Articles | Volume 19, issue 7
https://doi.org/10.5194/hess-19-3181-2015
© Author(s) 2015. 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-19-3181-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments
UNESCO-IHE Institute for Water Education, Delft, the Netherlands
currently at: Department of Civil Engineering, Middle East Technical University, Ankara, Turkey
P. López López
UNESCO-IHE Institute for Water Education, Delft, the Netherlands
Deltares, Delft, the Netherlands
now at: Utrecht University (Utrecht) and Deltares (Delft), the Netherlands
D. P. Solomatine
UNESCO-IHE Institute for Water Education, Delft, the Netherlands
Delft University of Technology, Delft, the Netherlands
A. H. Weerts
Deltares, Delft, the Netherlands
Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University, Wageningen, the Netherlands
D. L. Shrestha
CSIRO Land and Water, Highett, Victoria, Australia
Viewed
Total article views: 5,631 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 10 Sep 2014)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
3,567 | 1,955 | 109 | 5,631 | 126 | 122 |
- HTML: 3,567
- PDF: 1,955
- XML: 109
- Total: 5,631
- BibTeX: 126
- EndNote: 122
Total article views: 4,434 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 23 Jul 2015)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
2,782 | 1,568 | 84 | 4,434 | 89 | 96 |
- HTML: 2,782
- PDF: 1,568
- XML: 84
- Total: 4,434
- BibTeX: 89
- EndNote: 96
Total article views: 1,197 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 10 Sep 2014)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
785 | 387 | 25 | 1,197 | 37 | 26 |
- HTML: 785
- PDF: 387
- XML: 25
- Total: 1,197
- BibTeX: 37
- EndNote: 26
Cited
45 citations as recorded by crossref.
- Post-Processing of Stream Flows in Switzerland with an Emphasis on Low Flows and Floods K. Bogner et al. 10.3390/w8040115
- Quantile-Based Hydrological Modelling H. Tyralis & G. Papacharalampous 10.3390/w13233420
- Performance Assessment of Hydrological Models Considering Acceptable Forecast Error Threshold Q. Dong & F. Lu 10.3390/w7116173
- Bayesian flood forecasting methods: A review S. Han & P. Coulibaly 10.1016/j.jhydrol.2017.06.004
- Multi-source error correction for flood forecasting based on dynamic system response curve method Z. Liang et al. 10.1016/j.jhydrol.2020.125908
- Dynamic quantile regression for trend analysis of streamflow time series L. Lima et al. 10.1002/rra.3983
- Spatial mode-based calibration (SMoC) of forecast precipitation fields from numerical weather prediction models P. Zhao et al. 10.1016/j.jhydrol.2022.128432
- Merging Satellite and Gauge-Measured Precipitation Using LightGBM With an Emphasis on Extreme Quantiles H. Tyralis et al. 10.1109/JSTARS.2023.3297013
- Quantile regression as a generic approach for estimating uncertainty of digital soil maps produced from machine-learning B. Kasraei et al. 10.1016/j.envsoft.2021.105139
- Parametric uncertainty assessment of hydrological models: coupling UNEEC-P and a fuzzy general regression neural network A. Ahmadi et al. 10.1080/02626667.2019.1610565
- Temporally varied error modelling for improving simulations and quantifying uncertainty L. Liu et al. 10.1016/j.jhydrol.2020.124914
- 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
- Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system S. Sharma et al. 10.5194/hess-22-1831-2018
- Assessing future rainfall uncertainties of climate change in Taiwan with a bootstrapped neural network‐based downscaling model C. Li et al. 10.1111/wej.12443
- Hydrological post-processing for predicting extreme quantiles H. Tyralis & G. Papacharalampous 10.1016/j.jhydrol.2023.129082
- Uncertainty Estimation Using the Glue and Bayesian Approaches in Flood Estimation: A case Study—Ba River, Vietnam P. Cu Thi et al. 10.3390/w10111641
- 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
- Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: Methodology development and investigation using toy models G. Papacharalampous et al. 10.1016/j.advwatres.2019.103471
- Impact of Dataset Size on the Signature-Based Calibration of a Hydrological Model S. Mohammed et al. 10.3390/w13070970
- 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
- Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning L. Knoll et al. 10.1088/1748-9326/ab7d5c
- Extending a joint probability modelling approach for post-processing ensemble precipitation forecasts from numerical weather prediction models P. Zhao et al. 10.1016/j.jhydrol.2021.127285
- Comparison of different quantile regression methods to estimate predictive hydrological uncertainty in the Upper Chao Phraya River Basin, Thailand S. Acharya et al. 10.1111/jfr3.12585
- Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods O. Rahmati et al. 10.1016/j.scitotenv.2019.06.320
- Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models R. Taghizadeh-Mehrjardi et al. 10.1016/j.geoderma.2020.114793
- Technical note: Combining quantile forecasts and predictive distributions of streamflows K. Bogner et al. 10.5194/hess-21-5493-2017
- Quantifying climate and anthropogenic impacts on runoff using the SWAT model, a Budyko-based approach and empirical methods R. Xu et al. 10.1080/02626667.2023.2218551
- Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting O. Wani et al. 10.5194/hess-21-4021-2017
- 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
- Skill of ensemble flood inundation forecasts at short- to medium-range timescales M. Gomez et al. 10.1016/j.jhydrol.2018.10.063
- Comparative Study of Two State-of-the-Art Semi-Distributed Hydrological Models P. Paul et al. 10.3390/w11050871
- Exploring a copula-based alternative to additive error models—for non-negative and autocorrelated time series in hydrology O. Wani et al. 10.1016/j.jhydrol.2019.06.006
- Bayesian LSTM With Stochastic Variational Inference for Estimating Model Uncertainty in Process‐Based Hydrological Models D. Li et al. 10.1029/2021WR029772
- Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms G. Papacharalampous et al. 10.3390/w11102126
- Skill of Hydrological Extended Range Forecasts for Water Resources Management in Switzerland K. Bogner et al. 10.1007/s11269-017-1849-5
- Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale G. Papacharalampous et al. 10.1016/j.advwatres.2019.103470
- The risk assessment of arsenic contamination in the urbanized coastal aquifer of Rayong groundwater basin, Thailand using the machine learning approach N. Sumdang et al. 10.1016/j.ecoenv.2023.114665
- Probabilistic Water Demand Forecasting Using Quantile Regression Algorithms G. Papacharalampous & A. Langousis 10.1029/2021WR030216
- Diagnosing Credibility of a Large-Scale Conceptual Hydrological Model in Simulating Streamflow P. Paul et al. 10.1061/(ASCE)HE.1943-5584.0001766
- A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context L. Berthet et al. 10.5194/hess-24-2017-2020
- A Framework for Recalibrating Pedotransfer Functions Using Nonlinear Least Squares and Estimating Uncertainty Using Quantile Regression B. Heung et al. 10.2139/ssrn.4352027
- Changes in extreme rainfall and its implications for design rainfall using a Bayesian quantile regression approach S. Uranchimeg et al. 10.2166/nh.2020.003
- A review on statistical postprocessing methods for hydrometeorological ensemble forecasting W. Li et al. 10.1002/wat2.1246
- Characterizing distributed hydrological model residual errors using a probabilistic long short-term memory network D. Li et al. 10.1016/j.jhydrol.2021.126888
- Alternative configurations of quantile regression for estimating predictive uncertainty in water level forecasts for the upper Severn River: a comparison P. López López et al. 10.5194/hess-18-3411-2014
44 citations as recorded by crossref.
- Post-Processing of Stream Flows in Switzerland with an Emphasis on Low Flows and Floods K. Bogner et al. 10.3390/w8040115
- Quantile-Based Hydrological Modelling H. Tyralis & G. Papacharalampous 10.3390/w13233420
- Performance Assessment of Hydrological Models Considering Acceptable Forecast Error Threshold Q. Dong & F. Lu 10.3390/w7116173
- Bayesian flood forecasting methods: A review S. Han & P. Coulibaly 10.1016/j.jhydrol.2017.06.004
- Multi-source error correction for flood forecasting based on dynamic system response curve method Z. Liang et al. 10.1016/j.jhydrol.2020.125908
- Dynamic quantile regression for trend analysis of streamflow time series L. Lima et al. 10.1002/rra.3983
- Spatial mode-based calibration (SMoC) of forecast precipitation fields from numerical weather prediction models P. Zhao et al. 10.1016/j.jhydrol.2022.128432
- Merging Satellite and Gauge-Measured Precipitation Using LightGBM With an Emphasis on Extreme Quantiles H. Tyralis et al. 10.1109/JSTARS.2023.3297013
- Quantile regression as a generic approach for estimating uncertainty of digital soil maps produced from machine-learning B. Kasraei et al. 10.1016/j.envsoft.2021.105139
- Parametric uncertainty assessment of hydrological models: coupling UNEEC-P and a fuzzy general regression neural network A. Ahmadi et al. 10.1080/02626667.2019.1610565
- Temporally varied error modelling for improving simulations and quantifying uncertainty L. Liu et al. 10.1016/j.jhydrol.2020.124914
- 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
- Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system S. Sharma et al. 10.5194/hess-22-1831-2018
- Assessing future rainfall uncertainties of climate change in Taiwan with a bootstrapped neural network‐based downscaling model C. Li et al. 10.1111/wej.12443
- Hydrological post-processing for predicting extreme quantiles H. Tyralis & G. Papacharalampous 10.1016/j.jhydrol.2023.129082
- Uncertainty Estimation Using the Glue and Bayesian Approaches in Flood Estimation: A case Study—Ba River, Vietnam P. Cu Thi et al. 10.3390/w10111641
- 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
- Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: Methodology development and investigation using toy models G. Papacharalampous et al. 10.1016/j.advwatres.2019.103471
- Impact of Dataset Size on the Signature-Based Calibration of a Hydrological Model S. Mohammed et al. 10.3390/w13070970
- 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
- Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning L. Knoll et al. 10.1088/1748-9326/ab7d5c
- Extending a joint probability modelling approach for post-processing ensemble precipitation forecasts from numerical weather prediction models P. Zhao et al. 10.1016/j.jhydrol.2021.127285
- Comparison of different quantile regression methods to estimate predictive hydrological uncertainty in the Upper Chao Phraya River Basin, Thailand S. Acharya et al. 10.1111/jfr3.12585
- Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods O. Rahmati et al. 10.1016/j.scitotenv.2019.06.320
- Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models R. Taghizadeh-Mehrjardi et al. 10.1016/j.geoderma.2020.114793
- Technical note: Combining quantile forecasts and predictive distributions of streamflows K. Bogner et al. 10.5194/hess-21-5493-2017
- Quantifying climate and anthropogenic impacts on runoff using the SWAT model, a Budyko-based approach and empirical methods R. Xu et al. 10.1080/02626667.2023.2218551
- Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting O. Wani et al. 10.5194/hess-21-4021-2017
- 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
- Skill of ensemble flood inundation forecasts at short- to medium-range timescales M. Gomez et al. 10.1016/j.jhydrol.2018.10.063
- Comparative Study of Two State-of-the-Art Semi-Distributed Hydrological Models P. Paul et al. 10.3390/w11050871
- Exploring a copula-based alternative to additive error models—for non-negative and autocorrelated time series in hydrology O. Wani et al. 10.1016/j.jhydrol.2019.06.006
- Bayesian LSTM With Stochastic Variational Inference for Estimating Model Uncertainty in Process‐Based Hydrological Models D. Li et al. 10.1029/2021WR029772
- Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms G. Papacharalampous et al. 10.3390/w11102126
- Skill of Hydrological Extended Range Forecasts for Water Resources Management in Switzerland K. Bogner et al. 10.1007/s11269-017-1849-5
- Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale G. Papacharalampous et al. 10.1016/j.advwatres.2019.103470
- The risk assessment of arsenic contamination in the urbanized coastal aquifer of Rayong groundwater basin, Thailand using the machine learning approach N. Sumdang et al. 10.1016/j.ecoenv.2023.114665
- Probabilistic Water Demand Forecasting Using Quantile Regression Algorithms G. Papacharalampous & A. Langousis 10.1029/2021WR030216
- Diagnosing Credibility of a Large-Scale Conceptual Hydrological Model in Simulating Streamflow P. Paul et al. 10.1061/(ASCE)HE.1943-5584.0001766
- A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context L. Berthet et al. 10.5194/hess-24-2017-2020
- A Framework for Recalibrating Pedotransfer Functions Using Nonlinear Least Squares and Estimating Uncertainty Using Quantile Regression B. Heung et al. 10.2139/ssrn.4352027
- Changes in extreme rainfall and its implications for design rainfall using a Bayesian quantile regression approach S. Uranchimeg et al. 10.2166/nh.2020.003
- A review on statistical postprocessing methods for hydrometeorological ensemble forecasting W. Li et al. 10.1002/wat2.1246
- Characterizing distributed hydrological model residual errors using a probabilistic long short-term memory network D. Li et al. 10.1016/j.jhydrol.2021.126888
Saved (final revised paper)
Saved (preprint)
Latest update: 26 Sep 2023