A self-exciting marked point process approach is proposed to model clustered low-flow events. It combines a self-exciting ground process designed to capture the temporal clustering behavior of extreme values and an extended Generalized Pareto mark distribution for the exceedances over a subasymptotic threshold. The model takes into account the dependence between the magnitude and occurrence time of exceedances and allows for closed-form inference on tail probabilities and large quantiles. It is used to analyze daily water levels from the Rivière des Mille Îles (Québec, Canada) and to characterize drought patterns in the Montréal area. The model is useful to generate short-term probability forecasts and to estimate the return period of major droughts. This information on the drought events is critical to water resource professionals in planning, designing, building, and managing more efficient water resource systems to hedge against the water shortage in case of extreme droughts.

中文翻译：

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一种用于干旱分析的自激标记点过程模型

提出了一种自激标记点过程方法来模拟聚集的低流量事件。它结合了旨在捕获极值的时间聚类行为的自激地面过程和超过亚渐近阈值的扩展广义帕累托标记分布。该模型考虑了超标的幅度和发生时间之间的依赖性，并允许对尾部概率和大分位数进行封闭式推断。它用于分析 Rivière des Mille Îles（加拿大魁北克省）的每日水位并描述蒙特利尔地区的干旱模式。该模型可用于生成短期概率预测和估计重大干旱的重现期。这些关于干旱事件的信息对于水资源专业人员的规划至关重要，