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Understanding the spatial and temporal variability of rainfall is essential for effective water resource management, flood mitigation, and agricultural planning in tropical regions. The present study thus deals with the comprehensive statistical assessment of rainfall variability, trends, spatial dependence, and extremes using long-term (1985–2024) IMDAA 0.12° × 0.12° gridded rainfall in a tropical coastal basin of eastern India. Monthly, seasonal and annual rainfall characteristics were quantified, and temporal persistence was examined using autocorrelation analysis. Robust non-parametric M-K test with Sen’s slope estimator were employed to detect long-term trends. Extreme rainfall behavior was analyzed through GEV modeling, return period-based PMP estimation, and ETCCDI indices, providing a rigorous framework for understanding rainfall extremes and variability. Results reveal that rainfall in monsoon and Kharif seasons exhibit the highest standard deviation, yet the lowest CV%, indicating dominance with relative stability. At all temporal scales, mean rainfall exceeds the median, while skewness and kurtosis reveal that extremes are most pronounced at the monthly scale, particularly during core and transitional monsoon months. Spatially, nine coastal blocks receive higher rainfall than others, whereas Kamarda and Jaleswar are the driest. Lag-1 autocorrelation characterizes monsoon, Kharif, May and August rainfall. Although FDR rejects most local Mann-Kendall trends, field significance and mean areal rainfall analysis confirm significant regional increases in non-monsoon, Kharif and annual rainfall. A significant rise in Rx1day at Khejuri-II (2.3 mm·yr.−1, p < 0.05), with 2007–2008 found as extreme years over the study area, highlights increasing short-duration rainfall risk, supported by heavy-tailed and extreme value distributional behavior monthly and seasonally. Rainfall further shows increasing wet days and intensity, strong inter-annual variability, frequent extremes, high concentration, pronounced seasonality, and dominant monsoon control during 1985–2024.

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Analysis of Temporal Variation in Rainfall Using High-Resolution Dataset in a Coastal Basin of Eastern India

  • 会议论文

  • 发表日期：

    2026年

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  • Subhankar Ghosh,
  • Madan Kumar Jha,
  • Ataur Rahman

摘要

Understanding the spatial and temporal variability of rainfall is essential for effective water resource management, flood mitigation, and agricultural planning in tropical regions. The present study thus deals with the comprehensive statistical assessment of rainfall variability, trends, spatial dependence, and extremes using long-term (1985–2024) IMDAA 0.12° × 0.12° gridded rainfall in a tropical coastal basin of eastern India. Monthly, seasonal and annual rainfall characteristics were quantified, and temporal persistence was examined using autocorrelation analysis. Robust non-parametric M-K test with Sen’s slope estimator were employed to detect long-term trends. Extreme rainfall behavior was analyzed through GEV modeling, return period-based PMP estimation, and ETCCDI indices, providing a rigorous framework for understanding rainfall extremes and variability. Results reveal that rainfall in monsoon and Kharif seasons exhibit the highest standard deviation, yet the lowest CV%, indicating dominance with relative stability. At all temporal scales, mean rainfall exceeds the median, while skewness and kurtosis reveal that extremes are most pronounced at the monthly scale, particularly during core and transitional monsoon months. Spatially, nine coastal blocks receive higher rainfall than others, whereas Kamarda and Jaleswar are the driest. Lag-1 autocorrelation characterizes monsoon, Kharif, May and August rainfall. Although FDR rejects most local Mann-Kendall trends, field significance and mean areal rainfall analysis confirm significant regional increases in non-monsoon, Kharif and annual rainfall. A significant rise in Rx1day at Khejuri-II (2.3 mm·yr.−1, p < 0.05), with 2007–2008 found as extreme years over the study area, highlights increasing short-duration rainfall risk, supported by heavy-tailed and extreme value distributional behavior monthly and seasonally. Rainfall further shows increasing wet days and intensity, strong inter-annual variability, frequent extremes, high concentration, pronounced seasonality, and dominant monsoon control during 1985–2024.