The problem of estimating return levels of river discharge, relevant in flood frequency analysis, is tackled by relying on the extreme value theory. The Generalized Extreme Value (GEV) distribution is assumed to model annual maxima values of river discharge registered at multiple gauging stations belonging to the same river basin. The specific features of the data from the Upper Danube basin drive the definition of the proposed statistical model. Firstly, Bayesian P-splines are considered to account for the non-linear effects of station-specific covariates on the GEV parameters. Secondly, the problem of functional and variable selection is addressed by imposing a grouped horseshoe prior on the coefficients, to encourage the shrinkage of non-relevant components to zero. A cross-validation study is organized to compare the proposed modeling solution to other models, showing its potential in reducing the uncertainty of the ungauged predictions without affecting their calibration.

翻译：在洪水频率分析中，河流流量重现期水平的估计问题通过依赖极值理论得以解决。假设广义极值分布用于建模同一流域内多个测站记录的年度最大流量值。来自上多瑙河流域数据的特定特征驱动了所提出统计模型的定义。首先，考虑使用贝叶斯P样条来解释站点协变量对GEV参数的非线性影响。其次，通过将分组马蹄先验施加于系数上，以促使非相关分量收缩至零，解决了函数与变量选择问题。组织了一项交叉验证研究，将所提出的建模方案与其他模型进行比较，显示其在不影响校准精度的前提下减少未测站点预测不确定性的潜力。