Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models

Abstract

Determining the flow accumulation threshold (FAT) is a key task in the extraction of river networks from digital elevation models (DEMs). Several methods have been developed to extract river networks from Digital Elevation Models. However, few studies have considered the geomorphologic complexity in the FAT estimation and river network extraction. Recent studies estimated influencing factors’ impacts on the river length or drainage density without considering anthropogenic impacts and landscape patterns. This study contributes two FAT estimation methods. The first method explores the statistical association between FAT and 47 tentative explanatory factors. Specifically, multi-source data, including meteorologic, vegetation, anthropogenic, landscape, lithology, and topologic characteristics are incorporated into a drainage density-FAT model in basins with complex topographic and environmental characteristics. Non-negative matrix factorization (NMF) was employed to evaluate the factors’ predictive performance. The second method exploits fractal geometry theory to estimate the FAT at the regional scale, that is, in basins whose large areal extent precludes the use of basin-wide representative regression predictors. This paper’s methodology is applied to data acquired for Hubei and Qinghai Provinces, China, from 2001 through 2018 and systematically tested with visual and statistical criteria. Our results reveal key local features useful for river network extraction within the context of complex geomorphologic characteristics at relatively small spatial scales and establish the importance of properly choosing explanatory geomorphologic characteristics in river network extraction. The multifractal method exhibits more accurate extracting results than the box-counting method at the regional scale.

The Structural Equation Model of the Spatial Distribution of Hydraulic Facilities on the Beijing-Hangzhou Grand Canal and its Correlation with Natural Factors

“…After that, the change in river network density becomes more gentle. Therefore, the river network image generated by the threshold at the in ection point is selected for comparison and veri cation with the restored river network image of the Ming Dynasty through graphic superposition (Figure 4) [20] . It is found that the river network image generated when the threshold is set to 100,000 is relatively close to the restored river network image of the Ming Dynasty, and at the same time, it is basically consistent with the main stream image of the restored Ming Dynasty river network.…”

A phased creation of digital elevation model for hydrodynamic modeling in flat zones

“…Because of the terrain configuration in the study area, and built flood protection structures, the flow-accumulation image was not created and converted to main flow lines. The measures of the DEM quality are different in such cases and include number of broken flowlines, and average flow path displacement [17]. When fitting geodetic surveys of cross-section of the riverbed and levee points to LiDAR surveys, a hight consistency of 1-2 cm is found.…”