Articles | Volume 19, issue 6
https://doi.org/10.5194/hess-19-2859-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-19-2859-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| Highlight paper
| 
22 Jun 2015
Research article | Highlight paper |
| 22 Jun 2015
Towards observation-based gridded runoff estimates for Europe
L. Gudmundsson
CORRESPONDING AUTHOR
Institute for Atmospheric and Climate Science, ETH Zurich, Universitaetstrasse 16, 8092 Zurich, Switzerland
S. I. Seneviratne
CORRESPONDING AUTHOR
Institute for Atmospheric and Climate Science, ETH Zurich, Universitaetstrasse 16, 8092 Zurich, Switzerland
Viewed
Total article views: 8,178 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 18 Nov 2014)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 5,377 | 2,572 | 229 | 8,178 | 190 | 206 |
- HTML: 5,377
- PDF: 2,572
- XML: 229
- Total: 8,178
- BibTeX: 190
- EndNote: 206
Total article views: 6,916 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 22 Jun 2015)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 4,893 | 1,831 | 192 | 6,916 | 160 | 182 |
- HTML: 4,893
- PDF: 1,831
- XML: 192
- Total: 6,916
- BibTeX: 160
- EndNote: 182
Total article views: 1,262 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 18 Nov 2014)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 484 | 741 | 37 | 1,262 | 30 | 24 |
- HTML: 484
- PDF: 741
- XML: 37
- Total: 1,262
- BibTeX: 30
- EndNote: 24
Cited
38 citations as recorded by crossref.
- GRQA: Global River Water Quality Archive H. Virro et al. 10.5194/essd-13-5483-2021
- Large‐Scale Controls of the Surface Water Balance Over Land: Insights From a Systematic Review and Meta‐Analysis R. Padrón et al. 10.1002/2017WR021215
- Coupling physically based and data-driven models for assessing freshwater inflow into the Small Aral Sea G. Ayzel & A. Izhitskiy 10.5194/piahs-379-151-2018
- Improved data splitting methods for data-driven hydrological model development based on a large number of catchment samples J. Chen et al. 10.1016/j.jhydrol.2022.128340
- CLIMFILL v0.9: a framework for intelligently gap filling Earth observations V. Bessenbacher et al. 10.5194/gmd-15-4569-2022
- Deep hydrothermal fluid–rock interaction: the thermal springs of Da Qaidam, China I. Stober et al. 10.1111/gfl.12190
- Hydrology needed to manage droughts: the 2015 European case H. Van Lanen et al. 10.1002/hyp.10838
- The Impact of an Open Water Balance Assumption on Understanding the Factors Controlling the Long‐Term Streamflow Components A. Ballarin et al. 10.1029/2022WR032413
- Assessment of machine learning models to predict daily streamflow in a semiarid river catchment A. Kumar et al. 10.1007/s00521-024-09748-1
- Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation H. Meyer et al. 10.1016/j.envsoft.2017.12.001
- GRUN: an observation-based global gridded runoff dataset from 1902 to 2014 G. Ghiggi et al. 10.5194/essd-11-1655-2019
- Optimizing Soil Moisture Station Networks for Future Climates V. Bessenbacher et al. 10.1029/2022GL101667
- G‐RUN ENSEMBLE: A Multi‐Forcing Observation‐Based Global Runoff Reanalysis G. Ghiggi et al. 10.1029/2020WR028787
- Shifting from traditional landslide occurrence modeling to scenario estimation with a “glass-box” machine learning F. Caleca et al. 10.1016/j.scitotenv.2024.175277
- A global reconstruction of climate‐driven subdecadal water storage variability V. Humphrey et al. 10.1002/2017GL072564
- A random forests-based hedonic price model accounting for spatial autocorrelation E. Tepe 10.1007/s10109-024-00449-w
- Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction H. Meyer et al. 10.1016/j.ecolmodel.2019.108815
- Streamflow sensitivity to water storage changes across Europe W. Berghuijs et al. 10.1002/2016GL067927
- High-end climate change impact on European runoff and low flows – exploring the effects of forcing biases L. Papadimitriou et al. 10.5194/hess-20-1785-2016
- A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources H. Tyralis et al. 10.3390/w11050910
- The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment L. Gudmundsson et al. 10.5194/essd-10-787-2018
- Evaluation and comparison of CMIP6 and CMIP5 model performance in simulating the runoff H. Guo et al. 10.1007/s00704-022-04118-0
- Historical Trends in Mean and Extreme Runoff and Streamflow Based on Observations and Climate Models B. Asadieh et al. 10.3390/w8050189
- Dominant process zones in a mixed fluvial–tidal delta are morphologically distinct M. Perignon et al. 10.5194/esurf-8-809-2020
- Spatio-temporal modeling of parcel-level land-use changes using machine learning methods E. Tepe & A. Safikhani 10.1016/j.scs.2023.104390
- Mapping urban temperature using crowd-sensing data and machine learning M. Zumwald et al. 10.1016/j.uclim.2020.100739
- A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework Z. Liang et al. 10.1007/s00704-017-2186-6
- Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review G. Balsamo et al. 10.3390/rs10122038
- Observation-based gridded runoff estimates for Europe (E-RUN version 1.1) L. Gudmundsson & S. Seneviratne 10.5194/essd-8-279-2016
- Global evaluation of runoff from 10 state-of-the-art hydrological models H. Beck et al. 10.5194/hess-21-2881-2017
- The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a daily streamflow archive and metadata H. Do et al. 10.5194/essd-10-765-2018
- Challenges in Applying Machine Learning Models for Hydrological Inference: A Case Study for Flooding Events Across Germany L. Schmidt et al. 10.1029/2019WR025924
- Compound droughts under climate change in Switzerland C. von Matt et al. 10.5194/nhess-24-1975-2024
- Application of Machine Learning to Model Wetland Inundation Patterns Across a Large Semiarid Floodplain S. Shaeri Karimi et al. 10.1029/2019WR024884
- Estimation of Alpine Grassland Forage Nitrogen Coupled with Hyperspectral Characteristics during Different Growth Periods on the Tibetan Plateau J. Gao et al. 10.3390/rs11182085
- Understanding climate phenomena with data-driven models B. Knüsel & C. Baumberger 10.1016/j.shpsa.2020.08.003
- Spatial representativeness of soil moisture using in situ, remote sensing, and land reanalysis data N. Nicolai‐Shaw et al. 10.1002/2015JD023305
- Basin‐scale runoff prediction: An Ensemble Kalman Filter framework based on global hydrometeorological data sets C. Lorenz et al. 10.1002/2014WR016794
37 citations as recorded by crossref.
- GRQA: Global River Water Quality Archive H. Virro et al. 10.5194/essd-13-5483-2021
- Large‐Scale Controls of the Surface Water Balance Over Land: Insights From a Systematic Review and Meta‐Analysis R. Padrón et al. 10.1002/2017WR021215
- Coupling physically based and data-driven models for assessing freshwater inflow into the Small Aral Sea G. Ayzel & A. Izhitskiy 10.5194/piahs-379-151-2018
- Improved data splitting methods for data-driven hydrological model development based on a large number of catchment samples J. Chen et al. 10.1016/j.jhydrol.2022.128340
- CLIMFILL v0.9: a framework for intelligently gap filling Earth observations V. Bessenbacher et al. 10.5194/gmd-15-4569-2022
- Deep hydrothermal fluid–rock interaction: the thermal springs of Da Qaidam, China I. Stober et al. 10.1111/gfl.12190
- Hydrology needed to manage droughts: the 2015 European case H. Van Lanen et al. 10.1002/hyp.10838
- The Impact of an Open Water Balance Assumption on Understanding the Factors Controlling the Long‐Term Streamflow Components A. Ballarin et al. 10.1029/2022WR032413
- Assessment of machine learning models to predict daily streamflow in a semiarid river catchment A. Kumar et al. 10.1007/s00521-024-09748-1
- Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation H. Meyer et al. 10.1016/j.envsoft.2017.12.001
- GRUN: an observation-based global gridded runoff dataset from 1902 to 2014 G. Ghiggi et al. 10.5194/essd-11-1655-2019
- Optimizing Soil Moisture Station Networks for Future Climates V. Bessenbacher et al. 10.1029/2022GL101667
- G‐RUN ENSEMBLE: A Multi‐Forcing Observation‐Based Global Runoff Reanalysis G. Ghiggi et al. 10.1029/2020WR028787
- Shifting from traditional landslide occurrence modeling to scenario estimation with a “glass-box” machine learning F. Caleca et al. 10.1016/j.scitotenv.2024.175277
- A global reconstruction of climate‐driven subdecadal water storage variability V. Humphrey et al. 10.1002/2017GL072564
- A random forests-based hedonic price model accounting for spatial autocorrelation E. Tepe 10.1007/s10109-024-00449-w
- Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction H. Meyer et al. 10.1016/j.ecolmodel.2019.108815
- Streamflow sensitivity to water storage changes across Europe W. Berghuijs et al. 10.1002/2016GL067927
- High-end climate change impact on European runoff and low flows – exploring the effects of forcing biases L. Papadimitriou et al. 10.5194/hess-20-1785-2016
- A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources H. Tyralis et al. 10.3390/w11050910
- The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment L. Gudmundsson et al. 10.5194/essd-10-787-2018
- Evaluation and comparison of CMIP6 and CMIP5 model performance in simulating the runoff H. Guo et al. 10.1007/s00704-022-04118-0
- Historical Trends in Mean and Extreme Runoff and Streamflow Based on Observations and Climate Models B. Asadieh et al. 10.3390/w8050189
- Dominant process zones in a mixed fluvial–tidal delta are morphologically distinct M. Perignon et al. 10.5194/esurf-8-809-2020
- Spatio-temporal modeling of parcel-level land-use changes using machine learning methods E. Tepe & A. Safikhani 10.1016/j.scs.2023.104390
- Mapping urban temperature using crowd-sensing data and machine learning M. Zumwald et al. 10.1016/j.uclim.2020.100739
- A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework Z. Liang et al. 10.1007/s00704-017-2186-6
- Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review G. Balsamo et al. 10.3390/rs10122038
- Observation-based gridded runoff estimates for Europe (E-RUN version 1.1) L. Gudmundsson & S. Seneviratne 10.5194/essd-8-279-2016
- Global evaluation of runoff from 10 state-of-the-art hydrological models H. Beck et al. 10.5194/hess-21-2881-2017
- The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a daily streamflow archive and metadata H. Do et al. 10.5194/essd-10-765-2018
- Challenges in Applying Machine Learning Models for Hydrological Inference: A Case Study for Flooding Events Across Germany L. Schmidt et al. 10.1029/2019WR025924
- Compound droughts under climate change in Switzerland C. von Matt et al. 10.5194/nhess-24-1975-2024
- Application of Machine Learning to Model Wetland Inundation Patterns Across a Large Semiarid Floodplain S. Shaeri Karimi et al. 10.1029/2019WR024884
- Estimation of Alpine Grassland Forage Nitrogen Coupled with Hyperspectral Characteristics during Different Growth Periods on the Tibetan Plateau J. Gao et al. 10.3390/rs11182085
- Understanding climate phenomena with data-driven models B. Knüsel & C. Baumberger 10.1016/j.shpsa.2020.08.003
- Spatial representativeness of soil moisture using in situ, remote sensing, and land reanalysis data N. Nicolai‐Shaw et al. 10.1002/2015JD023305
Saved (final revised paper)
Saved (final revised paper)
Saved (preprint)
Latest update: 13 Dec 2024
Short summary
Water storages and fluxes on land are key variables in the Earth system. To provide context for local investigations and to understand phenomena that emerge at large spatial scales, information on continental freshwater dynamics is needed. This paper presents a methodology to estimate continental-scale runoff on a 0.5° spatial grid, which combines the advantages of in situ observations with the power of machine learning regression. The resulting runoff estimates compare well with observations.
Water storages and fluxes on land are key variables in the Earth system. To provide context for...
