论文标题
贝叶斯对河流洪水危害估计的不确定性的表征
Bayesian Characterization of Uncertainties Surrounding Fluvial Flood Hazard Estimates
论文作者
论文摘要
河流洪水驱动了河流社区的严重风险。有强有力的证据表明，世界许多地区都会增加洪水危害。洪水危害估计中使用的方法和假设的选择会影响风险管理策略的设计。在这项研究中，我们表征了以不确定的模型结构，模型参数和参数的先验分布为条件的预期洪水危害。我们使用非平稳统计模型构建了一个贝叶斯框架，用于河流阶段的回流水平估计，该模型仅依赖于印度洋偶极指数。我们表明，忽略不确定性会导致对预期洪水危害的估计。我们发现，所考虑的模型参数不确定性比模型结构和模型先验更具影响力。我们的结果强调了将不确定性纳入河流估计值的重要性，并且在不断变化的气候下为水基础设施设计提供了实际使用。
Fluvial floods drive severe risk to riverine communities. There is a strong evidence of increasing flood hazards in many regions around the world. The choice of methods and assumptions used in flood hazard estimates can impact the design of risk management strategies. In this study, we characterize the expected flood hazards conditioned on the uncertain model structures, model parameters and prior distributions of the parameters. We construct a Bayesian framework for river stage return level estimation using a nonstationary statistical model that relies exclusively on Indian Ocean Dipole Index. We show that ignoring uncertainties can lead to biased estimation of expected flood hazards. We find that the considered model parametric uncertainty is more influential than model structures and model priors. Our results highlight the importance of incorporating uncertainty in river stage estimates, and are of practical use for informing water infrastructure designs in a changing climate.
