G-RUN ENSEMBLE

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.<br>------------------------------------------------------------------------------<b>File description </b><b><br></b>- <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.<br>- <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.<br>- 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><b> </b>------------------------------------------------------------------------------<br><b>References</b><br>- Ghiggi, G., Humphrey, V., Seneviratne, S. I., &amp; 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><br>- Ghiggi, G., Humphrey, V., Seneviratne, S. I., &amp; 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 <br>

G-RUN ENSEMBLE（发音为GeRUN）是一套多强迫全球月径流量再分析数据集，通过机器学习方法与全球河流流量观测（river discharge observations）集合构建而成。G-RUN ENSEMBLE能够提供前所未有的全球陆地水动态视角，时间尺度覆盖数月至一整个世纪。对大气强迫数据（atmospheric forcing data）带来的不确定性进行量化，使得G-RUN ENSEMBLE成为开展可靠且稳健的水资源评估的理想选择。<br>------------------------------------------------------------------------------<br><b>文件说明</b><br>- <i>G-RUN_ENSEMBLE_MMM.nc</i> 涵盖1902年至2019年的时间范围，提供G-RUN ENSEMBLE各集合成员（ensemble members）的中位数结果。若仅需单一估算结果，此文件即为您的目标选择。<br>- <i>G-RUN_ENSEMBLE_MEMBERS.zip</i> 包含21套不同大气强迫数据对应的集合平均（ensemble mean）再建结果，其时间范围取决于所采用的强迫数据集。<br>- 其余命名格式为<i>G-RUN_ENSEMBLE_*.zip</i>的文件（其中*代表用于驱动模型的大气强迫数据集的缩写），均包含25套径流再建结果（runoff reconstructions），这些结果通过基于可用径流观测的子集（subsets of the available runoff observations）训练模型得到。<br>------------------------------------------------------------------------------<br><b>参考文献</b><br>- Ghiggi, G., Humphrey, V., Seneviratne, S. I., & Gudmundsson, L. (2021). G-RUN ENSEMBLE：基于观测的多强迫全球径流再分析数据集。《水资源研究（Water Resources Research）》，57(5), e2020WR028787. https://doi.org/10.1029/2020WR028787<br><br>- Ghiggi, G., Humphrey, V., Seneviratne, S. I., & Gudmundsson, L. (2019). GRUN：1902-2014年基于观测的全球网格化径流数据集。《地球系统科学数据（Earth System Science Data）》，11(4), 1655–1674. https://doi.org/10.5194/essd-11-1655-2019