Articles | Volume 26, issue 1
https://doi.org/10.5194/hess-26-197-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-197-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jan 2022
Research article |
| 14 Jan 2022
| 14 Jan 2022
Choosing between post-processing precipitation forecasts or chaining several uncertainty quantification tools in hydrological forecasting systems
Emixi Sthefany Valdez
CORRESPONDING AUTHOR
Dept. of Civil and Water Engineering, Université Laval, 1065 Avenue de la Médecine, Quebec G1V 0A6, Canada
François Anctil
Dept. of Civil and Water Engineering, Université Laval, 1065 Avenue de la Médecine, Quebec G1V 0A6, Canada
Maria-Helena Ramos
Université Paris-Saclay, INRAE, UR HYCAR, 1 Rue Pierre-Gilles de Gennes, 92160 Antony, France
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Cited
16 citations as recorded by crossref.
- Producing reliable hydrologic scenarios from raw climate model outputs without resorting to meteorological observations S. Ricard et al. 10.5194/hess-27-2375-2023
- A methodological framework for the evaluation of short-range flash-flood hydrometeorological forecasts at the event scale M. Charpentier-Noyer et al. 10.5194/nhess-23-2001-2023
- Sensitivity analysis of the hyperparameters of an ensemble Kalman filter application on a semi-distributed hydrological model for streamflow forecasting B. Sabzipour et al. 10.1016/j.jhydrol.2023.130251
- Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop A. Dasgupta et al. 10.1111/jfr3.12880
- Was the extreme rainfall that caused the August 2022 flood in Pakistan predictable? I. Malik et al. 10.1088/2752-5295/acfa1a
- Global streamflow modelling using process-informed machine learning M. Magni et al. 10.2166/hydro.2023.217
- Quantifying the contributions of hydrological pre-processor, post-processor, and data assimilator to ensemble streamflow prediction skill J. Zhang et al. 10.1016/j.jhydrol.2024.132611
- Improve streamflow simulations by combining machine learning pre-processing and post-processing Y. Zhang et al. 10.1016/j.jhydrol.2025.132904
- Improving Probabilistic Quantitative Precipitation Forecasts Using Short Training Data through Artificial Neural Networks M. Ghazvinian et al. 10.1175/JHM-D-22-0021.1
- Subbasin Spatial Scale Effects on Hydrological Model Prediction Uncertainty of Extreme Stream Flows in the Omo Gibe River Basin, Ethiopia B. Gebeyehu et al. 10.3390/rs15030611
- Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River N. Dong et al. 10.5194/hess-29-2023-2025
- Combining large-scale and regional hydrological forecasts using simple methods N. Fontaine et al. 10.1080/07011784.2023.2265893
- Rainfall nowcasting models: state of the art and possible future perspectives D. De Luca et al. 10.1080/02626667.2025.2490780
- Stochastic Watershed Model Ensembles for Long‐Range Planning: Verification and Validation G. Shabestanipour et al. 10.1029/2022WR032201
- Enhancing streamflow ensemble forecasting: evaluating pre- and post-processing strategies for a spatially distributed model A. Askarinejad et al. 10.1080/07011784.2025.2541629
- A hybrid process-data driven framework for real-time hydrological forecasting with interpretable deep learning F. Zhu et al. 10.1016/j.jhydrol.2025.134082
16 citations as recorded by crossref.
- Producing reliable hydrologic scenarios from raw climate model outputs without resorting to meteorological observations S. Ricard et al. 10.5194/hess-27-2375-2023
- A methodological framework for the evaluation of short-range flash-flood hydrometeorological forecasts at the event scale M. Charpentier-Noyer et al. 10.5194/nhess-23-2001-2023
- Sensitivity analysis of the hyperparameters of an ensemble Kalman filter application on a semi-distributed hydrological model for streamflow forecasting B. Sabzipour et al. 10.1016/j.jhydrol.2023.130251
- Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop A. Dasgupta et al. 10.1111/jfr3.12880
- Was the extreme rainfall that caused the August 2022 flood in Pakistan predictable? I. Malik et al. 10.1088/2752-5295/acfa1a
- Global streamflow modelling using process-informed machine learning M. Magni et al. 10.2166/hydro.2023.217
- Quantifying the contributions of hydrological pre-processor, post-processor, and data assimilator to ensemble streamflow prediction skill J. Zhang et al. 10.1016/j.jhydrol.2024.132611
- Improve streamflow simulations by combining machine learning pre-processing and post-processing Y. Zhang et al. 10.1016/j.jhydrol.2025.132904
- Improving Probabilistic Quantitative Precipitation Forecasts Using Short Training Data through Artificial Neural Networks M. Ghazvinian et al. 10.1175/JHM-D-22-0021.1
- Subbasin Spatial Scale Effects on Hydrological Model Prediction Uncertainty of Extreme Stream Flows in the Omo Gibe River Basin, Ethiopia B. Gebeyehu et al. 10.3390/rs15030611
- Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River N. Dong et al. 10.5194/hess-29-2023-2025
- Combining large-scale and regional hydrological forecasts using simple methods N. Fontaine et al. 10.1080/07011784.2023.2265893
- Rainfall nowcasting models: state of the art and possible future perspectives D. De Luca et al. 10.1080/02626667.2025.2490780
- Stochastic Watershed Model Ensembles for Long‐Range Planning: Verification and Validation G. Shabestanipour et al. 10.1029/2022WR032201
- Enhancing streamflow ensemble forecasting: evaluating pre- and post-processing strategies for a spatially distributed model A. Askarinejad et al. 10.1080/07011784.2025.2541629
- A hybrid process-data driven framework for real-time hydrological forecasting with interpretable deep learning F. Zhu et al. 10.1016/j.jhydrol.2025.134082
Latest update: 28 Aug 2025
Short summary
We investigated how a precipitation post-processor interacts with other tools for uncertainty quantification in a hydrometeorological forecasting chain. Four systems were implemented to generate 7 d ensemble streamflow forecasts, which vary from partial to total uncertainty estimation. Overall analysis showed that post-processing and initial condition estimation ensure the most skill improvements, in some cases even better than a system that considers all sources of uncertainty.
We investigated how a precipitation post-processor interacts with other tools for uncertainty...
