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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Publications at this Location » Publication #356288

Research Project: Design and Implementation of Monitoring and Modeling Methods to Evaluate Microbial Quality of Surface Water Sources Used for Irrigation

Location: Environmental Microbial & Food Safety Laboratory

Title: New methodology to develop high-resolution rainfall data using weather radar for watershed-scale water quality model

Author
item Jeon, Dong Jin - Gwangju Institute Of Science And Technology
item Pachepsky, Yakov
item Kim, Bumjo - Gwangju Institute Of Science And Technology
item Kim, Joon Ha - Gwangju Institute Of Science And Technology

Submitted to: Desalination and Water Treatment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/13/2019
Publication Date: 2/21/2019
Citation: Jeon, D., Pachepsky, Y.A., Kim, B., Kim, J. 2019. New methodology to develop high-resolution rainfall data using weather radar for watershed-scale water quality model. Desalination and Water Treatment. 138:248-256.

Interpretive Summary: For the same rainfall events, variability of rainfall intensity, durations, and amounts across watersheds can be substantial. This variability must be accounted for in modeling fate and transport and estimating pollution risks. Typical number of rain gages across watersheds is small. Weather radars provide a high resolution spatial coverage for the rainfall forecast but the accuracy of rainfall estimates is low. We tested the hypothesis that calibrating rainfall data with rain gage data and using then rain gage and radar data together can improve the rainfall estimates across the entire watershed. We found that indeed combining gauge rainfall and corrected radar rainfall data improved simulations of total suspended solids and total phosphorus. Results of this work will be used by consultants and water managers hydrologic modeling to improve the water quality and quantity simulations.

Technical Abstract: Watershed-scale water quality models are often used for interpreting changes in complex environmental systems. Precipitation is a primary control affecting the output of watershed-scale water quality model, and higher resolution of precipitation data is highly desirable. The objective of this study was to investigate whether the radar rainfall estimates can improve the accuracy of stream flow, TSS load, and TP load simulations with the Soil and Water Assessment Tool (SWAT) for high- and low-flow conditions. Yeongsan River watershed (YRW) was selected for this study. This watershed is located south-west of Korean Peninsula, has an area of about 2,938 km2, and is divided into 25 sub-watersheds. The simulations were conducted under different rainfall datasets: 1) rainfall observations from nine ground rain gauges (GR), 2) 25 corrected radar rainfall estimates (RR), and 3) a combination of nine ground rain gauges and 16 corrected radar rainfall estimates that represent the 16 ungauged sub-watersheds in YRW (GARR). Simulation results under different the rainfall datasets were compared using the Nash-Sutcliffe efficiency coefficient (NSE) and percentage bias (PBIAS). The prediction of both high and low stream flows using GARR was better than using GR and RR data. The model performance for predicting TSS load was significantly better under GARR data than under GR and RR data. In case of TP, the model performances using RR and GARR data were significantly better than that using GR data. Overall, combining gauge rainfall and corrected radar rainfall led to an improvement in the prediction accuracy for the watershed-scale water quality model.