Articles | Volume 17, issue 2
https://doi.org/10.5194/hess-17-795-2013
© Author(s) 2013. 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-17-795-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
22 Feb 2013
Research article |
| 22 Feb 2013
A Bayesian joint probability post-processor for reducing errors and quantifying uncertainty in monthly streamflow predictions
P. Pokhrel
CSIRO Land and Water, Graham Road, Highett, Victoria, Australia
D. E. Robertson
CSIRO Land and Water, Graham Road, Highett, Victoria, Australia
Q. J. Wang
CSIRO Land and Water, Graham Road, Highett, Victoria, Australia
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Cited
16 citations as recorded by crossref.
- Hydrological uncertainty processor based on a copula function Z. Liu et al. https://doi.org/10.1080/02626667.2017.1410278
- Streamflow forecast uncertainty evolution and its effect on real-time reservoir operation L. Chen et al. https://doi.org/10.1016/j.jhydrol.2016.06.015
- An error model for long-range ensemble forecasts of ephemeral rivers J. Bennett et al. https://doi.org/10.1016/j.advwatres.2021.103891
- A Bayesian Hierarchical Framework for Postprocessing Daily Streamflow Simulations across a River Network Á. Ossandón et al. https://doi.org/10.1175/JHM-D-21-0167.1
- Evaluating post-processing approaches for monthly and seasonal streamflow forecasts F. Woldemeskel et al. https://doi.org/10.5194/hess-22-6257-2018
- Quantifying predictive uncertainty of streamflow forecasts based on a Bayesian joint probability model T. Zhao et al. https://doi.org/10.1016/j.jhydrol.2015.06.043
- Statistical calibration and bridging of ECMWF System4 outputs for forecasting seasonal precipitation over China Z. Peng et al. https://doi.org/10.1002/2013JD021162
- Climate index weighting of ensemble streamflow forecasts using a simple Bayesian approach A. Bradley et al. https://doi.org/10.1002/2014WR016811
- Which precipitation forecasts to use? Deterministic versus coarser‐resolution ensemble NWP models P. Zhao et al. https://doi.org/10.1002/qj.3952
- How well do the ERA‐Interim, ERA‐5, GLDAS‐2.1 and NCEP‐R2 reanalysis datasets represent daily air temperature over the Tibetan Plateau? L. Liu et al. https://doi.org/10.1002/joc.6867
- Using a simple post-processor to predict residual uncertainty for multiple hydrological model outputs L. Ehlers et al. https://doi.org/10.1016/j.advwatres.2019.05.003
- Bayesian Framework for Uncertainty Quantification and Bias Correction of Projected Streamflow in Climate Change Impact Assessment J. George & P. Athira https://doi.org/10.1007/s11269-024-03876-y
- Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting D. Robertson et al. https://doi.org/10.5194/hess-17-3587-2013
- On the prediction of persistent processes using the output of deterministic models H. Tyralis & D. Koutsoyiannis https://doi.org/10.1080/02626667.2017.1361535
- Innovative Analysis of Runoff Temporal Behavior through Bayesian Networks J. Molina et al. https://doi.org/10.3390/w8110484
- Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework A. Gragne et al. https://doi.org/10.5194/hess-19-3695-2015
16 citations as recorded by crossref.
- Hydrological uncertainty processor based on a copula function Z. Liu et al. https://doi.org/10.1080/02626667.2017.1410278
- Streamflow forecast uncertainty evolution and its effect on real-time reservoir operation L. Chen et al. https://doi.org/10.1016/j.jhydrol.2016.06.015
- An error model for long-range ensemble forecasts of ephemeral rivers J. Bennett et al. https://doi.org/10.1016/j.advwatres.2021.103891
- A Bayesian Hierarchical Framework for Postprocessing Daily Streamflow Simulations across a River Network Á. Ossandón et al. https://doi.org/10.1175/JHM-D-21-0167.1
- Evaluating post-processing approaches for monthly and seasonal streamflow forecasts F. Woldemeskel et al. https://doi.org/10.5194/hess-22-6257-2018
- Quantifying predictive uncertainty of streamflow forecasts based on a Bayesian joint probability model T. Zhao et al. https://doi.org/10.1016/j.jhydrol.2015.06.043
- Statistical calibration and bridging of ECMWF System4 outputs for forecasting seasonal precipitation over China Z. Peng et al. https://doi.org/10.1002/2013JD021162
- Climate index weighting of ensemble streamflow forecasts using a simple Bayesian approach A. Bradley et al. https://doi.org/10.1002/2014WR016811
- Which precipitation forecasts to use? Deterministic versus coarser‐resolution ensemble NWP models P. Zhao et al. https://doi.org/10.1002/qj.3952
- How well do the ERA‐Interim, ERA‐5, GLDAS‐2.1 and NCEP‐R2 reanalysis datasets represent daily air temperature over the Tibetan Plateau? L. Liu et al. https://doi.org/10.1002/joc.6867
- Using a simple post-processor to predict residual uncertainty for multiple hydrological model outputs L. Ehlers et al. https://doi.org/10.1016/j.advwatres.2019.05.003
- Bayesian Framework for Uncertainty Quantification and Bias Correction of Projected Streamflow in Climate Change Impact Assessment J. George & P. Athira https://doi.org/10.1007/s11269-024-03876-y
- Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting D. Robertson et al. https://doi.org/10.5194/hess-17-3587-2013
- On the prediction of persistent processes using the output of deterministic models H. Tyralis & D. Koutsoyiannis https://doi.org/10.1080/02626667.2017.1361535
- Innovative Analysis of Runoff Temporal Behavior through Bayesian Networks J. Molina et al. https://doi.org/10.3390/w8110484
- Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework A. Gragne et al. https://doi.org/10.5194/hess-19-3695-2015
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