| dc.description.abstract | Playa-lunette systems (PLSs) are common landscape features in the Great Plains that have been investigated from various perspectives including ecologic, geographic, hydrologic, and geologic. One of the region’s most important water sources is the High Plains Aquifer (HPA), an expansive groundwater reservoir located beneath the PLSs, which has been susceptible to groundwater shortages, water table declines, and pollution throughout much of the High Plains due primarily to anthropogenic influences. This investigation takes on a new perspective, that of the geomorphology of playa basins and associated lunettes using LiDAR in a GIS environment. Playa-lunette systems were mapped using an objective method, or the largest closed contour (LCC) method, and a subjective method, also known as the subjectively chosen contour method (SC), with contours derived from a high-resolution LiDAR DEM. Basic 2-D attributes, e.g., area, perimeter, and orientation, were computed for the PLSs. The basin and lunette locations are consistent (perpendicular and parallel long axes, respectively) with late Pleistocene-early Holocene northwesterly paleo-winds of the last glacial period, with lunettes commonly being located south to south-easterly of the basins. This dataset fills the need for the mapping of the basins and compares two mapping methods with contours generated from high-resolution data. The assumption is that the SC method produces a visually ideal dataset, but the cons are that it is subjective and it requires more knowledge of PLSs. The LCC method is objective and has a set criteria to follow, producing more consistent results. A subset of 40 ideal playa-lunette systems (out of the 104 study sites from Bowen et al., 2018) were selected for a directional trends analysis and other 2-D metrics. Overall, the LCC method was comparable to the SC method, though the LCC method had a slight overestimation of the features comprising the PLSs. The LCC method and SC method mapped playas more consistently with the same contour than lunettes. A relatively high correlation was found with regression plots of long axial orientation values between pairs of the datasets, as well as t-test results showing that these results are statistically similar. | |
