Original scientific paper

https://doi.org/10.17559/TV-20171211112204

Spring Flood Forecasting Based on the WRF-TSRM Mode

Xianyong Meng orcid.org/0000-0003-4182-5028 ; State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin & China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Beijing, 100038, P. R. China
Zhiqun Sun ; College of Resource and Enviroment Sciences & Xinjiang University, Shengli Road, Urumqi, 830046, P. R. China
Honggang Zhao ; College of Chemical Engineering, Xinjiang Normal University, Xinyi Road, Urumqi, 830046, P. R. China
Xiaonan Ji ; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Urumqi, 830046, P. R. China
Hao Wang ; State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin & China Institute of Water Resources and Hydropower Research, Room A966, No.1 Fuxing Road, Beijing, 100038, P. R. China
Lianqing Xue ; Hohai University, Gulou Road, Jiangsu, 210098, P. R. China
Hongjing Wu ; NRPOP Lab, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL, T 709 749 5201, Canada
Yongnan Zhu ; State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin & China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Beijing, 100038, P. R. China

Abstract

The snowmelt process is becoming more complex in the context of global warming, and the current existing studies are not effective in using the short-term prediction model to drive the distributed hydrological model to predict snowmelt floods. In this study, we selected the Juntanghu Watershed in Hutubi County of China on the north slope of the Tianshan Mountains as the study area with which to verify the snowmelt flood prediction accuracy of the coupling model. The weather research and forecasting (WRF) model was used to drive a double-layer distributed snowmelt runoff model called the Tianshan Snowmelt Runoff Model (TSRM), which is based on multi-year field snowmelt observations. Moreover, the data from NASA’s moderate resolution imaging spectroradiometer (MODIS) was employed to validate the snow water equivalent during the snow-melting period. Results show that, based on the analysis of the flow lines in 2009 and 2010, the WRF-driven TSRM has an overall 80% of qualification ratios (QRs), with determination coefficients of 0.85 and 0.82 for the two years, respectively, which demonstrates the high accuracy of the model. However, due to the influence of the ablation of frozen soils, the forecasted flood peak is overestimated. This problem can be solved by an improvement to the modeled frozen soil layers. The conclusion reached in this study suggests that the WRF-driven TSRM can be used to forecast short-term snowmelt floods on the north slope of the Tianshan Mountains, which can effectively improve the local capacity for the forecasting and early warning of snowmelt floods.

Keywords

early warning; MODIS; snowmelt model; TSRM; WRF

Hrčak ID:

193608

URI

https://hrcak.srce.hr/193608

Publication date:

10.2.2018.

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