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Spatiotemporal Evaluation of Simulated Evapotranspiration and Streamflow over Texas Using the WRF‐Hydro‐RAPID Modeling Framework
Author(s) -
Lin Peirong,
Rajib Mohammad Adnan,
Yang ZongLiang,
SomosValenzuela Marcelo,
Merwade Venkatesh,
Maidment David R.,
Wang Yan,
Chen Li
Publication year - 2018
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/1752-1688.12585
Subject(s) - streamflow , evapotranspiration , environmental science , surface runoff , hydrology (agriculture) , base flow , baseflow , weather research and forecasting model , hydrological modelling , precipitation , drainage basin , atmospheric sciences , climatology , meteorology , geology , geography , ecology , cartography , geotechnical engineering , biology
This study assesses a large‐scale hydrologic modeling framework ( WRF ‐Hydro‐ RAPID ) in terms of its high‐resolution simulation of evapotranspiration ( ET ) and streamflow over Texas (drainage area: 464,135 km 2 ). The reference observations used include eight‐day ET data from MODIS and FLUXNET , and daily river discharge data from 271 U.S. Geological Survey gauges located across a climate gradient. A recursive digital filter is applied to decompose the river discharge into surface runoff and base flow for comparison with the model counterparts. While the routing component of the model is pre‐calibrated, the land component is uncalibrated. Results show the model performance for ET and runoff is aridity‐dependent. ET is better predicted in a wet year than in a dry year. Streamflow is better predicted in wet regions with the highest efficiency ~0.7. In comparison, streamflow is most poorly predicted in dry regions with a large positive bias. Modeled ET bias is more strongly correlated with the base flow bias than surface runoff bias. These results complement previous evaluations by incorporating more spatial details. They also help identify potential processes for future model improvements. Indeed, improving the dry region streamflow simulation would require synergistic enhancements of ET , soil moisture and groundwater parameterizations in the current model configuration. Our assessments are important preliminary steps towards accurate large‐scale hydrologic forecasts.