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Understanding non-stationarity of hydroclimatic extremes and resilience in Peninsular catchments, India

Climate change significantly impacts the global hydrological cycle, leading to pronounced shifts in hydroclimatic extremes such as increased duration, occurrence, and intensity. Despite these significant changes, our understanding of hydroclimatic risks and hydrological resilience remains limited, p...

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Autores principales: Kumar, Nikhil, Patel, Piyush, Singh, Shivam, Goyal, Manish Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10397228/
https://www.ncbi.nlm.nih.gov/pubmed/37532763
http://dx.doi.org/10.1038/s41598-023-38771-w
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author Kumar, Nikhil
Patel, Piyush
Singh, Shivam
Goyal, Manish Kumar
author_facet Kumar, Nikhil
Patel, Piyush
Singh, Shivam
Goyal, Manish Kumar
author_sort Kumar, Nikhil
collection PubMed
description Climate change significantly impacts the global hydrological cycle, leading to pronounced shifts in hydroclimatic extremes such as increased duration, occurrence, and intensity. Despite these significant changes, our understanding of hydroclimatic risks and hydrological resilience remains limited, particularly at the catchment scale in peninsular India. This study aims to address this gap by examining hydroclimatic extremes and resilience in 54 peninsular catchments from 1988 to 2011. We initially assess extreme precipitation and discharge indices and estimate design return levels using non-stationary Generalized Extreme Value (GEV) models that use global climate modes (ENSO, IOD, and AMO) as covariates. Further, hydrological resilience is evaluated using a convex model that inputs simulated discharge from the best hydrological model among SVM, RVM, random forest, and a conceptual model (abcd). Our analysis shows that the spatial patterns of mean extreme precipitation indices (R1 and R5) mostly resemble with extreme discharge indices (Q1 and Q5). Additionally, all extreme indices, including R1, Q1, R5, and Q5, demonstrate non-stationary behavior, indicating the substantial influence of global climate modes on extreme precipitation and flooding across the catchments. Our results indicate that the random forest model outperforms the others. Furthermore, we find that 68.52% of the catchments exhibit low to moderate hydrological resilience. Our findings emphasize the importance of understanding hydroclimatic risks and catchment resilience for accurate climate change impact predictions and effective adaptation strategies.
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spelling pubmed-103972282023-08-04 Understanding non-stationarity of hydroclimatic extremes and resilience in Peninsular catchments, India Kumar, Nikhil Patel, Piyush Singh, Shivam Goyal, Manish Kumar Sci Rep Article Climate change significantly impacts the global hydrological cycle, leading to pronounced shifts in hydroclimatic extremes such as increased duration, occurrence, and intensity. Despite these significant changes, our understanding of hydroclimatic risks and hydrological resilience remains limited, particularly at the catchment scale in peninsular India. This study aims to address this gap by examining hydroclimatic extremes and resilience in 54 peninsular catchments from 1988 to 2011. We initially assess extreme precipitation and discharge indices and estimate design return levels using non-stationary Generalized Extreme Value (GEV) models that use global climate modes (ENSO, IOD, and AMO) as covariates. Further, hydrological resilience is evaluated using a convex model that inputs simulated discharge from the best hydrological model among SVM, RVM, random forest, and a conceptual model (abcd). Our analysis shows that the spatial patterns of mean extreme precipitation indices (R1 and R5) mostly resemble with extreme discharge indices (Q1 and Q5). Additionally, all extreme indices, including R1, Q1, R5, and Q5, demonstrate non-stationary behavior, indicating the substantial influence of global climate modes on extreme precipitation and flooding across the catchments. Our results indicate that the random forest model outperforms the others. Furthermore, we find that 68.52% of the catchments exhibit low to moderate hydrological resilience. Our findings emphasize the importance of understanding hydroclimatic risks and catchment resilience for accurate climate change impact predictions and effective adaptation strategies. Nature Publishing Group UK 2023-08-02 /pmc/articles/PMC10397228/ /pubmed/37532763 http://dx.doi.org/10.1038/s41598-023-38771-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Kumar, Nikhil
Patel, Piyush
Singh, Shivam
Goyal, Manish Kumar
Understanding non-stationarity of hydroclimatic extremes and resilience in Peninsular catchments, India
title Understanding non-stationarity of hydroclimatic extremes and resilience in Peninsular catchments, India
title_full Understanding non-stationarity of hydroclimatic extremes and resilience in Peninsular catchments, India
title_fullStr Understanding non-stationarity of hydroclimatic extremes and resilience in Peninsular catchments, India
title_full_unstemmed Understanding non-stationarity of hydroclimatic extremes and resilience in Peninsular catchments, India
title_short Understanding non-stationarity of hydroclimatic extremes and resilience in Peninsular catchments, India
title_sort understanding non-stationarity of hydroclimatic extremes and resilience in peninsular catchments, india
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10397228/
https://www.ncbi.nlm.nih.gov/pubmed/37532763
http://dx.doi.org/10.1038/s41598-023-38771-w
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