估算河流极值流量的统计区划
Statistical regionalization for estimation of extreme river discharges
摘要：
区域化方法长期以来一直被用来估计河流流量的高回报水平在未测量的位置在河流网络。在这些方法中，一组类似的，测量，站的记录流量测量用于估计在目标集水区，没有观测的高分位数。这一组被称为影响区域，其与未测量位置的相似性是根据物理和气象流域属性来衡量的。我们开发了一种基于广义极值分布参数区域化的高收益水平估计的统计方法。以最佳方式选择影响区域，确保相似性和组内同质性。我们的方法被施加到从瑞士莱茵河流域的流量数据，其性能在无测量的位置相比，经典的区域化方法。对于测量位置，我们展示了我们的方法如何通过将本地测量与影响区域的测量相结合来提高长重现期的估计不确定性。
英文摘要：
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.
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