论文标题
洪水变化的知情归因于上奥地利大气，集水区和河流驾驶员的际变化
Informed attribution of flood changes to decadal variation of atmospheric, catchment and river drivers in Upper Austria
论文作者
论文摘要
洪水的变化可能归因于属于三个主要类别的变化驱动因素：大气，集水和河流系统驱动程序。在这项工作中，我们提出了一种基于数据的归因方法，用于选择哪种驱动程序最能与洪水频率曲线的变化有关。假定洪水峰遵循牙龈分布，其位置参数的时间随着以下替代协变量之一的衰减变化的函数而变化：不同持续时间的年度和极端降水，农业土地利用强化索引，以及在集水区中的储层构建，并由指数定量。该归因模型的参数由贝叶斯推断估计。从现有文献中获取有关这些参数之一的事先信息，即对各个驱动器的洪水峰的弹性，以提高该方法的鲁棒性，以使洪水与协变时间序列之间的虚假相关性。因此，归因模型以两种方式告知：通过使用协变量，代表变革的驱动因素和先验，代表了对这些协变量如何影响洪水的水文理解。 Watanabe-Akaike信息标准用于比较涉及替代协变量的模型。我们将这种方法应用于上奥地利的96个集水区，在过去的50年中，人们观察到了积极的洪水峰值趋势。结果表明，在上奥地利，一到七天的极端降水通常是洪水频率曲线变化的更好的协变量，而不是在较长时间尺度上降水。农业土地利用强化很少是最好的协变量，而储层指数从来没有，这表明集水区和河流驾驶员不如大气重要。
Flood changes may be attributed to drivers of change that belong to three main classes: atmospheric, catchment and river system drivers. In this work, we propose a data-based attribution approach for selecting which driver best relates to variations in time of the flood frequency curve. The flood peaks are assumed to follow a Gumbel distribution, whose location parameter changes in time as a function of the decadal variations of one of the following alternative covariates: annual and extreme precipitation for different durations, an agricultural land-use intensification index, and reservoir construction in the catchment, quantified by an index. The parameters of this attribution model are estimated by Bayesian inference. Prior information on one of these parameters, the elasticity of flood peaks to the respective driver, is taken from the existing literature to increase the robustness of the method to spurious correlations between flood and covariate time series. Therefore, the attribution model is informed in two ways: by the use of covariates, representing the drivers of change, and by the priors, representing the hydrological understanding of how these covariates influence floods. The Watanabe-Akaike information criterion is used to compare models involving alternative covariates. We apply the approach to 96 catchments in Upper Austria, where positive flood peak trends have been observed in the past 50 years. Results show that, in Upper Austria, one or seven day extreme precipitation is usually a better covariate for variations of the flood frequency curve than precipitation at longer time scales. Agricultural land-use intensification rarely is the best covariate, and the reservoir index never is, suggesting that catchment and river drivers are less important than atmospheric ones.
