Assessment of drainage network extractions in a low-relief area of the Cuvelai Basin (Namibia) from multiple sources: LiDAR, topographic maps, and digital aerial orthophotographs

Abstract

Accurate delineation of drainage networks (DNs) is crucial for hydrological or hydraulic modelling, and the com-24 prehension of fluvial processes. This task presents challenging aspects in complex lowland terrains with subtle 25 relief and particularly for data poor-areas like the Cuvelai river basin (CRB), Namibia, where the present study 26 takes place. In the CRB standard methods of drainage network extraction from low resolution gridded digital 27 elevation models (DEMs) are unsuitable, hence airborne Light Detection and Ranging (LiDAR) solutions have 28 been utilized. However, LiDAR also presents challenges to large areal applications, especially with a surface 29 roughness exceeding the capacity of numerous algorithms. Indeed, LiDAR-based DEMs (2 and 50 m resolutions) 30 need to be hydrologically corrected and smoothed to enable the extraction of scale-relevant geomorphologic 31 features such as DNs. In the present contribution, channels from topographic maps (blue lines) were compared 32 to those from hydrologically corrected and uncorrected LiDAR DEMs, heads-up digitized channels from high-33 resolution digital aerial orthophotographs, field-mapped channels and auxiliary data. The 'maximum gradient 34 deterministic eight (D8)' GIS algorithm was applied to the corrected and uncorrected LiDAR DEMs using two 35 network extraction methods: area threshold support and curvature/drop analysis. Different progressive flow 36 accumulation threshold values (12) were used to delineate channels with these methods. Validation was per-37 formed between the field-mapped channels, the modelled channels and those derived from multiple sources. 38 Additionally, spatial and quantitative analyses were performed on geomorphologic parameters and indices.39 The results have shown that hydrologically corrected LiDAR DEMs offer useful details for identifying low order 40 stream segments in headwaters, while blue lines derived from the national hydrography datasets for watersheds, 41 located in elevated and low-lying areas of the study area, underestimated total stream lengths for field-mapped 42 channels by −15.3% and −88.5%, respectively. This study also confirmed that DNs can be extracted from com-43 plex low-terrain areas with standard GIS algorithms and accurate field data. The results will aid national mapping 44 agencies in data-poor regions to modernize their national hydrography datasets and to account for changing land 45 surface conditions that can affect channel spatial arrangements over time. Angola and Namibia (Fig. 1), encompasses these and many other 64 challenges related to flood risk management. 65The CRB, where this research has taken place, displays a series of

Drainage from the Critical Zone: Lithologic Controls on the Persistence and Spatial Extent of Wetted Channels during the Summer Dry Season

“…Zimmer and McGlynn () focused on runoff generation processes in a small (0.03 km 2 ), low‐slope watershed, and observed variable flowhead position, often upslope of geomorphic channel heads in response to storm events. Remote sensing technology has recently been used to map wetted channels in larger watersheds in nonvegetated environments, using high‐resolution multispectral band analysis (e.g., Hamada et al, ), LiDAR (e.g., Liu et al, ), and digital aerial orthophotographs (e.g., Persendt & Gomez, ). Additional work has used a combination of orthophotographs and on‐ground mapping (e.g., González‐Ferreras & Barquín, ) to quantify the ubiquitous underrepresentation of ephemeral channels, and to illuminate their importance, both ecologically and hydrologically.…”

Global‐scale river network extraction based on high‐resolution topography and constrained by lithology, climate, slope, and observed drainage density

“…This is true in the field because of stream intermittency, artificial ditches, or hydraulic obstructions in flat areas. Additional problems arise when implementing traditional methods to map the "blue lines," usually from airborne or satellite imagery, because of insufficient resolution, vegetation masking, or scarce field data [Tarboton et al, 1991;Lehner and Döll, 2004;Benstead and Leigh, 2012;Persendt and Gomez, 2016]. It is believed that river networks extracted from very high resolution DEMs are more comprehensive, assuming that they are properly calibrated [Benstead and Leigh, 2012;Stein et al, 2014].…”

Tectonics controls on fluvial landscapes and drainage development in the westernmost part of Switzerland: Insights from DEM-derived geomorphic indices

“…Different algorithms have been developed to extract drainage systems from a DEM by using GIS tools. D8 is the most popular used algorithm for automatic drainage extraction (e.g., Anders et al, 2009;Ariza-Villaverde et al, 2013, 2015Liu and Zhang, 2011;Nigel and Rughooputh, 2010;Persendt and Gomez, 2015;Turcotte et al, 2000). This algorithm is specifically relied on determining direction of water flow from every cell in the DEM raster to one of its eight neighbors, either adjacent or diagonal, in the direction of the steepest descending slope (O'Callaghan and Mark, 1984;Tarboton, 1997).…”