Multi-temporal downscaling of daily to sub-daily streamflow for flash flood watersheds at ungauged stations using a hybrid framework

Budamala, V and Wadhwa, A and Das Bhowmik, R and Mahindrakar, A and Satyaji Rao Yellamelli, R and Kasiviswanathan, KS (2023) Multi-temporal downscaling of daily to sub-daily streamflow for flash flood watersheds at ungauged stations using a hybrid framework. In: Journal of Hydrology, 625 .

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Abstract

Unprecedented flash floods (FFs) in urban regions have been rising due to the increasing intensity and magnitude of heavy rainfall resulting from human-induced climate and land-use changes. Modelling of FF along different spatiotemporal scales is extremely complex since FF models require multi-resolution forcing and observed information. In the absence of sub-daily, and multi-site streamflow data, multi-temporal downscaling (MTD) plays a crucial role for FF modeling. While a wide range of methods are available for the spatio-temporal downscaling of climate data, the applicability of the MTD strategy for streamflow has not been investigated yet. The current study proposed a MTD methodology for yielding the daily to sub-daily streamflow of gauged and ungauged stations using adaptive emulator modelling concepts. The proposed MTD framework for ungauged stations is a hybrid model that draws on conceptual and machine learning-based approaches to analyze catchment behavior. The study selected the Peachtree Creek watershed (United States) since it frequently experiences FFs. Results suggest that model-derived sub-daily streamflow had minimal uncertainty in capturing hydrological signatures and nearly 95 accuracy in predicting flow attributes over ungauged stations. The proposed framework can be useful for planning, mitigation, and management, where the fine resolution data is required. © 2023 Elsevier B.V.

Item Type:	Journal Article
Publication:	Journal of Hydrology
Publisher:	Elsevier B.V.
Additional Information:	The copyright for this article belongs to the Elsevier B.V.
Keywords:	Catchments; Floods; Land use; Rain; Stream flow; Watersheds, Down-scaling; Flash-floods; Flood modeling; Hybrid framework; Hybrid model; Hydroclimatology; Machine-learning; Multi-temporal; Temporal downscaling; Ungaged, Machine learning
Department/Centre:	Division of Interdisciplinary Sciences > Interdisciplinary Centre for Water Research
Date Deposited:	30 Oct 2023 13:11
Last Modified:	30 Oct 2023 13:11
URI:	https://eprints.iisc.ac.in/id/eprint/83121

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