Deconstructing the spatiotemporal characteristics of extreme precipitation events from multiple data products during Indian summer monsoon
Study Region: India Study Focus: The rising frequency of extreme precipitation events (EPEs) alters Earth systems processes and poses growing risks to socio-economic stability, intensified by climate change. This study analyzes the spatiotemporal characteristics of EPEs across the Indian subcontinen...
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Elsevier
2025-10-01
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| Series: | Journal of Hydrology: Regional Studies |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581825004963 |
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| author | Sandipan Paul Priyank J. Sharma Ramesh S.V. Teegavarapu |
| author_facet | Sandipan Paul Priyank J. Sharma Ramesh S.V. Teegavarapu |
| author_sort | Sandipan Paul |
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| description | Study Region: India Study Focus: The rising frequency of extreme precipitation events (EPEs) alters Earth systems processes and poses growing risks to socio-economic stability, intensified by climate change. This study analyzes the spatiotemporal characteristics of EPEs across the Indian subcontinent during the monsoon season, critical for the region’s water resources and agriculture. Using observational (IMD, APHRODITE), reanalysis (IMDAA, GLDAS, ERA5-Land), satellite (CHIRPS, PERSIANN-CDR), and hybrid (MSWEP) datasets, we assess their ability to reproduce EPE intensity, detectability, timing, trends, and statistical properties. Results identify MSWEP as the most reliable alternative to IMD in data-scarce regions, providing valuable insights for hydrologic studies, climate impact assessments, disaster risk management and enhancing socio-economic resilience. New Hydrological Insights for the Region: The study reveals that EPE intensity and frequency are highest along India’s western coast and northeast, moderate in central regions, and lowest in arid western and peninsular areas. Wet-to-wet, dry-to-dry, and wet-to-dry transitions follow similar regional patterns. Satellite datasets generally underestimate, while reanalysis datasets overestimate EPE intensities, introducing wet and dry biases in moderate-intensity event frequencies, respectively. In contrast, both datasets report an overestimation of low-intensity event frequencies. MSWEP shows the best performance with the lowest bias and highest detectability, while MSWEP and APHRODITE best preserve spatial patterns of median EPEs. No consistent EPE trend clusters are found. These findings support adaptive hydrologic design and disaster risk mitigation to combat climate change. |
| format | Article |
| id | doaj-art-3d1c0944b1a8414e9e64d6ed92dee9c8 |
| institution | Kabale University |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-3d1c0944b1a8414e9e64d6ed92dee9c82025-08-20T03:57:58ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-10-016110266710.1016/j.ejrh.2025.102667Deconstructing the spatiotemporal characteristics of extreme precipitation events from multiple data products during Indian summer monsoonSandipan Paul0Priyank J. Sharma1Ramesh S.V. Teegavarapu2Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, IndiaHydroinformatics Lab, Department of Civil Engineering, Indian Institute of Technology Indore, Madhya Pradesh 453552, India; Corresponding author.Department of Civil, Environmental and Geomatics Engineering, Florida Atlantic University, Boca Raton, FL 33431, United StatesStudy Region: India Study Focus: The rising frequency of extreme precipitation events (EPEs) alters Earth systems processes and poses growing risks to socio-economic stability, intensified by climate change. This study analyzes the spatiotemporal characteristics of EPEs across the Indian subcontinent during the monsoon season, critical for the region’s water resources and agriculture. Using observational (IMD, APHRODITE), reanalysis (IMDAA, GLDAS, ERA5-Land), satellite (CHIRPS, PERSIANN-CDR), and hybrid (MSWEP) datasets, we assess their ability to reproduce EPE intensity, detectability, timing, trends, and statistical properties. Results identify MSWEP as the most reliable alternative to IMD in data-scarce regions, providing valuable insights for hydrologic studies, climate impact assessments, disaster risk management and enhancing socio-economic resilience. New Hydrological Insights for the Region: The study reveals that EPE intensity and frequency are highest along India’s western coast and northeast, moderate in central regions, and lowest in arid western and peninsular areas. Wet-to-wet, dry-to-dry, and wet-to-dry transitions follow similar regional patterns. Satellite datasets generally underestimate, while reanalysis datasets overestimate EPE intensities, introducing wet and dry biases in moderate-intensity event frequencies, respectively. In contrast, both datasets report an overestimation of low-intensity event frequencies. MSWEP shows the best performance with the lowest bias and highest detectability, while MSWEP and APHRODITE best preserve spatial patterns of median EPEs. No consistent EPE trend clusters are found. These findings support adaptive hydrologic design and disaster risk mitigation to combat climate change.http://www.sciencedirect.com/science/article/pii/S2214581825004963Extreme Precipitation IndicesTransitional ProbabilitiesIndian Summer MonsoonComposite Performance ScoreCategorical MetricsContinuous Measures |
| spellingShingle | Sandipan Paul Priyank J. Sharma Ramesh S.V. Teegavarapu Deconstructing the spatiotemporal characteristics of extreme precipitation events from multiple data products during Indian summer monsoon Journal of Hydrology: Regional Studies Extreme Precipitation Indices Transitional Probabilities Indian Summer Monsoon Composite Performance Score Categorical Metrics Continuous Measures |
| title | Deconstructing the spatiotemporal characteristics of extreme precipitation events from multiple data products during Indian summer monsoon |
| title_full | Deconstructing the spatiotemporal characteristics of extreme precipitation events from multiple data products during Indian summer monsoon |
| title_fullStr | Deconstructing the spatiotemporal characteristics of extreme precipitation events from multiple data products during Indian summer monsoon |
| title_full_unstemmed | Deconstructing the spatiotemporal characteristics of extreme precipitation events from multiple data products during Indian summer monsoon |
| title_short | Deconstructing the spatiotemporal characteristics of extreme precipitation events from multiple data products during Indian summer monsoon |
| title_sort | deconstructing the spatiotemporal characteristics of extreme precipitation events from multiple data products during indian summer monsoon |
| topic | Extreme Precipitation Indices Transitional Probabilities Indian Summer Monsoon Composite Performance Score Categorical Metrics Continuous Measures |
| url | http://www.sciencedirect.com/science/article/pii/S2214581825004963 |
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