GRUN : Global Runoff Reconstruction

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

本数据集包含一套网格化的全球逐月径流时间序列重建成果。本研究采用GSIM数据集（GSIM dataset）的原位河道流量观测数据训练机器学习算法，基于全球土壤湿度项目第三阶段（Global Soil Wetness Project Phase 3, GSWP3）气象强迫数据集提供的前期降水与气温数据，逐月预测径流速率。我们感谢Hyungjun Kim教授开发GSWP3数据集并向我们提供数据的早期访问权限。本数据集采用NetCDFv4格式存储，分辨率为逐月，时间覆盖范围为1902年至2014年。
本数据集提供的GRUN重建成果（对应文件为"GRUN_v1_GSWP3_WGS84_05_1902_2014.nc"）采用0.5度（WGS84）网格，单位为毫米/日。该径流时间序列为通过使用不同数据子集训练机器学习模型所得到的50次重建结果的集合平均。各集合成员数据已打包存储于"Realizations_GRUN_v1_GSWP3_WGS84_05_1902_2014.zip"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：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 Syst. Sci. Data）, 2019, DOI: https://doi.org/10.5194/essd-2019-32
GSIM档案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
关于GSIM数据集的更多详细信息，请参考以下链接：
- https://doi.org/10.5194/essd-10-765-2018
- https://doi.org/10.5194/essd-10-787-2018
关于GSWP3的更多详细信息，请参考以下链接：
- https://doi.org/10.20783/DIAS.501
- https://hyungjun.github.io/GSWP3.DataDescription
- http://hydro.iis.u-tokyo.ac.jp/GSWP3/exp1.html

搜集汇总

背景与挑战

背景概述

GRUN数据集是一个全球网格化的月径流重建时间序列，覆盖1902年至2014年，基于机器学习算法利用GSWP3气象数据和GSIM原位观测训练生成。数据以0.5度空间分辨率提供，包括集合平均和50个集合成员，适用于水文学、气候研究和自然灾害分析等领域。