Quantifying Climate and Catchment Control on Hydrological Drought in the Continental United States
Quantifying Climate and Catchment Control on Hydrological Drought in the Continental United States
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DOI：
10.1029/2018wr024620
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发表时间：
2020-01-01
影响因子：
5.4
通讯作者：
Mishra, Ashok
中科院分区：
文献类型：
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作者：
Konapala, Goutam;Mishra, Ashok
水文干旱事件的演变是气候和流域过程之间复杂（非线性）相互作用的结果。为了研究这种非线性关系，我们将基于随机森林（RF）算法的机器学习建模框架与解释框架相结合，以量化气候和流域控制对水文干旱的作用。更具体地说，我们的框架解释了构建的 RF 机器学习模型，以识别主导变量，并利用最小深度、交互深度和部分依赖的概念可视化它们对水文干旱特征的功能依赖和交互影响。我们基于 1979 年至 2010 年期间人类干扰最小的一组 652 个美国大陆流域来测试我们提出的建模框架。该框架的应用表明存在三种不同的干旱状况，其中包括： 状况 1：持续时间较长、频率较低、强度较小的干旱；类型2：中等持续时间、中等频率、中等强度的干旱；机制3：干旱持续时间短、频率高、强度大。 RF 算法能够根据选定变量对所有三种干旱状况的干旱特征（强度、持续时间和事件数量）进行准确建模。据观察，主导变量的类型及其与水文干旱特征的非线性函数关系在三个选定的制度之间可能有所不同。我们的解释框架表明，流域特征在控制流域水文干旱（机制1）方面具有重要作用，而气候和流域特征都控制流域2和3中的水文干旱。
The evolution of hydrological drought events is a result of complex (nonlinear) interactions between climate and catchment processes. To investigate such nonlinear relationship, we integrated a machine learning modeling framework based on the random forest (RF) algorithms with an interpretation framework to quantify the role of climate and catchment controls on hydrological drought. More particularly, our framework interprets a built RF machine-learning model to identify dominant variables and visualize their functional dependence and interaction effects on hydrological drought characteristics utilizing concepts of minimal depth, interactive depth, and partial dependence. We test our proposed modeling framework based on a set of 652 continental United States catchments with minimal human interference for a period of 1979-2010. Application of this framework indicated presence of three distinct drought regimes, which includes, Regime 1: droughts with longer duration, less frequent and lesser intensity; Regime 2: droughts with moderate duration, moderate frequency, and moderate intensity; and Regime 3: droughts with shorter duration, more frequent, and more intense. RF algorithm was able to accurately model the drought characteristics (intensity, duration, and number of events) for all the three drought regimes as a function of selected variables. It was observed that the type of dominant variables as well as their nonlinear functional relationship with hydrological droughts characteristics can vary between three selected regimes. Our interpretation framework indicated that catchment characteristics have a significant role in controlling the hydrologic drought for catchments (regime 1), whereas both climate and catchment characteristics control hydrological drought in regimes 2 and 3.