08 Nov 2017
Technical note | 08 Nov 2017
Technical note: Combining quantile forecasts and predictive distributions of streamflows
Konrad Bogner et al.
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13 citations as recorded by crossref.
- 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 Finite Mixture Modelling Perspective for Combining Experts’ Opinions with an Application to Quantile-Based Risk Measures D. Makariou et al. 10.3390/risks9060115
- 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
- Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms G. Papacharalampous et al. 10.3390/w11102126
- Temporally varied error modelling for improving simulations and quantifying uncertainty L. Liu et al. 10.1016/j.jhydrol.2020.124914
- Sequential aggregation of probabilistic forecasts—Application to wind speed ensemble forecasts M. Zamo et al. 10.1111/rssc.12455
- 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
- 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
Discussed (final revised paper)
Latest update: 08 Aug 2022