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modified on 2021-11-23, 20:46
G-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.

File description

- 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.

- 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.

- 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.

- 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.