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

Ghiggi, Gionata and Humphrey, Vincent and Seneviratne, Sonia I. and Gudmundsson, Lukas (2019) GRUN: an observation-based global gridded runoff dataset from 1902 to 2014. Earth System Science Data, 11 (4). pp. 1655-1674. ISSN 1866-3516. https://resolver.caltech.edu/CaltechAUTHORS:20191205-140258222

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Abstract

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 38 452 km³ yr⁻¹ 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 https://doi.org/10.6084/m9.figshare.9228176 (Ghiggi et al., 2019).


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.5194/essd-11-1655-2019DOIArticle
https://doi.org/10.5194/essd-11-1655-2019-supplementDOISupplement
https://doi.org/10.6084/m9.figshare.9228176DOIGRUN dataset
ORCID:
AuthorORCID
Ghiggi, Gionata0000-0002-0818-0865
Humphrey, Vincent0000-0002-2541-6382
Gudmundsson, Lukas0000-0003-3539-8621
Additional Information:© 2019 Author(s). This work is distributed under the Creative Commons Attribution 4.0 License. Published by Copernicus Publications. Received: 14 Feb 2019 – Discussion started: 12 Mar 2019 – Revised: 12 Aug 2019 – Accepted: 03 Oct 2019 – Published: 13 Nov 2019. Author contributions. LG initiated this investigation. GG, VH, SIS and LG designed the study. GG developed the model code and performed the analysis. GG prepared the paper with contributions from all co-authors. The authors declare that they have no conflict of interest. We thank Hyungjun Kim for providing us with early access to the GSWP3 dataset and GRDC for the river discharge observations. Sonia I. Seneviratne acknowledges partial support from the ERC DROUGHT-HEAT project funded by the European Community’s Seventh Framework Programme (grant agreement FP7-IDEAS-ERC-617518). Vincent Humphrey acknowledges support from the Swiss National Science Foundation (grant agreement P400P2_180784). This paper was edited by Alexander Gelfan and reviewed by two anonymous referees.
Funders:
Funding AgencyGrant Number
European Research Council (ERC)617518
Swiss National Science Foundation (SNSF)P400P2_180784
Issue or Number:4
Record Number:CaltechAUTHORS:20191205-140258222
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20191205-140258222
Official Citation:Ghiggi, G., Humphrey, V., Seneviratne, S. I., and Gudmundsson, L.: GRUN: an observation-based global gridded runoff dataset from 1902 to 2014, Earth Syst. Sci. Data, 11, 1655–1674, https://doi.org/10.5194/essd-11-1655-2019, 2019
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:100213
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:06 Dec 2019 17:30
Last Modified:06 Dec 2019 17:30

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