Abstract
Abstract. 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.
Highlights
Floods are one of the most common and damaging disasters of India
Sone River is a tributary of Beas River having catchment area of 230 km2 with outlet at Kandhapatan, Mandi district, Himachal Pradesh (HP)
National Centres for Environmental Prediction (NCEP)’s Global Forecasting System (GFS) downscaled rainfall data at 3 km from Weather Research and Forecasting (WRF) simulation, IMD (Indian Meteorological Department) grid of 50 km resolution, INSAT 3D IMR data at 0.5 degree and CPC daily rainfall inputs were the different rainfall datasets analysed for hydrological modelling
Summary
Floods are one of the most common and damaging disasters of India. Early warning of floods is most critical for minimizing the losses due to reoccurring floods in many parts of India. Numbers of flash floods, cloud burst or extreme rainfall events and floods due to glacier lake outburst have increased in frequency. Flood early warning activities are mainly part of pre-flood management plans. In India, near real time streamflow forecast system has been developed for BhakraBeas Basin and Bhima Basin under Hydrology Project II (RTDSS 2015). Results of the developed systems depends on the accuracy of near real time meteorological data used in hydrological models. In all the flood early warning studies, accurate prediction of rainfall remains most important task (Mukhopadhyay, 2015), other important factors are parameterization of hydrological models, accuracy and resolution of Digital Elevation Models (DEM) (Alemseged and Rientjes, 2005), scale and details of LULC and soil map. In this study attempt has been made to establish flood early warning system using integration of weather forecasting, hydrological and hydrodynamic models
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