The GRAPESCGlobal-Regional Assimilation and PrEdiction System)_Meso model developed by China Meteorological Administration is coupled with a hydrological model to increase lead-time of flood forecast. GRAPES_Meso model is run in 15 km× 15 km horizontal resolution and 5 km×5 km horizontal resolution. The initial fields and lateral boundaries of 15 km×15 km horizontal resolution of GRAPES is provided by global NCEP forecast datasets, and the initial fields and lateral boundaries of 5 km×5 km horizontal resolution of GRAPES is provided by 15 km×15 km horizontal resolution of GRAPES. In order to match the input scale of hydrological model, quantitative precipitation forecasts of GRAPES_Meso model is down-scaled to 5 km×5 km horizontal resolution. Xin'anjiang model and grid-based distributed Xin'anjiang model are used, which have been widely applied and proven effective in flood forecasting and hydrological simulation in humid and semi-humid regions of China for a long term. Wangjiaba Station and Xixian Basin in the upper reaches of the Huai River are chosen as sensitive areas. The two hydrological models are driven by forecast datasets of GRAPES. Upstream Wangjiaba Station, the basin is divided into 10 sub-basins for the coupling experiment of Xin'anjiang model. And Xixian Basin is for the coupling experiment of grid-based distributed Xin'anjiang model. A flood which maintains from 0800 BT 28 August to 1400 BT 29 September in 2009 is forecasted by these two models. The experiment results show that compared with ob-served precipitation, quantitative products of GRAPES model in 15 km× 15 km and 5 km×5 km horizontal resolutions are well consistent. The quantitative products of GRAPES model with 5 km×5 km are larger than the quantitative products of GRAPES model with 15 km×15 km. A promising tool is given by GRAPES meteorological and hydrological coupled hydrologic model to increase lead-time of real-time flood forecast, compared with that driven by raingauge observation. The accuracy of the flood forecasting based on the precipitation prediction of GRAPES model is approximate to the precipitation prediction. The performance may be better if the input requirements for hydrological models are exactly met.%尝试将GRAPES(Global-Regional Assimilation and PrEdiction System)模式与水文模型结合,构建GRAPES气象-水文单向耦合模式,进行洪水预报.气象模式选取GRAPES_Meso模式,分别采用15 km× 15 km和5 km×5 km水平分辨率,15 km×15 km的GRAPES模式由NCEP全球预报场提供初始场和侧边界条件；5km×5 km的GRAPES模式由15 km×15 km GRAPES模式提供初始场和侧边界条件,将GRAPES_Meso模式的定量降水预报分辨率统一降尺度到5 km×5 km分辨率,用于驱动水文模式.水文模型选取新安江模型与分布式新安江模型.以淮河王家坝站以上流域和息县流域为试验流域,将GRAPES降水预报场驱动水文模型进行单向耦合,构建GRAPES气象-水文单向耦合模式,选择2009年8月28日08:00(北京时,下同)-9月9日14:00汛期一次洪水过程,进行实际预报试验.结果表明:15 km×15 km和5 km×5 km的GRAPES模式预报降水与实况降水分布相一致；与水文站观测降水驱动水文模型洪水模拟结果相比,GRAPES气象水文模式对洪水预报的预见期延长效果明显,对洪水模拟精度也较高,与水文模型输入场分辨率要求相匹配的降水产品对洪水模拟的精度更高.
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