Abstract
In terrain analysis and hydrological modeling, surface depressions (or sinks) in a digital elevation model (DEM) are commonly treated as artifacts and thus filled and removed to create a depressionless DEM. Various algorithms have been developed to identify and fill depressions in DEMs during the past decades. However, few studies have attempted to delineate and quantify the nested hierarchy of actual depressions, which can provide crucial information for characterizing surface hydrologic connectivity and simulating the fill-merge-spill hydrological process. In this paper, we present an innovative and efficient algorithm for delineating and quantifying nested depressions in DEMs using the level-set method based on graph theory. The proposed level-set method emulates water level decreasing from the spill point along the depression boundary to the lowest point at the bottom of a depression. By tracing the dynamic topological changes (i.e., depression splitting/merging) within a compound depression, the level-set method can construct topological graphs and derive geometric properties of the nested depressions. The experimental results of two fine-resolution Light Detection and Ranging-derived DEMs show that the raster-based level-set algorithm is much more efficient (~150 times faster) than the vector-based contour tree method. The proposed level-set algorithm has great potential for being applied to large-scale ecohydrological analysis and watershed modeling.
Highlights
Surface depressions, known as sinks or pits, are abundant in digital elevation models (DEMs)
The Cottonwood Lake Study Area (CLSA) is one of the three long-term wetland ecosystem monitoring sites established by the U.S Geological Survey (USGS) in the Prairie Pothole Region of North America (Mushet and Euliss, 2012)
Field-based wetland identifier points were established by CLSA USGS researchers to aid in the identification of wetlands and facilitate research that investigates the impacts of land use change and natural climate variability on the wetland ecosystem in the Prairie Pothole Region (Mushet and Erickson, 2017)
Summary
Known as sinks or pits, are abundant in digital elevation models (DEMs). Light Detection and Ranging (LiDAR) technology has increasingly been used to acquire elevation data and produce fine-resolution DEMs. it is no longer justifiable to treat all depressions in DEMs as artifacts and eliminate them from hydrological analysis. The Prairie Pothole Region of North America is dotted with millions of depressional wetlands, which are typically small and shallow. These wetlands provide important ecological and hydrological functions (Cohen et al, 2016; Evenson et al, 2016; Rains et al, 2016; Vanderhoof et al, 2016, 2017; Waz and Creed, 2017; Wu, 2018; Wu and Lane, 2017). It is critical to delineate these wetland depressions, integrate them into hydrological modeling and assess their impacts on downstream waters (Lane et al, 2018; US EPA, 2015)
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