Abstract Droughts impact both the natural and human communities worldwide. Understanding the complex process underlying community resilience to drought is crucial to coping with the hazard. This study examined the dynamics of resilience to drought hazard for 503 counties in South-Central USA using a Bayesian Network (BN) approach. We first applied the Resilience Inference Measurement (RIM) framework and found 10 out of 52 variables contributed most to the resilience level of the county. We then applied the bootstrapped Hill-Climbing algorithm to Bayesian Network learning to identify the significant links among two resilience outcomes (population change, agricultural damage) and resilience predictors. The final BN, which included eight predictors, was used to find the probabilities of population decline and agricultural damage conditional upon different levels of hazard intensity (drought incidence) and two levels of resilience predictors (at Years 2000 and 2015). The study reveals that an increase in drought incidence will likely lead to higher agricultural damage, but it will unlikely curb population growth. This is probably due to the higher impact of drought hazard on agriculture than the general population. These probabilities and associated findings could be used as decision-support tools for stakeholders and practitioners in the communities affected by drought.

中文翻译：

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使用贝叶斯网络模拟美国中南部干旱恢复力的动态

摘要 干旱影响着全世界的自然和人类社区。了解社区抵御干旱的复杂过程对于应对灾害至关重要。本研究使用贝叶斯网络 (BN) 方法检查了美国中南部 503 个县的干旱灾害恢复力动态。我们首先应用了弹性推理测量 (RIM) 框架，发现 52 个变量中有 10 个对县的弹性水平贡献最大。然后，我们将引导式爬山算法应用于贝叶斯网络学习，以识别两种弹性结果（人口变化、农业破坏）和弹性预测因子之间的重要联系。最终的 BN，其中包括八个预测器，被用来寻找以不同水平的灾害强度（干旱发生率）和两个水平的复原力预测因子（2000 年和 2015 年）为条件的人口下降和农业损害的概率。该研究表明，干旱发生率的增加可能会导致更大的农业损失，但不太可能抑制人口增长。这可能是由于干旱灾害对农业的影响比一般人群更大。这些概率和相关发现可用作受干旱影响社区的利益相关者和从业者的决策支持工具。但它不太可能抑制人口增长。这可能是由于干旱灾害对农业的影响比一般人群更大。这些概率和相关发现可用作受干旱影响社区的利益相关者和从业者的决策支持工具。但它不太可能抑制人口增长。这可能是由于干旱灾害对农业的影响比一般人群更大。这些概率和相关发现可用作受干旱影响社区的利益相关者和从业者的决策支持工具。