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>跨广袤地理区域的高精度地表径流与河道流量数据的可用性，对诸多应用场景至关重要。当前仅有少数国家（如美国）可提供高分辨率的此类数据，绝大多数国家尚未具备相关能力。详细空间数据的缺失，以及高强度数据处理带来的技术瓶颈，长期制约着大空间尺度下高分辨率河道流量数据集的研发。为满足这一特定需求，本研究采用成熟的径流曲线数（Curve Number, CN）法构建高精度地表径流数据集：依托公开可得且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，通过空间化应用CN方程，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，结合HydroSHEDS与HydroBASINS提供的高精度全球河流水系及流域边界数据集，通过空间耦合将地表径流转化为河道流量，最终估算得到覆盖全球河网的年均径流量。