Comparative evaluation of high-resolution rainfall products over South Peninsular India in characterising precipitation extremes

M. R. Sneha () and Archana Nair ()
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M. R. Sneha: Amrita Vishwa Vidyapeetham
Archana Nair: Amrita Vishwa Vidyapeetham

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 117, issue 2, No 32, 1969-1999

Abstract: Abstract Extreme rainfall events are becoming more frequent in South Peninsular India (SPI), which is resulting in an increase in flash floods, landslides, and damage to agriculture and infrastructure. However, because of the scarcity of rainfall data over remote areas and oceans, the reanalysis datasets are a boon for understanding various meteorological phenomenon. India's first high-resolution reanalysis dataset, Indian Monsoon Data Assimilation and Analysis (IMDAA), simulates past climate data at the regional or local levels. In this study, a comprehensive evaluation of IMDAA is carried out with respect to Indian Meteorological Department (IMD) daily gridded dataset over SPI during 2000–2020. It was found that monsoon and post-monsoon seasons demonstrated strong compatibility whereas annual and pre-monsoon seasons displayed some dissimilarity as depicted by Mahalanobis metric. Spatiotemporal analysis of IMDAA in capturing seasonal and annual climatic variations implied that the reanalysis product considerably showed a similar pattern to that of IMD neglecting some overestimations. According to the study, an analogy of 96% can be seen between IMDAA and IMD on an average scale. Results also suggest the efficiency of IMDAA model in capturing some extreme rainfall episodes better than the IMD. Consequently, the findings provide insight into the reanalysis product's ability to depict climatic variability and reliability in employing precipitation data estimated by IMDAA in modelling extreme events over SPI.

Keywords: IMDAA; IMD; Rainfall extremes; South Peninsular India; Mahalanobis distance (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-023-05936-9

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