Abstract. The availability of detailed surface runoff and river flow data across large geographic areas is needed for several scientific applications, such as refined freshwater environmental risk assessments. Some limiting factors in developing detailed river flow datasets over large spatial scales have been paucity of detailed input spatial data and challenges in processing of these data. The well-established USDA Curve Number (CN) method was applied for spatially distributed hydrologic processing to estimate surface runoff. Publicly available global datasets for hydrologic soil groups, land cover, and precipitation were spatially processed by applying the CN equations to create a global mean annual surface runoff grid of 50 meters. Runoff was spatially combined with global hydrology of catchments and rivers from publicly available datasets to estimate daily mean annual flow (MAF) across the globe. Estimated daily MAF were compared with measured gauge flow at rivers in several countries which showed good correlation (R² of 0.76–0.98). These flow estimates can be used for diverse applications at local watersheds to larger regions across the globe. The two spatial data products of this project representing MAF at the global scale are publicly available for download at https://doi.org/10.6084/m9.figshare.22694146 (Heisler, et al., 2023).

中文翻译：

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基于曲线数法从地表径流开发的全球河流流量数据

摘要。一些科学应用需要大量地理区域的详细地表径流和河流流量数据的可用性，例如精细的淡水环境风险评估。在大空间尺度上开发详细的河流流量数据集的一些限制因素是缺乏详细的输入空间数据和处理这些数据的挑战。成熟的 USDA 曲线数 (CN) 方法应用于空间分布式水文处理以估算地表径流。通过应用 CN 方程创建 50 米的全球年平均地表径流网格，对水文土壤组、土地覆盖和降水量的公开可用全球数据集进行了空间处理。径流在空间上与来自公开数据集的集水区和河流的全球水文学相结合，以估算全球的日平均年流量 (MAF)。将估计的每日 MAF 与几个国家河流的测量仪表流量进行了比较，显示出良好的相关性（R²的 0.76–0.98）。这些流量估算可用于从当地流域到全球更大区域的各种应用。该项目在全球范围内代表 MAF 的两个空间数据产品可在 https://doi.org/10.6084/m9.figshare.22694146（Heisler 等人，2023 年）上公开下载。