Downscaling of real-time coastal flooding predictions for decision support
Downscaling of real-time coastal flooding predictions for decision support
复制标题
缩小实时沿海洪水预测的规模以支持决策
DOI：
10.1007/s11069-021-04634-8
复制
发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Luettich, R. A.
中科院分区：
文献类型：
--
作者：
Rucker, C. A.;Tull, N.;Dietrich, J. C.;Langan, T. E.;Mitasova, H.;Blanton, B. O.;Fleming, J. G.;Luettich, R. A.
在沿海风暴期间，预报员和研究人员使用数值模型来预测沿海洪水的规模和程度。这些模型必须表示可能受风暴影响的大区域，因此，它们的计算成本可能很高，并且可能不会使用最高的地理空间分辨率。但是，作为后处理步骤，可以缩小预测的洪水范围(通过提高分辨率)。现有的缩小尺度方法要么使用洪水的静态外推作为平坦的表面，要么依赖于后续以更高分辨率嵌套的全物理模型的模拟。这项研究探索了一种中间方式，在这种方式下，降低尺度包括简化物理以提高精度。利用最新模型的结果，我们用三种方法缩小了洪水预报的范围：(1)静态方法，其中水面高程被水平外推，直到它们与地面相交；(2)斜坡，其中使用水面的坡度；以及(3)水头损失，它考虑了由于土地覆盖特性造成的能量损失。然后对缩小尺度方法进行评估，以预测和预测2018年飓风佛罗伦萨，这场飓风在北卡罗来纳州造成了大范围的洪水。静态方法和斜率方法往往高估了洪水的范围。然而，水头损失方法产生的缩小的洪水范围与更高分辨率的全物理模型的预测非常接近。这些结果对于使用这些缩小尺度的方法来支持沿海风暴期间的决策是令人鼓舞的。
During coastal storms, forecasters and researchers use numerical models to predict the magnitude and extent of coastal flooding. These models must represent the large regions that may be affected by a storm, and thus, they can be computationally costly and may not use the highest geospatial resolution. However, predicted flood extents can be downscaled (by increasing resolution) as a post-processing step. Existing downscaling methods use either a static extrapolation of the flooding as a flat surface, or rely on subsequent simulations with nested, full-physics models at higher resolution. This research explores a middle way, in which the downscaling includes simplified physics to improve accuracy. Using results from a state-of-the-art model, we downscale its flood predictions with three methods: (1) static, in which the water surface elevations are extrapolated horizontally until they intersect the ground surface; (2) slopes, in which the gradient of the water surface is used; and (3) head loss, which accounts for energy losses due to land cover characteristics. The downscaling methods are then evaluated for forecasts and hindcasts of Hurricane Florence (2018), which caused widespread flooding in North Carolina. The static and slopes methods tend to over-estimate the flood extents. However, the head loss method generates a downscaled flooding extent that is a close match to the predictions from a higher-resolution, full-physics model. These results are encouraging for the use of these downscaling methods to support decision-making during coastal storms.
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影响因子：
--
作者：
N. Knowles
通讯作者：
N. Knowles
在飓风艾琳期间利用水文和沿海水动力耦合模型对实时预报系统进行技能评估（2011 年）
影响因子：
2.3
作者：
K. Dresback;J. Fleming;B. Blanton;Carola Kaiser;J. Gourley;E. Tromble;R. Luettich;R. Kolar;Y. Hong;S. V. Cooten;H. Vergara;Z. Flamig;H. Lander;K. Kelleher;Kodi L. Nemunaitis
通讯作者：
Kodi L. Nemunaitis
影响因子：
4
作者：
Hartnett, Michael;Nash, Stephen
通讯作者：
Nash, Stephen
影响因子：
4.4
作者：
Cyriac, R.;Dietrich, J. C.;Luettich, R. A.
通讯作者：
Luettich, R. A.
影响因子：
3.8
作者：
Aerts, Jeroen C. J. H.;Lin, Ning;de Moel, Hans
通讯作者：
de Moel, Hans