Articles | Volume 20, issue 8
https://doi.org/10.5194/hess-20-3277-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/hess-20-3277-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
12 Aug 2016
Research article |
| 12 Aug 2016
ENSO-conditioned weather resampling method for seasonal ensemble streamflow prediction
Joost V. L. Beckers
CORRESPONDING AUTHOR
Deltares, Delft, the Netherlands
Albrecht H. Weerts
Deltares, Delft, the Netherlands
Department of Environmental Sciences, Wageningen University, Wageningen, the Netherlands
Erik Tijdeman
Department of Hydrology, University of Freiburg, Freiburg, Germany
Edwin Welles
Deltares USA Inc, Silver Spring, Maryland, USA
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Cited
22 citations as recorded by crossref.
- Assessment of an ensemble seasonal streamflow forecasting system for Australia J. Bennett et al. 10.5194/hess-21-6007-2017
- Long-term ensemble forecast of snowmelt inflow into the Cheboksary Reservoir under two different weather scenarios A. Gelfan et al. 10.5194/hess-22-2073-2018
- Mitigating the Impacts of Climate Nonstationarity on Seasonal Streamflow Predictability in the U.S. Southwest F. Lehner et al. 10.1002/2017GL076043
- A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments A. Schepen et al. 10.5194/hess-22-1615-2018
- Utilizing Bivariate Climate Forecasts to Update the Probabilities of Ensemble Streamflow Prediction J. Sung et al. 10.3390/su12072905
- The role of probabilistic precipitation forecasts in hydrologic predictability S. Seo & J. Sung 10.1007/s00704-020-03273-6
- Subseasonal to seasonal streamflow forecasting in a semiarid watershed P. Broxton et al. 10.1111/1752-1688.13147
- Augmenting geophysical interpretation of data-driven operational water supply forecast modeling for a western US river using a hybrid machine learning approach S. Fleming et al. 10.1016/j.jhydrol.2021.126327
- Assessing the new Natural Resources Conservation Service water supply forecast model for the American West: A challenging test of explainable, automated, ensemble artificial intelligence S. Fleming et al. 10.1016/j.jhydrol.2021.126782
- Improving Meteorological Seasonal Forecasts for Hydrological Modeling in European Winter N. Stringer et al. 10.1175/JAMC-D-19-0094.1
- Improving monthly streamflow prediction in alpine regions: integrating HBV model with Bayesian neural network W. Ren et al. 10.1007/s00477-018-1553-x
- Benchmarking ensemble streamflow prediction skill in the UK S. Harrigan et al. 10.5194/hess-22-2023-2018
- Monthly streamflow forecasting at varying spatial scales in the Rhine basin S. Schick et al. 10.5194/hess-22-929-2018
- Evaluation of the potential of using subsets of historical climatological data for ensemble streamflow prediction (ESP) forecasting B. Sabzipour et al. 10.1016/j.jhydrol.2020.125656
- The development and evaluation of a hydrological seasonal forecast system prototype for predicting spring flood volumes in Swedish rivers K. Foster et al. 10.5194/hess-22-2953-2018
- Influence of internal variability on population exposure to hydroclimatic changes J. Mankin et al. 10.1088/1748-9326/aa5efc
- Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times S. Donegan et al. 10.5194/hess-25-4159-2021
- Enhancing Ensemble Seasonal Streamflow Forecasts in the Upper Colorado River Basin Using Multi‐Model Climate Forecasts S. Baker et al. 10.1111/1752-1688.12960
- An intercomparison of approaches for improving operational seasonal streamflow forecasts P. Mendoza et al. 10.5194/hess-21-3915-2017
- Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting O. Wani et al. 10.5194/hess-21-4021-2017
- Linking interannual river flow river variability across New Zealand to the Southern Annular Mode, 1979–2011 N. Li & G. McGregor 10.1002/hyp.11184
- Using a simple post-processor to predict residual uncertainty for multiple hydrological model outputs L. Ehlers et al. 10.1016/j.advwatres.2019.05.003
22 citations as recorded by crossref.
- Assessment of an ensemble seasonal streamflow forecasting system for Australia J. Bennett et al. 10.5194/hess-21-6007-2017
- Long-term ensemble forecast of snowmelt inflow into the Cheboksary Reservoir under two different weather scenarios A. Gelfan et al. 10.5194/hess-22-2073-2018
- Mitigating the Impacts of Climate Nonstationarity on Seasonal Streamflow Predictability in the U.S. Southwest F. Lehner et al. 10.1002/2017GL076043
- A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments A. Schepen et al. 10.5194/hess-22-1615-2018
- Utilizing Bivariate Climate Forecasts to Update the Probabilities of Ensemble Streamflow Prediction J. Sung et al. 10.3390/su12072905
- The role of probabilistic precipitation forecasts in hydrologic predictability S. Seo & J. Sung 10.1007/s00704-020-03273-6
- Subseasonal to seasonal streamflow forecasting in a semiarid watershed P. Broxton et al. 10.1111/1752-1688.13147
- Augmenting geophysical interpretation of data-driven operational water supply forecast modeling for a western US river using a hybrid machine learning approach S. Fleming et al. 10.1016/j.jhydrol.2021.126327
- Assessing the new Natural Resources Conservation Service water supply forecast model for the American West: A challenging test of explainable, automated, ensemble artificial intelligence S. Fleming et al. 10.1016/j.jhydrol.2021.126782
- Improving Meteorological Seasonal Forecasts for Hydrological Modeling in European Winter N. Stringer et al. 10.1175/JAMC-D-19-0094.1
- Improving monthly streamflow prediction in alpine regions: integrating HBV model with Bayesian neural network W. Ren et al. 10.1007/s00477-018-1553-x
- Benchmarking ensemble streamflow prediction skill in the UK S. Harrigan et al. 10.5194/hess-22-2023-2018
- Monthly streamflow forecasting at varying spatial scales in the Rhine basin S. Schick et al. 10.5194/hess-22-929-2018
- Evaluation of the potential of using subsets of historical climatological data for ensemble streamflow prediction (ESP) forecasting B. Sabzipour et al. 10.1016/j.jhydrol.2020.125656
- The development and evaluation of a hydrological seasonal forecast system prototype for predicting spring flood volumes in Swedish rivers K. Foster et al. 10.5194/hess-22-2953-2018
- Influence of internal variability on population exposure to hydroclimatic changes J. Mankin et al. 10.1088/1748-9326/aa5efc
- Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times S. Donegan et al. 10.5194/hess-25-4159-2021
- Enhancing Ensemble Seasonal Streamflow Forecasts in the Upper Colorado River Basin Using Multi‐Model Climate Forecasts S. Baker et al. 10.1111/1752-1688.12960
- An intercomparison of approaches for improving operational seasonal streamflow forecasts P. Mendoza et al. 10.5194/hess-21-3915-2017
- Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting O. Wani et al. 10.5194/hess-21-4021-2017
- Linking interannual river flow river variability across New Zealand to the Southern Annular Mode, 1979–2011 N. Li & G. McGregor 10.1002/hyp.11184
- Using a simple post-processor to predict residual uncertainty for multiple hydrological model outputs L. Ehlers et al. 10.1016/j.advwatres.2019.05.003
Saved (preprint)
Latest update: 02 Nov 2024
Short summary
Oceanic–atmospheric climate modes, such as El Niño–Southern Oscillation (ENSO), are known to affect the streamflow regime in many rivers around the world. A new method is presented for ENSO conditioning of the ensemble streamflow prediction (ESP) method, which is often used for seasonal streamflow forecasting. The method was tested on three tributaries of the Columbia River, OR. Results show an improvement in forecast skill compared to the standard ESP.
Oceanic–atmospheric climate modes, such as El Niño–Southern Oscillation (ENSO), are known to...
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