Zeng, Ziyue听1听;听Hong, Yang听2听;听Shen, Xinyi听3
1听Institute of Arctic and Alpine Research, 乐播传媒, Boulder, CO
2听School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK
3听Civil and Environmental Engineering, University of Connecticut, CT
Due to complex hydrometeorological and geographic conditions, China is continuously affected by severe floods, which often lead to significant losses on human lives and property. Aiming to support the progressive forecasting, analysis and evaluation of flood disasters, we developed a distributed high-resolution Flood Monitoring and Forecasting System and applied it at global, national and regional scales on the platform of China Meteorological Administration (CMA). In this system, based on the SMAP satellite soil moisture data and gauge-combined GSMaP satellite precipitation product, the annual global runoff (April 2015-March 2016, 0.1掳脳0.1掳) is estimated using a new version of global Curve Number (CN) dataset. The estimated monthly runoff shows consistency with the observed data in Jialing River Basin. Furthermore, a distributed hydrological model, the Coupled Routing and Excess STorage (CREST) version 2.1, was been used to realize systematical and dynamical simulation of hydrological processes in a fine resolution in China (0.125?脳0.125?and daily for the nation, 1km脳1km and hourly for basins). Embedded a global geomorphology variable database, an Inundation Mapping module (iMap) using CREST simulated streamflow as the main input to calculate flood areas and depths was developed, dependent on which the time series of spatial and temporal dynamic inundation became available. Driven by the merged precipitation product of CMORPH and observations from automatic rainfall stations, simulation results demonstrate good skills in forecasting storm-triggered floods. The performance of iMap also indicated that this system is capable of estimating flood process in Gan river basin and Jialing river basin, thus offering guidance in flood disaster prevention and mitigation for users in China and even in the whole Asia. Enhanced by this system in the ability of flood forecasting and risk assessment modelling, CMA has adopted it as an operational system since 2013. Despite positive performance, more accurate forecasting precipitation data (e.g. Quantitative Precipitation Estimation products) and social-economic data (e.g. GDP, population, prevention measures) are need to improve the predictive capability and robustness of this system.