1/2
G-RUN ENSEMBLE
dataset
posted on 2021-05-03, 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.
------------------------------------------------------------------------------
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.
------------------------------------------------------------------------------
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
History
References
Usage metrics
Categories
Keywords
runofffreshwater resourcesdroughthydrological droughtsreconstructionhistorical dataland surface modelglobal hydrological modelmachine learningstatistical modelclimatologyhydrologyatmospheric forcingwater cycleclimateclimate changeuncertaintyHydrologyNatural HazardsWater Resources EngineeringSurfacewater HydrologySimulation and ModellingClimate ScienceClimatology (excl. Climate Change Processes)