River discharge is an Essential Climate Variable (ECV) and is one of the best monitored components of the terrestrial water cycle. Nonetheless, gauging stations are distributed unevenly around the world, leaving many white spaces on global freshwater resources maps. Here, we use a machine learning algorithm and historical weather data to upscale sparse in situ river discharge measurements. We provide a global reanalysis of monthly runoff rates for periods covering decades to the past century at a resolution of 0.5° (about 55 km), and with up to 525 ensemble members based on 21 different atmospheric forcing data sets. This global runoff reconstruction, named Global RUNoff ENSEMBLE (G‐RUN ENSEMBLE), is evaluated using independent observations from large river basins and benchmarked against other publicly available runoff data sets over the period 1981–2010. The accuracy of the data set is evaluated on observed river flow from basins not used for model calibration and is found to compare favorably against state‐of‐the‐art global hydrological model simulations. The G‐RUN ENSEMBLE estimates the global mean runoff volume to range between 3.2 × 10⁴ and 3.8 × 10⁴ km³ yr⁻¹. This publicly available data set (https://doi.org/10.6084/m9.figshare.12794075) has a wide range of applications, including regional water resources assessments, climate change attribution studies, hydro‐climatic process studies as well as the evaluation, calibration and refinement of global hydrological models.

中文翻译：

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G-RUN ENSEMBLE：基于多强迫观测的全球径流重新分析

河流排放是基本气候变量（ECV），并且是陆地水循环中受监控最好的组成部分之一。尽管如此，测量站在世界各地分布不均，在全球淡水资源地图上仍留有许多空白。在这里，我们使用机器学习算法和历史天气数据来进行稀疏原位河流流量测量。我们以0.5°（约55 km）的分辨率对上世纪几十年的月径流率进行了全球重新分析，并基于21个不同的大气强迫数据集，最多有525个合奏成员。这种全球径流重建被称为全球径流ENSEMBLE（G‐RUN ENSEMBLE），使用来自大河流域的独立观测值进行评估，并以1981-2010年期间其他可公开获得的径流量数据集为基准。该数据集的准确性是通过对未用于模型校准的流域观测到的河流流量进行评估的，并且可以与最新的全球水文模型模拟进行比较。G‐RUN ENSEMBLE估计全球平均径流量在3.2×10之间⁴和3.8×10 ⁴  km ³ yr ^-1。这个公开可用的数据集（https://doi.org/10.6084/m9.figshare.12794075）具有广泛的应用，包括区域水资源评估，气候变化归因研究，水文气候过程研究以及评估，校准和完善全球水文模型。