GRUN : Global Runoff Reconstruction
datasetposted on 09.09.2019 by Gionata Ghiggi, Lukas Gudmundsson, Vincent Humphrey
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
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.
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.
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
The complete collection of in-situ streamflow observations from the GSIM archive can be found at:
For further information on the GSIM dataset see:
For further information on GSWP3, see: