Not logged in
Data Publisher for Earth & Environmental Science

Gudmundsson, Lukas; Seneviratne, Sonia I (2016): E-RUN version 1.1: Observational gridded runoff estimates for Europe, link to data in NetCDF format (69 MB). PANGAEA,, Supplement to: Gudmundsson, L; Seneviratne, SI (2016): Observation-based gridded runoff estimates for Europe (E-RUN version 1.1). Earth System Science Data, 8(2), 279-295,

Always quote citation above when using data! You can download the citation in several formats below.

RIS CitationBibTeX CitationShow MapGoogle Earth

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.
Latitude: 54.000000 * Longitude: 14.800000
Europe * Latitude: 54.000000 * Longitude: 14.800000
Monthly mean runoff rates for Europe (December 1950 - December 2015) on a regular 0.5 degree grid. The data are estimated on the basis of streamflow observations from small catchments which are combined with gridded observations of precipitation and temperature using machine learning. The resulting machine learning model allows to predict monthly mean runoff rates at all grid-cells of the considered atmospheric drivers.
69.8 MBytes

Download Data

Download dataset