Hydrologically significant surface depressions on grassy land surfaces, solar photovoltaic farms, and porous parking lots: identification and quantification using terrestrial laser scanning point cloud and triangulated irregular network
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Meneses Meneses, Diego Martin.
Hydrologically significant surface depressions on grassy land surfaces, solar photovoltaic farms, and porous parking lots: identification and quantification using terrestrial laser scanning point cloud and triangulated irregular network. Retrieved from
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TitleHydrologically significant surface depressions on grassy land surfaces, solar photovoltaic farms, and porous parking lots: identification and quantification using terrestrial laser scanning point cloud and triangulated irregular network
Date Created2023
Other Date2023-05 (degree)
Extent175 pages : illustrations
DescriptionChanges in the natural topography associated with urban development can lead to alterations in the hydrologic processes of land surfaces. Surface depression storage depth is described as the maximum depth filled by precipitation over a given catchment before runoff occurs. Identification and quantification of surface depressions are important to hydrologic and water quality modeling. A minor deviation could drastically alter the overall water budget in a watershed during small rainfall events that are critical to groundwater recharge, water quality, local flooding, and other stormwater management issues. Surface depression storage depth is also unique to the land cover, land use, and topography of a catchment area.The objectives of this dissertation research are to (1) demonstrate the applicability and capability of a terrestrial laser scanning (TLS) point cloud on characterizing the ground surface of a grassy land surface, solar photovoltaic (PV) farm, and a porous parking lot of different topographic attributes; (2) develop a new methodology to identify and quantify surface depressions using a TLS point cloud and a triangular irregular network (TIN); (3) quantify the surface depression storage depths of a grassy land surface, a solar PV farm, and a porous parking lot; (4) quantify the deviation of surface depression of a solar PV farm from that of the pre-development grassy land surface; and (5) explore relationships among surface topography, distance to sediment sources, and hydraulic conductivity on a porous parking lot.
TLS provides point clouds with densities orders of magnitude higher than the commonly used airborne laser scanning (ALS), with a spatial accuracy of as low as 1 mm. An investigation was completed to assess the applicability and capability of a TLS point cloud to characterize the ground surface of different study areas (i.e., grassy land surface, solar PV farm, porous parking lot). The TLS point cloud captured all topographic features of the study areas, including the ground surface covered by vegetation, vegetation elements such as tall grass, solar photovoltaic panel (PVP) structures, and the changes in ground elevation of the porous parking lot. The microtopography of the ground surfaces was well characterized in comparison to field observations.
A TIN is vector-based topographic data used to display terrain models. TIN uses a set of point cloud points connected by edges of contiguous irregular triangles, creating a continuous surface representing the terrain. A new methodology to accurately identify and quantify surface depressions was developed using TLS&TIN and validated with field observations. With the 15-mm-spatial resolution TLS&TIN method, the surface depression storage depths for the grassy land surface ranged from 1.73 to 14.28 mm, which is much wider compared to the established range (2.5 to 5 mm). The surface depression storage depth on the grassy land was also found to increase with the mildness of the ground surface slope. Additionally, the surface depression storage depths for the solar PV farm ranged from 0.91 to 12.63 mm, and the surface depression storage depth of the porous parking lot ranged from 0.40 to 1.87 mm. Among other applications, these quantified surface depression storage depths would provide new reference values for watershed models. Furthermore, sensitivity analyses on the point cloud spatial resolution (horizontal and vertical direction) were performed to assess the applicability of TLS point clouds to characterize the ground surface. The results indicated that TLS point clouds with resolutions equal to or finer than 30 mm and 15 mm in the horizontal and vertical directions respectively, can characterize all topographic features of the ground surface, including where occlusive structures are present.
A study was conducted to determine if solar PV farms change the surface depressions of pre-development grassy land surfaces. The surface depression storage depth was found to be positively correlated with the area ratio of solar PVPs and was found to differ from that of grassy land surfaces (without the installation of solar PVPs) when the area ratio deviates from 32.7% (approximately one-third). When the area ratio increased to the upper limit of 50%, the surface depression storage depth increased by 3.79 mm. This study accurately discerned the impact of solar PV farms on surface depression from the underlying grassy land surfaces and revealed the topographic alterations of the land surface and their subsequent hydrologic changes caused by the installation of solar PVPs.
Field testing was conducted to measure the infiltration capacity of the porous parking lot pavement. The characterized microtopography and associated stream network were used to evaluate the potential for reduction in infiltration rate (clogging). Results indicated that spatial variability of the infiltration rate of the porous pavement is well correlated with the distance to the sediment source along the streamline and the traffic volume on the parking lot.
The primary contributions of this dissertation research are the development of a framework for the identification and quantification of hydrologically significant surface depressions, the establishment of a new and broad reference data set for the surface depression storage depth commonly needed in the application of watershed models, and the assessment of surface depression impacts of solar PV farms separated from the underlying grassy land surface. The development of new methodology and the reference data represent progress in the watershed analysis and modeling of urban catchment areas and stormwater management.
NotePh.D.
NoteIncludes bibliographical references
Genretheses
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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