An artificial neural network-hydrodynamic coupledmodeling approach to assess the impacts of floods underchanging climate in the East Rapti Watershed, Nepal
This article aims to establish a rainfall-runoff relationship; estimate depth and extent of inundation under climate change scenarios; assess impacts on the socio-economy; and identify and evaluate adaptation strategies in Nepal's East Rapti Watershed (ERW). The ERW has been the site of devastating floods but effective mitigation and adaptation measures are lacking. The study uses an Artificial Neural Network (ANN) was to generate peak flows which were then entered into a hydraulic model to simulate inundation. The calibrated and validated RR and hydraulic models were fed with projected future climate derived from multiple regional-climate-models to assess the changes in inundation.
The study finds that the peak discharge likely exceeds 10,500 cubic meters per second at the ERW outlet in the extreme future flood scenario with corresponding inundation of 80 sq.km and up to a depth of 11 m sweeping away over 1000 houses and 19 sq.km. of agricultural land in the critical areas. Constructing a 17 km long embankment in the critical areas along the right bank of the East Rapti River could reduce the flood spread by 35%, safeguarding 78% of the houses and saving 51% agricultural land compared with the scenarios without the embankment.
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