Predictability of extreme floods

极端洪水的可预测性

• 批准号: 324101383
• 负责人: Professor Dr.-Ing. Uwe Haberlandt
• 金额: --
• 依托单位: Institut für Hydrologie und Wasserwirtschaft
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/324101383?language=en
• 关键词: Predictability; extreme; floods

The overall goal of the proposed research in the second phase will remain the investigation of the predictability of extreme floods as function of their catchment and climate drivers considering different space and time scales. Prediction of floods will be defined here as the estimation of flood probability distributions for unobserved locations and/ or future time - periods. Predictability refers then to the quantification and attribution of the uncertainty of this flood prediction. Predictability is extended to the quantification and attribution of the uncertainty of the estimation of multivariate and non-stationary flood probability distributions. Three work packages will be proposed. The first is dealing with the predictability of multivariate flood properties considering non-stationarity from data. The data based univariate regional flood frequency analyses will be extended to multivariate ones, i.e. taking into account flood volume and duration beside the peak flows. The second one is exploring stochastic weather generation using radar data considering non-stationarity. The weather generator which has been developed in phase one will be further advanced by utilizing weather radar data and employed to downscale future climate. It will be applied to generate rainfall and climate scenarios for the model based flood simulations in the third work package. This is investigating the predictability of multivariate flood properties considering non-stationarity from modelling. Flood prediction from modelling will also be extended to the analyses of the multivariate and non-stationarity flood frequency analyses. The outcomes of the three work packages may eventually result in a new generalized statistical model for prediction of flood frequencies directly from rainfall frequencies conditioned on catchment and climate.

第二阶段拟议研究的总体目标仍然是调查极端洪水的可预测性，将其作为考虑不同空间和时间尺度的集水区和气候驱动因素的函数。洪水预测在这里被定义为对未观测到的位置和/或未来时间段的洪水概率分布的估计。可预测性是指洪水预测的不确定性的量化和归因。可预报性扩展到多变量和非平稳洪水概率分布估计的不确定性的量化和归因。将提出三个工作包。第一个是考虑数据的非平稳性，处理多变量洪水属性的可预测性。基于单变量数据的区域洪水频率分析将扩展到多变量分析，即考虑洪峰流量旁边的洪水量和持续时间。第二个是探索利用雷达资料考虑非平稳性的随机天气生成。第一阶段开发的天气发生器将通过利用天气雷达数据进一步改进，并用于缩小未来气候的规模。它将被应用于生成降雨和气候情景，用于第三个工作包中基于模型的洪水模拟。这是调查的可预测性的多变量洪水属性考虑非平稳性建模。洪水预报模型也将扩展到多变量和非平稳洪水频率分析。三个工作包的结果可能最终导致一个新的广义统计模型预测洪水频率直接从降雨频率条件下的流域和气候。