STREAM FLOW ESTIMATION USING SPATIALLY DISTRIBUTED RAINFALL IN THE TRINITY RIVER BASIN, TEXAS

Abstract

Rainfall is the driving force behind all hydrologic processes in a watershed, and therefore the driving force in hydrologic modeling. In the past, raingauge data has been used as the primary input for these models. However, raingauge networks are generally sparse and insufficient to capture the spatial variability across large watersheds. A relatively new alternative is high-resolution radar rainfall data from weather radar systems, such as the Next Generation Weather Radar (NEXRAD) of the National Weather Service (NWS). In this study, raingauge data were compared to NEXRAD data at each raingauge location to evaluate the accuracy and validity of rainfall data measured by radar. The main objective of this study was to evaluate the use of spatially distributed rainfall on stream flow estimation using radar rainfall inputs to a hydrologic model. SWAT, a distributed-parameter continuous-time hydrologic/water quality model, was used to estimate stream flow for a watershed in the Trinity River Basin of northeast Texas. Results obtained from simulations using NEXRAD rainfall inputs were compared to those obtained using traditional raingauge data as input to the same model. Estimation efficiency analysis was used to compare the storage volume for the Cedar Creek Reservoir with daily, ten-day, and monthly accumulated flow from SWAT simulations using raingauge and NEXRAD rainfall inputs. The efficiency for both models was similar; however, NEXRAD rainfall inputs seem to provide a better flow estimate. The accuracy of the model results suggest that NEXRAD is a good alternative to raingauge data, and can be extremely valuable in large watersheds without readily available raingauge data or sparse raingauge networks. In addition, NEXRAD can capture rainfall from localized events that may be missed by raingauge networks but that still contribute to overland runoff, thus contributing to stream flow.