We propose a new model and estimation framework for spatiotemporal streamflow exceedances above a threshold that flexibly captures asymptotic dependence and independence in the tail of the distribution. We model streamflow using a mixture of processes with spatial, temporal and spatiotemporal asymptotic dependence regimes. A censoring mechanism allows us to use only observations above a threshold to estimate marginal and joint probabilities of extreme events. As the likelihood is intractable, we use simulation-based inference powered by random forests to estimate model parameters from summary statistics of the data. Simulations and modeling of streamflow data from the U.S. Geological Survey illustrate the feasibility and practicality of our approach.

翻译：我们提出了一种新的模型与估计框架，用于处理超过阈值的时空径流极值，该框架能够灵活捕捉分布尾部的渐近相依性与渐近独立性。我们采用混合过程对径流进行建模，该混合过程包含空间、时间以及时空渐近相依机制。通过引入截断机制，我们能够仅利用超过阈值的观测数据来估计极端事件的边缘概率与联合概率。由于似然函数难以直接处理，我们采用基于随机森林的模拟推断方法，通过数据的汇总统计量来估计模型参数。对美国地质调查局径流数据的模拟与建模分析验证了本方法的可行性与实用性。