G-RUN : Global Runoff Reconstruction

An updated and extended version of the dataset (referred as G-RUN ENSEMBLE) is available at https://figshare.com/articles/dataset/G-RUN_ENSEMBLE/12794075<br><br>-------------------------------------------------------------------------------------------------<br>The dataset contains a gridded global reconstruction of monthly runoff timeseries. In-situ streamflow observations from the GSIM dataset are used to train a machine learning algorithm that predicts monthly runoff rates based on antecedent precipitation and temperature from the Global Soil Wetness Project Phase 3 (GSWP3) meteorological forcing dataset. We thank Prof. Dr. Hyungjun Kim for developing the GSWP3 dataset and providing us with early access to the data. The data are provided in NetCDFv4 format at monthly resolution covering the period 1902-2014. <br>The GRUN reconstruction ("GRUN_v1_GSWP3_WGS84_05_1902_2014.nc" file) is provided on a 0.5 degrees (WGS84) grid in units of mm/day. The runoff time series correspond to the ensemble mean of 50 reconstructions obtained by training the machine learning model with different subsets of data. The individual ensemble members of the reconstruction are provided in the "Realizations_GRUN_v1_GSWP3_WGS84_05_1902_2014.zip" file.<br>When using this dataset, please cite: Ghiggi, G., Humphrey, V., Seneviratne, S. I., Gudmundsson (2019), GRUN: An observations-based global gridded runoff dataset from 1902 to 2014, Earth Syst. Sci. Data, 2019, DOI: https://doi.org/10.5194/essd-2019-32<br> The complete collection of in-situ streamflow observations from the GSIM archive can be found at: - https://doi.pangaea.de/10.1594/PANGAEA.887477 - https://doi.pangaea.de/10.1594/PANGAEA.887470 <br>For further information on the GSIM dataset see: - https://doi.org/10.5194/essd-10-765-2018 - https://doi.org/10.5194/essd-10-787-2018 <br>For further information on GSWP3, see: - https://doi.org/10.20783/DIAS.501 - https://hyungjun.github.io/GSWP3.DataDescription - http://hydro.iis.u-tokyo.ac.jp/GSWP3/exp1.html

本数据集的更新扩展版本（命名为G-RUN ENSEMBLE）可于以下链接获取：https://figshare.com/articles/dataset/G-RUN_ENSEMBLE/12794075

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本数据集包含网格化的全球逐月径流序列重建结果。本研究采用GSIM数据集（GSIM）中的原位河道流量观测数据，训练机器学习算法以基于全球土壤湿度计划第三期（Global Soil Wetness Project Phase 3, GSWP3）气象强迫数据集提供的前期降水与气温参数，实现逐月径流速率的预测。特此感谢金亨俊教授（Prof. Dr. Hyungjun Kim）开发GSWP3数据集并向我们提供早期数据访问权限。

数据集以NetCDFv4格式存储，时间分辨率为逐月，覆盖1902年至2014年的时段。
GRUN重建数据集（GRUN，对应文件为"GRUN_v1_GSWP3_WGS84_05_1902_2014.nc"）采用0.5°（WGS84）网格，单位为毫米/日。该径流序列对应50次模型重建结果的集合平均，这些重建结果通过采用不同的数据子集训练机器学习模型得到。本次重建的单个集合成员数据存储于"Realizations_GRUN_v1_GSWP3_WGS84_05_1902_2014.zip"压缩文件中。

使用本数据集时，请引用如下文献：Ghiggi G, Humphrey V, Seneviratne S I, Gudmundsson. 2019. GRUN：一套基于观测的1902-2014年全球网格化径流数据集. 地球系统科学数据（Earth Syst. Sci. Data）, 2019. DOI: https://doi.org/10.5194/essd-2019-32

GSIM档案库中的完整原位河道流量观测数据集可通过以下链接获取：
- https://doi.pangaea.de/10.1594/PANGAEA.887477
- https://doi.pangaea.de/10.1594/PANGAEA.887470

如需了解GSIM数据集的更多信息，请参考：
- https://doi.org/10.5194/essd-10-765-2018
- https://doi.org/10.5194/essd-10-787-2018

如需了解GSWP3的更多信息，请参考：
- https://doi.org/10.20783/DIAS.501
- https://hyungjun.github.io/GSWP3.DataDescription
- http://hydro.iis.u-tokyo.ac.jp/GSWP3/exp1.html