Effect of User Decision and Environmental Factors on Computationally Derived River Networks

Abstract

Despite recent developments of continental and global vector‐based river networks, the impact of digital elevation model selection, stream initiation area and environmental parameters including land cover, and elevation, remain unexplored at large scales. To fill this gap, vector river networks based on multiple data sets are compared to the National Hydrography Dataset Plus High Resolution flowpaths. Using TauDEM, river networks from three conditioned Digital Elevation Models (DEMs) were produced at multiple thresholds for stream initiation. OpenCLC, a software package for the comparison of hydrographic networks, was used to compare digital hydrographic networks with the NHDPlus HR flowlines data set over more than 35,00 basins. Networks derived from the 12 m Tandem‐X data set showed similar results as the MERIT Hydro with 90 m resolution until the application of a sophisticated stream burning methodology improved performance significantly. The optimal CLC is obtained at 1‐km threshold for Hydrological Data and Maps Based on SHuttle Elevation Derivatives at multiple Scales and MERIT Hydro‐gridded data sets, quality declined with smaller thresholds. Spatial patterns in river‐network quality were observed and were associated with dominant land classification, with greater forest coverage associated with significantly better quality and greater wetland presence with lower quality networks. This study demonstrates user selection of DEM, and threshold combined with environmental factors (vegetation, water coverage, and precipitation) play a significant role in river‐network quality compared to the DEM selection, and that without sophisticated conditioning, a higher resolution base DEM does not necessarily produce a better river network.

Identifying the Optimal Area Threshold of Mapping Units for Cultural Ecosystem Services in a River Basin

“…In the Qiantang River Basin, the OATMU for GI was 3.60 km 2 , which was similar to the studies of Wu et al [22] and Olsen et al [33]. Wu et al calculated very close results using three DEM datasets to evaluate the reasonableness of OATMU [22].…”