The DOI matches its Crossref record; 101 of the 101 references it cites were found in a registry or an index.
GRUN: an observation-based global gridded runoff dataset from 1902 to 2014
Open accessAbstract
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).
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Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
River flow forecasting through conceptual models part I — A discussion of principles
Nash, J. E., & Sutcliffe, J. V. (1970) · Journal of Hydrology · doi:10.1016/0022-1694(70)90255-6 · Cited by 23,674
Estimates of the Regression Coefficient Based on Kendall's Tau
Sen, P. K. (1968) · Journal of the American Statistical Association · doi:10.1080/01621459.1968.10480934 · Cited by 14,517
Updated world map of the Köppen-Geiger climate classification
Peel, M. C., Finlayson, B. L., & McMahon, T. A. (2007) · doi:10.5194/hessd-4-439-2007 · Cited by 13,640
Updated world map of the Köppen-Geiger climate classification
(2007) · doi:10.5194/hess-11-1633-2007 · Cited by 13,640
Global threats to human water security and river biodiversity
Vörösmarty, C. J., McIntyre, P. B., Gessner, M. O., Dudgeon, D., Prusevich, A., Green, P., Glidden, S., Bunn, S. E., Sullivan, C. A., Liermann, C. R., et al. (2010) · Nature · doi:10.1038/nature09440 · Cited by 7,582
Four billion people facing severe water scarcity
Mekonnen, M. M., & Hoekstra, A. Y. (2016) · Science Advances · doi:10.1126/sciadv.1500323 · Cited by 5,531
Global Water Resources: Vulnerability from Climate Change and Population Growth
Vörösmarty, C. J., Green, P., Salisbury, J., & Lammers, R. B. (2000) · Science · doi:10.1126/science.289.5477.284 · Cited by 5,036
Global hydrological cycles and world water resources
Oki, T., & Kanae, S. (2006) · Science · doi:10.1126/science.1128845 · Cited by 4,392
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Metadata
- Authors
- Ghiggi, G., Humphrey, V., Seneviratne, S. I., & Gudmundsson, L.
- Venue
- Earth System Science Data
- Publisher
- Copernicus GmbH
- Published
- 2019-11-13
- Volume
- 11
- Issue
- 4
- Pages
- 1655-1674
- ISSN
- 1866-3508 (ISSN-L)
- 1866-3516
- OpenAlex
- W2921518683
- Cited by
- 386
- References
- 101
- FWCI
- 18.67
- Type
- journal article
- Language
- en
- Open access
- gold
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- Ghiggi, G., Humphrey, V., Seneviratne, S. I., & Gudmundsson, L. (2019). GRUN: an observation-based global gridded runoff dataset from 1902 to 2014. Earth System Science Data. https://doi.org/10.5194/essd-11-1655-2019
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