Building treatments for urban flood inundation models and implications for predictive skill and modeling efficiency
Building treatments for urban flood inundation models and implications for predictive skill and modeling efficiency
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DOI：
10.1016/j.advwatres.2012.02.012
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发表时间：
2012-06
影响因子：
4.7
通讯作者：
J. Schubert;B. Sanders
中科院分区：
文献类型：
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作者：
J. Schubert;B. Sanders
城市地区由于经济和社会资产的密度而容易受到重大洪水的破坏，人们对局部洪水强度预测越来越感兴趣，以实施洪水风险降低措施。已经提出了一些模型，通过城市景观的非稳定洪水流量，但数据的需求和复杂性是不同的，它是不清楚的，增加的复杂性的好处是合理的改进预测技能。在这项研究中，我们比较了四种方法来模拟不稳定的，通过城市地区的多维流：建筑阻力（BR），积木（BB），建筑洞（BH）和建筑孔隙度（BP）。每种方法都适用于Baldwin Hills，CA城市溃坝的情况下，提供了极好的数据模型参数化，验证和整体性能评估，包括洪水范围的观察，流流量，冲刷path.Results结果表明，所有四种方法都能够使用独特的非结构化网格，利用每种方法的优势，洪水范围和流流量的高预测技能。然而，局部速度证明更难以预测，即使在非常精细的网格（约1000米）的限制下，也对构建方法敏感。1.5米分辨率）。此外，只有那些方法，占建筑物的几何形状（BB，BH和BP）捕捉建筑规模的变化速度场。预测技能，执行时间和设置时间之间的权衡确定建议的最佳方法，为特定的应用程序将取决于可用的数据，计算资源，时间限制，和特定的建模目标。
Urban areas are vulnerable to major flood damages due to the density of economic and social assets, and there is increasing interest in localized flood intensity predictions to implement flood risk reduction measures. A number of models have been proposed for unsteady flood flows through urban landscapes, but the data needs and complexity are varied and it is not clear that the benefits of added complexity are justified by improved predictive skill. In this study we compare four methods to model unsteady, multi-dimensional flow through urban areas: building resistance (BR), building block (BB), building hole (BH) and building porosity (BP). Each method is applied to the Baldwin Hills, CA urban dam break scenario which offers excellent data for model parameterization, validation and overall performance assessment including observations of flood extent, stream flow, and scour path. Results show that all four methods are capable of high predictive skill for flood extent and stream flow using unique unstructured meshes tailored to exploit the strengths of each approach. However, localized velocities prove more difficult to predict and are sensitive to the building method even in the limit of a very fine grid (ca. 1.5m resolution). In addition, only those methods that account for building geometries (BB, BH and BP) capture building-scale variability in the velocity field. Tradeoffs between predictive skill, execution time, and set-up time are identified suggesting that the best method for a particular application will depend on available data, computing resources, time constraints, and the specific modeling objectives.