CRWR Online Report 98-1
A GIS Assessment of the
Total Loads and Water Quality
in the Corpus Christi Bay System
by
Ann Marie Quenzer, M.S.C.E
Graduate Research Assistant
May 1998
CENTER FOR RESEARCH IN WATER RESOURCES
Bureau of Engineering Research ? The University of Texas at Austin
J.J. Pickle Research Campus ? Austin, TX 78712-4497
This document is available online via World Wide Web at
http://www.ce.utexas.edu/centers/crwr/reports/online.html
1
ACKNOWLEDGEMENTS
The study presented in this report is funded by the Corpus Christi Bay
National Estuary Program.  Their support is gratefully acknowledged.  The author
would also like to thank Dr. David Maidment, Ferdinand Hellweger, Dr. Nabil Eid,
Dr. George Ward, Dr. Neal Armstrong, Dr. Paul Montagna, and Dr. Edward R.
Holley of The University of Texas at Austin for providing technical expertise in all
aspects of the study.
Finally, many thanks are due to the members of the GIS Hydrology
Research Group at The University of Texas? Center for Research in Water
Resources for their support, advice and encouragement.
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A GIS Assessment of the
Total Loads and Water Quality
in the Corpus Christi Bay System
by
Ann Marie Quenzer, M.S.E.
The University of Texas at Austin
SUPERVISOR: David R. Maidment
ABSTRACT
A method is presented for determining raster maps of mean annual water
flow and pollutant loading from the land surface, and for determining the resulting
concentrations in receiving water bodies. The method is illustrated by application to
the Corpus Christi Bay system in South Texas.  A mesh of 100m digital elevation
model cells is laid over the drainage area and cell to cell connectivity established to
link each land surface cell with a corresponding water body segment. Nonpoint
source constituent loads are determined for each cell as the product of runoff and
expected mean concentration, and accumulated down to the bay system.  Point
source and atmospheric loads are added, water quality computed in each bay
system, and compared to observed data.  A strong South to North runoff gradient is
observed in the study area.  The majority of the constituent loading comes from
nonpoint sources, except for oil and grease, which arise mainly from point sources.
Nitrogen and phosphorus concentrations in the bay system are reproduced
reasonably well provided a decay rate of 0.01-0.02 day
-1
 is used.  Oil and grease are
reproduced well as conservative constituents.  The computed metals concentrations
are low and suggest a significant source in sediment or elsewhere that is presently
not accounted for.
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TABLE OF CONTENTS
ACKNOWLEDGEMENTS.......................................................................................1
ABSTRACT...............................................................................................................2
TABLE OF CONTENTS...........................................................................................3
LIST OF TABLES .....................................................................................................6
LIST OF FIGURES....................................................................................................8
1 INTRODUCTION............................................................................................1
1.1 Background....................................................................................................1
1.2 Objectives ......................................................................................................2
1.3 Study Area .....................................................................................................3
1.4 Literature Reviewed.......................................................................................6
1.4.1 Non-Point Source Models......................................................................7
1.4.1.1 HSPF..................................................................................................7
1.4.1.2 STORM..............................................................................................8
1.4.1.3 CREAMS/GLEAMS .........................................................................8
1.4.1.4 ANSWERS ........................................................................................9
1.4.1.5 AGNPS ............................................................................................10
1.4.1.6 WATERSHED.................................................................................11
1.4.2 GIS Based Models ...............................................................................12
1.4.2.1 Galveston Bay National Estuary Program .......................................12
1.4.2.2 ARMSED.........................................................................................12
1.4.2.3 BASINS2 .........................................................................................13
1.4.3 Surface Water Quality Models.............................................................14
1.4.3.1 QUAL2E..........................................................................................14
1.4.3.2 WASP ..............................................................................................15
1.4.4 Earlier Studies in CCBNEP .................................................................15
1.5 Report Outline..............................................................................................15
2 DATA DESCRITION ....................................................................................17
2.1 Map Projection.............................................................................................17
2.2 Establishing a Digital Database ...................................................................18
2.2.1 Political Boundaries.............................................................................18
2.2.2 Hydrologic Unit Codes (HUC) ............................................................23
2.2.3 Digital Elevation Model (DEM) ..........................................................25
2.2.3.1 Converting Files to Arc/Info Format ...............................................27
2.2.3.2 Merging the Grids............................................................................27
2.2.3.3 Reducing the DEM to the Study Area .............................................28
2.2.4 USGS Land Use Data ..........................................................................31
2.2.4.1 Converting the Files to Arc/Info Format..........................................32
2.2.4.2 Joining Land Use Maps ...................................................................33
4
2.2.4.3 Updating Land Use ..........................................................................36
2.2.5 USGS Stream Gauges..........................................................................42
2.2.5.1 Create a Stream Gauge Coverage ....................................................42
2.2.6 Precipitation Data ................................................................................47
2.2.7 Digital Line Graphs (DLG)..................................................................54
2.2.7.1 Converting the Files to Arc/Info Format..........................................55
2.2.7.2 Joining the DLG Coverages.............................................................55
2.2.8 Event Mean Concentration (EMC) ......................................................59
3 METHODOLOGY.........................................................................................61
3.1 Grid-Based Watershed Modeling Using Digital Elevation Data.................61
3.1.1 Recondition the DEM Surface Model..................................................61
3.1.2 Connecting the Bay System with the River Network ..........................65
3.1.2.1 Bay Coverage...................................................................................67
3.1.2.2 A Problem Encountered when Connecting the Land Surface with the
Bay System. .................................................................................................69
3.1.3 Eight-Direction Pour Point Model.......................................................72
3.2 Calculating the Precipitation/Runoff Relationship ......................................75
3.2.1 Digital Delineation of Sub-Watershed Drainage Areas From the United
States Geological Survey (USGS) Flow Gauges............................................75
3.2.2 Average Precipitation for each Delineated Drainage Areas.................79
3.2.3 Determining Average Runoff per Unit Area at each USGS Gauge.....80
3.2.4 Establishing a Mathematical Relationship between Precipitation
Percent Land Use and Streamflow .................................................................83
3.2.5 Computing Expected Runoff ...............................................................89
3.3 Linking Expected Mean Concentration (EMC) Values to Land Use ..........89
3.3.1 Computing Concentration Grid ...........................................................91
3.4 Estimating Annual Loads Throughout the Watersheds ...............................91
3.4.1 Computing Land Surface Loads ..........................................................91
3.4.2 Computing Point Source Loads ...........................................................92
3.4.3 Computing Upstream Watershed Loads ..............................................94
3.4.4 Computing Atmospheric Loads ...........................................................94
3.4.5 Computing Sediment Loads.................................................................96
3.5 Calibrating the Water Quality Model...........................................................97
3.5.1 Bay System Bathymetry.......................................................................99
3.5.2 Bay Evaporation and Precipitation ....................................................101
3.5.3 Inflow to Bay System.........................................................................102
3.5.4 Model Calibration Using Salinity......................................................104
3.6 Computing Equilibrium Water Quality Concentrations in the Bay System
106
3.7 Loads from Weighted Flow Accumulations ..............................................109
5
3.8 Running the Model ....................................................................................111
4 RESULTS AND DISCUSSION...................................................................112
4.1 Inflow into Bay System..............................................................................112
4.2 Total Constituent Loads into Bay System..................................................118
4.3 Constituent Concentrations in the Bay System..........................................126
4.3.1 Comparing the Expected Concentrations to the Observed
Concentrations using the T-Test ..................................................................126
4.3.2 Calculating the Decay Rates in the Three Bay Systems ....................130
4.3.3 Edroy and King Ranch EMC Study...................................................134
5 CONCLUSIONS..........................................................................................138
APPENDIX A: Data Dictionary ............................................................................146
APPENDIX B: Avenue Scripts and AML Programs.............................................156
BIBLIOGRAPHY..................................................................................................248
VITA......................................................................................................................253
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LIST OF TABLES
Table 2.1: Data for the Stream Gauges in the CCBNEP Study Region...................42
Table 2.2: Event Mean Concentration Values used in the CCBNEP Study............60
Table 3.1: Comparison between the Original Segmentation and the New Bay Model
Segmentation. ..........................................................................................................69
Table 3.2: Comparison of the USGS Calculated Drainage Areas and the Delineated
Drainage Areas for Each of the Sub-Watersheds.....................................................76
Table 3.3: Depth of Precipitation (mm/yr) for Each of the Drainage Areas............80
Table 3.4: Mean Annual Flow (cfs) of Record for Each of the Nine USGS Gauging
Stations.....................................................................................................................81
Table 3.5: Thirty-year Projected Mean Annual Flow for the Period 1961-1990.....82
Table 3.6: Streamflow Depths for Each of the Sub-watersheds. .............................83
Table 3.7: Input Values Used in the Excel Regression Tool. ..................................84
Table 3.8: Summary Output for the Agricultural Land Use, Precipitation, and
Runoff Analysis. ......................................................................................................85
Table 3.9: Summary Output for the Rangeland, Precipitation, and Runoff Analysis.
.................................................................................................................................86
Table 3.10: Segment Name, ID, Depth, Surface Area and Volume. .....................100
Table 3.11: Bay Segment Interface, Interface Line Length, Interface Length,
Interface Depth, and Interface Area. ......................................................................101
Table 3.12: Comparison of the Calculated Mean Annual Evaporation and
Precipitation with the Texas Water Development Board's Bay System's Mean
Annual Evaporation and Precipitation...................................................................102
Table 3.13: Comparison of the Calculated Runoff Inflows with Two Other Inflow
Studies by the USGS and the TWDB. ...................................................................103
Table 3.14: Salinity Interface Dispersion Coefficients, Bulk Dispersion
Coefficients, and Interface Flows. .........................................................................106
Table 4.1: Percent Inflow to the Bay System from Different Sources...................113
Table 4.2: Constituent Loadings for Each Bay Segment, a) Atmospheric Loads, b)
Point Source Loads, c) Land Surface (Non-point Loads), d) Nueces River
Watershed Loads, e) Total Loads. .........................................................................119
Table 4.3: Expected and Observed Concentrations, Standard Deviation, Number of
Samples, and the T-Test Result for Each Bay Segment: a) Total Nitrogen, b) Total
Phosphorus, c) Oil and Grease, d) Copper, e) Chromium, and f) Zinc..................128
Table 4.4: Average T-Test Result for Each Constituent........................................130
Table 4.5: Decay Rates for Total Nitrogen and Total Phosphorus. .......................133
Table 4.6:  Total Nitrogen and Total Phosphorus from Land Surface Sources to Bay
System Using Original and Provisional EMC Values. ..........................................135
7
Table 4.7:  Total Nitrogen and Total Phosphorus from all Sources to Bay System
Using Original and Provisional EMC Values........................................................135
Table 4.8:  Total Nitrogen Concentrations in the Bay System Using Original and
Provisional EMC Data Compared to the Observed Data.......................................136
Table 4.9:  Total Phosphorus Concentrations in the Bay System Using Original and
Provisional EMC Data Compared to the Observed Data.......................................136
Table 4.10:  Comparing Decay Rates for Total Nitrogen and Total Phosphorus
Using Original and Provisional Data. ....................................................................137
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LIST OF FIGURES
Figure 1.1: Study Region Location in the State of Texas. .........................................4
Figure 1.2: Hydrologic Features within the Study Region.........................................5
Figure 2.1: Counties Participating in the CCBNEP.................................................22
Figure 2.2: Hydrologic Unit Code Boundaries within the CCBNEP Study Area....26
Figure 2.3: Digital Elevation Model for the CCBNEP Study Area. ........................30
Figure 2.4: USGS Land Use in the CCBNEP Study Area.......................................35
Figure 2.5: Land Use in the CCBNEP Study Area with the Missing Data Area
Being Assigned Range Land....................................................................................37
Figure 2.6: Updated Land Use in the CCBNEP Study Region................................41
Figure 2.7: Stream Gauges in the CCBNEP Study Area. ........................................46
Figure 2.8: Original Precipitation Used in the Study...............................................48
Figure 2.9: Precipitation Trend Found Using the Grid Function Trend. .................52
Figure 2.10: Merged Precipitation Grids. ................................................................53
Figure 2.11: Digital Line Graph File Used in the CCBNEP Study. ........................58
Figure 3.1: Methodology of the Reconditioning System. ........................................63
Figure 3.2: Delineated Rivers Compared to the DLG File. .....................................64
Figure 3.3: Methodology of the Connection between the River Network and the
Bay System. .............................................................................................................66
Figure 3.4: Comparison between the a) New Bay Segmentation and the b) Clipped
Segmentation. ..........................................................................................................68
Figure 3.5: North Aransas Bay Segment Flow Problem Due to Extended DEM. ...71
Figure 3.6: Eight-Direction Pour Point Model. .......................................................73
Figure 3.7: Streams with Flow Accumulation Greater than 20,000 Cells Near Baffin
Bay. ..........................................................................................................................74
Figure 3.8: River Reach Files Compares to the Digital Line Graph File in the Oso
Creek Sub-watershed. ..............................................................................................77
Figure 3.9: Sub-watersheds in the CCBNEP Study Region. ...................................78
Figure 3.10: Depth of Precipitation (mm/yr) vs. Depth of Expected Runoff (mm/yr)
for Each of the Land Uses........................................................................................88
Figure 3.11: Runoff Grid Calculated Using the  Relationship between Precipitation,
Streamflow, and Percent Land Use..........................................................................90
Figure 3.12:  Texas Natural Resource Conservation Commission Water Quality
Segments Used in the CCBNEP Study....................................................................93
Figure 3.13: Water Quality Methodology Used in the Calculations of the
Equilibrium Concentrations.....................................................................................98
Figure 4.1: Precipitation and Runoff Gradient from South, A,  to North, B, along
the Bay System.......................................................................................................115
Figure 4.2: a) Precipitation and b) Runoff Gradient Locations in the South, A, and
9
the North, B, Portions of the Study Area. ..............................................................115
Figure 4.3: Water Balances for the a) North Bay Sytem, b) Middle Bay System, c)
South Bay System, and d) the Entire Bay System. ................................................118
Figure 4.4: Percent Constiuent Loads of the Total Load for the Entire System. ...123
Figure 4.5: Percent of Total Constituent Load which Comes from a Specific Source
for Entire System: (Total Nitrogen, Total Phosphorus, Oil and Grease,  Copper,
Chromium, and Zinc. .............................................................................................124
Figure 4.6: Percent of Total Constituent Load which Comes from a Specific Source
for the North, Middle and South Bay Sections: (Total Nitrogen, Total Phosphorus,
Oil and Grease, Copper, Chromium, and Zinc). ....................................................125
1
1 INTRODUCTION
1.1 Background
The National Estuary Program was established through the Federal Water
Quality Act of 1987 to identify and protect significant estuaries in our nation.
Studies on twenty-eight estuaries are now funded and administered by the United
States Environmental Protection Agency (EPA).  Governor Ann Richards formally
nominated Corpus Christi Bay for the program in 1992.  The Corpus Christi Bay
National Estuary Program is designed to coordinate the activities of all regulatory
authorities along with input from the non-government sector in assessing scientific
information about the nature of the bay system and formulate a management plan to
address environmental problems in the bay system.
This study is being done to determine the total constituent loadings to the
bay system.  Loads from the atmosphere, land surface, point sources, and the
Nueces River upstream of the study region are calculated and entered into a bay
water quality model to determine the equilibrium constituent concentrations.  Mean
annual flow conditions at steady state are the only flow conditions considered, so
the computation in the bay system is a mean annual mass balance.
The results of this study synthesize many components of the Corpus Christi
Bay National Estuary Program, including the results of separate studies on point,
land surface, and atmospheric sources, and the status and trends study of observed
water quality in the bay system.  The sum of the loadings coming into the bays were
used as inputs to a water quality model to try to match the observed concentrations
in the bay system, so that the significance of various pollution sources can be
judged against their contribution to impacts on the bay water quality.
2
1.2 Objectives
The total loadings project begins by simulating the watershed characteristics
using the ArcView Geographic Information System (GIS).  The land surface or land
surface load, point source loads, upstream watershed loads, and atmospheric loads
are also modeled using ArcView.  A 100 meter grid is laid over the land surface in
the study region.  Each cell accounts for elevation, land use, constituent
concentrations, mean annual precipitation, mean annual expected runoff, and
annual land surface loads.  In addition to the non-point source loads, loads from
point sources, the atmosphere and the Nueces River inflow to the study region are
added into the model.  Benthic loadings from the bay sediments have not been
explicitly accounted for because of lack of usable data.
By directing the flow of water from cell to cell, the loads are accumulated in
the river network and bay system.  The loads are then entered into the water quality
model to calculate an equilibrium concentration in each of 20 bay segments.
For purposes of summarizing the research results, the bay segments are grouped
into three regions: north, middle and south bays.  The north bays comprise Copano
and Aransas Bays and the smaller bays flowing into them.  The middle bays are
Nueces and Corpus Christi Bay, and those in the south are the Laguna Madre and
Baffin Bay.
The study had a number of limitations.  The use of mean annual calculations
consider the flow and loads to be steady state parameters from year to year.  The
effects of flushing of constituents through the system in substantial floods are not
considered.  Second, constituent concentrations on the land surface are related to
general land use categories and do not to vary with more specific types of land uses.
For example, different types of crops grown in an agricultural area may produce
different amounts of runoff, but that effect is not considered here.  Third,
3
constituent loads were considered to be conservative in the river network as they
are transported to the bay system, possibly causing an over-estimation of the
constituent loads to the bay system.  Fourth, some constituent loads are estimated
from limited data.  For example, the atmospheric data were collected at one site in
Beeville, TX, and this site has limited data.  Sediment loads are not estimated, and
this limitation is particularly pronounced in the case of the metals where it appears
that there are significant sediment sources for all metal constituents.
Despite all these limitations, the final model results bear a reasonable
resemblance to the observed concentration data in the bay system when the two
values are compared statistically.  Where there are consistent discrepancies between
observed and computed values, the explanations are readily apparent and can be
accounted for by missing source loadings or the adjustment of the decay rates, in
the case of metals and nutrients, respectively.
1.3 Study Area
The study area includes 75 miles of south-central Texas coastline; 550
square miles of water including all bays and saltwater bayous, Aransas Bay,
Copano Bay, Redfish Bay, Nueces Bay, Corpus Christi Bay, Baffin Bay, and
Laguna Madre.  The twelve counties covered by the study are McMullen, Live Oak,
Bee, Refugio, Aransas, San Patricio, Jim Wells, Duval, Nueces, Kleberg, Kenedy
and Brooks.  Padre Island also is included in the study.
The study area is bounded by the San Antonio Bay on the north and extends
down to the Lower Laguna Madre.  Three main rivers drain into the bay system;
Mission River, Aransas River, and Nueces River.  The Mission and Aransas Rivers
flow into the Copano Bay while the Nueces River drains into the Nueces Bay.
Figure 1.1 shows the location of the basin within the State of Texas, and Figure 1.2
4
Figure 1.1: Study Region Location in the State of Texas.
5
Figure 1.2: Hydrologic Features within the Study Region.
6
shows the hydrologic features in the region.  The large indentation on the inland
side of the study region is the drainage area of Nueces River upstream of Lake
Corpus Christi, which is treated as a point source so that no explicit representation
of its land surface features is required.  The water quality effluent from Lake
Corpus Christi can be quantified with sufficient confidence using this method, so
this simplification is reasonable.
The study area is characterized by pronounced topography in elevations in
the northwest portion of the study region while near the coast and in the south
portion of the area there is little change in elevation.  The southern region of the
study area is mostly covered by rangeland.  The middle portion of the area is
substantially cultivated for agriculture.  The northern portion of the study area is
used for a mix of agriculture and rangeland.  Small portions of urban land use are
scattered throughout the study region with the majority of the urban land use being
centered in and around the City of Corpus Christi.
1.4 Literature Reviewed
To help control the quality of natural waters, models have been developed
by government agencies, research and academic institutes, and consulting firms to
assess the non-point source loads in a watershed.  The agencies, institutes and
consulting firms have also developed models to calculate the water quality in the
receiving waters as well as to describe the biological and chemical processes in the
water.  Some of these models have been linked to the GIS system.
This section reviews some of the non-point source models which have been
developed along with some of the surface water quality models.  Finally, a
discussion is presented of earlier water quality studies that have taken place in the
Corpus Christi Bay National Estuary Program?s study area.
7
1.4.1 Non-Point Source Models
There are several types of non-point source loading models; constant
concentration, spreadsheet, statistical, rating curve or regression, and
buildup/washoff.  A constant concentration model runs on the assumption that
runoff has the same concentration for a specific constituent.  For example, an
annual runoff volume can be multiplied by a annual concentration to produce an
annual constituent load.  Many engineering calculations are done using
sophisticated spreadsheet analysis with water quality analysis being no exception.
Many of the spreadsheet non-point source load models use a runoff coefficient
multiplied by the precipitation depth with the advantage of having constituent
concentrations vary with land use.  The EPA statistical method assumes EMC
values are not constant, but rather have a log normal frequency distribution.
Regression has been used to relate loads and EMC?s to catchment, demographic,
and hydrologic characteristics.  Buildup/washoff models try to describe the
mechanics of dry weather constituent buildup with the accumulation of the solids
being washed off with the storm event (Donigian, 1991).
1.4.1.1 HSPF
The Hydrological Simulation Program-Fortran (HSPF) originated from the
Stanford Watershed Model in 1966 and eventually incorporated many non-point
sources models form the EPA Athens Laboratory (Bicknell et al., 1993).  This
model is used for non-point source modeling.  The purpose of the model is to
predict conventional and organic chemical concentrations in runoff, surface and
ground waters.  The model includes fate and transport of the constituents in a one-
dimensional stream network.  HSPF produces a time history of the runoff flow rate,
sediment load, constituent concentrations.  It also calculates a time history of water
8
quality and quantity at any point in the watershed.  The programs also simulate
three sediment types; sand, silt and clay.  The model includes transfer and reaction
processes in the form of hydrolysis, oxidation, photolysis, biodegradation,
volatilization and sorption.  Input data is extensive for HSPF, since it is a
continuous model.  Therefore, the model needs continuos data inputs, such as
precipitation, evapotranspiration, temperature, and solar intensity.
1.4.1.2 STORM
The Storage, Treatment, Overflow, Runoff Model (STORM) was developed
by the Corps of Engineers Hydrologic Engineering Center (Abbott, 1997).  Simple
runoff coefficients, and the SCS and unit hydrograph methods are used in the model
to calculate runoff depth.  STORM generates hourly runoff depths from hourly
precipitation depths.  The flow can be routed through a constant-rate treatment
facility with the excess flow being diverted to a storage device.  Flows that pass
through storage are not included in the receiving water loads.  Linear buildup and
first order washoff simulations can be done for six pre-specified constituents.
STORM is a surface water quality model used for runoff simulations in an urban
area.  Hourly precipitation data is used along with runoff coefficients which are
estimated from handbooks and textbooks.  Soil Conservation Service (SCS)
parameters are available if soil information is known.  Output includes storm event
summaries of runoff volume, loads and concentrations in the receiving waters.
1.4.1.3 CREAMS/GLEAMS
The US Department of Agriculture-Agricultural Research Service
developed the models Chemicals, Runoff, and Erosion from the Agricultural
Management System (CREAMS) to analyze agricultural best management practices
9
(Knisel, 1980).  It is a field-scale model that uses separate hydrology, erosion, and
chemistry submodels which are connected by pass files.  The CREAMS model
calculates runoff volume, peak flow, infiltration, evapotranspiration, soil water
content and percolation on a daily basis.  Infiltration can be calculated if detailed
precipitation is available.  The sediment and erosion yields are estimated at the edge
of field along with particle size distribution.  Nutrients and pesticides are simulated
along with their loads and average concentrations.  Dissolved chemicals are
simulated in the runoff, sediment, and through the root zone.
Ground Water Loading Effects of Agricultural Management Systems
(GLEAMS) was also developed by the US Department of Agriculture-Agricultural
Research Service.  It was developed to utilize the CREAMS model and incorporate
a vadose zone component (Knisel, 1980). The three main components in GLEAMS
are hydrology, erosion/sediment yield, and pesticides.  Precipitation is broken into
runoff and infiltration with water balance being calculated on a daily basis.  Runoff
is calculated using the SCS method where the soil is broken into various layers with
varying thickness and is used for water and pesticide routing.
Both models need extensive input data.  The hydrologic submodel requires
daily or breakpoint precipitation, monthly solar radiation, and monthly air
temperature.  Soil type, soil properties, and crop types are also needed.  The output
includes hydrology and nutrient summaries for storm, monthly, or annual
simulations.
1.4.1.4 ANSWERS
The model Areal Nonpoint Source Watershed Environmental Response
Simulation (ANSWERS) was developed by the Agricultural Engineering
Department at Purdue University.  It predicts the hydrologic and erosion response
10
of agricultural watersheds for surface waters (Beasley, 1982).    The model requires
the watershed to be broken into a grid of square elements.  The grid size must be
small enough for the parameters to be uniform over the cell, however parameters
are allowed to vary from cell to cell.  Within each element the following processes
are simulated; interception, infiltration, surface storage, surface flow, subsurface
drainage, sediment drainage, and sediment detachment, transport and deposition.
The output from one cell becomes the input to its adjacent cell downstream.  The
ANSWERS model needs descriptions of the watershed topography, drainage
network, soils, and land use.  The output can be analyzed on a cell by cell basis or
at the watershed level.
1.4.1.5 AGNPS
The Agricultural Nonpoint Source Pollution Model (AGNPS) was
developed by the US Department of Agriculture-Agriculture Research Service.  The
model calculates estimates of runoff water quality for a single storm event or for a
continuous simulation (Young et al., 1987).  The watershed should be divided into
a grid.  AGNPS can handle point source inputs from feedlots, wastewater treatment
plant discharges, and stream bank and gully erosion.  The constituents are routed
from cell to cell, so that the individual cells can be examined for its water quality.
The Modified Universal soil Loss Equation is used to predict soil erosion, while the
unit hydrograph is used to predict the flow in the watershed.  Input data includes
topographic information, soil data, daily precipitation, and land use.  The output
from the model includes runoff volume, peak runoff rate, sediment yield, upland
erosion, channel erosion, and nutrient concentrations.
11
1.4.1.6 WATERSHED
The point and non-point source loadings model WATERSHED was
developed by the US Environmental Protection Agency and the US Soil
Conservation Service as an accounting system to manage water quality (Walker,
1989).  The model is made up of a series of worksheets.  The first worksheet holds
the background information, such as basin name, area, and location.  Worksheets
two through four hold information about urban loads, rural non-cropland loads, and
rural cropland loads.  Worksheet five totals the loads, while worksheet six contains
information about the cost of load reduction programs.  Finally, worksheet seven
derives the cost effectiveness of the programs.  The model is started by dividing the
watershed into homogeneous sub-watersheds and entering the appropriate data into
the correct worksheets.  The loads are calculated in worksheets two through four for
normal and best management conditions.  Inputs are estimated equations, such as
the Universal Soil Loss Equation, or from measurements.  The loads are routed
downstream using transmission losses.
The WATERSHED model was used in a study in the Delavan Lake,
Wisconsin Basin.  The watershed was divided into six major basins and into
twenty-six smaller sub-basins depending on land use.  Four sources of loads were
considered: cropland loads, barnyard loads, non-point urban loads, and point urban
loads.  The cropland loads were estimated using the USLE.  Barnyard and non-
point urban loads were taken from previous studies in the area.  The point source
urban loads were sampled.  The model was calibrated against five stations using the
constituent phosphorus.  The model was run for normal conditions and for two best
management practices, conservation tillage and contour farming.  The cost
effectiveness of 1) conservation tillage alone, 2) contour farming alone, and 3) a
12
combination of the two practices.  The results showed that agriculture is a large
contributor to the phosphorus load in Delavan Lake, however best management
practices would not significantly reduce the load in the Lake.  Therefore, innovative
control measures were taken to reduce the phosphorus level in Lake Delavan.
1.4.2 GIS Based Models
1.4.2.1 Galveston Bay National Estuary Program
The Galveston Bay National Estuary Program collected a digital database to
estimate non-point source loads using a Geographic Information System (GIS)
(Rifai, 1993).  Eight water quality parameters were identified; total suspended
solids, total nitrogen, total phosphorus, fecal coliforms, heavy metals, oil and
grease, and synthetic organic chemicals.  The USGS 1:100,000 maps were used for
the hydrography and transportation networks.  The watersheds were digitized into
the database and the soil information was obtained from the Soil Conservation
Service.  LANDSAT images were used for land use delineation.  Three rainfall
cases were derived form rain-gauge data within the basin: normal year, wet year
with 10-year return period, and an individual storm event.  The first two cases were
used to determine the non-point source loads to the Bay.  The rainfall was
distributed over the watershed using the Thiessen polygon method, and the runoff
volume was estimated using the SCS method.  The runoff estimation was coded in
the GIS environment.  The constituent loads were calculated by multiplying the
runoff volume by Event Mean Concentration Values (EMC).
1.4.2.2 ARMSED
Another study linked a widely used model to the GIS system.  The Multiple
13
Watershed Sediment Routing (ARMSED), originally developed by Colorado State
University and New Mexico State University and later updated by the US Army
Construction Engineering Research Laboratory, is based on the Green-Ampt
equations and is a distributed physical process model.  A study by Hodge (1986)
integrated the ARMSED and GRASS models using a GIS interface.  The GRASS
computerized geographic information system contains typical map layers, such as
soils, vegetation cover, geology, satellite images, roads, water bodies, and digital
elevations.  The system calculates areas and distances, reclassify and combine
different maps together, etc.  The procedure involves reconditioning the digital
elevation model and delineating watersheds and stream networks within the
GRASS system.  An interface called WATERSHED is used to identify the
watershed area and channels.  A raster file is created and entered into the GRASS
memory.  The ARMSED model is accessed through GRASS.  The ARMSED
model is used to simulate rainfall; calculate channel geometric characteristics; and
determines soil, surface, rainfall and erosion characteristics.
1.4.2.3 BASINS2
The program BASINS (Better Assessment Science Point and Nonpoint
Sources), developed by the Environmental Protection Agencey (EPA), is a GIS
based program used to analyze watersheds and water quality.  The program is
downloadable from the EPA Internet site (EPA, 1998):
http://www.epa.gov/OST/BASINS.  The program allows the user to define the
watershed of interest, calculates non-point source loads in that watershed, analyze
point source loads, houses national watershed data, and integrates environmental
assessment models.  BASINS includes three assessment tools: TARGET, ASSESS,
and Data Mining.  These three tools assess the watershed data on a large- and
14
small-scale.   The nonpoint source loads are calculated using the Non-Point Source
Model (NPSM) using the HSPF model.  The output from the NSPM and point
source analysis can be entered into the TOXIROUTE and QUAL2E models to
assess the water quality in the receiving waters.  The point and non-point source
analysis is run within the BASINS program which runs within the GIS
environment, while the TOXIROUTE and QUAL2E models are called from the
BASINS program, but run within their own interface.  The output from the
TOXIROUTE and QUAL2E models can be assessed in the post processing option
within the BASINS program.
1.4.3 Surface Water Quality Models
1.4.3.1 QUAL2E
The Enhanced Stream Water Quality Model QUAL2E is maintained by the
U.S. Environmental Protection Agency?s Center for Exposure and Assessment
Modeling (EPA, 1995).  The model simulates several constituents in a stream
network using advective and dispersive mass transport in one-dimensional stream
segments.  The river network is broken into stream reaches which are further
broken into subreaches or computational elements.  For each element, a hydrologic
balance in terms of flow, a heat balance in terms of temperature, and a material
balance in terms of concentration is computed and written to an output file.  The
model is solved for steady state conditions using computations for nutrient cycles,
algal production, benthic and carbonaceous oxygen demand, atmospheric rearation,
and their effect on the dissolved oxygen balance.  A similar program QUAL2E-
UNCAS employs the same calculations as QUAL2-E, but also computes an
uncertainty analysis.
15
1.4.3.2 WASP
The U.S. Environmental Protection Agency?s Center for Exposure and
Assessment Modeling maintains the water quality modeling system WASP5 which
is the generalized framework for contaminant fate and transport in surface waters
(EPA, 1993).  The modeling system is composed of three sub-models which
analyze toxic materials, eutrophication and the hydrodynamics of the river network.
The Toxic Chemical Model (TOXI5) predicts dissolved and sorbed chemical
concentrations in the surface waters and the sediment.  The Eutrophication Model
(EUTRO5) predicts dissolved oxygen, carbonaceous biochemical oxygen demand,
phytoplankton, carbon, chlorophyll-a, ammonia, nitrate, organic nitrogen, and
ortho-phosphate in the surface water and the sediment.  The Hydrodynamic Model
(DYNHYD5) calculates the surface water?s flow, volumes, velocities, and depths.
1.4.4 Earlier Studies in CCBNEP
This study was completed to assess the non-point source pollution in the
San Antonio-Nueces Coastal Basin (Saunders, 1996).  The study uses publicly
available digital data, such as digital elevation maps, stream networks, rainfall,
stream flow, and land use data sets.  First, the DEM was made to agree with the
stream network through a process called ?burning-in?.  A mathematical relationship
was found between streamflow and precipitation to predict runoff.  Literature-based
Expected Mean Concentrations (EMC) were assigned to each of the land uses.  The
runoff was multiplied by the EMC grid to calculate a load grid which was then
routed downstream to the rivers and bays.
1.5 Report Outline
This study has a unique combination of land surface loadings from a
16
distributed parameter model of the landscape with a lumped parameter model of
water quality in the bay system, both connected in the GIS using a map-based
modeling approach that has not been accomplished previously.  The report is
divided into five chapters.  Chapter 2 describes the data sources used in the study
and how the data are compiled into a consistent database with a common
geographic reference frame.  A total of some 500MB of files were thus created, and
this database will be of continuing value for other studies in the Corpus Christi
region after this research is completed.  Chapter 3 shows how the runoff and
constituent load values are developed for input to the total loadings model and
demonstrates a very substantial south to north gradient in runoff from the land
surface.  Chapter 4 presents the results of the computations of water quality in the
bays and statistically compares the calculated values with those observed.  It shows
that the bay system functions as a sink for constituents because most of the inflow
to the bay is lost to evaporation and does not flow out into the Gulf of Mexico.
Chapter 5 presents the conclusions of the study.  The data developed in the study
are documented in Appendix A and the computer programs in Appendix B.
17
2 DATA DESCRITION
To complete the total loadings project, the study uses raster and vector data
to describe the landscape.  Raster data are also termed grids as the data set is stored
in a uniform rectangular array.  Vector data are also known as coverages and
contain points, lines, and/or polygons as the geographic features.  Point coverages
describe features located by a single point or a set of points.  Geographic features in
a line coverage are defined by a series of line segments with nodes specifying the
starting and ending points of each line.  Geographic features in a polygon coverage
are formed by a series of connected lines.  The data sets also have tables associated
with the coverage or grid that present descriptive data about the geographic feature
which they represent (Environmental System Research Institute, 1990).
2.1 Map Projection
Because of the different data sources and the different map projections, in
which each data source is found, a standard map projection is needed.  Spatial data
sets are also found in various map scales and in different coordinate systems.
Arc/Info allows raster and vector data to be viewed together as long as they have a
common datum, map projection and coordinate system.  Arc/Info converts the data
from one map projection to another.
The Texas State Mapping System (TSMS) coordinate system is used for this
study.  TSMS uses a Lambert conformal conic projection which preserves shape.
However, this study uses a variation of the TSMS, an Albers equal-area projection.
The equal-area projection is preferred for the study because of the calculations of
water and mass balances over a region which require preserving the correct earth
surface area in the map projection, which the Lambert projection does not do
(Snyder, 1987).
18
2.2 Establishing a Digital Database
To begin the total loadings study for the CCBNEP a digital database was
established.  This section of the report describes each of the databases needed for
the study, as well as the location of the data source.  The steps needed for the
database establishment are provided along with a short narrative describing the
process or information gathered.  The actual Arc/Info and ArcView commands are
listed when possible.  When the step is more efficiently completed using an
automated program, such as Avenue scripts and Arc Macro Language (AML)
programs, the scripts or programs are listed in Appendix B or in the report itself.
2.2.1 Political Boundaries
The twelve counties involved in the CCBNEP are McMullen, Live Oak,
Bee, Refugio, Aransas, San Patricio, Jim Wells, Duval, Nueces, Kleberg, Kenedy
and Brooks.  For a rough estimate of the amount of land within the study area, the
political boundaries were used to begin defining the study area.  The political
boundaries are obtained from a database established at the Center for Research in
Water Resources (CRWR).  The counties involved in the study were selected using
the Arc command Reselect.
Arc: reselect counties count
>: res name = "MCMULLEN"
>:~
Do you wish to re-enter expression? (y/n) n
Do you wish to enter another expression? (y/n) y
>: ares name = "LIVE OAK"
>:~
Do you wish to re-enter expression? (y/n) n
Do you wish to enter another expression? (y/n) y
>: ares name = "BEE"
19
>:~
Do you wish to re-enter expression? (y/n) n
Do you wish to enter another expression? (y/n) y
>: ares name = "REFUGIO"
>:~
Do you wish to re-enter expression? (y/n) n
Do you wish to enter another expression? (y/n) y
>: ares name = "ARANSAS"
>:~
Do you wish to re-enter expression? (y/n) n
Do you wish to enter another expression? (y/n) y
>: ares name = "SAN PATRICIO"
>:~
Do you wish to re-enter expression? (y/n) n
Do you wish to enter another expression? (y/n) y
>: ares name = "JIM WELLS"
>:~
Do you wish to re-enter expression? (y/n) n
Do you wish to enter another expression? (y/n) y
>: ares name = "DUVAL"
>:~
Do you wish to re-enter expression? (y/n) n
Do you wish to enter another expression? (y/n) y
>: ares name = "NUECES"
>:~
Do you wish to re-enter expression? (y/n) n
Do you wish to enter another expression? (y/n) y
>: ares name = "KLEBERG"
>:~
Do you wish to re-enter expression? (y/n) n
Do you wish to enter another expression? (y/n) y
>: ares name = "KENEDY"
>:~
Do you wish to re-enter expression? (y/n) n
Do you wish to enter another expression? (y/n) y
>: ares name = "BROOKS"
>:~
Do you wish to re-enter expression? (y/n) n
Do you wish to enter another expression? (y/n) n
20
The coverage file with the political boundaries is projected from a
LAMBERT projection to an ALBERS projection.  The ALBERS projection is used
in the Texas State Mapping System.  The projection file used is written in an Arc
Macro Language (AML) file called lamtsms.prj and is presented below.
LAMTSMS.PRJ
Input
projection LAMBERT
units meters
datum NAD83
parameters
34 00 0.000
27 25 0.000
-100 00 0.000
31 10 0.000
1000000.00000
1000000.00000
output
projection ALBERS
units meters
datum NAD83
parameters
27 25 00
34 55 00
-100 00 00
31 10 00
1000000.0
1000000.0
End
The Arc command Project is used to project the file to the Texas State
Mapping System.  Once the projection is done, the Arc command Build must be
used to establish polygon topology. 
21
Arc: project cover count countt lamtsms.prj
Arc: build countt poly
Figure 2.1 shows the twelve counties involved in the CCBNEP study area.
22
Figure 2.1: Counties Participating in the CCBNEP.
23
2.2.2 Hydrologic Unit Codes (HUC)
Hydrologic Unit Codes (HUC) boundaries are a subdivision of the United
States made by the United States Geological Survey (USGS), to show major and
minor river basins.  Hydrologic Unit Maps divide the United States into 21 major
hydrologic regions, 222 subregions, 352 accounting units, and 2,100 cataloging
units.  The major basins within the United States have a 2 digit HUC boundary
code, and smaller subbasins have 4, 6, and 8 digit codes (USGS, 1996).  The USGS
1:250,000-scale HUC gives approximate drainage basin boundaries for this study.
The HUC files were obtained from a database established at CRWR.  The data files
can be obtained from the Internet site:
http://nsdi.cr.usgs.gov/nsdi/products/huc.html
The HUC numbers were found using the identification icon in ArcView.
The HUCs in the study area were selected using the Arc command Reselect.
Arc: reselect huc250 cchuc
>: res huc >= 12110201
>:~
Do you wish to re-enter expression? (y/n) n
Do you wish to enter another expression? (y/n) y
>: res huc <= 12110207
>:~
Do you wish to re-enter expression? (y/n) n
Do you wish to enter another expression? (y/n) y
>: ares huc >= 12100404
>:~
Do you wish to re-enter expression? (y/n) n
Do you wish to enter another expression? (y/n) y
>: res huc >= 12100405
>:~
Do you wish to re-enter expression? (y/n) n
Do you wish to enter another expression? (y/n) y
24
>: res huc >= 12100406
>:~
Do you wish to re-enter expression? (y/n) n
Do you wish to enter another expression? (y/n) y
>: res huc >= 12100407
>:~
Do you wish to re-enter expression? (y/n) n
Do you wish to enter another expression? (y/n) y
>: res huc >= 12111011
>:~
Do you wish to re-enter expression? (y/n) n
Do you wish to enter another expression? (y/n) n
The HUC cover needs to be projected from ALBERS projection with datum
NAD27 to an ALBERS projection with datum NAD28.  The ALBERS projection
with datum NAD28 is used in the Texas State Mapping System.  The projection file
used is written in a file called huctsms.prj and is shown below.
HUCTSMS.PRJ
Input
projection ALBERS
units meters
datum NAD27
parameters
29 30 0.000
45 30 0.000
-96 00 0.000
23 00 0.000
0.00000
0.00000
output
projection ALBERS
units meters
datum NAD83
parameters
27 25 0.000
25
34 55 0.000
-100 00 0.000
31 00 0.000
1000000.00000
1000000.00000
End
The final projected HUC file must then be built to establish polygon
topology.  This can be done using the Arc command Project and the Arc command
Build.  Figure 2.2 shows the HUC files along with the names and numbers of each
of the HUCs.
Arc: project cover cchuc cchuctsms huctsms.prj
Arc: build cchuctsms poly
2.2.3 Digital Elevation Model (DEM)
Digital elevation models are a sampled array of elevations for ground
positions.  The maps are distributed by the USGS.  This study used 1-degree DEMs
which are also called 3-second or 1:250,000 scale DEM data (USGS, 1996).  The
nine compressed DEM files which are needed for the study area are Beeville-e,
Beeville- w, Crystal_City-e, Corpus_Christi-e, Corpus_Christi-w, Laredo-e,
Laredo-w, Port_Isabel-w, and McAllen-e. These files were downloaded via the
Internet from the USGS Internet site:
http://edcwww.cr.usgs.gov/doc/edchome/ndcdb/ndcdb.html
Once the Internet page is accessed, click on the area of interest.  A
magnified view of the area will appear.  Again, click on the desired area of interest.
Each map sheet has prompts for the uncompressed and compressed ftp sites. Click
on the uncompressed DEM file. Once the file is downloaded use the Save-as
command in the pulldown menu File of the web browser to save the file to the
desired directory.
26
Figure 2.2: Hydrologic Unit Code Boundaries within the CCBNEP Study
Area.
27
2.2.3.1 Converting Files to Arc/Info Format
The DEM files that were downloaded need to be converted into Arc/Info
format.  The following commands rearrange the block sizes for Arc/Info
processing.
$ dd if=beeville-e of=beee.dem cbs=1024 conv=unblock
This should be done for each of the files which are saved.  Next, the files
need to be converted to Arc/Info format.  This can be done by starting Arc and
typing the following:
Arc: demlattice beee.dem beeedem usgs
This produces an Arc/Info grid called beeedem which contains the raw
DEM data in geographic coordinates.
2.2.3.2 Merging the Grids
The nine grids are merged to form the large grid called corpus2.  The grid
can be viewed either by ArcView or the by using Grid display.  The following
commands enable the reader to view the merged grid via Grid display.
Arc: grid
Grid: corpus2 = merge ( beeedem, beewdem, ccedem, cowdem, coedem,
laedem, lawdem, piwdem, maedem )
Grid: display 9999
Grid: mape corpus2
Grid: gridpaint corpus2 value linear nowrap gray
Next, the DEM grid needs to be projected from GEOGRAPHIC projection
28
to an ALBERS projection.  The WGS84 datum is used in the Texas State Mapping
System.  This datum is functionally equivalent to the NAD83 datum used for the
previous data sets.  The projection file used is written in an AML file called
demtsms.prj and is shown below.
DEMTSMS.PRJ
Input
projection GEOGRAPHIC
datum WGS72
units ds
parameters
output
projection ALBERS
datum WGS84
units meters
parameters
27 25 00
34 55 00
-100 00 00
31 10 0.000
1000000.0
1000000.0
End
The Arc projection command is shown below.
Arc: project grid corpus2 corptsms demtsms.prj
2.2.3.3 Reducing the DEM to the Study Area
The DEM needs to be clipped to a smaller size to fit the study area.  This is
accomplished by making a 5000-m buffer from the projected HUC file.  Next, this
coverage is used to clip the DEM file to a more workable size.  The buffer needs to
include more than the study area to ensure all the drainage into the study area is
29
encompassed.
Arc: buffer cchuc2tsms tsmsbuff # # 5000 # poly flat #
Arc: grid
Grid: display 9999
Grid: mape tsmsbuff
Grid: arcs tsmsbuff
Grid: gridpaint corptsms value linear nowrap gray
Grid: setwindow tsmsbuff corptsms
Grid: corp2tsms = corptsms
Figure 2.3 shows the DEM of the study area within the buffered HUC
boundaries.
30
Figure 2.3: Digital Elevation Model for the CCBNEP Study Area.
31
2.2.4 USGS Land Use Data
Land use data files describe the vegetation, water, natural surface, and
cultural features on the land surface (TNRIS, 1996).  The land use data was
obtained from the Texas Natural Research Information System's Internet Site:
http://www.tnris.state.tx.us/pub/GIS/topography/LULC/.  Once at the site, click
on the download prompt, followed by clicking on the index of files prompt.
Finally, the land use prompt is selected, with the 250k files being selected last.
The land use files for the map sheets Beeville, McAllen, Corpus Christi,
Crystal City and Laredo were downloaded in export format (e.00).
The land use/land cover files use the Anderson Land Use Code
classification system, in which land use types are broken into 9 basic categories
with the second digit distinguishing subcategories of these principal categories.
1 = urban
2 = agriculture
3 = range land
4 = forest
5 = water
6 = wetlands
7 = barren
8 = tundra
9 = ice and snow
Subcategories
11 = urban residential
12 = urban commercial
13 = urban industrial
etc.
This land use classification was made in the late 1970?s, and the land use
has obviously changed in the years since, particularly urban areas have grown in
size.  However, the land use/land cover files are still the standard land use
32
classification of the United States as a whole.  Certain areas of the United States
have developed updated land use maps, for example the area surrounding Corpus
Christi Bay which was described with a separate land use coverage, as described in
Section 2.2.4.3.
2.2.4.1 Converting the Files to Arc/Info Format
The land use files need to be imported into a format which Arc/Info can
read.  Once this is done, the Arc Build command needs to be used to build polygon,
node and line topology.  Before the build command can be used, it is a good idea to
use the Clean command to trim extraneous arcs and to join arcs which are within
the fuzzy tolerance.
Arc: import cover beeville.e00 lubee
Arc: clean lubee
Arc: build lubee
Do this for all the files that are downloaded from the Texas Natural
Resource Information System's Internet Site.  The files also need to be dissolved.
This eliminates any overlaying lines in the maps.
Arc: dissolve lubee lubeed landuse-id poly
The maps then need to be edited using ArcEdit.  This is due to the lines and
nodes which do not line up due to map making errors.  The command to use in
ArcEdit is called Edgematch.  Use an edit coverage and a snap coverage.  Nodes are
used as the edit feature for both coverages.  The procedure is done manually using
the Add Automatically and Delete Many commands.
33
2.2.4.2 Joining Land Use Maps
Once the maps are edited, they must be joined together.
Arc: mapjoin lucorp
Enter the 1st coverage: lubeed
Enter the 2nd coverage: lucod
Enter the 3rd coverage: luccd
Enter the 4th coverage: lula
Enter the 5th coverage: luma
Enter the 6th coverage: ~
Done entering coverage names?(y/n): y
Do you wish to use the above coverages?(y/n): y
The land use maps need to be cleaned once the maps are joined to clip off
extraneous arcs.  They also needed to have node, line and polygon topology built.
Finally, the Arc command Dissolve needs to be used on the big map to rid the map
of overlapping arcs.
Arc: clean lucorp lucorp # # line
Arc: clean lucorp lucorp # # poly
Arc: build lucorp node
Arc: build lucorp line
Arc: build lucorp poly
Arc: dissolve lucorp lucorpd landuse poly
The polygon coverage needs to be projected from LAMBERT projection to
an ALBERS projection.  The projection file used is written in an AML file called
lamtsms.prj and was shown earlier in Section 2.2.1.
Arc: project cover lucorpd lucorptsms lamtsms.prj
The land use map was clipped using the buffered HUC file discussed in
Section 2.2.3.3.  This is done to reduce the file to a more usable size.
34
Arc: clip lucorptsms tsmsbuff landuse poly #
Figure 2.4 shows the land use map encompassing the study area.
35
Figure 2.4: USGS Land Use in the CCBNEP Study Area.
36
2.2.4.3 Updating Land Use
The patch of unknown land use which lies within the study region, south of
Baffin Bay and west of the South Laguna Madre was filled in manually using
ArcEdit.  The unknown portion was assigned to a land use classification of
rangeland.  Figure 2.5 shows the range land polygon added to the land use file.
An updated land use file became available later in the study.  The Texas
Park and Wildlife prepared the files (Hinson, 1995).  This new land use file covered
approximately 19,000 Km
2
 surrounding Corpus Christi Bay and was added to the
original land use file.  The land use files were downloaded from the TNRIS Internet
site format (TNRIS, 1997):
http://www.tnris.state.tx.us/pub/GIS/wetlands/aquatic/cwh/corpus/.  All 71
land use files in the directory were downloaded in a .e00.z.
Unlike the original land use files, the new land use files were grids rather
than coverages.  The files were unzipped using the Guzip command and imported
using the Arc command Import.
$ gunzip -d agua.e00.z
$ arc
Arc: import grid agua.e00 agua
:
$ gunzip -d yarpoee.e00.z
$ arc
Arc: import grid yarpoee.e00 yarpoee
37
Figure 2.5: Land Use in the CCBNEP Study Area with the Missing Data Area
Being Assigned Range Land.
38
The files were merged into six files and then those six files were merged
into one large file.
Arc: grid
Grid: nland1 = merge ( agua, alices, allyns, anna, aranpass, banq, bayside,
bense, bhead, chapman, cinw, cinwoee )
Grid: nland2 = merge ( cisw, conc, corp, cranell, dinero, drisce, driscw, edroy,
ella, escon, estes, greg )
Grid: nland3 = merge ( kinge, kingnw, kingw, kleberg, lamar, laureles, mathis,
mesq, mission, odem, orange, osone )
Grid: nland4 = merge ( osonw, paisano, panth, papa, petrne, pita, por,
portland, premonte, prtaran, prtingl, rbeach )
Grid: nland5 = merge ( rbne, rbnw, ricardo, rinc, riviera, robs, rock, sandia,
sanpat, sbi, sbinw, sbise )
Grid: nland6 = merge ( sintone, sintonw, stcb, stcbse, stcbsw, taft, tynan,
woods, wsinton, yarp, yarpoee )
Grid: nland = merge ( nland1, nland2, nland3, nland4, nland5, nland6 )
The final merged grid needs to be projected from UNIVERSAL
TRANSVERSE MERCATOR projection to an ALBERS projection. The projection
file used is written in an AML file called utmtex.prj and is shown below.
UTMTEX.PRJ
input
projection UTM
units meters
datum NAD83
zone 14
parameters
output
projection ALBERS
units meters
datum NAD83
parameters
27 25 00
39
34 55 00
-100 00 00
31 10 00
1000000.0
1000000.0
end
Arc: project cover nland newland utmtex.prj
The new land use files use a different land use classification numbering
system that the original files (Hinson, 1995).  The newland gridvalue field was
manually converted to reflect the Anderson Land Use System.  The ArcView
pulldown menu Edit was used to begin editing the newland grid attribute table.  A
new field was added called landuse.  The following list shows the gridvalue number
from the new land use file and the converted Anderson Land Use System?s
classification and number.
Updated Land Type of Anderson Land
Use Gridvalue             Land Use                          Use Number
0 no data 0
1 commercial 12
2 commercial 12
3 industrial 13
4 residential 11
5 residential 11
6-9 agricultural 20
10-27 range land 30
28-30 barren 70
31-70 wetlands 60
71-79 water 50
The part of the newland grid which contained zero as its grid cell value was
calculated to equal the original land use file.  This was done by first converting the
original land use files to grids using the Arc command Polygrid.  Next, the Grid
40
command Con was used to redefine the zero value grid cells to the old land use cell
values.
Arc: grid
Grid: biglandg = polygrid ( lucorptsms, landuse, #, #, 100 )
Grid: landuseg = polygrid ( landuse, landuse, #, #, 100 )
Grid: newlandbg = merge ( biglandg, landuseg )
Grid: newland2 = con ( newland == 0, biglandg, newland )
Grid: newlandbg2 = merge ( newlandbg, newland2 )
The new land use grid needs to be clipped to the study area size for a more
manageable file.  The command uses the delineated watershed grid for the study
area which is discussed in Chapter 3.  This states that the new land use file equals
no data if the file lies outside of the study area, but equals the land use grid value if
inside the study region.
Grid: landuseg2 = con ( big1 == 1, newlandg2, big1 )
The newly merged land use grid is converted to a polygon coverage and
cleaned.
Grid: landuse2 = gridpoly ( landuseg2 )
Grid: q
Arc: clean landuse2
Once this is completed, the landuse2 attribute table is opened in ArcView,
and a new field called landuse is added.  The new landuse field is calculated to
equal the gridvalue field using the ArcView pulldown menu Field with the
Calculate option.  Figure 2.6 shows the updated land use within the study region.
41
Figure 2.6: Updated Land Use in the CCBNEP Study Region.
42
2.2.5 USGS Stream Gauges
The stream gauge locations in the study area can be obtained from the Texas
USGS Internet site: http://txwww.cr.usgs.gov/cgi-bin/nwis1_server
At the web page, click on the item that will allow data to be viewed by an
Index of Gauging by HUC (140kb).  From this page, download the stream gauges in
each of the HUCs associated with the project which are West San Antonio Bay,
Aransas Bay, South Corpus Christi Bay, North Laguna Madre, Baffin Bay, Central
Laguna Madre, Palo Blanco, San Fernando, and North Corpus Christi Bay.
Table 2.1 shows the HUC number which the stream gauge is located, the
stream gauge number, the stream gauge location, the latitude and longitude, and the
dates which data is available. (TXUSGS, 1996).
Table 2.1: Data for the Stream Gauges in the CCBNEP Study Region.
HUC Sta Location Latitude Longitude Date
12110205 8212400 Los Omos Creek near Falfurrias, TX 27 15 51 098 08 08 Jan 1, 1967-Sept 30, 1983
12110204 8211900 San Fernando Creek at Alice, TX 27 46 20 098 02 00 Jan 1, 1965-Mar 5, 1987
8211800 San Diego Creek at Alice, TX 27 45 59 098 04 31 Oct 10, 1963-Oct 1, 1989
12110202 8211520 Oso Creek at Corpus Christi, TX 27 42 40 097 30 06 Sept 9, 1972-present
12100407 8189800 Chiltipon Creek at Sinton, TX 28 02 48 097 30 13 July 23, 1970-Apr, 06, 1987
Aug 4, 1987-Sept 4, 1991
8189700 Aransas River near Skidmore, TX 28 16 56 097 37 14 Apr 1 , 1964-present
12100406 8189500 Mission River at Refugio, TX 28 17 30 097 16 44 July 1, 1939-present
8189300 Medio Creek near Beeville,TX 28 28 58 097 39 23 Mar 1, 1962-Sept 17, 1977
12100405 8189200 Copano Creek near Refugio, TX 28 18 12 097 06 44 June 17, 1970-present
2.2.5.1 Create a Stream Gauge Coverage
Once the stream gauge files are downloaded, a data file needs to made using
the text editor listing the stream gauge latitude and longitude in decimal degree
format.  This can be done using the following formula:
DD = D + Min/60 + Sec/3600
43
The following is the beginning of the data file created for the study area.
The file is called lonlat.dat.
1 -98.1356 27.2642
2 -98.0333 27.7722
:
Note that West longitude is treated as negative in decimal degree format.  A
point coverage of the digital coordinate data is built using the ARC/Info Generate
command.  The lonlat.dat file is specified as the input and points as the geographic
feature type.  Once the coverage is created, point topology is established through
the Build command.  The digital coordinate values are added as attributes to each
point by using the Addxy command.
Arc: generate station
Generate: input lonlat.dat
Generate: points
Creating points with coordinates loaded from lonlat.dat
Generate: quit
Externally BND and TIC..
Arc: build station points
Arc: addxy station
A second data file is created for the station name and number.  The file was
called statname.dat.  The beginning of this file is shown below:
1 08212400 Los_Omos
2 08211900 San_Fernando
:
The following commands are used to create an attribute table for the stream
gauge point coverage.
44
Arc: tables
Enter Command: define attribut.dat
1
Item name: stations-id
Item width: 4
Item Output Width: 4
Item Type: i
5
Item Name: stat-num
Item width: 10
Item Output Width: 10
Item Type: c
15
Item Name: stat-name
Item width: 15 7
Item Output Width: 15
Item Type: c
Item Name:
Enter Command: add from statname.dat
Finally, the attribute tables need to be joined to the point coverage, and the
point coverage needs to be projected from GEOGRAPHIC projection to an
ALBERS projection.  The projection file used is written in an AML file called
geotsms.prj and is shown below.
GEOTSMS.PRJ
Input
projection GEOGRAPHIC
units dd
datum NAD83
spheroid GRS1980
parameters
output
projection ALBERS
units meters
datum NAD83
45
spheroid GRS1980
parameters
27 25 00
34 55 00
-100 00 00
31 10 00
1000000.0
1000000.0
end
Arc: Joinitem stations.pat attribut.dat stations.pat stations-id stations-id
Arc: project cover stations corpgages geotsms.prj
Figure 2.7 shows the stream gauges within the CCBNEP study area.
46
Figure 2.7: Stream Gauges in the CCBNEP Study Area.
47
2.2.6 Precipitation Data
The precipitation grid is obtained from an Oregon State University
anonymous ftp site: fsl.orst.edu.  The precipitation grid is projected to the Texas
State Mapping System which is an Albers.
The precipitation data from Oregon State University is a mean annual
precipitation grid for the United States based on the years 1961 to 1990.  The grid
was developed using an interpolation process called PRISM, and is verified by
consultation with State Climatologists (Oregon, 1996).  Figure 2.8 shows the
precipitation grid downloaded from Oregon State University.
No coordinate system was defined for the precipitation grid.  However, the
X and Y boundary values shown indicate that the precipitation grid is in a
geographic projection with the decimal degrees specified as the units of measure. In
order to select a smaller portion of the grid the buffered HUC file is projected from
the ALBERS projection used in the Texas State Mapping System to the
GEOGRAPHIC projection.  This newly projected file, geobuff, is used to reduce
the precipitation file to a more usable size.  The projection file tsmsgeo.prj is listed
below:
48
Figure 2.8: Original Precipitation Used in the Study.
49
TSMSGEO.PRJ
input
projection ALBERS
units meters
datum NAD83
spheroid GRS1980
parameters
27 25 00
34 55 00
-100 00 00
31 10 00
1000000.0
1000000.0
output
projection GEOGRAPHIC
units dd
datum NAD83
spheroid GRS1980
parameter
end
Arc: project cover tsmsbuff geobuff tsmsgeo.prj
The Grid Setwindow command is used to reduce the analysis window to the map
extent of the buffered coverage. A smaller precipitation grid which
contains the values from the large precipitation grid is defined within this window.
The smaller grid is then projected to the ALBERS projection from the
GEOGRAPHIC projection using the demtsms.prj projection file.  This file was
listed in Section 2.2.3.2.
Arc: grid
Grid: display 9999
Grid: mape geobuff
Grid: linecolor 3
Grid: arcs geobuff
50
Grid: setwindow geobuff p_ann
Grid: precip = p_ann
Arc: project grid precip precip2 geotsms.prj
The precipitation grid covered the land surface over the study area, however
much of the bay system did not have precipitation data.  A series of Grid commands
are used to fill in the missing data.  The Setcell command is used to set the cell size
to equal the original precipitation grid.  Setwindow is used to decrease the size of
the area of the precipitation grid to be analyzed.  The Grid command Gridpoint
converted the precipitation grid to a point coverage, which is used with the Trend
command to create the new grid.  The Trend command performs a trend
interpolation on a point coverage and calculates the new grid.  The inputs are a
point coverage, name of the item to be used for the interpolation, the order of the
equation to be used for the analysis, the cell size, and the extent of the analysis.
The Setwindow and Setcell commands determine the extent of the analysis and the
cell size of the new grid.  A second order equation is specified while using the
Trend function.  The point coverage was calculated using the Gridpoint command
where the grid value was named ?rain?.  The new precipitation grid and the original
precipitation grid are merged together to create a new grid with the no data cells of
the original grid filled in with the new grid.  The original precipitation grid was
displayed in the Grid Display window before the calculations were begun.
$: arc
Arc: grid
Grid: setcell precip2
Grid: setwindow precip2
Grid: mape precip2
Grid: gridpaint precip2 value linear nowrap gray
Grid: setwindow *
Define box
Grid: precip3 = precip2
51
Grid: precippnt = gridpoint ( precip3, rain )
Grid: precipgrid = trend ( precippnt, rain, 2, linear, 100, # )
  Coeff #               coeff  _
0 -306.535
1  0.003
2 -0.007
3  0.000
4  0.000
5  0.000  _
RMS Error = 25.002
Chi-square = 507561.475
Grid: setcell precip2
Grid: setwindow precip2
Grid: precipmerge = merge ( precip2, precipgrid )
Figure 2.9 shows the precipitation trend grid, and Figure 2.10 shows the
merged precipitation grids.  This process does not effect any of the cells in the
original precipitation grid, but fills in the missing cells over the bays and barrier
islands with reasonable precipitation values.
52
Figure 2.9: Precipitation Trend Found Using the Grid Function Trend.
53
Figure 2.10: Merged Precipitation Grids.
54
2.2.7 Digital Line Graphs (DLG)
The 1:100,000 DLG files include hydrologic features such as stream
networks, standing water, and coastlines. The USGS also produces DLG files for
roads, railways, and pipelines (USGS, 1996). The DLG files are obtained from the
USGS Internet site: http://edcwww.cr.usgs.gov/doc/edchome/ndcdb/ndcdb.html.
This Internet page has various sites to obtain information.  Page down to
the100,000-Scale Digital Line Graph (DLG)site and click on: via graphics (DLG)
The DLG files can also be obtained from a USGS 14-volume Compact
Disc-Read Only Memory (CD-ROM) set which can be obtained from the USGS
Earth Science Information Center in Reston, VA.  The DLG files for this study are
downloaded from Volume 8 (Texas and Oklahoma) of the CD-ROM series (USGS,
1993).  The Hydrology files were located in the 100k_dlg directory.  This directory
contains directories for each of the map sheet in the Texas/Oklahoma region.  The
Internet site is used to obtain the map sheet names needed for the study.  The
hydrology files for each of the map sheets were downloaded from the CD-ROM to
a PC workspace, and were then brought over to the UNIX workspace by ftp.  The
files needed for this study are listed below:
NAME                                     FILE
Goliad be1hydro.zip
Port Lavaca be2hydro.zip
Beeville be3hydro.zip
San Antonio Bay be4hydro.zip
Corpus Christi cc1hydro.zip
Allyns Bight cc2hydro.zip
Baffin Bay cc3hydro.zip
Laredo ly1hydro.zip
Alice ly2hydro.zip
San Ygnacio ly3hydro.zip
55
Falfuria ly4hydro.zip
Encino m32hydro.zip
Pleasanton x42hydro.zip
George West x44hydro.zip
Port Mansfield zi1hydro.zip
2.2.7.1 Converting the Files to Arc/Info Format
Each of these files needs to be unzipped. This creates eight coverages. An
example for the first file is shown.
$ unzip be1hydro.zip
Exploding: be3hyf01
Exploding: be3hyf02
:
Exploding: be3hyf08
The files then need to be converted from a DLG format to an Arc/Info
format.  The Dlgarc command is used for this.  After the files are converted, the
line topology must be built using the Build command.  An example for the first file
is shown.
Arc: dlgarc optional be1hyf01 be1f01
Arc: build be1f01
2.2.7.2 Joining the DLG Coverages
The border around each map needs to be removed.  This is done by
acknowledging that all arcs in a line coverage have left and right polygon numbers
associated with them.  The value of the exterior polygon number is always one.
The Arc Reselect command is used to remove the lines from the coverage.
Arc: reselect be1f01 be101 line # line
>: res rpoly# > 1
56
>: ~
Do you wish to re-enter expression? (Y/N) n
Do you wish to enter another expression? (Y/N) y
>: res lpoly# > 1
>: ~
Do you wish to re-enter expression? (Y/N) n
Do you wish to enter another expression? (Y/N) n
Once the border is removed, the DLG coverages can be joined together
using the Arc Append command.  The Build command is used to add line topology
to the large map.
Arc: append bigmap
Enter the 1st coverage: be101
Enter the 2st coverage: be102
:
:
Enter the 116st coverage: zi101
Done entering coverage names?(Y?N) y
Do you wish to use the above coverages? (Y/N) y
Appending coverages...
Arc: build bigmap line
Arc: project cover bigmap dlgtsms utmtsms.prj
Finally, the appended coverage is projected from the UNIVERSAL
TRANSVERSE MERCATOR (UTM) projection to the Texas State Mapping
System which is in an ALBERS projection with the projection file utmtsms.prj.
The projection file is shown below:
UTMTSMS.PRJ
Input
projection UTM
units meters
57
datum NAD27
spheroid CLARKE1866
zone 14
parameters
output
projection ALBERS
units meters
datum NAD83
spheroid GRS1980
parameters
27 25 00
34 55 00
-100 00 00
31 10 00
1000000.0
1000000.0
end
Figure 2.11 shows the DLG file for the study area.  Note how the low
amount of runoff draining to the South Laguna Madre leads to a poorly defined
stream network in this region.
58
Figure 2.11: Digital Line Graph File Used in the CCBNEP Study.
59
2.2.8 Event Mean Concentration (EMC)
Constituent concentrations need to be assigned to each cell in order to
calculate the land surface loads.  This study uses Event Mean Concentration (EMC)
values obtained from a previous CCBNEP analysis, Characterization of Non-point
Sources and Loadings to the Corpus Christi Bay National Estuary Program Study
Area (Baird,1996).  This study developed EMC values from water quality analysis
performed at the Oso Creek and Seco Creek USGS Stream Gauges.  The Oso Creek
gauge is located west of Corpus Christi and represents agricultural land use.  The
gauges located on Seco Creek are northwest of Hondo, TX, and represent range
land uses.
EMC values for 18 constituents were listed in this study and are shown in
Table 2.2.  The values in the table are typical concentrations of constituents found
in runoff water from each particular land use.  The values are compiled from many
studies done by the USGS and other organizations.
60
Table 2.2: Event Mean Concentration Values used in the CCBNEP Study.
Two EMC studies are being conducted near Edroy, TX and at the King
Ranch which is located in southeast Texas.  Both studies represent agricultural land
use.  Provisional results from the two studies are showing lower total nitrogen EMC
values for rangeland and agricultural land uses.  Once the EMC studies are
completed, it would be feasible to incorporate the data into the Total Loads and
Water Quality Project for the Corpus Christi National Estuary Program.  The
provisional data are analyzed in Section 4.3.3.
61
3 METHODOLOGY
The ArcView project described in this section calculates the atmospheric,
land surface, point source and Nueces River loads for the Corpus Christi National
Estuary Program?s total loadings study.  The project includes calculations for land
surface concentrations and runoff on a per cell basis.  The project has capabilities to
delineate watersheds.  Finally, the project is able to calculate the receiving water
body?s equilibrium concentrations due to point, non-point, atmospheric, and
upstream watershed loads.
In so far as possible, the commands used to accomplish the calculations are
written in bold type, while the computer prompts are in italics.  The scripts are
listed in Appendix B.
3.1 Grid-Based Watershed Modeling Using Digital Elevation Data
To begin the assessment of land surface loadings, it is necessary to
recondition the digital elevation model (DEM) which involves making the DEM
and the hydrology digital line graph (DLG) files agree.  This is so the precipitation
can be directed from the land surface to the receiving waters using the DEM.  The
reconditioning process involves digging a trench into the DEM where the riverbed
is located according to the DLG files.  Once the DEM has been reconditioned, the
river network and bay system are connected by dropping the centroid of the bay
coverage below the value of the surrounding cells, so that the flow is accumulated
at that point.
3.1.1 Recondition the DEM Surface Model
AGREE is a surface reconditioning system for a DEM (Hellweger, 1996).
62
The program is shown in Appendix B at the end of the report.  The system adjusts
the surface elevation of the DEM to be consistent with a vector coverage.  The
vector coverage can be a stream or ridge line coverage.  For the CCBNEP project,
the vector coverage is the DLG discussed in Section 2.2.7.  The AGREE system is
an alternative to the "burning in the streams" process which simply drops the
elevation of the cells corresponding to the DLG files.  The AGREE reconditioning
process lowers the grid cells corresponding to the DLG files by an amount
designated by the user, while the cells within a buffer distance of the rivers are
altered to have a smooth transition from the unmodified land surface to the lowered
river cells.  Figure 3.1 shows the AGREE methodology.
The program is run within the ArcView environment.  The elevation grid
and the vector coverage should be active in the view.  The script asks for five
parameters, which are the 1) elevation grid, 2) vector coverage, 3) buffer distance,
4) smooth drop/raise distance, and 5) the sharp drop/raise distance.  Once the
program is running, the temporary grids do not need to be saved.
Figure 3.2 shows a sample area near Corpus Christi Bay of the recondition
DEM streams and the DLG file.  This process forces the DEM drainage to follow
the mapped hydrology DLG streams and rivers.
Before further calculations can be done using the DEM, data errors must be
corrected.  The DEM may contain sinks, which are a single grid cell or groups of
cells surrounded by cells of higher elevation.  The errors are corrected by using the
Arc command Fill, which assigns the elevation of each sink to be equal to the value
of its lowest elevation neighbor.  This is accomplished by using the ArcView?s
Hydrology extension?s pulldown menu with the Fill option.  The reconditioned
grid must be active in the view.
63
ORIGINAL ELEVATION
MODIFIED ELEVATION
KNOWN STREAM LOCATION
AND STREAM DELINEATED
FROM MODIFIED ELEVATION
GRID CELL SIZESECTION A-A
STREAM DELINEATED
FROM ORIGINAL ELEVATION
ELEVATION
RESOLUTION
GRID
CELL SIZE
PLAN
AA
Figure 3.1: Methodology of the Reconditioning System.
64
Figure 3.2: Delineated Rivers Compared to the DLG File.
65
3.1.2 Connecting the Bay System with the River Network
CONNECT, listed in Appendix B, is an Avenue script which connects a
reconditioned DEM with a polygon coverage representing the segments of the bay
system by dropping the centroid of the polygon coverage below the elevation of the
surrounding cells (Hellweger, 1996).  The elevation grid is altered to create
artificial sinks corresponding to the centroid of the segments of the polygon
coverage.  Lowering the elevation in the segments an arbitrary amount creates the
sinks, and then the bottom of the segment is sloped towards the centroid of the
segment area.  Figure 3.3 shows the methodology of the connection between river
network and the bay system.
66
OCEAN
LAND SURFACE
BAY
SEGMENT
SINK
ELEVATION
MSL
SECTION
PLAN
CENTROID
FLOW PATH
Figure 3.3: Methodology of the Connection between the River Network and
the Bay System.
67
3.1.2.1 Bay Coverage
The bay coverage used in the total loadings study is developed using the
River Reach Files (RF1) downloaded from the USGS Internet site (USGS, 1996):
http://h2o.er.usgs.gov/nsdi/wais/water/rf1.html.
The bay segmentation is obtained from the report entitled Corpus Christi
Bay National Estuary Program, Ambient Water, Sediment and Tissue Quality of
Corpus Christi Bay Study Area: Present Status and Historical Trends, Summary
Report (Ward, 1996).  The bays are segmented according to natural bathymetry and
water flow in the system.  The original 189 segments defined in the report are too
detailed for the total constituent loading project due to the time that would be
involved in calibrating the equilibrium concentration model.  The original bay
coverage is clipped using the RF1 file to remove parts of the bay system which
covered the land surface.  A total of 14 segments are thus eliminated.  The
remaining segments are grouped together to form the 20 segments used in this study
keeping the natural bathymetry and water flow in mind.  Figure 3.4 compares the
clipped segmentation with the new bay model graphically, while Table 3.1
compares the clipped and grouped segmentation.
68
(a) (b)
Figure 3.4: Comparison between the a) New Bay Segmentation and the b)
Clipped Segmentation.
69
Table 3.1: Comparison between the Original Segmentation and the New Bay
Model Segmentation.
3.1.2.2 A Problem Encountered when Connecting the Land Surface with the
Bay System.
A problem was encountered at the bay system boundaries when first
Segment ID Ward and Armstrong Segments
St. Charles stc SC1, SC2, SC3
Copano cop M1, M2, AR1, PB1, PB2, CP01, CP02, CP03,
CP04, CP05, CP06, CP07, CP08, CP09, CP10
Aransas North aran I1, I2, I3, AYB, MB1, MB2, CB, CBY1, CBY2
Aransas Middle aram A1, A2, A3, A4, A5, I4, I5, A8, A9, A10, LB
Ar ans as  South ar as A6 , A1 1 , A1 2 , A1 3 , I6 , I7, I8 , LAC
Redfish red RB1, RB2, RB3, RB4, RB5, RB6, RB7, RB8,
RB9, CBH, HI1, HI2
Inlet inl INL
Nueces  Wes t nuew ND1, ND2, ND3, ND4, NB1
Nueces Middle nuem NR1, NR2, NR3, NR4, NR5, NB2, NB3, NB4
Nueces East nuee NB5, NB6, NB7, NB8, NB9
Inner Harbor inh IH1, IH2, IH3, IH4, IH5, IH6, IH7
Corpus North ccbn LQ1, LQ2, C15, C16, C17, C18, C19, C20,
C2 1 ,  C2 2 ,  C2 3 ,  CCC3 ,  CCC4 ,  CCC5 ,  CCC6 ,
CCC7 ,  CCC8
Corpus Middle ccbm C01, C02, C03, C04, C05, C06, C07, C08,
C1 0 ,  C1 1 ,  C1 3
Cor pu s  E as t ccbe CCC1 ,  CCC2 ,  E F ,  C1 2 ,  C0 9 ,  C1 4
Corpus South ccbs C24, I9, C25
Oso oso OS1, OS2, OS3, OS4, OS5, OS6, OS7
ULM North ulmn UL01, UL02, UL03, UL04, UL05, UL06, UL07,
UL08, UL09, UL10, I10, I11, I12, I13, I14
ULM Middle ulmm UL11, UL12, UL13, UL14, I15, I16, I17, I18
ULM South ulms none
Baffin baf GR1, GR2, LS1, LS2, AL1, AL2, BF1, BF2, BF3
70
applying the CONNECT methodology.  An extended portion of the DEM is used to
ensure all of the study site is encompassed.  Therefore, in the northeast corner of the
study site, some of the flow was directed into the north Aransas Bay segment.
However, the flow from the land surface may actually flow north rather than
directly into the bay.  This problem is corrected manually by setting the runoff
inflow into this bay segment to zero.
Figure 3.5 shows the flow error into the north segment of Aransas Bay
caused by the extended DEM.
71
Figure 3.5: North Aransas Bay Segment Flow Problem Due to Extended DEM.
72
3.1.3 Eight-Direction Pour Point Model
Once the reconditioned and filled elevation grid has been connected to the
bay system, the direction of flow for each cell can be calculated.  After the flow
direction is determined the number of cells upstream of a given grid cell can be
determined.  These two processes are called flow direction and flow accumulation,
respectively.  The calculations are based on the eight-direction pour point model.
The model begins by describing a cell surrounded by its eight neighbors.  A cell
drains to its neighbor of steepest descent as defined by the filled and connected
elevation model.  The drainage connections are traced from cell to cell to develop a
flow direction network.  The flow accumulation grid is calculated by counting the
number of cells that occur upstream of each particular cell (Maidment, 1996).
The flow direction grid is calculated using the ArcView Hydrology
extension?s pulldown menu with the Flow Direction option.  The connected grid
must be active in the view.  Again the ArcView Hydrology extension?s menu is
used, however this time the Flow Accumulation is used and the flow direction grid
must be active in the view.
By acknowledging that surface runoff accumulates in creeks and streams,
the flow accumulation values along streams should be greatest.  Therefore, the flow
accumulation grid can be queried to view the stream network that was embedded
into the elevation grid.  This is done using the ArcView extension?s pulldown menu
Analysis.  The Map Query option is used to query the flow accumulation grid for
all cell values above 20,000.  The resulting grid reflects the string of cells whose
flow accumulation values are greater than 20,000. Each cell is 100m in size or 0.01
Km
2
 in area, so 20,000 cells is equivalent to an upstream drainage area of 200 Km
2
.
Figure 3.6 shows the eight-direction pour point model, while Figure 3.7
shows the flow accumulation of the cells greater than 20,000 near Baffin Bay.
73
67 56 49 46 50
53 44 37 38 48
58 55 22 31 24
61 47 21 16 19
53 34 12 11 12
22448
12484
128 1 2 4 8
21444
111216
00000
0 9 17 40 0
0 093016
001 0
005 8
ELEVATION
FLOW DIRECTION
ACCUMULATED FLOW
4 5 12 17 23
3 3 10 12 16
226810
01478
00347
FLOW
133
163
Figure 3.6: Eight-Direction Pour Point Model.
74
Figure 3.7: Streams with Flow Accumulation Greater than 20,000 Cells Near
Baffin Bay.
75
3.2 Calculating the Precipitation/Runoff Relationship
Non-point source constituents are carried over the land surface into the river
networks and bay system by direct runoff.  For this study, the runoff is assumed to
be a function of precipitation and land use.  The Regression Tool in the software
package Microsoft Excel 5.0 is used to determine the relationship between
streamflow, precipitation and percent land use.
3.2.1 Digital Delineation of Sub-Watershed Drainage Areas From the United
States Geological Survey (USGS) Flow Gauges
 
The United States Geological Survey (USGS) streamflow gauges are
obtained from the USGS Internet site as discussed in Section 2.2.5.  Only gauges in
the study area with more than 10 years of record were used for the study.  The
gauges included are Los Omos, Chiltipin, Aransas, Medio, Mission, Oso, San
Fernando, San Diego, and Copano.
The digital drainage areas are compared to the USGS drainage areas for the
nine gauging stations used in the study for a quantitative check on the accuracy of
the digital drainage basins.  To begin the watershed delineation, a stream link grid
must first be calculated to determine a stream network and direction.  The Avenue
script STRMLNK, listed in Appendix B, is run to determine the stream link grid,
and the Avenue script WTRSHD, listed in Appendix B, is used to calculate the sub-
watersheds in the study area.
For most of the sub-watersheds, the delineated drainage area is comparable
to the USGS drainage area.  However, the delineated drainage areas for Oso and
Copano were smaller than the USGS drainage areas.  This could be due to errors
induced by the relatively flat topography near the coast.  Another explanation of the
drainage area disagreements is found when comparing the RF1 files and the DLG
76
files developed by the USGS.  The RF1 file has the Oso Creek extending farther
northwest than the DLG file.  Since the DLG file is used for the surface
reconditioning process, the Oso Creek sub-watershed is smaller than it would have
been using the RF1 files.
The problem was corrected by finding the depth of precipitation and depth
of runoff for each of the sub-watersheds rather than the total precipitation and total
runoff.  The depths of precipitation and runoff are found by dividing total
precipitation and total runoff by the appropriate drainage areas.  This procedure is
discussed in detail in Sections 3.2.2 and 3.2.3 of this report.
Table 3.2 compares the delineated watershed areas to the USGS watershed
areas, while Figure 3.8 shows the difference between the RF1 and the DLG files
that was encountered within the Oso Creek watershed.   Figure 3.9 shows the study
area defined by the bay system drainage area and the delineated sub-watersheds.
Table 3.2: Comparison of the USGS Calculated Drainage Areas and the
Delineated Drainage Areas for Each of the Sub-Watersheds.
77
Figure 3.8: River Reach Files Compares to the Digital Line Graph File in the
Oso Creek Sub-watershed.
78
Figure 3.9: Sub-watersheds in the CCBNEP Study Region.
79
3.2.2 Average Precipitation for each Delineated Drainage Areas
 
A weighted flow accumulation operation is used to determine the average
rainfall for each drainage area.  A weighted flow accumulation is an extension of
the flow accumulation discussed in Section 3.1.3.  Instead of counting the number
of cells upstream of a particular grid cell, the weighted flow accumulation sums the
grid values from a second grid called the weight grid.  Using the precipitation grid
as the weight grid, the total annual potential runoff is calculated for the study area.
The Arc/Info console is used to calculate the accumulated precipitation grid.
Grid: accprecip2 = flowaccumulation ( fdac2, precip ) * 10
Where the factor of ten is used to convert the precipitation units of depth
(mm) to units of volume (m
3
).  Each cell has an area of 10,000 m
2
.  Or:
Equation 3.1
Volume (m
3
) = Depth (mm) * Area(# cells) * 10
Once the accumulated precipitation grid is calculated, it is added to the
view.  The USGS stream gauge coverage is overlaid and the ArcView inquiry tool
is used to determine the accumulated precipitation for each of the delineated
watersheds by clicking on the grid cell which contains the gauging station.
The depth of precipitation for each sub-watershed is found by dividing the
accumulated precipitation by the delineated drainage area.  Table 3.3 shows the
depth of precipitation while Equation 3.2 is used to calculate the depth of
precipitation.
80
Equation 3.2
Depth (mm) = (Volume (m
3
) / Area of watershed (m
2
)) * 1000 mm/m
Table 3.3: Depth of Precipitation (mm/yr) for Each of the Drainage Areas.
 
Station Precipitation
Name (mm/yr)
Los Omos 618
Chiltipin 841
San Fernando 661
San Diego 665
Oso 775
Aransas 810
Mission 847
Medio 787
Copano 927
3.2.3 Determining Average Runoff per Unit Area at each USGS Gauge
The average streamflow record is determined using the daily streamflow
values from each of the nine gauging stations used in the study.  Only the USGS
stations with a flow record longer than 10 years are used for the study.  The mean
annual flows are found for the years 1961 through 1990 and are shown in Table 3.4.
The Mission River station is the only station of the nine which had the full
30 years of record.  For an accurate establishment of the precipitation/runoff
relationship, the same 30 year period should be used for both the precipitation and
streamflow data.  Therefore, a projected 30 year average annual streamflow at each
gauge, Q
g
, are estimated using the average annual 1961-1990 streamflow at the
Mission River gauge, Q
m
.  These estimates are calculated by multiplying Q
m
 by the
ratio of q
g
/q
m
, where q
g
 is the average annual streamflow at the gauge and q
m
 is the
average annual streamflow at the Mission gauge.  Equation 3.3 is used, and the
streamflow projections are shown in Table 3.5.
81
Table 3.4: Mean Annual Flow (cfs) of Record for Each of the Nine USGS
Gauging Stations.
Year LosOmos Chilt ipin SanFer n SanDiego Oso Ar ansas Mission Medio Copano
1961 64. 60
1962 45. 89 7. 34
1963 6. 38 4. 22
1964 1. 75 5. 84 11. 94 3. 51
1965 9. 85 4. 07 13. 98 52. 70 12. 55
1966 12. 05 2. 73 26. 68 119. 05 1. 60
1967 42. 59 86. 55 43. 77 206. 85 708. 51 182. 90
1968 2. 01 4. 31 0. 16 20. 73 147. 38 14. 42
1969 0. 82 4. 38 0. 26 16. 49 83. 24 3. 24
1970 0. 41 7. 42 4. 64 0. 34 16. 70 73. 72 8. 26 34. 77
1971 34. 43 131. 76 198. 26 103. 48 129. 33 424. 45 12. 56 109. 00
1972 3. 23 40. 15 7. 00 1. 09 18. 31 39. 07 198. 44 7. 27 64. 88
1973 10. 52 92. 30 15. 01 1. 64 74. 59 79. 28 398. 80 11. 63 85. 48
1974 4. 62 13. 85 13. 71 3. 70 10. 42 59. 34 119. 52 0. 83 24. 61
1975 1. 01 13. 17 2. 27 0. 29 16. 13 4. 96 39. 81 0. 62 1. 92
1976 4. 32 66. 67 23. 70 4. 23 55. 03 34. 38 282. 66 20. 48 47. 79
1977 0. 29 29. 63 3. 18 1. 24 16. 83 18. 57 131. 52 18. 89 16. 24
1978 0. 60 17. 84 3. 24 1. 14 36. 92 7. 46 69. 10 64. 73
1979 0. 55 61. 77 11. 22 2. 00 45. 74 18. 95 137. 79 53. 07
1980 5. 46 64. 28 37. 89 5. 82 50. 95 23. 57 128. 31 12. 07
1981 6. 80 48. 54 7. 99 1. 21 41. 41 62. 44 389. 56 150. 58
1982 12. 40 28. 42 2. 64 0. 50 24. 58 12. 76 126. 61 24. 54
1983 0. 00 51. 55 5. 66 2. 52 19. 70 29. 94 184. 85 95. 18
1984 45. 90 2. 70 0. 35 31. 95 8. 88 29. 10 8. 69
1985 58. 04 17. 65 4. 59 29. 44 21. 73 79. 07 15. 79
1986 1. 23 5. 40 0. 79 16. 90 3. 93 44. 69 13. 30
1987 62. 64 7. 81 30. 47 29. 81 101. 29 15. 94
1988 2. 49 0. 14 4. 86 10. 14 9. 22 0. 00
1989 0.47 0.16 2.35 2.34 1.24 0.52
1990 2. 08 18. 65 56. 05 200. 80 36. 75
Mean Annual
Flow ( cf s) 7. 65 40. 01 21. 79 7. 53 28. 70 35. 56 147. 01 19. 40 41. 71
Equation 3.3
Q
g
 = Q
m
 * (q
g
/q
m
 )
Where:Q
g
 = Mean annual flow at gauging station of interest calculated
from period of record.
Q
m
 = Mean annual flow at Mission gauging station calculated
from period of record.
q
g
 = Mean annual flow at gauging station of interest
q
m
 = Mean annual flow at Mission gauging station
82
Table 3.5: Thirty-year Projected Mean Annual Flow for the Period 1961-1990.
Year LosOmos Chilt ipin SanFer n SanDiego Oso Ar ansas Mission Medio Copano
1961 3. 36 17. 58 9. 58 3. 31 12. 61 15. 63 64. 60 8. 52 18. 33
1962 2. 39 12. 49 6. 80 2. 35 8. 96 11. 10 45. 89 7. 34 13. 02
1963 0.33 1.74 0.95 0.33 1.25 1.54 6.38 4.22 1.81
1964 0.62 3.25 1.77 1.75 2.33 5.84 11.94 3.51 3.39
1965 2. 74 14. 34 9. 85 4. 07 10. 29 13. 98 52. 70 12. 55 14. 95
1966 6. 20 32. 40 12. 05 2. 73 23. 24 26. 68 119. 05 1. 60 33. 78
1967 42. 59 192. 83 86. 55 43. 77 138. 32 206. 85 708. 51 182. 90 201. 02
1968 2. 01 40. 11 4. 31 0. 16 28. 77 20. 73 147. 38 14. 42 41. 81
1969 0. 82 22. 65 4. 38 0. 26 16. 25 16. 49 83. 24 3. 24 23. 62
1970 0. 41 7. 42 4. 64 0. 34 14. 39 16. 70 73. 72 8. 26 34. 77
1971 34. 43 131. 76 198. 26 103. 48 82. 86 129. 33 424. 45 12. 56 109. 00
1972 3. 23 40. 15 7. 00 1. 09 18. 31 39. 07 198. 44 7. 27 64. 88
1973 10. 52 92. 30 15. 01 1. 64 74. 59 79. 28 398. 80 11. 63 85. 48
1974 4. 62 13. 85 13. 71 3. 70 10. 42 59. 34 119. 52 0. 83 24. 61
1975 1. 01 13. 17 2. 27 0. 29 16. 13 4. 96 39. 81 0. 62 1. 92
1976 4. 32 66. 67 23. 70 4. 23 55. 03 34. 38 282. 66 20. 48 47. 79
1977 0. 29 29. 63 3. 18 1. 24 16. 83 18. 57 131. 52 18. 89 16. 24
1978 0. 60 17. 84 3. 24 1. 14 36. 92 7. 46 69. 10 9. 12 64. 73
1979 0. 55 61. 77 11. 22 2. 00 45. 74 18. 95 137. 79 18. 18 53. 07
1980 5. 46 64. 28 37. 89 5. 82 50. 95 23. 57 128. 31 16. 93 12. 07
1981 6. 80 48. 54 7. 99 1. 21 41. 41 62. 44 389. 56 51. 41 150. 58
1982 12. 40 28. 42 2. 64 0. 50 24. 58 12. 76 126. 61 16. 71 24. 54
1983 0. 00 51. 55 5. 66 2. 52 19. 70 29. 94 184. 85 24. 39 95. 18
1984 1. 51 45. 90 2. 70 0. 35 31. 95 8. 88 29. 10 3. 84 8. 69
1985 4. 11 58. 04 17. 65 4. 59 29. 44 21. 73 79. 07 10. 43 15. 79
1986 2. 33 1. 23 5. 40 0. 79 16. 90 3. 93 44. 69 5. 90 13. 30
1987 5. 27 62. 64 15. 01 7. 81 30. 47 29. 81 101. 29 13. 37 15. 94
1988 0. 48 2. 49 1. 37 0. 14 4. 86 10. 14 9. 22 1. 22 0. 00
1989 0.06 0.47 0.18 0.16 2.35 2.34 1.24 0.16 0.52
1990 10. 45 2. 08 29. 76 10. 29 18. 65 56. 05 200. 80 26. 50 36. 75
Mean Annual
Flow ( cf s) 5. 66 39. 25 18. 16 7. 07 29. 48 32. 95 147. 01 17. 23 41. 71
The mean annual depth of streamflow (runoff per unit area) is found by
dividing the 30-year average  streamflow by the drainage area determined by the
USGS and converting the units to mm/yr.  The streamflow depth is used to
represent the direct runoff for each of the drainage areas.  Table 3.6 shows the
streamflow depth for each sub-watershed.
83
Table 3.6: Streamflow Depths for Each of the Sub-watersheds.
Station StreamFlow
Name Depths (mm/yr)
Los Omos 4
Chiltipin 106
San Fernando 12
San Diego 8
Oso 113
Aransas 46
Mission 74
Medio 29
Copano 164
Note the varied runoff for each of the gauging stations.  The northern
stations (Chiltipin, Oso, Aransas, Mission, Medio, and Copano) have larger runoff
depths than the southern gauging stations (Los Omos, San Fernando and San
Diego).
3.2.4 Establishing a Mathematical Relationship between Precipitation
Percent Land Use and Streamflow
 
Using the Microsoft Excel 5.0 Regression Tool, an equation is found to
predict runoff using the relationship between streamflow depth, precipitation depth
and percent land use in each of the nine watersheds.  The depth of streamflow is
used to represent the direct runoff from each of the drainage basins.
The percent land use is found by clipping the land use file with the sub-
watershed coverage using the Arc command Clip.  The watershed land use file is
then queried in ArcView using the Field pulldown menu with the Summarize
option.  The landuse field is active in the watershed land use attribute table.  In the
Table Summary Menu, the field chosen is Area which is summarized by Sum.  The
Add button is clicked, and the file name is changed to reflect the watershed.  The
summary table is viewed in Microsoft Excel 5.0 where the individual summaries of
84
land use areas are divided by the total area and multiplied by 100% to find the
percent land use in the watershed.
Arc: clip landuse2 aransas landara polygon #
An equation is calculated for agricultural land use.  The multiple regression
tool is used with streamflow depth for the y-input and the precipitation depth and
percent agricultural land are used as the x-inputs.  Table 3.7 shows the input values
that are used in the Regression Tool.
Table 3.7: Input Values Used in the Excel Regression Tool.
Watershed Streamflow   Precipitation  %Agric  %Range
(mm/yr) (mm/yr)
Los Omos 4 618 12 87
Chiltipin 106 841 86 11
San Fernando 12 661 22 77
San Diego 8 665 13 86
Oso         113 775 97 1
Aransas 46 810 58 38
Mission      74 847 25 73
Medio     29 787 41 58
Copano      164 927 7 67
The Microsoft Excel 5.0 Regression output, shown in Table 3.8, is used to
determine the best equation to represent the relationship between precipitation and
runoff for agricultural land use.  The coefficients are plugged into Equation 3.4:
85
Table 3.8: Summary Output for the Agricultural Land Use, Precipitation, and
Runoff Analysis.
SUMMARY OUTPUT
Regression Statistics
Mult iple R 0. 956543917
R Squar e 0. 914976265
Adjusted R Squ 0. 88663502
St andar d Er r or 0. 439935201
Obser v ations 9
ANOVA
df SS MS F Significance F
Regr ession 2 12. 49678582 6. 24839291 32. 28427 0. 00061464
Residual 6 1. 161257884 0. 193542981
T ot al 8 13. 6580437
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
I nt er cept - 4. 79004056 1. 247523775 - 3. 839638696 0. 008563 - 7. 842623502 - 1. 7374576 - 7. 8426235 - 1. 7374576
X  V ar iable 1 0. 01141489 0. 001546613 7. 380574565 0. 000317 0. 007630463 0. 01519932 0. 00763046 0. 01519932
X  V ar iable 2 0. 346167599 0. 16942563 2. 043183194 0. 08706 - 0. 068402285 0. 76073748 - 0. 0684023 0. 76073748
Equation 3.4
lnQ = lna + b*P + c*ln(%A)
where: Q = runoff depth (mm/yr)
b = coefficient calculated using Regression tool
P = precipitation depth (mm/yr)
c = coefficient calculated using Regression tool
%A = Percent agricultural land use
The equation is solved for runoff, Q.  If percent agriculture is assumed to be
100%, the last term in the equation, which represent percent agriculture, equals one
and can then be dropped from the equation.  Equation 3.5 results:
Equation 3.5
Q = 0.008312 * exp ( 0.011415 * P )
where: Q = runoff depth (mm/yr)
P = precipitation depth (mm/yr)
0.008312 = exp (-4.79004056)
Again, the Microsoft Excel 5.0 Regression output, shown in Table 3.9, is
used to determine the best equation to represent the relationship between
86
precipitation and runoff for rangeland.  The coefficients are plugged into Equation
3.6, and are solved for runoff, Q.
Table 3.9: Summary Output for the Rangeland, Precipitation, and Runoff
Analysis.
SUMMARY OUTPUT
Regression Statistics
Mult iple R 0. 989748737
R Squar e 0. 979602563
Adjusted R Squ 0. 972803417
St andar d Er r or 0. 215479731
Obser v at ions 9
ANOVA
df SS MS F Significance F
Regr ession 2 13. 37945462 6. 689727308 144. 0773 8. 48646E- 06
Residual 6 0. 278589088 0. 046431515
T ot al 8 13. 6580437
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
I nt er cept - 5. 239701761 0. 583176391 - 8. 984763169 0. 000106 - 6. 66668403 - 3. 812719 - 6. 666684 - 3. 81271949
X Var iable 1 0. 010993081 0. 000763542 14. 39748217 7. 03E- 06 0. 00912476 0. 0128614 0. 00912476 0. 012861402
X Var iable 2 - 0. 315150467 0. 052227559 - 6. 034179522 0. 000936 - 0. 44294679 - 0. 187354 - 0. 4429468 - 0. 18735414
Equation 3.6
lnQ = lna + b*P + c*ln(%R)
where: Q = runoff depth (mm/yr)
b = coefficient calculated using Regression tool
P = precipitation depth (mm/yr)
c = coefficient calculated using Regression tool
%R = Percent rangeland land use
If percent rangeland is assumed to be 100%, the last term in the equation,
which represented percent rangeland, equals one and can then be dropped from the
equation.  Equation 3.7 results:
87
Equation 3.7
Q = 0.0053 * exp ( 0.010993 * P )
where: Q = runoff depth (mm/yr)
P = precipitation depth (mm/yr)
0.0053 = exp (-5.239702)
Since, there are no gauged watersheds in the study region which represent
urban land use, an expected runoff depth was taken from a study being done in the
City of Austin (Barrett, 1997).  The City of Austin was chosen because it receives a
similar average annual precipitation as Corpus Christi, the largest city in the
CCBNEP study region.  The typical depth of runoff in the city of Austin ranges
from 170 mm/yr to 200 mm/yr.  The average between the two values, 185 mm/yr, is
used for the CCBNEP study.  The average annual precipitation in the Oso Creek
watershed is used due to the proximity of Corpus Christi to the gauging station. The
expected runoff depth (185mm/yr) is divided by the mean annual precipitation
depth (774.75 mm/yr) resulting in a runoff coefficient of 0.24.  Therefore, the land
use grid cells representing urban area are assigned a runoff value of 24% of the
mean annual precipitation for that grid cell.  Equation 3.8 is the final equation.
Equation 3.8
Q = 0.24 * P
where: Q = runoff depth (mm/yr)
P = precipitation depth (mm/yr)
An attempt to find the urban land use runoff equation was done using the
Regression tool, however the results gave unreasonably high runoff depths
throughout the study area for urban land use areas due to the low percentage of
88
urban land use in the study region.
The areas representing water are assigned a value of zero runoff because the
precipitation over the bay system is already accounted for in the water quality
model.  By assigning a value of zero, the precipitation is not accounted for twice.
Runoff in the wetland and barren land use areas were assigned the same equation as
the rangeland.  Figure 3.10 shows the precipitation versus expected runoff curves
for urban, rangeland and agricultural land uses.  It can be seen that the relationship
between runoff and precipitation is not the same shape for urban land use as for
agriculture and rangeland, but the lack of urban runoff data in the region precludes
a more definitive analysis of this factor.
Precipitation vs. Runoff Curves
Chiltipin
Copano
Oso
Mission
Aransas
Medio
San Diego
Los Omos
San Fernando
0.00
50.00
100.00
150.00
200.00
250.00
0 200 400 600 800 1000
Depth of Precipitation (mm/yr)
D
e
pt
h of
 
E
x
pe
c
t
e
d
 R
u
n
o
f
f
 (
m
m
Range Land
Agriculture
Urban
Stations
Figure 3.10: Depth of Precipitation (mm/yr) vs. Depth of Expected Runoff
(mm/yr) for Each of the Land Uses.
89
3.2.5 Computing Expected Runoff
The Avenue script ROGRIDLAND, listed in Appendix B, is used to
compute the runoff grid.  The script runs in the ArcView environment and
calculates the runoff grid using the percent land use, precipitation and runoff
relationships developed in Section 3.2.4 of this report.
The runoff grid is then divided by the precipitation grid to calculate a runoff
coefficient grid.  Any runoff coefficient greater than the urban runoff coefficient of
0.24 is set equal to 0.24.  The final runoff grid is calculated by multiplying the
precipitation grid by the runoff coefficient grid.  Figure 3.11 shows the runoff grid
for the study region.
3.3 Linking Expected Mean Concentration (EMC) Values to Land Use
The study uses the Event Mean Concentration (EMC) values discussed in
Chapter 2.  The EMC values are obtained from a previous CCBNEP analysis,
Characterization of Non-point Sources and Loadings to the Corpus Christi Bay
National Estuary Program Study Area (Baird,1996).  The Baird study developed
EMC values from water quality analysis performed at the Oso Creek and Seco
Creek USGS Stream Gauges.  The Oso Creek gauge is located west of Corpus
Christi and represents agricultural land use.  The gauges located on Seco Creek are
northwest of Hondo, TX, and represent range land uses.  Refer to Table 2.2 in
Section 2.2.8 for the EMC table used in the total loads study.
90
Figure 3.11: Runoff Grid Calculated Using the  Relationship between
Precipitation, Streamflow, and Percent Land Use.
91
3.3.1 Computing Concentration Grid
The Avenue script CONCGRID, shown in Appendix B, is used to compute
the concentration grids (Hellweger, 1996).  CONCGRID runs in the ArcView
environment and converts the land use polygons into a concentration grid.  In this
case, the cell value is equal to the constituent EMC field of choice.
3.4 Estimating Annual Loads Throughout the Watersheds
The purpose of the project is to estimate the total constituent loads
throughout the study area.  The non-point sources are estimated by multiplying the
concentration grids developed from the EMC values by the runoff grid.  The point
source loads are calculated from the point source study done by Armstrong (1997)
for the Corpus Christi National Estuary Program?s Point Source Data Study.  The
upstream watershed load from the Nueces River watershed is calculated using Lake
Corpus Christi as a point source assuming the concentrations in the Lake and the
flow out of the Lake represented the loads from the upstream watershed.  The
atmospheric load is obtained from a National Atmospheric Deposition Program
weather station in Beeville, TX (USGSNADP, 1997).
3.4.1 Computing Land Surface Loads
The Avenue script LOADGRID, listed in Appendix B, is used to compute
the land surface load grids.  The script runs in the ArcView environment and
calculates the land surface loads by multiplying the concentration grid by the runoff
grid and a conversion factor.  To run the script properly, the concentration grid and
the runoff grid must be in the view.
The script converts the concentration grid to a grid showing the land surface
loads in Kg/day.  The script makes a conversion of mg/l (concentration grid) *
92
mm/yr (runoff grid) to the Kg/day (load grid).  The conversion constant of 1/36525
is used to the inputs units of mm/yr and mg/l, and to the output units of Kg/d.
Equation 3.9
Load (Kg/d) = Runoff (mm/yr) * Concentration (mg/l) * Cell Area (10,000
m
2
) * (1m /1,000mm) * (1yr/365.25d) * (1 Kg/10
6
mg) *
(1000liters/1m
3
)
Load (Kg/d) = Runoff  * Concentration  * Cell Area * (1/36525)
3.4.2 Computing Point Source Loads
The point source grid is based on the point source discharge data compiled
by Armstrong (1997) for each of the surface water quality segments of the Texas
Natural Resources Conservation Commission (TNRCC).  Figure 3.12 shows the
TNRCC water quality segments used in the study.
To create the point source load grid, a digital map of surface water quality
segments was obtained from the TNRCC via their Internet Site (TNRCC, 1997):
http://www.tnris.state.tx.us/ftparea.html.  The files need to be converted into an
Arc/Info format.
The Avenue script POINTLD, listed in Appendix B, is used to convert the
water quality segment coverages to a point source grid (Hellweger, 1996).  For
streamlines the cell corresponding to the first point in the line was valued with the
load and for polygon segments in the bay, a cell inside the area was valued with the
loads.
93
Figure 3.12:  Texas Natural Resource Conservation Commission Water
Quality Segments Used in the CCBNEP Study.
94
3.4.3 Computing Upstream Watershed Loads
The loads from the upstream Nueces watershed are determined by using
Lake Corpus Christi as a point source.  The flow leaving the Lake at the Nueces
River gauging station near Mathis, TX, is calculated as a thirty year average for the
years 1961 - 1990, similar to the streamflow calculations used in the
precipitation/runoff analysis.  The resulting streamflow leaving Lake Corpus Christi
is calculated to be 20 m
3
/s.  The water quality data representing the Lake are found
from the TNRCC Internet site: http://www.tnris.state.tx.us/ftparea.html.  The
constituents used in the study were total nitrogen, total phosphorus and iron.  The
concentration values for the other constituents are too small to measure or no data
existed at this location.  The constituent load is found by multiplying the
constituent concentration of the Lake by the flow out of the Lake.
Equation 3.10
Load (Kg/d) = Concentration (mg/l) * Flow (m
3
/s) * (1Kg/ 10
6
mg)
* (1000 liters/1 m
3
) * (86,400sec/d)
Load (Kg/d) = Concentration (mg/l) * Flow (m
3
/s) * 86.4
The total nitrogen, total phosphorus, and iron loads are added into the water
quality segment coverage to the segment number which represented Lake Corpus
Christi.  The total phosphorus load is 58.52 Kg/d, while total nitrogen and iron
loads are 81.11 Kg/d, and 2.23 Kg/d respectively.
3.4.4 Computing Atmospheric Loads
The atmospheric load is only applied to the bay system because on the land
surface it is accounted for in the EMC values.  The atmospheric deposition data was
95
obtained from the USGS National Atmospheric Deposition Program?s Internet Site
(USGSNADP, 1997): http://h20.usgs.gov/nwc/NWC/pH/html/ph.html.  The
Beeville, Texas, station was used due to its location in the study area.  The specific
Internet site for the Beeville station is:
http://nadp.nrel.colostate.edu/NADP/cgi_scripts/sitepics.cgi?TX03.
The Beeville constituent concentrations were compared with a study
completed by Liljestrand et al. (1986).  The ammonia and nitrate concentrations are
similar in both the study and at the USGS Internet site.  The study by Liljestrand
found that atmospheric concentrations are 0.82 mg/l for nitrate and 0.25 mg/l for
ammonium while the nitrate concentrations for the Beeville station are 0.81 mg/l
and 0.29 mg/l for ammonium.  The Beeville station data are used for the total
loadings study because the data is more current.
The data from the Internet site is downloaded as a wet deposition annual
average loading in units of Kg/ha/yr.  The data are averaged for the years 1986 to
1997, and converted to Kg/m
2
/day.  Finally, the data is multiplied by the surface
area of each of the bay segments.  This gives a loading of Kg/d, which is the correct
unit needed in the water quality model.  The total nitrogen load is found by adding
the nitrate load to the ammonium load.  Equation 3.11 explains the unit conversions
used in the calculations:
Equation 3.11
Load (Kg/d) = Load (Kg/ha/yr) *  Area (m
2
) * (1/(3.6525 * 10
6
))
A calculation is done to compare the amount of atmospheric load which
falls on the land surface to the total land surface load.  The loading data from the
Beeville site is applied to the entire land surface using Equation 3.11.  The entire
load is then divided by the number of cells in the study area to determine a load per
96
cell.  The load per cell is multiplied by the runoff coefficient for each cell to
calculate the amount of atmospheric load that is found in the runoff.  Using the
Zonalmean function in Arc, the mean load per cell is found over the land surface
(0.001 Kg/d).  The total atmospheric load over the land surface is the product of the
mean load per cell and the number of cells over the land surface (2733 Kg/d).  The
atmospheric load over the land surface is approximately 35% of the total land
surface load.
3.4.5 Computing Sediment Loads
Sediment fluxes for nutrients are obtained from a report entitled Benthic
Nutrient Fluxes of Selected Estuaries in the Gulf of Mexico (Montagna et al., 1997).
The data are obtained from graphs which show the variance and the mean values.
The mean values are extracted from the graphs as fluxes in Pmol/m
2
/hr.  The report
has data for Corpus Christi Bay and the Laguna Madre.  The following flux
assignments are made to the bays without data due to their geographic location to
the bays with flux data.  Baffin Bay is assigned the same flux as the Laguna Madre
while Nueces Bay, Inner Harbor, and Oso Bay are assigned the same flux as Corpus
Christi Bay.  The mean flux for San Antonio Bay is also extracted from the graph to
determine a linear trend northward from Corpus Christi Bay to San Antonio Bay,
which is the northern boundary of the study area.  The nitrate + nitrite flux data is
assigned an upward trend from Corpus Christi Bay (75 Pmol/m2 hr) through Red
Fish Bay, Copano Bay and Aransas Bay to San Antonio Bay (175 Pmol/m
2
/hr).
Red Fish Bay and the Aransas Bay south segments are assigned a flux of 112.5
Pmol/m
2
/hr, which is 1.5 times higher than the Corpus Christi Bay flux.  Copano
Bay, and the Aransas Bay middle and north segments are assigned a nitrate plus
nitrite flux of 150 Pmol/m
2
 hr which is twice as large as the Corpus Christi Bay
97
flux.  This gives linear flux values from Corpus Christi Bay to San Antonio Bay.
The San Antonio Bay mean ammonium flux is outside the range of the
variance.  The trend from Corpus Christi Bay to San Antonio Bay is decreasing,
however the mean value is negative.  Therefore, a downward linear trend is used to
compute the Red Fish Bay, Aransas Bay and Copano Bay fluxes.  Red Fish Bay and
Aransas Bay south segment were assigned a value of 37.5 Pmol/m
2
/hr which is 75%
of the flux measured in Corpus Christi Bay.  Copano Bay and the Aransas Bay
north and middle segments are assigned a value of 25 Pmol/m
2
/hr, which is 50% of
the flux measured in Corpus Christi Bay.
Once the fluxes are calculated, the loads for each segment are calculated
based on the surface area of the segment using Equation 3.12:
Equation 3.12
Load (Kg/d) = flux (Pmol/m
2
 hr) * 10
-6
 (mol/(Pmol) * 24 hr/d
* # grams/mole * Area (m
2
) * Kg/1000 g
The resulting nutrient sediment loads are two orders of magnitude larger
than the other calculated loads.  This is could be due to the study being a measure
of nutrient flux out of the sediment rather than being a net flux where the flux into
the sediment is also measured.  Therefore, the sediment load data is not used as part
of the total loads to the water quality model.
3.5 Calibrating the Water Quality Model
A series of Avenue scripts making up the water quality model is used to
calculate the equilibrium constituent concentration in the bay system (Hellweger,
1996).  The model uses an explicit finite difference algorithm.  The software runs
within the ArcView environment.  The model uses the methodology presented by
98
Thomann and Mueller (1987).  Figure 3.13 is a graphical depiction of the
methodology of the program.
WA
E(ss)A,B A B
??
Qs
A,B A
?
W
B
E(ss)
B, BND B BND
??
Qs
B,BND B
?
SEGMENT A
V
A
, K
A
, s
A
BOUNDARY
s
BND
SEGMENT B
V
B
, K
B
, s
B
?KV
AA
?
s
A
?
KV
BB
?
s
B
E(ss)O,A O A
??
Qs
O,A A
?
Figure 3.13: Water Quality Methodology Used in the Calculations of the
Equilibrium Concentrations.
Equation 3.13
 ds
B 
 * V
B
 = Q
A,B
*s
A
 - Q
B
,
BND
*s
B
 + E
A,B
*(s
A
 - s
B
) + E
B
,
BND
*(s
B
 - s
BND
) - K
B
*V
B
*s
B
 + W
B
  dt
where: ds
B
 = change in concentration of segment B / change in time
dt
V
B
 = volume of segment B; (m
3
)
Q
A,B
 = flow from segment A to B; (m
3
/s)
Q
B
,
BND 
= flow from segment B to BND; (m
3
/s)
s
A
 = concentration of constituent in segment A; (mg/l)
s
B
 = concentration of constituent in segment B; (mg/l)
s
BND
 = concentration of constituent at boundary; (mg/l)
E
A,B
 = bulk dispersion coefficient between segments A,B; (m
3
/s)
E
B
,
BND
 = bulk dispersion coefficient between segments B,BND; (m
3
/s)
K
B
 = decay rate in segment B; (/d)
W
B
 = waste load in segment B; (kg/yr)
Equation 3.13 is a mass balance around Segment B of Figure 3.13.
Assuming steady state, the (dsB/dt) VB term is zero.  The rest of the terms represent:
99
x the advective load entering from segment A,  QA,B * sA
x the advective load leaving at the boundary, QB,BND * sB
x the dispersive load across the segment A and segment B interface, EA,B(s
A
 ? sB)
x the dispersive load across the segment B and boundary interface, EB,BND(s
B
 ? sBND)
x the load lost to decay, KB * VB* sB
x the waste load into segment, WB
The model is first calibrated against salinity data for dispersion coefficients.
The calibration process began with segmenting the bay system using the 20
segment partitioning of the bay system described earlier.  The water quality model
reaches steady state by doing time varying computations from an assumed set of
initial concentrations in each bay until concentration values converge to a steady
value.  The dispersion coefficients are obtained by a trial and error method which is
described in detail in Section 3.5.4.
3.5.1 Bay System Bathymetry
The bathymetry data for the water quality model is taken from a file from
the Texas Water Development Board hydrodynamic model of the bay system
developed by Junji Matsumoto (1993).  The segment surface areas are calculated
using the CALCAREA Avenue script, shown in Appendix B (Hellweger, 1996).
The segment and interface depths are calculated using the Surface Water Modeling
System (SMS) graphical user interface.  The segment volumes are calculated by
multiplying the surface area and the segment depths.  Because the inlet segment is
small compared to other segments, it controlled the numerical stability of the
model.  The volume of the Inlet segment is increased by a factor of 1000 to allow
for this.  This did not change the model results for salinity because the model is
solved for a steady state solution without decay.  Table 3.10 shows the segment ID,
segment depth, segment area, and segment volume.
100
Table 3.10: Segment Name, ID, Depth, Surface Area and Volume.
Depth Area Volume
Segment ID (m) (m^2) (m^3)
St. Charles stc 0.7 33173300 23221300
Copano cop 1.3 19193100 249510000
Aransas North aran 1.4 38094600 53332400
Aransas Middle aram 2.2 110235000 242518000
Aransas South aras 2.6 120451000 313173000
Redfish red 1.6 98067500 156908000
Inlet inl 14.0 4117510 57645100000
Nueces West nuew 0.7 12096600 8467650
Nueces Middle nuem 0.8 20349600 16279700
Nueces East nuee 1.0 37515700 37515700
Inner Harbor inh 14.0 6204910 86868700
Corpus North ccbn 5.3 108541000 575267000
Corpus Middle ccnm 3.4 200240000 680816000
Corpus East ccbe 3.4 101090000 343707000
Corpus South ccbs 0.6 35924600 21554800
Oso oso 0.7 17746600 12422600
ULM North ulmn 1.5 173796000 260694000
ULM Middle ulmm 1.8 78999500 142199000
ULM South ulms 1.8 217413000 391344000
Baffin baf 1.3 239474000 311317000
The Avenue script CALCLENGTH, shown in Appendix B, is used to
determine the interface length between each of the segments (Hellweger, 1996).
The line between Corpus Christi South and Lower Laguna Madre North is adjusted
manually from 6449 meters to 1000 meters.  This interface represents the JFK
Causeway and the opening between the two bays is much smaller than the
calculated interface length.  The interface areas are calculated by multiplying the
interface depth and length.  Table 3.11 shows the segment interface identification,
the interface line length, the interface length which is used in the model, the
interface depth, and the interface area.
101
Table 3.11: Bay Segment Interface, Interface Line Length, Interface Length,
Interface Depth, and Interface Area.
Interface Line Interface Depth Area
From To  Length (m) Length (m) (m) (m^2)
St. Charles Aransas Middle 3924 1600 0.80 1280
Copano Aransas Middle 2946 2946 1.60 4714
Aransas North Aransas Middle 4257 4257 1.70 7237
Aransas Middle Aransas South 5953 5953 3.00 17859
Aransas South Redfish 16607 830 2.10 1743
Aransas South Inlet 2211 800 7.50 6000
Redfish Corpus North 353 353 3.60 1271
Redfish Inlet 1552 200 3.00 600
Inlet Corpus East 2281 1100 9.30 10230
Corpus North Corpus Middle 21123 211223 6.70 1415194
Corpus North Corpus East 978 978 10.10 9878
Corpus North Inner Harbor 522 522 14.00 7308
Nueces West Nueces Middle 2618 2600 0.80 2080
Nueces Middle Nueces East 4574 4100 0.90 3690
Nueces East Corpus North 3458 2900 1.20 3480
Corpus Middle Oso 385 200 1.30 260
Corpus Middle Corpus East 19070 18070 3.50 63245
Corpus East Corpus South 5637 4000 1.40 5600
Corpus East Redfish 11235 4494 3.50 15729
Corpus South ULM North 6449 1000 5.50 5500
ULM North ULM Middle 3588 3000 1.60 4800
Baffin ULM Middle 6798 6700 1.50 10050
ULM Middle ULM South 5990 4200 1.90 7980
3.5.2 Bay Evaporation and Precipitation
The evaporation data are obtained from a grid of mean annual open water
evaporation provided by Reed et al. (1996).  The evaporation grid was originally
calculated by extrapolating pan evaporation data from the Texas Water
Development Board.
Table 3.12 compares the evaporation and precipitation to the Texas Water
Development Board?s (Matsumoto, 1993) mean annual precipitation and
evaporation.  The two sets of values are comparable.
102
Table 3.12: Comparison of the Calculated Mean Annual Evaporation and
Precipitation with the Texas Water Development Board's Bay System's Mean
Annual Evaporation and Precipitation.
Mean Annual TWDB Pan TWDB
Precipitation Precipitation Evaporation Evaporation
Segment (m/yr) (m/yr) (m/yr) (m/yr)
stc 0.93 0.83 1.55 1.58
cop 0.89 0.83 1.57 1.58
aran 0.93 0.83 1.55 1.58
aram 0.91 0.83 1.55 1.58
aras 0.88 0.83 1.62 1.58
red 0.86 0.83 1.63 1.58
ccbn 0.83 0.83 1.63 1.58
nuee 0.81 0.83 1.63 1.58
nuew 0.81 0.83 1.63 1.58
nuem 0.81 0.83 1.63 1.58
inl 0.84 0.83 1.63 1.58
ccbe 0.84 0.83 1.63 1.58
inh 0.81 0.83 1.63 1.58
ccbm 0.82 0.83 1.63 1.58
oso 0.78 0.83 1.63 1.58
ccbs 0.81 0.83 1.63 1.58
ulmn 0.79 0.83 1.63 1.58
baf 0.73 0.83 1.63 1.58
ulmm 0.74 0.83 1.63 1.58
ulms 0.69 0.83 1.62 1.58
3.5.3 Inflow to Bay System
The inflows are determined by calculating a weighted flow accumulation
with the runoff grid as the weight grid.  The weighted flow accumulation routes the
runoff over the land surface downstream into the river network and into the bay
system.  Table 3.13 shows the calculated runoff compared to the runoff calculated
by the USGS (Ward, 1996) and the TWDB (Matsumoto, 1993).  The three sets of
numbers are comparable with the calculated runoffs from this study, for the most
part, being in between the other data sets.
103
Table 3.13: Comparison of the Calculated Runoff Inflows with Two Other
Inflow Studies by the USGS and the TWDB.
Calc. USGS TWDB
Runoff Runoff Runoff
Segment (m^3/s) (m^3/s) (m^3/s)
stc 5.4 2.9 3.3
cop 19.0 25.9 17.5
aran 0.0 0.0 0.0
aram 0.2 0.0 0.0
aras 0.0 0.0 0.0
red 0.2 0.5 0.0
ccbn 0.3 16.5 0.0
nuee 0.3 0.0 0.0
nuew 21.9 20.0 13.5
nuem 0.1 0.0 0.0
inl 0.0 0.0 0.0
ccbe 0.0 0.0 0.0
inh 0.3 0.0 0.0
ccbm 0.4 0.0 0.0
oso 1.1 0.0 2.7
ccbs 0.0 0.0 0.0
ulmn 0.2 0.0 0.0
baf 5.6 4.2 2.1
ulmm 0.0 0.0 0.0
ulms 1.2 0.2 0.0
total 56.1 70.2 39.1
Since the bay system extends outside the CCBNEP study area, some inflow
is routed into the Aransas North segment, which would normally be routed into
another bay segment.  This problem was discussed in Section 3.1.2.2 and is
corrected manually by setting the runoff to zero.
To determine the north boundary condition, an estimation of the inflow
from the San Antonio Bay is calculated.  The USGS gauging stations on the San
Antonio River near Goliad, TX, and the gauging station on the Guadalupe River
near Victoria, TX, are used to estimate the inflow into the San Antonio Bay. The
flow data was downloaded from the USGS Internet site discussed in Chapter 2 of
this report.  The resulting inflow, 77.44 m
3
/s, from the two rivers is found by taking
a 30 year average over the time period from the years 1960 ? 1991.   The northern
104
interface boundary is set equal to 20% of the inflow into the San Antonio Bay from
the Guadalupe River and the San Antonio River.  The north boundary inflow is set
to 15.49 m
3
/s.  This result compares to a study done by the Texas Water
Development Board where an estimate of 11 m
3
/s was calculated for the freshwater
portion of the inflow to the Aransas Bay from the San Antonio Bay (Brock, 1997).
The Gulf of Mexico boundary is fixed as 6.33 m
3
/s to close the water mass balance.
The south interface boundary flow is treated as a no flow boundary.
3.5.4 Model Calibration Using Salinity
The salinity data are used to manually calibrate the interface flows and the
dispersion coefficients.  The interface flows and the dispersion coefficients are
found using a trial-and-error method, and are modified as needed.  The process is
repeated until the calculated salinity?s matched the observed salinity?s.
A file of salinity measurements is obtained from Ward (1996).  The
measurements are grouped together according to this original segmentation.  A
time- and space-weighted average is used to average the salinity data because the
two dimensions, time and space, needed to be considered when averaging the data.
This method employs a process where each measurement is given a weight
proportional to the time between the last measurement and the next measurement.
If the time between the measurements exceeds one month, a weight corresponding
to one month is assigned.  This is done to prevent measurements at the end of a
long no-measurement period from being assigned too much weight.  The maximum
time period is set to be close to the response time of the system.  The Avenue script
TIMEAVE, shown in Appendix B, is used for the time-weighted average
(Hellweger, 1996).  The observed data are then weighted according to the segment
surface area Equation 3.14:
105
Equation 3.14
Conc = )*(
1
sA
A
ii
i
?
?
where:
Ai
= surface area
si
 = observed concentration in segment
The north and south boundaries of the bay segmentation are assigned
salinity?s similar to their adjacent segments because observed salinity data are not
available at these locations.  The north boundary is set to 19.10 ppt while the south
boundary is set to 37.65 ppt.  The Gulf of Mexico boundary is set to the observed
salinity concentration of 29.37 ppt.
The model calibrates the tidal dispersion coefficients.  Because the model is
based on a mean annual flow, the dispersion load calculations need to consider
molecular diffusion, eddy diffusion, turbulent conditions, and tidal mixing.
Therefore, the bulk dispersion coefficients need to be calculated.  The calibrated
dispersion coefficients and the bulk dispersion coefficients are related by Equation
3.15 (Thomman and Mueller, 1987).
Equation 3.15
E
bulk
 = E
cal
 * A / ?x
Where:E
bulk
 = bulk dispersion coefficient
E
cal
 = calibrated dispersion coefficient
A = surface area
?x = length of segment
The bulk dispersion coefficients are calculated using the Avenue script
CALCEP which is listed in Appendix B (Hellweger, 1996).  Table 3.14 shows the
dispersion coefficients, the bulk dispersion coefficients, and the interface flows
106
calculated from the calibration process.
Table 3.14: Salinity Interface Dispersion Coefficients, Bulk Dispersion
Coefficients, and Interface Flows.
Dispersion Bulk Disp. Flow
Interface Coeff. (m^3/s) Coeff. (m^3/s) (m^3/s)
aran/boun 5000.00 1819.69 -15.49
aram/stc 160.00 14.85 -4.71
aram/cop 160.00 52.43 -14.87
aram/aran 500.00 275.64 -15.94
aras/aram 35.00 39.99 -33.49
red/aras 320.00 58.02 -28.35
aras/inl 50.00 27.96 2.33
red/inl 500.00 31.00 2.00
ccbe/inl 1000.00 757.99 2.08
ocean/inl 200.00 301.96 -6.33
ccbn/nuee 550.00 194.35 -20.41
nuem/nuew 300.00 195.75 -21.55
ccbe/red 30.00 27.74 -24.17
ccbn/red 250.00 17.19 0.00
nuem/nuee 420.00 241.99 21.14
ccbn/ccbe 500.00 309.97 0.00
inh/ccbn 7.00 3.39 0.10
ccbm/ccbn 100.00 1544.60 -18.05
ccbm/oso 5000.00 131.71 -0.57
ccbm/ccbe 1000.00 4901.24 13.93
ccbs/ccbe 1000.00 395.81 -33.48
ulmn/ccbs 500.00 153.13 -32.56
ulmm/ulmn 1000.00 182.47 -28.13
ulmm/baf 500.00 393.50 1.32
ulms/ulmm 5000.00 909.10 -24.57
ulms/boun 5000.00 1738.79 0.00
3.6 Computing Equilibrium Water Quality Concentrations in the Bay
System
The variables used in the water quality model are:
_deltat = timestep, [T]
s = concentration, [M/ L
3
]
sb = boundary concentration, [M/ L
3
]
q = flow, [L
3
/T]
ep = bulk dispersion coefficient, [L
3
/T]
fad = advective mass flux, [M/T]
fdi = diffusive mass flux, [M/T]
v = volume, [L
3
]
k = decay rate, [1/T]
107
wnp = non-point source load, [M/T]
wps = point source load, [M/T]
wat = atmospheric load, [M/T]
wot = other load, [M/T]
wse = sediment load, [M/T]
wad = net advective load, [M/T]
wdi = net diffusive load, [M/T]
wd = decay load, [M/T]
There are various inputs which BALANCE needs to run properly.  The
inputs should be listed in the attribute table.  If the following attributes are not
present in the attribute tables of the active themes, BALANCE will add the fields to
the attribute tables:
Line Attributes
q = flow
ep = bulk dispersion coefficient
sb boundary concentration (only for boundary lines)
Polygon Attributes
v = volume
k = decay rate
wnp = non-point source load
wps = point source load
wat = atmospheric load
wse = sediment load
wot other load
so = initial concentration
The output data is stored in the attribute tables of the polygon and line
coverages.  The output is a mass balance at the last time step.  The following
attributes are computed during the water quality mass balance:
Line Attributes
fad = advective mass transport
fdi = diffusive mass transport
108
Polygon Attributes
s = concentration
wad = net advective load
wdi = net diffusive load
wd = decay load
To use the system, the attribute table should be filled with as much input
data as possible.  The units should be configured to SI (There is a bug in the
program when using the English or Generic units).  Data may be entered into the
attribute tables by using the Graphical User Interface (GUI) tools, or by manually
entering the data into the attribute table.  The GUI tool, C, allows the configuration
of the units.  The GUI tool, M, allows the modification of the active theme?s
attribute table.  This tool allows the user to click on the polygon or line of interest
and make the change.  The GUI tool, B, starts the BALANCE program.
The following control parameters are needed for the water quality program
to run properly.
Delta t = Time step used
Converge delta s = If the maximum change in concentration from one time step to
another is smaller than this value, the system assumes the steady state is
reach, and the program is stopped
Diverge delta s = If the maximum change in concentration from one time step to
the next is larger that this value, the system assumes unstable conditions,
and the program is stopped.
Max t = Maximum time for the computation
User Observation Level = Specifies the amount of the information conveyed to the
user during the computation
where: level 1 = The ArcView status bar is updated periodically.
Information displayed includes time, max change in s, and
percent of max time.
level 2 = In addition to level 1, the legend is periodically
updated.  This is useful if, the polygons are colored based on
concentration.
109
level 3 = In addition to level 2, the mass balance terms are
plotted.
level 4 = In addition to level 3, the system pauses after every
time step and displays the time and maximum change in
concentration from the last step.
The model can be stopped for four reasons, 1) convergence, 2) divergence,
3) a time limit was reached, or 4) it can be stopped by user.  To examine the results
of the model, the GUI tool, I, may be used.  The theme of interest should be active,
and then click the polygon or line of interest.  The message box will display the
outputs discussed earlier in the report.  The GUI tool, P, can be used to plot mass
fluxes and loads.  To configure the plot parameters, use the GUI, C, with the plot
parameter option.  Choose the parameters, which are desired on the plot.
An Excel 5.0 worksheet is used for some calculations instead of the
BALANCE program (Eid, 1997).  The worksheet uses the same methodology as the
water quality model, but reduces the run time significantly.  The worksheet is used
to calibrate the water quality model using the salinity data, and it is also used to
calculate the equilibrium constituent concentrations due to the total loads.  The
operation of the spreadsheet and the ArcView program are checked against each
one another for consistency.
3.7 Loads from Weighted Flow Accumulations
The non-point source load is determined by multiplying the concentration
grid by the land based runoff grid.  The point source grid is calculated by using the
CCBNEP point source data (Armstrong, 1997).  The upstream load is found using
Lake Corpus Christi as a point source to represent the Nueces River Watershed.
The weighted flow accumulation command is used to calculate the land
surface, point source, and loads from the Nueces River Watershed.  A weighted
110
flow accumulation is an extension of the Flowaccumulation command discussed in
Section 3.1.3.  Instead of counting the number of cells upstream of a particular grid
cell, the weighted flow accumulation sums the grid values from a second grid called
the weight grid.  Using a load grid as the weight grid, the annual load is calculated
for the study area.  All loads are an average daily load in Kg/d.
First, the load grids were clipped to the size of the study area using the grid
big1.  The Arc/Info console is used to calculate the accumulated load grids.  Each
command is repeated for the constituents being modeled; nitrate + nitrite,
ammonium, total nitrogen, total phosphorus, oil & grease, copper, cadmium,
chromium, zinc, lead, iron, arsenic, and mercury.  Three Arc Macro Language
programs (AMLs) (pogridwf.aml, logridwf.aml, and lkgridwf.aml) are written for
the calculations and are listed in Appendix B.  Each AML is responsible for
calculating the weighted flow accumulation of the point source grids, land surface
load grids, and the upstream load grids.
The weighted flow accumulation can also be calculated by using the Avenue
script WFACGRID, shown in Appendix B.  The script uses the same methodology
as the weighted flow accumulation command used in the Grid module (Hellweger,
1996).
After the weighted flow accumulation is calculated, the Avenue script
PICKLOAD, listed in Appendix B, is used to determine the loads from the land
surface, point source and Nueces River (Hellweger, 1996).  PICKLOAD only
works in the UNIX ArcView system.
The need for PICKLOAD is due to the unstability of the grid algorithms
near the bay centroid.  At the bottom of a sink the flow direction is undefined.  This
causes the flow accumulation algorithm to break down, therefore the cell located at
the centroid of the segment does not contain the correct value.  To avoid this
111
problem, the grids are not queried at the centroid of the segment areas, but rather
around the perimeter of a five-by-five square centered on the centroid.  The
program PICKLOAD does this calculation.  The program automatically writes the
load to the wnp field of the bay attribute table, thus connecting land surface loads
into bay segment attributes.
The wnp field is used to obtain the loads to each of the segments.  The land
surface loads for the constituents, lead, copper, zinc, cadmium, chromium, and
nickel are divided by 1000 since the concentration is in ug/l.  The Nueces River
iron load is also divided by 1000 due to units of concentration being ug/l.
3.8 Running the Model
Before the water quality model can be run, the total loads need to be entered
into the appropriate field of the bay attribute table.  This can be done using
PICKLOAD, entering the values in manually, or by using the tool button M.  To
run the water quality model, the bay polygon and bay arc coverages need to be
active in the view, and the tool button B needs to be clicked.
The run control parameters used for the CCBNEP Total Loadings project
are as follows:
Delta t [hr] = 12
Converge delta s [mg/l] = 0
Diverge delta s [mg/l] = 10,000
Max t [hr] = 60,000
The total loads can either be entered into the appropriate field in the wnp
load column or into the Excel 5.0 spreadsheet.  The spreadsheet is used for the
thirteen constituents; ammonium, nitrate + nitrite, total nitrogen, total phosphorus,
oil and grease, copper, cadmium, chromium, lead, zinc, iron, arsenic, and mercury;
and salinity.
112
4 RESULTS AND DISCUSSION
To understand the bay system, it is important to look at where the water
enters into the system.  The system is driven by the inflow that enters into the bays
from the north portion of the study region while the south portion of the study
region contributes little inflow and most of the water in the bays is evaporated.
Total loads are discussed as well as the specific source by which the load is
contributed.  Some of the data needed in the total loads calculations are missing,
however overall equilibrium concentrations in the bay system can still be calculated
within a reasonable error.  For most of the metals, the expected concentrations are
under-estimated which suggests that a load source is missing in the calculations.
The nutrient concentrations are over-estimated compared to the observed
concentrations, however, decay is not considered in the water quality model which
causes the over-estimation.  The calculated constituent concentration for oil and
grease is estimated with in a reasonable error.
4.1 Inflow into Bay System
The inflow into the bay system is calculated by running a weighted flow
accumulation on the expected runoff grid.  The runoff is accumulated into the
centroid of each bay segment.  Two other inflows are added into the model, 1)
inflow from the Nueces River Watershed, and 2) inflow from the San Antonio Bay.
The total inflow into the entire bay system is 71.6 m
3
/s as shown in Table 4.1.
For the runoff analysis, the bay system is broken into three sections; north,
middle, and south.  The land that drains into Aransas Bay, Copano Bay, and
Redfish Bay constitute the north portion of the study region.   The land that drains
into Nueces Bay, Corpus Christi Bay, the Inner Harbor, and Oso Bay, and the
Nueces River Watershed constitute the middle portion of the study region.  The
113
land that drains into Baffin Bay and the Laguna Madre, constitute the south portion
of the study region.
The majority of the inflow into the system is from the north portion of the
study region.  Approximately, 56% of the inflow enters into this portion of the bay
system.  San Antonio Bay contributes approximately 21.5% of the inflow to the
Aransas Bay, while the land surface drainage in the north portion contributes
approximately 34.5% of the inflow into the system.
The total inflow into the middle section of the bay system is 24 m
3
/s.  The
Nueces River contributes approximately 28% of the inflow, while the land surface
in the middle portion of the study region contributes approximately 6% of the
inflow.
Very little inflow enters into the system in the south portion of the study
area.  Only 10% of the inflow enters in from the south portion of the study region.
Table 4.1 shows the total and percent inflow from each source.
Table 4.1: Percent Inflow to the Bay System from Different Sources.
Total Inflow % Inflow
(m^3/s)
15.5 21.6    San Antonio Bay
24.8 34.6    Land Surface Inflow From North
40.3 56.2    Total Inflow from North
20.0 27.9    Nueces River Inflow
4.4 6.1    Land Surface Inflow from Middle
24.4 34.0    Total Inflow from Middle
7.0 9.8    Land Surface Inflow from South
71.6 100.0    Total Inflow into Bay System
The larger inflow into the bay system in the north portion of the system
versus the south portion of the bay system can be explained by the precipitation and
runoff gradient as you move from south to north in the study region.  The
114
precipitation is lower in the south than in the north portion of the study region.  The
runoff gradient has a steeper incline from south to north than the precipitation
gradient.  As the ground saturates from the increase in precipitation, less water
infiltrates due to the saturated soil which in turn causes more of the water to
become runoff.
Figure 4.1 shows graphically the precipitation and runoff gradients from
south to north, while Figure 4.2 shows the runoff and precipitation gradients
geographically.
At location A, south of Baffin Bay, a mean annual precipitation of
600mm/yr yields a runoff of 10 mm/yr, or a runoff coefficient of 0.02.  At location
B, near Copano Bay, 1000 mm/yr of mean annual precipitation converts to 100
mm/yr of runoff, or a runoff coefficient of 0.1.  Thus, as one moves North, the
precipitation increases by a fraction of 1.7, but the runoff increases by a factor of 10
between the same two points.  The wetter ground in the North yields a much greater
percentage of the precipitation as runoff, as well as having more precipitation to
begin with.
The average precipitation onto the bays is approximately 800 mm/yr and the
average evaporation is approximately double that figure, or 1600 mm/yr.  On
average, nearly all the inflow to the bays is dissipated by the net evaporation from
the bays, and the mean annual outflow from the bay system to the Gulf of Mexico is
only 6 m
3
/s.  The remainder of the flow is lost by evaporation.
There is a significant amount of uncertainty as to the amount of flow
entering the bay system from the San Antonio and Guadalupe Rivers.  If this inflow
is omitted, essentially all the drainage into the Corpus Christi Bay system is lost to
evaporation and does not flow to the oceans.  It follows that on average, the bay
system is not substantially flushed by inflow, therefore it is a large sink that has to
115
Precipitation and Runoff Gradients along
 Bay System
A
B
A
B
1
10
100
1000
South                                                       North
Precipitation (mm/yr)
Expected Runoff 
(mm/yr)
Precipitation
Runoff
Figure 4.1: Precipitation and Runoff Gradient from South, A,  to North, B,
along the Bay System.
a) b)
Figure 4.2: a) Precipitation and b) Runoff Gradient Locations in the South, A,
and the North, B, Portions of the Study Area.
116
absorb most of the loads that come into it from the land surface, atmosphere, and
point sources.
The conclusion that the system acts as a sink coincides with a study by
Ward (1997), Current Status and Historical Trends of Estuarine Circulation.  The
Ward study states that there are three components to the Gulf of Mexico
astronomical tides; 12.4-hour semidiurnal, 24.8-hour lunar diurnal, and the 13.6-
day fortnightly cycle in the magnitude of declination of the moon.  The bays act as
stilling wells due to the fact that the slower, longer-period variations are passed
through the connecting conduits, but the shorter-period variations are significantly
filtered out.
Figure 4.3 shows the water balance for each of the three bay systems and the
entire bay system.  As it is shown, the inflow into the north bay system is largely
contributed by the runoff, and the flow is lost to the middle bay and evaporation.
The inflow to the middle bay is largely due to the runoff and inflow from the north
bay system, and the flow is lost to the ocean, evaporation and the south bay system.
The south bay system loses its inflow to evaporation.  The entire bay system loses
most of its inflow to evaporation.
117
a)
Water Balance for North Bay System
-80.0
-60.0
-40.0
-20.0
0.0
20.0
40.0
60.0
80.0
Precip Evap Runoff Inflow from
other bays
Outflow to
other bays
Net Flow
Water Source
Flow (m^3/s)
b)
Water Balance for Middle Bay System
-80.0
-60.0
-40.0
-20.0
0.0
20.0
40.0
60.0
80.0
Precip Evap Runoff Inflow from
other bays
Outflow to
other bays
Net Flow
Water Source
Flow (m^3/s)
118
c)
Water Balance for the South Bay System
-80.0
-60.0
-40.0
-20.0
0.0
20.0
40.0
60.0
80.0
Precip Evap Runoff Inflow  from
other bays
Outflow  to
other bays
Net Flow
Water Source
Flow (m^3/s)
d)
Water Balance for Entire Bay System
-150.0
-100.0
-50.0
0.0
50.0
100.0
150.0
Precip Evap Runoff Inflow
from other
bays
Outflow to
other
bays
Net Flow
Water Source
Flow (m^3/s)
Figure 4.3: Water Balances for the a) North Bay Sytem, b) Middle Bay
System, c) South Bay System, and d) the Entire Bay System.
4.2 Total Constituent Loads into Bay System
The constituent loads are determined from the land surface, point sources,
atmosphere, and the Nueces River Watershed.  Some of the data is missing for
119
certain constituents at specific sources.  For example, data are available for
ammonium only in the atmosphere.  There is ammonium in the point source and
non-point source loads, however data are only available for total nitrogen for the
point sources and total nitrogen and nitrate + nitrite for the non-point sources.
Table 4.2 shows the constituent loads for each bay segment, the source in which the
load originated, and where data is missing.
Table 4.2: Constituent Loadings for Each Bay Segment, a) Atmospheric
Loads, b) Point Source Loads, c) Land Surface (Non-point Loads), d) Nueces
River Watershed Loads, e) Total Loads.
a)
b)
120
c)
d)
e)
Atmospheric Loads
The atmospheric loads show constituent load contributions from
121
ammonium, nitrate, and total nitrogen.  Many of the other constituents are not
found in rain water or are not measured at the sampling site at Beeville, TX.
Point Source Loads
Point source loads show constituent load contributions from total nitrogen,
total phosphorus, oil and grease, copper, cadmium, chromium, lead, zinc, iron,
arsenic and mercury.  No data is found for ammonium and nitrate + nitrite, however
these constituents are present in the point source.  Because there is a total nitrogen
estimate of the point source loads, these other constituents are present implicitly.
Since the percentage of these two components of the total nitrogen is not known,
the constituent loads for ammonium and nitrate + nitrite could not be calculated.
Land Surface Loads
Non-point source loads show constituent load contributions from nitrate +
nitrite, total nitrogen, total phosphorus, oil and grease, copper, cadmium,
chromium, lead, and zinc.  The ammonium constituent is not accounted for, and the
metals; iron, arsenic, and mercury; have no data.  These constituents are not present
in the EMC table used in the study.
The atmospheric load over the land surface is 35% of the total land surface
load (Section 3.4.4).  However, the atmospheric load is only added directly to the
bay system because the EMC values account for both land surface applications and
atmospheric deposition over the land surface.
Nueces River Watershed Loads
The Nueces River has loads for total nitrogen, total phosphorus and iron.
The constituents ammonium, nitrate + nitrite, oil and grease, copper, cadmium,
122
chromium, lead, zinc, arsenic, and mercury are not accounted for.  These
constituents are either non-detectable or are not measured.
Relative Total Loads
For the constituents analyzed, the largest load contribution to the entire bay
system is the total nitrogen load with nitrate + nitrite being the second largest load.
Total phosphorus, oil and grease and ammonium are large load contributors to the
bay system.  The ammonium and nitrate + nitrite load would be a large contributor
if more were known about the constituents coming from point, non-point and the
Nueces River sources.  Very little of the total load is from the metal loads.
Figure 4.4 shows the percentage of loads from the total load to the entire
system from each of the constituents.  The metals are lumped into one category so
that the small load can be compared to the larger loads presented with the data.
123
Percent Load for Each Constituent
Total Daily Load = 21,000 Kg/d
71%
15%
13%
1%
Total Nitrogen
Total Phos
Oil & Grease
Total Metals
Figure 4.4: Percent Constiuent Loads of the Total Load for the Entire System.
When each constituent load is broken down into its percent source
contribution, it can be determined which source the majority of the load is coming
from.  Figure 4.5 shows the percentage of the constituent load that is contributed
from a specific source to the entire bay system.
The largest contributor to the total nitrogen load is from the land surface.
The atmosphere and the point sources also largely contribute to the total nitrogen
load. The total phosphorus load comes mostly from the land surface with large
contribution from point sources and a smaller contribution from the Nueces River.
The oil and grease load is due to point sources and non-point sources.  The land
surface and the point sources largely contribute to the copper, chromium and zinc
loads in the bay system.
124
0%
20%
40%
60%
80%
100%
Total
Nitrogen
Total
Phosphorus
Oil and
Grease
Copper Chromium Zinc
Nueces
Land
Point
Atmo
Figure 4.5: Percent of Total Constituent Load which Comes from a Specific
Source for Entire System: (Total Nitrogen, Total Phosphorus, Oil and Grease,
Copper, Chromium, and Zinc.
When each constituent load is broken down into its percent bay
contribution, it can be determined where majority of the load is going.  Figure 4.6
shows the percentage of the constituent load that is contributing to a specific bay
region
The majority of the total nitrogen and total phosphorus load is received in
the middle and north portions of the bay.  The oil and grease load ends up mostly in
the middle section of the bay system.  The middle and north sections of the bay
system receive the majority of the copper load.   The north portion of the bay
system receives almost half of the chromium load with the middle and south
125
portions receiving the rest of the load.  The zinc load is received in the north and
middle section of the bay system with the south section of the bay system receiving
a smaller portion of the load.  Almost all of the constituent loads are received in the
middle and north portions of the bay system due to the increased runoff in those
areas and the majority of the point sources being located in those areas.
0%
20%
40%
60%
80%
100%
Total
Nitrogen
Total
Phosphorus
Oil and
Grease
Copper Chromium Zinc
South
Middle
North
Figure 4.6: Percent of Total Constituent Load which Comes from a Specific
Source for the North, Middle and South Bay Sections: (Total Nitrogen, Total
Phosphorus, Oil and Grease, Copper, Chromium, and Zinc).
126
4.3 Constituent Concentrations in the Bay System
After the water quality model is run on the constituents, the expected
concentrations are compared to the observed concentrations.  A statistical analysis
is run on the observed concentrations to determine an area- and time-weighted
mean as well as a standard deviation.  A statistical t-test is calculated between the
expected and the observed concentrations to determine if the model predicts the
equilibrium concentration within a reasonable range.
4.3.1 Comparing the Expected Concentrations to the Observed
Concentrations using the T-Test
The t-test is used to compare the expected concentrations calculated using
the water quality model to the observed concentrations.  The t-test is a statistical
analysis which compares the observed concentrations with the expected
concentrations normalized with the standard deviation and the square root of the
number of samples (Hirsch et al., 1993).  A positive t-test result indicates the
observed concentrations are greater than the expected concentrations while a
negative t-test result indicates the observed concentrations are less than the
expected concentrations.  If the t-test result is larger than r2, the expected value is
not statistically the same as the observed values.  The Equation 4.1 is used to
calculate the t-statistic.
127
Equation 4.1
t-test = 
n
s
x P 
where: x  = mean of observed concentrations, mg/l
   = )*(
1
sn
n
ii
i
?
?
where:
ni
= number of samples
si
 = observed concentration in segment
P = expected concentration computed from model, mg/l
   = )*(
1
sA
A
ii
i
?
?
where:
Ai
= surface area
si
 = observed concentration in segment
s = standard deviation of observed concentrations, mg/l
n = number of samples
Table 4.3 shows the expected concentration, the observed concentration, the
standard deviation, the number of samples, and the t?test result for each of the bay
segments.
For the most part, the t-test result is negative for the nutrient constituents,
total nitrogen and total phosphorus.  This suggests a higher expected concentration
than the observed concentrations.  Because decay is not accounted for in the water
quality model, this result is reasonable.
The t-test results for the metals are positive for most of the metal
constituents.  This suggests lower expected concentration than the observed
concentrations.  This is probably due to a missing load source in the calculations.
128
Since the bays do not flush frequently, there exist long periods of time during which
the metals are washed into the bays and deposited in bay sediments, creating a
reservoir of metals in the sediments.  A portion of these sediment metals may then
re-enter the water column due to resuspension and physical and chemical process.
The net flux of metal concentrations out of the sediment is not accounted for in the
water quality model due to lack of metal sediment flux data.  There could also be a
missing data source from the atmosphere, land surface and/or point sources.
Oil and grease, for the most part, has a t-statistic within the r2 range, which
indicates a reasonable estimation of the constituent concentrations in the bay
system.
Table 4.3: Expected and Observed Concentrations, Standard Deviation,
Number of Samples, and the T-Test Result for Each Bay Segment: a) Total
Nitrogen, b) Total Phosphorus, c) Oil and Grease, d) Copper, e) Chromium,
and f) Zinc.
a) b)
Total Nitrogen Copper
segment exp (mg/l) obs (mg/l) std dev # samples t-test segment exp (ug/l) obs (ug/l) std dev # samples t-test
stc 0.9 0.1 0.4 176 -30.6 stc 2.4 6.8 2.1 2 2.9
cop 1.1 0.6 0.7 514 -15.5 cop 2.6 24.5 34.4 8 1.8
aran 0.2 0.1 0.3 262 -2.0 aran
aram 0.4 0.1 0.1 191 -47.9 aram 1.1 2.9 2.3 57 5.8
aras 0.4 0.1 0.2 288 -24.8 aras 1.8 3.5 1.6 15 4.1
red 0.3 0.1 0.6 348 -6.5 red 2.3 13.2 31.2 10 1.1
ccbn 0.3 0.1 0.3 1335 -18.1 ccbn 3.0 9.8 37.8 133 2.1
nuee 0.3 0.2 0.6 707 -5.6 nuee 3.0 15.0 26.6 15 1.7
nuew 0.4 0.6 0.1 130 19.0 nuew
nuem 0.4 0.1 0.3 631 -20.0 nuem
inl 0.2 0.1 0.4 4 -0.5 inl
ccbe 0.3 0.1 0.3 691 -17.4 ccbe 3.0 0.8 2.1 10 -3.2
inh 0.5 0.4 1.1 482 -0.1 inh 3.8 9.9 13.8 92 4.2
ccbm 0.3 0.1 0.1 711 -32.7 ccbm 3.0 4.5 4.7 5 0.7
oso 0.4 0.2 4.9 192 -0.6 oso 3.4 41.4 52.4 3 1.3
ccbs 0.3 0.2 0.5 287 -3.7 ccbs
ulmn 0.2 0.3 0.1 417 15.7 ulmn 5.7 10.1 23.4 18 0.8
baf 0.3 0.1 0.2 802 -21.3 baf 7.7 26.9 31.8 8 1.7
ulmm 0.2 0.1 0.2 248 -5.8 ulmm
129
c) d)
Total Phosphorus Chromium
segment exp (mg/l) obs (mg/l) std dev # samples t-test segment exp (ug/l) obs (ug/l) std dev # samples t-test
stc 0.3 0.1 0.1 177 -24.9 stc 3.9 10.0 0.0 2
cop 0.3 0.1 0.1 519 -56.5 cop 4.1 28.8 31.1 8 2.3
aran 0.1 0.1 0.1 216 -1.1 aran 2.5 21.8 2.3 13 29.8
aram 0.2 0.1 0.1 193 -22.4 aram 3.0 5.8 2.1 57 9.9
aras 0.1 0.1 0.0 293 -33.4 aras 3.8 7.6 2.9 14 4.8
red 0.1 0.1 0.1 346 -27.6 red 4.2 12.5 30.9 10 0.8
ccbn 0.1 0.1 0.1 941 -36.2 ccbn 4.6 15.3 21.0 132 5.9
nuee 0.2 0.1 0.1 298 -8.5 nuee 4.4 19.9 25.2 15 2.4
nuew 0.2 0.2 0.1 15 1.4 nuew
nuem 0.2 0.2 0.3 297 -2.0 nuem
inl 0.1 0.1 0.0 2 -0.2 inl
ccbe 0.1 0.0 0.1 352 -27.9 ccbe
inh 0.2 0.1 0.2 429 -6.7 inh 4.8 16.4 21.0 84 5.1
ccbm 0.1 0.1 0.1 589 -32.4 ccbm 4.6 24.0 21.4 5 2.0
oso 0.2 0.4 1.5 182 1.7 oso 4.9 42.1 52.0 3 1.2
ccbs 0.1 0.1 0.0 240 -19.8 ccbs
ulmn 0.1 0.1 0.0 186 -20.4 ulmn 4.3 13.9 22.6 18 1.8
baf 0.1 0.1 0.7 306 0.3 baf 4.1 42.2 36.4 10 3.3
ulmm 0.1 0.1 0.0 127 -7.7 ulmm
e) f)
Oil and Grease Zinc
segment exp (mg/l) obs (mg/l) std dev # samples t-test segment exp (ug/l) obs (ug/l) std dev # samples t-test
stc stc 9.7 13.2 12.4 2 0.4
cop cop 12.9 34.4 288.1 8 0.2
aran 10.2 10.4 9.4 7 0.1 aran 5.9 5.7 51.9 13 0.0
aram 9.2 9.1 6.3 26 -0.1 aram 7.8 19.2 33.6 57 2.6
aras aras 10.8 34.6 28.1 15 3.3
red red 12.6 13.2 31.3 10 0.1
ccbn 2.7 2.0 1.2 34 -3.4 ccbn 14.1 80.7 222.5 134 3.5
nuee nuee 13.7 51.3 49.2 14 2.9
nuew nuew
nuem nuem
inl 2.7 6.3 7.8 2 0.6 inl 14.0 7.5 8.9 5 -1.6
ccbe ccbe 14.0 2.1 3.8 10 -9.9
inh 2.8 7.3 11.8 27 2.0 inh 22.9 47.8 50.4 89 4.7
ccbm ccbm 14.1 133.8 152.5 5 1.8
oso oso 15.3 44.9 50.4 3 1.0
ccbs ccbs
ulmn ulmn 11.8 12.8 22.9 18 0.2
baf baf 10.3 51.3 34.0 10 3.8
ulmm ulmm 9.8 8.6 113.1 2 0.0
The  average t-test results for each bay system is calculated based on a
number of sample weighted average over each of the three bay sections.  Equation
4.2 illustrates this calculation.  Table 4.4 shows the average t-test result for each of
the constituents.  The nutrient t-test results are negative, again suggesting that decay
should be used in the water quality model.  The average t-test result for copper,
chromium, and zinc, are positive which suggests a data source is missing in the
load calculations.  Oil and Grease has a low average t-test result which suggests the
model predicts the equilibrium concentrations close to the observed concentrations.
130
Equation 4.2
Ave T-Test = )*(
1
tn
n
ii
i
?
?
where:
ni
= number of samples
si
 = observed concentration in segment
Table 4.4: Average T-Test Result for Each Constituent.
Constituent Bay System Average t-test
Total Nitrogen North -18.2
Middle -14.5
South -8.2
Total -14.2
Total Phosphorus North -33.1
Middle -21.9
South -7.5
Total -23.8
Oil and Grease North -0.1
Middle -1.0
South
Total 0.0
Copper North 4.6
Middle 2.6
South 1.1
Total 3.0
Chromium North 10.0
Middle 5.2
South 2.3
Total 6.4
Zinc North 1.9
Middle 3.2
South 1.4
Total 2.7
4.3.2 Calculating the Decay Rates in the Three Bay Systems
The previous calculations assumed that there is no constituent decay or
deposition in the bays.  The appropriate decay rates to bring the computed and
observed concentrations into balance can be determined.  To do this, the bay system
is broken into three smaller systems being the north, middle and south sections.
The decay rates calculated included all abiotic and biotic processes.
131
A simplified model is used to determine the load which is not transported
out of the system.  The same mass balance equation is used for the simplified model
as is used for the model which broke the system into twenty segments, however the
equation is solved for the decay rate rather than the equilibrium concentrations.
The simplified system is used to calculate the decay rate needed to reach the
observed concentrations given the constituent load to the system.  The model
assumes the only unknown into the system is the decay rate. The average observed
concentrations are calculated using the time- and space-weighted average observed
concentrations for each segment explained in Section 3.5.4.  The concentrations are
averaged over time and space for each of the 20 segments.  The average observed
concentrations for the three sections is a weighted averaged based on the number of
samples taken for that segment.  This is done using Equation 4.3.
Equation 4.3
Cave
= )*(
1
sn
n
ii
i
?
where:
Cave
 = average observed concentration
ni
= number of samples
si
 = observed concentration in segment
Once the average concentrations for each bay segment are calculated, the
decay rates are calculated using Equation 4.4.  The methodology for the decay rate
calculations is the same as the water quality mass balance (Figure 3.13).
132
Equation 4.4
K = ( QN,M*sN ? QM,S* sM ? QM,Oc*sM + EN,M(sN-sM) + EM,Oc(sM-sOc)
       + EM,S(sM-sS) + WM ) / ( VM*sM )
where: K = the decay rate
QN,M*sN = Advective load from the north segment
QM,S* sM = Advective load leaving the middle segment to the
south segment
QM,Oc* sM = Advective load leaving the middle segment to the
ocean
EN,M(sN-sM) = dispersive load between the north and middle
segments
EM,Oc(sM-sOc) = dispersive load between the middle segment
and the ocean
EM,S(sM-sS) = dispersive load between the middle and south
segments
WM = waste load entering the middle segment
VM*sM = load volume in the segment
For the north system, the advective load from the boundary is added to total
waste load from the land surface, point sources, non-point sources, and atmospheric
deposition.  The advective load to middle segment is subtracted from the system,
and the dispersive loads are added or subtracted depending on the concentration
gradient.
For the middle system, the advective load from the first segment is added to
total load from the land surface, point sources, non-point sources, and atmospheric
deposition.  The advective load to south segment and the ocean are subtracted from
the system, and the dispersive loads are added or subtracted depending on the
concentration gradient.
For the south system, the advective load from the middle section is added to
total load from the land surface, point sources, non-point sources, and atmospheric
deposition.  The advective load leaving at the south boundary is subtracted from the
133
system, and the dispersive loads are added or subtracted depending on the
concentration gradient.
The interface flows, bulk dispersion coefficients, segment volumes, and
boundary conditions are the same as the twenty segment model.  Because the
boundary dispersion coefficients are large, they govern the mass balance equation.
Therefore, the boundary conditions are set equal to the concentration in the bay
adjacent to the boundary interface making the dispersion flow at the boundaries
negligible.  Table 4.5 shows the decay rates calculated from the mass balance for
the constituents total nitrogen and total phosphorus.
Table 4.5: Decay Rates for Total Nitrogen and Total Phosphorus.
Constituent Bay Decay Rate Ave Decay
Section ( /d )  Rate ( /d )
Total Nitrogen North 0.030
Middle 0.011
South 0.024 0.022
Total Phosphorus North 0.013
Middle 0.004
South 0.009 0.009
The long residence time of the three bay segments makes the concentrations
sensitive to the overall gain and loss within the system.  The decay rate takes into
account all the mechanisms of the gain and loss process either by biotic or abiotic
processes.  These processes include photosynthetic uptake, excretion, chemical
transformations, hydrolysis of dissolved organic nutrients, detritus decomposition,
sediment decomposition and release, and external loading (EPA, 1997).
The small decay rate values suggest they are within the right order of
magnitude and are comparable to the United States Environmental Protection
Agency (EPA) suggested decay rates in the surface water quality modeling online
report at the Internet site:
http://www.epa.gov/ORD/WebPubs/surfaceH2O/surface.htm.
134
4.3.3 Edroy and King Ranch EMC Study
Two runoff studies as part of the Corpus Christi National Estuary Program
and supervised by the USGS are being done to determine representative EMC
values for nutrients from agricultural landuse within the Corpus Christi National
Estuary Programs study area.  The studies are being completed on the King Ranch
and on an agricultural area near the Town of Edroy.  Because the studies are still in
progress, the data is provisional.  The following calculation made from these data is
for comparison purposes with results of the original EMC data in Section 2.2.8.
The provisional EMC values are 1.49 mg/l and 0.47 mg/l for total nitrogen
and total phosphorus, respectively.  The provisional values are calculated from 29
runoff samples for total nitrogen and 32 runoff samples for total phosphorus.  The
median values are reported by the USGS (Ockerman, 1998).  For comparison, the
original EMC values are 4.40 mg/l for total nitrogen and 1.30 mg/l for total
phosphorus, as discussed in Section 2.2.8, Table 2.2.
The EMC results from the studies at the King Ranch and Edroy agricultural
fields are entered into the land surface runoff model by replacing the original
agricultural land use EMC values, with the new provisional EMC values.  The
EMC values are changed for the Anderson Land Use Classification number 21
which represents agricultural crop and pasture land.
Table 4.6 shows the land surface load for nitrogen and phosphorus to the
bay system using the original and the provisional EMC data.  The land surface
loads are reduced by 54% for total nitrogen and 60% for total phosphorus using the
provisional EMC data compared to the original EMC data.
135
Table 4.6:  Total Nitrogen and Total Phosphorus from Land Surface Sources
to Bay System Using Original and Provisional EMC Values.
         Total Nitrogen       Total Phosphorus
Original Provisional Original Provisional
Segment (Kg/d) (Kg/d) (Kg/d) (Kg/d)
North 5021.27 2268.98 1283.48 498.46
Middle 1184.66 530.17 338.45 151.78
South 1412.24 669.7 356.19 144.38
Total 7618.17 3468.85 1978.12 794.62
Table 4.7 shows the total loads to the bay system for nitrogen and
phosphorus using the original and the provisional EMC data.  Total loads to the bay
system are found by adding together the land surface loads, atmospheric loads,
point source loads, and Nueces River Watershed loads as discussed in Section 3.4.
The total load is reduced by 27% and 38% for total nitrogen and total phosphorus,
respectively.
Table 4.7:  Total Nitrogen and Total Phosphorus from all Sources to Bay
System Using Original and Provisional EMC Values.
         Total Nitrogen       Total Phosphorus
Original Provisional Original Provisional
Segment (Kg/d) (Kg/d) (Kg/d) (Kg/d)
North 6576.35 3824.06 1409.7 624.68
Middle 4025.84 3371.35 1203.25 1016.58
South 4566.3 3823.76 496.37 284.56
Total 15168.49 11019.17 3109.32 1925.82
In section 3.4.4, the atmospheric contribution to the total nitrogen land
surface load is found to be 2,733 Kg/d.  This is 35% of the original nitrogen load
from land surface sources and 79% of the provisional nitrogen load from land
surface sources.  It is important to note that the atmospheric station is near Beeville,
TX.  Since the station is located inland, the atmosphere it samples is influenced by
many sources such as volatilization of land surface loads, industries, etc.  The mean
136
annual concentration of total nitrogen present in the atmosphere is 1.10 mg/l
(USGSNADP, 1997).
The water quality model is run using the provisional loads to the bay
system, assuming conservative transport (no decay).  As with the original data, the
expected concentrations are again higher than the observed constituent
concentrations because no decay is considered in the calculation. Tables 4.8 and 4.9
compare the observed nutrient concentrations in the bay system to the original
provisional concentrations.  The concentrations in the north bay system drop
considerably when the provisional data is used.  However, the concentrations in the
middle and south bay systems stay relatively the same.
Table 4.8:  Total Nitrogen Concentrations in the Bay System Using Original
and Provisional EMC Data Compared to the Observed Data.
Original Provisional
Segment exp (mg/l) exp (mg/l) obs (mg/l)
North 0.55 0.34 0.19
Middle 0.33 0.32 0.22
South 0.23 0.21 0.18
Table 4.9:  Total Phosphorus Concentrations in the Bay System Using
Original and Provisional EMC Data Compared to the Observed Data.
Original Provisional
Segment exp (mg/l) exp (mg/l) obs (mg/l)
North 0.20 0.12 0.08
Middle 0.15 0.13 0.13
South 0.09 0.08 0.07
The system is again broken into the north, middle and south portions to
calculate the decay rates needed to match the calculated and the observed
concentrations in the bay system.  Table 4.10 shows the decay rates calculated for
the original loads and the provisional loads to the bay system.  The decay rates
137
needed for new total nitrogen and total phosphorus data are the smaller than the
decay rates calculated for the original data in the north bay system.  However, the
decay rates in the middle and south bay segments are relatively the same for both
the original and provisional loads.  In all cases, the decay rates are within
reasonable limits.
Table 4.10:  Comparing Decay Rates for Total Nitrogen and Total Phosphorus
Using Original and Provisional Data.
Constituent Bay Decay Rate Decay Rate
Segment Original (/d) Provisional (/d)
Total Nitrogen North 0.030 0.017
Middle 0.011 0.009
South 0.024 0.021
Average 0.022 0.016
Total Phosphorus North 0.013 0.005
Middle 0.004 0.003
South 0.009 0.008
Average 0.009 0.005
The provisional data calculations show that a reduction in the EMC value
produces a reduction in the land surface load to the bay system.  However, the total
load to the system does not show as large of a reduction in load.  The
concentrations in the north bay system are reduced while the middle and south bay
system concentrations did not show as much of a reduction.  This is expected since
the runoff gradient increases as you move North through the study area bringing
along with it a larger load to the bay system.  Since, the two agricultural runoff
studies are being conducted in the south and middle portions of the study area, and
the majority of the runoff and load is in the north portion of the study area, a runoff
study could be conducted in that region in order to obtain a true EMC value
representative of agricultural land use in the study region.
138
5 CONCLUSIONS
A Geographic Information System (GIS) model of total constituent loadings
and their impacts on receiving water quality is presented for the Corpus Christi Bay
system. The model uses publicly available elevation, stream network, precipitation,
stream discharge, water quality, and land use data sets.  The model development
begins by laying a fine mesh of 100m cells over a watershed within which the
nonpoint source constituent loads are estimated from each cell of the land surface.
Point sources and atmospheric deposition on the bay system are separately
accounted for, as is the load from the Nueces River inflow to the study region.  The
loads from the model are input to the receiving water to calculate the equilibrium
concentrations in the bay system.  The coefficients of dispersion for the Bay
segments are calibrated using salinity as a conservative tracer.  The resulting
constituent concentrations are compared with the average of those observed in each
bay segment.
For summary purposes, the overall bay system is subdivided geographically
into three large bay systems, north, middle and south.  The middle bay system refers
to the Corpus Christi and Nueces Bays and the smaller bays flowing into them.  The
other two categories refer to the bays north and south of the middle bay system;
Aransas/Copano Bays, and Baffin Bay/Laguna Madre, respectively.  The study
methodology can be broken down into three components: first, the water balance of
inflow, outflow, precipitation and evaporation; second, the total loadings of
constituents from point, land surface, atmospheric and the Nueces River Basin; and
third, the mass balance of each constituent in the bay system.  The constituent mass
balance analysis includes a comparison of the observed and calculated
concentrations.  Finally, the possible management uses of the model are assessed.
139
(1) Water Balance
The mean annual precipitation ranges from approximately 600 mm/yr (24
in/yr) at the southern end of the study region to approximately 1000 mm/yr (40
in/yr) at the northern end.  At the southern end less than 2% of the precipitation
becomes runoff while at the northern end more than 10% becomes runoff.
Therefore, the runoff gradient from South to North is much more pronounced than
is the gradient of precipitation.  The runoff averages less than 10 mm/yr (0.4 in/yr)
in the south to more than 100 mm/yr (4 in/yr) in the north. The reason for the steep
runoff gradient is that the larger precipitation in the north saturates the soil more
frequently causing a greater percentage of the precipitation to become runoff than in
the south.
The runoff into the bay system from the land surface in the study region
totals 36 m
3
/s.  An additional 20 m
3
/s enters the bay system from the Nueces River
outflow from Lake Corpus Christi.  At the northern boundary of the bay system, an
inflow of 16 m
3
/s is assumed to come from the very substantial runoff of the
Guadalupe and San Antonio Rivers which flow into San Antonio Bay, this estimate
being 20% of the mean annual gauged discharge of those two rivers.  At the
southern boundary of the bay system, it assumed that there is no inflow from the
Lower Laguna Madre. The total inflow to the bay system is 72 m
3
/s of which 56%
enters the north bays, 34% the middle bays and 10% the southern bays.
The average direct precipitation onto the surface of the bays is
approximately 800 mm/yr and the average evaporation is approximately double that
figure, or 1600 mm/yr.  The mean annual outflow from the bay system to the Gulf
of Mexico is only 6 m
3
/s, so the remainder of the inflow is lost by evaporation.
Since very little tidal flushing occurs and the bay system is not frequently flushed
by inflow from the land surface, the bay system acts as a large sink that absorbs
140
most of the loads that come into it from the land surface, atmosphere, and point
sources.
The calculations in this study are all based on mean annual conditions.  In
years of high flow, there is a correspondingly greater outflow to the Gulf, while in
years of low flow there is a net inflow of water from the Gulf to the bay system to
sustain the net evaporation from the bay system.   The disparity between inflow and
outflow is particularly acute in the south bay system, in part because the
precipitation is lowest there and in part because the surface area of the south bay
system is larger than the combined surface areas of the north and central bay
systems.  There is also no point of exchange of water with the Gulf of Mexico in
the south bay region.  Hence, the south bay system flushes itself much less
frequently and less completely than do the north or the central bay systems.
(2) Total Loadings
Total load calculations were made for thirteen constituents: ammonium,
nitrite plus nitrate, total nitrogen, total phosphorus, oil and grease, and eight metals
(copper, cadmium, chromium, lead, zinc, iron, arsenic, and mercury).  There are
significant gaps in the available data, notably for point source loadings of
ammonium and nitrite plus nitrate, non-point source loadings of ammonium,
atmospheric loadings of many constituents, and sediment loads of all constituents.
Because of these data limitations, results are discussed for six constituents with the
most reliable data: total nitrogen, total phosphorus, oil and grease, copper,
chromium, and zinc.
The nutrients nitrogen and phosphorus have a combined loading of
approximately 18,100 kg/day, of which 15,000 kg/day is total nitrogen and 3,100
kg/day is total phosphorus.  Oil and Grease have a total load of 2,800 kg/day.  The
141
loadings for the metal constituents are much smaller, approximately 30 kg/day for
copper, 26 kg/day for chromium and 108 kg/day for zinc.  For total phosphorus and
the metals, from one half to two thirds of the loads are derived from the land
surface. Oil and grease source are derived mostly from point sources.  Atmospheric
sources contribute about one third of the load of total nitrogen, about one half of
this load comes from non-point sources on the land surface and one sixth from
point sources.  The atmospheric load in wet deposition over the land surface is
approximately 35% of the land surface load for total nitrogen.  For the metals,
about one third of the load comes from point sources and two thirds from non-point
sources.  No sediment loads from the bay floor are included in these estimates, and
atmospheric loads are also not accounted for in the case of phosphorus and the
metal constituents.
(3) Water Quality
The impacts of the loadings on the bay system are evaluated using a water
quality model, which takes into account advection, dispersion, and first order decay,
but not detailed biological or chemical processes.  The dispersion coefficients for
each of the 20 bay segments within the three main bay segments were calibrated
using salinity as a conservative constituent and held constant for all the other
constituents.  A set of mass balance calculations are first made assuming all
constituents are conservative.  A t-test was used to compare the predicted and
observed concentrations in the bay segments.  The t-test results showed that the
conservative constituent assumption results in accurate prediction of observed
concentrations of oil and grease, over-prediction of total nitrogen and phosphorus,
and under-prediction of metals.
The long residence time of the three bay segments makes the concentrations
142
sensitive to the overall gain and loss within the system.  A second set of mass
balance calculations was carried out to determine constituent decay.  The decay
rates take into account all the mechanisms (abiotic and biotic) of the gain and loss
process. To reach the average observed concentrations in the bays for total nitrogen
requires a decay rate of approximately 0.02 d
-1
.  This means that 2% of the mass of
nitrogen is removed from the bay waters each day by decay processes.  A similar
computation for total phosphorus yields a decay rate of approximately 0.01 d
-1
.
These decay rates are reasonable when compared to corresponding values reported
in the literature.
Using the conservative mass balance, the metals concentrations are under-
predicted by a factor of three to four, which suggests that there are metals sources
that are presently unaccounted for.  Since the bays do not flush frequently, there
exist long periods of time during which the metals are washed into the bays and
deposited in bay sediments, creating a reservoir of metals in the sediments.  A
portion of these sediment metals may then re-enter the water column due to re-
suspension and physical and chemical process.  It is also possible that a reservoir of
metals has been accumulated in the Bay sediments from industrial discharges of
previous decades when wastewater discharge standards were not as stringent as
they are now.  The calculations suggest that this flux from the sediment reservoir
may be the dominant control on the metals concentrations in the bay waters rather
than current loadings from the land surface.  It is also possible that the metal
loadings from the other sources have been underestimated, either in the point
sources, land surface or atmospheric loads.  Clarifying this discrepancy in the mass
balance for metals in the bay system requires further investigation.
The nonpoint source loadings for total nitrogen and total phosphorus are
largely driven by runoff from agricultural lands.  Ongoing field studies at the King
143
Ranch and near Edroy, TX, suggest that the expected mean concentrations of
nitrogen and phosphorus are less than half of those assumed originally in the mass
balance study, whose expected mean concentration values were derived from data
observed outside the Corpus Christi region.  When other sources of nitrogen and
phosphorus are also accounted for, the resulting reductions in total loads are about
27% from total nitrogen (from 15,000 Kg/d to 11,000 Kg/d) and about 28% for
total phosphorus (from 3,1000 Kg/d to 1,900 Kg/d).
This study did not incorporate the City of Corpus Christi?s water supply
diversion from the Nueces River.  It was assumed that all of the water leaving the
Corpus Christi Dam enters the bay system.  However, only fifteen percent of the
discharge in the Nueces River is diverted to the City, with two thirds of that is
discharged back to the bay system through the City?s wastewater discharge (Ward,
1997).  Overall, the constituent load from the Nueces River Basin upstream of
Lake Corpus Christi has very little impact on the bay system.
(4)  Model Uses and Developments
The original intent of the study was to determine the total loads and water
quality to the Corpus Christi Bay System.  However, the way that the GIS model is
prepared, the study could be used for management purposes.  By determining the
source of load, different best management practices (BMP) can be analyzed in order
to reduce that constituent load.  The models can then be run again to calculate the
reduction in loads due to the BMPs.  This has been done in a parallel study
concerned with water quality master planning being carried out for the City of
Austin.
The Expected Mean Concentration (EMC) table, which links pollutant
concentrations in runoff to land use, can be updated as new information is obtained.
144
For example, once the Edroy and King Ranch studies are complete and the data is
analyzed, the EMC table used in the model can be updated by changing the EMC
values associated with the rangeland and agricultural land uses.  The models can be
run again in order to assess the change in loads due to the change in EMC values
for these two land uses.  The land use data employed in this study do not
distinguish between improved pasture and row-crop agriculture.  Once new land
use are obtained, the land use files can be updated.  The new land use would then
be linked to the EMC table and the models could be run again to determine the
change in loads due to the new land use data.
The model can be used to assess the loads in small scale studies in problem
areas in the Corpus Christi Bay System.  For example, the area draining into the
Inner Harbor can be delineated and separated from the rest of the study area.  The
land surface and water quality models can be run on that area to determine the
source of the loads ending up in the Inner Harbor.  Different BMPs could be
analyzed to determine the best solution to control the loads entering the receiving
water in the Inner Harbor.
Since the study used mean annual values for flow, precipitation, and
evaporation, the model results are a representation of total loads and the resulting
water quality that occur during a year of normal weather.  The model could be run
using flow, precipitation, and evaporation representing flood and drought years in
order to assess the water quality of the bay system during high and low flows.
Also, the model could be run using the seasonal variations in flow, precipitation
and evaporation by finding the average values for each of the seasons.  The water
quality for the seasonal variations could then be assessed.
Once new data is obtained, the model can be updated.  For example, there
are separate studies being done in the CCBNEP for atmospheric deposition.  Once
145
these studies are complete and the data is analyzed, the new loadings can be entered
into the water quality model and the new equilibrium constituent concentrations can
be calculated.  This same calculation can be done once new load information is
obtained for sediment fluxes, point sources, and land surface EMC data.
146
APPENDIX A: Data Dictionary
147
Data File Description Class Attributes
accprecip2 Accumulation of the precipitation grid. Grid Accumulated
Units are cubic meters/year. Precipitation
AGREE Avenue script that reconditions the digital
elevation surface to coincide with the DLG
files.
aransas Sub-watershed delineated using the Grid/Poly
script WTRSHD.  For the Aransas River gauge.
BALANCE Avenue scripts for calculating the equilibrium
constituent concentrations in the bay system.
bay Bay coverage which is the final 20 Poly Area
segments.  Contains information for each Volume
of the segments. etc.
bayevap Zonal mean of evaporation over the bay Poly Evaportation
system which are integer values.
bayprecip Zonal mean of precipitation over the bay Poly Precipitation
system which are integer values.
big Copy of the grid of the study are obtained Grid
from the delineated watersheds.
big1 Grid of the study are obtained from the Grid
delineated watersheds where all cells equal 
one and the Lake Corpus Christi Watershed
is equal to no data.
biglandg Original land use file converted to a grid Grid Land Use
from the lucorptsms file.
bigmap Appended DLG files.  Original files were Arc Land Use
obtained from the USGS CD-ROM.
baygrid Grid of the bay segments Grid
CALCAREA Avenue script used to calculated the surface
area of the bay segments.
CALCEP Avenue scripts used to calculate the bulk
dispersion coeffiecents from the dispersion
coeffiecents.
CALCLENGTH Avenue script used to calculated the interface
length between each of the bay segments.
ccbnep.apr ArcView project file containing all the scripts
needed to complete the CCBNEP total
loadings and water quality project.
cchuc HUC files for the study area. Poly HUC number
Albers projection with NAD27 datum. and name
148
Data File Description Class Attributes
cchuc2tsms HUC files for the study area. Poly HUC number
Albers projection with NAD28 datum. and name
chiltipin Sub-watershed delineated using the Grid/Poly
script WTRSHD.  For the Chiltipin Creek gauge.
cogrid Land surface constituent concentration grid. Grid Constituent
Constituent abbreviation is tacked onto the Concentration
end of the cogrid label.  Ten constituents 
were analyzed over the land surface.
Units are mg/l.
CONCGRID Avenue script which calculated the land
surface concentrations from the EMC values.
CONNECT Avenue script which connects the DEM
to the bay coverage.
copano Sub-watershed delineated using the Grid/Poly
script WTRSHD.  For the Copano Creek gauge.
corpgages Point file with all the gauging stations in the Point Latitude
study region projected from a georaphic Longitude
projection to the Texas State Mapping
System.
corptsms Projected digital elevation grid.  Clipped Grid Elevation
using the buffered HUC files.  Elevation
values in each grid cell are in units 
of meters above sea level.
corpus2 Merged DEM files originating from nine Grid Elevation
smaller DEM files obtained from the 
USGS Internet site.  Map sheet = Beeville-e,
Beeville-w,Crystal_City-e, Corpus_Christi-e,
Corpus_Christi-w, Laredo-e, Laredo-w,
Port_Isabel-w, and McAllen-e.  Elevation
values in each grid cell are in units of 
meters above sea level.
count A coverage of the counties in the Poly County name
study area.  Lambert Projection
countt A coverage of the counties in the Poly County name
study area.  Texas State Mapping
System.
counties A coverage of the counties in Texas. Poly County name
dem Projected digital elevation grid renamed. Grid Elevation
Elevation values in each grid cell
are in units of meters above sea
level.
149
Data File Description Class Attributes
demtsms.prj Projection file writtem in the Arc Macro
Language to project from a geographic 
projection to the Texas State 
Mapping System.
dlgtsms The projected and appended DLG file. Arc
Projected from the UTM projection to the 
Texas State Mapping System.
dlg2 The renamed projected DLG file. Arc
evap Grid coverage of the evaporation taken from Grid Evaporation
the quadsp file of evaporation.
evapzone Zonal mean of evaporation over the bay Grid Evaporation
system. Units are mm/yr.
evapzone2 Zonal mean of evaporation over the bay Grid Evaporation
system which are integer values.
facac2 Integer number of cells that fall upstream Grid Flow
of each cell.  A flow accumulation after Accumulation
the Avenue scripts AGREE and CONNECT
are run on the original DEM.
fdac2 A flow direction after the Avenue scripts Grid Flow
scripts AGREE and CONNECT are run. Direction
geotsms.prj Projection file writtem in the Arc Macro
Language to project from a geographic 
projection to the Texas State 
Mapping System.
gridagree2 Digital elevation grid which was Grid Elevation
calculated using the script AGREE.
Units are meters above sea level.
150
Data File Description Class Attributes
gridconnect2 Digital elevation grid which was Grid Elevation
calculated after using the script CONNECT.
Elevation values in each grid cell are in 
units of meters above sea level.
gridfilla2 Digital elevation grid which was filled Grid Elevation
after the AGREE script was run.  Elevation 
values in each grid cell are in units of 
meters above sea level.
huc250 HUC files for the United States Poly HUC number
and name
lamtsms.prj Projection file writtem in the Arc Macro
Language to project from a lambert projection
to the Texas State Mapping System.
landara Land use coverage for the sub-watershed Poly Land Use
Aransas.
landchil Land use coverage for the sub-watershed Poly Land Use
Chiltipin.
landcop Land use coverage for the sub-watershed Poly Land Use
Copano.
land.dbf Database file which contains the EMC
values for ten constituents.
landlos Land use coverage for the sub-watershed Poly Land Use
Los Omos.
landmed Land use coverage for the sub-watershed Poly Land Use
Medio.
landmis Land use coverage for the sub-watershed Poly Land Use
Mission.
landoso Land use coverage for the sub-watershed Poly Land Use
Oso.
landsdie Land use coverage for the sub-watershed Poly Land Use
San Diego.
landsfer Land use coverage for the sub-watershed Poly Land Use
San Fernando.
landuse Original land use cover created by Poly Land Use
clipping the lucortsms coverage with  
the outline coverage.
landuse2 Updated land use file converted to a polygon Poly Land Use
coverage from the landuseg2 file.
landuseg Original land use file converted to a grid. Grid Land Use
from the landuse file.
151
Data File Description Class Attributes
landuseg2 Merged land use file clipped using the big1 Grid Land Use
file which outlines the study area.
lkcc Grid of the Lake Corpus Christi watershed. Grid
lkg Nueces River load grid clipped to study area. Grid
lkgrid Nueces River Watershed source Grid
constituent loadings grid.  Three constituent
loads were analyzed.  Constituent
abbreviations are tacked onto the
end of the lkgrid label.  Units are Kg/d.
lkgridwf Weighted flow accumulation for the Nueces Grid
River Watershed load grids.  Constituent 
abbreviationsare tacked on the end of lkwfgrid.
lkgridwf.aml AML used to calculate the weighted flow
accumulations for the Nueces River loads.
LOADGRID Avenue script which calculated the land
surface laods from the concentration grids.
log Land surface load grid clipped to study area. Grid Load
logrid Land surface constituent loadings grid. Grid Load
Constituent abbreviation is tacked onto the
end of the logrid label.  Ten constituents 
were analyzed over the land surface.
Units are Kg/d.
logridwf Weighted flow accumulation for the land Grid Load
surface load grids.  Constituent abbreviations Accumulation
are tacked on the end of lowfgrid.
lonlat.dat File containing the longitude and latitude
of all the gauging stations in the study area
in decimal degree format.
losomos Sub-watershed delineated using the Grid/Poly
script WTRSHD.  For the Los Omos
Creek gauge.
152
Data File Description Class Attributes
lucorp Merged land use cover downloaded from Poly Land Use
the TNRIS Internet site.  Map Sheets =
Beeville, McAllen, Corpus Christi, Chrystal
City, and Laredo.
lucorpd Dissolved land use cover downloaded Poly Land Use
from the TNRIS Internet site.
lucorptsms The projected land use cover downloaded Poly Land Use
from the TNRIS Internet site.  Texas State 
Mapping System.
map Outline grid of the study area obtained Grid
from the merged delineated watersheds.
medio Sub-watershed delineated using the Grid/Poly
script WTRSHD.  For the Medio gauge.
mission Sub-watershed delineated using the Grid/Poly
script WTRSHD.  For the Mission River gauge.
newland Updated land use map projected from a Grid Land Use
UTM projetion to the Texas State Mapping
System.
newland2 New land use file where no data cells are Grid Land Use
equal to biglandg else they are equal to
newland.
newlandbg Original land use file created by merging Grid Land Use
the biglandg and landuseg files.
newlandbg2 Merged land use file created from the Grid Land Use
newlandbg and newland2 files.
nland1-6 Updated land use files which were created Grid Land Use
by merging the smaller land use files.
nland Final updated and merged land use file. Grid Land Use
oso Sub-watershed delineated using the Grid/Poly
script WTRSHD.  For the Oso Creek gauge.
outline A buffered outline coverage of the Poly
study area renamed.
outlineg Outline grid of the study area. Grid
153
Data File Description Class Attributes
p_ann Precipitation grid obtained from the CRWR Grid Precipitation
database.  Covers all of the United States 
and is a mean annual precipitation in mm/yr.
PICKLOAD Avenue script which extracts the loading to
the centroid of the bay system.
pog Point source load grid clipped to study area. Grid Load
pogrid Point source constituent loadings grid. Grid Load
Constituent abbreviation is tacked onto the
end of the pogrid label. Units are Kg/d.
pogridwf Weighted flow accumulation for the point Grid Load
source grids.  Constituent abbreviations Accumulation
are tacked on the end of powfgrid.
pogridwf.aml AML used to calculate the weighted flow
accumulations for the point source loads.
POINTLD Avenue script which calculates the point
loads by placing the load in the centroid of
the stream polygon or the beginning of the
stream segment.
precip Clipped precipitation grid using the buffered Grid Precipitation
HUC files.
precip2 Clipped and projected precipitation grid. Grid Precipitation
projected from Geographic projection to the
Texas State Mapping System.
precip3 A portion of the clipped and projected Grid Precipitation
precipitation grid along the coast line.
precipgrid Precipitation grid created using the trend Grid Precipitation
function and the precipitation point file
along the coast line.
precipmerge Preciptation grid created by merging the Grid Precipitation
original preciptation grid and the trend grid.
precippnt Point file of the precipitation grid along the Grid Precipitation
coast line.
precipzone Zonal mean of precipitation over the bay Grid Precipitation
system. Units are mm/yr.
precipzone2 Zonal mean of precipitation over the bay Grid Precipitation
system which are integer values.
quadsp Large file of evaporation for the state of Texas. Poly Anntot
rf1 The clipped and projected river reach files. Arc
154
Data File Description Class Attributes
riv20000 Flow accumulation with cells greater Grid Flow
than 20,000 accumulation. Accumulation
rogrid Large runoff grid caclulated by merging Grid Runoff
the small Anderson Land Use classification
grids.  Units are mm/yr.
rogrida Runoff grid with Anderson Land Use Grid Runoff
Classification >=20 and <=29.
rogridr1 Runoff grid with Anderson Land Use Grid Runoff
Classification >=30 and <=49.
rogridr2 Runoff grid with Anderson Land Use Grid Runoff
Classification >=70 and <=79.
rogridr3 Runoff grid with Anderson Land Use Grid Runoff
Classification <=10.
rogridu Runoff grid with Anderson Land Use Grid Runoff
Classification >=11 and <=19.
rogridw Runoff grid with Anderson Land Use Grid Runoff
Classification >=50 and <=59.
rogridwe Runoff grid with Anderson Land Use Grid Runoff
Classification >=60 and <=69.
runcoeff Runoff coefficient grid calculated by Grid Runoff
dividing the runoff grid by the precipitation Coefficient
grid.
runcoeff2 Runoff coefficient grid with all cells being Grid Runoff
less than 0.24. Coefficient
runland2 Runoff grid calculated by multiplying the runoff Grid Runoff
coefficient grid with values less than 0.24
and the precipitation grid. Units are mm/yr.
sandiego Sub-watershed delineated using the Grid/Poly
script WTRSHD.  For the San 
Diego gauge.
sanfern Sub-watershed delineated using the Grid/Poly
script WTRSHD.  For the San 
Fernando Creek gauge.
segments TNRCC stream segment coverage. Arc/Poly
station Point coverage file of the gauging stations in Point
the study area.
statname.dat Data file which contains gauging station name
and number.
155
Data File Description Class Attributes
strmlnk Stream Link grid calculated from the script Grid Stream
STRMLNK and used to delineate the sub- Links
watersheds in the study area.
STRMLNK Avenue script which calculates the stream
link grid used to delineate sub-watersheds.
TIMEAVE Avenue scripts for calculating the average
observed concentrations over time and space.
tsmsbuff A buffered outline coverage of the Poly
study area created from the HUC files.
WTRSHD Avenue script which delineates watersheds.
156
APPENDIX B: Avenue Scripts and AML Programs
SCRIPT/AML DESCRIPTION
Agree Reconditions the DEM to coincide with the DLG
Balance Steady State Water Quality Model
Calcarea Calculates the area of the bay segment
Calcep Calculates the bulk dispersion coefficient from the calibrated
dispersion coefficient
Concgrid Calculates the concentration grid from the EMC field in the
landuse attribute table
Connect Connects the land surface with the bay segments by dropping
the centroid of the bay segment below the surrounding cells
Loadgrid Calculates the load grid by multiplying the concentration grid
by the runoff grid
Lkgridwf.aml AML which calculates the weighted flow accumulation for
the Nueces River loading grids
Logridwf.aml AML which calculates the weighted flow accumulation for
the land surface loading grids
Pickload Picks the loads from the centroid of the bay segment after the
flow accumulation is run
Pogridwf.aml AML which calculates the weighted flow accumulation for
the point source loading grids
Pointld Calculates the point sources grids based on the TNRCC
segmentation
Rogridland Calculates runoff over the land surface based on landuse and
precipitation
Strmlnk Calculates the stream link grid needed to delineate
watersheds
Timeave Calculates the average observed concentration of a
constituent for each bay segment based on time and space
Wfacgrid Calculates the weighted flow accumulation
Wtrshd Delineates watersheds from a specified point
157
AGREE
'
'----------------------------
'--- Creation information ---
'----------------------------
'
'Name: agree.ave
'Version: 1.0.av
'Date: 03/01/97
'Author: Ferdi Hellweger
'        Center for Research in Water Resources
'        The University of Texas at Austin
'        ferdi@crwr.utexas.edu
'
'---------------------------
'--- Purpose/Description ---
'---------------------------
'
'AGREE is a surface reconditioning system for Digital Elevation
'Models (DEMs).  The system adjusts the surface elevation of the
'DEM to be consistent with a vector coverage.  The vecor coverage
'can be a stream or ridge line coverage.
'
'----------------
'--- Get view ---
'----------------
'
theview = av.getactivedoc
'
'------------------
'--- Get themes ---
'------------------
'
theactivethemes = theview.getactivethemes
if (theactivethemes.count = 0) then
    msgbox.error("No active themes found", "AGREE")
    exit
end
if (theactivethemes.count = 1) then
    msgbox.error("Only one active theme found", "AGREE")
158
    exit
end
if (theactivethemes.count > 2) then
    msgbox.error("Too many active themes found", "AGREE")
    exit
end
if (theactivethemes.count = 2) then
    oegfound = false
    lfound = false
    for each activetheme in theactivethemes
        if (activetheme.getclass.getclassname = "gtheme") then
            oegtheme = activetheme
            oegfound = true
        end
        if (activetheme.getclass.getclassname = "ftheme") then
            theftab = activetheme.getftab
            theshapef = theftab.findfield("shape")
            theshape = theftab.returnvalue(theshapef,0)
            if (theshape.getclass.getclassname = "polyline") then
                theltheme = activetheme
                lfound = true
            end
        end
    end
end
if (not oegfound) then
    msgbox.error("One theme needs to be a grid theme", "AGREE")
    exit
end
if (not lfound) then
    msgbox.error("One theme needs to be a line theme", "AGREE")
    exit
end
'
'-----------------
'--- Get input ---
'-----------------
'
labels = list.make
labels = labels.add("Buffer distance")
159
labels = labels.add("Smooth drop/raise distance (+ = up)")
labels = labels.add("Sharp drop/raise distance (+ = up)")
labels = labels.add("Keep temporary data sets?")
'
defaults = list.make
defaults = defaults.add("0")
defaults = defaults.add("0")
defaults = defaults.add("0")
defaults = defaults.add("no")
'
inputs = msgbox.multiinput("Enter run control parameters", "AGREE", labels,
defaults)
if (inputs.count = 0) then
    exit
end
'
buffer = inputs.get(0).asnumber
smoothdist = inputs.get(1).asnumber
sharpdist = inputs.get(2).asnumber
if (inputs.get(3).ucase.left(1) = "Y") then
    keeptemp = true
else
    keeptemp = false
end
'
'---------------------
'--- Set up themes ---
'---------------------
'
'grid theme
'
oegrid = oegtheme.getgrid
'
'check below causes error crash for some reason
'
'if (thegrid = nil) then
'    msgbox.error("Can't open grid theme","AGREE")
'    exit
'end
oegextend = oegrid.getextent
160
oegcellsize = oegrid.getcellsize
'
'line theme
'
lftab = theltheme.getftab
if (lftab = nil) then
    msgbox.error("Can't open line theme","AGREE")
    exit
end
'
'-----------------
'--- Calculate ---
'-----------------
'
'--- Initial set up ---
'
'setwindow
'
grid.setanalysisextent(#GRID_ENVTYPE_VALUE, oegextend)
'
'setcell
'
grid.setanalysiscellsize(#GRID_ENVTYPE_VALUE, oegcellsize)
'
'--- AGREE method ---
'
'vectgrid
'
vectgrid = grid.makefromftab(lftab, prj.makenull, nil, nil)
if (keeptemp) then
    vectgridfilename = av.getproject.makefilename("vectgrid", "")
    vectgrid.savedataset(vectgridfilename)
    vectgridgtheme = gtheme.make(vectgrid)
    theview.addtheme(vectgridgtheme)
    vectgridgtheme.setvisible(true)
end
'
'smogrid
'
smogrid = (vectgrid.isnull).setnull(oegrid + smoothdist.asgrid).int
161
if (keeptemp) then
    smogridfilename = av.getproject.makefilename("smogrid", "")
    smogrid.savedataset("smogrid".asfilename)
    smogridgtheme = gtheme.make(smogrid)
    theview.addtheme(smogridgtheme)
    smogridgtheme.setvisible(true)
end
'
'vectdist and vectallo
'
vectallofilename = av.getproject.makefilename("vectallo", "")
vectdist = smogrid.eucdistance(nil, vectallofilename, nil)
vectallosrcname = grid.makesrcname(vectallofilename.asstring)
vectallo = grid.make(vectallosrcname)
if (keeptemp) then
    vectdistfilename = av.getproject.makefilename("vectdist", "")
    vectdist.savedataset(vectdistfilename)
    vectdistgtheme = gtheme.make(vectdist)
    theview.addtheme(vectdistgtheme)
    vectdistgtheme.setvisible(true)
    vectallo.savedataset("vectallo".asfilename)
    vectallogtheme = gtheme.make(vectallo)
    theview.addtheme(vectallogtheme)
    vectallogtheme.setvisible(true)
end
'
'bufgrid1 and bufgrid2
'
bufgrid1 = (vectdist > (buffer - (oegcellsize / 2)).asgrid).con(1.asgrid, 0.asgrid)
bufgrid2 = (not bufgrid1).setnull(oegrid).int
if (keeptemp) then
    bufgrid1filename = av.getproject.makefilename("bufgrid1", "")
    bufgrid1.savedataset(bufgrid1filename)
    bufgrid1gtheme = gtheme.make(bufgrid1)
    theview.addtheme(bufgrid1gtheme)
    bufgrid1gtheme.setvisible(true)
    bufgrid2filename = av.getproject.makefilename("bufgrid2", "")
    bufgrid2.savedataset("bufgrid2".asfilename)
    bufgrid2gtheme = gtheme.make(bufgrid2)
    theview.addtheme(bufgrid2gtheme)
162
    bufgrid2gtheme.setvisible(true)
end
'
'bufdist and bufallo
'
bufallofilename = av.getproject.makefilename("bufallo", "")
bufdist = bufgrid2.eucdistance(nil, bufallofilename, nil)
    bufallosrcname = grid.makesrcname(bufallofilename.asstring)
    bufallo = grid.make(bufallosrcname)
if (keeptemp) then
    bufdistfilename = av.getproject.makefilename("bufdist", "")
    bufdist.savedataset(bufdistfilename)
    bufdistgtheme = gtheme.make(bufdist)
    theview.addtheme(bufdistgtheme)
    bufdistgtheme.setvisible(true)
    bufallo.savedataset("bufallo".asfilename)
    bufallogtheme = gtheme.make(bufallo)
    theview.addtheme(bufallogtheme)
    bufallogtheme.setvisible(true)
end
'
'smoelev
'
smoelev = vectallo + (((bufallo - vectallo) / (bufdist + vectdist)) * vectdist)
if (keeptemp) then
    smoelevfilename = av.getproject.makefilename("smoelev", "")
    smoelev.savedataset("smoelev".asfilename)
    smoelevgtheme = gtheme.make(smoelev)
    theview.addtheme(smoelevgtheme)
    smoelevgtheme.setvisible(true)
end
'
'shagrid
'
shagrid = (vectgrid.isnull).setnull(oegrid + sharpdist.asgrid).int
if (keeptemp) then
    shagridfilename = av.getproject.makefilename("shagrid", "")
    shagrid.savedataset("shagrid".asfilename)
    shagridgtheme = gtheme.make(shagrid)
    theview.addtheme(shagridgtheme)
163
    shagridgtheme.setvisible(true)
end
'
'elevgrid
'
elevgrid = (vectgrid.isnull).con(smoelev, shagrid)
elevgridfilename = av.getproject.makefilename("elevgrid", "")
elevgrid.savedataset("gridagree".asfilename)
elevgridgtheme = gtheme.make(elevgrid)
theview.addtheme(elevgridgtheme)
elevgridgtheme.setvisible(true)
'
'
'final message to user
'
message = "Grid agreed"
msgbox.info(message,"AGREE")
'
'-----------
'--- End ---
'-----------
'
164
BALANCE
'
'----------------------------
'--- Creation information ---
'----------------------------
'
'Name: balance.ave
'Version: 1.0
'Date: 01/11/97
'Author: Ferdi Hellweger
'        Center for Research in Water Resources
'        The University of Texas at Austin
'        ferdi@crwr.utexas.edu
'
'---------------------------
'--- Purpose/Description ---
'---------------------------
'
'This program computes the constituent mass balance.
'
'---------------------------------------------
'--- Check if units configuration happened ---
'---------------------------------------------
'
if (not _configu) then
    configure = msgbox.yesno("Units are not configured.  Configure it
now?","BALANCE",true)
    if (configure) then
        av.run("balconu", nil)
    else
        exit
    end
end
'
'----------------
'--- Get view ---
'----------------
'
theview = av.getactivedoc
'
165
'------------------
'--- Get themes ---
'------------------
'
theactivethemes = theview.getactivethemes
if (theactivethemes.count = 0) then
    msgbox.error("No active themes found", "BALANCE")
    exit
end
if (theactivethemes.count = 1) then
    msgbox.error("Only one active theme found", "BALANCE")
    exit
end
if (theactivethemes.count > 2) then
    msgbox.error("Too many active themes found", "BALANCE")
    exit
end
if (theactivethemes.count = 2) then
    lfound = false
    pfound = false
    for each activetheme in theactivethemes
        theftab = activetheme.getftab
        theshapef = theftab.findfield("shape")
        theshape = theftab.returnvalue(theshapef,0)
        if (theshape.getclass.getclassname = "polyline") then
            theltheme = activetheme
            lfound = true
        end
        if (theshape.getclass.getclassname = "polygon") then
            theptheme = activetheme
            pfound = true
        end
    end
end
if (not lfound) then
    msgbox.error("One theme needs to be a line theme", "BALANCE")
    exit
end
if (not pfound) then
    msgbox.error("One theme needs to be a polygon theme", "BALANCE")
166
    exit
end
'
'---------------------
'--- Set up themes ---
'---------------------
'
'line theme
'
lftab = theltheme.getftab
if (lftab = nil) then
    msgbox.error("Can't open line theme","BALANCE")
    exit
end
'
lshapef = lftab.findfield("shape")
if (lshapef = nil) then
    msgbox.error("Can't find 'shape' field in line theme","BALANCE")
    exit
end
'
lpf = lftab.findfield("lpoly#")
if (lpf = nil) then
   lpf = lftab.findfield("lpoly_")
end
if (lpf = nil) then
    msgbox.error("Can't find 'lpoly#' field in line theme","BALANCE")
    exit
end
'
rpf = lftab.findfield("rpoly#")
if (rpf = nil) then
   rpf = lftab.findfield("rpoly_")
end
if (rpf = nil) then
    msgbox.error("Can't find 'rpoly#' field in line theme","BALANCE")
    exit
end
'
qf = lftab.findfield("q")
167
if (qf = nil) then
    addfield = msgbox.yesno("Can't find 'q' field in line theme.  Add
it?","BALANCE", true)
    if (addfield) then
        lftab.seteditable(true)
        qf = field.make("q", #FIELD_DECIMAL, 16, 4)
        lftab.addfields({qf})
        lftab.seteditable(false)
    else
        exit
    end
end
'
epf = lftab.findfield("ep")
if (epf = nil) then
    addfield = msgbox.yesno("Can't find 'ep' field in line theme.  Add
it?","BALANCE", true)
    if (addfield) then
        lftab.seteditable(true)
        epf = field.make("ep", #FIELD_DECIMAL, 16, 4)
        lftab.addfields({epf})
        lftab.seteditable(false)
    else
        exit
    end
end
'
sbf = lftab.findfield("sb")
if (sbf = nil) then
    addfield = msgbox.yesno("Can't find 'sb' field in line theme.  Add
it?","BALANCE", true)
    if (addfield) then
        lftab.seteditable(true)
        sbf = field.make("sb", #FIELD_DECIMAL, 16, 4)
        lftab.addfields({sbf})
        lftab.seteditable(false)
    else
        exit
    end
end
168
'
fadf = lftab.findfield("fad")
if (fadf = nil) then
    addfield = msgbox.yesno("Can't find 'fad' field in line theme.  Add
it?","BALANCE", true)
    if (addfield) then
        lftab.seteditable(true)
        fadf = field.make("fad", #FIELD_DECIMAL, 16, 4)
        lftab.addfields({fadf})
        lftab.seteditable(false)
    else
        exit
    end
end
'
fdif = lftab.findfield("fdi")
if (fdif = nil) then
    addfield = msgbox.yesno("Can't find 'fdi' field in line theme.  Add
it?","BALANCE", true)
    if (addfield) then
        lftab.seteditable(true)
        fdif = field.make("fdi", #FIELD_DECIMAL, 16, 4)
        lftab.addfields({fdif})
        lftab.seteditable(false)
    else
        exit
    end
end
'
'polygon theme
'
pftab = theptheme.getftab
if (pftab = nil) then
    msgbox.error("Can't open polygon theme","BALANCE")
    exit
end
'
pshapef = pftab.findfield("shape")
if (pshapef = nil) then
    msgbox.error("Can't find 'shape' field in polygon theme","BALANCE")
169
    exit
end
'
if (theptheme.getsrcname.asstring.right(4) = ".shp") then
    pname = theptheme.getsrcname.asstring.astokens(".").get(0)+"#"
    polyf = pftab.findfield(pname)
end
polyf = nil
if (polyf = nil) then
    pname = theptheme.getsrcname.asstring.astokens(".").get(0)+"_"
    polyf = pftab.findfield(pname)
end
if (polyf = nil) then
    pname = theptheme.getsrcname.asstring+"#"
    polyf = pftab.findfield(pname)
end
if (polyf = nil) then
    pname = theptheme.getsrcname.asstring+"_"
    polyf = pftab.findfield(pname)
end
if (polyf = nil) then
    msgbox.error("Can't find polygon number field in polygon theme","BALANCE")
    exit
end
'
sof = pftab.findfield("so")
if (sof = nil) then
    addfield = msgbox.yesno("Can't find 'so' field in polygon theme.  Add
it?","BALANCE", true)
    if (addfield) then
        pftab.seteditable(true)
        sof = field.make("so", #FIELD_DECIMAL, 16, 4)
        pftab.addfields({sof})
        pftab.seteditable(false)
    else
        exit
    end
end
'
wnpf = pftab.findfield("wnp")
170
if (wnpf = nil) then
    addfield = msgbox.yesno("Can't find 'wnp' field in polygon theme.  Add
it?","BALANCE", true)
    if (addfield) then
        pftab.seteditable(true)
        wnpf = field.make("wnp", #FIELD_DECIMAL, 16, 4)
        pftab.addfields({wnpf})
        pftab.seteditable(false)
    else
        exit
    end
end
'
watf = pftab.findfield("wat")
if (watf = nil) then
    addfield = msgbox.yesno("Can't find 'wat' field in polygon theme.  Add
it?","BALANCE", true)
    if (addfield) then
        pftab.seteditable(true)
        watf = field.make("wat", #FIELD_DECIMAL, 16, 4)
        pftab.addfields({watf})
        pftab.seteditable(false)
    else
        exit
    end
end
'
wotf = pftab.findfield("wot")
if (wotf = nil) then
    addfield = msgbox.yesno("Can't find 'wot' field in polygon theme.  Add
it?","BALANCE", true)
    if (addfield) then
        pftab.seteditable(true)
        wotf = field.make("wot", #FIELD_DECIMAL, 16, 4)
        pftab.addfields({wotf})
        pftab.seteditable(false)
    else
        exit
    end
end
171
'
wsef = pftab.findfield("wse")
if (wsef = nil) then
    addfield = msgbox.yesno("Can't find 'wse' field in polygon theme.  Add
it?","BALANCE", true)
    if (addfield) then
        pftab.seteditable(true)
        wsef = field.make("wse", #FIELD_DECIMAL, 16, 4)
        pftab.addfields({wsef})
        pftab.seteditable(false)
    else
        exit
    end
end
'
kf = pftab.findfield("k")
if (kf = nil) then
    addfield = msgbox.yesno("Can't find 'k' field in polygon theme.  Add
it?","BALANCE", true)
    if (addfield) then
        pftab.seteditable(true)
        kf = field.make("k", #FIELD_DECIMAL, 16, 4)
        pftab.addfields({kf})
        pftab.seteditable(false)
    else
        exit
    end
end
'
vf = pftab.findfield("v")
if (vf = nil) then
    addfield = msgbox.yesno("Can't find 'v' field in polygon theme.  Add
it?","BALANCE", true)
    if (addfield) then
        pftab.seteditable(true)
        vf = field.make("v", #FIELD_DECIMAL, 16, 4)
        pftab.addfields({vf})
        pftab.seteditable(false)
    else
        exit
172
    end
end
'
sf = pftab.findfield("s")
if (sf = nil) then
    addfield = msgbox.yesno("Can't find 's' field in polygon theme.  Add
it?","BALANCE", true)
    if (addfield) then
        pftab.seteditable(true)
        sf = field.make("s", #FIELD_DECIMAL, 16, 4)
        pftab.addfields({sf})
        pftab.seteditable(false)
    else
        exit
    end
end
'
wadf = pftab.findfield("wad")
if (wadf = nil) then
    addfield = msgbox.yesno("Can't find 'wad' field in polygon theme.  Add
it?","BALANCE", true)
    if (addfield) then
        pftab.seteditable(true)
        wadf = field.make("wad", #FIELD_DECIMAL, 16, 4)
        pftab.addfields({wadf})
        pftab.seteditable(false)
    else
        exit
    end
end
'
wdif = pftab.findfield("wdi")
if (wdif = nil) then
    addfield = msgbox.yesno("Can't find 'wdi' field in polygon theme.  Add
it?","BALANCE", true)
    if (addfield) then
        pftab.seteditable(true)
        wdif = field.make("wdi", #FIELD_DECIMAL, 16, 4)
        pftab.addfields({wdif})
        pftab.seteditable(false)
173
    else
        exit
    end
end
'
wdf = pftab.findfield("wd")
if (wdf = nil) then
    addfield = msgbox.yesno("Can't find 'wd' field in polygon theme.  Add
it?","BALANCE", true)
    if (addfield) then
        pftab.seteditable(true)
        wdf = field.make("wd", #FIELD_DECIMAL, 16, 4)
        pftab.addfields({wdf})
        pftab.seteditable(false)
    else
        exit
    end
end
'
'----------------------------------
'--- Set up plot parameter list ---
'----------------------------------
'
plotlist = list.make
plotlist = plotlist.add(theview)
plotlist = plotlist.add(theltheme)
plotlist = plotlist.add(theptheme)
plotlist = plotlist.add(lftab)
plotlist = plotlist.add(pftab)
plotlist = plotlist.add(lshapef)
plotlist = plotlist.add(qf)
plotlist = plotlist.add(fadf)
plotlist = plotlist.add(fdif)
plotlist = plotlist.add(pshapef)
plotlist = plotlist.add(wnpf)
plotlist = plotlist.add(watf)
plotlist = plotlist.add(wotf)
plotlist = plotlist.add(wsef)
plotlist = plotlist.add(wadf)
plotlist = plotlist.add(wdif)
174
plotlist = plotlist.add(wdf)
'
'----------------------------------
'--- Get run control parameters ---
'----------------------------------
'
labels = list.make
labels = labels.add("Delta t " + _tlab)
labels = labels.add("Converge delta s " + _slab)
labels = labels.add("Diverge delta s " + _slab)
labels = labels.add("Max t " + _tlab)
labels = labels.add("User Observation Level (0-4)")
'
defaults = list.make
defaults = defaults.add((_deltat * _tout).asstring)
defaults = defaults.add(_convs.asstring)
defaults = defaults.add(_divs.asstring)
defaults = defaults.add((_maxt * _tout).asstring)
defaults = defaults.add(_oblevel.asstring)
'
inputs = msgbox.multiinput("Enter run control parameters", "BALANCE", labels,
defaults)
if (inputs.count = 0) then
    exit
end
'
_deltat = inputs.get(0).asnumber * _tin
_convs = inputs.get(1).asnumber
_divs = inputs.get(2).asnumber
_maxt = inputs.get(3).asnumber * _tin
_oblevel = inputs.get(4).asnumber
'
'-----------------
'--- Calculate ---
'-----------------
'
'--- Initial set up ---
'
'make attribute tables editable
'
175
lftab.seteditable(true)
pftab.seteditable(true)
'
'set initial variables
'
time = 0
maxdeltas = 0
converged = false
diverged = false
'
'set starting concentrations to initial concentrations
'rezero advective and diffusive loads
'make index for polygon attribute table
'
polydict = dictionary.make(pftab.getnumrecords * 2)
for each prec in pftab
    so = pftab.returnvalue(sof, prec)
    pftab.setvalue(sf, prec, so)
    '
    pftab.setvalue(wadf, prec, 0)
    pftab.setvalue(wdif, prec, 0)
    '
    poly = pftab.returnvalue(polyf, prec)
    polydict.add(poly, prec.asstring.asnumber)
end
'
'dictionaries for speed processing
'
if (_oblevel <= 1) then
'
pftabdict = dictionary.make(pftab.getnumrecords * 15)
for each prec in pftab
    '
    poly = pftab.returnvalue(polyf, prec).clone
    '
    pdrec = list.make
    pdrec = pdrec.add(pftab.returnvalue(sof, prec).clone)
    pdrec = pdrec.add(pftab.returnvalue(wnpf, prec).clone)
    pdrec = pdrec.add(pftab.returnvalue(watf, prec).clone)
    pdrec = pdrec.add(pftab.returnvalue(wotf, prec).clone)
176
    pdrec = pdrec.add(pftab.returnvalue(wsef, prec).clone)
    pdrec = pdrec.add(pftab.returnvalue(kf, prec).clone)
    pdrec = pdrec.add(pftab.returnvalue(vf, prec).clone)
    pdrec = pdrec.add(pftab.returnvalue(sf, prec).clone)
    pdrec = pdrec.add(pftab.returnvalue(wadf, prec).clone)
    pdrec = pdrec.add(pftab.returnvalue(wdif, prec).clone)
    pdrec = pdrec.add(pftab.returnvalue(wdf, prec).clone)
    pdrec = pdrec.add(prec.clone)
    '
    pftabdict.add(poly, pdrec.clone)
end
'
lftablist = list.make
for each lrec in lftab
    '
    llrec = list.make
    llrec = llrec.add(lftab.returnvalue(lpf, lrec).clone)
    llrec = llrec.add(lftab.returnvalue(rpf, lrec).clone)
    llrec = llrec.add(lftab.returnvalue(qf, lrec).clone)
    llrec = llrec.add(lftab.returnvalue(epf, lrec).clone)
    llrec = llrec.add(lftab.returnvalue(sbf, lrec).clone)
    llrec = llrec.add(lftab.returnvalue(fadf, lrec).clone)
    llrec = llrec.add(lftab.returnvalue(fdif, lrec).clone)
    llrec = llrec.add(lrec.clone)
    '
    lftablist.add(llrec.clone)
end
'
end
'
'user observation
'
if (_oblevel >= 1) then
    av.setstatus(0)
    av.showstopbutton
end
if (_oblevel >= 2) then
    thedisplay = theview.getdisplay
end
'
177
'--- Time step loop ---
'
'regular processing
'
if (_oblevel > 1) then
    while ((not converged) and (not diverged) and (time < _maxt))
        '
        'increment time
        '
        time = time + _deltat
        '
        'user observation
        '
        message = "BALANCE Time: " + (time * _tout).asstring + " " + _tlab +" Max
Delta S: " + maxdeltas.asstring + " " + _slab
        av.showmsg(message)
        running = av.setstatus(((time / _maxt) * 100))
        if (not running) then
            message = "Time: "+(time * _tout).asstring + " " + _tlab
            message = message + nl + "Maximum delta s: " + maxdeltas.asstring + " " +
_slab
            message = message + nl + "BALANCE stopped by user."
            msgbox.info(message,"BALANCE")
            av.showmsg("")
            av.setstatus(100)
            lftab.seteditable(false)
            pftab.seteditable(false)
            exit
        end
        '
        '--- Exchanges ---
        '
        'rezero
        '
        for each prec in pftab
            pftab.setvalue(wadf, prec, 0)
            pftab.setvalue(wdif, prec, 0)
        end
        '
        for each lrec in lftab
178
            '
            'get line attributes
            '
            lp = lftab.returnvalue(lpf, lrec)
            rp = lftab.returnvalue(rpf, lrec)
            q = lftab.returnvalue(qf, lrec)
            ep = lftab.returnvalue(epf, lrec)
            sb = lftab.returnvalue(sbf, lrec) * _sin
            '
            'find adjacent concentrations
            '
            sl = 0
            sr = 0
            if (lp > 1) then
                sl = pftab.returnvalue(sf, polydict.get(lp))
            end
            if (rp > 1) then
                sr = pftab.returnvalue(sf, polydict.get(rp))
            end
            '
            'calculate advection
            '
            lftab.setvalue(fadf, lrec, 0)
            if ((q > 0) and (lp > 1) and (rp > 1)) then
                lftab.setvalue(fadf, lrec, ((sl * q) * _wout))
                wadold = pftab.returnvalue(wadf, polydict.get(lp)) * _win
                wad = wadold - (sl * q)
                pftab.setvalue(wadf, polydict.get(lp), wad * _wout)
                wadold = pftab.returnvalue(wadf, polydict.get(rp)) * _win
                wad = wadold + (sl * q)
                pftab.setvalue(wadf, polydict.get(rp), wad * _wout)
            end
            if ((q < 0) and (lp > 1) and (rp > 1)) then
                lftab.setvalue(fadf, lrec, ((sr * q) * _wout))
                wadold = pftab.returnvalue(wadf, polydict.get(rp)) * _win
                wad = wadold + (sr * q)
                pftab.setvalue(wadf, polydict.get(rp), wad * _wout)
                wadold = pftab.returnvalue(wadf, polydict.get(lp)) * _win
                wad = wadold - (sr * q)
                pftab.setvalue(wadf, polydict.get(lp), wad * _wout)
179
            end
            if ((q > 0) and (lp = 1)) then
                lftab.setvalue(fadf, lrec, ((sb * q) * _wout))
                wadold = pftab.returnvalue(wadf, polydict.get(rp)) * _win
                wad = wadold + (sb * q)
                pftab.setvalue(wadf, polydict.get(rp), wad * _wout)
            end
            if ((q > 0) and (rp = 1)) then
                lftab.setvalue(fadf, lrec, ((sl * q) * _wout))
                wadold = pftab.returnvalue(wadf, polydict.get(lp)) * _win
                wad = wadold - (sl * q)
                pftab.setvalue(wadf, polydict.get(lp), wad * _wout)
            end
            if ((q < 0) and (lp = 1)) then
                lftab.setvalue(fadf, lrec, ((sr * q) * _wout))
                wadold = pftab.returnvalue(wadf, polydict.get(rp)) * _win
                wad = wadold + (sr * q)
                pftab.setvalue(wadf, polydict.get(rp), wad * _wout)
            end
            if ((q < 0) and (rp = 1)) then
                lftab.setvalue(fadf, lrec, ((sb * q) * _wout))
                wadold = pftab.returnvalue(wadf, polydict.get(lp)) * _win
                wad = wadold - (sb * q)
                pftab.setvalue(wadf, polydict.get(lp), wad * _wout)
            end
            '
            'calculate diffusion
            '
            lftab.setvalue(fdif, lrec, 0)
            if ((lp > 1) and (rp > 1)) then
                lftab.setvalue(fdif, lrec, (ep * (sl - sr) * _wout))
                wdiold = pftab.returnvalue(wdif, polydict.get(lp)) * _win
                wdi = wdiold - (ep * (sl - sr))
                pftab.setvalue(wdif, polydict.get(lp), wdi * _wout)
                wdiold = pftab.returnvalue(wdif, polydict.get(rp)) * _win
                wdi = wdiold + (ep * (sl - sr))
                pftab.setvalue(wdif, polydict.get(rp), wdi * _wout)
            end
            if ((rp = 1) and (ep > 0)) then
                lftab.setvalue(fdif, lrec, (ep * (sl - sb) * _wout))
180
                wdiold = pftab.returnvalue(wdif, polydict.get(lp)) * _win
                wdi = wdiold - (ep * (sl - sb))
                pftab.setvalue(wdif, polydict.get(lp), wdi * _wout)
            end
            if ((lp = 1) and (ep > 0)) then
                lftab.setvalue(fdif, lrec, (ep * (sb - sr) * _wout))
                wdiold = pftab.returnvalue(wdif, polydict.get(rp)) * _win
                wdi = wdiold + (ep * (sb - sr))
                pftab.setvalue(wdif, polydict.get(rp), wdi * _wout)
            end
        end
        '
        '--- Polygons ---
        '
        maxdeltas = 0
        '
        for each prec in pftab
            '
            wnp = pftab.returnvalue(wnpf, prec) * _win
            so = pftab.returnvalue(sof, prec) * _sin
            wat = pftab.returnvalue(watf, prec) * _win
            wot = pftab.returnvalue(wotf, prec) * _win
            wse = pftab.returnvalue(wsef, prec) * _win
            k = pftab.returnvalue(kf, prec) * _kin
            v = pftab.returnvalue(vf, prec)
            sold = pftab.returnvalue(sf, prec) * _sin
            wad = pftab.returnvalue(wadf, prec) * _win
            wdi = pftab.returnvalue(wdif, prec) * _win
            '
            wt = wnp + wat + wot + wse - (k * v * sold) + wad + wdi
            snew = sold + (wt * (_deltat / v))
            pftab.setvalue(sf, prec, snew * _sout)
            '
            pftab.setvalue(wdf, prec, (k * v * sold) * _wout)
            '
            thisdeltas = (snew - sold).abs
            if (thisdeltas > maxdeltas) then
                maxdeltas = thisdeltas
            end
        end
181
        '
        if ((maxdeltas > 0) and (maxdeltas <= _convs)) then
            converged = true
        end
        if (maxdeltas >= _divs) then
            diverged = true
        end
        if (_oblevel >=3) then
            av.run("balplot",plotlist)
        end
        if (_oblevel >= 2) then
            theltheme.updatelegend
            theptheme.updatelegend
            theview.draw(thedisplay)
        end
        if (_oblevel >= 4) then
            message = "Time: "+(time * _tout).asstring+" "+_tlab+nl+"Maximum delta
s: "+maxdeltas.asstring+ " "+_slab+nl+"User Observation Level:"
            default = _oblevel.asstring
            newoblevel = msgbox.input(message, "BALANCE", default)
            if (newoblevel = nil) then
                av.showmsg("")
                av.setstatus(100)
                lftab.seteditable(false)
                pftab.seteditable(false)
                exit
            end
            _oblevel = newoblevel.asnumber
        end
    end
end
'
'speed processing
'
if (_oblevel <= 1) then
    while ((not converged) and (not diverged) and (time < _maxt))
        '
        'increment time
        '
        time = time + _deltat
182
        '
        'user observation
        '
        message = "BALANCE Time: " + (time * _tout).asstring + " " + _tlab +" Max
Delta S: " + maxdeltas.asstring + " " + _slab
        av.showmsg(message)
        running = av.setstatus(((time / _maxt) * 100))
        if (not running) then
            message = "Time: "+(time * _tout).asstring + " " + _tlab
            message = message + nl + "Maximum delta s: " + maxdeltas.asstring + " " +
_slab
            message = message + nl + "BALANCE stopped by user."
            msgbox.info(message,"BALANCE")
            av.showmsg("")
            av.setstatus(100)
            for each llrec in lftablist
                lftab.setvalue(fadf, llrec.get(7), llrec.get(5))
                lftab.setvalue(fdif, llrec.get(7), llrec.get(6))
            end
            for each pdrec in pftabdict
                pftab.setvalue(sf, pdrec.get(11), pdrec.get(7))
                pftab.setvalue(wadf, pdrec.get(11), pdrec.get(8))
                pftab.setvalue(wdif, pdrec.get(11), pdrec.get(9))
                pftab.setvalue(wdf, pdrec.get(11), pdrec.get(10))
            end
            lftab.seteditable(false)
            pftab.seteditable(false)
            exit
        end
        '
        '--- Exchanges ---
        '
        'rezero
        '
        for each pdrec in pftabdict
            pdrec.set(8, 0)
            pdrec.set(9, 0)
        end
        '
        for each llrec in lftablist
183
            '
            'get line attributes
            '
            lp = llrec.get(0)
            rp = llrec.get(1)
            q = llrec.get(2)
            ep = llrec.get(3)
            sb = llrec.get(4) * _sin
            '
            'find adjacent concentrations
            '
            sl = 0
            sr = 0
            if (lp > 1) then
                sl = pftabdict.get(lp).get(7)
            end
            if (rp > 1) then
                sr = pftabdict.get(rp).get(7)
            end
            '
            'calculate advection
            '
            llrec.set(5, 0)
            if ((q > 0) and (lp > 1) and (rp > 1)) then
                llrec.set(5,((sl * q) * _wout).clone)
                wadold = pftabdict.get(lp).get(8) * _win
                wad = wadold - (sl * q)
                pftabdict.get(lp).set(8, (wad * _wout).clone)
                wadold = pftabdict.get(rp).get(8) * _win
                wad = wadold + (sl * q)
                pftabdict.get(rp).set(8, (wad * _wout).clone)
            end
            if ((q < 0) and (lp > 1) and (rp > 1)) then
                llrec.set(5, ((sr * q) * _wout).clone)
                wadold = pftabdict.get(rp).get(8) * _win
                wad = wadold + (sr * q)
                pftabdict.get(rp).set(8, (wad * _wout).clone)
                wadold = pftabdict.get(lp).get(8) * _win
                wad = wadold - (sr * q)
                pftabdict.get(lp).set(8, (wad * _wout).clone)
184
            end
            if ((q > 0) and (lp = 1)) then
                llrec.set(5, ((sb * q) * _wout).clone)
                wadold = pftabdict.get(rp).get(8) * _win
                wad = wadold + (sb * q)
                pftabdict.get(rp).set(8, (wad * _wout).clone)
            end
            if ((q > 0) and (rp = 1)) then
                llrec.set(5, ((sl * q) * _wout).clone)
                wadold = pftabdict.get(lp).get(8) * _win
                wad = wadold - (sl * q)
                pftabdict.get(lp).set(8, (wad * _wout).clone)
            end
            if ((q < 0) and (lp = 1)) then
                llrec.set(5, ((sr * q) * _wout).clone)
                wadold = pftabdict.get(rp).get(8) * _win
                wad = wadold + (sr * q)
                pftabdict.get(rp).set(8, (wad * _wout).clone)
            end
            if ((q < 0) and (rp = 1)) then
                llrec.set(5, ((sb * q) * _wout).clone)
                wadold = pftabdict.get(lp).get(8) * _win
                wad = wadold - (sb * q)
                pftabdict.get(lp).set(8, (wad * _wout).clone)
            end
            '
            'calculate diffusion
            '
            llrec.set(6, 0)
            if ((lp > 1) and (rp > 1)) then
                llrec.set(6, (ep * (sl - sr) * _wout).clone)
                wdiold = pftabdict.get(lp).get(9) * _win
                wdi = wdiold - (ep * (sl - sr))
                pftabdict.get(lp).set(9, (wdi * _wout).clone)
                wdiold = pftabdict.get(rp).get(9) * _win
                wdi = wdiold + (ep * (sl - sr))
                pftabdict.get(rp).set(9, (wdi * _wout).clone)
            end
            if ((rp = 1) and (ep > 0)) then
                llrec.set(6, (ep * (sl - sb) * _wout).clone)
185
                wdiold = pftabdict.get(lp).get(9) * _win
                wdi = wdiold - (ep * (sl - sb))
                pftabdict.get(lp).set(9, (wdi * _wout).clone)
            end
            if ((lp = 1) and (ep > 0)) then
                llrec.set(6, (ep * (sb - sr) * _wout).clone)
                wdiold = pftabdict.get(rp).get(9) * _win
                wdi = wdiold + (ep * (sb - sr))
                pftabdict.get(rp).set(9, (wdi * _wout).clone)
            end
        end
        '
        '--- Polygons ---
        '
        maxdeltas = 0
        '
        for each pdrec in pftabdict
            '
            wnp = pdrec.get(1) * _win
            so = pdrec.get(0) * _sin
            wat = pdrec.get(2) * _win
            wot = pdrec.get(3) * _win
            wse = pdrec.get(4) * _win
            k = pdrec.get(5) * _kin
            v = pdrec.get(6)
            sold = pdrec.get(7) * _sin
            wad = pdrec.get(8) * _win
            wdi = pdrec.get(9) * _win
            '
            wt = wnp + wat + wot + wse - (k * v * sold) + wad + wdi
            snew = sold + (wt * (_deltat / v))
            pdrec.set(7, (snew * _sout).clone)
            '
            pdrec.set(10, ((k * v * sold) * _wout).clone)
            '
            thisdeltas = (snew - sold).abs
            if (thisdeltas > maxdeltas) then
                maxdeltas = thisdeltas
            end
        end
186
        '
        if ((maxdeltas > 0) and (maxdeltas <= _convs)) then
            converged = true
        end
        if (maxdeltas >= _divs) then
            diverged = true
        end
    end
end
'
'write data to attribute tables for speed processing
'
if (_oblevel <= 1) then
for each llrec in lftablist
    lftab.setvalue(fadf, llrec.get(7), llrec.get(5))
    lftab.setvalue(fdif, llrec.get(7), llrec.get(6))
end
for each pdrec in pftabdict
    pftab.setvalue(sf, pdrec.get(11), pdrec.get(7))
    pftab.setvalue(wadf, pdrec.get(11), pdrec.get(8))
    pftab.setvalue(wdif, pdrec.get(11), pdrec.get(9))
    pftab.setvalue(wdf, pdrec.get(11), pdrec.get(10))
end
end
'
'make attribute tables non editable
'
lftab.seteditable(false)
pftab.seteditable(false)
'
'final message to user
'
if (converged) then
    message = "Time: " + (time * _tout).asstring + " " + _tlab
    message = message + nl + "Maximum delta s: " + maxdeltas.asstring + " " _slab
    messsage = message + nl + "BALANCE converged."
    msgbox.info(message,"BALANCE")
end
if (diverged) then
    message = "Time: " + (time * _tout).asstring + " " + _tlab
187
    message = message + nl + "Maximum delta s: " + maxdeltas.asstring + " " _slab
    message = message + nl + "BALANCE diverged."
    msgbox.info(message,"BALANCE")
end
if ((not converged) and (not diverged)) then
    message = "Time: " + (time * _tout).asstring + " " + _tlab
    message = message + nl + "Maximum delta s: " + maxdeltas.asstring + " " +
_slab
    message = message + nl + "BALANCE time limit reached."
    msgbox.info(message,"BALANCE")
end
if (_oblevel >= 1) then
    av.showmsg("")
    av.setstatus(100)
end
'
'-------------
'--- End ----
'-------------
188
CALCAREA
'
'----------------------------
'--- Creation information ---
'----------------------------
'
'Name: calcarea.ave
'Version: 1.0
'Date: 02/16/97
'Author: Ferdi Hellweger
'        Center for Research in Water Resources
'        The University of Texas at Austin
'        ferdi@crwr.utexas.edu
'
'---------------------------
'--- Purpose/Description ---
'---------------------------
'
'This program computes the area.
'
'---------------------------------------------
'--- Check if units configuration happened ---
'---------------------------------------------
'
if (not _configu) then
    configure = msgbox.yesno("Units are not configured.  Configure it
now?","BALANCE",true)
    if (configure) then
        av.run("balconu", nil)
    else
        exit
    end
end
'
'----------------
'--- Get view ---
'----------------
'
theview = av.getactivedoc
'
189
'-----------------
'--- Get theme ---
'-----------------
'
theactivethemes = theview.getactivethemes
if (theactivethemes.count = 0) then
    msgbox.error("No active themes found", "BALANCE")
    exit
end
if (theactivethemes.count > 1) then
    msgbox.error("Too many active themes found", "BALANCE")
    exit
end
theptheme = theactivethemes.get(0)
pftab = theptheme.getftab
pshapef = pftab.findfield("shape")
pshape = pftab.returnvalue(pshapef,0)
if (not (pshape.getclass.getclassname = "polygon")) then
    msgbox.error("The theme needs to be a polygon theme", "BALANCE")
    exit
end
'
'--------------------
'--- Set up theme ---
'--------------------
'
af = pftab.findfield("a")
if (af = nil) then
    addfield = msgbox.yesno("Can't find 'a' field in polygon theme.  Add
it?","BALANCE", true)
    if (addfield) then
        pftab.seteditable(true)
        af = field.make("a", #FIELD_DECIMAL, 16, 4)
        pftab.addfields({af})
        pftab.seteditable(false)
    else
        exit
    end
end
'
190
'
'-----------------
'--- Calculate ---
'-----------------
'
'--- Initial set up ---
'
'make polygon attribute table editable
'
pftab.seteditable(true)
'
'--- Loop ---
'
for each prec in pftab
    '
    'calculate area
    '
    pshape = pftab.returnvalue(pshapef, prec)
    lshape = pshape.aspolyline
    mpoint = lshape.asmultipoint
    plist = mpoint.aslist
    n = plist.count
    i = 1
    a = 0
    for each prec in plist
        yterm = plist.get(i - 1).gety
        if (i < n ) then
            xterma = plist.get(i).getx
        end
        if (i = n ) then
            xterma = plist.get(0).getx
        end
        if (i > 1) then
            xtermb = plist.get(i - 2).getx
        end
        if (i = 1) then
            xtermb = plist.get(n - 1).getx
        end
        a = a + (yterm * (xterma - xtermb))
        i = i + 1
191
    end
    a = 0.5 * a.abs
    '
    'write a to polygon attribute table
    '
    pftab.setvalue(af, prec, a)
    '
end
'
'make polygon attribute table non editable
'
pftab.seteditable(false)
'
'final message to user
'
message = "Area, a calculated"
msgbox.info(message,"BALANCE")
'
'-----------
'--- End ---
'-----------
'
192
CALCEP
'
'----------------------------
'--- Creation information ---
'----------------------------
'
'Name: calcep.ave
'Version: 1.1
'Date: 02/16/97
'Author: Ferdi Hellweger
'        Center for Research in Water Resources
'        The University of Texas at Austin
'        ferdi@crwr.utexas.edu
'
'---------------------------
'--- Purpose/Description ---
'---------------------------
'
'This program computes the bulk dispersion coefficient.
'
'---------------------------------------------
'--- Check if units configuration happened ---
'---------------------------------------------
'
if (not _configu) then
    configure = msgbox.yesno("Units are not configured.  Configure it
now?","BALANCE",true)
    if (configure) then
        av.run("balconu", nil)
    else
        exit
    end
end
'
'----------------
'--- Get view ---
'----------------
'
theview = av.getactivedoc
'
193
'------------------
'--- Get themes ---
'------------------
'
theactivethemes = theview.getactivethemes
if (theactivethemes.count = 0) then
    msgbox.error("No active themes found", "BALANCE")
    exit
end
if (theactivethemes.count = 1) then
    msgbox.error("Only one active theme found", "BALANCE")
    exit
end
if (theactivethemes.count > 2) then
    msgbox.error("Too many active themes found", "BALANCE")
    exit
end
if (theactivethemes.count = 2) then
    lfound = false
    pfound = false
    for each activetheme in theactivethemes
        theftab = activetheme.getftab
        theshapef = theftab.findfield("shape")
        theshape = theftab.returnvalue(theshapef,0)
        if (theshape.getclass.getclassname = "polyline") then
            theltheme = activetheme
            lfound = true
        end
        if (theshape.getclass.getclassname = "polygon") then
            theptheme = activetheme
            pfound = true
        end
    end
end
if (not lfound) then
    msgbox.error("One theme needs to be a line theme", "BALANCE")
    exit
end
if (not pfound) then
    msgbox.error("One theme needs to be a polygon theme", "BALANCE")
194
    exit
end
'
'------------------------------
'--- Get calculation option ---
'------------------------------
'
choices = list.make
choices = choices.add("Area Field, a")
choices = choices.add("Depth Field, h and length")
'
mychoice = msgbox.listasstring(choices, "Take area from", "BALANCE")
if (mychoice = nil) then
    exit
end
'
'---------------------
'--- Set up themes ---
'---------------------
'
'line theme
'
lftab = theltheme.getftab
if (lftab = nil) then
    msgbox.error("Can't open line theme","BALANCE")
    exit
end
'
lshapef = lftab.findfield("shape")
if (lshapef = nil) then
    msgbox.error("Can't find 'shape' field in line theme","BALANCE")
    exit
end
'
lpf = lftab.findfield("lpoly#")
if (lpf = nil) then
   lpf = lftab.findfield("lpoly_")
end
if (lpf = nil) then
    msgbox.error("Can't find 'lpoly#' field in line theme","BALANCE")
195
    exit
end
'
rpf = lftab.findfield("rpoly#")
if (rpf = nil) then
   rpf = lftab.findfield("rpoly_")
end
if (rpf = nil) then
    msgbox.error("Can't find 'rpoly#' field in line theme","BALANCE")
    exit
end
'
ef = lftab.findfield("e")
if (ef = nil) then
    addfield = msgbox.yesno("Can't find 'e' field in line theme.  Add
it?","BALANCE", true)
    if (addfield) then
        lftab.seteditable(true)
        ef = field.make("e", #FIELD_DECIMAL, 16, 4)
        lftab.addfields({ef})
        lftab.seteditable(false)
    else
        exit
    end
end
'
epf = lftab.findfield("ep")
if (epf = nil) then
    addfield = msgbox.yesno("Can't find 'ep' field in line theme.  Add
it?","BALANCE", true)
    if (addfield) then
        lftab.seteditable(true)
        ef = field.make("ep", #FIELD_DECIMAL, 16, 4)
        lftab.addfields({epf})
        lftab.seteditable(false)
    else
        exit
    end
end
'
196
if (mychoice = "Area Field, a") then
af = lftab.findfield("a")
if (af = nil) then
    addfield = msgbox.yesno("Can't find 'a' field in line theme.  Add
it?","BALANCE", true)
    if (addfield) then
        lftab.seteditable(true)
        epf = field.make("a", #FIELD_DECIMAL, 16, 4)
        lftab.addfields({af})
        lftab.seteditable(false)
    else
        exit
    end
end
end
if (mychoice = "Depth Field, h and length") then
hf = lftab.findfield("h")
if (hf = nil) then
    addfield = msgbox.yesno("Can't find 'h' field in line theme.  Add
it?","BALANCE", true)
    if (addfield) then
        lftab.seteditable(true)
        hf = field.make("h", #FIELD_DECIMAL, 16, 4)
        lftab.addfields({hf})
        lftab.seteditable(false)
    else
        exit
    end
end
end
'
'polygon theme
'
pftab = theptheme.getftab
if (pftab = nil) then
    msgbox.error("Can't open polygon theme","BALANCE")
    exit
end
'
pshapef = pftab.findfield("shape")
197
if (pshapef = nil) then
    msgbox.error("Can't find 'shape' field in polygon theme","BALANCE")
    exit
end
'
polyf = nil
if (theptheme.getsrcname.asstring.right(4) = ".shp") then
    pname = theptheme.getsrcname.asstring.astokens(".").get(0)+"#"
    polyf = pftab.findfield(pname)
end
if (polyf = nil) then
    pname = theptheme.getsrcname.asstring.astokens(".").get(0)+"_"
    polyf = pftab.findfield(pname)
end
if (polyf = nil) then
    pname = theptheme.getsrcname.asstring+"#"
    polyf = pftab.findfield(pname)
end
if (polyf = nil) then
    pname = theptheme.getsrcname.asstring+"_"
    polyf = pftab.findfield(pname)
end
if (polyf = nil) then
    msgbox.error("Can't find polygon number field in polygon theme","BALANCE")
    exit
end
'
'-----------------
'--- Calculate ---
'-----------------
'
'--- Initial set up ---
'
'make line attribute table editable
'
lftab.seteditable(true)
'
'make index for polygon attribute table
'
polydict = dictionary.make(pftab.getnumrecords * 2)
198
for each prec in pftab
    poly = pftab.returnvalue(polyf, prec)
    polydict.add(poly, prec.asstring.asnumber)
end
'
'--- Loop ---
'
for each lrec in lftab
    '
    'get line attributes
    '
    lp = lftab.returnvalue(lpf, lrec)
    rp = lftab.returnvalue(rpf, lrec)
    lshape = lftab.returnvalue(lshapef, lrec)
    e = lftab.returnvalue(ef, lrec)
    if (mychoice = "Area Field, a") then
        a = lftab.returnvalue(af, lrec)
    end
    if (mychoice = "Depth Field, h and length") then
        h = lftab.returnvalue(hf, lrec)
        pointlist = lshape.aslist
        length = 0
        for each twopoints in pointlist
            frompoint = twopoints.get(0)
            topoint = twopoints.get(1)
            thislength = frompoint.distance(topoint)
            length = length + thislength
        end
        a = length * h
    end
    '
    'calculate mixing length
    '
    if ((lp > 1) and (rp > 1)) then
        lpshape = pftab.returnvalue(pshapef, polydict.get(lp))
        lpcentroid = lpshape.returncenter
        rpshape = pftab.returnvalue(pshapef, polydict.get(rp))
        rpcentroid = rpshape.returncenter
        dx = lpcentroid.distance(rpcentroid)
    end
199
    if ((rp = 1) or (lp = 1)) then
        pointlist = lshape.asmultipoint.aslist
        fulllength = 0
        i = 1
        while (i < pointlist.count)
            fx = pointlist.get(i - 1).getx
            fy = pointlist.get(i - 1).gety
            tx = pointlist.get(i).getx
            ty = pointlist.get(i).gety
            length = (((fx - tx)^2) + ((fy - ty)^2)^0.5)
            fulllength = fulllength + length
            i = i + 1
        end
        partlength = 0
        i = 1
        centerfound = false
        while (i < pointlist.count)
            fx = pointlist.get(i - 1).getx
            fy = pointlist.get(i - 1).gety
            tx = pointlist.get(i).getx
            ty = pointlist.get(i).gety
            length = (((fx - tx)^2) + ((fy - ty)^2)^0.5)
            partlength = partlength + length
            if ((partlength >= (fulllength * 0.5)) and (not centerfound)) then
                fraction = ((fulllength * 0.5) - partlength + length) / length
                cx = fx + ((tx - fx) * fraction)
                cy = fy + ((ty - fy) * fraction)
                centerpoint = point.make(cx,cy)
                centerfound = true
            end
            i = i + 1
        end
    end
    if (rp = 1) then
if (polydict.get(lp) = nil) then
msgbox.info(lp.asstring, "DEBUG")
exit
end
        lpshape = pftab.returnvalue(pshapef, polydict.get(lp))
200
        lpcentroid = lpshape.returncenter
        dx = lpcentroid.distance(centerpoint) * 2
    end
    if (lp = 1) then
        rpshape = pftab.returnvalue(pshapef, polydict.get(rp))
        rpcentroid = rpshape.returncenter
        dx = rpcentroid.distance(centerpoint) * 2
    end
    '
    'calculate ep
    '
    ep = (e * a) / dx
    '
    'write ep to line attribute table
    '
    lftab.setvalue(epf, lrec, ep)
    '
end
'
'make line attribute table non editable
'
lftab.seteditable(false)
'
'final message to user
'
message = "Bulk Dispersion Coefficients, ep calculated"
msgbox.info(message,"BALANCE")
'
'-----------
'--- End ---
'-----------
'
201
CONCGRID
'
'----------------------------
'--- Creation information ---
'----------------------------
'
'Name: concgrid.ave
'Version: 1.0
'Date: 02/17/97
'Author: Ferdi Hellweger
'        Center for Research in Water Resources
'        The University of Texas at Austin
'        ferdi@crwr.utexas.edu
'
'Modified: 02/28/97
'          Ann Quenzer
'          Center for Research in Water Resources
'          The University of Texas at Austin
'          quenzer@mail.utexas.edu
'          1) changed the message box description to reflect the
'script
'          2) added purpose and description
'          3) changed pathname for the data file to be saved
'          4) took out error message of only one theme found
'
'---------------------------
'--- Purpose/Description ---
'---------------------------
'
'A concentration grid is computed for the land surface using the
'landuse cover which is linked to the EMC values.  The resulting
'grid is called concgrid and is in mg/l.
'
'----------------
'--- Get view ---
'----------------
'
theview = av.getactivedoc
'
'
202
'-----------------
'--- Get theme ---
'-----------------
'
thethemes = theview.getthemes
if (thethemes.count = 0) then
    msgbox.error("No themes found", "CONC GRID")
    exit
end
thefthemes = list.make
for each thetheme in thethemes
    if (thetheme.getclass.getclassname = "ftheme") then
        thefthemes.add(thetheme)
    end
end
if (thefthemes.count = 0) then
    msgbox.error("No feature themes found", "CONC GRID")
    exit
end
theptheme = msgbox.listasstring(thefthemes, "Landuse theme", "CONC GRID")
if (theptheme = nil) then
    exit
end
thepftab = theptheme.getftab
thepshapef = thepftab.findfield("shape")
thepshape = thepftab.returnvalue(thepshapef, 0)
if (not (thepshape.getclass.getclassname = "polygon")) then
    msgbox.error("Theme needs to be a polygon theme", "CONC GRID")
    exit
end
'
'-----------------
'--- Get field ---
'-----------------
'
thepfields = thepftab.getfields
thecfield = msgbox.listasstring(thepfields, "Choose concentration field", "CONC
GRID")
'
'
203
'-----------------
'--- Calculate ---
'-----------------
'
cellsize = number.makenull
dummmy = grid.getanalysiscellsize(cellsize)
extent = rect.makenull
dummy = grid.getanalysisextent(extent)
concgrid = grid.makefromftab(thepftab, prj.makenull, thecfield, {cellsize, extent})
'exit
concfilename = av.getproject.makefilename("concgrid", "")
concgrid.savedataset("concgrid".asfilename)
concgtheme = gtheme.make(concgrid)
theview.addtheme(concgtheme)
concgtheme.setvisible(true)
'
'final message to user
'
message = "Concentration grid calculated."
msgbox.info(message,"CONC GRID")
'
'-----------
'--- End ---
'-----------
'
204
CONNECT
'
'----------------------------
'--- Creation information ---
'----------------------------
'
'Name: connect.ave
'Version: 1.0
'Date: 02/13/97
'Author: Ferdi Hellweger
'        Center for Research in Water Resources
'        The University of Texas at Austin
'        ferdi@crwr.utexas.edu
'
'---------------------------
'--- Purpose/Description ---
'---------------------------
'
'This program modifies an elevation grid based on a polygon grid.
'It digs sinks for the polygons so the flowaccumulation will
'accumulate at the polygon centroids.  Use the pickload program
'to pick of loads.
'
'---------------------------------------------
'--- Check if units configuration happened ---
'---------------------------------------------
'
if (not _configu) then
    configure = msgbox.yesno("Units are not configured.  Configure it
now?","CONNECT",true)
    if (configure) then
        av.run("balconu", nil)
    else
        exit
    end
end
'
'
'
'
205
'----------------
'--- Get view ---
'----------------
'
theview = av.getactivedoc
vgraphics = theview.getgraphics
'
'------------------
'--- Get themes ---
'------------------
'
theactivethemes = theview.getactivethemes
if (theactivethemes.count = 0) then
    msgbox.error("No active themes found", "CONNECT")
    exit
end
if (theactivethemes.count = 1) then
    msgbox.error("Only one active theme found", "CONNECT")
    exit
end
if (theactivethemes.count = 2) then
    msgbox.error("Only two active theme found", "CONNECT")
    exit
end
if (theactivethemes.count > 3) then
    msgbox.error("Too many active themes found", "CONNECT")
    exit
end
if (theactivethemes.count = 3) then
    oegfound = false
    pfound = false
    lfound = false
    for each activetheme in theactivethemes
        if (activetheme.getclass.getclassname = "gtheme") then
            oegtheme = activetheme
            oegfound = true
        end
        if (activetheme.getclass.getclassname = "ftheme") then
            theftab = activetheme.getftab
206
            theshapef = theftab.findfield("shape")
            theshape = theftab.returnvalue(theshapef,0)
            if (theshape.getclass.getclassname = "polyline") then
                theltheme = activetheme
                lfound = true
            end
            if (theshape.getclass.getclassname = "polygon") then
                theptheme = activetheme
                pfound = true
            end
        end
    end
end
if (not oegfound) then
    msgbox.error("One theme needs to be a grid theme", "CONNECT")
    exit
end
if (not pfound) then
    msgbox.error("One theme needs to be a polygon theme", "CONNECT")
    exit
end
if (not lfound) then
    msgbox.error("One theme needs to be a line theme", "CONNECT")
    exit
end
'
'-----------------
'--- Get input ---
'-----------------
'
keeptemp = msgbox.yesno("Keep temporary data sets?", "CONNECT", false)
'
'---------------------
'--- Set up themes ---
'---------------------
'
'grid theme
'
oegrid = oegtheme.getgrid
'
207
'check below causes error crash for some reason
'
'if (thegrid = nil) then
'    msgbox.error("Can't open grid theme","CONNECT")
'    exit
'end
oegextend = oegrid.getextent
oegcellsize = oegrid.getcellsize
'
'polygon theme
'
pftab = theptheme.getftab
if (pftab = nil) then
    msgbox.error("Can't open polygon theme","CONNECT")
    exit
end
'
pshapef = pftab.findfield("shape")
if (pshapef = nil) then
    msgbox.error("Can't find 'shape' field in polygon theme","CONNECT")
    exit
end
'
'line theme
'
lftab = theltheme.getftab
if (lftab = nil) then
    msgbox.error("Can't open line theme","CONNECT")
    exit
end
'
'-----------------
'--- Calculate ---
'-----------------
'
'--- Initial set up ---
'
'setwindow
grid.setanalysisextent(#GRID_ENVTYPE_VALUE, oegextend)
208
'
'setcell
'
grid.setanalysiscellsize(#GRID_ENVTYPE_VALUE, oegcellsize)
'
'centroid grid
'
cpfilename = av.getproject.makefilename("cenp", "shp")
cpftab = ftab.makenew(cpfilename, point)
cpshapef = cpftab.findfield("shape")
cpfields = list.make
cpgridvalf = field.make("gridval", #FIELD_DECIMAL, 16, 4)
cpfields.add(cpgridvalf)
cpftab.addfields(cpfields)
cpftab.seteditable(true)
for each prec in pftab
    pshape = pftab.returnvalue(pshapef, prec)
    cenp = pshape.returncenter
    cengs = graphicshape.make(cenp)
    vgraphics.add(cengs)
    inside = cenp.iscontainedin(pshape)
    if (not inside) then
        msgbox.error("Polygon centroid not inside polygon", "CONNECT")
    end
    theoutrec = cpftab.addrecord
    cpftab.setvalue(cpshapef, theoutrec, cenp)
    cpftab.setvalue(cpgridvalf, theoutrec, 1)
end
cpftab.seteditable(false)
cengrid = grid.makefromftab(cpftab, prj.makenull, nil, nil)
if (keeptemp) then
    cenfilename = av.getproject.makefilename("cengrid", "")
    cengrid.savedataset("cen".asfilename)
    cengtheme = gtheme.make(cengrid)
    theview.addtheme(cengtheme)
    cengtheme.setvisible(true)
end
'
'boundary grid
'
209
bndgrid = grid.makefromftab(lftab, prj.makenull, nil, nil)
if (keeptemp) then
    bndfilename = av.getproject.makefilename("bndgrid", "")
    bndgrid.savedataset("bnd".asfilename)
    bndgtheme = gtheme.make(bndgrid)
    theview.addtheme(bndgtheme)
    bndgtheme.setvisible(true)
end
'
'polygon grid
'
polygrid = grid.makefromftab(pftab, prj.makenull, nil, nil)
if (keeptemp) then
    polyfilename = av.getproject.makefilename("polygrid", "")
    polygrid.savedataset("poly".asfilename)
    polygtheme = gtheme.make(polygrid)
    theview.addtheme(polygtheme)
    polygtheme.setvisible(true)
end
'
'drop grid
'
dropgrid = (polygrid.isnull).con(0.asgrid, (bndgrid.isnull).con(1.asgrid, 0.asgrid))
if (keeptemp) then
    dropfilename = av.getproject.makefilename("dropgrid", "")
    dropgrid.savedataset("drop".asfilename)
    dropgtheme = gtheme.make(dropgrid)
    theview.addtheme(dropgtheme)
    dropgtheme.setvisible(true)
end
'
'cost grid
'
costgrid = (dropgrid = 1.asgrid).con(1.asgrid, 1000000.asgrid)
if (keeptemp) then
    costfilename = av.getproject.makefilename("costgrid", "")
    costgrid.savedataset("cost".asfilename)
    costgtheme = gtheme.make(costgrid)
    theview.addtheme(costgtheme)
    costgtheme.setvisible(true)
210
end
'
'dist grid
'
distgrid = cengrid.costdistance(costgrid, nil, nil, nil)
if (keeptemp) then
    distfilename = av.getproject.makefilename("distgrid", "")
    distgrid.savedataset("dist".asfilename)
    distgtheme = gtheme.make(distgrid)
    theview.addtheme(distgtheme)
    distgtheme.setvisible(true)
end
'
'modification parameters
'
tmpgrid = (distgrid < 1000000.asgrid).con(distgrid, 0.asgrid)
maxdist = tmpgrid.getstatistics.get(1)
sinkelev = (-100 - (maxdist * 0.1))
'
'modified elevation grid
'
megrid = (dropgrid = 0.asgrid).con(oegrid, (sinkelev.asgrid + (0.1.asgrid *
distgrid)))
mefilename = av.getproject.makefilename("megrid", "")
megrid.savedataset("gridconnect".asfilename)
megtheme = gtheme.make(megrid)
theview.addtheme(megtheme)
megtheme.setvisible(true)
'
'final message to user
'
message = "Grid connected"
msgbox.info(message,"CONNECT")
'
'-----------
'--- End ---
'-----------
'
211
LOADGRID
'
'----------------------------
'--- Creation information ---
'----------------------------
'
'Name: loadgrid.ave
'Version: 1.0
'Date: 02/28/97
'Author: Ann Quenzer
'        Center for Research in Water Resources
'        The University of Texas at Austin
'        quenzer@mail.utexas.edu
'
'---------------------------
'--- Purpose/Description ---
'---------------------------
'
'Makes a land surface loading grid (Kg/day) to be used in
'a weighted flow accumulation which determines the loadings
'to the bay.  All concentration grid inputs should be in mg/l.
'
'----------------
'--- Get view ---
'----------------
'
theview = av.getactivedoc
'
'-------------------------------
'--- Get themes for loadgrid ---
'-------------------------------
'
thethemes = theview.getthemes
if (thethemes.count = 0) then
    msgbox.error("No themes found", "LOAD GRID")
    exit
end
thegthemes = list.make
for each thetheme in thethemes
    if (thetheme.getclass.getclassname = "gtheme") then
212
        thegthemes.add(thetheme)
    end
end
if (thegthemes.count = 0) then
    msgbox.error("No grid themes found", "LOAD GRID")
    exit
end
conctheme = msgbox.listasstring(thegthemes, "Concentration Grid", "LOAD
GRID")
if (conctheme = nil) then
    exit
end
'
'
thethemes = theview.getthemes
if (thethemes.count = 0) then
    msgbox.error("No themes found", "LOAD GRID")
    exit
end
thegthemes = list.make
for each thetheme in thethemes
    if (thetheme.getclass.getclassname = "gtheme") then
        thegthemes.add(thetheme)
    end
end
if (thegthemes.count = 0) then
    msgbox.error("No grid themes found", "LOAD GRID")
    exit
end
rotheme = msgbox.listasstring(thegthemes, "Runoff Grid", "LOAD GRID")
if (rotheme = nil) then
    exit
end
'
'
'
'-----------------
'--- Calculate ---
'-----------------
'
213
concgrid = conctheme.getgrid
rogrid = rotheme.getgrid
'
cellsize = number.makenull
dummmy = grid.getanalysiscellsize(cellsize)
extent = rect.makenull
dummy = grid.getanalysisextent(extent)
'
loadgrid = ((concgrid * rogrid) / 36525.asgrid)
loadfilename = av.getproject.makefilename("loadgrid", "")
loadgrid.savedataset("loadgrid".asfilename)
loadgtheme = gtheme.make(loadgrid)
theview.addtheme(loadgtheme)
loadgtheme.setvisible(true)
'
'final message to user
'
message = "Load grid calculated."
msgbox.info(message,"LOAD GRID")
'
'
'-----------
'--- End ---
'-----------
'
214
LKGRIDWF
'
'------------------------------
'----Creation Information------
'------------------------------
'
'Name: lkgridwf.aml
'Date: 9/15/97
'Author:  Ann Quenzer
'     Center for Research in Water Resources
'     The University of Texas at Austin
'     quenzer@mail.utexas.edu
'
'------------------------------
'----Purpose/Description-------
'------------------------------
'Find the weighted flow accumulation of the Nueces
'River Watershed Loads from each constituent's
'load grids.
'
'
'
grid
lkgtn = con ( big1 == 1, lkgridtn, big1 )
lkwfgridtn = flowaccumulation ( fdac2, lkgtn )
lkgtp = con ( big1 == 1, lkgridtp, big1 )
lkwfgridtp = flowaccumulation ( fdac2, lkgtp )
lkgfe = con ( big1 == 1, lkgridfe, big1 )
lkwfgridfe = flowaccumulation ( fdac2, lkgfe )
q
q
end
215
LOGRIDWF
'
'------------------------------
'----Creation Information------
'------------------------------
'
'Name: logridwf.aml
'Date: 9/15/97
'Author:  Ann Quenzer
'     Center for Research in Water Resources
'     The University of Texas at Austin
'     quenzer@mail.utexas.edu
'
'------------------------------
'----Purpose/Description-------
'------------------------------
'Find the weighted flow accumulation of the Land
'Surface Loads from each constituent's load grids.
'
'
'
grid
setcell fdac2
setwindow fdac2
logridnn = ( runland2 * cogridnn ) / 36525
lognn = con ( big1 == 1, logridnn, big1 )
lowfgridnn = flowaccumulation ( fdac2, lognn )
logridtn = ( runland2 * cogridtn ) / 36525
logtn = con ( big1 == 1, logridtn, big1 )
lowfgridtn = flowaccumulation ( fdac2, logtn )
logridtp = ( runland2 * cogridtp ) / 36525
logtp = con ( big1 == 1, logridtp, big1 )
lowfgridtp = flowaccumulation ( fdac2, logtp )
logridog = ( runland2 * cogridog ) / 36525
logog = con ( big1 == 1, logridog, big1 )
lowfgridog = flowaccumulation ( fdac2, logog )
logridtc = ( runland2 * cogridtc ) / 36525
logtc = con ( big1 == 1, logridtc, big1 )
lowfgridtc = flowaccumulation ( fdac2, logtc )
logridtca = ( runland2 * cogridtca ) / 36525
216
logtca = con ( big1 == 1, logridtca, big1 )
lowfgridtca = flowaccumulation ( fdac2, logtca )
logridtch = ( runland2 * cogridtch ) / 36525
logtch = con ( big1 == 1, logridtch, big1 )
lowfgridtch = flowaccumulation ( fdac2, logtch )
logridtl = ( runland2 * cogridtl ) / 36525
logtl = con ( big1 == 1, logridtl, big1 )
lowfgridtl = flowaccumulation ( fdac2, logtl )
logridtz = ( runland2 * cogridtz ) / 36525
logtz = con ( big1 == 1, logridtz, big1 )
lowfgridtz = flowaccumulation ( fdac2, logtz )
q
q
end
217
PICKLOAD
'
'----------------------------
'--- Creation information ---
'----------------------------
'
'Name: pickload.ave
'Version: 1.0
'Date: 02/11/97
'Author: Ferdi Hellweger
'        Center for Research in Water Resources
'        The University of Texas at Austin
'        ferdi@crwr.utexas.edu
'
'---------------------------
'--- Purpose/Description ---
'---------------------------
'
'This program picks up the nonpoint source load for each
'polygon from a nonpoint source load (weighted flow accumulation)
'grid with the cellvalue request. The nonpoint source load is
'written to the wnp field in the polygon attribute table.
'
'---------------------------------------------
'--- Check if units configuration happened ---
'---------------------------------------------
'
if (not _configu) then
    configure = msgbox.yesno("Units are not configured.  Configure it
now?","BALANCE",true)
    if (configure) then
        av.run("balconu", nil)
    else
        exit
    end
end
'
'----------------
'--- Get view ---
'----------------
218
'
theview = av.getactivedoc
vgraphics = theview.getgraphics
'
'------------------
'--- Get themes ---
'------------------
'
theactivethemes = theview.getactivethemes
if (theactivethemes.count = 0) then
    msgbox.error("No active themes found", "PICKLOAD")
    exit
end
if (theactivethemes.count = 1) then
    msgbox.error("Only one active theme found", "PICKLOAD")
    exit
end
if (theactivethemes.count > 2) then
    msgbox.error("Too many active themes found", "PICKLOAD")
    exit
end
if (theactivethemes.count = 2) then
    gfound = false
    pfound = false
    for each activetheme in theactivethemes
        if (activetheme.getclass.getclassname = "gtheme") then
            thegtheme = activetheme
            gfound = true
        end
        if (activetheme.getclass.getclassname = "ftheme") then
            theptheme = activetheme
            pfound = true
        end
    end
end
if (not gfound) then
    msgbox.error("One theme needs to be a grid theme", "PICKLOAD")
    exit
end
if (not pfound) then
219
    msgbox.error("One theme needs to be a polygon theme", "PICKLOAD")
    exit
end
'
'---------------------
'--- Set up themes ---
'---------------------
'
'grid theme
'
thegrid = thegtheme.getgrid
'
'check below causes error crash for some reason
'
'if (thegrid = nil) then
'    msgbox.error("Can't open grid theme","PICKLOAD")
'    exit
'end
thecellsize = thegrid.getcellsize
'
'polygon theme
'
pftab = theptheme.getftab
if (pftab = nil) then
    msgbox.error("Can't open polygon theme","PICKLOAD")
    exit
end
'
pshapef = pftab.findfield("shape")
if (pshapef = nil) then
    msgbox.error("Can't find 'shape' field in polygon theme","PICKLOAD")
    exit
end
'
wnpf = pftab.findfield("wnp")
if (wnpf = nil) then
    addfield = msgbox.yesno("Can't find 'wnp' field in polygon theme.  Add
it?","PICKLOAD", true)
    if (addfield) then
        pftab.seteditable(true)
220
        wnpf = field.make("wnp", #FIELD_DECIMAL, 16, 4)
        pftab.addfields({wnpf})
        pftab.seteditable(false)
    else
        exit
    end
end
'
'-----------------
'--- Calculate ---
'-----------------
'
'Note that due to instabilities in the flowaccumulation algorithm
'at sinks the wnp load can not be simply picked of the grid at the
'centroid (the sink).  The wnp load is the integral of the grid
'values on a 5x5 box around the centroid.  This assumes that the
'flow is directly to the centroid and not around the perimeter of
'the box.  Observations support this assumption.  The flow direction
'grid could be used to check and/or modify the algorithm.
'
'--- Initial set up ---
'
'make polygon attribute table editable
'
pftab.seteditable(true)
'
'--- Loop ---
'
for each prec in pftab
    '
    'get centroid
    '
    pshape = pftab.returnvalue(pshapef, prec)
    cenp = pshape.returncenter
    cenx = cenp.getx
    ceny = cenp.gety
    '
    'plot centroid
    '
    cengs = graphicshape.make(cenp)
221
    vgraphics.add(cengs)
    '
    'check if centroid is in polygon
    '
    inside = cenp.iscontainedin(pshape)
    if (not inside) then
        msgbox.error("Polygon centroid not inside polygon", "PICKLOAD")
    end
    '
    'get wnp
    '
    wnp = 0
    '
    pickx = cenx + (0 * thecellsize)
    picky = ceny + (2 * thecellsize)
    pickp = point.make(pickx, picky)
    wnpi = thegtheme.returncellvalue(pickp)
    wnp = wnp + wnpi.clone
    '
    pickx = cenx + (1 * thecellsize)
    picky = ceny + (2 * thecellsize)
    pickp = point.make(pickx, picky)
    wnpi = thegtheme.returncellvalue(pickp)
    wnp = wnp + wnpi.clone
    '
    pickx = cenx + (2 * thecellsize)
    picky = ceny + (2 * thecellsize)
    pickp = point.make(pickx, picky)
    wnpi = thegtheme.returncellvalue(pickp)
    wnp = wnp + wnpi.clone
    '
    pickx = cenx + (2 * thecellsize)
    picky = ceny + (1 * thecellsize)
    pickp = point.make(pickx, picky)
    wnpi = thegtheme.returncellvalue(pickp)
    wnp = wnp + wnpi.clone
    '
    pickx = cenx + (2 * thecellsize)
    picky = ceny + (0 * thecellsize)
    pickp = point.make(pickx, picky)
222
    wnpi = thegtheme.returncellvalue(pickp)
    wnp = wnp + wnpi.clone
    '
    pickx = cenx + (2 * thecellsize)
    picky = ceny + (-1 * thecellsize)
    pickp = point.make(pickx, picky)
    wnpi = thegtheme.returncellvalue(pickp)
    wnp = wnp + wnpi.clone
    '
    pickx = cenx + (2 * thecellsize)
    picky = ceny + (-2 * thecellsize)
    pickp = point.make(pickx, picky)
    wnpi = thegtheme.returncellvalue(pickp)
    wnp = wnp + wnpi.clone
    '
    pickx = cenx + (1 * thecellsize)
    picky = ceny + (-2 * thecellsize)
    pickp = point.make(pickx, picky)
    wnpi = thegtheme.returncellvalue(pickp)
    wnp = wnp + wnpi.clone
    '
    pickx = cenx + (0 * thecellsize)
    picky = ceny + (-2 * thecellsize)
    pickp = point.make(pickx, picky)
    wnpi = thegtheme.returncellvalue(pickp)
    wnp = wnp + wnpi.clone
    '
    pickx = cenx + (-1 * thecellsize)
    picky = ceny + (-2 * thecellsize)
    pickp = point.make(pickx, picky)
    wnpi = thegtheme.returncellvalue(pickp)
    wnp = wnp + wnpi.clone
    '
    pickx = cenx + (-2 * thecellsize)
    picky = ceny + (-2 * thecellsize)
    pickp = point.make(pickx, picky)
    wnpi = thegtheme.returncellvalue(pickp)
    wnp = wnp + wnpi.clone
    '
    pickx = cenx + (-2 * thecellsize)
223
    picky = ceny + (-1 * thecellsize)
    pickp = point.make(pickx, picky)
    wnpi = thegtheme.returncellvalue(pickp)
    wnp = wnp + wnpi.clone
    '
    pickx = cenx + (-2 * thecellsize)
    picky = ceny + (0 * thecellsize)
    pickp = point.make(pickx, picky)
    wnpi = thegtheme.returncellvalue(pickp)
    wnp = wnp + wnpi.clone
    '
    pickx = cenx + (-2 * thecellsize)
    picky = ceny + (1 * thecellsize)
    pickp = point.make(pickx, picky)
    wnpi = thegtheme.returncellvalue(pickp)
    wnp = wnp + wnpi.clone
    '
    pickx = cenx + (-2 * thecellsize)
    picky = ceny + (2 * thecellsize)
    pickp = point.make(pickx, picky)
    wnpi = thegtheme.returncellvalue(pickp)
    wnp = wnp + wnpi.clone
    '
    pickx = cenx + (-1 * thecellsize)
    picky = ceny + (2 * thecellsize)
    pickp = point.make(pickx, picky)
    wnpi = thegtheme.returncellvalue(pickp)
    wnp = wnp + wnpi.clone
    '
    'write wnp to polygon attribute table
    '
    pftab.setvalue(wnpf, prec, wnp)
    '
end
'
'make polygon attribute table non editable
'
pftab.seteditable(false)
'
'final message to user
224
'
message = "Nonpoint Source Load, wnp picked"
msgbox.info(message,"PICKLOAD")
'
'-----------
'--- End ---
--------------
225
POGRIDWF
'
'------------------------------
'--Creation Information---
'------------------------------
'
'Name: pogridwf.aml
'Date: 9/15/97
'Author:  Ann Quenzer
'     Center for Research in Water Resources
'     The University of Texas at Austin
'     quenzer@mail.utexas.edu
'
'------------------------------
'----Purpose/Description--
'------------------------------
'
'Find the weighted flow accumulation of the Point Source Loads from each
constituent'sload grids.
'
'------------------------------
'----------Calculate---------
'------------------------------
'
grid
pogtn = con ( big1 == 1, pogridtn, big1 )
pwfgridtn = flowaccumulation ( fdac2, pogtn )
pogtp = con ( big1 == 1, pogridtp, big1 )
pwfgridtp = flowaccumulation ( fdac2, pogtp )
pogog = con ( big1 == 1, pogridog, big1 )
pwfgridog = flowaccumulation ( fdac2, pogog )
pogtc = con ( big1 == 1, pogridtc, big1 )
pwfgridtc = flowaccumulation ( fdac2, pogtc )
pogtca = con ( big1 == 1, pogridtca, big1 )
pwfgridtca = flowaccumulation ( fdac2, pogtca )
pogtch = con ( big1 == 1, pogridtch, big1 )
pwfgridtch = flowaccumulation ( fdac2, pogtch )
pogtl = con ( big1 == 1, pogridtl, big1 )
pwfgridtl = flowaccumulation ( fdac2, pogtl )
pogtz = con ( big1 == 1, pogridtz, big1 )
226
pwfgridtz = flowaccumulation ( fdac2, pogtz )
pogfe = con ( big1 == 1, pogridfe, big1 )
pwfgridfe = flowaccumulation ( fdac2, pogfe )
pogar = con ( big1 == 1, pogridar, big1 )
pwfgridar = flowaccumulation ( fdac2, pogar )
poghg = con ( big1 == 1, pogridhg, big1 )
pwfgridhg = flowaccumulation ( fdac2, poghg )
q
q
end
227
POINTLD
'
'----------------------------
'--- Creation information ---
'----------------------------
'
'Name: pointld.ave
'Version: 1.0
'Date: 06/2/97
'Author: Ferdi Hellweger
'        Center for Research in Water Resources
'        The University of Texas at Austin
'        ferdi@crwr.utexas.edu
'
'
'---------------------------
'--- Purpose/Description ---
'---------------------------
'
'A point load grid is computed from a point, polyline and/or polygon ftheme based
'on the value in a field in the feature attribute table.
'
'--------------------
'--- User's guide ---
'--------------------
'
'1. Set the analysis cell size and extent.
'2. Highlight the input feature theme(s).  Multiple themes can processed.
'3. Run the program.
'4. Supply the field(s) holding the point load.
'
'-------------
'--- Notes ---
'-------------
'
'1. This program supports loads attached to points, polylines and polygons.
'   The loads are placed on the grid as follows:
'      Points: at the point.
'      Polylines: at the first point of the line.
228
'      Polygons: half way between the centroid and the first point of of the boundary.
'
'2. When using this with the CONNECT and PICKLOAD methodology the
program output
'   has to be checked to see that the loads for polygons are not placed in the
'   5x5 rectangle around the centroid of the water quality segments.  If that is
'   the case the methodology for locating the load for polygons should be altered.
'
'3. The program uses a rather crude way to value the point load grid.  First a
'   point shape file is created, which is then converted to a grid with the
'   makefromftab request.  It would be much easier if cells could be valued in
'   the same as as with the ARC/INFO grid fillcell command.  An equivalent has
'   not been found in the spatial analyst.
'
'4. The output of this program is a point source load grid in the same units as
'   the load field in the input feature theme.
'
'5. The point source load grid should be added to the nonpoint source load grid
'   to create a total load grid before a weighted flow accumulation is done.
'
'----------------
'--- Get view ---
'----------------
'
theview = av.getactivedoc
'
'--------------------
'--- Get theme(s) ---
'--------------------
'
thethemes = theview.getactivethemes
if (thethemes.count = 0) then
    msgbox.error("No themes found", "POINT LOAD GRID")
    exit
end
thefthemes = list.make
for each thetheme in thethemes
    if (thetheme.getclass.getclassname = "ftheme") then
        thefthemes.add(thetheme)
    end
229
end
if (thefthemes.count = 0) then
    msgbox.error("No feature themes found", "POINT LOAD GRID")
    exit
end
'
'------------------------------
'--- Set up point load grid ---
'------------------------------
'
plgrid = grid.makefromnumb(0.0)
'
'------------------------
'--- Set up temp ftab ---
'------------------------
'
tempftab = ftab.makenew(("tempname").asfilename, point)
tempftab.seteditable(true)
tempshapef = tempftab.findfield("shape")
tempfields = list.make
temploadf = field.make("load", #FIELD_DECIMAL, 16, 4)
tempfields = tempfields.add(temploadf)
tempftab.addfields(tempfields)
temprec = tempftab.addrecord
'
'--------------------------
'--- Process each theme ---
'--------------------------
'
for each thetheme in thefthemes
    '
    '--- Set up theme  ---
    '
    theftab = thetheme.getftab
    theshapef = theftab.findfield("shape")
    '
    '--- Get field ---
    '
    thefields = theftab.getfields
    theloadf = msgbox.listasstring(thefields, "Choose point load field for " +
230
thetheme.getname, "POINT LOAD GRID")
    if (theloadf = nil) then
        exit
    end
    '
    '--- Process each feature ---
    '
    for each thefeature in theftab
        'check load
        theload = theftab.returnvalue(theloadf, thefeature)
        if (theload > 0) then
            'get point
            theshape = theftab.returnvalue(theshapef, thefeature)
            if (theshape.getclass.getclassname = "point") then
                thepoint = theshape
            end
            if (theshape.getclass.getclassname = "polyline") then
                thepoint = theshape.asmultipoint.aslist.get(0)
            end
            if (theshape.getclass.getclassname = "polygon") then
                thecentroid = theshape.returncenter
                cenx = thecentroid.getx
                ceny = thecentroid.gety
                theboundarypoint = theshape.aspolyline.asmultipoint.aslist.get(0)
                boundx = theboundarypoint.getx
                boundy = theboundarypoint.gety
                pointx = ((cenx + boundx) / 2)
                pointy = ((ceny + boundy) / 2)
                thepoint = point.make(pointx, pointy)
                if (not (thepoint.iscontainedin(theshape))) then
                    msgbox.info("Point load assigned outside polygon.", "POINT LOAD
GRID")
                end
            end
            'value temp ftab
            tempftab.seteditable(true)
            tempftab.setvalue(tempshapef, temprec, thepoint.clone)
            tempftab.setvalue(temploadf, temprec, theload.clone)
            tempftab.seteditable(false)
            'make temp grid
231
            tempgrid = grid.makefromftab(tempftab, prj.makenull, temploadf, nil)
            'add temp grid to point load grid
            plgrid = tempgrid.isnull.con(plgrid, (plgrid + tempgrid))
        end
    end
end
'
'---------------------------------
'--- Save grid and add to view ---
'---------------------------------
'
plgtheme = gtheme.make(plgrid)
theview.addtheme(plgtheme)
plgtheme.setvisible(true)
'
'-----------------------------
'--- Final message to user ---
'-----------------------------
'
message = "Point load grid calculated."
msgbox.info(message,"POINT LOAD GRID")
'
'-----------
'--- End ---
'-----------
'
232
ROGRIDLAND
'
'----------------------------
'--- Creation information ---
'----------------------------
'
'Name: rogridland.ave
'Version: 1.0
'Date: 09/02/97
'Author:   Ann Quenzer
'          Center for Research in Water Resources
'          The University of Texas at Austin
'          quenzer@mail.utexas.edu
'
'---------------------------
'--- Purpose/Description ---
'---------------------------
'
'Computes a runoff grid in mm/yr from the precipitation grid.
'Uses the runoff equation calculated from Microsoft Excel 5.0
'regression tool using the relationship between percent land
'use, precipitation, and streamflow.
'
'----------------
'--- Get view ---
'----------------
'
theview = av.getactivedoc
'
'------------------
'--- Get themes ---
'------------------
'
thethemes = theview.getthemes
if (thethemes.count = 0) then
    msgbox.error("No themes found", "RUNOFF GRID")
    exit
end
thegthemes = list.make
for each thetheme in thethemes
233
    if (thetheme.getclass.getclassname = "gtheme") then
        thegthemes.add(thetheme)
    end
end
if (thegthemes.count = 0) then
    msgbox.error("No grid themes found", "RUNOFF GRID")
    exit
end
pretheme = msgbox.listasstring(thegthemes, "Precipitation Grid", "RUNOFF
GRID")
if (pretheme = nil) then
    exit
end
landtheme = msgbox.listasstring(thegthemes, "Land Use Grid", "RUNOFF GRID")
if (pretheme = nil) then
    exit
end
'
'-----------------
'--- Calculate ---
'-----------------
'
pregrid = pretheme.getgrid
landgrid = landtheme.getgrid
'
cellsize = number.makenull
dummmy = grid.getanalysiscellsize(cellsize)
extent = rect.makenull
dummy = grid.getanalysisextent(extent)
'
rogridr1 = ((landgrid >= 30.asgrid) and (landgrid <= 49.asgrid)).con((0.0053.asgrid
* (0.010993.asgrid * pregrid).exp), (0.asgrid)).int
rogridr2 = ((landgrid >= 70.asgrid) and (landgrid <= 79.asgrid)).con((0.0053.asgrid
* (0.010993.asgrid * pregrid).exp), (0.asgrid)).int
rogridr3 = (landgrid <= 10.asgrid).con((0.0053.asgrid * (0.010993.asgrid *
pregrid).exp), (0.asgrid)).int
rogrida = ((landgrid >= 20.asgrid) and (landgrid <=
29.asgrid)).con((0.008312.asgrid * (0.011415.asgrid * pregrid).exp), (0.asgrid)).int
rogridu = ((landgrid >= 11.asgrid) and (landgrid <= 19.asgrid)).con((pregrid *
0.24.asgrid), (0.asgrid)).int
234
rogridwe = ((landgrid >= 60.asgrid) and (landgrid <= 69.asgrid)).con((0.0053.asgrid
* (0.010993.asgrid * pregrid).exp), (0.asgrid)).int
rogridw = ((landgrid >= 50.asgrid) and (landgrid <= 59.asgrid)).con((0.asgrid),
(0.asgrid)).int
rogrid = (rogridr1 + rogridr2 + rogridr3 + rogrida + rogridu + rogridw +
rogridwe).int
rofilename = av.getproject.makefilename("rogrid", "")
rogrid.savedataset("runland".asfilename)
rogtheme = gtheme.make(rogrid)
theview.addtheme(rogtheme)
rogtheme.setvisible(true)
'
'final message to user
'
message = "Runoff grid calculated."
msgbox.info(message,"RUNOFF GRID")
'
'-----------
'--- End ---
'-----------
'
235
STRMLNK
'----------------------------
'--- Creation information ---
'----------------------------
'Name: strmlnk.ave
'Version: 1.0
'Date: 05/07/97
'Author: Ann Quenzer
'        Center for Research in Water Resources
'        The University of Texas at Austin
'        quenzer@mail.utexas.edu
'
'---------------------------
'--- Purpose/Description ---
'---------------------------
'
'This script makes a stream link grid.
'
'----------------
'--- Get view ---
'----------------
'
theview = av.getactivedoc
theDisplay = theView.GetDisplay
'
'------------------
'--- Get themes ---
'------------------
'
thethemes = theview.getthemes
if (thethemes.count = 0) then
    msgbox.error("No themes found", "WTRSHD")
    exit
end
if (thethemes.count = 1) then
    msgbox.error("Only one theme found", "WTRSHD")
    exit
end
thegthemes = list.make
for each thetheme in thethemes
236
    if (thetheme.getclass.getclassname = "gtheme") then
        thegthemes.add(thetheme)
    end
end
if (thegthemes.count = 0) then
    msgbox.error("No grid themes found", "WTRSHD")
    exit
end
if (thethemes.count = 1) then
    msgbox.error("Only grid one theme found", "WTRSHD")
    exit
end
fdrtheme = msgbox.listasstring(thegthemes, "Flow direction theme", "WTRSHD")
if (fdrtheme = nil) then
    exit
end
factheme = msgbox.listasstring(thegthemes, "Flow accumulation theme",
"WTRSHD")
if (factheme = nil) then
    exit
end
rivtheme = msgbox.listasstring(thegthemes, "Stream Grid theme", "WTRSHD")
if (rivtheme = nil) then
    exit
end
'
'-----------------
'--- Calculate ---
'-----------------
'
fdrgrid = fdrtheme.getgrid
facgrid = factheme.getgrid
strgrid = rivtheme.getgrid
'
' delineate stream links
lnk1grid = strgrid.StreamLink(fdrgrid)
'
'Save the New Grid
lnk1grid.SaveDataSet("lnk1grid".AsFileName)
'
237
'Create Grid Themes
lnk1theme = GTheme.Make(lnk1grid)
'
'Add Themes to the View
TheView.AddTheme(lnk1theme)
'
'-----------
'--- End ---
'-----------
238
TIMEAVE
'
'----------------------------
'--- creation information ---
'----------------------------
'
'Name: timeave.ave
'Version: 1.0
'Date: 03/09/97
'Author: Ferdi Hellweger
'        Center for Research in Water Resources
'        The University of Texas at Austin
'        ferdi@crwr.utexas.edu
'        www.ce.utexas.edu/stu/ferdi/
'
'---------------------------
'--- purpose/description ---
'---------------------------
'
'This is a time averaging utility for sample data.
'
'-----------------
'--- get table ---
'-----------------
'
intable = av.getactivedoc
'
'--------------------
'--- set up table ---
'--------------------
'
invtab = intable.getvtab
insegidf = invtab.findfield("segid")
indatef = invtab.findfield("date")
inmsmtf = invtab.findfield("msmt")
'
'---------------------------
'--- set up output table ---
'---------------------------
'
239
outfilenamestring = msgbox.input("Output file name", "WARDIMP",
"c:\warddata\wqsalsur3.dbf")
outfilename = filename.make(outfilenamestring)
outvtab = vtab.makenew(outfilename, dBASE)
outfields = list.make
outsegidf = field.make("segid", #FIELD_CHAR, 4, 0)
outavef = field.make("ave", #FIELD_DECIMAL, 16, 4)
outnvalf = field.make("nval", #FIELD_DECIMAL, 16, 4)
outfields = outfields.add(outsegidf)
outfields = outfields.add(outavef)
outfields = outfields.add(outnvalf)
outvtab.addfields(outfields)
outvtab.seteditable(true)
'
'--------------------
'--- process data ---
'--------------------
'
'algorithm assumes data in chronological order
'time handled as decimal years, due to problem with early, ca. 68 dates
'arcview crashes
'
nval = 0
tottime = 0
totweight = 0
thissegid = invtab.returnvalue(insegidf, 0)
'datestring = invtab.returnvalue(indatef, 0).asstring.Split({2,4}, ":")
'lastdate = date.make(datestring, "yy:MM:dd")
datestr = invtab.returnvalue(indatef, 0).asstring
datenum1 = datestr.left(2).asnumber
datenum2 = datestr.left(4).right(2).asnumber / 12
datenum3 = datestr.right(2).asnumber / 30 / 12
datenum = datenum1 + datenum2 + datenum3
lastdate = datenum.clone
for each inrec in invtab
    insegid = invtab.returnvalue(insegidf, inrec)
    datestr = invtab.returnvalue(indatef, inrec).asstring
    datenum1 = datestr.left(2).asnumber
    datenum2 = datestr.left(4).right(2).asnumber / 12
    datenum3 = datestr.right(2).asnumber / 30 / 12
240
    datenum = datenum1.clone + datenum2.clone + datenum3.clone
    indate = datenum.clone
    inmsmt = invtab.returnvalue(inmsmtf, inrec)
    if (thissegid <> insegid) then
        ave = totweight.clone / tottime.clone
'        message = "thissegid = " + thissegid.asstring
'        message = message + nl + "insegid = " + insegid.asstring
'        message = message + nl + "nval = " + nval.asstring
'        message = message + nl + "totweight = " + totweight.asstring
'        message = message + nl + "tottime = " + tottime.asstring
'        message = message + nl + "ave = " + ave.asstring
'        keepgoing = msgbox.yesno(message, "WARDIMP", true)
'        if (not keepgoing) then
'            exit
'        end
        outrec = outvtab.addrecord
        outvtab.setvalue(outsegidf, outrec, thissegid.clone)
        outvtab.setvalue(outavef, outrec, ave.clone)
        outvtab.setvalue(outnvalf, outrec, nval.clone)
        nval = 0
        tottime = 0
        totweight = 0
        thissegid = insegid.clone
        lastdate = indate.clone
    end
    mytime = indate - lastdate
'    message = "indate = " + indate.asstring
'    keepgoing = msgbox.yesno(message, "WARDIMP", true)
'    if (not keepgoing) then
'        exit
'    end
    weight = mytime.clone * inmsmt.clone
    nval = nval.clone + 1
    tottime = tottime.clone + mytime.clone
    totweight = totweight.clone + weight.clone
    lastdate = indate.clone
end
'close up
'
msgbox.info("Averages calculated", "WARDIMP")
241
WFACGRID
'
'----------------------------
'--- Creation information ---
'----------------------------
'
'Name: wfacgrid.ave
'Version: 1.0
'Date: 02/17/97
'Author: Ferdi Hellweger
'        Center for Research in Water Resources
'        The University of Texas at Austin
'        ferdi@crwr.utexas.edu
'
'Modified: 02/28/97
'          Ann Quenzer
'          Center for Research in Water Resources
'          The University of Texas at Austin
'          quenzer@mail.utexas.edu
'          1) changed the conversions to reflect project
'          2) changed the message box descriptions to
' reflect the script
'          3) computes an integer grid
'          4) added purpose and description
'
'---------------------------
'--- Purpose/Description ---
'---------------------------
'
'Computes the weighted flow accumulation using the flow
'direction grid and the land surface loading grid.  The
'resulting grid produces a grid (wfacload) with the
'loadings to the bay in Kg/day.
'
'----------------
'--- Get view ---
'----------------
'
theview = av.getactivedoc
'
242
'
'---------------------
'--- Get themes ---
'---------------------
'
thethemes = theview.getthemes
if (thethemes.count = 0) then
    msgbox.error("No themes found", "WFAC GRID")
    exit
end
if (thethemes.count = 1) then
    msgbox.error("Only one theme found", "WFAC GRID")
    exit
end
thegthemes = list.make
for each thetheme in thethemes
    if (thetheme.getclass.getclassname = "gtheme") then
        thegthemes.add(thetheme)
    end
end
if (thegthemes.count = 0) then
    msgbox.error("No grid themes found", "WFAC GRID")
    exit
end
if (thethemes.count = 1) then
    msgbox.error("Only grid one theme found", "WFAC GRID")
    exit
end
fdrtheme = msgbox.listasstring(thegthemes, "Flow direction theme", "WFAC
GRID")
if (fdrtheme = nil) then
    exit
end
ldtheme = msgbox.listasstring(thegthemes, "Load theme", "WFAC GRID")
if (ldtheme = nil) then
    exit
end
'
'
'
243
'-----------------
'--- Calculate ---
'-----------------
'
fdrgrid = fdrtheme.getgrid
ldgrid = ldtheme.getgrid
'
cellsize = number.makenull
dummmy = grid.getanalysiscellsize(cellsize)
extent = rect.makenull
dummy = grid.getanalysisextent(extent)
'
facgrid = (fdrgrid.flowaccumulation(ldgrid)).int
facfilename = av.getproject.makefilename("facgrid", "")
facgrid.savedataset("wfacload1".asfilename)
facgtheme = gtheme.make(facgrid)
theview.addtheme(facgtheme)
facgtheme.setvisible(true)
'
'final message to user
'
message = "Accumulated load grid calculated."
msgbox.info(message,"WFAC GRID")
'
'-----------
'--- End ---
'-----------
'
244
WTRSHD
'
'----------------------------
'--- Creation information ---
'----------------------------
'
'Name: wtrshd.ave
'Version: 1.0
'Date: 03/20/97
'Author: Ann Quenzer
'        Center for Research in Water Resources
'        The University of Texas at Austin
'        quenzer@mail.utexas.edu
'
'---------------------------
'--- Purpose/Description ---
'---------------------------
'
'Delineates the watersheds within the study area.
'
'----------------
'--- Get view ---
'----------------
'
theview = av.getactivedoc
theDisplay = theView.GetDisplay
'
'--------------------------------
'--- Get themes for watershed ---
'--------------------------------
'
'
thethemes = theview.getthemes
if (thethemes.count = 0) then
    msgbox.error("No themes found", "WATERSHED")
    exit
end
thegthemes = list.make
for each thetheme in thethemes
    if (thetheme.getclass.getclassname = "gtheme") then
245
        thegthemes.add(thetheme)
    end
end
if (thegthemes.count = 0) then
    msgbox.error("No grid themes found", "WATERSHED")
    exit
end
fdrtheme = msgbox.listasstring(thegthemes, "Flow Direction Grid",
"WATERSHED")
if (fdrtheme = nil) then
    exit
end
'
'
if (thethemes.count = 0) then
    msgbox.error("No themes found", "WATERSHED")
    exit
end
thegthemes = list.make
for each thetheme in thethemes
    if (thetheme.getclass.getclassname = "gtheme") then
        thegthemes.add(thetheme)
    end
end
'
'
if (thegthemes.count = 0) then
    msgbox.error("No grid themes found", "WATERSHED")
    exit
end
factheme = msgbox.listasstring(thegthemes, "Flow Accumulation Grid",
"WATERSHED")
if (factheme = nil) then
    exit
end
'
'
if (thethemes.count = 0) then
    msgbox.error("No themes found", "WATERSHED")
    exit
246
end
thegthemes = list.make
for each thetheme in thethemes
    if (thetheme.getclass.getclassname = "gtheme") then
        thegthemes.add(thetheme)
    end
end
'
'
if (thegthemes.count = 0) then
    msgbox.error("No grid themes found", "WATERSHED")
    exit
end
demtheme = msgbox.listasstring(thegthemes, "DEM Grid", "WATERSHED")
if (factheme = nil) then
    exit
end
'
'-----------------
'--- Calculate ---
'-----------------
'
fdrgrid = fdrtheme.getgrid
facgrid = factheme.getgrid
demgrid = demtheme.getgrid
'
'
thePoint = theDisplay.ReturnUserPoint
mPoint = MultiPoint.Make({thePoint})
'
'Delineate the watersheds for each of these stream segments
sourcegrid = demgrid.ExtractByPoints(mPoint,Prj.MakeNull,FALSE)
wshedgrid = fdrgrid.Watershed(sourcegrid.SnapPourPoint(facgrid,240))
' create a theme
theGTheme = GTheme.Make(wshedgrid)
' check if output is ok
if (wshedgrid.HasError) then
  return NIL end
247
'wshedgrid = fdrgrid.watershed(linkgrid)
'
'Save the New Grids
wshedgrid.savedataset("watershed".AsFileName)
'
'Create Grid Themes
'linktheme = gtheme.make(linkgrid)
wshedtheme = gTheme.make(wshedgrid)
'
'Add Themes to the View
theview.addtheme(wshedtheme)
'
'final message to user
'
message = "Watershed calculated."
msgbox.info(message,"WATERSHED")
'
'-----------
'--- End ---
'---------------------
248
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253
     VITA
Ann Marie Quenzer was born in Aberdeen, South Dakota on November 6,
1972, the daughter of JaniceEdna Quenzer and Harlan John Quenzer.  After
completing he work at Mitchell Senior High School, Mitchell, South Dakota, in
1991, she entered South Dakota State University in Brookings, South Dakota.  She
received the degree of Bachelor of Science from South Dakota State University in
December, 1995.  For the following months, she was employed as a Civil Engineer
for Shchmucker, Paul, Nohr and Associates of Mitchell, South Dakota.  In
September, 1996, she entered the Graduate School at the University of Texas at
Austin.
Permanent Address: 1409 Pebble Beach Road
Mitchell, South Dakota 57301
This thesis was typed by the author.