An efficient and comprehensive method for drainage network extraction from DEM with billions of pixels using a size-balanced binary search tree

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

doi:10.1016/j.geomorph.2015.02.028

Cited by 63 publications

(39 citation statements)

References 56 publications

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“…Digital land surface analysis has been used to extract the basic terrain derivatives (e.g., slope and aspect) [15,16] and to study complicated applications (e.g., paleo-topographic reconstruction and landform classification) [17][18][19][20][21] of the Loess Plateau. The terrain points [22][23][24], lines [25][26][27], and surfaces [28][29][30] studies mainly focused on research with the view of developing and evaluating technologies and methods, and there is a lack of research and discussion on the role of terrain data in the study of the topography and geomorphology of the Loess Plateau. Tang et al [1] established the theory of the slope topo-sequence, which they successfully applied to study the Loess landform.…”

Section: Introductionmentioning

confidence: 99%

Topographic Spatial Variation Analysis of Loess Shoulder Lines in the Loess Plateau of China Based on MF-DFA

Cao

et al. 2017

IJGI

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Abstract:The Loess Plateau in China is internationally known for its unique geographical features and has therefore been studied by many researchers. This research exploits the regional differences in the spatial morphological characteristics of Loess shoulder lines in different landform types as an important basis for geomorphological regionalization. In this study, we used ensemble empirical mode decomposition (EEMD), multi-fractal detrended fluctuation analysis (MF-DFA), and detrended cross-correlation analysis (DCCA) to analyze topographic data series extracted from shoulder lines. Loess shoulder-line land variations series data from the Suide, Ganquan, and Chunhua areas on the Loess Plateau were selected and a combination of the two above-mentioned methods was used to study land variations at these three sample sites. The results revealed differences in the topographic variations of the multi-fractal characteristics and the topographic spatial variation in the Loess shoulder line of the three sample sites. Furthermore, the extent to which the results were affected by noise and the analysis scale differed among the three areas.

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Section: Introductionmentioning

confidence: 99%

Topographic Spatial Variation Analysis of Loess Shoulder Lines in the Loess Plateau of China Based on MF-DFA

Cao

et al. 2017

IJGI

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“…All the basic units of a river basin constitute a drainage network [60], which is generally extracted from digital elevation model (DEM) data [61], e.g., the 30-m-resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM dataset [62,63] used in this paper. Runoff yield is simulated on each hillslope using a two-layer soil model to reflect both the infiltration-excess and storage-excess mechanisms, in fine time steps (e.g., 6 min).…”

Section: Digital Yellow River Integrated Modelmentioning

confidence: 99%

Analysis of the Influence of Rainfall Spatial Uncertainty on Hydrological Simulations Using the Bootstrap Method

Zhang

Shi

et al. 2018

Atmosphere

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Abstract:Rainfall stations of a certain number and spatial distribution supply sampling records of rainfall processes in a river basin. Uncertainty may be introduced when the station records are spatially interpolated for the purpose of hydrological simulations. This study adopts a bootstrap method to quantitatively estimate the uncertainty of areal rainfall estimates and its effects on hydrological simulations. The observed rainfall records are first analyzed using clustering and correlation methods and possible average basin rainfall amounts are calculated with a bootstrap method using various combinations of rainfall station subsets. Then, the uncertainty of simulated runoff, which is propagated through a hydrological model from the spatial uncertainty of rainfall estimates, is analyzed with the bootstrapped rainfall inputs. By comparing the uncertainties of rainfall and runoff, the responses of the hydrological simulation to the rainfall spatial uncertainty are discussed. Analyses are primarily performed for three rainfall events in the upstream of the Qingjian River basin, a sub-basin of the middle Yellow River; moreover, one rainfall event in the Longxi River basin is selected for the analysis of the areal representation of rainfall stations. Using the Digital Yellow River Integrated Model, the results show that the uncertainty of rainfall estimates derived from rainfall station network has a direct influence on model simulation, which can be conducive to better understand of rainfall spatial characteristic. The proposed method can be a guide to quantify an approximate range of simulated error caused by the spatial uncertainty of rainfall input and the quantified relationship between rainfall input and simulation performance can provide useful information about rainfall station network management in river basins.

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“…All the basic units of a river basin constitute a 30 drainage network (Li et al, 2010a), which is generally extracted from digital elevation model (DEM) data (Bai et al, 2015), e.g., the 30-m-resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM Hydrol. Earth Syst.…”

Section: Digital Yellow River Integrated Modelmentioning

confidence: 99%

A bootstrap method to estimate the influence of rainfall spatial uncertainty in hydrological simulations

Zhang

Shi

et al. 2017

Preprint

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Abstract. Rainfall stations with a certain number and spatial distribution supply sampling records of rainfall processes in a river basin. Uncertainty may be introduced when the station records are spatially interpolated for the purpose of hydrological 10 simulations. This study adopts a bootstrap method to quantitatively estimate the uncertainty of areal rainfall estimates and its effects on hydrological simulations. The observed rainfall records are first analysed using clustering and correlation methods, and possible average basin rainfall amounts are calculated with a bootstrap method using various combinations of rainfall station subsets. Then, the uncertainty of simulated runoff, which is propagated through a hydrological model from the spatial uncertainty of rainfall estimates, is analysed with the bootstrapped rainfall inputs. By comparing the uncertainties of rainfall 15 and runoff, the responses of the hydrological simulation to the spatial uncertainty of rainfall are discussed. Analyses are performed for three rainfall events in the upstream of the Qingjian River basin, a sub-basin of the Yellow River. Using the Digital Yellow River Integrated Model, the results show that the uncertainty of rainfall estimates derived from rainfall station network has a direct influence on simulated runoff processes. This quantified relationship between rainfall input and simulation performance can provide useful information on managing rainfall station density in river basins. The proposed 20 method could be a guide to quantify an approximate range of simulated error caused by the spatial uncertainty of rainfall input.

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An efficient and comprehensive method for drainage network extraction from DEM with billions of pixels using a size-balanced binary search tree

Cited by 63 publications

References 56 publications

Topographic Spatial Variation Analysis of Loess Shoulder Lines in the Loess Plateau of China Based on MF-DFA

Topographic Spatial Variation Analysis of Loess Shoulder Lines in the Loess Plateau of China Based on MF-DFA

Analysis of the Influence of Rainfall Spatial Uncertainty on Hydrological Simulations Using the Bootstrap Method

A bootstrap method to estimate the influence of rainfall spatial uncertainty in hydrological simulations

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