Global_HydroRIVERS_River_Network_MAF.zip

The availability of detailed surface runoff and river flow data across large geographic areas is crucial for diverse applications. A few countries (e.g., U.S.) offer such data at a high-resolution but most countries do not. Lack of detailed spatial data and challenges with intense processing have been the limiting factors in developing high-resolution river flows over large spatial scales. To address this specific need, the well-established Curve Number (CN) method was applied to develop a detailed surface runoff dataset. Publicly available, scientifically accepted and high-resolution global datasets for hydrologic soil groups, land cover, and precipitation were spatially processed by applying the CN equations to generate a contiguous global mean-annual surface runoff grid at a very high-resolution of 50m x 50m. Surface runoff was converted to river flow by spatially combining with a detailed global hydrology of rivers and catchment boundaries from HydroSHEDS and HydroBASINS to estimate mean-annual flows across the global river network. <br>

大地理尺度下详细地表径流与河道流量数据的可获取性，对众多应用场景均具有关键支撑作用。少数国家（例如美国）可提供高分辨率的此类数据，但绝大多数国家暂未具备该条件。详细空间数据的匮乏以及高强度数据处理的技术瓶颈，长期以来制约了大空间尺度高分辨率河道流量数据集的开发。为解决这一特定需求，研究人员采用成熟的径流曲线数（Curve Number, CN）法构建了高精度地表径流数据集。研究人员依托径流曲线数方程，对公开可得、经科学认可的高分辨率全球水文土壤组、土地覆盖与降水数据集开展空间处理，最终生成了分辨率为50米×50米的连续全球年均地表径流栅格数据。随后，通过将地表径流数据与来自HydroSHEDS和HydroBASINS的详细全球河道及流域边界水文数据进行空间融合，将地表径流转换为河道流量，以此估算全球河网的年均流量。