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In cooperation with the Trinity River Authority

Computed and Estimated Pollutant Loads, West Fork Trinity River, Fort Worth, Texas, 1997

By Paul W. McKee and Harry C. McWreath

U.S. Geological Survey
Water-Resources Investigations Report 01–4253


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pdf (506 KB)


Contents

Abstract

Introduction

Purpose and Scope

Description of Study Area

Previous Studies

Acknowledgments

Methods of Computing and Estimating Pollutant Loads

Computing Loads

Estimating Loads With Deterministic Model

Estimating Loads With Statistical Model

Computed and Estimated Pollutant Loads

Summary

References Cited

Figures

1.  
Map showing location of study area and monitoring sites in the Dallas-Fort Worth metropolitan area
2.  
Map showing land-use categories in the study area
3.  
Hydrographs showing accumulated rainfall and discharge at streamflow-gaging station 08048543 for storms of October 23–24 and December 7–8, 1997
4–6.  
Graphs showing:
4.  
Comparison of use of point rainfall and areal-averaged rainfall, deterministic model—differences between estimated and computed pollutant loads for 12 properties and constituents for two storms (October 23–24 and December 7–8, 1997)
5.  
Comparison of use of point rainfall and areal-averaged rainfall, statistical model—differences between estimated and computed pollutant loads for 12 properties and constituents for two storms (October 23–24 and December 7–8, 1997)
6.  
Comparison of results of deterministic model and statistical model, both using areal-averaged rainfall—mean absolute differences between estimated and computed pollutant loads for 12 properties and constituents for two storms (October 23–24 and December 7–8, 1997)

Tables

1.  
Land-use characteristics
2.  
Median event-mean concentrations by land-use category
3.  
Regression equation coefficients for estimating pollutant loads for residential, commercial, industrial, and nonurban land uses
4.  
Regression equation coefficients for estimating pollutant loads for highway land use
5.  
Computed and estimated pollutant loads for 12 selected properties and constituents

Abbreviations

BCF, bias-correction factor     LUR, residential land use
DA, total contributing drainage area   mi, mile

DFW, Dallas-Fort Worth

  NEXRAD, NEXt Generation Weather RADar
EMC, event-mean concentration   NPDES, National Pollutant Discharge Elimination System
ft3/s, cubic foot per second   R2 adj., adjusted coefficient of determination
GIS, Geographic Information System   SE, standard estimate of error
IA, impervious area   TRN, total storm rainfall
LUC, commercial land use   USGS, U.S. Geological Survey
LUI, industrial land use   WMM, Watershed Management Model
LUN, nonurban land use    

Abstract

In 1998 the U.S. Geological Survey, in cooperation with the Trinity River Authority, did a study to estimate storm-runoff pollutant loads using two models—a deterministic model and a statistical model; the estimated loads were compared to loads computed from measured data for a large (118,000 acres) basin in the Dallas-Fort Worth, Texas, metropolitan area. Loads were computed and estimated for 12 properties and constituents in runoff from two 1997 storms at streamflow-gaging station 08048543 West Fork Trinity River at Beach Street in Fort Worth. Each model uses rainfall as a primary variable to estimate pollutant load. In addition to using point rainfall at the Beach Street station to estimate pollutant loads, areal-averaged rainfall for the basin was computed to obtain a more representative estimate of rainfall over the basin. Loads estimated by the models for the two storms, using both point and areal-averaged rainfall, generally did not compare closely to computed loads for the 12 water-quality properties and constituents. Both models overestimated loads more frequently than they underestimated loads. The models tended to yield similar estimates for the same property or constituent. In general, areal-averaged rainfall data yielded better estimates of loads than point rainfall data for both models. Neither the deterministic model nor the statistical model (both using areal-averaged rainfall) was consistently better at estimating loads. Several factors could account for the inability of the models to estimate loads closer to computed loads. Chief among them is the fact that neither model was designed for the specific application of this study.

 


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