【摘要】 为了提高洪水预报的精度和预见期,本文提出了一种耦合数值天气预报和多目标参数优化的洪水预报方法。利用基于ε网格的带精英策略的非支配排序遗传算法(ε-NSGAⅡ)对分布式水文–土壤–植被模型(DHSVM)进行了自动率定,并将欧洲中期天气预报中心(ECMWF)的24小时累积降水预报信息进行等权重加权集合平均,驱动DHSVM模型进行洪水预报。结果表明对于整体流量过程线而言,预见期在8天以内较为可靠,其预报值与实测值的相对平均误差(RME)在20%范围之内。相对于传统的确定性洪水预报,引入集合数值天气预报后能够延长洪水预报的预见期,为发展洪水预报方法提供一种有效途径。更多还原

【Abstract】 A flood forecasting method coupled with numerical weather prediction and multi-objective optimization for parameter calibration is presented to improve accuracy and lead time of flood forecasting. In this method, the epsilon-dominance non-dominated sorted genetic algorithm II(?-NSGA II) is used for multi-objective auto-calibration of the Distributed Hydrology Soil Vegetation Model(DHSVM), and ensemble averaging precipitation data from the European Centre for Medium-Range Weather Forecasts(ECMWF) is adopted to drive DHSVM for flood forecasting. The results demonstrate that for overall flow, forecasts of lead time within eight days are reliable and the relative mean error(RME) is within 20%. Compared with traditional deterministic forecasting, adopting ensemble precipitation forecasts can prolong the lead time of flood forecasting and provide an effective way for developing flood forecasting methods.更多还原