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Terrain-driven unstructured mesh development through semi-automatic vertical feature extraction

机译:通过半自动垂直特征提取进行地形驱动的非结构化网格开发

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A se ini-automated vertical feature terrain extraction algorithm is described and applied to a two-dimensional, depth-integrated, shallow water equation inundation model. The extracted features describe what are commonly sub-mesh scale elevation details (ridge and valleys), which may be ignored in standard practice because adequate mesh resolution cannot be afforded. The extraction algorithm is semi-automated, requires minimal human intervention, and is reproducible. A lidar-derived digital elevation model (DEM) of coastal Mississippi and Alabama serves as the source data for the vertical feature extraction. Unstructured mesh nodes and element edges are aligned to the vertical features and an interpolation algorithm aimed at minimizing topographic elevation error assigns elevations to mesh nodes via the DEM. The end result is a mesh that accurately represents the bare earth surface as derived from lidar with element resolution in the floodplain ranging from 15 m to 200 m. To examine the influence of the inclusion of vertical features on overland flooding, two additional meshes were developed, one without crest elevations of the features and another with vertical features withheld. All three meshes were incorporated into a SWAN+ADCIRC model simulation of Hurricane Katrina. Each of the three models resulted in similar validation statistics when compared to observed time-series water levels at gages and post-storm collected high water marks. Simulated water level peaks yielded an R-2 of 0.97 and upper and lower 95% confidence interval of similar to +/- 0.60 m. From the validation at the gages and HWM locations, it was not clear which of the three model experiments performed best in terms of accuracy. Examination of inundation extent among the three model results were compared to debris lines derived from NOAA post-event aerial imagery, and the mesh including vertical features showed higher accuracy. The comparison of model results to debris lines demonstrates that additional validation techniques are necessary for state-of-the-art flood inundation models. In addition, the semi-automated, unstructured mesh generation process presented herein increases the overall accuracy of simulated storm surge across the floodplain without reliance on hand digitization or sacrificing computational cost. (C) 2015 Elsevier Ltd. All rights reserved.
机译:描述了一种自动的垂直特征地形提取算法,并将其应用于二维,深度综合的浅水方程淹没模型。提取的特征描述了通常属于子网格比例的高程细节(山脊和山谷),由于无法提供足够的网格分辨率,因此在标准实践中可能会忽略这些细节。提取算法是半自动化的,需要最少的人工干预,并且可重现。密西西比州和阿拉巴马州沿海地区的激光雷达衍生数字高程模型(DEM)用作垂直特征提取的源数据。非结构化的网格节点和元素边缘与垂直特征对齐,旨在最小化地形高程误差的插值算法通过DEM将高程分配给网格节点。最终结果是一个网格,该网格可以精确表示从激光雷达得出的裸露地球表面,洪泛区中的元素分辨率范围为15 m至200 m。为了检查垂直要素的包含对陆上洪水的影响,开发了两个附加的网格,一个不包含要素的波峰高程,另一个不包含垂直要素。将所有三个网格都合并到卡特里娜飓风的SWAN + ADCIRC模型仿真中。与在量具和暴风雨后收集的高水位线处观察到的时间序列水位相比,这三个模型中的每一个得出的验证统计数据都相似。模拟的水位峰值得出的R-2为0.97,上下95%置信区间接近+/- 0.60 m。根据量具和HWM位置的验证,尚不清楚这三个模型实验中哪一个在准确性方面表现最佳。将这三个模型结果中淹没程度的检查与源自NOAA事件后航空影像的碎片线进行了比较,并且包括垂直特征的网格显示出更高的精度。模型结果与泥石流线的比较表明,对于最新的洪水淹没模型,还需要其他验证技术。此外,本文介绍的半自动,非结构化网格生成过程可提高整个洪泛区模拟风暴潮的整体精度,而无需依赖手工数字化或牺牲计算成本。 (C)2015 Elsevier Ltd.保留所有权利。

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