An integrated stochastic approach for extreme rainfall analysis in the National Capital Region of India,Journal of Earth System Science

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An integrated stochastic approach for extreme rainfall analysis in the National Capital Region of India
Journal of Earth System Science ( IF 1.9 ) Pub Date : 2021-02-04 , DOI: 10.1007/s12040-020-01510-0
Ranjana Ray Chaudhuri , Prateek Sharma

Abstract

The National Capital Region of India (NCR Delhi) receives around 26 rainy days with majority of short duration high intensity rainfall events. Yet, the city faces severe waterlogging during south-west monsoon, and shortage of water in other seasons due to rapid urbanization and changing hydrological flow patterns. In an uncertain scenario, where spatiotemporal variability of rainfall at local scale is not very well understood, a location-specific robust model applicable for the megacity through interpretation of parameter estimates will improve understanding of extreme rainfall pattern with duration. Identification of the best-fit statistical model for prediction of short duration extreme events is done and parameters of the model are evaluated for different durations. The study finds that the 2-parameter gamma distribution and 3-parameter generalized extreme value (GEV) predict similar return levels of extreme intensity for short durations and short return periods. The shape parameter in GEV and shape and scale parameters in gamma explain the extreme quantile in the distribution responsible for prediction of high magnitude events. The more generic gamma model is robust and applicable at local scale, with pronounced shape parameter variations across durations (1–11.534, 2–8.264, 3–6.609 h). It is concluded that the knowledge of hourly variation in extreme rainfall events will help in informed decision making in this acutely water-stressed region of the world.

Highlights

Short duration extreme rainfall events are frequent in semi-arid urban regions and characterization of these storms is important. The extreme rainfall pattern characteristics is defined through the parameter estimates of shape and scale of gamma distribution.
Within 1 h duration rainfall events pattern, the higher shape factor identifies storms in a more wet regime (α-14.05) than the mean (α-11.534), while the lower shape factor identifies dry regime (α-9.018).
The 4 h and 6 h events have the lowest shape factor and high scale factor, implying variation, and unpredictability.
Short duration storms have the potential to cause flash floods, the hydrological insights provided by this study will be useful in similar geographical regions.

中文翻译：

印度国家首都辖区极端降雨分析的综合随机方法

摘要

印度国家首都地区（NCR德里）大约有26个雨天，大部分是短期的高强度降雨事件。然而，该市在西南季风期间面临严重的涝灾，而由于快速的城市化和水文流量模式的变化，其他季节的水资源短缺。在不确定的情况下，如果对当地尺度降雨的时空变化了解得不太清楚，则通过解释参数估计值，适用于特大城市的特定于位置的鲁棒模型将增进对持续时间的极端降雨模式的理解。确定了用于预测短期持续性极端事件的最佳拟合统计模型，并针对不同持续时间评估了模型的参数。研究发现，2参数伽玛分布和3参数广义极值（GEV）预测了短期和短期返回期间极端强度的相似返回水平。GEV中的形状参数和gamma中的形状和比例参数说明了负责预测高强度事件的分布中的极高分位数。更为通用的伽马模型具有较强的鲁棒性，并适用于局部规模，并且在整个持续时间（1-111.534、2-8.264、3-6.609小时）内，形状参数都有明显的变化。结论是，极端降雨事件中每小时变化的知识将有助于在这个世界上水资源严重短缺的地区做出明智的决策。GEV中的形状参数和gamma中的形状和比例参数解释了负责预测高强度事件的分布中的极高分位数。更为通用的伽马模型具有较强的鲁棒性，并适用于局部规模，并且在整个持续时间（1-111.534、2-8.264、3-6.609小时）内，形状参数都有明显的变化。结论是，极端降雨事件中每小时变化的知识将有助于在这个世界上水资源严重短缺的地区做出明智的决策。GEV中的形状参数和gamma中的形状和比例参数解释了负责预测高强度事件的分布中的极高分位数。更为通用的伽马模型具有较强的鲁棒性，并适用于局部规模，其形状参数在整个持续时间（1-111.534、2-8.264、3-6.609 h）中都有明显的变化。结论是，极端降雨事件中每小时变化的知识将有助于在这个世界上水资源严重短缺的地区做出明智的决策。