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ABSTRACT: Current data collection technologies such as light detection and ranging (LIDAR) produce dense digital terrain data that result in more accurate digital terrain models (DTMs) for engineering applications. However, such data are redundant and often cumbersome for hydrologic and hydraulic modeling purposes. Data filtering provides a means of eliminating redundant points and facilitates model preparation. This paper demonstrates the impact of varied data resolution on a case study completed for a 2.3 mi 2 area with mild slopes (about 001 ft/ft) along Leith Creek near Laurinburg, North Carolina. For the original data set and seven filtered data sets, filtering induced changes in elevation, area, and hydraulic radius were determined for 10 water depths at 23 cross sections. Water surface elevations resulting from HEC‐RAS (Hydrologic Engineering Center‐River Analysis System) models for each data set were then compared. A hydraulic model sensitivity analysis was also conducted to compare filtering error to error introduced by variation in flow rates and roughness values. Finally, automated floodplain delineation was performed for each filter level based on the computed hydraulic model results and the filtered LIDAR elevations. Data filtering results indicate that significant time savings are achieved throughout the modeling process and that filtering to four degrees can be performed without compromising cross‐sectional geometry, hydraulic model results, or floodplain delineation results.
Omer et al. (Tue,) studied this question.