Estimation of minimum assured rainfall using probability of exceedance: A suitable approach for planning rainfed rice
Rainfall is one of the most important components of agricultural productivity as it forms the basis of rural livelihoods and food security. In rainfed agricultural regions, where irrigation infrastructure is limited, the rainfall variability directly influences planting schedules, crop growth stage...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Association of agrometeorologists
2025-06-01
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| Series: | Journal of Agrometeorology |
| Subjects: | |
| Online Access: | https://journal.agrimetassociation.org/index.php/jam/article/view/2977 |
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| Summary: | Rainfall is one of the most important components of agricultural productivity as it forms the basis of rural livelihoods and food security. In rainfed agricultural regions, where irrigation infrastructure is limited, the rainfall variability directly influences planting schedules, crop growth stages, and yield outcomes. The study presents a systematic approach for estimating minimum assured rainfall using the probability of exceedance (P) by analyzing 50 years (1973–2022) of weekly rainfall data from three Agro-climatic zones of West Bengal: Undulating Red and Laterite Zone, Gangetic Alluvial Zone, and Terai-Teesta Alluvial Zone. Using the CumFreq software, various probability distributions were fitted to historical rainfall data corresponding to the Standard Meteorological Weeks (SMW) 20 to 41. The best-fit models, primarily Generalized Laplace, Generalized Extreme Value (GEV), and Generalized Normal were used to estimate weekly rainfall at 25%, 50%, 75%, and 90% exceedance probabilities. The 75% probability level, representing assured rainfall, was used as a threshold to determine the number of rainy days and guide varietal recommendations for rice cultivation. Results revealed significant variability in rainfall patterns across zones, with notable implications for selecting suitable rice varieties. The findings provide a probabilistic framework to inform agricultural planning and risk mitigation strategies, especially under increasing climate variability in rainfed ecosystems.
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| ISSN: | 0972-1665 2583-2980 |