Statistical Analysis of Extreme Rainfall Events
Summary
Extreme rainfall events pose significant challenges to infrastructure, water management and hazard mitigation worldwide. Statistical analysis provides tools to quantify the frequency and intensity of such events, drawing on probability theory and extreme value theory to estimate the likelihood of rare but impactful rainfall intensities. By fitting observed rainfall data to theoretical distributions, researchers derive return periods and confidence intervals for thresholds of interest, enabling planners to assess flood risk and design resilient hydraulic structures. Methods such as L-moments and maximum likelihood estimation have been refined to improve parameter estimation, while goodness-of-fit tests ensure the robustness of chosen models. Recent advances incorporate non-stationary frameworks to account for trends linked to climate variability, and novel distributions offer better fits for heavy tails. This body of work is inherently interdisciplinary, integrating climatology, hydrology and statistical theory to inform adaptation strategies in diverse climatic zones. Concrete applications range from urban drainage design to watershed management and agricultural planning. As extreme rainfall patterns evolve under climate change, statistical methods continue to adapt, emphasising the need for regionally calibrated models and real-time risk assessment tools.
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Statistical Analysis of Extreme Rainfall Events publication trend
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Technical terms
Generalized Extreme Value distribution (GEV): A family of models combining Gumbel, Fréchet and Weibull distributions to characterise block maxima of variables.
Return period: The average interval between events exceeding a given magnitude over a long-term record.
L-moments: Statistical measures based on linear combinations of order statistics used to estimate distribution parameters with reduced sampling variability.
Block maxima: The largest observed value of a variable within a predefined time block, often annual or seasonal, used in extreme value analysis.
Goodness-of-fit tests: Hypothesis tests, such as Anderson-Darling or Kolmogorov-Smirnov, evaluating how closely a theoretical distribution matches observed data.
References
- Best-Fit Probability Distributions and Return Periods for Maximum Monthly Rainfall in Bangladesh. Climate (2018).
- Selection of Hydrological Probability Distributions for Extreme Rainfall Events in the Regions of Colombia. Water (2020).
- Statistical Study of Rainfall Control: The Dagum Distribution and Applicability to the Southwest of Spain. Water (2019).
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