G-RUN ENSEMBLE

<div>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. </div><div>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.</div><div><br></div><div>------------------------------------------------------------------------------</div><div><b>File description </b></div><div><b><br></b></div><div><div>- <i>G-RUN_ENSEMBLE_MMM.nc </i>covers<i> </i>the time period from 1902 to 2019 and provide the<i> </i>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.</div><div><br></div><div>- <i>G-RUN_ENSEMBLE_MEMBERS.zip </i>contains ensemble mean reconstructions for 21 different atmospheric forcing datasets. The time range depends on the considered forcing.</div></div><div><br></div><div>- Each remaining file called <i>G-RUN_ENSEMBLE_*.zip (</i><i></i>where * denotes the acronym of the atmospheric forcing dataset used to force the model)<i>, </i>contains 25 runoff reconstructions obtained by training models on different subsets of the available runoff observations.<br></div><div><b> </b></div><div>------------------------------------------------------------------------------<br></div><div><b>References</b></div><div><br></div><div>- 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<br></div><div><br></div><div>- Ghiggi, G., Humphrey, V., Seneviratne, S. I., & Gudmundsson, L. (2019). GRUN: an observation-based global gridded runoff dataset from 1902 to 2014. <i>Earth System Science Data</i>, <i>11</i>(4), 1655–1674. https://doi.org/10.5194/essd-11-1655-2019 </div><div><br></div><div><div><div> </div></div></div>