A common statistical problem in hydrology is the estimation of annual maximal river flow distributions and their quantiles, with the objective of evaluating flood protection systems. Typically, record lengths are short and estimators imprecise, so that it is advisable to exploit additional sources of information. However, there is often uncertainty about the adequacy of such information, and a strict decision on whether to use it is difficult. We propose penalized quasi-maximum likelihood estimators to overcome this dilemma, allowing one to push the model towards a reasonable direction defined a priori. We are particularly interested in regional settings, with river flow observations collected at multiple stations. To account for regional information, we introduce a penalization term inspired by the popular Index Flood assumption. Unlike in standard approaches, the degree of regionalization can be controlled gradually instead of deciding between a local or a regional estimator. Theoretical results on the consistency of the estimator are provided and extensive simulations are performed for the reason of comparison with other local and regional estimators. The proposed procedure yields very good results, both for homogeneous as well as for heterogeneous groups of sites. A case study consisting of sites in Saxony, Germany, illustrates the applicability to real data.

水文学中一个常见的统计问题是估算年度最大河流量分布及其分位数，目的是评估防洪系统。通常，记录长度短且估计量不精确，因此建议利用其他信息源。但是，此类信息的适当性通常存在不确定性，因此很难决定是否使用该信息。我们提出了惩罚拟最大似然估计器来克服这一难题，使人们能够将模型推向先验定义的合理方向。我们对区域环境特别感兴趣，在多个站点收集了河流流量观测数据。为了说明区域信息，我们引入了一个受流行的Index Flood假设启发的惩罚术语。与标准方法不同，可以逐渐控制区域化程度，而不必在本地或区域估计量之间做出决定。提供了有关估计量一致性的理论结果，并且出于与其他本地和区域估计量进行比较的目的，进行了广泛的仿真。所提出的方法对于均质的和异质的位点组均产生非常好的结果。由德国萨克森州站点组成的案例研究说明了对真实数据的适用性。所提出的方法对于均质的和异质的位点组均产生非常好的结果。由德国萨克森州站点组成的案例研究说明了对真实数据的适用性。所提出的方法对于均质的和异质的位点组均产生非常好的结果。由德国萨克森州站点组成的案例研究说明了对真实数据的适用性。