Evaluating flood potential in the Mahanadi River Basin, India, using Gravity Recovery and Climate Experiment (GRACE) data and topographic flood susceptibility index under non-stationary framework.

Autor: Bhere S; Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, 4000076, India. studentsachinbhere@gmail.com., Reddy MJ; Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, 4000076, India.; Interdisciplinary Program (IDP) in Climate Studies, Indian Institute of Technology Bombay, Mumbai, 4000076, India.
Jazyk: angličtina
Zdroj: Environmental science and pollution research international [Environ Sci Pollut Res Int] 2024 Mar; Vol. 31 (11), pp. 17206-17225. Date of Electronic Publication: 2024 Feb 09.
DOI: 10.1007/s11356-024-32105-7
Abstrakt: Extreme flood events have been recorded recently in the Mahanadi River basin in India with a high destructive potential that causes large social and economic damages. Because fewer hydrometeorological stations can record the flood magnitude in the basin, exploring new datasets like Gravity Recovery and Climate Experiment (GRACE) becomes important to overcome the barriers of assessing the hydrological extremes. The study estimates the flood potential using the GRACE-based terrestrial water storage (TWS) and analytical hierarchy process (AHP)-based topographic flood susceptibility to model the non-stationary flood frequency. During extreme flood events, the magnitude of the combined flood potential index (CFPI) is high (CFPI > 0.6), which correlates with higher river discharge. The CFPI value for the 2012 flood event with a discharge of 11,000 m 3 /sec (corresponds to a 35-year return period) is recorded at 0.67. Likewise, the CFPI for the flood event in 2011, which corresponds to a return period of 17 years, also stands at 0.63. The overall correlation between the discharge values of various flood events and CFPI values is above 0.8 for all locations, indicating GRACE-based CFPI's applicability for identifying the flood risk for larger basins like Mahanadi. Furthermore, on integrating CFPI as a covariate in non-stationary flood frequency modeling, the study found its superior performance when compared to both stationary models and non-stationary models with time or other climate indices as covariates, thus, helping in accurate estimation of flood return levels that are very useful in the hydrological design of water resources projects.
(© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Databáze: MEDLINE
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