Drainage network extraction from a high-resolution DEM using parallel programming in the .NET Framework

Du, Chao; Ye, Aizhong; Gan, Yanjun; You, Jinjun; Duan, Qingyun; Ma, Feng; Hou, Jingwen

doi:10.1016/j.jhydrol.2017.10.034

Cited by 12 publications

(4 citation statements)

References 24 publications

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“…First, given a threshold of the sub-basin (100 km 2 in this study), we use the drainage network extraction method introduced by Du et al [56] to spit the whole YLJ basin into 522 sub-basins. The minimum threshold for sub-basin is based on the complexity of hydrological modeling, the running time step, the area of the Yalong River basin, the time scale of the hydrological simulation and the resolution of the forcing data.…”

Section: Hydrological Modelmentioning

confidence: 99%

Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modeling

Zhang

Nguyen

et al. 2021

Remote Sensing

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Satellite precipitation estimates (SPEs) are promising alternatives to gauge observations for hydrological applications (e.g., streamflow simulation), especially in remote areas with sparse observation networks. However, the existing SPEs products are still biased due to imperfections in retrieval algorithms, data sources and post-processing, which makes the effective use of SPEs a challenge, especially at different spatial and temporal scales. In this study, we used a distributed hydrological model to evaluate the simulated discharge from eight quasi-global SPEs at different spatial scales and explored their potential scale effects of SPEs on a cascade of basins ranging from approximately 100 to 130,000 km2. The results indicate that, regardless of the difference in the accuracy of various SPEs, there is indeed a scale effect in their application in discharge simulation. Specifically, when the catchment area is larger than 20,000 km2, the overall performance of discharge simulation emerges an ascending trend with the increase of catchment area due to the river routing and spatial averaging. Whereas below 20,000 km2, the discharge simulation capability of the SPEs is more randomized and relies heavily on local precipitation accuracy. Our study also highlights the need to evaluate SPEs or other precipitation products (e.g., merge product or reanalysis data) not only at the limited station scale, but also at a finer scale depending on the practical application requirements. Here we have verified that the existing SPEs are scale-dependent in hydrological simulation, and they are not enough to be directly used in very fine scale distributed hydrological simulations (e.g., flash flood). More advanced retrieval algorithms, data sources and bias correction methods are needed to further improve the overall quality of SPEs.

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Section: Hydrological Modelmentioning

confidence: 99%

Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modeling

Zhang

Nguyen

et al. 2021

Remote Sensing

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“…ArcGIS and SWAT commonly calibrate the threshold against river channels retrieved from satellites. The calibration is a trial and error since the threshold varies with DEM resolution and geomorphic complexity (Ariza‐Villaverde et al., 2015; Du et al., 2017). As for step (d), the outlet is manually relocated onto the stream network based on users' knowledge of the outlet station, which may be impractical for a large number of watersheds to be delineated (Lindsay et al., 2008).…”

Section: Introductionmentioning

confidence: 99%

Rapid Watershed Delineation Using an Automatic Outlet Relocation Algorithm

Xie

Liu

et al. 2022

Water Resources Research

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Delineating the boundaries of numerous watersheds is essential for hydrological analysis in the big data era. Various techniques, such as ArcGIS, have been developed to delineate watershed boundaries. However, existing techniques suffer from two laborious manual processes: (a) Determine flow accumulation thresholds through trial and error to derive digital stream networks; (b) Manually relocate watershed outlets to the correct stream networks. Here, we proposed an automatic outlet relocation (AOR) algorithm for rapid watershed delineation without manual processing. The AOR algorithm relocates outlets to correct stream networks by analyzing flow accumulation gradients along the flow directions. An experiment using the AOR algorithm in 1,398 watersheds across the contiguous United States showed that 94.1% of the watersheds were correctly delineated, compared to 81.3% accuracy of the ArcGIS relocation algorithm (Snap Pour Point). In addition, the AOR algorithm took 58 min to delineate the 1,398 watersheds on a 90 m‐resolution flow direction, while the ArcGIS algorithm required 586 min to perform the same task. Thus, the AOR algorithm offers an opportunity to automatically delineate a large number of watersheds for hydrological simulations in the big data era. Software with a graphical user interface for the AOR algorithm is freely available on GitHub (https://github.com/xiejx5/watershed_delineation).

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“…Conventionally, in gridded data sets, the issue of sinks is resolved either by filling up the depression or by carving a channel through the surrounding landscape, resulting in a modified, “hydrologically corrected” landscape (O'Callaghan & Mark, ; Planchon & Darboux, ; Rieger, ; Zhang et al, ). Other approaches avoid this modification by directing flow uphill out of a sink toward the outlet of that sink (Du et al, ; Wang et al, ). Here, we propose an approach that modifies the FFN without a modification of the underlying TIN.…”

Section: Methodsmentioning

confidence: 99%

A Network‐Based Flow Accumulation Algorithm for Point Clouds: Facet‐Flow Networks (FFNs)

2019

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Flow accumulation algorithms estimate the steady state of flow on real or modeled topographic surfaces and are crucial for hydrological and geomorphological assessments, including delineation of river networks, drainage basins, and sediment transport processes. Existing flow accumulation algorithms are typically designed to compute flows on regular grids and are not directly applicable to arbitrarily sampled topographic data such as lidar point clouds. In this study we present a random sampling scheme that generates homogeneous point densities, in combination with a novel flow path tracing approach-the Facet-Flow Network (FFN)-that estimates flow accumulation in terms of specific catchment area (SCA) on triangulated surfaces. The random sampling minimizes biases due to spatial sampling and the FFN allows for direct flow estimation from point clouds. We validate our approach on a Gaussian hill surface and study the convergence of its SCA compared to the analytical solution. Here, our algorithm outperforms the multiple flow direction algorithm, which is optimized for divergent surfaces. We also compute the SCA of a 6-km 2 -steep, vegetated catchment on Santa Cruz Island, California, based on airborne lidar point-cloud data. Point-cloud-based SCA values estimated by our method compare well with those estimated by the D ∞ or multiple flow direction algorithm on gridded data. The advantage of computing SCA from point clouds becomes relevant especially for divergent topography and for small drainage areas: These are depicted with much more detail due to the higher sampling density of point clouds.

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Drainage network extraction from a high-resolution DEM using parallel programming in the .NET Framework

Cited by 12 publications

References 24 publications

Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modeling

Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modeling

Rapid Watershed Delineation Using an Automatic Outlet Relocation Algorithm

A Network‐Based Flow Accumulation Algorithm for Point Clouds: Facet‐Flow Networks (FFNs)

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