Abbaszadeh Peyman, Muñoz David F, Moftakhari Hamed, Jafarzadegan Keighobad, Moradkhani Hamid
Department of Civil, Construction and Environmental Engineering, Center for Complex Hydrosystems Research, Tuscaloosa, AL, USA.
iScience. 2022 Sep 23;25(10):105201. doi: 10.1016/j.isci.2022.105201. eCollection 2022 Oct 21.
This perspective discusses the importance of characterizing, quantifying, and accounting for various sources of uncertainties involved in different layers of hydrometeorological and hydrodynamic model simulations as well as their complex interactions and cascading effects (e.g., uncertainty propagation) in forecasting compound flooding (CF). Over the past few decades, CF has come to attention across the globe as this natural hazard results from a combination of either concurrent or successive flood drivers with larger economic, societal, and environmental impacts than those from isolated drivers. A warming climate and increased urbanization in flood-prone areas are expected to contribute to an escalation in the risk of CF in the near future. Recent advances in remote sensing and data science can provide a wide range of possibilities to account for and reduce the predictive uncertainties; hence improving the predictability of CF events, enabling risk-informed decision-making, and ensuring a sustainable CF risk governance.
本视角探讨了在水文气象和水动力模型模拟的不同层次中，对各种不确定性来源进行特征描述、量化和核算的重要性，以及它们在复合洪水（CF）预测中的复杂相互作用和级联效应（如不确定性传播）。在过去几十年里，CF已在全球范围内受到关注，因为这种自然灾害是由同时发生或相继出现的洪水驱动因素共同作用导致的，其经济、社会和环境影响比单一驱动因素造成的影响更大。预计气候变暖以及洪水易发地区城市化进程的加快，将在不久的将来导致CF风险升级。遥感和数据科学的最新进展为解释和减少预测不确定性提供了广泛的可能性；从而提高CF事件的可预测性，实现基于风险的决策，并确保可持续的CF风险管理。
