城市环境中的洪涝灾害

IF 6.5 3区 工程技术 Q1 ENGINEERING, GEOLOGICAL
L. Gao, Limin Zhang, Yang Hong, Hong-Xin Chen, Shijin Feng
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引用次数: 3

摘要

摘要除了估计降水、洪水和风暴潮的大小外,还需要对人口稠密的城市地区的暴雨水流进行建模,以设计应对策略和做出决策。结合地表径流和管道流量建模能力,可以预测城市洪水灾害。本研究综合了城市环境中模拟洪水过程和评估洪水危害的方法。总结了现有的模型及其相关的不确定性,并说明了建立模拟城市洪水的数值模型的最先进技术及其在具体案例中的应用。提出了城市洪水灾害预测的示意性框架,其中包括多源观测检索、基于物理的建模、参数优化、不确定性估计、模型观测融合、多因素复合效应评估和数字孪生技术。洪水过程建模的主要挑战和不确定性来自输入数据、模型结构、验证过程和复合效应。应开发用于估算输入数据和提高洪水评估效率和准确性的多学科技术。在理解依赖过程的指标、耦合建模和数据模型同化方面需要做出巨大努力。确定复合洪水的概率和了解驱动因素对于评估洪水风险也至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Flood hazards in urban environment
ABSTRACT Apart from the estimation of magnitudes of precipitation, floods and storm surges, modelling of storm water flows in a densely populated urban area is required for designing coping strategies and making decisions. Incorporating surface runoff and conduit flow modelling capabilities has enabled the prediction of urban flood hazards. This study synthesises methodologies for simulating flood processes and evaluating flood hazards in urban environment. Existing models and their associated uncertainties are summarised, and state-of-the-art techniques to build up a numerical model for simulating urban floods and the applications to specific cases are illustrated. A schematic framework for urban flood hazard prediction is proposed, within which multi-source observation retrieval, physics-based modelling, parameter optimisation, uncertainty estimation, model-observation fusion, evaluation of compound effects of multiple factors and digital twin techniques are included. The major challenges and uncertainties in flood process modelling originate from input data, model structures, validation processes and compounding effects. Multidisciplinary techniques for estimating the input data and enhancing the efficiency and accuracy of the flood evaluation should be developed. Great efforts are needed in understanding the process-dependent indicators, coupled modelling and data-model assimilation. Determining the probability of compound floods and understanding the driving factors are also essential for evaluating flood risks.
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来源期刊
CiteScore
8.70
自引率
10.40%
发文量
31
期刊介绍: Georisk covers many diversified but interlinked areas of active research and practice, such as geohazards (earthquakes, landslides, avalanches, rockfalls, tsunamis, etc.), safety of engineered systems (dams, buildings, offshore structures, lifelines, etc.), environmental risk, seismic risk, reliability-based design and code calibration, geostatistics, decision analyses, structural reliability, maintenance and life cycle performance, risk and vulnerability, hazard mapping, loss assessment (economic, social, environmental, etc.), GIS databases, remote sensing, and many other related disciplines. The underlying theme is that uncertainties associated with geomaterials (soils, rocks), geologic processes, and possible subsequent treatments, are usually large and complex and these uncertainties play an indispensable role in the risk assessment and management of engineered and natural systems. Significant theoretical and practical challenges remain on quantifying these uncertainties and developing defensible risk management methodologies that are acceptable to decision makers and stakeholders. Many opportunities to leverage on the rapid advancement in Bayesian analysis, machine learning, artificial intelligence, and other data-driven methods also exist, which can greatly enhance our decision-making abilities. The basic goal of this international peer-reviewed journal is to provide a multi-disciplinary scientific forum for cross fertilization of ideas between interested parties working on various aspects of georisk to advance the state-of-the-art and the state-of-the-practice.
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