GRUN: An observations-based global gridded runoff dataset from 1902 to 2014

Abstract

Abstract. Freshwater resources are of high societal relevance and understanding their past variability is vital to water management in the context of current and future climatic change. This study introduces a global gridded monthly reconstruction of runoff covering the period from 1902 to 2014. In-situ streamflow observations are used to train a machine learning algorithm that predicts monthly runoff rates based on antecedent precipitation and temperature from an atmospheric reanalysis. The accuracy of this reconstruction is assessed with cross-validation and compared with an independent set of discharge observations for large river basins. The presented dataset agrees on average better with the streamflow observations than an ensemble of 13 state-of-the art global hydrological model runoff simulations. We estimate a global long-term mean runoff of 37 419 km3 yr−1 in agreement with previous assessments. The temporal coverage of the reconstruction offers an unprecedented view on large-scale features of runoff variability also in regions with limited data coverage, making it an ideal candidate for large-scale hydro-climatic process studies, water resources assessments and for evaluating and refining existing hydrological models. The paper closes with example applications fostering the understanding of global freshwater dynamics, interannual variability, drought propagation and the response of runoff to atmospheric teleconnections. The GRUN dataset is available from the ETHZ Research Collection at https://doi.org/10.3929/ethz-b-000324386 (Ghiggi et al., 2019).

Historical and future changes in global flood magnitude – evidence from a model-observation investigation

“…GHMs also enable 'factorial' experiments to explore the individual roles of atmospheric forcing, land use change and other drivers of change on streamflow trends. However, unlike climate models, for which the performance in terms of reproducing trends of extreme precipitation has been evaluated substantially (Kiktev et al, 2003;Kiktev et al, 2007;Kumar et al, 2013;Sakaguchi et al, 2012), the performance of GHMs has been assessed mostly on their capacity to represent physical features of the hydrological regime, such as streamflow percentiles, the seasonal cycle or the timing of peak discharge (Gudmundsson et al, 2012a;Zaherpour et al, 2018;Beck et al, 2017;Zhao et al, 2017;Veldkamp et al, 2018;Pokhrel et al, 2012;Biemans et al, 2011;Giuntoli et al, 2018). Streamflow variability can be subject not only to long-term changes in atmospheric forcing, but also to climate variability (e.g.…”

“…The particular interest is in reconciling observed and simulated trends in historical streamflow extremes at the global and continental scale using the Global Streamflow Indices and Metadata (GSIM) archive Gudmundsson et al, 2018b), to-date the largest possible streamflow observations database. GSIM has been used in recent global scale investigations and is also an important source for the production of GRUN, a data-driven century long runoff reconstruction (Ghiggi et al, 2019). The second objective is to determine the representativeness of observation locations (streamflow gauges) in GHM simulations by comparing trends simulated at these locations to trends simulated across all land grid points of GHMs.…”

Artificial Intelligence and Machine Learning to Assist Climate Change Monitoring

“…It is presently more made that the Earth's atmosphere is warming that the primary driver will be the development of regular house gases (GHGs) in the Journal of Artificial Intelligence and Systems planet, created by anthropogenic non-renewable energy source burning just as change in land use (generally deforestation) [30]. Environmental change has offered ascend to a ton of unmistakable impacts, especially advancement of the Earth's forceful region temperature and of marine warming content, dissolving of marine ice and glaciers and damage of ice mass from the Greenland and Antarctica ice sheets [31]. Sea warming prompts winter improvement of marine waters, thus ocean level ascent.…”

Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges

“…soil moisture). The gap between these two fields is however quickly reducing, with the development of gridded streamflow observations (Fekete et al 2002, Gudmundsson and Seneviratne 2016, Ghiggi et al 2019, ever larger domains covered by rainfallrunoff models (Beck et al 2016), the ever finer resolution of large-scale models (Wood et al 2011, assessments of the influence of catchment-scale processes on the performances of large-scale models (Kauffeldt et al 2016, Fang et al 2017, Veldkamp et al 2018, Zaherpour et al 2018, and the inclusion of streamflow simulations from macroscale models in LSH investigations (Rakovec et al 2016, Zink et al 2017, Do et al 2019.…”