2023
DOI: 10.3390/hydrology10060116
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Discerning Watershed Response to Hydroclimatic Extremes with a Deep Convolutional Residual Regressive Neural Network

Abstract: The impact of climate change continues to manifest itself daily in the form of extreme events and conditions such as droughts, floods, heatwaves, and storms. Better forecasting tools are mandatory to calibrate our response to these hazards and help adapt to the planet’s dynamic environment. Here, we present a deep convolutional residual regressive neural network (dcrrnn) platform called Flux to Flow (F2F) for discerning the response of watersheds to water-cycle fluxes and their extremes. We examine four United… Show more

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Cited by 2 publications
(1 citation statement)
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“…An issue to analyze with climate change is its correlation with the hydroclimatic systems of the Earth. Larson et al [42] proposed a deep convolutional residual regressive neural network to determine river basins' response to the water cycle's flows. The analysis revealed that this architecture and the catchment flow data exhibited satisfactory prediction performance for various locations at different time scales.…”
Section: Related Workmentioning
confidence: 99%
“…An issue to analyze with climate change is its correlation with the hydroclimatic systems of the Earth. Larson et al [42] proposed a deep convolutional residual regressive neural network to determine river basins' response to the water cycle's flows. The analysis revealed that this architecture and the catchment flow data exhibited satisfactory prediction performance for various locations at different time scales.…”
Section: Related Workmentioning
confidence: 99%