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G-RUN ENSEMBLE
dataset
posted on 03.05.2021, 20:46 authored by Gionata GhiggiGionata Ghiggi, Vincent HumphreyVincent Humphrey, Lukas Gudmundsson, Sonia I SeneviratneG-RUN ENSEMBLE (pronounced GeRUN) consists in a multi-forcing global reanalysis of monthly runoff rates created by means of machine learning and a global collection of river discharge observations.
G-RUN ENSEMBLE allows for an unprecedented view on global terrestrial water dynamics on time scales ranging from months to a full century. Quantification of the uncertainty stemming from the atmospheric forcing data makes G-RUN ENSEMBLE the ideal candidate for reliable and robust water resources assessments.
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File description
- G-RUN_ENSEMBLE_MMM.nc covers the time period from 1902 to 2019 and provide the median of the G-RUN ENSEMBLE members. If you want to rely on one single estimate this is likely the file you are interested in.
- G-RUN_ENSEMBLE_MEMBERS.zip contains ensemble mean reconstructions for 21 different atmospheric forcing datasets. The time range depends on the considered forcing.
- Each remaining file called G-RUN_ENSEMBLE_*.zip (where * denotes the acronym of the atmospheric forcing dataset used to force the model), contains 25 runoff reconstructions obtained by training models on different subsets of the available runoff observations.
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References
- Ghiggi, G., Humphrey, V., Seneviratne, S. I., & Gudmundsson, L. (2021). G-RUN ENSEMBLE: A multi-forcing observation-based global runoff reanalysis. Water Resources Research, 57(5), e2020WR028787. https://doi.org/10.1029/2020WR028787
- 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,
11(4), 1655–1674. https://doi.org/10.5194/essd-11-1655-2019
CC BY 4.0