Journal cover Journal topic
Earth System Science Data The Data Publishing Journal
Earth Syst. Sci. Data, 8, 279-295, 2016
https://doi.org/10.5194/essd-8-279-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
07 Jul 2016
Observation-based gridded runoff estimates for Europe (E-RUN version 1.1)
Lukas Gudmundsson and Sonia I. Seneviratne Institute for Atmospheric and Climate Science, ETH Zurich, Universitaetstrasse 16, 8092 Zurich, Switzerland
Abstract. 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 databases. Observed monthly runoff rates are subsequently 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°  ×  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. The newly derived data are made publicly available at doi:10.1594/PANGAEA.861371.

Citation: Gudmundsson, L. and Seneviratne, S. I.: Observation-based gridded runoff estimates for Europe (E-RUN version 1.1), Earth Syst. Sci. Data, 8, 279-295, https://doi.org/10.5194/essd-8-279-2016, 2016.
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Short summary
Despite the scientific and societal relevance of freshwater, there are to date no observation-based pan-European runoff estimates available. Here we employ state-of-the-art techniques to estimate monthly runoff rates in Europe. The new data product is based on an unprecedented collection of river flow observations which are combined with atmospheric variables using machine learning. Potential applications of the presented product include climatological assessments and drought monitoring.
Despite the scientific and societal relevance of freshwater, there are to date no...
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