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首页> 外文期刊>International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences >EXPERIMENTAL FLOOD EARLY WARNING SYSTEM IN PARTS OF BEAS BASIN USING INTEGRATION OF WEATHER FORECASTING, HYDROLOGICAL AND HYDRODYNAMIC MODELS
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EXPERIMENTAL FLOOD EARLY WARNING SYSTEM IN PARTS OF BEAS BASIN USING INTEGRATION OF WEATHER FORECASTING, HYDROLOGICAL AND HYDRODYNAMIC MODELS

机译:利用天气预测,水文和水力模型的集成对流域部分盆地进行实验洪水预警系统

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The flood early warning for any country is very important due to possible saving of human life, minimizing economic losses and devising mitigation strategies. The present work highlights the experimental flood early warning study in parts of Beas Basin, India for the monsoon season of 2015. The entire flood early warning was done in three parts. In first part, rainfall forecast for every three days in double nested Weather Research and Forecasting (WRF) domain (9?km for outer domain and 3?km for inner domain) was done for North Western Himalaya NWH using National Centres for Environmental Prediction (NCEP) Global Forecasting System (GFS) 0.25 degree data as initialization state. Rainfall forecast was validated using Indian Meteorological Department (IMD) data, the simulation accuracy of WRF in rainfall prediction above 100?mm is about 60%. Rainfall induced flood event of August 05–08, 2015 in Sone River (tributary of Beas River) Basin, near Dharampur, Mandi district of Himachal Pradesh caused very high damages. This event was picked three days in advance by WRF model based rainfall forecast. In second part, mean rainfall at sub-basin scale for hydrological model (HEC-HMS) was estimated from forecasted rainfall at every three hours in netcdf format using python script and flood hydrographs were generated. In third part, flood inundation map was generated using Hydrodynamic (HD) model (MIKE 11) with flood hydrographs as boundary condition to see the probable areas of inundation.
机译:由于可能挽救生命,最大程度地减少经济损失并制定缓解策略,因此任何国家的洪水预警都是非常重要的。本工作重点介绍了印度Beas盆地部分地区2015年季风季节的洪水预警试验研究。整个洪水预警分为三个部分。在第一部分中,使用国家环境预测中心(National Centers for Environmental Prediction)对喜马拉雅西北部西北部的双嵌套天气研究与预测(WRF)域(外域9?km,内域3?km)中每三天进行一次降雨预报( NCEP)全球预报系统(GFS)0.25度数据作为初始化状态。利用印度气象局(IMD)数据验证了降雨预报,在100?mm以上的降雨预报中,WRF的模拟精度约为60%。降雨引发的洪灾事件于2015年8月5日至08日在喜马al尔邦Mandi区Dharampur附近的Sone河(Beas河支流)流域造成了很高的破坏。该事件是基于WRF模型的降雨预报提前三天进行的。在第二部分中,使用python脚本以netcdf格式根据每三小时的预报降雨量估算了水文模型(HEC-HMS)的亚流域尺度的平均降雨量,并生成了洪水水位图。第三部分,使用水动力模型(MIKE 11)以洪水水文图为边界条件,生成洪水淹没图,以查看可能的洪水淹没区域。

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