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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 #328341
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: Assessing the radar rainfall estimates in watershed-scale water quality model

Author
item JEON, DONG JIN - GWANGJU INSTITUTE OF SCIENCE AND TECHNOLOGY
item KIM, JOON HA - GWANGJU INSTITUTE OF SCIENCE AND TECHNOLOGY
item Pachepsky, Yakov

Submitted to: BARC Poster Day
Publication Type: Abstract Only
Publication Acceptance Date: 4/13/2016
Publication Date: 4/27/2016
Citation: Jeon, D., Kim, J., Pachepsky, Y.A. 2016. Assessing the radar rainfall estimates in watershed-scale water quality model. BARC Poster Day. 2016 BARC Poster Day on April 27, 2016.

Interpretive Summary:

Technical Abstract: Watershed-scale water quality models are effective science-based tools for interpreting change in complex environmental systems that affect hydrology cycle, soil erosion and nutrient fate and transport in watershed. Precipitation is one of the primary input data to achieve a precise rainfall-runoff simulation, and having its detailed spatial distribution is highly desirable. The objective of this work was to investigate whether the radar rainfall estimates can improve the accuracy of stream flow, TSS load, and TP load simulations using the Soil and Water Assessment Tool (SWAT). We intended to investigate separately model performance in high-flow 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 drivers: 1) rainfall observations from nine ground rain gauges (GR), 2) 25 corrected radar rainfall estimates (RR), and 3) combination of nine ground rain gauges and 16 corrected radar rainfall estimates that represent the 16 un-gauged sub-watersheds in YRW (GARR). Simulation results under different rainfall sources were compared using Nash-Sutcliffe efficiency coefficient (NSE) and Williams-Kloot test. Stream flow estimation results show high NSE value under three different rainfall sources, there were little differences. However, when we applied the Williams-Kloot test by dividing high and low stream flow, we confirmed statistically that prediction of low stream flow using RR was better than with GR and with GARR. Predictions of the high stream flow showed no significant difference. NSE value for prediction of TSS load was higher under RR and GARR than under GR. In case of TP, the GARR shows the highest NSE value, followed by RR and GR. These results suggest that radar rainfall estimates can help to accurate prediction of low stream flow and nutrient loads. Therefore, radar rainfall estimates are able to contribute to understanding aspects of watershed functioning affected by rainstorms of intermediate and low intensity such as biomass of crops and other plants, loss of nutrients to groundwater, availability of nutrients to plants, partitioning of nutrients between surface runoff and infiltration.