EVALUATION OF SWAT AND HSPF WITHIN BASINS PROGRAM FOR THE UPPER NORTH BOSQUE RIVER WATERSHED IN CENTRAL TEXAS

摘要

USEPA's water program staff and their counterparts in state pollution control agencies increasingly emphasize watershed and water quality-based assessment and integrated analysis of point and nonpoint sources of pollution. BASINS 3.0 (Better Assessment Science Integrating point and Nonpoint Sources) is a system developed to meet the needs of such agencies. This study was conducted to evaluate the major watershed-scale models, SWAT (Soil and Water Assessment Tool) and HSPF (Hydrological Simulation Program - Fortran), included within the BASINS 3.0 system. SWAT and HSPF were calibrated and verified with data from the Upper North Bosque River watershed (UNBRW), an intense dairy producing region located in central Texas. The model output was calibrated for daily flow, sediment, and nutrients measured at five stream sites within the UNBRW for the period of January 1994 through June 1995 and verified (daily and monthly time-step) for the period of July 1995 to July 1999. Nash-Sutcliffe model efficiency (E) and mean error (ME) were used to evaluate the accuracy of the models. The average daily flow, sediment, and nutrient loading simulated by SWAT were closer to measured values than HSPF during both calibration and verification periods at the outlet of UNBRW. As indicated by E values, the temporal variations of daily flow (E = 0.72 and 0.70 for HSPF, while E = 0.17 and 0.62 for SWAT during the calibration and verification, respectively) and sediment (E = 0.11 and 0.23 for HSPF, while E = -2.50 and -3.51 for SWAT during the calibration and verification, respectively) were better described by HSPF during the calibration and verification periods. However, the model efficiencies of both models for monthly flow (E = 0.91 and 0.86 for HSPF, while E = 0.50 and 0.78 for SWAT during the calibration and verification, respectively) and sediment (E = 0.72 and 0.88 for HSPF, while E = 0.83 and 0.59 for SWAT during the calibration and verification, respectively) significantly improved. SWAT generally proved to be a better predictor of nutrient loading during both the calibration and verification periods.