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在印度模拟极端降雨事件时整合区域化,不确定性和非静止性

Ankush¹, Narendra Kumar Goel², Vinnarasi Rajendran³

¹Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India.

Journal of environmental management

|February 6, 2025

概括

这项研究量化了极端降雨预测中的不确定性,发现参数不确定性占主导地位. 区域化气候共变量简化了分析,并改善了对非静止降雨模式的理解,以更好地设计基础设施.

科学领域:

水文学和气候科学气象学和气候科学
对极端事件的统计建模.

背景情况:

极端降雨分析对于基础设施设计至关重要,但非静止模式带来了建模挑战.
准确捕捉动态极端降雨需要先进的方法来避免复杂性和过度装配.
在共同变量选择中量化不确定性对于可靠的风险评估至关重要.

研究的目的:

为了探索由于共变量选择而导致的降雨回归水平的不确定性.
为了比较非静止降雨模型中的共变量不确定性与参数不确定性.
确定和区域化印度极端降雨非静止性的关键物理驱动因素.

主要方法:

使用贝叶斯推理,方差通胀因子 (VIF) 和主要组件分析 (PCA) 的非静止建模.
利用区域化分析来简化共变量计算并提供物理解释.
在不同的时间间隔验证了模型,并分析了极端降雨驱动因素的空间可变性.

主要成果:

参数不确定性通常主导着共变量不确定性,除了在特定地区.
协变量的区域化简化了分析,并提供了对极端降雨驱动因素的物理见解.
非静止模型显示,极端降雨回报水平可以根据趋势的方向和大小增加或减少.

结论:

非静止降雨量分析提供了比基础设施规划的静止模型更明智的决策.
了解区域气候动态和极端降雨的驱动因素可以增强适应策略和气候弹性.
双极模式指数 (DMI) 被确定为印度沿海地区 (第2区) 的显著共变量.

关键词:

印度印度印度模型的不确定性多对线性是多对线性的.不稳定性的不稳定性极端的降雨情况.地区化区域化是指区域化.

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