A data set of global river networks and corresponding water resources zones divisions

Yan, Denghua; Wang, Kun; Qin, Tianling; Weng, Baisha; Wang, Hao; Bi, Wuxia; Li, Xiangnan; Ли, Менг; Lv, Zhenyu; Liu, Fang; He, Shan; Ma, Jun; Shen, Zhenqian; Wang, Jianwei; Bai, Hongliang; Man, Zihao; Sun, Congwu; Liu, Meiyu; Shi, Xinzheng; Jing, Lanshu; Sun, Renren; Cao, Shuang; Hao, Cailian; Wang, Lina; Pei, Mengtong; Dorjsuren, Batsuren; Gedefaw, Mohammed; Girma, Abel; Abiyu, Asaminew

doi:10.1038/s41597-019-0243-y

Cited by 36 publications

(26 citation statements)

References 18 publications

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“…High-accuracy hydrography data delineating global river networks and basin boundaries lay the foundation for many important geoscience applications, such as global hydrologic modeling [1][2][3] , ecohydrological analysis 4 , geomorphological analysis 5 , and water resources management 6 . During the past two decades, improvements in the resolution and accuracy in spaceborne digital elevation models (DEMs) have greatly advanced the delineation of such hydrographic data -prominent recent examples include the HydroSHEDS 7 benchmarking the global hydrography dataset since the release of the Shuttle Radar Topography Mission (SRTM) 7 , and its recent variant HydroATLAS 2 that contains millions of river flowlines with hydro-environmental information.…”

Section: Background and Summarymentioning

confidence: 99%

A new vector-based global river network dataset accounting for variable drainage density

et al. 2021

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Spatial variability of river network drainage density (Dd) is a key feature of river systems, yet few existing global hydrography datasets have properly accounted for it. Here, we present a new vector-based global hydrography that reasonably estimates the spatial variability of Dd worldwide. It is built by delineating channels from the latest 90-m Multi-Error-Removed Improved Terrain (MERIT) digital elevation model and flow direction/accumulation. A machine learning approach is developed to estimate Dd based on the global watershed-level climatic, topographic, hydrologic, and geologic conditions, where relationships between hydroclimate factors and Dd are trained using the high-quality National Hydrography Dataset Plus (NHDPlusV2) data. By benchmarking our dataset against HydroSHEDS and several regional hydrography datasets, we show the new river flowlines are in much better agreement with Landsat-derived centerlines, and improved Dd patterns of river networks (totaling ~75 million kilometers in length) are obtained. Basins and estimates of intermittent stream fraction are also delineated to support water resources management. This new dataset (MERIT Hydro–Vector) should enable full global modeling of river system processes at fine spatial resolutions.

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Section: Background and Summarymentioning

confidence: 99%

A new vector-based global river network dataset accounting for variable drainage density

et al. 2021

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“…The sample and test areas are depicted in Figure 2a,b, respectively. These areas were obtained from a global dataset (https://doi.org/10.6084/m9.figshare.8044184.v6, accessed on 1 July 2020) with a 3-arcsecond resolution which has higher precision than the HDMA dataset released by the USGS [64].…”

Section: Sample and Test Areasmentioning

confidence: 99%

Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models

Zhang

Loáiciga

Liao

et al. 2021

IJGI

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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.

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“…Each time the satellite returns on the given pass, new observations of the river can be added to the WSE time series at the virtual station. The river line is from the open data set of global river networks from Yan et al (2019), which is based on two DEMs (Digital Elevation Model): the SRTM (Shuttle Radar Topography Mission) and ASTER GDEM v.2 (Advanced Spaceborne Thermal Emission and Reflection…”

Section: Virtual Station Localizationmentioning

confidence: 99%

Sentinel-3 radar altimetry for river monitoring – a catchment-scale evaluation of satellite water surface elevation from Sentinel-3A and Sentinel-3B

Kittel¹,

Jiang²,

Tøttrup³

et al. 2020

Preprint

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Abstract. Sentinel-3 is the first satellite altimeter to operate in Synthetic Aperture Radar (SAR) mode and in open-loop tracking mode nearly globally. Both features are expected to improve the ability of the altimeters to observe inland water bodies. In this study we evaluate the possibility to extract river water surface elevation (WSE) at catchment level from Sentinel-3A and Sentinel-3B radar altimetry, using Level-1b and Level-2 data from two public platforms. The objectives of the study are to evaluate the density of valuable observations and establish a WSE monitoring network. Additionally, we demonstrate the potential application of Sentinel-3 for monitoring river interactions with wetlands and floodplains. In the Zambezi basin, 175 virtual stations (VS) contain useful WSE information in both datasets, far exceeding the number of VS available in standard databases. The RMSD is between 2.7 cm and 31.2 cm at six in-situ stations and the VS reflect the observed WSE climatology throughout the basin. Additional VS are available in both the Copernicus Open Access Hub and GPOD (Grid Processing on Demand), highlighting the value of considering multiple processing options. In particular, we show that the processing options available on GPOD strongly affect the number of useful VS; in particular, extending the size of the receiving window, considerably improved data at 13 Sentinel-3 VS. The number of VS delivering usable data increased after the Open-Loop Tracking Command (OLTC) on-board Sentinel-3A was updated. However, the open-loop tracking mode poses two new challenges: correct on-board elevation information is crucial, and steep changes in the receiving window position can have detrimental effects on the WSE observations. Finally, we extract Sentinel-3 observations over key wetlands in the Zambezi basin. We show that clear seasonal patterns are captured in the Sentinel-3 WSE, reflecting flooding events in the floodplains. These results highlight the potential of using Sentinel-3 as a SWOT (Surface Water and Ocean Topography) surrogate while awaiting the mission launch. The results show the benefit of the high-resolution Synthetic Aperture Radar (SAR) altimeter, as well as the benefits and disadvantages of the open-loop tracking mode.

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A data set of global river networks and corresponding water resources zones divisions

Cited by 36 publications

References 18 publications

A new vector-based global river network dataset accounting for variable drainage density

A new vector-based global river network dataset accounting for variable drainage density

Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models

Sentinel-3 radar altimetry for river monitoring – a catchment-scale evaluation of satellite water surface elevation from Sentinel-3A and Sentinel-3B

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