An Improved Coupled Routing and Excess Storage (CREST) Distributed Hydrological Model and Its Verification in Ganjiang River Basin, China

Water

Youbing Hu¹  Xiaoyan He¹  Lian He¹  Guoqiang Tang¹  Yuan Yang²  Yang Hong²  Jiren Li²  Yaokui Cui³  Liuqian Ding⁴  Zhansheng Li⁴  Guangyuan Kan⁴  Ke Liang⁴ 

[1] Drought Disaster Reduction of the Ministry of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China;State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China;State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing IWHR Corporation, Beijing 100048, China;;State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Research Center on Flood &

关键词: flood simulation;    distributed hydrological model;    remote sensing;    CREST;   

DOI  :  10.3390/w9110904

来源: DOAJ

【 摘 要 】

The coupled routing and excess storage (CREST) distributed hydrological model has been applied regionally and globally for years. With the development of remote sensing, requirements for data assimilation and integration have become new challenges for the CREST model. In this paper, an improved CREST model version 3.0 (Tsinghua University and China Institute of Water Resources and Hydropower Research, Beijing, China) is proposed to enable the use of remotely-sensed data and to further improve model performance. Version 3.0 model’s runoff generation, soil moisture, and evapotranspiration based on three soil layers to make the CREST model friendly to remote sensing products such as soil moisture. A free water reservoir-based module which separates three runoff components and a four mechanism-based cell-to-cell routing module are also developed. Traditional CREST and CREST 3.0 are applied in the Ganjiang River basin, China to compare their simulation capability and applicability. Research results indicate that CREST 3.0 outperforms the traditional model and has good application prospects in data assimilation, flood forecasting, and water resources planning and management applications.

【 授权许可】

Unknown