River floods are usually caused by a combination of wind-driven flood surges, low barometric pressure, and massive waves colliding with high upstream river flows. These floods are known to be the most devastating natural hazards. In India, wide sectoral and regional variables impact country climate change adaptability [1]. According to Markus et al. [2] climate change effects in the catchment, this change in climate is the leading cause of floods, mostly due to deforestation, urbanization, etc. *Author for correspondence Statistics suggest that floods account for almost 15% of all deaths from natural disasters and that the economic damages caused by floods have been seen to surpass billions of dollars worldwide, not just in developed countries in Asia but also in the countries of the European Union [3].The occurrence of catastrophic weather conditions in recent years has proven the need of realistic flood models, which function as early warning systems in severe storm situations, reducing economic damage. An early warning system comprises both structural and non-structural measures. While structural incorporates physical construction of embankments, canals, and so on, non structural initiatives include
The river Vamsadhara runs through the states of Andhra Pradesh (AP) and Odisha along a stretch of coastline that is prone to cyclonic storms. Riverine flow, along with cyclonic gales, is a crucial issue for most of India's coastal districts. As a result, a developed flood forecasting model is required to mitigate the danger of flooding to a certain level. For evaluating maximum water depth and inundation of flood plains induced by storms under existing and future land use circumstances, hydrological and hydraulic models are more prominently studied. The current work employs hydrological and hydrodynamic models to simulate a real flood inundation model for unsteady flow conditions. The technique involves the processing of the digital elevation models (DEM) to generate flood hydrographs that serve as boundary conditions for the reaches. In the present analysis, Manning's roughness coefficient is used as a sensitive parameter. Calibration and validation of extreme flood events that occurred in 2006, 2010, and 2013 using observed water levels yielded good results, with model performances of 0.79, 0.68, and 0.84, respectively. The results of the depth, velocity, and flood extent maps that were generated are presented. These maps can be used to plan for flood disasters and long-term watershed management.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.