Satellite‐Based Probable Maximum Precipitation Estimation and Spatiotemporal Analysis of Rainfall in Nepal Himalayas: Implications for Hydroclimatic Disaster Management

ABSTRACT Probable maximum precipitation (PMP) is crucial for water‐related disaster management, especially in regions like Nepal, where extreme weather poses significant risks. Despite its importance, PMP studies in Nepal remain limited. This study quantifies PMP using Hershfield's method, applied to daily precipitation data at 0.05° resolution from CHIRPS v2.0 (1992–2022), compared against in situ observations (correlation coefficient = 0.80, index of agreement = 0.68, RMSE = 4.56 mm). Intense rainfall (> 70 mm/day) is prevalent in central, eastern, and lower western Nepal from June to September, with an increasing trend in pre‐monsoon (March–May) rainfall, suggesting a potential shift toward an earlier monsoon onset. In contrast, post‐monsoon (October–November) rainfall is declining, particularly in western Nepal. The trend in extreme precipitation (total precipitation on heaviest 1% of days) shows a statistically significant increase of 16 mm/year ( p < 0.05) across the eastern and Churia range. In contrast, the western and northern regions experienced a significant decline of approximately 14 mm/year ( p < 0.05). PMP values peak at around 920 mm/day in certain grids over western Nepal and 540 mm/day in the central Himalayas and mid‐hills, highlighting serious hydrological risks such as floods, landslides, and dry spells. The findings underscore the urgency of improved early warning systems, resilient infrastructure, and adaptive climate policies.

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Publication Details

Journal
Atmospheric Science Letters
Published
2026-05-01
DOI
https://doi.org/10.1002/asl2.70031
Primary Topic
Precipitation Measurement and Analysis
Type
article
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article

Satellite‐Based Probable Maximum Precipitation Estimation and Spatiotemporal Analysis of Rainfall in Nepal Himalayas: Implications for Hydroclimatic Disaster Management

Ram Avtar, Shivaang Sinha, Konduru Rakesh Teja, Sudip Pandey
Atmospheric Science Letters
Precipitation Measurement and Analysis
article

Satellite‐Based Probable Maximum Precipitation Estimation and Spatiotemporal Analysis of Rainfall in Nepal Himalayas: Implications for Hydroclimatic Disaster Management

Ram Avtar, Shivaang Sinha, Konduru Rakesh Teja, Sudip Pandey
article en

Abstract

ABSTRACT Probable maximum precipitation (PMP) is crucial for water‐related disaster management, especially in regions like Nepal, where extreme weather poses significant risks. Despite its importance, PMP studies in Nepal remain limited. This study quantifies PMP using Hershfield's method, applied to daily precipitation data at 0.05° resolution from CHIRPS v2.0 (1992–2022), compared against in situ observations (correlation coefficient = 0.80, index of agreement = 0.68, RMSE = 4.56 mm). Intense rainfall (> 70 mm/day) is prevalent in central, eastern, and lower western Nepal from June to September, with an increasing trend in pre‐monsoon (March–May) rainfall, suggesting a potential shift toward an earlier monsoon onset. In contrast, post‐monsoon (October–November) rainfall is declining, particularly in western Nepal. The trend in extreme precipitation (total precipitation on heaviest 1% of days) shows a statistically significant increase of 16 mm/year ( p < 0.05) across the eastern and Churia range. In contrast, the western and northern regions experienced a significant decline of approximately 14 mm/year ( p < 0.05). PMP values peak at around 920 mm/day in certain grids over western Nepal and 540 mm/day in the central Himalayas and mid‐hills, highlighting serious hydrological risks such as floods, landslides, and dry spells. The findings underscore the urgency of improved early warning systems, resilient infrastructure, and adaptive climate policies.

Atmospheric Science LettersVol. 27(5)
Chennai Mathematical Institute (IN), Hokkaido University (JP), National Institute of Ocean Technology (IN), Japan Aerospace Exploration Agency (JP)
Climate action
Openalex Percentile: Top 5%
Precipitation Measurement and Analysis
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