An artificial neural network-hydrodynamic coupled modeling approach to assess the impacts of floods under changing climate in the East Rapti Watershed, Nepal
Article
Article Title | An artificial neural network-hydrodynamic coupled modeling approach to assess the impacts of floods under changing climate in the East Rapti Watershed, Nepal |
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ERA Journal ID | 41778 |
Article Category | Article |
Authors | Bhattarai, Roshika, Bhattarai, Utsav, Pandey, Vishnu Prasad and Bhattarai, Pawan Kumar |
Journal Title | Journal of Flood Risk Management |
Journal Citation | 15 (4) |
Article Number | e12852 |
Number of Pages | 19 |
Year | 2022 |
Publisher | John Wiley & Sons |
Place of Publication | United Kingdom |
ISSN | 1753-318X |
Digital Object Identifier (DOI) | https://doi.org/10.1111/jfr3.12852 |
Web Address (URL) | https://onlinelibrary.wiley.com/doi/10.1111/jfr3.12852 |
Abstract | Recurring floods have devastating consequences on the East Rapti Watershed (ERW), but effective mitigation/adaptation measures are lacking. This article aims at establishing a rainfall-runoff (RR) relationship; estimating depth and extent of inundation under climate change scenarios; assessing impacts on the socio-economy; and identifying and evaluating adaptation strategies in the ERW. Artificial Neural Network (ANN) was used to generate peak flows which were then entered into a hydraulic model to simulate inundation. Results were validated with field survey. The calibrated and validated RR and hydraulic models were fed with projected future climate (2021–2050) derived from multiple regional-climate-models to assess the changes in inundation. Results showed the peak discharge likely exceeds 10,500 m3/s at the ERW outlet in the extreme future flood scenario with corresponding inundation of 80 km2 and up to a depth of 11 m sweeping away over 1000 houses and 19 km2 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. |
Keywords | adaptation strategies; HEC-RAS; flood modelling; East Rapti Watershed; climate change; ANN |
ANZSRC Field of Research 2020 | 410103. Human impacts of climate change and human adaptation |
Byline Affiliations | Tribhuvan University, Nepal |
Water Modeling Solutions, Nepal | |
Institute for Life Sciences and the Environment |
https://research.usq.edu.au/item/z01qq/an-artificial-neural-network-hydrodynamic-coupled-modeling-approach-to-assess-the-impacts-of-floods-under-changing-climate-in-the-east-rapti-watershed-nepal
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J Flood Risk Management - 2022 - Bhattarai.pdf | ||
License: CC BY 4.0 | ||
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