2016/11/10 by Peiman Asadi, Asadi, Peiman, Sebastian Engelke +4
Environmental Science · Mathematics · #Applications (stat.AP) #Climate variability and models #FOS: Computer and information sciences #Hydrology and Drought Analysis #Hydrology and Watershed Management Studies #stat.AP
paper · pdf · doi:10.48550/arxiv.1611.03219
arxiv created 2016/11/10 · openalex publication_date 2016/11/10 · arxiv updated 2016/11/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Regionalization methods have long been used to estimate high return levels of river discharges at ungauged locations on a river network. In these methods, the recorded discharge measurements of a group of similar, gauged, stations is used to estimate high quantiles at the target catchment that has no observations. This group is called the region of influence and its similarity to the ungauged location is measured in terms of physical and meteorological catchment attributes. We develop a statistical method for estimation of high return levels based on regionalizing the parameters of a generalized extreme value distribution. The region of influence is chosen in an optimal way, ensuring similarity and in-group homogeneity. Our method is applied to discharge data from the Rhine basin in Switzerland, and its performance at ungauged locations is compared to that of classical regionalization methods. For gauged locations we show how our approach improves the estimation uncertainty for long return periods by combining local measurements with those from the region of influence.