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GRUN: an observation-based global gridded runoff dataset from 1902 to 2014


Title (Dublin Core)

GRUN: an observation-based global gridded runoff dataset from 1902 to 2014

Description (Dublin Core)

<p>Freshwater resources are of high societal relevance, and understanding their
past variability is vital to water management in the context of ongoing
climate change. This study introduces a global gridded monthly
reconstruction of runoff covering the period from 1902 to 2014. In situ
streamflow observations are used to train a machine learning algorithm that
predicts monthly runoff rates based on antecedent precipitation and
temperature from an atmospheric reanalysis. The accuracy of this
reconstruction is assessed with cross-validation and compared with an
independent set of discharge observations for large river basins. The
presented dataset agrees on average better with the streamflow observations
than an ensemble of 13 state-of-the art global hydrological model runoff
simulations. We estimate a global long-term mean runoff of 38&thinsp;452&thinsp;km<span class="inline-formula"><sup>3</sup></span>&thinsp;yr<span class="inline-formula"><sup>−1</sup></span> in agreement with previous assessments. The temporal coverage of
the reconstruction offers an unprecedented view on large-scale features of
runoff variability in regions with limited data coverage, making it an
ideal candidate for large-scale hydro-climatic process studies, water
resource assessments, and evaluating and refining existing hydrological
models. The paper closes with example applications fostering the
understanding of global freshwater dynamics, interannual variability,
drought propagation and the response of runoff to atmospheric
teleconnections. The GRUN dataset is available at
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(Ghiggi et al.,

Creator (Dublin Core)

G. Ghiggi
V. Humphrey
S. I. Seneviratne
L. Gudmundsson

Subject (Dublin Core)

Environmental sciences

Publisher (Dublin Core)

Copernicus Publications

Date (Dublin Core)


Type (Dublin Core)


Identifier (Dublin Core)


Source (Dublin Core)

Earth System Science Data, Vol 11, Pp 1655-1674 (2019)

Language (Dublin Core)


Relation (Dublin Core)

Provenance (Dublin Core)

Journal Licence: CC BY