E-RUN version 1.1: Observational gridded runoff estimates for Europe, link to data in NetCDF format (69 MB)

River runoff is an essential climate variable as it is directly linked to the terrestrial water balance and controls a wide range of climatological and ecological processes. Despite its scientific and societal importance, there are to date no pan-European observation-based runoff estimates available. Here we employ a recently developed methodology to estimate monthly runoff rates on regular spatial grid in Europe. For this we first assemble an unprecedented collection of river flow observations, combining information from three distinct data bases. Observed monthly runoff rates are first tested for homogeneity and then related to gridded atmospheric variables (E-OBS version 12) using machine learning. The resulting statistical model is then used to estimate monthly runoff rates (December 1950 - December 2015) on a 0.5° x 0.5° grid. The performance of the newly derived runoff estimates is assessed in terms of cross validation. The paper closes with example applications, illustrating the potential of the new runoff estimates for climatological assessments and drought monitoring.

河道径流（River runoff）是一类关键气候变量，因其直接与陆地水平衡（terrestrial water balance）相关联，并调控着诸多气候与生态过程。尽管其兼具科学与社会价值，但截至目前，尚无基于观测数据的泛欧洲径流估算数据集问世。为此，本研究采用新近开发的方法，对欧洲区域规则空间网格下的月径流速率进行估算。我们首先整合了三个不同数据库的河流流量观测数据，构建了规模空前的观测数据集。随后对观测得到的月径流速率开展均一性检验，并借助机器学习方法，将其与网格化大气变量（E-OBS version 12）建立关联。利用所得的统计模型，在0.5°×0.5°的网格上估算1950年12月至2015年12月的月径流速率。本研究通过交叉验证对新生成的径流估算结果的性能进行了评估。最后，本文通过实例应用，展示了该径流估算数据集在气候评估与干旱监测领域的应用潜力。