A hierarchical pyramid method for managing large-scale high-resolution drainage networks extracted from DEM

Bai, Rui; Li, Tiejian; Huang, Yuefei; Li, Jiaye; Wang, Guangqian; Yin, Dafei

doi:10.1016/j.cageo.2015.06.019

Cited by 11 publications

(5 citation statements)

References 40 publications

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“…However, the map in this study can show the stream channels more clearly than that produced by GWSP Digital Water Atlas [20]. This is mainly because the map in this study is from the 0.01-degree-resolution global drainage network extracted from the 30-m-resolution DEM data [22,44], which has the much higher spatial resolution than that (i.e., 0.5-degree) used by GWSP Digital Water Atlas [20]. In addition, by comparing Figures 1 and 3b, it is found that the distributions of the colored parts in these two maps have overall similar characteristics; however, more colored regions shaped as stream channels can be observed in Figure 3b (e.g., central Asia, northern and southern Africa, central North America, and southern South America).…”

Section: Map Of Global River Dischargementioning

confidence: 95%

An Efficient Method for Mapping High-Resolution Global River Discharge Based on the Algorithms of Drainage Network Extraction

Liu

et al. 2018

Water

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River discharge, which represents the accumulation of surface water flowing into rivers and ultimately into the ocean or other water bodies, may have great impacts on water quality and the living organisms in rivers. However, the global knowledge of river discharge is still poor and worth exploring. This study proposes an efficient method for mapping high-resolution global river discharge based on the algorithms of drainage network extraction. Using the existing global runoff map and digital elevation model (DEM) data as inputs, this method consists of three steps. First, the pixels of the runoff map and the DEM data are resampled into the same resolution (i.e., 0.01-degree). Second, the flow direction of each pixel of the DEM data (identified by the optimal flow path method used in drainage network extraction) is determined and then applied to the corresponding pixel of the runoff map. Third, the river discharge of each pixel of the runoff map is calculated by summing the runoffs of all the pixels in the upstream of this pixel, similar to the upslope area accumulation step in drainage network extraction. Finally, a 0.01-degree global map of the mean annual river discharge is obtained. Moreover, a 0.5-degree global map of the mean annual river discharge is produced to display the results with a more intuitive perception. Compared against the existing global river discharge databases, the 0.01-degree map is of a generally high accuracy for the selected river basins, especially for the Amazon River basin with the lowest relative error (RE) of 0.3% and the Yangtze River basin within the RE range of ±6.0%. However, it is noted that the results of the Congo and Zambezi River basins are not satisfactory, with RE values over 90%, and it is inferred that there may be some accuracy problems with the runoff map in these river basins.

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Section: Map Of Global River Dischargementioning

confidence: 95%

An Efficient Method for Mapping High-Resolution Global River Discharge Based on the Algorithms of Drainage Network Extraction

Liu

et al. 2018

Water

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“…Our identification of river basins is based on a previously described method (Elkhrachy, 2018;Bajirao et al, 2019;Garrote, 2022;Samsonov, 2022). The theoretical and methodological foundations of delineating river basins and sub-basins were extensively discussed by Bai et al (2015a) (Bai et al, 2015b). We automated it using the built-in ArcGIS Model Builder (Figure 2).…”

Section: Figurementioning

confidence: 99%

Identification of river basins within northwestern slope of Crimean Mountains using various digital elevation models (ASTER GDEM, ALOS World 3D, Copernicus DEM, and SRTM DEM)

Tabunshchik,

Gorbunov,

Gorbunova

et al. 2023

Front. Earth Sci.

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Since the end of the 20th century, the use of geographic information systems and digital elevation models has reduced the time required for and improved the quality of morphometric analysis of the relief within river basins. However, researchers are constantly faced with the problem of choosing the most accurate and suitable digital terrain model for their task. Many global, regional, and local digital elevation models are available. In this study, we comparatively analyzed the accuracy of the ASTER GDEM, ALOS World 3D, Copernicus DEM, and SRTM DEM spatial datasets for the purpose of catchment basin modeling for the river basins of the northwestern slope of the Crimean Mountains (Zapadnyy Bulganak, Alma, Kacha, Belbek, and Chernaya Rivers) as an example. For each river basin, we calculated the systematic, root mean square, mean absolute, standard root mean square (Bessel’s correction), and centered mean absolute errors by comparing ASTER GDEM, ALOS World 3D, Copernicus DEM, and SRTM DEM data with a 1:100,000 topographic map within the considered river basins. We found the smallest error values for the Copernicus DEM and ALOS World 3D datasets; furthermore, we used the Copernicus DEM dataset to model the river basins and sub-basins of the northwestern slope of the Crimean Mountains. As a result, we identified these river basins and sub-basins for the Zapadnyy Bulganak, Alma, Kacha, Belbek, and Chernaya Rivers, which are represented by stream basins, valleys, gullies, and ravine systems.

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“…However, such conventional quantitative geometrical metrics of fluvial systems are unlikely to be sufficient to define, or discriminate between, channel types (Carling et al, 2014). Meanwhile, the definition of river network topologies (Dodds and Rothman, 2000;Rodriguez and Rinaldo, 2000) and their stream ordering laws (Tokunaga, 1966;Williams and Rust, 1969;Bai et al, 2015) demonstrates that river networks can be treated as real-world, non-random networks of varying complexity. In this view, channel bifurcations (whether divergent or convergent) are nodes, with the individual channels between nodes regarded as links.…”

Section: Introductionmentioning

confidence: 99%

An automatic graph-based method for characterizing multichannel networks

Liu

Carling

Wang

et al. 2022

Computers & Geosciences

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A hierarchical pyramid method for managing large-scale high-resolution drainage networks extracted from DEM

Cited by 11 publications

References 40 publications

An Efficient Method for Mapping High-Resolution Global River Discharge Based on the Algorithms of Drainage Network Extraction

An Efficient Method for Mapping High-Resolution Global River Discharge Based on the Algorithms of Drainage Network Extraction

Identification of river basins within northwestern slope of Crimean Mountains using various digital elevation models (ASTER GDEM, ALOS World 3D, Copernicus DEM, and SRTM DEM)

An automatic graph-based method for characterizing multichannel networks

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