Articles | Volume 21, issue 11
https://doi.org/10.5194/hess-21-5493-2017
© Author(s) 2017. 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-21-5493-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Technical note: Combining quantile forecasts and predictive distributions of streamflows
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Katharina Liechti
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Massimiliano Zappa
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
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Cited
20 citations as recorded by crossref.
- Multi-model approach in a variable spatial framework for streamflow simulation C. Thébault et al. 10.5194/hess-28-1539-2024
- Quantile-Based Hydrological Modelling H. Tyralis & G. Papacharalampous 10.3390/w13233420
- Machine Learning Techniques for Predicting the Energy Consumption/Production and Its Uncertainties Driven by Meteorological Observations and Forecasts K. Bogner et al. 10.3390/su11123328
- Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models K. Nikhil Teja et al. 10.1016/j.jhydrol.2023.130176
- Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms G. Papacharalampous et al. 10.3390/w11102126
- Online Aggregation of Probabilistic Predictions of Hourly Electrical Loads V. V’yugin & V. Trunov 10.1134/S1064226922060201
- Sequential Aggregation of Probabilistic Forecasts—Application to Wind Speed Ensemble Forecasts M. Zamo et al. 10.1111/rssc.12455
- Review of Recent Developments in Hydrologic Forecast Merging Techniques M. Sheikh & P. Coulibaly 10.3390/w16020301
- A stochastic deep-learning-based approach for improved streamflow simulation N. Dolatabadi & B. Zahraie 10.1007/s00477-023-02567-1
- The Value of Subseasonal Hydrometeorological Forecasts to Hydropower Operations: How Much Does Preprocessing Matter? D. Anghileri et al. 10.1029/2019WR025280
- Online Aggregation of Probabilistic Forecasts Based on the Continuous Ranked Probability Score V. V’yugin & V. Trunov 10.1134/S1064226920060285
- Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS H. Tyralis et al. 10.1016/j.jhydrol.2019.123957
- Adjusting for Conditional Bias in Process Model Simulations of Hydrological Extremes: An Experiment Using the North Wyke Farm Platform S. Curceac et al. 10.3389/frai.2020.565859
- Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years M. Troin et al. 10.1029/2020WR028392
- A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting G. Papacharalampous & H. Tyralis 10.3389/frwa.2022.961954
- A Finite Mixture Modelling Perspective for Combining Experts’ Opinions with an Application to Quantile-Based Risk Measures D. Makariou et al. 10.3390/risks9060115
- Comparison of Tree-Based Ensemble Algorithms for Merging Satellite and Earth-Observed Precipitation Data at the Daily Time Scale G. Papacharalampous et al. 10.3390/hydrology10020050
- Temporally varied error modelling for improving simulations and quantifying uncertainty L. Liu et al. 10.1016/j.jhydrol.2020.124914
- Seasonal Drought Prediction: Advances, Challenges, and Future Prospects Z. Hao et al. 10.1002/2016RG000549
- A dynamical statistical framework for seasonal streamflow forecasting in an agricultural watershed L. Slater et al. 10.1007/s00382-017-3794-7
18 citations as recorded by crossref.
- Multi-model approach in a variable spatial framework for streamflow simulation C. Thébault et al. 10.5194/hess-28-1539-2024
- Quantile-Based Hydrological Modelling H. Tyralis & G. Papacharalampous 10.3390/w13233420
- Machine Learning Techniques for Predicting the Energy Consumption/Production and Its Uncertainties Driven by Meteorological Observations and Forecasts K. Bogner et al. 10.3390/su11123328
- Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models K. Nikhil Teja et al. 10.1016/j.jhydrol.2023.130176
- Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms G. Papacharalampous et al. 10.3390/w11102126
- Online Aggregation of Probabilistic Predictions of Hourly Electrical Loads V. V’yugin & V. Trunov 10.1134/S1064226922060201
- Sequential Aggregation of Probabilistic Forecasts—Application to Wind Speed Ensemble Forecasts M. Zamo et al. 10.1111/rssc.12455
- Review of Recent Developments in Hydrologic Forecast Merging Techniques M. Sheikh & P. Coulibaly 10.3390/w16020301
- A stochastic deep-learning-based approach for improved streamflow simulation N. Dolatabadi & B. Zahraie 10.1007/s00477-023-02567-1
- The Value of Subseasonal Hydrometeorological Forecasts to Hydropower Operations: How Much Does Preprocessing Matter? D. Anghileri et al. 10.1029/2019WR025280
- Online Aggregation of Probabilistic Forecasts Based on the Continuous Ranked Probability Score V. V’yugin & V. Trunov 10.1134/S1064226920060285
- Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS H. Tyralis et al. 10.1016/j.jhydrol.2019.123957
- Adjusting for Conditional Bias in Process Model Simulations of Hydrological Extremes: An Experiment Using the North Wyke Farm Platform S. Curceac et al. 10.3389/frai.2020.565859
- Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years M. Troin et al. 10.1029/2020WR028392
- A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting G. Papacharalampous & H. Tyralis 10.3389/frwa.2022.961954
- A Finite Mixture Modelling Perspective for Combining Experts’ Opinions with an Application to Quantile-Based Risk Measures D. Makariou et al. 10.3390/risks9060115
- Comparison of Tree-Based Ensemble Algorithms for Merging Satellite and Earth-Observed Precipitation Data at the Daily Time Scale G. Papacharalampous et al. 10.3390/hydrology10020050
- Temporally varied error modelling for improving simulations and quantifying uncertainty L. Liu et al. 10.1016/j.jhydrol.2020.124914
Discussed (final revised paper)
Latest update: 05 Nov 2024
Short summary
The enhanced availability of many different weather prediction systems nowadays makes it very difficult for flood and water resource managers to choose the most reliable and accurate forecast. In order to circumvent this problem of choice, different approaches for combining this information have been applied at the Sihl River (CH) and the results have been verified. The outcome of this study highlights the importance of forecast combination in order to improve the quality of forecast systems.
The enhanced availability of many different weather prediction systems nowadays makes it very...