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.

Erosion Gully Networks Extraction Based on InSAR Refined Digital Elevation Model and Relative Elevation Algorithm—A Case Study in Huangfuchuan Basin, Northern Loess Plateau, China

“…Subsequently, a stream network was generated by selecting a threshold on the flow accumulation raster. The turning point where river network density ceases to substantially increase with the threshold is often used as an optimal threshold [59]. However, to better detect small gullies, a relatively smaller threshold could be selected.…”

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

“…The mapping unit dataset with the OATMU for CES was defined as the OATMU group; the mapping unit dataset with the left ATMUs adjacent to the OATMU was defined left-OATMU group; the mapping unit dataset with the right ATMU adjacent to the OATMU was defined as the right-OATMU group; and the administrative village dataset within the Qiantang River Basin was defined as the administrative village group. By comparing the mean area, area quartile distance, and area maximum interval of the 3 ATMU groups and the administrative village group [26], the group with the minimum percent error of all three is the OATMU group for CES, and the corresponding ATMU is the OATMU for CES in the river basin. Validation was conducted using descriptive statistics and box plots in IBM SPSS Statistics 26 software.…”

“…Therefore, with the establishment of appropriate units, CES can be accurately quantified [25]. Several studies in environmental hydraulics have used multiple catchment area thresholds for selecting the optimal area thresholds to obtain relatively homogeneous and refined mapping units [20,22,26]. To enhance mapping precision alongside computational efficiency, this study introduced the concept of area threshold, offering both a theoretical framework and a practical approach for accurately determining the appropriate area of mapping units.…”

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.…”