%0 Generic %A Ghiggi, Gionata %A Gudmundsson, Lukas %A Humphrey, Vincent %D 2019 %T GRUN : Global Runoff Reconstruction %U https://figshare.com/articles/dataset/GRUN_Global_Runoff_Reconstruction/9228176 %R 10.6084/m9.figshare.9228176.v1 %2 https://ndownloader.figshare.com/files/16807844 %2 https://ndownloader.figshare.com/files/17046731 %K runoff %K freshwater resources %K drought %K hydrological droughts %K reconstruction %K historical data %K hydrological model %K land surface model %K statistical model %K machine learning %K climatology %K hydrology %K water cycle %K climate %K climate variability %K climate change %K Hydrology %K Natural Hazards %K Water Resources Engineering %K Surfacewater Hydrology %K Simulation and Modelling %K Climate Science %K Climatology (excl. Climate Change Processes) %X 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.