Open Access: A self-exciting marked point process model for drought analysis

Each week, we select a recently published Open Access article to feature. This week’s article comes from the Environmetrics and proposes a model to predict drought events and daily water levels.

The article’s abstract is given below, with the full article available to read here.

Li, X.Genest, C., & Jalbert, J. (2021). A self-exciting marked point process model for drought analysisEnvironmetrics, e2697.

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.

More Details