Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, 08544, USA.
Institute of Industrial Science, University of Tokyo, Tokyo, Japan.
Sci Data. 2021 Jan 26;8(1):28. doi: 10.1038/s41597-021-00819-9.
Spatial variability of river network drainage density (D) 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 D 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 D based on the global watershed-level climatic, topographic, hydrologic, and geologic conditions, where relationships between hydroclimate factors and D 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 D 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.
河网排水密度（D）的空间变异性是河流系统的一个关键特征，但现有少数全球水文数据集未能妥善考虑这一点。在这里，我们提出了一种新的基于矢量的全球水文数据集，该数据集合理地估计了全球 D 的空间变异性。它是通过从最新的 90 米多误差消除改进地形（MERIT）数字高程模型和流向/累积中描绘河道构建的。开发了一种机器学习方法来根据全球流域水平的气候、地形、水文和地质条件来估计 D，其中利用高质量的国家水文数据集加（NHDPlusV2）数据来训练水文气候因子与 D 之间的关系。通过将我们的数据集与 HydroSHEDS 和几个区域水文数据集进行基准测试，我们表明新的河流流线与 Landsat 衍生的中心线更为一致，并且获得了改进的河网 D 模式（总长约 7500 万公里）。还划分了流域和间歇性溪流部分的估计值，以支持水资源管理。这个新的数据集（MERIT Hydro-Vector）应该能够在精细的空间分辨率下实现对河流系统过程的全面全球建模。
