational effort. This cell-
based approach enables nonpoint pollution to be examined at any location
within the watershed and allows easy identification of pollution "hotspots", or
localized areas of high pollution potential. More importantly, however, this
approach permits manipulation of specific landscape characteristics,
allowing nonpoint pollution to be evaluated under a number of different
landscape scenarios.
The model requires twenty-two specific inputs for each grid cell (Table
1). Most input variables can be derived from one or a combination of four
spatial data sets: land use/cover, soils, topography, and hydrography. These
variables are used in a sequence of calculations that (i) determines the amount
of runoff, soil erosion, and sediment yield generated within each cell, (ii)
Table 1. Cell variables required by the AGNPS model to
simulate runoff, sediment and nutrient transport for specified
storm events. Italicized variables were set to uniform values

across the watershed (given in parenthesis).
Variable # |Description Source (Set value)
1 Cell ID number User defined
2 Receiving Cell number DEM
3 SCS Curve number Land Use/Soils
4 Slope DEM
5 Slope Shape (1)
6 Field Slope Length Soil
7 Channel Slope DEM/Hydrography
8 Channel Side Slope DEM/Hydrography
9 Manning's Roughness Land Use
10 Soil Erodibility Soils
11 Cropping factor Land Use
12 Practice factor (1)
13 Surface condition constant ILand Use
14 Aspect DEM
15 Soil texture Soils
16 Fertilization factor (2)
17 Availability factor (10)
18 Point source indicator (0)
19 Gully source level (0)
20 COD factor Land Use
21 Impoundment indicator (0)
22 Channel indicator Hydrography
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estimates the amount of nutrients (N and P) and chemical oxygen demand
(COD) either adsorbed by these sediments or dissolved in the runoff, and (iii)
simulates the routing of these sediments and nutrients from cell to cell,
downslope, until reaching an outlet cell at the base of the watershed. AGNPS
uses a modified universal soil loss equation (Wischmeyer and Smith, 1978) to
determine soil erosion and sediment yield, while runoff, sediment and
nutrient transport are calculated using a set of hydrologic equations
developed for an earlier nonpoint pollution model, the Chemical Runoff and
Erosion from Agricultural Management Systems (CREAMS) model (Knisel,
1980).
Simulations are run for a specified storm event, defined either by its
energy-intensity (EI) value or by the duration of the storm and the amount of
precipitation. The model produces a number of output parameters (Table 2),
Table 2. Hydrologic, sediment and nutrient output information
generated by the AGNPS model. These data are available for each
cell within the watershed as well as for the complete watershed,
measured at the watershed outlet cell.

Variable Group Output Information (unit)
Hydrology Runoff Volume (inches)
Peak Flow (cfs)
Sediments Upland [Cell] Erosion (tons)
Sediment Concentration (ppm)
Sediment Yield (tons/acre)
Rate of Deposition (%)
Sediment into Cell (tons)
Sediment out of Cell (tons)
Channel Erosion (tons/acre)
Delivery Ratio (%)
Enrichment Ratio (%)
Nutrients (soluble) Cell contribution (lbs/acre)
[N, P, COD] Cumulative at cell (lbs/acre)
Concentration at cell (ppm)
Nutrients (in sediment) Cell contribution (lbs/acre)
[N, P] Cumulative at cell (lbs/acre)
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which can be obtained for a single cell, a subset of cells, or the entire
watershed. Results are generated either in tabular or spatial formats, or can be
written to a spreadsheet file that is easily imported into other applications for
further analysis.
Integrating GIS with the AGNPS model
The distributed parameter (cell based) approach used by AGNPS
offers several distinct advantages over lumped parameter models, but these
advantages come at the cost of greatly increased input and computational
effort. For example, subdividing the Saline River watershed (334 km2) into
the maximum cell size recommended for use with AGNPS (16 ha) would
result in 2,088 total cells. At 22 inputs per cell, each run of the model would
therefore require over 45,000 inputs - a clear limitation if handled manually.
Fortunately, geographic information systems offer many unique
features that can greatly reduce the effort required to generate such a massive
database. More specifically, each of the 22 cell inputs can be organized as
individual spatially-referenced map layers. GIS can be used both to generate
these layers from existing spatial databases and to perform the spatial
operations needed to reclassify and organize these maps into a format
readable by the AGNPS model program. Furthermore, since the map layers
are stored digitally, the input values can easily be modified to represent a
variety of different landscape scenarios.
Using GIS to Build the AGNPS Database
One goal of this research was to develop a procedure for easily
repeating simulations of nonpoint pollution under a variety of landscape
scenarios, capitalizing on GIS' capability to integrate spatial data sets from a
number of different sources, its capacity to perform operations on multiple
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layers of spatial data to create new map layers, and its power to easily update
or modify particular data layers. The following text describes how these
capabilities of GIS were used to facilitate building the AGNPS model
database. A more detailed, step-by-step account of these procedures is given
in Appendices A – J.
Integrating Data from Multiple Sources
The use of pre-existing spatial data sets to derive most of the 22 input
variables required by AGNPS is one of the key features that make this model
so easy to build. Many federal, state, and local organizations are currently
assembling enormous archives of digital spatial data from satellite images,
aerial photos and other sources. Many of these archives contain data useful
for various stream and landscape investigations, but often the data are
collected and stored at scales, resolutions, and projections that make them
difficult to use for particular purposes. In this study, making use of existing
digital data Sources via GIS has dramatically reduced the time necessary to
collect and organize the massive database required by the AGNPS model.
Fifteen of the 22 input variables can be derived from one or a
combination of four available spatial data layers. Three of these data layers -
land use, soils, and hydrography - were extracted from the Michigan
Resource Information System (MIRIS), a statewide digital archive of spatial
data maintained by the Michigan Department of Natural Resources and the
Library of Michigan. The fourth layer, topography, was derived from
1:250,000 scale digital elevation models (DEM) generated by the United States
Defense Mapping Agency and distributed by the United States Geologic
Survey.
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Two GIS software packages, C-Map (Michigan State University 1991)
and ERDAS (Earth Resources Data Analysis Systems 1990), were used to
import these data layers and combine them into a single, multi-layer spatial
database. The MIRIS data layers, which are stored as vector GIS files, first had
to be converted into raster coverages. This was done by importing each
MIRIS layer into C-Map where they were cleaned and built into topologically
meaningful coverages. These coverages were then converted into ERDAS
files and gridded into 1 ha raster cells georeferenced to the southern zone of
the Michigan State Plane coordinate system.
The DEM topographic data, already in raster format, were imported
directly into ERDAS and georectified to the Michigan State Plane coordinate
system as 1.0 ha cells, the highest resolution afforded by the 1:250,000 DEM
data. Also, to keep measurements consistent with the AGNPS model, cell
elevation values of the DEM data, originally in meters, were converted into
feet using a simple ERDAS recode command. The final result of these efforts,
then, was a four layer GIS database consisting of MIRIS land use, soils and
hydrography, and DEM topography, all referenced to the same Michigan
State Plane coordinate system. This GIS database could then be used as a
master database to derive many of the specific input variables required by the
AGNPS model.
Using GIS to generate the AGNPS database from the master GIS database
While GIS is a useful tool for integrating data from a number of
different sources into a uniform database, a more powerful application of GIS
is its ability to generate new information from existing map layers through
various spatial operations and analyses. One of the simplest GIS operations is
recoding cell values of an existing database to new cell values based on
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information specified by the user. Eight of the 22 AGNPS input variables
were created by recoding layers contained in the master database. The recode
tables used to convert these cell values were obtained from a variety of
published sources (Table 3).
A slightly more complex GIS operation, which assigned new cell
values based on a matrix of cell values in two or more existing layers, was
used to compute cell runoff curve values. This variable is based on specific
combinations of land cover and soil hydrologic regimes. To calculate cell
values, a recode table based on information contained in Young et al. (1987)
and Nelischer (1989) was set up to assign appropriate curve number values to
each cell based on the information contained in the land use and the soil
layers for that cell.
More sophisticated GIS operations were used to calculate values for
other AGNPS variables. Cell slope and aspect were determined using GIS
operations that compare one cell's elevation value to the values of
surrounding cells. AGNPS channel slope and channel side slope variable
values were calculated using the proximity analysis capabilities of GIS: the
hydrography layer was used as a reference to locate cells within certain
distances of waterways and these cells were assigned values as set forth by
Table 3. Several of the AGNPS input variables were derived by recoding values of cells in
the land use, soils, and hydrology data layers. This table lists the variables derived by
recoding, the data layer from which they were recoded, and the source of the information
used to recode them.

Variable Derivation Recode Table
Manning's Roughness Coefficient Land Use Young et al. (1987), Nelischer (1989)
Cropping Management (“C”)Factor || Land Use SEMCOG (1979)
Surface Condition Constant Land Use Young et al. (1987), Nelischer (1989)
Chemical Oxygen Demand Land Use Young et al. (1987), Nelischer (1989)
Soil Erosivity (“K”) Factor Soils NRCS Soil Surveys
Soil Texture Soils NRCS Soil Surveys
Field Slope Length Soils SEMCOG (1979)
Channel Indicator Hydrology Young et al. (1987)
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AGNPS documentation based on their distances from the stream course.
GIS operations can also be combined to compose maps of new
information, as illustrated in the creation of cell identification and receiving
cell variable layers. Cell identification numbers, a set of sequentially
increasing integers used to label each cell, were assigned using a macro
program written in C programming language (Appendix K). Then, the
receiving cell numbers, indicating which adjacent cell would receive the
majority of runoff of that cell, were determined using a GIS subroutine that
used the aspect layer to identify and insert the appropriate cell identification
number.
Fifteen of the twenty-two AGNPS input variables were created and
stored as individual GIS layers using the various GIS operations described
above. The remaining seven inputs were either set to uniform values
representing worst-case scenarios or were not used in analyses. Practice
management ("P") factor and fertilization incorporation factor were both set
to 1, representing worst-case pollution scenarios, and fertilizer application
was set to a mid-range background rate of 50 lbs/acre. Determining the
actual cell values for these parameters would have required a comprehensive
survey of agricultural practices, which was beyond the scope of this research.
Furthermore, these values vary widely from year to year, making such
surveys accurate only for short durations. Setting them to worst-case
scenarios allows a more general application of the model and is still useful for
qualitative comparisons of nonpoint pollution events (Young et al. 1987).
Four other input layers – slope shape, point source indicator, gully
source level, and impoundment factor – were also set to uniform values
across the watershed. Slope shape was set to represent uniformly shaped
(non-convex or concave) cells across the watershed. Point source indicator,
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gully source level, and impoundment factor inputs were all set to zero,
omitting them from the analyses. This procedure has been used in other
applications of the AGNPS model where such data were not readily available
(Young et al. 1987, Gordon and Simpson 1990).
GIS and its spatial analyses capabilities have thus proven extremely
valuable in creating the AGNPS input database. Fifteen of the twenty-two
variables were easily extracted from the four data layers in the master
database, and the remaining seven were created as needed to meet other
model requirements. Once all of the twenty-two input map layers were
completed, they were reorganized and converted into an AGNPS spreadsheet
data input file using a file conversion program (Appendix L). Additional
information, including storm precipitation and duration data, were added to
the data file, and the AGNPS model was run. For most runs, a twenty-five
year storm event (3.71 inches over 24 hours – NOAA 1990) was used.
Using GIS to modify the AGNPS database to test different landscape scenarios
Beyond the data integration and spatial analysis capabilities of GIS, this
technology also enables the user to interactively update and modify maps.
This feature adds great power to GIS-driven hydrologic modeling by
allowing easy manipulation of data layers to reflect different landscape
configurations. These modified layers can then be run through the model to
compare their relative impacts on the terrestrial and aquatic systems.
Some of the more basic data manipulation techniques include recoding
data in one of the master data layers to reflect basin-wide changes. For
example, all cells coded as forest in the land-use master layer could be
recoded as cropland cells to represent total conversion of forest land uses to
agricultural uses. Using this modified land use layer, the 22 AGNPS input
variables can be automatically recalculated using the GIS procedures
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described above to create an alternative landscape scenario that can then be
put into the model to generate a new set of results.
The GIS is also capable of many more complex hypothetical landscape
scenarios. For instance, the GIS can be directed only to change cells within a
certain distance of a given feature. This technique is used later in this paper
to simulate growth of certain land use types: cells identified as being within a
certain distance of urban-labeled cells, for example, are overwritten as urban
cells to reflect urban sprawl of that distance. This technique can also be used
to simulate different types and widths of riparian buffers by specifying that
only cells within a certain distance of stream corridors be altered.
The number of different landscape scenarios that can be modeled is up
to the creativity of the user. Input data can be manipulated on a cell-by-cell
basis or through any number of combined spatial operations such as
buffering, overlaying, and proximity analyses. The ease with which these
modifications can be done and re-run through the model truly makes for a
much more powerful and comprehensive analytical tool.
In sum, then, the benefits of incorporating GIS with the AGNPS model
are indeed profound. First, the ability of GIS to incorporate data from
multiple existing sources such as MIRIS and the USGS spatial data archive
greatly reduces the time and effort required to assemble the massive database
needed to run AGNPS. Second, the range of spatial analyses of which GIS is
capable enables these data to be quickly and accurately recoded and
reorganized into the specific formats required by the model. And lastly,
because GIS stores its information digitally, input data can be easily modified
to represent a wide number of possible scenarios. The following section
should show how these benefits combine to produce an extremely useful and
powerful tool for ecological research.
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Using the Model: A Case Study
To demonstrate the utility of GIS-driven hydrologic modeling as a
robust and efficient tool for studying landscape-stream interactions, the
model was used in a case study examining how runoff, sediment and
nutrients respond to changes in land use within the Saline River basin. While
the model is capable of many more applications, this case study illustrates the
ease and flexibility with which the model can be adapted to explore a specific
research question. It also provides a sampling of the model's wide range of
analytical capabilities and offers an opportunity to identify where
improvements in such modeling techniques might be made.
Evaluating the Impacts of Land Use Change in the Saline River Basin
Like many regions of the midwestern United States, the landscape of
the Saline River basin is constantly changing, whether from the sprawl of
nearby cities, the cultivation of more cropland, or the slow encroachment of
forests back onto fallow land. Understanding how these changes affect
underlying physical processes such as storm runoff, soil erosion and nutrient
transport is essential if we are to manage our natural resources effectively.
This study demonstrates how GIS-driven hydrologic modeling can be an
important and useful tool in helping us gain such an understanding.
In this study, model output from a set of hypothetical land use
scenarios was used to evaluate the effects of different changes in the landscape
on the generation and transport of runoff, sediments and nutrients in the
Saline River basin. While changes in the landscape can take many shapes and
forms, this study focuses on the impacts of expanding urban, agricultural,
and forested lands -- the three dominant land use types in the Saline River
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basin. Under the assumption that such expansion likely could occur around
the perimeter of existing land use types (i.e., urban land is likely to increase
via the expansion of existing urban areas), 100 m buffers up to 500 m were
created around existing urban, agricultural, and forested cell clusters in the
original land use cover and recoded into each respective type (Figure 5). The
process was easily accomplished using a set of basic GIS operations described
in Appendix C.
The rates of increase in urban, agricultural and forested cover under
the respective land use expansion scenarios are shown in Figure 6. The
differences among these curves arise from the relative proportions of land use
types in the original land use layer. Agricultural lands, which comprise
two-thirds of the Saline River basin area, have a larger total perimeter than
Figure 5. Diagram of buffering method used to simulate land use expansion.
Each land use polygon of a specific type was expanded outwardly in hundred
meter increments to a maximum distance of 500 m.
22

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Current 100 m 200 m 300 m 400 m 500 m
Conditions Distance of Outward Expansion
—Q-Urban increase under urban expansion
— — — —Agricultural increase under agricultural expansion
--A--Forest increase under forest expansion
—
Figure 6. Rates of change in basin land use under the different land use scenarios. Each
scenario was modeled by expanding all urban, agricultural and forested polygons in
100 m increments up to 500 m. Note that in the case of agricultural land, such
expansion quickly results in the entire catchment becoming agricultural.
does either urban or forest land and thus engulfs more land per buffer
distance than does the other types (Figure 7). However, with two-thirds of the
watershed area originally agricultural, only the remaining third can be
converted from other land uses. Thus, after its initial rapid rate of change,
agricultural expansion slows as it nears the point where the entire basin has
been recoded into agricultural usages, which occurs at the 500 m buffer
distance. Urban and forest cover, in contrast, have much more room to
expand and thus can continue to expand at much more gradual and
consistent rates.
The nonpoint source impacts associated with these land use changes, as
predicted by the model, were used to construct a more complete and detailed
account of the influence of landscape structure on environmental processes.
23

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24









Predicted levels of runoff, Sediment and nutrients measured at the watershed
outlet cell were used to evaluate the basin-wide cumulative impacts of the
different land use changes, while thematic maps of the watershed and model
output from selected upstream locations were used to examine spatial
differences in the responses to the alternative landscape changes. These
findings were then merged to formulate a better understanding of the degree
to which modified landscapes can alter runoff, sediment, and nutrient fluxes,
and also to identify specific locations within the basin that appear
particularly sensitive to certain landscape changes. In addition, these
findings were compared to observations of other watersheds and to our
current understanding of these processes to confirm the viability of the
predictions made by the model developed here and to identify possible
shortcomings of the model structure.
Basin-wide impacts
Examination of changes in runoff and transport of sediment and
nutrients at the watershed outlet suggest that the three land use changes
studied can have widely different cumulative impacts on the generation and
movement of materials within the Saline River basin. Charts of model output
(runoff volume, peak flow rate, sediment concentration, sediment yield,
nutrient concentration and nutrient yield) plotted against the total area of
land recoded under the different land use scenarios reveal differences in the
relative rates at which the respective land use changes either increase or
decrease the given variable (Figure 8).
25
(a)
i
Runoff Volume
50 100
Expansion (km *2)
150
(b)
200
9000
8000
7000
6000
2 5000
o 4000
3000
2000
1000
Peak Flow Rate
50 100 150 200
Expansion (kmaz)
(c)
Sediment Yield
Sediment Concentration
expansion.
5000
4500
4000
3500
a 3000
-
3 2500
2000
1500
1000
500
O
50 100 150 200 0 50 100 150 200
Expansion (kmaz) Expansion (km"2)
(e) Nitrogen Concentration (f) Phosphorus Concentration
4.00 gº 0.50 ------------------------------------------------------
3.50 0.45
0.40
3.00
0.35
– 2.50 – 0.30
£200 on 0.25
1.50 0.20
1.00 0.15
0.10
0.00 0.00
50 100 150 200 o 50 100 150 200
Expansion (km *2) Expansion (kmaz)
-
(g) Nitrogen Yield (h) Phosphorus Yield
2.50 - ---------------------------------- 0.50 --- ---
0.45
2.00 0.40
º 0.35
5 1.50 *- 0.30
s -- Sº 0.25
# 1.00 3 0.20
- 0.15
0.50 0.10
0.05
0.00 0.00
50 100 150 200 0 50 100 150 200
Expansion (kmaz) Expansion (kmaz)
-º-Urban -* Agriculture -ā- Forest
Figure 8. Predicted relationships between land use change and nonpoint source
pollution levels at the Saline River basin outlet. Existing parcels of urban, agricultural
and forested lands were expanded outwardly to simulate the expansion of individual
land use types. Changes in runoff, sediments and nutrients as predicted by model runs of
these scenarios plotted against the area of land converted into the respective land use
type indicate the rate of change corresponding to urban, agricultural and forest
26








Hydrologic Response
Runoff volume, a measure of the amount of precipitation not
intercepted, infiltrated, or otherwise prevented from moving downslope
across the ground surface, and peak flow, an indication of the velocity at
which this runoff moves, are markedly affected by land use change. Runoff
and peak flows increase steadily as urban land cover is expanded from 9% of
the catchment area to 59%. Expansion of agricultural land resulted in a
smaller rate of increase in these variables, and increasing forest cover
markedly decreased runoff and peak flow rate (Figure 8a-b).
Model prediction of gradual increases in runoff volume and peak flow
rates with increased urban cover are expected. The preponderance of
impermeable surfaces such as paved roads and rooftops in urban areas
dramatically reduces infiltration rates and thus converts more rainfall into
runoff, resulting in high runoff volumes and increased flow rates (Anderson
1968, Hammer 1972). Urban land also contributes to high runoff volumes and
flow rates through the implementation of sewers and other drainage systems
(Leopold 1972). AGNPS, however, was designed for predominantly
agricultural watersheds and does not incorporate factors other than surface
characteristics into its equations (Young et al. 1987). Thus, these results
probably underestimate the hydrologic impacts of urban expansion.
Model predictions of decreased runoff volume and flow rate as forest
cover is increased also concur with existing knowledge of hydrologic
processes. Undisturbed forested lands generally have high infiltration
capacities and may thus generate less runoff by absorbing more rainfall into
the soil (Kostadinov and Mitrovic 1994, Gray 1973). Forest cover also reduces
peak flows by increasing interception and by lowering the antecedent
moisture condition in the soil through increased evapo-transpiration rates
27
(Anderson et al. 1976, Hornbeck et al. 1986). Protruding roots and other
vegetative structures on forest floors can also pool rainfall and slow its lateral
movement, further reducing runoff volume and peak flows (Cheng 1975).
The less pronounced but still notable increases in runoff volume and
peak flow rate due to agricultural expansion can also be understood in terms
of ground surface characteristics. The high degree of exposed soil in most
agricultural land reduces the infiltration capacity of the soil, again resulting
in larger amounts of surface runoff (Burwell 1969). Agricultural areas,
especially those supporting row crops, generally also have smoother surfaces
than more densely vegetated covers and therefore generate higher peak flows
(USDA 1972). The implementation of subsurface drainage systems (drainage
tiles) may also contribute to accelerated fluxes of water to streams (Burkart et
al. 1994). However, the effect of drainage tiles are not expressly incorporated
in the AGNPS model and thus these results could underestimate the
hydrologic impacts associated with agricultural expansion.
Sediment Response
Changes in sediment concentrations and sediment yields predicted by
the model suggest that agricultural expansion has the largest impact of the
three land-use scenarios, dramatically increasing both variables (Figure 8c-d).
Forest and urban expansion, in contrast, decrease sediment yield and
concentration at the outlet cell. Forest expansion lowers concentration
slightly more and yield much more than does urban expansion, and decreases
yield to a greater degree.
Model predictions of increases in sediment concentration and yield
associated with agricultural expansion conform to a number of other studies
monitoring the impacts of agriculture on stream water quality (Costa 1975,
28
Lenat et al. 1979, Clark 1987). Row crop cultivation, in particular, is known to
be strongly associated with high instream sediment levels (Robinson 1971).
Such high sediment concentrations and yields generally are attributed to the
relatively high degree of direct exposure of soil surfaces to the impact of rain
drops and to the scouring of surface flow, both of which act to dislodge
sediment particles that eventually reach stream channels (Brooks et al. 1991).
The increased runoff and flow rates noted above can also accelerate both the
erosion process and the delivery rate of these materials to stream courses
(Lenat and Crawford 1994).
The model's predictions that increased forest cover should lower
sediment concentrations and yields also are supported by studies conducted
on other catchments (Patric et al. 1984, Likens et al. 1978). The differences are
believed to be attributable to surface vegetation and root structure, both of
which inhibit surface and channel erosion, the source of high sediment
concentration and yields (Hornbeck et al. 1986). Decreased runoff volume
and peak flow in forested areas may also decrease channel erosion and
sediment delivery by reducing the scouring energy of the flow as well as
allow some suspended particles to settle out of the water column.
Reductions in sediment concentration and yield with increased urban
cover has also been reported in other catchments. In a Piedmont region stream
undergoing landscape changes from forest to agriculture to urban, Wolman
(1967) observed urbanization to cause a sharp initial increase in sediment
yields, then a reduction nearly back to levels observed under forested
conditions. The initial spike is believed to result from a construction phase,
where much of the ground surface is exposed and dislodged by heavy
construction machinery. The subsequent stabilization of sediment sources by
pavement or similar structures then begins to inhibit the dislodgment of
29
Sediment, lowering sediment concentrations in rivers and streams. The
AGNPS model does not account for construction activities, and thus should
not show any spike in sediment output corresponding to urban growth. The
decline in sediment levels shown by the model then likely reflects the
stabilization phase of urbanization which is represented as the cropping
management (C-factor) value (Appendix E).
Lenat and Crawford (1994), however, found yearly suspended
sediment levels to be consistently higher in urban-dominated catchments
under high flow conditions than in either agricultural or forested watersheds.
These findings may suggest that the AGNPS model either is failing to
consider important factors relating to urban surface characteristics, or that the
spatial arrangement of urban, forest, agricultural and other land use types in
the landscape can have significant impacts on the generation and transport of
Sediments. Direct comparison of model output and water quality data
collected from the same stream system should provide more insight into these
discrepancies.
Nutrient Response
Model predictions of the effect of landscape change on nitrogen and
phosphorus concentration are highly dependent on how the land is altered.
Increasing urban cover appears to significantly increase both the
concentration and total yield of each nutrient, while forest expansion causes
nearly equally dramatic decreases (Figure 8e-f, g-h). Increasing the extent of
agricultural land slightly lowers nitrogen and slightly increases phosphorus
concentrations in runoff, but has little effect on total yield of both nutrients.
Model predictions of an increase in nutrient levels with increased
urban cover agree with several studies on urban nonpoint Source pollution
30
(USEPA 1990). However, the AGNPS model does not expressly incorporate
specific urban non-point sources into its calculations; instead, predicted
nutrient levels primarily reflect contributions from rainfall, a background
fertilization rate and interactions (adsorption and leaching) with soils (Young
et al. 1987). The model-generated increase in nitrogen and phosphorous
concentrations in runoff associated with increased urban cover thus most
likely is due to the low infiltration rates of urban surfaces which prevent
leaching of nitrogen and phosphorus into the soil (Brooks et al. 1991, see
Figure 9 for a diagrammatic explanation of this model behavior). Also, the
influence of higher concentrations coupled with higher runoff volumes
should increase the total yield of both nutrients as urban cover increases. The
addition of urban nonpoint pollution processes not currently included in the
AGNPS model, such as combined sewer overflows and concentrated storm
runoff, would likely cause an even greater response.
Declines in nutrient concentrations with increased forest cover have
also been observed in a number of studies. When streams draining urban,
agricultural and forested lands were compared, forested areas in the eastern
United States were found to have the lowest mean total nitrogen and total
phosphorus concentrations (Omernik 1976). Low nutrient concentrations are
attributed to higher rates of nutrient uptake to support vegetation growth
combined with decreased nitrification rates resulting from cooler soils
shaded by the canopy (Likens et al. 1978, Martin et al. 1984).
Perhaps the most surprising prediction made in these modeled
scenarios is the decrease in nutrient concentration and near constancy of
yields associated with increased agricultural cover. Although the predicted
decreases in nitrogen and phosphorus concentration are only slight, most
studies report agricultural lands to contribute to high nitrogen and
31
(a) Nu t r i e n tº I n p u ts
RAINFALL FERTILIZER
RunOFFSQ |Impermeable Surface
(b) N u t r i e n tº I n_p u ts
RAINFALL FERTILIZER
RUINOFF Permeable Surface
WWWW
LEACHING
Figure 9. Diagram of nutrient outflow as modeled under impermeable
(urban) and permeable (agriculture and forest) conditions. Impermeable
surfaces (a) prevent leaching of nutrients downward into the soil surface,
thus contributing to higher concentrations in surface runoff. Permeable
surfaces (b) allow leaching of nutrients into the soil which reduces nitrogen
and phosphorus concentrations in runoff.
phosphorus levels in runoff and receiving streams (Omernik 1976, Burkart et
al. 1994, Lenat and Crawford 1994). This discrepancy is likely due to the
omission of fertilizer as a variable input in these model runs. AGNPS permits
input of fertilizer application and incorporation rates, but these were set to


32
uniform levels regardless of land use because of the difficulty in acquiring
specific data for localized land practices. Inclusion of land-use specific
fertilizer data would almost certainly boost nutrient levels associated with
agricultural and possibly urban land use.
Still, these predictions which suggest agricultural expansion will
reduce nitrogen concentrations even more than does expansion of forest cover
(Figure 8e), initially appear contrary to the model mechanics. AGNPS
computes nutrient concentrations based on the interactions of rainfall and
fertilizer inputs with characteristics of the land surface (flow rate and soil
porosity; Young et al. 1987). With forested surfaces generally being more
rough and porous than agricultural surfaces (Nelischer 1987), nutrients in
runoff would be more likely to be lost to leaching and adsorption (Brooks et
al. 1991), which is contrary to the predictions of the model. A possible
explanation for this apparent contradiction may be that agricultural
expansion displaces more urban land than does forest expansion, which
would result in a slightly larger decrease in nutrient concentrations.
Clearly, further examination of the model mechanics and these results
are necessary to reveal the cause of the apparent inconsistency between model
nutrient predictions and empirical observations. First, data on fertilization
application and incorporation rates should better reflect actual land use
practices, thus enabling the model to more accurately compute the
accumulation and movement of nutrients across different land types. Even
without detailed information on the fertilization practices of individual
farmers, additional scenarios where fertilization levels are increased in
agricultural lands but not in urban and forested lands could provide useful
insight into nutrient-runoff dynamics. Alternatively, fertilizer application
could be omitted entirely from the model to explore the impact of land use
33
alone. Furthermore, analyses of model output should also include
information on which types of land are being replaced in the different land
use scenarios, as disproportionate replacement of given land types may
significantly alter findings. Finally, the model itself may be improved with
better accounting for specific types of urban nonpoint sources.
In summary, the AGNPS model coupled to different scenarios of
changing land use predicts widely different outcomes in terms of hydrologic,
Sediment and nutrient responses. Generally these outcomes conform well to
empirical studies of watersheds actually undergoing such changes, although
Some nutrient predictions should be further investigated. The model
Scenarios probably underestimate nutrient responses to increased agriculture
because fertilizer inputs were not included. This shortcoming can be
rectified by obtaining further information on the practices of local farmers,
using survey or interview methodologies. However, the AGNPS model does
not allow inclusion of some factors that are considered important based on
empirical studies. Specifically, artificial drainage systems in urban (sewers)
and agricultural (drainage tiles) lands may increase runoff and peak flows
more than is represented in these scenarios. In addition, construction,
combined sewer overflows, and urban nonpoint source pollution dynamics
may cause model predictions to underestimate effects caused by urban land
use changes.
Spatial Variation in Response to Land Use Change
The next stage of this case study used the model to investigate spatial
variation in the response variables to the various land use changes. Spatial
variation was examined in two ways. First, thematic grid maps were
constructed using shading to display levels of runoff, sediment and nutrients
34
to investigate spatial patterns. Second, model outputs at specific upstream
locations were compared to identify any noticeable differences among
Selected sub-watersheds within the Saline River basin.
Thematic Grid Maps
Grid maps of the predicted levels of each variable at the 500 m
expansion increment provide easy visualization of the impacts of the different
land use changes across the basin. Under current conditions, levels of runoff
appear slightly lower in the upper (northern and western) regions of the
watershed (Figure 10). Urban expansion intensifies this pattern and also
dramatically increases runoff across the entire basin, except perhaps for some
regions in the extreme upper portions of the basin. Agricultural expansion
also increases runoff volume, but less intensively and more consistently
across the watershed, perhaps because much less land is actually converted in
the agricultural scenario (Figure 6). Forest expansion reduces runoff and has
a more noticeable effect in the upper reaches of the catchment.
Grid maps of soil loss under the different scenarios suggest that the
southern and extreme western reaches of the upper watershed are the most
susceptible to erosion under severe storm events (Figure 11). The expansion
of urban cover appears to stabilize much of the eastern side of the basin as
well as small portions of the upper western regions, while agriculture, in
sharp contrast, dramatically increases soil loss throughout the entire basin.
The expansion of forest cover markedly reduces soil loss across the entire
watershed.
Grid maps of nitrogen and phosphorus concentrations also show high
values under current conditions to be clustered in the northeastern edge of
35
3.
Runoff Volume (in)
0.00 - 1.10
1.11 - 1.90
1.91 - 2.55
.56 - 2.80
2.84
3.71
.
--
Figure 10.
Runoff volume at unmodified and
500 m expansion scenarios. Refer
to Figure 6 for information on how
land use is affected by these expansion
scenarios.
Current Land Use
Urban Expansion
Agricultural Expansion
Forest Expansion








9
Soil Loss (tons/acre)
0 - 75
75 - 93
93 – 116
116 - 160
160 - 400
Figure 11.
Soil loss at unmodified and
500 m expansion scenarios.
Current Land Use
Urban Expansion
Agricultural Expansion
Forest Expansion






the catchment. Urban expansion intensifies this effect and generally increases
levels across most of the watershed (Figures 12 & 13). Agricultural expansion
results in slight decreases in nutrient concentrations that are fairly evenly
distributed throughout the watershed. Forest expansion results in dramatic
decreases in concentrations across much of the watershed, although some
noticeably high concentrations still remain in parts of the eastern and
southeastern portions of the watershed.
In general, most of these patterns of change are consistent with the
changes in land use occurring under the different scenarios (Figure 7). The
concentration of urban cover in northeastern and central regions of the
watershed under current conditions and the intensification of this pattern
when urban land is expanded corresponds well to locations of high levels of
runoff and nutrient concentrations. Agricultural and forested land cover are
much more evenly distributed across the watershed, and thus the expansion
of these land types affect runoff, sediment and nutrient levels in a much more
consistent fashion. Thus, the patterns of change appear strongly to reflect the
current mosaic of land use types of the watershed and also indicate where
management efforts to control the different forms of nonpoint pollution might
best be directed.
Sub-Watershed Comparisons
Comparisons of the impacts of land use change among selected sub-
basins within the Saline River (Figure 14) provide more detailed evidence of
spatial variation in model response to changes in land use. The average
predicted percent increase or decrease in each nonpoint source variable
against the percent of sub-basin area converted into urban, agricultural or
forested land, respectively, is shown in Figure 15. A greater height
38
3
Nitrogen Concentration (mg/l)
0.0 - 1.6
1.6 - 3.1
3.1 - 3.6
3.6 - 4.0
4.0 - 11.6
Figure 12.
Nitrogen concentration at unmodified
and 500 m expansion scenarios.
Current Land Use
Urban Expansion
Agricultural Expansion
Forest Expansion








5
Phosphorus Concentraton (mg/l)
0.0 - 2.0
2.0 - 6.0
6.0 - 7.0
7.0 - 8.0
8.0 - 46.0
Figure 13.
Phosphorus concentration at unmodified
and 500 m expansion scenarios.
Current Land Use
Urban Expansion
Agricultural Expansion
Forest Expansion







！！！！！ **
41
1OIl.
ine River modeled
1 variation in
13.
ine spat
delivery and routing of NPS pollut
Sub-basins of the Sal
Figure 14.
to exam


Runoff Volume (b) Peak Flow Rate
# #
- tº:
-- +
O O
sº § -
Sub-488. Sub-243 Sub-517 Upper Outlet Sub-488. Sub-243 Sub-517 Upper Outlet
Sub-basin Sub-basin
Sediment Concentration (d) Sediment Yield
§ §
- -
º º
-- --
O O
& §
* -º-º-º-º-º-º-º: - - 2.0 -
Sub-488 Sub-243 Sub-517 Upper Outlet Sub-488. Sub-243 Sub-517 Upper Outlet
Sub-basin Sub-basin
(e) Nitrogen Concentration (f) Phosphorus Concentration
§ §
- -
- º:
-- --
O O
sº §
Sub-488 Sub-243 Sub-517 Upper Outlet Sub-488 Sub-243 Sub-517 Upper Outlet
Sub-basin Sub-basin
(g) Nitrogen Yield (h) Phosphorus Yield
§ &
- -
º tº:
-- --
O O
& &
Sub-488 Sub-243 Sub-517 Upper Outlet Sub-488 Sub-243 Sub-517 Upper Outlet
Sub-basin Sub-basin
ITUrban El Agriculture a Forest
Figure 14. Average rates of change in runoff, sediment and nutrient generated within
different sub-basins of the Saline River basin. Bar values reflect the average percent increase
or decrease of pollution levels from current levels per fraction of sub-basin area converted
into urban, agriculture or forest under the different land-use scenarios.
42




indicates a greater response per equal proportion sub-basin area modified
under the given scenario.
Runoff volume and peak flow rates appear to respond fairly
consistently across the different sub-basins. The slight variation in response
likely reflects the unequal displacement of land use types by the expanding
land use. For example, because the difference in runoff volume and peak
flow rates generated under urban and forest cover types is much larger than
the difference between urban and agricultural types (see previous section),
urban expansion that displaces forest land will have a much larger impact
than expansion that displaces agricultural land. This would explain why
urban expansion in sub-basin 488, which has the highest initial percentage of
forest land (Figure 15a-b), appears to be the most sensitive to urban expansion.
Similarly, the relatively small response to agricultural expansion seen at the
watershed outlet likely reflects the high proportion of urban land
downstream of the Upper Saline station: because the new agricultural land is
overtaking more urban land than forest in the lower watershed, agricultural
expansion in this part of the basin will appear to have a lesser impact on
runoff volume and peak flow rates.
The impacts of land use change on sediment concentrations and yields
show a much higher degree of spatial variability among the sub-basins
(Figure 14c-d). Particularly striking is the extreme sensitivity of sediment
concentrations and yields to agricultural expansion in sub-basin 243. While
disproportionate land use displacement may explain the elevated sensitivities
of the upper Saline and the watershed outlet (converting urban land into
agriculture will have the largest impact), it is an unlikely explanation for the
extreme case of sub-basin 243. Instead, the relatively erosive soils (Figure 16)
and steep slopes (Figure 17) of sub-basin 243 may cause its heightened
43
Soil K Factor
0.00 - 0.09
0.10 - 0.19
0.20 - 0.29
0.30 - 0.39
0.40 - 0.49
0.50 - 0.59
0.60 - 0.69
0.70 - 0.79
0.80 - 0.89
0.90 - 0.99
Figure 16.
Soil Erosivity ("K") Factor (NRCS Soils Surveys).
44


- º
º
--~~~~~~~
º
Figure 17.
Digital elevation relief map of the Saline Basin (Derived from
1:100,000 USGS Digital Line Graph data).
45

Sensitivity to the erosive impacts of agricultural land. Further research is
needed to confirm this explanation.
Nitrogen and phosphorus concentrations also show some distinctly
different responses to land use change among the different sub-basins (Figure
15e-f). In particular, urban expansion appears to have much a larger impact
on concentrations in the upper sub-basins than it does at the watershed outlet.
One explanation for this pattern may be the relatively small proportion of
forest cover displaced by urban expansion in the lower reaches of the basin.
Also surprising are the increases in nutrient concentration from agricultural
expansion in sub-basin 243. Like sediment concentration and yield, this
peculiarity may result from combinations of steeper slopes and highly
erodible soils particular to this catchment. Further research into other
potential characteristics would be necessary to determine the exact cause for
these observations.
Rates of change in nitrogen and phosphorus yields appear remarkably
consistent under agricultural and forest cover expansion scenarios.
Responses to urban expansion show minor variation among the sub-basins
with basins 243 and 517 appearing slightly more sensitive to urban expansion
than the other sub-basins (Figure 15 g-h). Again, further analyses into the
specific characteristics of these sub-basins should reveal the specific reasons
behind the observed variation and, in the case of agricultural and forest
expansion, lack of variation.
In summary, these sub-basin analyses document spatial variation in
expected responses to storm events, and also that different variables exhibit
different degrees of spatial variation. Variation in the two hydrologic
variables, runoff volume and peak flow rate, possibly are driven by the
46
disproportionate displacement of certain land types during the expansion of
another land cover type. Variation in sediment concentration and sediment
yields appears to be driven by other properties of the landscape, possibly soil
and topographic characteristics. Finally, variation in nutrient concentrations
and nutrient yields also may be driven by interacting landscape factors. In
short, these analyses prove useful in revealing specific spatial differences
among selected regions within the larger Saline basin, but further research is
required to explore the actual landscape interactions that drive these different
Sensitivities to changing land use. Without further analysis, it also is difficult
to rule out model error as a factor behind the observed differences. However,
the consistency between results predicted at the basin outlet and empirical
observations of actual catchments undergoing land use changes as described
in the previous section suggest that the model results are qualitatively
aCCurate.
Summary/Conclusions
The responses of hydrology, sediments and nutrients to the several
Scenarios of land use change included in the case studies together provide
insight into the various interactions involving the landscape and the
generation and transport of nonpoint pollutants. Predictions made at the
watershed outlet indicate that urban sprawl may be the largest threat to
hydrologic and nutrient stability in the watershed, while posing little threat to
sediment pollution. The cultivation of more cropland in the Saline may
dramatically increase the amount of sediment entering the stream, while
having relatively minor impacts on the other variables. Forest expansion, as
expected, reduces the amounts of each pollutant entering the stream course.
Thematic maps of sediment and nutrient responses to a storm reveal
which regions of the watershed are most likely to be affected by changes in
47
land use. The northeast portion of the basin generally appears most
susceptible to urban and agricultural changes while the northwest portion
appears most responsive to forested changes.
Strong spatial differences were observed in model response to different
land-use changes, and through comparison of selected sub-watersheds in the
Saline River basin. Specific characteristics of sub-watersheds can result in
greater responsiveness of some variables, such as sediment yield to
agricultural expansion, while other variables exhibit little spatial variation in
their response to the respective landscape change.
In short, these analyses, although confined only to evaluating the
impacts of urban, agricultural, and forested land cover expansion, provide us
with a much more detailed and thorough understanding of the potential
impacts of landscape change in the Saline River basin. Although many of the
predictions made by the model, such as the increase in runoff volume and
peak flow with urban expansion and the raised sediment yields with
agricultural expansion, may seem somewhat intuitive given our existing
understanding of runoff and erosion, this model also allows us to compare
the relative magnitude of responses and to gauge effects on a spatial scale.
Furthermore, by generating additional scenarios using GIS, one can further
explore initial model predictions such as why agricultural expansion had
opposite effects on some variables at different locations in the Saline basin. In
all, the GIS-based modeling approach provides a simple but powerful tool
capable of examining landscape interactions on scales that otherwise are
virtually impossible to address.
48
Discussion
The integration of geographic information systems technology with the
AGNPS hydrologic model has proven crucial to the development of an easy-
to-build and easy-to-use analytical tool with a wide range of modeling
capabilities. The power of GIS to incorporate data from multiple existing
sources such as MIRIS and the USGS spatial data archive greatly reduces the
time and effort required to assemble the massive databases often required to
drive complex hydrologic models. Furthermore, the ability of GIS to
manipulate and rearrange these spatial data not only allows the
transformation of information into the exact specifications required by a given
hydrologic model, but also allows for the controlled modification of specific
variables in ways that can represent a multitude of hypothetical scenarios.
Prior to the development of GIS-driven hydrologic modeling,
landscape influences on stream systems were primarily determined by
rigorous water sampling programs followed by varying levels of statistical
analyses on the data collected (Johengen 1991, Bright 1995). While these
studies often provide quantitative data on existing conditions that is much
more accurate than modeled data, they do not permit exploration of likely
changes under alternative land use scenarios. Conversely, the development
and application of the model presented in this research allows a broad range
of terrestrial-aquatic analyses for widely different landscape scenarios, but
does so with accuracy limited by the model's inputs and mechanics. Thus,
the question arises: given the two approaches (intensive stream sampling
versus GIS-driven watershed modeling), how should stream ecology and
watershed management proceed?
Certainly, the answer is to continue pursuing both approaches, but we
must pay more attention to how each approach might complement the other.
49
For example, although the output of a model such as the one presented here
may be less than accurate in some instances, it can be quite useful in
determining where to focus stream sampling sites and what should be
Sampled, making such sampling efforts much more efficient. On the other
hand, a hydrologic model can always be improved with better validation of
its principles, which are frequently derived empirically based on actual
stream and landscape measurements. In fact, between the time this research
was first initiated and its completion, the AGNPS model had been revised to
include improved flow, sediment, and nutrient decay equations (Young et al.
1994). Thus, the two approaches working cooperatively should produce
mutual benefits that eventually should provide scientists and managers with
research tools that are overall both more accurate and capable of a much
broader range of analytic scenarios.
Additionally, it is also worth mentioning that, while water sampling
programs will certainly continue to contribute a great deal to the advance of
GIS-driven watershed modeling, several other technological advances will
almost certainly improve the modeling approach to studying landscape-
stream interactions. These include advance in desktop computing power,
remote sensing capabilities, global data sharing networks, and geographic
information system software.
Desktop computers, which continue to increase in affordability, speed,
and overall capability at astonishing rates, are quickly diminishing concerns
over the ability of such machines to handle the enormous number of
computations required by increasingly complex models.
The availability and accuracy of remotely sensed data are also
improving at unprecedented rates. At present, satellite images are available
for the entire globe at 10 meter resolutions and are obtained repeatedly
50
approximately every 18 days. Before the end of the decade it is likely that
images at the resolution of 1 meter will be publicly available (Corbley 1996).
Such data will not only improve the accuracy of model inputs for hydrologic
models, but also will relieve the limitation of data being available only for
regions of the world with developed mapping programs.
In addition to improved satellite imagery, developing global data
sharing networks and spatial data archives will also become an increasingly
valuable asset to the development of GIS-driven modeling. Just as the
Michigan Resource Information System (MIRIS) and the USGS digital spatial
data archive greatly assisted the development of this model, similar such data
archives, which are rapidly emerging at internet sites across the world, will
provide the inputs necessary for present and future modeling efforts.
Finally, enhancements in the functionality of GIS software applications
and in techniques used in spatial analyses will provide the basis for new
capabilities of data capture, data visualization, and spatial modeling that may
not even be understood today.
In conclusion, geographic information systems appear to have a role of
growing importance not just in examining the processes behind nonpoint
Source pollution or in determining the influence of landscape structure on
aquatic systems, but in helping us examine and better understand the world
around us. GIS has the potential of being a revolutionary tool in the scientific
community and it is essential that people continue to move forward in
research that explores new uses for this application.
51
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55
Appendix A: Data Acquisition and Conversion: Building the Master Database
The first step in building the AGNPS model database was to acquire
the necessary digital spatial data layers and translate them into ERDAS-
readable formats. Two types of data were used to build the database: MIRIS
data and DEM data. This section provides step-by-step methodology on how
these data were obtained, converted to ERDAS raster GIS files, and then
prepared for further processing into the twenty-two specific input variables
needed to run the AGNPS model.
MIRIS Data
The Michigan Resource Information System (MIRIS) data, which were
used to generate the land use/land cover, soils association, and hydrographic
master data layers, are distributed by the Michigan Department of Natural
Resources as Intergraph design files grouped by county. Each county file
contains arc and attribute data for individual townships which must be
merged together and topologically cleaned and built before further
processing. These steps were executed in C-Map, a vector-based GIS
developed by the Michigan State University's Center for Remote Sensing.
The MIRIS files for Monroe and Washtenaw counties were first loaded
into C-Map format using C-Map’s IMPORT module. Land use/land cover
filenames were automatically assigned standard two-digit number codes of 05
and 50 for arc and attribute data, respectively. Soils filenames were likewise
given codes of 26 and 27. The hydrography layer, a subset of the MIRIS base
map layers, were given codes of 07 for perennial rivers and 08 for drains and
intermittent streams. No attribute data are included with the hydrographic
layers; they contain only arc data for rivers and streams. Once imported, the
A-1
township files for each layer, including both arc and attribute data layers,
were merged together to create basin-wide coverages.
Both the land use/land cover and the soils data were then cleaned and
built into polygon topologies using C-Map’s CLEAN and BUILD modules.
Default values of 40.0 ft. for the node tolerance and 5.0 ft. for the point
tolerance were used in the cleaning process. Some editing was necessary to
correct mislabeled and sliver polygons. The hydrographic layers were
cleaned, but not built, as building is inappropriate for non-polygon data.
Exporting C-Map files to ERDAS requires that all attribute labels be
converted to integers if not already existing as such. The land use/land cover
labels do exist as integers, but the class values needed to be coordinated so
that all classes were designated to the same level (Level III). Thus, any two-
digit (Level II) labels such as those for cropland polygons ("21") were
multiplied by ten (“210"). Likewise, the few Level IV classified categories
were reduced to Level III by dropping the fourth digit (e.g. “1131" became
"113”).
Converting the soils layer was slightly more complicated as two sets of
data – soil type and slope class – had to be exported. Both sets of data are
contained in the single three character soils labels; the first two characters
indicate soil type and the third represents slope class. To export them as
separate coverages, these labels had to be split into separate fields and then
recoded as integers. This was accomplished by first importing the C-Map
polygon attribute table (*.POL) into a spreadsheet utility (Microsoft Excel)
where the label field was parsed into separate components for Soil type and
slope class. C-Map recode tables (*.RTB), consisting of each soil type or slope
category linked with a unique integer recode, were then created using a text
editor (MS-DOS Editor) and used with C-Map’s RECODE module (MANAGE
menu) to convert character codes into the integer values.
The hydrography layer required no additional processing before being
converted to ERDAS. All three coverages were exported using C-Map’s
EXPORT module (ERDAS sub-option, under the GIS MENU). The land use/land
cover layer was exported as polygons, using the label field as the export
attribute. Soils polygon layer was exported twice into ERDAS, once using the
Soil type as the export attribute, then a second time using slope class. The two
hydrographic layers were exported individually as separate arc files,
assigning perennial rivers (07) to lines with an attribute value of 1, and
intermittent streams a value of 2.
The final step in processing the MIRIS data into master database layers
involved converting the land use, soil type, and slope category files, still in
vector format, into a raster gridwork. This was accomplished using the
ERDAS GRDPOL command. Cell resolutions were set to 330 x 330 ft. (2.5 acres),
which represents the minimum size of features included in the MIRIS land
use/land cover database. Each new raster layer required as many classes as
integer recode values in the initial C-Map export files: land use required 650;
Soils for Washtenaw and Monroe counties required 250; and slope class
needed 10 classes. This produced three raster GIS files serving as land use,
soils, and soil slope class layers in the master database. The hydrography
layers remained in vector format for later processing.
Digital Elevation Model (DEM) Data
The remaining component in the master database is the topographic
data layer, which is derived from 1:250,000 digital elevation models generated
by the United States Defense Mapping Agency. These data are available free
A-3
of charge and can be obtained electronically by downloading them from the
USGS internet archive (anonymous ftp from edftp.cr.usgs.gov). The data are
downloaded in 1-degree sections containing 1,441,201 cells in a 1200 x 1201
cell grid. At the latitude of lower Michigan, each cell thus works out to be
roughly 225 x 225 ft (68.58 x 68.58 m).
Although ERDAS (version 7.5) is able to read DEM data directly, it can
only do so from digital tapes. Therefore, other methods were used to get the
data into ERDAS. The original DEM data were first downloaded to a
Macintosh supporting the MapII GIS application. MapII can readily import
DEM data files and convert them into SPANS format, a standard data format
common to both MapII and ERDAS. These SPANS files were then imported
into ERDAS using the RDSPANS command, and georectified to the Michigan
State Plane system, keeping them consistent with the other layers in the master
database. Rectifying the DEM data consisted of first assigning the
latitude/longitude coordinates of the upper-left cell of each DEM coverage
(FIXHED), and then performing a second-order rectification (NRECTIFY using
cubic convolution) with a coordinate file matrix (PROGCP, COORDN) to convert
the file from planimetric to State Plane zone 6401 (southern Michigan). These
steps were done for each DEM section included in the basin area. All sections
were then merged together (STITCH) into a basin-wide topographic coverage
containing 225 x 225 ft cells.
An additional elevation layer, consisting of enhanced topographic
features, was also included in the master database. Because DEM data
indicate elevational changes only at meter intervals, much of the Saline River
landscape, which can stretch for great distances without changing a full meter
in elevation, appears as a series of flat terraces separated by sudden ridges
found where two elevation intervals meet. This terracing effect, in turn,
A-4
creates problems in the model's attempts to determine hydrologic routing as
So many cells appear to have a flat aspect. An enhanced topographic layer, in
which valleys were amplified by artificially lowering cells containing
watercourses, was developed to overcome this problem.
The first step in creating the enhanced topographic layer was to
increase the vertical resolution of the original (unrectified) DEM data file by
rescaling the elevation values from meter to millimeter intervals, i.e.,
multiplying all cell values by 1000. Increasing the vertical resolution enabled
slight differences in cell elevations to be preserved rather than be rounded to
the nearest meter interval. Thus, when the DEM file was resampled during the
rectification process (NRECTIFY), elevations are represented much more
Smoothly since a new terrace occurs at every millimeter change rather than at
every meter.
Even with this change, however, resampling the data using the 225 x 225
ft. cells still results in significant terracing. The resampling process in the
rectification procedure only affects cells in close proximity to the “ledges,” or
where elevation changes occur in the original DEM data. To circumvent this
problem, a larger output cell size (2000 x 2000 ft.) was used when rectifying
the original elevation layer. Increasing the output cell size boosts the
probability that two abutting cells have different elevation values, thus
spreading the elevation changes over a broader area.
The above steps greatly enhance elevation for the purposes of
computing cell aspect, but also sacrifices cell accuracy to Some degree. In
regions where terrain is naturally varied enough to generate working cell
drainage paths, these modifications are artificially smoothing the elevation
data where it is unnecessary. Thus to preserve the accuracy in these cells a
composite map was created in which the modified elevation cells were used
A-5
only where necessary, i.e., when the aspect of the original elevation layer was
flat. This map was created simply by masking out all the flat regions of the
unaltered elevation map, and overlaying the remainder (the acceptable cells)
on top of the modified layer.
The resulting map provided much improved drainage patterns
compared to the sink-hole riddled map generated from the original elevation
layer, but it still had some problems in surface flow direction. In cases where
river channels flowed along, rather than across, topographic gradients the
elevation model would interpret surface flow incorrectly, generating
drainage paths that flowed in and out of river channels.
This problem was remedied by creating artificial river valleys in the
modified elevation layer, a modification based on the principle that water
runs downhill towards streams. A ten-cell buffer was created along rivers and
made to drain such that cells farther away from a watercourse would have a
higher cell value. Thus, river cells would have a value of 0 and cells 10 cell
lengths away from a waterway would have a value of 10; cells greater than 10
cell lengths away from any river or drain were all given a value of 11. This
buffer layer was then combined with the modified elevation using the
following equation: (A-(11-B)*10), where A is the modified elevation layer and
B is the buffer file. The result was a new elevation layer with valleys ranging
from 110 ft. below normal at the base [(11 - 0) * 10 = 110] to 10 ft. below normal
[(11 - 10) * 10 = 10) ten cell lengths away from the waterway.
Admittedly, these changes to the original elevation data seem drastic.
However, they are based on sound principles: the first modification is simply
an enhancement and resampling of existing topographic information using
the same techniques used to generate the original digital elevation model
data, and the second is based on the principle that surface water generally
A-6
moves towards the nearest waterway. Comparisons between these modified
maps and 1:24,000 USGS topographic quadrangles also showed good
conformity, indicating that the modifications did not significantly sacrifice
data integrity. In short, the modifications appear to be a valid means of
adapting an existing spatial data set to meet the requirements of an unusually
flat watershed.
Preparatory Files
Also included in the master database were a set of files designed to
prepare the master data layers for further processing into the twenty-two
specific input parameters used in the AGNPS model. These files include a
descriptor librarian, land use/cover descriptor file, a soils descriptor file, and
a GIS model librarian.
The Descriptor Librarian
The descriptor librarian (AGNPS.DSL) contains six scripts (Appendix E)
used to recode MIRIS land use/cover classes into AGNPS input variables. It
was designed specifically to work with MIRIS data and thus can be used
universally with any MIRIS-based AGPNS model database. The first of these
scripts, "MIRIS->NAME," groups the Level III land use classes in the master land
use/cover coverage into sixteen categories for which AGNPS recode values
exist. Most groupings were made simply by truncating the Level III attribute
data to Level II; however, certain Level III classes for which specific AGNPS
recodes could be found were preserved (e.g., cemeteries, open space). The
second script, “NAME->CODE," assigned a numeric code, used in generating the
runoff curve number, to each of these land use name classes. The five
remaining scripts, “NAME->SURFACE,” “NAME->ROUGHNESS,” “NAME->CROPPING,"
A-7
and "NAME->COD," each recoded the land use groupings with appropriate
values for the respective fields.
The Land Use/Cover Descriptor File
The land use/cover descriptor file (LAND.DSC) stores the variable
recodes created by the descriptor librarian. Four descriptor fields were
defined: “Class Code," "Surface Condition,” “Manning's Roughness,”
“Cropping Factor," and “COD." Recodes values were inserted into these
fields by enabling the AGNPS descriptor librarian and running the
appropriate script for each variable.
Descriptor files are especially useful in this instance because they link
these recode data to the land use/cover classes and not to particular cells.
Thus, if the land use/cover layer is later modified to represent an alternative
landscape scenario, the recodes in the descriptor files will be updated
automatically since the classes remain the same; only the spatial distribution
is altered.
The Soils Descriptor File
The soils descriptor file (SOILS.DSC) also stored variable recodes, but was
developed in a slightly different way. Five descriptor fields were defined:
“Textureş", "Symbol,” “Texture,” “Hydrologic Group,” and "Kfactor.” Data
were inserted into these fields by reading in an ASCII file containing soil
recode values (SOILS.TXT). The ASCII file, typed in manually, contained
each of the above fields in their respective order separated by commas
(Appendix F).
A-8
The GISMO Model Library
The ERDAS modeling language, GISMO, was used in several instances
to combine, recode, and otherwise reshape data layers to fit the requirements
of the AGNPS model. A total of thirteen models were stored in the AGNPS
model librarian (AGNPS.MLB). These models are similar to the descriptor
librarian in that they were designed to work with MIRIS- and DEM-based
input data and thus can work with any master database constructed from
these data sources. Each model is listed in Appendix D.
Summary
The results of the data acquisition and conversion process include a
total of seven GIS layers stored as the master database. Land use, soil types
and soil slope class, each derived from MIRIS data, are stored as ERDAS
raster files with a cell resolution of 330 x 330 ft. Two other layers also derived
from the MIRIS data, perennial and intermittent streams, are kept as ERDAS
vector files for later processing. And finally two elevation layers, one
standard for calculating slope, and one modified for calculating aspect, are
stored as raster image files at a 225 x 225 ft. resolution. Descriptor files for the
land use and soil type coverages, which contain recode values for certain
AGNPS input variables, and a GISMO model library also are included to aid
in the subsequent processing of these master data layers into the AGNPS
model databases.
A-9
Appendix B: Generating AGNPS Input Variables: The Subwatershed Databases.
Once all the necessary source data were captured and stored in the
master database files, the twenty-two specific variables required to run
AGNPS were created simply by recoding, recombining, or otherwise
transforming these master data files into AGNPS-readable subwatershed
databases. This process involved three steps: (1) generating a set of basin-wide
input layers, (2) generating a complete set of AGNPS input layers at the
subwatershed scale, and (3) converting these ERDAS databases into proper
AGNPS-formatted data files. This section outlines the specific procedures
involved in each step, including some discussion as to why each step was
included.
Generating Basin-Wide Input Layers
Rationale
As previously explained, the Saline River watershed is too large to be
processed by the AGNPS model as a whole and therefore was processed as a
series of subwatersheds, each modeled separately. However, to avoid having
to continually reprocess the master data into the twenty-two AGNPS inputs
for each subwatershed, many of the input layers were generated just once, but
for the entire watershed area, and then divided into the smaller subwatershed
databases. This two-stage process not only reduced the overall number of
processing steps, but for many layers, it also preserved greater accuracy
throughout the resampling process, as explained below.
B-1
To extract data from the high resolution master data layers to the lower
resolution subwatershed data layers, the smaller cells of the master data layer
must be grouped, or resampled, into larger cells. For cells containing
quantitative values (i.e., where cell values represent actual measured
quantities, such as Soil erosivity) the resampling process uses a weighted
average of the values of the smaller cells to determine the values of the larger
ones. However, cells containing qualitative data (i.e., where cell values
represent discrete categories or types, such as soil textures) cannot be
averaged as these averages would be meaningless: the “average" of type 1 and
type 3 is not necessarily type 2. Instead, resampling of qualitative data is
accomplished by assigning the dominant or most frequent data type of the
group of smaller cells as the value of the larger cell.
The distinction between the two resampling processes is subtle but
important. The quantitative approach of using weighted averages retains
more accuracy during the resampling process than does the qualitative
method, and thus it is the preferable of the two. Because many of the AGNPS
inputs are quantitative variables recoded from qualitative maps, it makes
sense to recode them first, and then resample them as qualitative values rather
than discrete classes. For example, soil erosivity, a quantitative variable, is
generated by recoding qualitative soil type classes. By recoding the
qualitative soils data into quantitative soil erosivity values, the resampling
process can take the more accurate weighted averages approach instead of the
qualitative approach it would have if the soil type map were resampled first.
Hence, not only did the two-stage process save steps, it also proved to be a
more accurate procedure in many respects.
B-2
The MASTER.AUD Batch File
Creating the basin-wide input layers was further simplified by
combining all the ERDAS commands needed to generate them into a single
batch file named MASTER.AUD (Appendix G).
The first procedure contained in the MASTER.AUD batch file was
generating the runoff curve number, a variable reflecting specific
combinations of land use and the soil types. This process began by running
the “SCSCN PREP" GISMO model (Appendix D) which extracts two sets of
information contained in the descriptor files, class code from the land
use/cover file and hydrologic group from the soils file, and inserts them into
separate GIS layers. Then, using the ERDAS MATRIX command, these maps
were overlaid and specific combinations of land uses and soil types were
assigned appropriate curve number values determined from a table supplied
by the USDA (1972).
The remaining procedures contained in the MASTER.AUD batch files were
straightforward recodes of master map layers executed by running three
separate GISMO models: “LAND.DSC->VARS." “SOILS.DSC->VARS,” and “SLOPE-
>VAR6" (Appendix D). The “LAND.DSC->VARS" model extracted Manning's
roughness, cropping management, surface condition constant, fertilization
index, and chemical oxygen demand factor from the land descriptor file, and
placed each into a separate GIS data layer. Similarly, the “SOILS.DSC->VARS"
model extracted soil erosivity and soil texture from the soils descriptor files
and placed these data into their own coverages. And finally, the "SLOPE-
>VAR6" model simply recoded the slope class master data layer into field
slope length categories based on a table supplied by the Southeast Michigan
Council of Governments (SEMCOG, 1979).
The outputs of the MASTER.AUD batch file then were seven new data
layers, each constructed at the basin-wide scale and each still retaining the
same resolution as the original master data layers (330 x 330 ft.). Subsets of
these layers would later be re-rectified into individual subwatershed
databases, as explained below. Some AGNPS variables were not created at the
basin-wide scale for one of two reasons. Elevation-based variables (slope,
channel slope, channel side slope, and aspect) were not because these
variables are more accurately calculated if done so from elevation layers
sharing the same cell resolutions as the final data sets. Other layers not created
at the basin-wide scale included those variables either not used in the analysis
or set to uniform values; these variable layers were more easily created
directly in the subwatershed databases themselves.
Building the subwatershed databases
The process for building each subwatershed database began with
creating a subdirectory within the master database directory, and inserting an
ERDAS vector file of the subwatershed boundary, created by digitizing
watershed boundaries delineated from 1:24,000 USGS topographic maps, into
the subdirectory. Once this subdirectory was made and the boundary file
inserted, the remaining procedure consisted of running a sequence of batch
files explained below.
B-4
SUIB1. A UID
The first batch file, SUB1.AUD (Appendix H), executed five steps. First, it
generated a series of empty map layers of the subwatershed area by
converting the digitized watershed boundary file into a blank raster grid and
then copying this raster grid into a number of additional empty coverages for
individual AGNPS input layers. The second step involved filling many of
these empty coverages with data extracted either from the master data layers
or from the basin-wide variable layers discussed above. This was done by re-
rectifying the original layers directly into the subwatershed layers, a process
that would automatically subset and resample the master data into the
appropriate cell sizes of the subwatershed database. The third step included
calculating cell slope and aspect from a re-rectified elevation layer, done
easily with specific ERDAS commands. Fourth, the SUB1.AUD batch file created
a raster layer of the river and drain network by rasterizing the vector
hydrographic files from the master database, and subsequently combined this
layer with the slope layer to generate the channel id, channel slope, and
channel side slope data layers. The final step included overlaying a river
aspect file, where the aspects of river and stream containing cells were, when
necessary, redirected to slope downstream, on top of the original aspect file.
This step was one final aspect modification created to ensure that cells routed
properly in a landscape as flat as the Saline River's.
SUIB2. A UID
Before the second batch file, which was designed to determine the
receiving cell numbers, could be run, the cell id layer had to be produced.
B-5
No simple ERDAS procedures were able to create cell id number (sequential
integers assigned to each cell within the watershed), so this procedure could
not be included in a batch file. Instead, it was created by exporting (DATATAB)
a coverage of the watershed into a tabular text file from which a DOS
executable batch file programmed in ANSI-C (Appendix K) would recode all
cells within the watershed (i.e., non-zero cells) with a sequentially increasing
integer value. The batch output file was then reconverted into ERDAS and
restructured back into a GIS layer using the ERDAS TABDATA command.
The SUB2.AUD (Appendix I) batch file then used this cell id layer and the
aspect layer generated in the previous batch file to determine the receiving
cell id for each raster in the subwatershed area. This process began by creating
eight new coverages consisting of the cell id file offset one cell in each of the
eight cardinal directions. Thus, one layer where all cell id numbers were
shifted one cell east, for example, would represent the receiving cell number
if all cells faced west. Next, three GISMO models (“VAR2 I,” “VAR2 II,” and
“ASPFIx", Appendix D) were run". VAR2 I and VAR 2 II were used to determine
which shifted cell id layer should be inserted into a particular cell based on
the aspect of that cell. The ASPfix model adjusted the remaining cells with a
flat aspect (sink hole cells) to a format required by the AGNPS model: aspects
were set to 0 and receiving cells were assigned to their own cell id numbers.
'Problems arose during many model runs when these model were included in the SUB2.AUD batch file and
thus these models were run manually (not within the batch files) by calling up the ERDAS GISMO
routine.
B-6
SUIB3. A UID
At the completion of the second batch file, all of the twenty-two
AGNPS input variables were stored in individual, spatially referenced GIS
map layers. The final batch file, SUB3.AUD (Appendix J), merged these GIS
layers into a single twenty-two layer coverage, which was converted into a
large tabular data file and eventually translated into the specific format
needed by the AGNPS program. The merging of the data files was done
simply by creating an empty twenty-two layer image file, and then subsetting
each variable file into a separate layer in this image file in the order used by
the AGNPS model database. This multi-layer file was then exported as a
tabular data file. The net result was a single file, named INPUT.TXT, which
contained all twenty-two AGNPS input values for each cell, identified by that
cell's id number, organized in tabular format.
Converting the ERDAS Output File to an AGNPS Readable Data File
While the INPUT.TXT file contained all the data for the model to run, and
even in proper sequence, the AGNPS program further requires that each field
be in a rigidly specified format. A DOS executable program, ERD2AGN.EXE
(Appendix L), was written to handle this task. This program, written in ANSI-
C, simply read the input.txt file and restructured it according to the format
specified by the AGNPS model, naming the output OUTPUT.TXT.
In addition to converting the format of the ERDAS file, a proper header
also had to be created and attached to the file. This was accomplished by first
creating a new dummy data file in the AGNPS program itself, consisting of
B-7
the proper header information (cell size, number of cells, storm duration,
etc.). Then, using a text editor, this header was copied and pasted to the
beginning of the OUTPUT.TXT file. The resulting file, containing all the necessary
variable information, could then be loaded into AGNPS and run. Most data
files, however, still needed some debugging in the AGNPS program; this
debugging most frequently consisted of locating sink hole cells on the fringes
of the subwatershed boundary and also identifying the outlet cell.
B-8
Appendix C: Generating Land-use Expansion Scenarios.
Alternative landscape scenarios were evaluated by replacing the land
use layer in the master database with a modified land use layer, and then
reprocessing the subwatershed specific databases just as described in
Appendices A and B. This section describes the procedures used in creating
these modified landscape layers.
Agricultural Expansion
The expansion of agricultural land, specifically cropland, was
simulated by recoding the cells along the perimeter of cropland cells to 210,
or the MIRIS Level III value for cropland. Because the cells in the master data
layers were 100 m (330 ft) wide, this change represented a radial expansion of
100 m for each clump of agricultural cells. Thus, to represent increased levels
of change, more cells along the perimeter would be recoded, each occurring
in 100 m intervals for every increment of perimeter cells changed. Expansions
of up to 500 m, or five cells away, were used in the simulation event. Water
cells, however, were not recoded since it seems unlikely that croplands will
expand on top of lakes or ponds.
The cells were recoded using a combination of the ERDAS SEARCH
command and a specifically designed GISMO model (AGRILIZER, Appendix
M). The SEARCH command was used to create a five-cell buffer around all
cropland clumps, and the AGRILIZER model would create a new land use
layer by recoding the cells in each successive buffer as cropland, with the
exception of cells valued between 500 and 599, which are MIRIS water cells.
C-1
Urban Expansion
Urban expansion was simulated just as was agricultural expansion. The
SEARCH command was used to create a five-cell buffer of all urban lands
(MIRIS levels 110 through 130), followed by a GISMO model (URBANIZER)
which recoded all cells in these buffers to urban, with the exception of water
cells.
Forest Expanion
Forest expansion again was simulated much like the urban and
agricultural expansion scenarios. Buffers were created around all cells with
MIRIS level values between 400 and 499, which correspond to forest cover
types. These buffers were then recoded into new forest cells.
C-2
Appendix D: ERDAS model (GISMO) scripts contained in AGNPS.MLB library.
1. Land.dsc->vars
# This model generates separate layers from information contained
# in the land use descriptor files.
DATA
INPUT landuse FILE ASK "land"; # Asks for land use file name
OUTPUT ft FILE "varl3"; # Surface Condition constant
OUTPUT cf FILE "varl1"; # Cropping factor file created
OUTPUT ro FILE "varo"; # Manning's roughness file created
OUTPUT col FILE "var?0"; # Chemical Oxygen Demand file
INTEGER surf; # (Temporary variable)
START
Cf landuse. "cropping factor" * 100; # Converts decimal to integer
ro = landuse. "Mannings roughness" * 100; # Converts decimal to integer
cq = landuse. "cod";
surf = landuse. "surface condition" * 100; # Converts decimal to integer
ft = CONDITIONAL (
(landuse >= 500 AND landuse < 600) 0 # Recodes water cells to 0
(default) surf ) ; # Non-water cells = code
# This model generates a channel slope (VART)
# from the slope layer (VAR4) and a buffer file.
# The land use file must also be accessed so that
# water and marsh cells can be identified and
# given a value of zero.
DATA
INPUT land FILE "land"; # Land use file
INPUT slope FILE "VAR4. lan"; # Slope layer
INPUT river FILE "river"; # River file
OUTPUT csp FILE "VART. lan"; # Channel slope (% x 10)
OUTPUT sqsp FILE "VAR8. lan"; # Channel side slope (%)
START
csp = CONDITIONAL {
(land. "class code" >= 14) 0 # Water & marsh cells have value = 0
(river >= 1) slope # River & drain cells = land slope
(default) slope/2 ) ; # All other cells = 1/2 slope
sdsp = EITHER O IF land. "class code" >= 14 OR 10 OTHERWISE;
- = = - - - - = = = = = = - = = = a- = = me = * = * * = - = - - - - - - - - - - - - - - - - - " * * * * * * * * * * * * * *
D-1
3. Side Slope
# This model generates a channel side slope (VAR8)
# from the slope layer (VAR4) and a buffer file.
# The land use file must also be accessed so that
# water and marsh cells can be identified and
# given a value of zero.
DATA
INPUT land FILE "Land"; # Land use file
INPUT slope FILE "VAR4. lan"; # Slope layer
INPUT bf FILE "buffer"; # River buffer
OUTPUT csp FILE "VAR8. lan"; # Channel side slope (%)
START
csp = CONDITIONAL {
(bf EQ 1) slope # adjacent cells have value = slope
(bf GT 1) slope/2 # other cells = 1/2 land slope
(land. "class code" == 14) 0 # Water cells have value = 0
(land. "class code" == 16) 0}; # Marsh cells have value = 0
END
#This converts the lirectified slope data into proper variable 4
#format (setting water values to zero, as AGNPS states)
DATA
INPUT land FILE "land";
INPUT slope FILE "slope";
OUTPUT slp FILE "var4. lan";
START
slp = conditional {
(land. "class code" == 14) 0
(default) slope };
D-2
5. Ch Ind
# Configures the channel layer according to agnps
DATA
INPUT riv FILE ASK "riv";
INPUT land FILE ASK "land";
OUTPUT ch FILE "var32. lan";
START
ch = CONDITIONAL (
(riv == 2) 7 #River cells take the AGNPS value of perrenial streams
(riv == 1) 6 #Drain cells take the AGNPS value of intermittent. . .
(land. "class code" == 14) 0
(default) 1 };
END
6. Var? I
DATA
Input as FILE "varl A. lan";
Input n FILE "north";
Input en FILE "neast";
Input e FILE "east";
Input se FILE "seast";
Input s FILE "south";
#Input sw FILE "swest"; # No room! Moved to part II
#Input w FILE "west"; #
#Input nw FILE "nwest"; #
Input v1 FILE "varl. lan";
Output x FILE "x1";
START
x = conditional {
(as == 1) n
(as == 2) en
(as == 3) e
(as == 4) se
(as == 5) s
# (as == 6) sw
# (as == 7) w
# (as == 8) nw
(default) v1 };
END
D-3
7. Var? II
DATA
Input as FILE
#Input n FILE "north";
#Input en FILE "neast";
#Input e FILE "east";
#Input se FILE "seast";
#Input s FILE "south";
Input x1 FILE "x1";
Input sw FILE "swest";
Input w FILE "west";
Input nw FILE "nwest";
#Input v1 FILE "varl";
Output x FILE "var2. lan";
START
x = conditional {
# (as == 1) n
# (as == 2) en
# (as == 2) e
# (as == 4) se
# (as == 5) s
(as == 6) sw
(as == 7) w
(as == 8) nw
(default) x1 };
END
"varl 4. lan";
* * * * * * * * * * * * * *m mm - mm wºn amam am sºme = - mem. - = = = m = m.
Done in part I
#
#
#
#
#
# Determines slope length from SEMCOG recode tables
DATA
INPUT slp FILE ASK "slope class";
OUTPUT ls FILE "varó";
START
ls = CONDITIONAL (
(slp == 1) 400
(slp == 2) 300
(slp == 3) 150
(default) 80
END
m= mm amº m = - mºm am, ammº ºme = amm amº m = mm ammº me mº mº m sºme sm: mº- mº mº - mºm º ºmº - - - - - * m mss m = m, sº mm mm. ººm mºm - sº- - - * = - *= - - *-* - *-* *- =
D-4
9. Soil.dsc->vars
data
input soils file "soils";
input land file "land";
Output erosiv file "varlO";
output texture file "varl B.";
Start
erosiv = soils. "kfactor";
texture = either 0 if land. "class code" >= 14 or soils. "texture"
otherwise;
end
DATA
INPUT asp FILE ASK "aspect";
INPUT riv FILE ASK "river";
OUTPUT ra FILE "rivasp";
START
ra = EITHER asp IF riv GT 0 OR 0 OTHERWISE;
# Fixes recieving cell/aspect problems in sink hole cells
# (sets aspect to zero and receiving cell to cell id for sink holes }
DATA
input var2 file "var2. lan";
input varl file "varl. lan";
input asp file "varl A. lan";
output v2a file "var2a. lan";
output asb file "varl Aa. lan";
START
v2a = either varl if var2 == 0 or var2 otherwise;
asb = either 0 if (var2 == 0) or asp otherwise;
END
12. Agrilizer
DATA
input skirt file "agskirt";
input land file "landz";
integer stop;
Output out file ask "out";
START
stop = 4;
Out = either 210 if (skirt gt 0 and skirt - stop) or land otherwise;
out = either land if land gt 500 and land lt 600 or out otherwise;
END
DATA
input skirt file "rpskt";
input land file "landz";
integer stop;
output out file ask "out";
START
stop = 5;
out = either 410 if (skirt >= 0 and skirt - stop) or land otherwise;
Appendix E: ERDAS Scripts Contained in the AGNPS.DSL Descriptor Library
1. MIRIS->Name
CONDITIONAL (
(class >=110 and class < 120) "residential"
(class >=120 and class < 130) "commercial"
(class >=130 and class < 140) "industrial"
(class >=140 and class < 150) "transportation"
(class >=170 and class < 180) "extractive"
(class >=190 and class < 194) "recreation"
(class ==194) "cemetery"
(class >=210 and class < 220) "cropland"
(class >=220 and class < 230) "Orchard"
(class >=230 and class < 240) "feedlot"
(class >=240 and class < 250) "pasture"
(class >=290 and class < 300) "farmstead."
(class >=310 and class < 320) "herbaceous"
(class >=320 and class < 330) "shrubland"
(class >=410 and class < 430) "deciduous"
(class >=420 and class < 429) "Conifer"
(class ==429) "Xmas trees"
(class >=500 and class < 600) "Water"
(class >=610 and class < 620) "wooded wetland"
(class >=620 and class < 630) "Wetland"
(default) " ––– " };
2. Name->Code
Conditional {
('class name' == "residential") 1.
('class name' == "industrial") 2
('class name' == "commercial") 2
('class name' == "transportation") 3
('class name' == "extractive") 4
('class name' == "recreation") 5
('class name' == "cemetery") 5
('class name' == "cropland") 6
('class name' == "orchard") 7
('class name' == "feedlot") 8
('class name' == "pasture") 9
('class name' == "farmstead") 10
('class name' == "herbaceous") 11
('class name' == "shrubland") 11
('class name' == "deciduous") 12
('class name' == "conifer") 13
('class name' == "xmas trees") 7
('class name' == "water") 14
('class name' == "wooded wetland") 15
('class name' == "wetland") 16
(default) 0};
3. Name->Surface
# Written 14 Sept 93; modified 15 sept 94
Conditional {
('class name' == "residential") 0.01 # AGNPS Table 23.
('class name' == "commercial") 0.01 # AGNPS Table 23. \ Urban Non-
('class name' == "industrial") 0.01 # AGNPS Table 23. / residential
('class name' == "transportation") 0.01 # AGNPS Table 23.
('class name' == "extractive") 0.80 # AGNPS Table 23.
('class name' == "recreation") 0.22 # AGNPS Table 2. (good pasture)
('class name' == "cemetery") 0.22 # AGNPS Table 2. (good pasture)
('class name' == "cropland") 0.17 # Big Darby Table 2–5
('class name' == "orchard") 0.17 # Big Darby Table 2–5
('class name' == "feedlot") 0.01 # AGNPS Table 2. (poor pasture)
('class name' == "pasture") 0.15 # AGNPS Table 2. (fair pasture)
('class name' == "farmstead") 0.01 # AGNPS Table 2.
('class name' == "herbaceous") 0.59 # AGNPS Table 2. (perm.meadow)
('class name' == "shrubland") 0.59 # AGNPS Table 2. (perm.meadow)
('class name' == "deciduous") 0.59 # AGNPS Table 2.
('class name' == "confifer") 0.29 # AGNPS Table 2.
('class name' == "xmas trees") 0.29 # AGNPS Table 2.
('class name' == "water") 0.01 # AGNPS Table 2.
('class name' == "wooded wetland") 0.01 # AGNPS Table 2. (marsh)
('class name' == "wetland") 0.01 # AGNPS Table 2. (marsh)
(default) 0.01};
- m - sma ººm- -º mº ºn mºs. ººm- -as ims - -ºn mºs = -ºme mºms - -s mºm - mºms me -m sºme ºw ººms sºme - sºme mºm - mºre mºm wºme sºme ºn mºms sºme - wºmam ºmºs º-ºn mºms me -ms sºme ºw ºms isºme tº wºmm wºman ºm *
4. Name->Roughness
#Written 14 Sept 93, modified 15 Sept 94
Conditional {
('class name' == "residential") 0.02 # AGNPS Table 23.
('class name' == "commercial") 0.02 # AGNPS Table 23. \ Urban Non-
('class name' == "industrial") 0.02 # AGNPS Table 23. / residential
('class name' == "transportation") 0.02 # AGNPS Table 23.
('class name' == "extractive") 0.10 # AGNPS Table 23.
('class name' == "open space") 0.13 # AGNPS Table 5. (excel. grass)
('class name' == "cropland") 0.13 # AGNPS Table 5. (excel. grass)
('class name' == "cropland") 0.03 # Big Darby Table 2–5
('class name' == "orchard") 0.03 # Big Darby Table 2-5
('class name' == "feedlot") 0.04 # AGNPS Table 5. (sparse grass)
('class name' == "pasture") 0.15 # AGNPS Table 5. (fair pasture)
('class name' == "farmstead") 0.01 # AGNPS Table 5. (residential)
('class name' == "herbaceous") 0.15 # Nelisher Table 2.2 (grarie)
('class name' == "shrubland") 0.13 # Nelisher Table 2.2 (range)
('class name' == "deciduous") 0.80 # Nelisher Table 2.2 (h.woods)
('class name' == "conifer") 0.40 # Nelisher Table 2.2 (l.woods)
('class name' == "xmas trees") 0.40 # Nelscher Table 2.2 (1.woods)
('class name' == "water") 0.99 # AGNPS Table 5.
('class name' == "wooded wetland") 0.99 # AGNPS Table 5. (marsh)
('class name' == "wetland") 0.99 # AGNPS Table 5. (marsh)
(default) 0.001 };
E-4
5. Name->Cropping
AGNPS Table
AGNPS Table
AGNPS Table
AGNPS Table
AGNPS Table
23
23
23.
. \ Urban NOn-
... / residential
23.
23.
SEMCOG Table 8.
SEMCOG TABLE 8.
AGNPS Table 23.
# AGNPS Table 23.
# W&S p. 33
# AGNPS Table 23.
# Big Darby Table 4–1.
# SEMCOG Table 8.
# SEMCOG Table 8.
# AGNPS Table
# AGNPS Table
# AGNPS Table
# AGNPS Table
# AGNPS Table
# AGNPS Table
23
23.
23.
. (Cultiv.)
23.
* *- - - - - * * = = * * = = - * = - m = = - * = * * * = ** - - * - - - - - - - - * = - = - - = = - - - - - * * *
Conditional {
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
Iname '
Iname '
name'
name '
Iname '
Iname '
name ' =
Iname'
name '
name '
Iname '
Iname '
Iname '
name '
Iname '
name '
Iname '
Iname '
Iname '
name '
(default) 0};
"residential")
"COmmercial")
"industrial")
"transportation")
"extractive")
"recreation")
"cemetery")
"cropland")
"Orchard")
"feedlot")
"pasture")
"farmstead")
"herbaceous")
"shrubland")
"deciduous")
"Conifer")
"xmas trees")
"Water")
"WOOded wetland")
"wetland")
.01
.01
.01
.01
. 10
. 06
. 003
. 39
.43
. 20
. 03
.01
. 003
23
23
. (marsh)
. (marsh)
E-5
"residential")
"commercial")
"industrial")
"transportation")
"extractive")
"recreation")
"cemetery")
"Cropland")
"Orchard")
"feedlot")
"pasture")
"farmstead")
"herbaceous")
"shrubland")
"deciduous")
"conifer")
"xmas trees")
"Water")
"WOOded wetland")
"wetland")
6. Name->COD
Conditional {
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
('class
(default)
Iname '
Iname'
Iname '
Iname '
Iname '
name '
name '
name '
name '
name '
name'
Iname '
name'
Iname '
Iname '
name '
Iname'
name '
name '
Iname '
0};
110
80
80
110
80
60
60
170
170
4000
50
80
60
60
65
65
65
25
25
# AGNPS
# AGNPS
# AGNPS
# AGNPS
# AGNPS
# AGNPS
AGNPS
AGNPS
AGNPS
AGNPS
AGNPS
AGNPS
# AGNPS
# AGNPS
# AGNPS
# AGNPS
# AGNPS
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
23.
23. \ Urban Non-
23. / residential
23.
23.
8.
8.
Big Darby Table 2–5
Big Darby Table 2–5
Young, et al.
Table 2
. (fair pasture)
. (pasture/Open)
. (pasture/Open)
. (forested)
. (marsh)
. (marsh)
E-6
Appendix F: ASCII file used to insert soils descriptor information
to soils map
Adrian, muck, 38, 4, 1, 0
Adrian, muck, Ad, 4, 1, 0
AQuents, muck, 31, 4, 1, 0
Arkport-Okee, loamy fine_sand, 35, 1, 2, 17
Barry, loam, 17, 2, 2, 28
Barry, sandy loam, Ba, 2, 2, 28
Barry–Brady-urban, urban, 57, 4, 2, 24
Beaches, sand, 27, 1, 9, 99
Belleville, loamy fine sand, Ba, 1, 2, 17
Belleville, loamy fine sand, 36, 1, 2, 17
Berrien, loamy sand, Bb, 1, 9, 99
Berrien, sandy loam, Bc, 1, 9, 99
Berville, loam, Bd, 2, 9, 99
Blount, loam, Bf, 2, 3, 43
Blount, loam, 13, 2, 3, 43
Blount, loam, Bb, 2, 3, 43
Blount-Pewamo, loam, Bg, 2, 3, 43
Blount-Pewamo, loam, Bc, 2, 3, 43
Blount-Pewamo-Metamora, complex, 62, 2, 3, 29
Blount-Urban, urban, 56, 2, 3, 43
Boyer, loamy sand, Bn, 1, 2, 17
Boyer-Kidder, complex, Bo, 1, 2, 17
Boyer-Oshtemo, sandy loam, 11, 2, 2, 24
Brady, sandy loam, 16, 2, 2, 20
Brady, sandy loam, Bh, 2, 2, 20
Brady-Macomb, loam, Bk, 2, 2, 24
Brady-Macomb, sandy loam, Bm, 2, 2, 24
Bronson, loam, Bn, 2, 9, 99
Brookston, loam, Bo, 2, 2, 28
Brookston, loam, Bp, 2, 2, 28
Brookston, loam, 61, 2, 2, 28
Brookston, loam, Br, 2, 2, 28
Cadmus, loam, Ca, 2, 9, 99
Cadmus, sandy_loam, Cb, 2, 9, 99
Cadmus–Blount, loam, Cc, 2, 9, 99
Capac, loam, 65, 2, 2, 32
Carlisle, muck, Cd, 4, 4, 0
Ceresco, fine_sandy loam, 46, 2, 2, 20
Channahon, loam, 45, 2, 4, 37
Cohoctah, fine sandy loam, CC, 2, 2, 28
Cohoctoh, fine sandy loam, 22, 2, 2, 28
Colwood, sandy loam, Ce, 2, 2, 28
Colwood, loam, 29, 2, 2, 28
Colwood-Wauseon, sandy loam, Cf, 2, 2, 24
Conover, loam, CG, 2, 2, 28
Conover, loam, Co, 2, 2, 28
Conover-Brookston, loam, Cp, 2, 2, 28
Corunna, sandy loam, 24, 2, 2, 20
Corunna, fine_sandy loam, Co, 2, 2, 20
Covover, loam, 60, 2, 2, 24
F-1
Cut land, , Cu, 0, 4, 0
Del Rey, silt loam, 62, 2, 3, 43
Del Rey, silt loam, 14, 2, 3, 43
Dixboro, fine sandy loam, 43, 2, 2, 20
Dixboro-Kibbie, fine sandy loam, DO, 2, 2, 20
Dumps, , 32, 0, 4, 0
Edwards, muck, 30, 4
Edwards, muck, Ea, 4
Edwards, muck, Ed, 4
Edwards, muck, Ee, 4
Eleva, sandy loam, 55, 2, 2, 2
Eleva, fine sandy loam, 66,
Fill_land, , Fá, 0, 4, 0
Fox, sand, Fa, 1, 2, 24
Fox, loam, Fb, 2, 2, 24
Fox, sandy loam, FC , 2, 2, 24
Fox, eroded, Fq., 2, 2, 24
Fox, sandy loam, Fo, 2, 2, 24
Fox, cobbly sandy loam, Fo, 2, 2, 24
Fulton, silty clay loam, 15, 2, 4, 43
Genesee, loam, Ga., 2, 9, 99
Genesee, sandy loam, Gb, 2, 9, 99
Genesee-Eel, loam, GC, 2, 9, 99
Gilford, sandy loam, 55, 2, 2, 20
Gilford, sandy loam, Gf, 2, 2, 20
Gilford-Colwood, complex, 18, 2, 2, 24
Granby, loamy sand, Gd, 1, 1, 17
Granby, sandy loam, Ge, 2, 1, 17
Granby, loamy sand, 18, 1, 1, 17
Granby, fine sand, Gr, 1, 1, 15
Griffin-Genesee, sandy loam, Gf, 2, 9, 99
Griffin–Sloan, loam, Gg, 2, 9, 99
Griffin–Sloan, sandy loam, Gh, 2, 9, 99
Henrietta, muck, 63, 4, 4, 0
Hillsdale, sandy loam, Ha, 2, 2, 24
Hillsdale-Riddles, sandy loam, 49, 2, 2, 24
Histosols–Aquentis, muck, 47, 4, 4, 0
Houghton, muck, Hb, 4, 1, 0
Houghton, muck, Hn, 4, 1, 0
Houghton, muck, 20, 4, 1, 0
Hoytville, clay loam, HC, 2, 4, 28
Hoytville, mucky_clay loam, Hól, 2, 4, 28
Hoytville, clay loam, He, 2, 4, 28
Hoytville, clay loam, 42, 2, 4, 28
Hoytville, clay, Ho, 3, 4, 28
Hoytville-Wauseon, complex, Hf, 3, 4, 24
Ionia, loam, Ia, 2, 4, 0
Kendalville, loam, Ka, 2, 2, 37
Kendalville, sandy loam, Kb, 2, 2, 37
Kendalville, loam, Kc, 2, 2, 37
Kendalville, loam, Ke, 2, 2, 37
Kerston, loam, Kd, 2, 9, 99
Kibbie, fine sandy loam, 29, 2, 2, 20
Kibbie, sandy loam, Ke, 2, 2, 20
Kibbie, sandy loam, 28, 2, 2, 20
, 2, 0
, 2, 0
, 2, 0
, 2, 0
, 2
4
2, 2, 17
F-2
Kibbie, fine sandy loam, Kn, 2, 2, 20
Kidder, sandy loam, Kr, 2, 2, 24
Kokomo-Barry, loam, Kf, 2, 9, 99
Kokomo-Barry–Wallkill, loam, Kg, 2, 9, 99
Lake, , La., 0, 4, 0
Lamson-Colwood, fine sandy loam, Ln, 2, 4, 28
Lenawee, silt loam, 40, 2, 2, 28
Lenawee, silty clay loam, Lb, 2, 2, 28
Lenawee, silty clay loam, 10, 2, 2, 28
Lenawee, silty clay loam, 21, 2, 2, 28
Lenawee–Urban, urban, 57, 0, 2, 28
Leoni, gravelly sandy loam, 44, 2, 2, 10
Linwood, muck, Lc, 4, 2, 0
Macomb, sandy loam, Ma, 2, 2, 28
Macomb, loam, Mc, 2, 2, 28
Macomb–Hoytville, sandy clay loam, Mb, 2, 2, 28
Made land, , Mb, 0, 4, 0
Marlette-Owosso, complex, 64, 2, 2, 28
Martisco, muck, 45, 2, 4, 0
Matherton, sandy loam, Md, 2, 2, 20
Maumee, loamy sand, Mc, 1, 9, 99
Metamora, sandy loam, 23, 2, 2, 20
Metamora, sandy loam, Me, 2, 2, 20
Metamora-Corunna, sandy loam, 17, 2, 2, 20
Metamora-Pewamo, sandy loam, Mf, 2, 2, 24
Metea, sand, 41, 1, 2, 17
Metea, loamy sand, Mh, 1, 2, 17
Miami, loam, Md, 2, 2, 37
Miami, loam, Mf, 2, 2, 37
Miami, loam, Mm, 1, 2, 37
Miami-Boyer, sandy loam, Me, 1, 2, 27
Miami-Boyer, sandy loam, Mg, 2, 2, 27
Millsdale, clay loam, 47, 2, 2, 32
Milton, clay loam, 26, 2, 3, 37
Morley, loam, Mh, 2, 1, 37
Morley, loam, Mk, 2, 1, 37
Morley, loam, Mo, 2, 1, 37
Napoleon, muck, 48, 4, 1, 0
Nappanee, silt loam, Na, 2, 4, 43
Nappanee, clay, Na, 3, 4, 43
Nappanee, loam, Nb, 2, 4, 43
Nappanee, loam, 43, 2, 4, 43
Oakville, fine sand, 11, 1, 1, 15
Oakville, fine_sand, 49, 1, 1, 15
Oakville, fine sand, Oa, 1, 1, 15
Oakville–Urban, urban, 58, 0, 1, 15
Ogden, muck, Oa, 4, 4, 0
Ormas-Spinks, complex, 14, 1, 1, 17
Oshtemo, loamy sand, Ob, 1, 2, 24
Oshtemo, loamy sand, Os, 1, 2, 24
Oshtemo-Leoni, complex, 68, 1, 2, 17
Oshtemo-urban, urban, 58, 0, 2, 24
Ottawa, loamy sand, OC, 1, 9, 99
Ottokee, fine_sand, 37, 1, 1, 15
Ottokee, fine_sand, 50, 1, 1, 17
F-3
Palms, muck, 37, 4, 4
Palms, muck, Pa, 4, 4
Palms, muck, Pa, 4, 4
Pella, loam, PC, 2, 4, 28
Pewamo, clay loam, Pb, 2, 3
Pewamo, clay loam, 22, 2, 3
Pewamo, clay, Pe, 3, 3, 24
Pits, gravel, 52, 1, 4, 0
Pits, quarries, 53, 0, 4, 0
Pits-Aquents, , 33, 0, 4, 0
Pits-Quarries, , 51, 0, 4, 0
Plainfield–Berrien, loamy sand, Pd, 1, 9, 99
Plainfield–Ottawa, loamy sand, Pe, 1, 9, 99
Randolf, clay loam, 25, 2, 4, 37
Riddles, sandy loam, 42, 2, 2, 24
Riddles, sandy loam, Rd, 2, 2, 24
Riddles-Leoni, complex, 56, 2, 2, 17
Riddles–urban, urban, 59,0, 2, 24
Rifle, peat, Ra, 4, 4, 0
Rollin, muck, Rb, 4, 4, 0
Saylesville, silt loam, 61, 2, 3, 37
Sebewa, loam, 46, 2, 2, 24
Selfridge, loamy sand, 19, 1, 3, 17
Selfridge, loamy sand, Se, 1, 3, 17
Selfridge-Pewamo, complex, 20, 2, 3, 20
Selfridge-Pewamo, complex, 59, 2, 3, 20
Selfridge-Pewamo, complex, Sf, 2, 3, 17
Seward, loamy fine sand, Se, 1, 2, 17
Seward, loam, Sf, 2, 2, 17
Seweba, loam, Sa, 2, 2, 24
Seweba, sandy loam, Sb, 2, 2, 24
Seweba, loam, Sb, 2, 2, 24
Shoals, clay, Sh, 3, 2, 37
Sisson, fine sandy loam, Sn, 2, 2, 24
Sloan, loam, 30, 2, 2, 37
Sloan, loam, So, 2, 4, 28
Spinks, loamy sand, 12, 1, 1, 17
Spinks, loamy sand, Sp, 1, 1, 17
Spinks-Boyer—-Plainfield-Hillsd, complex, Sc, 1, 1, 17
Spinks-Oshtemo, loamy sand, Sr, 1, 1, 17
StClair, loam, Sq, 2, 4, 37
StClair, loam, Se, 2, 4, 37
StClaire, clay, St, 3, 4, 37
Tawas, muck, Ta, 4, 4, 0
Teasdale, fine sandy loam, 15, 2, 2, 24
Tedrow, loamy sand, 16, 1, 2, 17
Tedrow, loamy fine_sand, Te, 1, 2, 17
Tedrow, loamy fine sand, Tf, 1, 2, 17
Thetford, loamy sand, 40, 1, 1, 17
Thetford, loamy sand, Th, 1, 1, 17
Toledo, silty clay loam, 48, 2, 4, 28
Udorthentis, , 51, 0, 4, 0
Udorthents–urban, urban, 60, 0, 4, 0
Urban, urban, 63, 0, 4, 0
Wallkill, loam, Wa, 2, 9, 99
, 0
, 0
, 0
, 2
, 24
, 24
F-4
Warners, muck, Wh, 4, 4, 0
Warners, silt loam, 52, 2, 4, 43
Wasepi, sandy loam, 44, 2, 2, 20
Wasepi, sandy loam, Wa, 2, 2, 20
Wasepi, loamy sand, We, 1, 2, 17
Wauseon, loam, WC, 2, 2, 20
Wauseon, fine sandy loam, Ws, 2, 2, 20
Whalan, loam, 67, 2, 2, 32
Willette, muck, Wa, 4, 4, 0
Ypsi, sandy loam, Yp, 2, 3, 24
Ypsi-Wauseon, complex, 39, 2, 3, 24
Owosso–Miami, complex, Ow, 2, 2, 24
Water, water, W, 0, 0, 0
Appendix G: The MASTER.AUD batch file
* * = * *m, ºm mºm ºm me - m ºs º- “m * - - sº “ - ºn m * * - - sº m ºm me sº- * = * * = * * * * * * * - * * *-* * * - amº m - me sm - mºms sm - sºms m - mºss amº - mºre º- -
>ERDAS|NOMENU
ERDS-
>del vº.gis
press any key to continue
º
ERD-
>del V’ - trl
press any key to continue
P
ERD-
>del vº. sta
press any key to continue
º
ERD-
>gismo
GISMO –– GIS Modeling
Version 7. 4.03.447
# 1
72 111 0 Land. dsc->vars
Enter Model Library filename:
Pagnps
# 1
72 129 0 slope->var4
7 129 0 Slope->Varó
T2 13 O O LAND->CODE
2 13 O LAND->CODE
Enter Land filename:
'Pland
# 1
:
i
Enter maximum output file value for clipping
217
# O
# 1
2 130 O SOIL->HYDROCON
2 13 O SOIL–S-HYDROCON
Enter Soil filename:
2soils
# 1
:
i
Enter maximum output file value for clipping
G-1
20 O SOIL->HYDROCON
ERD-
>matrix
MATRIX –– GIS Class Matrix
Version 7. 4.02 .434
Enter Input GIS filename:
?lcode
# 1
# 1
Is this the variable you wanted 2
2Yes
Enter Input GIS filename:
'Phydcon
# 1
# 1
Is this the variable you wanted 2
'PYes
Enter output matrix size (columns, rows)
217, 4
Enter range of values to recode (-1 to recode as is)
21, 17
Enter recode option:
Individual, Block, or Offset
2Individual
71
22
73
T24
75
26
27
28
79
T210
T211
712
713
214
715
716
Press any key to continue
º
217
Recode another range of values?
T2NO
Enter range of values to recode (-1 to recode as is)
21, 4
Enter recode option:
Individual, Block, or Offset
2Individual
71
TP2
73
T24
Recode another range of values?
2NO
Recode the matrix output
2Yes
Enter range of values to recode (–1 to recode as is)
20, 68
Enter recode option:
Individual, Block, or Offset
2Individual
O O O
T2100
1 1 1
277
2 2 1.
289
3 3 1
798
4 4 1
230
5 5 1
249
6 6 1
G-3
272
T243
268
249
7.59
735
235
225
736
2100
10
11
12
13
14
15
Press
285
785
285
292
298
258
269
781
16
17
18
19
20
21
22
23
24
10
11
12
13
14
15
any key to
16
17
continue
G-4
765
25
279
26
769
27
774
28
756
29
256
30
755
31
760
Press
P
32
TP100
33
785
34
785
35
790
36
794
37
298
38
271
39
279
40
788
41
776
10
11
12
13
14
to
15
16
17
any key
continue
G-5
42
TP86
43
279
44
282
45
270
46
270
47
270
Press
P
48
273
49
TP100
50
785
51
10
11
12
13
to
14
15
16
17
285
52
292
53
292
54
298
55
278
56
7284
57
7.91
58
282
59
any key
continue
G-6
TP89
60 9 4
T284
61 10 4
786
62 11 4
277
63 12 4
277
Press any key to continue
P
64 13 4
277
65 14 4
279
66 15 4
7100
67 16 4
785
68 17 4
285
Do you want to recode another range of values?
T2NO
Enter Output GIS filename:
2VAR3
# O
# 0
Please enter a description for the new GIS variable
2SCS Curve Numbers
Do you want to enter class names for the output variable
T2NO
Use the whole image?
72Yes
\
ERD-
>del loode. *
>del hydcon. *
ERD-
>gismo
GISMO –– GIS Modeling
Version 7. 4.03.447
# 1
G-7
2 111
Enter
Pagnps
# 1
Tº 13
Enter
º
l
alſ]
d
Enter
2100
# 0
# 4
Enter
TP100
# O
# 4
Enter
2100
# O
# 4
Enter
2250
# 0
# 4
Enter
T24
# O
# 1
Tº 129
TP 129
2 13
Enter
2 slope
# 1
:
Enter
7400
# 0
# 1
0 Land. dsc->vars
Model Library filename:
0 Land. dsc->vars
land filename:
maximum output file value for clipping
maximum output file value for clipping
maximum output file value for clipping
maximum output file value for clipping
maximum output file value for Clipping
0 slope->var4
0 Slope–-Varó
0 Slope–-Varó
slope class filename:
maximum output file value for clipping
G-8
Tº 130 O LAND->CODE
7 130 O SOIL->HYDROCON
7 130 0 Soil. dsc->vars
TP 1.3 0 Soil. dsc->vars
# 1
# 1
# 1
# 1
# O
# 0
# 1
# 1
# 4
Enter maximum output file value for clipping
2100
# 0
# 4
Enter maximum output file value for clipping
T24
# 0
# 1
7 120 0 Soil. dsc->vars
\
ERD-
>noaud
G-9
Appendix H: The SUB1.AuD batch file
>ERDAS|NOMENU
ERD-
>GRDPOL
GRDPOL –– Grid Polygons
Version 7.4.02 .445
Enter Output GIS filename:
TPSUB
# 0
Get Input Polygon file names from the Keyboard, Name file or
Directory selection
2Keyboard
Enter Input Polygon filename:
2SUB
# 1
TPNO
Enter pixel size (x, y) for Output
21320, 1320
Enter upper left map X coordinate
º
Enter upper left map Y coordinate
º
Enter lower right map X coordinate
º
Enter lower right map Y coordinate
º
Enter the number of classes
TP1900
Is this correct?
T2Yes
Enter initialization value
2O
# 1
\
ERDP-
>ERDAS|NOMENU
ERDS.
>copy sub. GIS varl. lan
ERD-
>copy sub. GIS vars . lan
ERD-
>copy sub. GIS varS. lan
ERDP-
>copy sub. GIS varó. lan
ERD-
>copy sub. GIS varS. lan
ERD-
>copy sub. GIS varl O. lan
ERDP-
>copy Sub. GIS varl1. lan
ERD-
>copy sub. GIS varl2. lan
ERD-
>copy sub. GIS varl3. lan
ERD-
>copy sub. GIS varl S. lan
ERD-
>copy sub. GIS varló. lan
ERD-
>copy sub. GIS varl'7. lan
ERD-
>copy sub. GIS varl 8. lan
ERD-
>copy sub. GIS varl 9. lan
ERDS-
>copy sub. GIS var20. lan
ERD-
>copy sub. GIS var21. lan
ERDS.
>copy sub. GIS land.gis
ERD-
>copy . . \land. dsc
ERD-
>copy sub. GIS river.gis
ERD-
>copy sub. GIS elev. lan
ERDS-
>copy sub. GIS elevix. lan
ERD-
>Copy Sub.gis hydro.gis
ERD-
>LRECTIFY
LRECTIFY —— Linear Rectification
Version 7.4.04. 445
Rectify a GIS or Image file?
2Image
Enter Input Image filename:
2 . . \VAR3 . GIS
# 1
Enter Option :
2G – Read . CFN File
Enter Coefficient filename:
2 . . \hires
# 1
Enter Option :
2J – Proceed
Enter Output Image filename:
2VAR3
# 1
Overwrite the file?
72Yes
Map coordinates of upper left corner (X, Y)
H-2
º
Map coordinates of lower right corner (X, Y)
2
Should zeroes from the input file overwrite values
in the output file 2
T2Yes
Available techniques for data resampling:
Nearest Neighbor
Bilinear Interpolation
Cubic Convolution
Use which technique?
2Bilinear Interpolation
\
ERD-
>LRECTIFY
LRECTIFY —— Linear Rectification
Version 7.4.04. 445
Rectify a GIS or Image file?
2Image
Enter Input Image filename:
2 . . \VAR6. GIS
# 1
Enter Option
2G – Read . CFN File
Enter Coefficient filename:
2 . . \HIRES
# 1
Enter Option
2J – Proceed
Enter Output Image filename:
TVAR6
# 1
Overwrite the file?
T2Yes
Map coordinates of upper left Corner (X, Y)
2
Map coordinates of lower right corner (X, Y)
2
Should zeroes from the input file overwrite values
in the output file 2
72Yes
Available techniques for data resampling:
Nearest Neighbor
Bilinear Interpolation
Cubic Convolution
H-3
Use which technique?
2Bilinear Interpolation
\
ERD-
>LRECTIFY
LRECTIFY –– Linear Rectification
Version 7.4.04. 445
Rectify a GIS or Image file?
2Image
Enter Input Image filename:
7 . . \VAR9. GIS
# 1
Enter Option
2G – Read . CFN File
Enter Coefficient filename:
2 . . \HIRES
# 1
Enter Option
2J – Proceed
Enter Output Image filename:
2VAR9
# 1
Overwrite the file?
T2Yes
Map coordinates of upper left corner (X, Y)
º
Map coordinates of lower right corner (X, Y)
2
Should zeroes from the input file Overwrite values
in the output file 2
2Yes
Available techniques for data resampling:
Nearest Neighbor
Bilinear Interpolation
Cubic Convolution
Use which technique?
2Bilinear Interpolation
\
ERD-
>LRECTIFY
LRECTIFY —— Linear Rectification
Version 7.4.04. 445
Rectify a GIS or Image file?
2Image
Enter Input Image filename:
2 . . \VAR10. GIS
# 1
Enter Option
2G – Read . CFN File
Enter Coefficient filename:
2 . . \HIRES
# 1
Enter Option
2J – Proceed
Enter Output Image filename:
2VAR10
# 1
Overwrite the file?
T2Yes
Map coordinates of upper left corner (X, Y)
º
Map coordinates of lower right corner (X, Y)
2
Should zeroes from the input file overwrite values
in the output file 2
2Yes
Available techniques for data resampling:
Nearest Neighbor
Bilinear Interpolation
Cubic Convolution
Use which technique?
2Bilinear Interpolation
\
ERD-
>LRECTIFY
LRECTIFY —— Linear Rectification
Version 7.4.04.445
Rectify a GIS or Image file?
2Image
Enter Input Image filename:
2 . . \VAR11 . GIS
# 1
Enter Option
2G – Read . CFN File
Enter Coefficient filename:
2 . . \HIRES
# 1
Enter Option :
20 – Proceed
Enter Output Image filename:
2VAR11
# 1
Overwrite the file?
T2Yes
Map coordinates of upper left Corner (X, Y)
º
Map coordinates of lower right corner (X, Y)
2
Should zeroes from the input file overwrite values
in the output file 2
T2Yes
Available techniques for data resampling:
Nearest Neighbor
Bilinear Interpolation
Cubic Convolution
Use which technique?
2Bilinear Interpolation
\
ERD-
>LRECTIFY
LRECTIFY —— Linear Rectification
Version 7.4.04. 445
Rectify a GIS or Image file?
?Image
Enter Input Image filename:
7 . . \VAR13 . GIS
# 1
Enter Option
2G – Read . CFN File
Enter Coefficient filename:
2 . . \HIRES
# 1.
Enter Option
20 – Proceed
Enter Output Image filename:
TVAR13
# 1
Overwrite the file?
T2Yes
Map coordinates of upper left Corner (X, Y)
º
Map coordinates of lower right corner (X, Y)
2
Should zeroes from the input file overwrite values
in the output file 2
T2Yes
Available techniques for data resampling:
Nearest Neighbor
Bilinear Interpolation
Cubic Convolution
Use which technique?
2Bilinear Interpolation
\
ERD-
>LRECTIFY
LRECTIFY –– Linear Rectification
Version 7.4.04.445
Rectify a GIS or Image file?
2Image
Enter Input Image filename:
2 . . \VAR15 . GIS
# 1
Enter Option
2G – Read . CFN File
Enter Coefficient filename:
7 . . \HIRES
# 1
Enter Option
2J – Proceed
Enter Output Image filename:
TVAR15
# 1
Overwrite the file?
T2Yes
Map coordinates of upper left corner (X, Y)
º
Map coordinates of lower right corner (X, Y)
2
Should zeroes from the input file overwrite values
in the output file 2
T2Yes
Available techniques for data resampling:
Nearest Neighbor
Bilinear Interpolation
Cubic Convolution
Use which technique?
2Bilinear Interpolation
\
ERD->
>LRECTIFY
LRECTIFY —— Linear Rectification
Version 7.4.04. 445
Rectify a GIS or Image file?
2Image
Enter Input Image filename:
2 . . \VAR16. GIS
# 1
Enter Option
2G – Read . CFN File
H-7
Enter Coefficient filename:
2 . . \HIRES
# 1
Enter Option
2J – Proceed
Enter Output Image filename:
TPVAR16
# 1
Overwrite the file?
T2Yes
Map coordinates of upper left corner (X, Y)
º
Map coordinates of lower right corner (X, Y)
2
Should zeroes from the input file overwrite values
in the output file 2
T2Yes
Available techniques for data resampling:
Nearest Neighbor
Bilinear Interpolation
Cubic Convolution
Use which technique?
2Bilinear Interpolation
\
ERD-
>LRECTIFY
LRECTIFY —— Linear Rectification
Version 7.4.04. 445
Rectify a GIS or Image file?
'PImage
Enter Input Image filename:
2 . . \VAR20. GIS
# 1
Enter Option
2G – Read . CFN File
Enter Coefficient filename:
2 . . \HIRES
# 1
Enter Option
20 – Proceed
Enter Output Image filename:
2VAR20
# 1
Overwrite the file?
T2Yes
Map coordinates of upper left Corner (X, Y)
2
H-8
Map coordinates of lower right corner (X, Y)
2
Should zeroes from the input file overwrite values
in the output file 2
T2Yes
Available techniques for data resampling:
Nearest Neighbor
Bilinear Interpolation
Cubic Convolution
Use which technique?
2Bilinear Interpolation
\
ERD-
>LRECTIFY
LRECTIFY —— Linear Rectification
Version 7.4.04. 445
Rectify a GIS or Image file?
'PImage
Enter Input Image filename:
2 . . \ELEV
# 1
Enter Option
2G – Read . CFN File
Enter Coefficient filename:
2 . . \HIRES
# 1
Enter Option
2J – Proceed
Enter Output Image filename:
TPELEV
# 1
Overwrite the file?
2Yes
Map coordinates of upper left corner (X, Y)
º
Map coordinates of lower right Corner (X, Y)
2
Should zeroes from the input file overwrite values
in the output file 2
T2NO
Available techniques for data resampling:
Nearest Neighbor
Bilinear Interpolation
Cubic Convolution
Use which technique?
2Cubic Convolution
H-9
\
ERD-
>LRECTIFY
LRECTIFY –– Linear Rectification
Version 7.4.04.445
Rectify a GIS or Image file?
2Image
Enter Input Image filename:
2 . . \ELEVX
# 1
Enter Option :
2G – Read . CFN File
Enter Coefficient filename:
2 . . \LORES
# 1
Enter Option
2J – Proceed
Enter Output Image filename:
TPELEVx
# 1
Overwrite the file?
T2Yes
Map coordinates of upper left corner (X, Y)
2
Map coordinates of lower right corner (X, Y)
2
Should zeroes from the input file overwrite values
in the output file 2
TPNO
Available techniques for data resampling:
Nearest Neighbor
Bilinear Interpolation
Cubic Convolution
Use which technique?
2Cubic Convolution
\
ERD-
>SLOPE
SLOPE –– Slope and Aspect
Version 7.5.00.447
Enter Input TOPO filename:
Tº ELEVX
# 1
Topo is in units of Feet, Meters, Other?
T2Feet
Enter Output GIS filename:
TPASPECT
# 0
# 0
H-10
Use the whole image?
2Yes
Compute Slope or Aspect?
'PAspect
Output 8-directional aspect or Degrees
28–directional
\
>ERDAS|MENU
ERD-
>recode
RECODE –– Recode GIS Classes
Version 7. 4.02 .434
Enter Input GIS filename:
'Paspect
# 1
# 1
Is this the variable you wanted?
72Yes
Enter range of values to recode (-1 to recode as is)
20, 9
Enter recode option:
Individual, Block, or Offset
2Individual
Background
2O
East
73
North East
72
North
71
North West
28
West
27
South West
26
South
25
South East
TP4
Flat
70
H-11
Do you want to recode another range of values?
TPNO
Enter Output GIS filename:
2varl A. lan
# 0
# 0
Please enter a description for the new GIS variable
Do you want to enter class names for the output variable
TPNO
Use the whole image?
2Yes
\
ERD-
>slope
SLOPE -- Slope and Aspect
Version 7.5.00.447
Enter Input TOPO filename:
2elev
# 1
Topo is in units of Feet, Meters, Other?
2Other
Enter conversion factor (topo units / map units)
2.1
Enter Output GIS filename:
2var4. lan
# 0
# O
Use the whole image?
2Yes
Compute Slope or Aspect?
2Slope
Output slope as Percent or Degrees?
72 Percent
\
ERDP-
>lrectify
LRECTIFY —— Linear Rectification
Version 7.4.04. 445
Rectify a GIS or Image file?
TPGIS
Enter Input GIS filename:
2 . . \land
# 1
Enter Option
2G – Read . CFN File
Enter Coefficient filename:
2 . . \HIRES
# 1
Enter Option
H-12
720 – Proceed
Enter Output GIS filename:
2land
# 1
Overwrite the file?
72Yes
Map coordinates of upper left corner (X, Y)
º
Map coordinates of lower right corner (X, Y)
2
Should zeroes from the input file overwrite values
in the output file 2
72Yes
# 1
# 0
\
ERD-
>grópol
GRDPOL –– Grid Polygons
Version 7. 4.02 .445
Enter Output GIS filename:
2hydro
# 1
Get Input Polygon file names from the Keyboard, Name file or
Directory selection
2Keyboard
Enter Input Polygon filename:
2 . . \river
# 1
TPNO
\
ERDP-
>gismo
GISMO –– GIS Modeling
Version 7. 4.03.447
# 1
7 111 0 Land. dsc->vars
Enter Model Library filename:
. . \agnps
1
130 0 CHslope
13 0 CHslope
H-13
Enter maximum output file value for clipping
Enter maximum output file value for clipping
Tº 129 0 Ch Ind
T 13 0 Ch Ind
Enter riv filename:
Enter land filename:
Enter maximum output file value for clipping
TP 120 0 Ch Ind
\
ERD-
>del aspect. *
press any key to
2
ERD-
>del land. *
press any key to
º
ERDP-
>del elev. *
press any key to
2
ERDP-
>del buffer. *
press any key to
º
ERDP-
>del hydro. *
press any key to
continue
continue
continue
continue
continue
H-14
7
ERDP-
>del elevix. *
press any key to continue
º
ERD-
>del river. *
press any key to continue
7
ERD-
>lrectify
LRECTIFY —— Linear Rectification
Version 7.4.04.445
Rectify a GIS or Image file?
TPGIS
Enter Input GIS filename:
2 . . \rivasp
# 1
Enter Option :
2G – Read . CFN File
Enter Coefficient filename:
2 . . \rivasp
# 1
Enter Option
2J – Proceed
Enter Output GIS filename:
2varl A. lan
# 1
Overwrite the file?
T2Yes
Map coordinates of upper left Corner (X, Y)
2
Map coordinates of lower right corner (X, Y)
2
Should zeroes from the input file overwrite values
in the output file 2
TPNO
1
1.
:
ERD-
>tabdata
TABDATA – Import from ASCII Data Tabular Format
Version 7.5.01. 449
Enter ASCII Input filename:
2cellid. c.sv
# 1
H-15
Is the input file referenced to map coordinates?
T2NO
Number of columns in the output file
º
Number of rows in the output file
º
Enter starting X position of upper left corner
71
Enter starting Y position of upper left corner
71
Create a 4-bit, 8-bit, or 16-bit output file?
71.
# 0
Press any key to continue
º
Enter LAN filename:
Tºvar 1
# 1
Overwrite the file?
T2Yes
\
ERD-
>copy varl A. trl Varl. trl
ERD->
>noaud
H-16
Appendix I: The SUB2.AUD batch file
>ERDAS|NOMENU
ERD-
>SCall
SCAN –– GIS Filtering
Version 7. 4.02 .449
Enter Input GIS filename:
2varl. lan
# 1
# 1
Is this the variable you wanted?
Tºyes
Enter range of values to change (-1 to use as is)
7–1
Use the whole image?
2Yes
Use polygon file to restrict scan area
TPNO
Use gis file as mask for scan
TPNO
Enter option
2A = Total
Select Border option (Duplication or Initialization)
2Duplication
Enter scan window option (Rectangle, Circle, Donut, or Arbitrary
window)
2Arbitrary window
Enter box dimensions (X, Y)
23, 3
48
49
48
48
48
48
48
48
48
120
:.2
Is this window correct?
T2Yes
Exclude zero input values from analysis
TPNO
I-1
Enter Output GIS filename:
2north
# 0
# 0
Please enter a description for the new GIS variable
2
\
ERD-
>SCall
SCAN –– GIS Filtering
Version 7.4.02 .449
Enter Input GIS filename:
2varl. lan
# 1
# 1
Is this the variable you wanted?
T2Yes
Enter range of values to change (-1 to use as is)
7–1
Use the whole image?
2Yes
Use polygon file to restrict scan area
T2NO
Use gis file as mask for scan
T2NO
Enter option
2A = Total
Select Border option (Duplication or Initialization)
2Duplication
Enter scan window option (Rectangle, Circle, Donut, or Arbitrary
window)
2Arbitrary window
Enter box dimensions (X, Y)
23, 3
48
48
49
48
48
48
48
48
48
120
.2:2.
I-2
Is this window correct?
2Yes
Exclude zero input values from analysis
TPNO
Enter Output GIS filename:
2neast
# 0
# 0
Please enter a description for the new GIS variable
2
\
ERD-
> S Carl
SCAN –– GIS Filtering
Version 7.4.02 .449
Enter Input GIS filename:
2varl. lan
# 1
# 1
Is this the variable you wanted?
T2Yes
Enter range of values to change (-1 to use as is)
7–1
Use the whole image?
2Yes
Use polygon file to restrict scan area
TPNO
Use gis file as mask for scan
T2NO
Enter option
2A = Total
Select Border option (Duplication or Initialization)
2Duplication
Enter scan window option (Rectangle, Circle, Donut, or Arbitrary
window)
2Arbitrary window
Enter box dimensions (X, Y)
23, 3
48
48
48
48
48
49
i
:.
TP 48 O
2 48 O
TP 48 O
2 120 O
Is this window correct?
2Yes
Exclude zero input values from analysis
T2NO
Enter Output GIS filename:
2 east
# 0
# 0
Please enter a description for the new GIS variable
2
\
ERD-
>SCall
SCAN –– GIS Filtering
Version 7. 4.02 .449
Enter Input GIS filename:
2varl. lan
# 1
# 1
Is this the variable you wanted?
T2Yes
Enter range of values to change (-1 to use as is)
7–1
Use the whole image?
T2Yes
Use polygon file to restrict scan area
T2NO
Use gis file as mask for scan
T2NO
Enter option
2A = Total
Select Border option (Duplication or Initialization)
2Duplication
Enter scan window option (Rectangle, Circle, Donut, or Arbitrary
window)
2Arbitrary window
Enter box dimensions (X, Y)
23, 3
2 48 O
I-4
48
48
48
48
48
48
48
49
120
.2:;
Is this window correct?
2Yes
Exclude zero input values from analysis
TPNO
Enter Output GIS filename:
2Seast
# 0
# 0
Please enter a description for the new GIS variable
2
\
ERD-
>SCall
SCAN –– GIS Filtering
Version 7. 4.02 .449
Enter Input GIS filename:
2varl. lan
# 1
# 1
Is this the variable you wanted?
72Yes
Enter range of values to change (-1 to use as is)
2–1
Use the whole image?
2Yes
Use polygon file to restrict scan area
TPNO
Use gis file as mask for scan
TPNO
Enter option
2A = Total
Select Border option (Duplication or Initialization)
2Duplication
Enter scan window option (Rectangle, Circle, Donut, or Arbitrary
window)
I-5
2Arbitrary window
Enter box dimensions (X, Y)
23, 3
48
48
48
48
48
48
49
48
120
222222222
Is this window correct?
T2Yes
Exclude zero input values from analysis
TPNO
Enter Output GIS filename:
2south
# 0
# 0
Please enter a description for the new GIS variable
2
\
ERD->
>SCall
SCAN –– GIS Filtering
Version 7. 4.02 .449
Enter Input GIS filename:
2varl. lan
# 1
# 1
Is this the variable you wanted?
T2Yes
Enter range of values to change (-1 to use as is)
2–1
Use the whole image?
T2Yes
Use polygon file to restrict scan area
TPNO
Use gis file as mask for scan
T2NO
Enter option
2A = Total
Select Border option (Duplication or Initialization)
2Duplication
Enter scan window option (Rectangle, Circle, Donut,
window)
2Arbitrary window
Enter box dimensions (X, Y)
23, 3
TP 48 O
TP 48 O
Tº 48 O
Tº 48 O
TP 48 O
TP 48 O
TP 49 O
TP 48 O
7 48 O
TP 120 O
Is this window correct?
72Yes
Exclude zero input values from analysis
T2NO
Enter Output GIS filename:
2SWest
# 0
# 0
Please enter a description for the new GIS variable
2
\
ERD-
> S Carl
SCAN –– GIS Filtering
Version 7. 4.02 .449
Enter Input GIS filename:
2varl. lan
# 1
# 1
Is this the variable you wanted?
T2Yes
Enter range of values to change (-1 to use as is)
2–1
Use the whole image?
72Yes
Use polygon file to restrict scan area
TPNO
Use gis file as mask for Scan
TPNO
Enter option
or Arbitrary
I-7
7A = Total
Select Border option (Duplication or Initialization)
2Duplication
Enter scan window option (Rectangle, Circle, Donut, or Arbitrary
window)
?Arbitrary window
Enter box dimensions (X, Y)
23, 3
7 48 O
TP 48 O
TP 48 O
2 49 O
TP 48 O
TP 48 O
2 48 O
TP 48 O
TP 48 O
7 120 O
Is this window correct?
T2Yes
Exclude zero input values from analysis
TPNO
Enter Output GIS filename:
2West
# 0
# 0
Please enter a description for the new GIS variable
2
\
ERD-
>SCall
SCAN —— GIS Filtering
Version 7. 4.02 .449
Enter Input GIS filename:
2varl. lan
# 1
# 1
Is this the variable you wanted?
T2Yes
Enter range of values to Change (-1 to use as is)
2–1
Use the whole image?
T2Yes
Use polygon file to restrict scan area
7NO
I-8
Use gis file as mask for scan
TPNO
Enter option
2A = Total
Select Border option (Duplication or Initialization)
2Duplication
Enter scan window option (Rectangle, Circle, Donut, or Arbitrary
window)
2Arbitrary window
Enter box dimensions (X, Y)
23, 3
49
48
48
48
48
48
48
48
48
120
2222222222
Is this window correct?
TPYes
Exclude zero input values from analysis
TPNO
Enter Output GIS filename:
2nWest
# 0
# 0
Please enter a description for the new GIS variable
2
\
ERDP-
>noaud
Appendix J: The SUB3.AUD batch file
>ERDAS|NOMENU
ERDS-
> . . \del2
ERD-
>fixhed
FIXHED -- Fix Header
Version 7.4.02 .434
Is this an Image file or a GIS file?
2Image
Enter Image filename:
T2a
# 1
Number of columns in this data set
º
Number of rows in this data set
º
Number of bands in this data set
222
Enter starting X position of upper left corner
71.
Enter starting Y position of upper left corner
21
Is data packed as 4-bit, 8-bit, or 16-bit?
216 bit
Is data base associated with any type of map?
(i.e., georeferenced or rectified)
T2Yes
Available Coordinate Types:
1 – UTM 99 = Other Map
2 = State Plane 100 = Geographic (Lat/Lon)
3 = Albers Conical Equal Area
4 = Lambert Conformal Conic
5 = Mercator
6 = Polar Stereographic
7 = Polyconic
8 = Equidistant Conic
9 = Transverse Mercator
10 = Stereographic
11 = Lambert Azimuthal Equal Area
12 = Azimuthal Equidistant
13 = Gnomonic
14 = Orthographic
15 = General Vertical Near-Side Perspective
16 = Sinusoidal
17 = Equirectangular
18 = Miller Cylindrical
Van der Grinten
Oblique Mercator
1
9
-
J-1
Specify coordinate type
22
Enter X map coordinate of upper left cell
7
Enter Y map coordinate of upper left cell
T
Enter X cell size
º
Enter Y cell size
2
What are the units of area?
(Acres, Hectares, Other Units, or None)
2ACres
Enter no. of Acres per cell
º
\
ERDS-
>subset
SUBSET –– Subset/Mosaic Files
Version 7. 4.01. 434
Image or GIS file?
2Image
Enter Input Image filename:
2varl
# 1
Use the whole image?
2Yes
Enter Output Image filename:
2a
# 1
Overwrite the file?
T2Yes
Enter output file coordinates at which to place
upper left corner of input subset
21, 1
Should input zero values Overwrite data in the output file?
T2Yes
Copy all bands in Order?
T2Yes
\
ERDP-
>subset
SUBSET –– Subset/Mosaic Files
Version 7. 4.01. 434
Image or GIS file?
2Image
Enter Input Image filename:
2var2
J-2
# 1
Use the whole image?
2Yes
Enter Output Image filename:
T2a
# 1
Overwrite the file?
2Yes
Enter output file coordinates at which to place
upper left corner of input subset
21, 1
Should input zero values overwrite data in the output file?
72Yes
Copy all bands in order?
TPNO
For input band 1, enter output band
72
\
ERD-
>subset
SUBSET –– Subset/Mosaic Files
Version 7.4.01. 434
Image or GIS file?
'PImage
Enter Input Image filename:
2Var3
# 1
Use the whole image?
T2Yes
Enter Output Image filename:
T2a.
# 1
Overwrite the file?
TPYes
Enter output file coordinates at which to place
upper left corner of input subset
21, 1
Should input zero values overwrite data in the output file?
2Yes
Copy all bands in order?
TPNO
For input band 1, enter output band
TP4
\
ERD-
>subset
SUBSET –– Subset/Mosaic Files
Version 7. 4.01. 434
Image or GIS file?
2Image
Enter Input Image filename:
2var4
# 1
Use the whole image?
T2Yes
Enter Output Image filename:
2a
# 1
Overwrite the file?
72Yes
Enter output file coordinates at which to place
upper left corner of input subset
21, 1
Should input zero values overwrite data in the output file?
T2Yes
Copy all bands in order?
TPNO
For input band 1, enter output band
25
\
ERD-
>subset
SUBSET –– Subset/Mosaic Files
Version 7. 4.01. 434
Image or GIS file?
'PImage
Enter Input Image filename:
72 Varb
# 1
Use the whole image?
2Yes
Enter Output Image filename:
T2a
# 1
Overwrite the file?
2Yes
Enter output file coordinates at which to place
upper left corner of input subset
21, 1
Should input zero values Overwrite data in the output file?
TPYes
Copy all bands in Order?
T2NO
For input band 1, enter output band
TP6
J-4
\
ERD-
>subset
SUBSET –– Subset/Mosaic Files
Version 7.4.01. 434
Image or GIS file?
2Image
Enter Input Image filename:
2var6
# 1
Use the whole image?
T2Yes
Enter Output Image filename:
T2a
# 1
Overwrite the file?
T2Yes
Enter output file coordinates at which to place
upper left corner of input subset
21, 1
Should input zero values overwrite data in the output file?
T2Yes
Copy all bands in order?
TPNO
For input band 1, enter output band
27
\
ERD-
>subset
SUBSET –– Subset/Mosaic Files
Version 7.4.01. 434
Image or GIS file?
2Image
Enter Input Image filename:
TPVar'7
# 1
Use the whole image?
T2Yes
Enter Output Image filename:
2a
# 1
Overwrite the file?
T2Yes
Enter output file coordinates at which to place
upper left corner of input subset
21, 1
Should input zero values Overwrite data in the output file?
72Yes
Copy all bands in order?
TPNO
For input band 1, enter output band
221
\
ERDS-
>subset
SUBSET –– Subset/Mosaic Files
Version 7. 4.01. 434
Image or GIS file?
2Image
Enter Input Image filename:
Tºvar8
# 1.
Use the whole image?
2Yes
Enter Output Image filename:
2a
# 1
Overwrite the file?
T2Yes
Enter output file coordinates at which to place
upper left corner of input subset
21, 1
Should input zero values overwrite data in the output file?
T2Yes
Copy all bands in order?
TPNO
For input band 1, enter output band
222
\
ERD-
>subset
SUBSET -- Subset/Mosaic Files
Version 7. 4.01. 434
Image or GIS file?
2Image
Enter Input Image filename:
2varS
# 1
Use the whole image?
TPYes
Enter Output Image filename:
T2a
# 1.
Overwrite the file?
T2Yes
Enter output file coordinates at which to place
J–6
upper left corner of input subset
21, 1
Should input zero values overwrite data in the output file?
T2Yes
Copy all bands in order?
T2NO
For input band 1, enter output band
78
\
ERD-
>subset
SUBSET –– Subset/Mosaic Files
Version 7.4.01. 434
Image or GIS file?
2Image
Enter Input Image filename:
Tºvar 10
# 1
Use the whole image?
TPYes
Enter Output Image filename:
T2a
# 1
Overwrite the file?
TPYes
Enter output file coordinates at which to place
upper left corner of input subset
21, 1
Should input zero values overwrite data in the output file?
T2Yes
Copy all bands in order?
TPNO
For input band 1, enter output band
29
\
ERDP-
>subset
SUBSET –– Subset/Mosaic Files
Version 7.4.01. 434
Image or GIS file?
2Image
Enter Input Image filename:
2varl1
# 1.
Use the whole image?
T2Yes
Enter Output Image filename:
2a
J-7
# 1
Overwrite the file?
T2Yes
Enter output file coordinates at which to place
upper left corner of input subset
21, 1
Should input zero values overwrite data in the output file?
T2Yes
Copy all bands in order?
TPNO
For input band 1, enter output band
710
\
ERD-
>subset
SUBSET –– Subset/Mosaic Files
Version 7. 4.01. 434
Image or GIS file?
2Image
Enter Input Image filename:
2Varl2
# 1
Use the whole image?
TPYes
Enter Output Image filename:
T2a
# 1.
Overwrite the file?
72Yes
Enter output file coordinates at which to place
upper left corner of input subset
21, 1
Should input zero values overwrite data in the output file?
72Yes
Copy all bands in order?
2NO
For input band 1, enter output band
211
\
ERD-
>subset
SUBSET –– Subset/Mosaic Files
Version 7.4.01. 434
Image or GIS file?
2Image
Enter Input Image filename:
2varl3
J–8
# 1
Use the whole image?
TPYes
Enter Output Image filename:
T2a
# 1
Overwrite the file?
T2Yes
Enter output file coordinates at which to place
upper left corner of input subset
21, 1
Should input zero values overwrite data in the output file?
T2Yes
Copy all bands in order?
T2NO
For input band 1, enter output band
712
\
ERD-
>subset
SUBSET -- Subset/Mosaic Files
Version 7.4.01. 434
Image or GIS file?
2Image
Enter Input Image filename:
2varl A.
# 1
Use the whole image?
T2Yes
Enter Output Image filename:
T2a
# 1
Overwrite the file?
2Yes
Enter Output file coordinates at which to place
upper left corner of input subset
21, 1
Should input zero values overwrite data in the output file?
T2Yes
Copy all bands in order?
TPNO
For input band 1, enter output band
23
\
ERDP-
>subset
SUBSET -- Subset/Mosaic Files
Version 7. 4.01. 434
J–9
Image or GIS file?
2Image
Enter Input Image filename:
Tºvar 15
# 1
Use the whole image?
7Yes
Enter Output Image filename:
Tºa
# 1
Overwrite the file?
T2Yes
Enter output file coordinates at which to place
upper left corner of input subset
21, 1
Should input zero values overwrite data in the output file?
T2Yes
Copy all bands in order?
TPNO
For input band 1, enter output band
713
\
ERD-
>subset
SUBSET –– Subset/Mosaic Files
Version 7. 4.01. 434
Image or GIS file?
2Image
Enter Input Image filename:
2varlG
# 1
Use the whole image?
T2Yes
Enter Output Image filename:
T2a
# 1
Overwrite the file?
T2Yes
Enter Output file coordinates at which to place
upper left corner of input subset
21, 1
Should input zero values overwrite data in the output file?
TPYes
Copy all bands in order?
TPNO
For input band 1, enter output band
214
J–10
\
ERDS-
>subset
SUBSET –– Subset/Mosaic Files
Version 7.4.01. 434
Image or GIS file?
2Image
Enter Input Image filename:
2Varl 7
# 1
Use the whole image?
T2Yes
Enter Output Image filename:
T2a
# 1
Overwrite the file?
T2Yes
Enter output file coordinates at which to place
upper left corner of input subset
21, 1
Should input zero values overwrite data in the output file?
TPYes
Copy all bands in order?
TPNO
For input band 1, enter output band
215
\
ERD-
>subset
SUBSET -- Subset/Mosaic Files
Version 7.4.01. 434
Image or GIS file?
2Image
Enter Input Image filename:
2Varl 8
# 1
Use the whole image?
2Yes
Enter Output Image filename:
2a
# 1
Overwrite the file?
TPYes
Enter Output file coordinates at which to place
upper left corner of input subset
21, 1
Should input zero values overwrite data in the output file?
T2Yes
J–11
Copy all bands in order?
T2NO
For input band 1, enter output band
T16
\
ERDS-
>subset
SUBSET –– Subset/Mosaic Files
Version 7. 4.01. 434
Image or GIS file?
'PImage
Enter Input Image filename:
2var].9
# 1
Use the whole image?
T2Yes
Enter Output Image filename:
2a
# 1.
Overwrite the file?
TPYes
Enter output file coordinates at which to place
upper left corner of input subset
21, 1
Should input zero values overwrite data in the output file?
TPYes
Copy all bands in order?
TPNO
For input band 1, enter output band
217
\
ERD-
>subset
SUBSET –– Subset/Mosaic Files
Version 7. 4.01. 434
Image or GIS file?
2Image
Enter Input Image filename:
2var20
# 1
Use the whole image?
T2Yes
Enter Output Image filename:
T2a
# 1
Overwrite the file?
T2Yes
Enter output file coordinates at which to place
J–12
upper left corner of input subset
21, 1
Should input zero values overwrite data in the output file?
T2Yes -
Copy all bands in order?
TPNO
For input band 1, enter output band
TP18
\
ERD-
>subset
SUBSET –– Subset/Mosaic Files
Version 7. 4.01. 434
Image or GIS file?
'PImage
Enter Input Image filename:
2var21
# 1
Use the whole image?
T2Yes
Enter Output Image filename:
2a
# 1.
Overwrite the file?
2Yes
Enter output file coordinates at which to place
upper left corner of input subset
21, 1
Should input zero values overwrite data in the output file?
T2Yes
Copy all bands in order?
TPNO
For input band 1, enter output band
TP19
\
ERD-
>subset
SUBSET -- Subset/Mosaic Files
Version 7. 4.01. 434
Image or GIS file?
2Image
Enter Input Image filename:
2var22
# 1
Use the whole image?
T2Yes
Enter Output Image filename:
T2a
J–13
# 1
Overwrite the file?
T2Yes
Enter output file coordinates at which to place
upper left corner of input subset
21, 1
Should input zero values overwrite data in the output file?
T2Yes
Copy all bands in order?
T2NO
For input band 1, enter output band
220
\
ERD-
>datatab
DATATAB -- ASCII Data Tabular Format
Version 7. 4.04. 449
Dump an Image or GIS file
2Image
Enter Image filename:
T2a
# 1
Include any other files?
TPNO
Select option:
2Data file
Enter upper left file coordinates
21, 1
Enter lower right file coordinates
º
Enter x skip factor
71.
Enter y skip factor
71
Enter Ascii Output filename:
2Output. xls
# 0
\
ERD-
>noaud
J-14
Appendix K: ANSI-C Source Code for CELLID.EXE
/*
* This program Converts the output generated by the ERDAS SUB1.AUD batch file
* into a cell identification file (AGNPS variable 1).
*/
#include <stdio.h>
#include <stdlib.h>
/* file constants */
#define inputEile "input. tzt"
#define inputEile "output. tzt"
/* constant definitions */
#define kMaxline 150
#define kFirstRow 4
int main ()
{
FILE *input,
*Output;
char inbfr[kMax Line] ;
int templnt,
rOW,
Col,
Stop,
Il,
lineIndex;
/* open the input file */
if (! (input = fopen (inputEile, "r"))) {
fprintf (stderr, "Can't open the input file\n");
exit (-1);
}
/* open the output file */
if (! (output = fopen (outputEile, "w"))) {
fprintf (stderr, "Can't open the output file\n");
exit (-1);
}
/* read input file */
for (row = 0; fgets (inbfr, kNaxLine, input); row----)
{
/* ignore the first kFirstRow rows " /
if (row < kFirstRow) continue;
for (lineIndex = 0, col = 1; col - kWaxCol; col----, lineIndex += n.) {
/*read in the next integer */
K-1
ssCanf (&inbfr[lineIndex) , " $dºn", &templnt, &n);
/*read the Column value */
switch (col) {
case kFirstCol:
case kSecondCol:
fprintf(output, "%07d" kColBreak, templnt);
break;
case kThirdCol:
if (templnt == 0) fprintf(output, "%07d" kColBreak, templnt);
else {
fprintf(output, "%07d" kColBreak, newlnt);
newſnt-H+;
}
break;
default:
break;
}
/* close the files */
folose (input);
folose (output);
return (0);
Appendix L: ANSI-C Source Code for ERD2AGN.EXE
/*
* This program converts the output generated by the ERDAS SUB3. AUD batch file
* into AGNPS format.
*
*/
#include <stdio.h>
#include <stdlib.h>
/* file Constants */
#define kTnputEileName "input. tzt"
#define kCutputEileName "output. tzt"
#define kRead "r"
#define kCverWrite "W"
/* constant definitions */
#define kFirstRow 4
#define kMaxline 150
#define kNaxCol kTwentysecondCol + 1
enum columns (
kFirstCol = 2,
kSecondCol,
kThirdCol,
kFourthCol,
kFifthCol,
kSixthCol,
kSeventhCol,
kEighthcol,
kNinthCol,
kTenthCol,
kEleventhCol,
kTwelfthCol,
kThirteenthCol,
kFourteenthCol,
kFifteenthCol,
kSixteenthCol,
kSeventeenthCol,
kEighteenthCol,
kNineteenthCol,
kTwentiethCol,
kTwentyfirstCol.,
kTwentysecondCol
};
/* multipliers */
#define
#define
#define
#define
#define
#define
kFirstCol Mult
kSecondColMult
kFifthCollſult
kEighthcolMult
kNinthColl/ſult
kTwentyfirstCol Mult
1000
1000
0.1
0.01
0.01
0.1
L-1
/* Column constants */
#define kSixthColVal
#define kBleventhColVal
#define kFifteenthColVal 10
#define kSixteenthColVal
#define kSeventeenthColVal
#define kNineteenthColVal
i
/* debugging macros “/
/*#define DEBUG" /
#ifdef DEBUG
#define kCol.Break "|"
#define DBGMSG (m) fprintf (stderr, m" \n")
#define DBGPAR (m, p) fprintf (stderr, m"\n", p)
#define PRT DBG HDR (f) fprintf(f, " 1| 2|14 || 3 |
10| 11| 12| 13||15|16|| 17|\n");
#else
#define kColbreak
#define DBGMSG (m)
#define DBGPAR (m, p)
#define PRT DBG HDR (f)
#endif
int main ()
{
FILE *input,
*output;
Char inbfr[kMax Line] ;
int templint,
rOW,
Col,
Stop,
Il,
lineIndex;
/* open the files */
if (! (input = fopen (kTnputEileName, kRead))) {
fprintf (stderr, "Can't open the input file\n");
exit (–1);
}
if (! (output = fopen (kDutputEileName, kCverWrite))) {
fprintf (stderr, "Can't open the output file\n");
exit (–1);
}
/* debugg file header */
PRT DBG HDR (output);
4|| 5 || 6 || 9 |
/* reset row and begin reformatting the text – exit: fgets returns NULL when EOF
*/
for (row = 0; fgets (inbfr, kNaxLine, input); row--4-) {
/* ignore the first kFirstRow rows “/
if (row < kFirstRow)
Continue;
for (lineIndex = 0, stop =
+= n.) {
0, Col = 0; col 3 kMaxCol && ! stop; col----, lineIndex
/* read in the next integer */
ssCanf (&inbfr[lineIndex.] , " $dºn", &templnt, &n);
/* read the column value */
switch (col) {
case kFirstCol:
/* if column one
if (! templnt)
stop = 1;
break;
}
fprintf(output,
break;
case kSecondCol:
fprintf(output,
break;
case kThirdCol:
case k[hirteenthCol:
fprintf(output,
break;
Case kFourthCol:
case kSeventhCol:
case kEighteenthCol:
fprintf(output,
break;
case kEifthCol:
fprintf(output,
break;
case kSixthCol:
fprintf(output,
break;
case kEighthCol:
fprintf(Output,
break;
case kNinthCol:
case kiſenthCol:
case kTwelfthCol:
fprintf(output,
break;
case kEleventhCol:
fprintf(Output,
is zero, delete the line */
{
"%07d" kCol.Break, templnt * kFirstCol Mult);
"%07d" kColBreak, templnt * kSecondColMult);
"%02d"kColBreak, templnt);
"%04d"kColbreak, templnt);
"%05.1 f" kColBreak, (float) templnt * kFifthColMult);
"%02d" kColbreak, kSixthColWal);
"%05.3f"kColBreak, (float) templnt * kEighthcol Mult);
"%04.2f"kCol.Break, (float) templnt * kNinthColMult);
"%05.2f"kColBreak, (float) kEleventhColVal) ;
break;
case kFourteenthCol:
fprintf(output, "%02d"kColBreak, templnt);
break;
case kFifteenthCol:
fprintf(output, "%04d"kColBreak, kFifteenthColWal);
break;
case kSixteenthCol:
fprintf(output, "%02d"kColBreak, kSixteenthColVal);
break;
case kSeventeenthCol:
fprintf(output, "%04d"kColBreak, kSeventeenthColWal);
break;
case kNineteenthCol:
fprintf(output, "%03d"kColBreak, kNineteenthColWal);
break;
case kTwentiethCol:
if (templnt <= 1)
stop = 1;
fprintf(output, "%02d Nn", templnt);
break;
case k[wentyfirstCol:
fprintf(output, "%07.1 f"kColBreak, (float) templnt *
kTwentyfirstCol Mult);
break;
case kiſwentysecondCol:
fprintf(output, "%07.1 f\n", (float) templnt);
break;
default:
/* delete columns not specified */
break;
}
/* close the files */
folose (input);
folose (output);
/* bye bye */
return (0);
L-4
UNIVERSITY OF MICHIGAN