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整体参数灵敏度:一个新的视角,以应对水域管理多来源深度不确定性带来的风险

Pengfei Du¹, Yi Rong², Siyu Zeng³

¹Dept.of Environmental Science & Engineering, Tsinghua University. Beijing, 100084, China.

Journal of environmental management

|November 21, 2024

概括

本研究引入了整体灵敏度分析,以解决流域模型中的多源深度不确定性 (MSDU). 该方法有效量化MSDU如何影响参数灵敏度,改善流域管理决策.

科学领域:

水文学水文学
环境建模环境建模
水资源管理水资源管理

背景情况:

流域模型对于管理至关重要,但面临着来自多源深度不确定性 (MSDU) 的挑战.
了解MSDU下参数灵敏度如何变化对于可靠的模型应用至关重要.
现有的方法往往无法完全捕捉MSDU对模型参数的复杂影响.

研究的目的:

开发和应用一个整体的灵敏度分析方法来定量评估MSDU下的参数灵敏度.
调查不同MSDU源对流域模型参数灵敏度的影响.
确定改善流域管理的关键敏感参数.

主要方法:

将深层不确定性分类为三个不同的来源.
开发一个整体灵敏度分析 (HSA) 框架.
使用土壤和水资源评估工具 (SWAT) 模型应用HSA方法,在上Chishui河流盆地.
分析了307152个多源深度不确定性场景.

主要成果:

整体灵敏度指数 (HSI) 表现出空间异质性,并受到MSDU的显著影响.
参数灵敏度指标因MSDU而变化高达73.3% (增加) 和36.4% (减少).
在49个敏感参数中,确定了34个敏感参数 (13个极其敏感,3个空间敏感).
揭示了驾驶条件和灵敏度指数之间的影响机制.

结论:

开发的HSA方法在处理MSDU对参数灵敏性的影响方面比传统方法更有效.
该方法可以检测因气候变化和人类活动而增加的参数灵敏度.
这种方法减少了参数值识别中偏差的风险,以改善流域建模和管理.

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