Blanton Brian, Dresback Kendra, Colle Brian, Kolar Randy, Vergara Humberto, Hong Yang, Leonardo Nicholas, Davidson Rachel, Nozick Linda, Wachtendorf Tricia
Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK, USA.
Risk Anal. 2020 Jan;40(1):117-133. doi: 10.1111/risa.13004. Epub 2018 Apr 25.
Hurricane track and intensity can change rapidly in unexpected ways, thus making predictions of hurricanes and related hazards uncertain. This inherent uncertainty often translates into suboptimal decision-making outcomes, such as unnecessary evacuation. Representing this uncertainty is thus critical in evacuation planning and related activities. We describe a physics-based hazard modeling approach that (1) dynamically accounts for the physical interactions among hazard components and (2) captures hurricane evolution uncertainty using an ensemble method. This loosely coupled model system provides a framework for probabilistic water inundation and wind speed levels for a new, risk-based approach to evacuation modeling, described in a companion article in this issue. It combines the Weather Research and Forecasting (WRF) meteorological model, the Coupled Routing and Excess STorage (CREST) hydrologic model, and the ADvanced CIRCulation (ADCIRC) storm surge, tide, and wind-wave model to compute inundation levels and wind speeds for an ensemble of hurricane predictions. Perturbations to WRF's initial and boundary conditions and different model physics/parameterizations generate an ensemble of storm solutions, which are then used to drive the coupled hydrologic + hydrodynamic models. Hurricane Isabel (2003) is used as a case study to illustrate the ensemble-based approach. The inundation, river runoff, and wind hazard results are strongly dependent on the accuracy of the mesoscale meteorological simulations, which improves with decreasing lead time to hurricane landfall. The ensemble envelope brackets the observed behavior while providing "best-case" and "worst-case" scenarios for the subsequent risk-based evacuation model.
飓风的路径和强度可能会以意想不到的方式迅速变化,因此飓风及其相关灾害的预测具有不确定性。这种内在的不确定性常常导致决策结果不理想,比如不必要的疏散。因此,在疏散规划及相关活动中体现这种不确定性至关重要。我们描述了一种基于物理的灾害建模方法,该方法(1)动态考虑灾害各组成部分之间的物理相互作用,(2)使用集合方法捕捉飓风演变的不确定性。这个松散耦合的模型系统为概率性洪水淹没和风速水平提供了一个框架,用于一种新的基于风险的疏散建模方法,本期刊的一篇配套文章对此进行了描述。它结合了天气研究与预报(WRF)气象模型、耦合路由与过量存储(CREST)水文模型以及高级环流(ADCIRC)风暴潮、潮汐和风浪模型,来计算一组飓风预测的淹没水平和风速。对WRF初始条件和边界条件以及不同模型物理特性/参数化的扰动会生成一组风暴解,然后用于驱动耦合的水文+水动力模型。以2003年的飓风伊莎贝尔为例来说明基于集合的方法。淹没、河流径流和风灾结果强烈依赖于中尺度气象模拟的准确性,随着飓风登陆时间的缩短,这种准确性会提高。集合包络涵盖了观测到的行为,同时为后续基于风险的疏散模型提供了“最佳情况”和“最坏情况”的情景。