Aerial Characterization of Surface Depressions in Urban Watersheds
Aerial Characterization of Surface Depressions in Urban Watersheds
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DOI：
10.1016/j.jhydrol.2023.129954
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发表时间：
2023-07
影响因子：
6.4
通讯作者：
Lapone Techapinyawat;Ian Goulden-Brady;Hannah Garcia;Hua Zhang
中科院分区：
文献类型：
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作者：
Lapone Techapinyawat;Ian Goulden-Brady;Hannah Garcia;Hua Zhang
数字高程模型（DEM）是水文模拟最基本的输入之一。通常的做法是删除DEM中的所有表面凹陷，因为它们被认为是数据错误。新兴的无人机系统（UAS）技术提供了一个机会，重新审视这一假设在超空间分辨率。这项研究是第一次尝试使用UAS图像来描述城市环境中的小表面凹陷。使用在得克萨斯州南部的一个城市地区作为研究地点，无人机系统的飞行进行产生混合DEM的分辨率为8-14厘米，再加上全面的地面实况收集。首先根据DEM的垂直精度对从UAS DEM中识别出的地表凹陷进行了校正，然后通过实地调查进行了验证，并与两个现有的LiDAR DEM（1米和10米）进行了比较。水文影响的不同DEM派生的估计集水区洼地存储使用的曲线数方法在不同的设计暴雨。结果表明，UAS DEM在描述城市地表径流的微地形控制以及建筑和自然特征之间的相关水文连通性方面优于LiDAR DEM。8厘米的无人机系统DEM显示926%以上的抑郁症存储比10米激光雷达DEM。这表明了一个令人信服的相关性增加DEM分辨率和增强量化的抑郁症体积。因此，在两年设计暴雨和200年设计暴雨下，增加的洼地蓄水量使地表径流减少了41%和13%。结果表明，DEM的分辨率和派生的抑郁症估计之间有很强的关系，符合流域系统的分形性质。此外，结果表明，厘米级UAS DEM也不能幸免于问题。它们可以产生由植被、临时街道物体和地下下水道管道等因素造成的假凹陷。这项研究的结果表明，需要量化DEM分辨率和相关的水文属性之间的关系，并开发新的数字排水分析算法，可以有效地将无人机系统的数据到城市水文建模。
Digital elevation models (DEM) are one of the most fundamental inputs for hydrological modeling. It has been a common practice to remove all surface depressions in a DEM as they are assumed to be data errors. The emerging technology of unmanned aircraft systems (UAS) provides an opportunity to re-examine this assumption at the hyperspatial resolution. This study was the first attempt to characterize small surface depressions in urban environments using UAS imagery. Using an urban area in south Texas as the study site, UAS flights were conducted to yield hybrid DEMs at the resolution of 8–14 cm, coupled with comprehensive ground truth collection. Surface depressions identified from the UAS DEMs were first corrected based on the vertical accuracy of DEMs and then validated through field surveys, with comparisons to two existing LiDAR DEMs (1-m and 10-m). The hydrological impacts of different DEM-derived estimates of catchment depression storage were examined using the Curve Number method across different design storms. Results show that the UAS DEMs outperformed the LiDAR DEMs in describing the microtopographic control of urban overland flow and associated hydrological connectivity across built and natural features. The 8-cm UAS DEM revealed 926% more depression storage than the 10-m LiDAR DEM. This demonstrates a compelling correlation between increasing DEM resolution and enhanced quantification of depression volume. Consequently, the increased depression storage reduced surface runoff by 41% under a two-year design storm and 13% under a 200-year design storm. The results suggest a strong relationship between the DEM resolution and the derived depression estimates, aligning with the fractal nature of watershed systems. Also, the results indicate that the centimeter-level UAS DEMs were not immune from problems. They could yield fake depressions caused by factors such as vegetation, temporary street objects, and underground sewer pipes. The findings of this study suggest the need to quantify the relationships between DEM resolution and associated hydrological attributes and develop new digital drainage analysis algorithms that could effectively incorporate UAS data into urban hydrological modeling.