Surface depression storage is grass-covered land that stores precipitation that might otherwise become runoff. Identifying the surface depression storage depth helps stormwater engineers with hydrologic and water quality modeling, so they can better plan for rainfall events. But how do you identify surface depressions? Lasers, such as light detection and ranging have long been used for measuring distance and mapping terrain. Another promising new technology is terrestrial laser scanning, a ground-type measurement technology that has a higher accuracy than LIDAR in horizontal and vertical directions. Researchers Diego M. Meneses, Lin Zheng, and Qizhong Guo explored using TLS to identify surface depressions in grassy land surfaces of different topographic attributes.
Their paper, “Identification and Quantification of Surface Depressions on Grassy Land Surfaces of Different Topographic Attributes Using High-Resolution Terrestrial Laser Scanning Point Cloud and Triangulated Irregular Network,” in the Journal of Hydrologic Engineering used TLS as well as a triangulated irregular network on five different subwatersheds to test their theory. Learn about their methodology and how it can more accurately identify topography than current methods used for watershed modeling at https://doi.org/10.1061/JHYEFF.HEENG-5823. The abstract is below.
Abstract
The objective of this study was to identify and quantify surface depressions on grass-covered land surfaces using a high-resolution terrestrial laser scanning (TLS) point cloud, and a triangulated irregular network (TIN). The entire grassy land surface in the study area was divided into five subwatersheds of different topographic attributes (i.e., depression depth and surface slope). Surface depressions were identified and quantified using a TIN generated from a high-resolution TLS point cloud. The results indicated that microtopography of the grassy land surface was well-characterized within each subwatershed in comparison with field observations. With the terrestrial light detection and ranging (LIDAR) point cloud of 15-mm point spacing and the TIN method, surface depression storage depths of the five subwatersheds ranged from 1.73 to 14.28 mm in the study area. The surface depression storage depth, as expected, increased with the maximum depth of surface depression. It was also found to increase when the land surface slope became milder. A sensitivity analysis indicated that a point cloud with a point spacing of 30 mm was sufficient to obtain an accurate representation of the terrain surface in the study area. This study also indicated the TIN method can represent the ground surface and the surface depression more realistically than the commonly used digital elevation model (DEM) method due to the TIN method’s higher capability of identifying and filtering out surface obstructions such as blades of grass. Moreover, by using the high-resolution TLS technology and the TIN method, our study provides an important and broad range of reference data on the surface depression storage depth commonly needed in application of the Storm Water Management Model (SWMM) and other watershed models.
Learn more about using terrestrial laser scanning to detect surface depressions in the ASCE Library: https://doi.org/10.1061/JHYEFF.HEENG-5823.
