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结果。本研究采用GSIM数据集的原位河道流量观测数据，训练机器学习算法，基于全球土壤湿度计划第三阶段（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。谨感谢金炯俊（Hyungjun Kim）教授开发GSWP3数据集并向我们提供早期数据访问权限。本数据集以NetCDFv4格式提供，时间分辨率为逐月，覆盖1902年至2014年时段。
GRUN重建结果（文件名为"GRUN_v1_GSWP3_WGS84_05_1902_2014.nc"）采用0.5度（WGS84）网格格式，单位为毫米/日。该径流时间序列为基于不同数据子集训练机器学习模型得到的50组重建结果的集合平均值。单组集合成员结果存储于"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. GRUN：一套基于观测的1902-2014年全球网格化径流数据集[J]. 地球系统科学数据（Earth Syst. Sci. Data）, 2019. DOI: https://doi.org/10.5194/essd-2019-32
GSIM数据集的完整原位河道流量观测档案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，请访问：
- 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，请访问：
- https://doi.org/10.20783/DIAS.501
- https://hyungjun.github.io/GSWP3.DataDescription
- http://hydro.iis.u-tokyo.ac.jp/GSWP3/exp1.html

AI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。