Articles | Volume 11, issue 4
https://doi.org/10.5194/essd-11-1655-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-11-1655-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
GRUN: an observation-based global gridded runoff dataset from 1902 to 2014
Institute for Atmospheric and Climate Science, ETH Zurich,
Universitaetstrasse 16, 8092 Zurich, Switzerland
Environmental Remote Sensing Laboratory (LTE), EPFL, 1005 Lausanne,
Switzerland
Vincent Humphrey
Institute for Atmospheric and Climate Science, ETH Zurich,
Universitaetstrasse 16, 8092 Zurich, Switzerland
Division of Geological and Planetary Sciences, California Institute
of Technology, Pasadena, CA, USA
Sonia I. Seneviratne
Institute for Atmospheric and Climate Science, ETH Zurich,
Universitaetstrasse 16, 8092 Zurich, Switzerland
Lukas Gudmundsson
Institute for Atmospheric and Climate Science, ETH Zurich,
Universitaetstrasse 16, 8092 Zurich, Switzerland
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Latest update: 03 Nov 2024
Short summary
Freshwater resources are of high societal relevance and understanding their past variability is vital to water management in the context of current and future climatic change. This study introduces GRUN: the first global gridded monthly reconstruction of runoff covering the period from 1902 to 2014. The dataset agrees on average much better with the streamflow observations than an ensemble of 13 state-of-the-art global hydrological models and will foster the understanding of freshwater dynamics.
Freshwater resources are of high societal relevance and understanding their past variability is...
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