Articles | Volume 15, issue 1
https://doi.org/10.5194/hess-15-255-2011
© Author(s) 2011. 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-15-255-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Estimation of predictive hydrological uncertainty using quantile regression: examples from the National Flood Forecasting System (England and Wales)
A. H. Weerts
Deltares, P.O. Box 177, 2600 MH Delft, The Netherlands
H. C. Winsemius
Deltares, P.O. Box 177, 2600 MH Delft, The Netherlands
J. S. Verkade
Deltares, P.O. Box 177, 2600 MH Delft, The Netherlands
Delft University of Technology, Department of Flood Risk Management and Hydraulic Structures,Delft, The Netherlands
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- Post-Processing of Stream Flows in Switzerland with an Emphasis on Low Flows and Floods K. Bogner et al. 10.3390/w8040115
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- Anomaly Kriging Helps to Remove Bias in Spatial Model Runoff Estimates N. Loonat et al. 10.1029/2019WR026240
- Flood Forecasting and Decision Making in the new Millennium. Where are We? E. Todini 10.1007/s11269-017-1693-7
- Transferring global uncertainty estimates from gauged to ungauged catchments F. Bourgin et al. 10.5194/hess-19-2535-2015
- Recent developments in predictive uncertainty assessment based on the model conditional processor approach G. Coccia & E. Todini 10.5194/hess-15-3253-2011
- On selection of the optimal data time interval for real-time hydrological forecasting J. Liu & D. Han 10.5194/hess-17-3639-2013
- Statistical Postprocessing of High-Resolution Regional Climate Model Output P. Mendoza et al. 10.1175/MWR-D-14-00159.1
- Quantile-Based Hydrological Modelling H. Tyralis & G. Papacharalampous 10.3390/w13233420
- Ensemble-based flash-flood modelling: Taking into account hydrodynamic parameters and initial soil moisture uncertainties S. Edouard et al. 10.1016/j.jhydrol.2017.04.048
- A global database of historic and real-time flood events based on social media J. de Bruijn et al. 10.1038/s41597-019-0326-9
- Tercile Forecasts for Extending the Horizon of Skillful Hydrological Predictions K. Bogner et al. 10.1175/JHM-D-21-0020.1
- Statistical downscaling of precipitation using quantile regression R. Tareghian & P. Rasmussen 10.1016/j.jhydrol.2013.02.029
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- Post‐processing hydrological ensemble predictions intercomparison experiment S. van Andel et al. 10.1002/hyp.9595
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- Real-time correction of water stage forecast during rainstorm events using combination of forecast errors S. Wu et al. 10.1007/s00477-011-0514-4
- Model uncertainty analysis by variance decomposition P. Willems 10.1016/j.pce.2011.07.003
- Real-time correction of water stage forecast using combination of forecasted errors by time series models and Kalman filter method J. Shen et al. 10.1007/s00477-015-1074-9
- Trend Analysis of Extreme Precipitation Using Quantile Regression B. So et al. 10.3741/JKWRA.2012.45.8.815
- Development of a New Quantile-Based Method for the Assessment of Regional Water Resources in a Highly-Regulated River Basin S. Abbas & Y. Xuan 10.1007/s11269-019-02290-z
- Real-time error correction of two-dimensional flood-inundation simulations during rainstorm events S. Wu et al. 10.1007/s00477-020-01792-2
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- Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System G. Matthews et al. 10.5194/hess-26-2939-2022
- A multi-model evaluation of probabilistic streamflow predictions via residual error modelling J. Romero-Cuellar et al. 10.1016/j.jhydrol.2024.131152
- Quantifying predictive uncertainty of streamflow forecasts based on a Bayesian joint probability model T. Zhao et al. 10.1016/j.jhydrol.2015.06.043
- A non-parametric data-based approach for probabilistic flood forecasting in support of uncertainty communication N. Van Steenbergen et al. 10.1016/j.envsoft.2012.01.013
- Towards Safer Data-Driven Forecasting of Extreme Streamflows J. Matos et al. 10.1007/s11269-017-1834-z
- Bayesian LSTM With Stochastic Variational Inference for Estimating Model Uncertainty in Process‐Based Hydrological Models D. Li et al. 10.1029/2021WR029772
- Ensemble flood forecasting: Current status and future opportunities W. Wu et al. 10.1002/wat2.1432
- 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
- Improving Forecast Skill of Lowland Hydrological Models Using Ensemble Kalman Filter and Unscented Kalman Filter Y. Sun et al. 10.1029/2020WR027468
- Development of roughness updating based on artificial neural network in a river hydraulic model for flash flood forecasting J. Fu et al. 10.1007/s12040-015-0644-z
- Deep Learning-Based Predictive Framework for Groundwater Level Forecast in Arid Irrigated Areas W. Liu et al. 10.3390/w13182558
- Reduction of the uncertainties in the water level-discharge relation of a 1D hydraulic model in the context of operational flood forecasting J. Habert et al. 10.1016/j.jhydrol.2015.11.023
- The multi temporal/multi-model approach to predictive uncertainty assessment in real-time flood forecasting S. Barbetta et al. 10.1016/j.jhydrol.2017.06.030
- Streamflow Modelling: A Primer on Applications, Approaches and Challenges D. Bourdin et al. 10.1080/07055900.2012.734276
- Evaluating uncertainty estimates in hydrologic models: borrowing measures from the forecast verification community K. Franz & T. Hogue 10.5194/hess-15-3367-2011
- Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments N. Dogulu et al. 10.5194/hess-19-3181-2015
- Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years M. Troin et al. 10.1029/2020WR028392
- Verification of short-term runoff forecasts for a small Philippine basin (Marikina) D. Kneis et al. 10.1080/02626667.2016.1183773
- Data-driven methods to improve baseflow prediction of a regional groundwater model T. Xu & A. Valocchi 10.1016/j.cageo.2015.05.016
- 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
- Forecasting flash floods using data-based mechanistic models and NORA radar rainfall forecasts P. Smith et al. 10.1080/02626667.2013.842647
- Use of the data depth function to differentiate between case of interpolation and extrapolation in hydrological model prediction S. Singh et al. 10.1016/j.jhydrol.2012.11.034
- Objective hydrograph baseflow recession analysis B. Thomas et al. 10.1016/j.jhydrol.2015.03.028
- Performance and robustness of probabilistic river forecasts computed with quantile regression based on multiple independent variables F. Hoss & P. Fischbeck 10.5194/hess-19-3969-2015
- Comparison of Different Configurations of Quantile Regression in Estimating Predictive Hydrological Uncertainty M. Muthusamy et al. 10.1016/j.proeng.2016.07.546
- Research on out-of-sample prediction method of water quality parameters based on dual-attention mechanism Z. Zheng et al. 10.1016/j.envsoft.2024.106020
- Probabilistic postprocessing models for flow forecasts for a system of catchments and several lead times K. Engeland & I. Steinsland 10.1002/2012WR012757
- MERLIN: a flood hazard forecasting system for coastal river reaches I. Fraga et al. 10.1007/s11069-020-03855-7
- A framework to assess the realism of model structures using hydrological signatures T. Euser et al. 10.5194/hess-17-1893-2013
- Comparative Study of Two State-of-the-Art Semi-Distributed Hydrological Models P. Paul et al. 10.3390/w11050871
- Technical note: Combining quantile forecasts and predictive distributions of streamflows K. Bogner et al. 10.5194/hess-21-5493-2017
- Hydrologic post-processing of MOPEX streamflow simulations A. Ye et al. 10.1016/j.jhydrol.2013.10.055
- A review on statistical postprocessing methods for hydrometeorological ensemble forecasting W. Li et al. 10.1002/wat2.1246
- Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms G. Papacharalampous et al. 10.3390/w11102126
- Technical Note: The normal quantile transformation and its application in a flood forecasting system K. Bogner et al. 10.5194/hess-16-1085-2012
- 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
- Ensemble prediction and data assimilation for operational hydrology D. Seo et al. 10.1016/j.jhydrol.2014.11.035
- Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts L. Crochemore et al. 10.5194/hess-20-3601-2016
- 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
- Evaluation and Bias Correction of S2S Precipitation for Hydrological Extremes W. Li et al. 10.1175/JHM-D-19-0042.1
- Quantifying forcing uncertainties in the hydrodynamics of the Gironde estuary V. Laborie et al. 10.1007/s10596-019-09907-7
- 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
- Multisite and multivariable statistical downscaling using a Gaussian copula quantile regression model M. Ben Alaya et al. 10.1007/s00382-015-2908-3
- Geostatistical upscaling of rain gauge data to support uncertainty analysis of lumped urban hydrological models M. Muthusamy et al. 10.5194/hess-21-1077-2017
- Uncertainty assessment of radar-raingauge merged rainfall estimates in river discharge simulations N. Nanding et al. 10.1016/j.jhydrol.2021.127093
- Comparing Approaches to Deal With Non‐Gaussianity of Rainfall Data in Kriging‐Based Radar‐Gauge Rainfall Merging F. Cecinati et al. 10.1002/2016WR020330
- Operational forecast uncertainty assessment for better information to stakeholders and crisis managers L. Berthet et al. 10.1051/e3sconf/20160718005
- Real-time flood forecasting downstream river confluences using a Bayesian approach S. Barbetta et al. 10.1016/j.jhydrol.2018.08.043
- Post-processing ECMWF precipitation and temperature ensemble reforecasts for operational hydrologic forecasting at various spatial scales J. Verkade et al. 10.1016/j.jhydrol.2013.07.039
- Agricultural drought prediction using climate indices based on Support Vector Regression in Xiangjiang River basin Y. Tian et al. 10.1016/j.scitotenv.2017.12.025
- Enhancing SWAT model with modified method to improve Eco-hydrological simulation in arid region Y. Cai et al. 10.1016/j.jclepro.2023.136891
- On the complexity of model complexity: Viewpoints across the geosciences J. Baartman et al. 10.1016/j.catena.2019.104261
- Using a simple post-processor to predict residual uncertainty for multiple hydrological model outputs L. Ehlers et al. 10.1016/j.advwatres.2019.05.003
- From Changing Environment to Changing Extremes: Exploring the Future Streamflow and Associated Uncertainties Through Integrated Modelling System S. Gaur et al. 10.1007/s11269-021-02817-3
- Vers la généralisation de la prévision hydrologique probabiliste au sein du réseau Vigicrues : estimation, évaluation et communication A. Belleudy et al. 10.1080/27678490.2024.2374079
- Mincer–Zarnowitz quantile and expectile regressions for forecast evaluations under aysmmetric loss functions K. Guler et al. 10.1002/for.2462
- Enhancing real-time streamflow forecasts with wavelet-neural network based error-updating schemes and ECMWF meteorological predictions in Variable Infiltration Capacity model T. Nanda et al. 10.1016/j.jhydrol.2019.05.051
- A multilinear discrete Nash-cascade model for stage-hydrograph routing in compound river channels B. Sahoo et al. 10.1080/02626667.2019.1699243
- Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system S. Sharma et al. 10.5194/hess-22-1831-2018
- A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion A. Sun et al. 10.5194/hess-26-5163-2022
- Estimating predictive hydrological uncertainty by dressing deterministic and ensemble forecasts; a comparison, with application to Meuse and Rhine J. Verkade et al. 10.1016/j.jhydrol.2017.10.024
- Hillslope-storage Boussinesq model for simulating subsurface water storage dynamics in scantily-gauged catchments S. Sahoo et al. 10.1016/j.advwatres.2018.08.016
- Probabilistic runoff forecasting considering stepwise decomposition framework and external factor integration structure C. Cao et al. 10.1016/j.eswa.2023.121350
- Application of independent component analysis in regional flood frequency analysis: Comparison between quantile regression and parameter regression techniques A. Rahman et al. 10.1016/j.jhydrol.2019.124372
- A Novel Approach to Uncertainty Quantification in Groundwater Table Modeling by Automated Predictive Deep Learning A. Abbaszadeh Shahri et al. 10.1007/s11053-022-10051-w
- Hydrological uncertainty processor based on a copula function Z. Liu et al. 10.1080/02626667.2017.1410278
- Statistical Post-Processing to Improve Hydrometeorological Forecasts 青. 段 10.12677/JWRR.2012.14023
- Meteolakes: An operational online three-dimensional forecasting platform for lake hydrodynamics T. Baracchini et al. 10.1016/j.watres.2020.115529
- Ensemble-based data assimilation for operational flood forecasting – On the merits of state estimation for 1D hydrodynamic forecasting through the example of the “Adour Maritime” river S. Barthélémy et al. 10.1016/j.jhydrol.2017.06.017
- Post-Processing of Stream Flows in Switzerland with an Emphasis on Low Flows and Floods K. Bogner et al. 10.3390/w8040115
- Multivariate postprocessing techniques for probabilistic hydrological forecasting S. Hemri et al. 10.1002/2014WR016473
- Anomaly Kriging Helps to Remove Bias in Spatial Model Runoff Estimates N. Loonat et al. 10.1029/2019WR026240
- Flood Forecasting and Decision Making in the new Millennium. Where are We? E. Todini 10.1007/s11269-017-1693-7
- Transferring global uncertainty estimates from gauged to ungauged catchments F. Bourgin et al. 10.5194/hess-19-2535-2015
- Recent developments in predictive uncertainty assessment based on the model conditional processor approach G. Coccia & E. Todini 10.5194/hess-15-3253-2011
- On selection of the optimal data time interval for real-time hydrological forecasting J. Liu & D. Han 10.5194/hess-17-3639-2013
- Statistical Postprocessing of High-Resolution Regional Climate Model Output P. Mendoza et al. 10.1175/MWR-D-14-00159.1
- Quantile-Based Hydrological Modelling H. Tyralis & G. Papacharalampous 10.3390/w13233420
- Ensemble-based flash-flood modelling: Taking into account hydrodynamic parameters and initial soil moisture uncertainties S. Edouard et al. 10.1016/j.jhydrol.2017.04.048
- A global database of historic and real-time flood events based on social media J. de Bruijn et al. 10.1038/s41597-019-0326-9
- Tercile Forecasts for Extending the Horizon of Skillful Hydrological Predictions K. Bogner et al. 10.1175/JHM-D-21-0020.1
- Statistical downscaling of precipitation using quantile regression R. Tareghian & P. Rasmussen 10.1016/j.jhydrol.2013.02.029
- Estimation of uncertainty in flood forecasts—A comparison of methods L. Boelee et al. 10.1111/jfr3.12516
- Postprocessing continental-scale, medium-range ensemble streamflow forecasts in South America using Ensemble Model Output Statistics and Ensemble Copula Coupling V. Siqueira et al. 10.1016/j.jhydrol.2021.126520
- Estimation and uncertainty analysis of groundwater quality parameters in a coastal aquifer under seawater intrusion: a comparative study of deep learning and classic machine learning methods M. Taşan et al. 10.1007/s11356-022-22375-4
- Bias-correction schemes for calibrated flow in a conceptual hydrological model K. Bum Kim et al. 10.2166/nh.2021.043
- Two novel error-updating model frameworks for short-to-medium range streamflow forecasting using bias-corrected rainfall inputs: Development and comparative assessment A. Khatun et al. 10.1016/j.jhydrol.2023.129199
- MERLIN: Una nueva herramienta para la predicción del riesgo de inundaciones en la demarcación hidrográfica Galicia-Costa I. Fraga et al. 10.4995/ia.2021.15565
- Estimation of flood-exposed population in data-scarce regions combining satellite imagery and high resolution hydrological-hydraulic modelling: A case study in the Licungo basin (Mozambique) L. Cea et al. 10.1016/j.ejrh.2022.101247
- Evaluer et communiquer les incertitudes associées aux prévisions hydrologiques pour mieux partager l'information L. Berthet et al. 10.1051/lhb/2016035
- Assessing Hydrologic Uncertainty Processor Performance for Flood Forecasting in a Semiurban Watershed S. Han et al. 10.1061/(ASCE)HE.1943-5584.0001828
- Validation of a national hydrological model H. McMillan et al. 10.1016/j.jhydrol.2016.07.043
- A novel smoothing-based long short-term memory framework for short-to medium-range flood forecasting A. Khatun et al. 10.1080/02626667.2023.2173012
- Creating consistent datasets by combining remotely-sensed data and land surface model estimates through Bayesian uncertainty post-processing: The case of Land Surface Temperature from HIRS G. Coccia et al. 10.1016/j.rse.2015.09.010
- A new modelling framework to assess changes in groundwater level I. Kalu et al. 10.1016/j.ejrh.2022.101185
- Multi-step Ahead Urban Water Demand Forecasting Using Deep Learning Models B. Sahoo et al. 10.1007/s42979-023-02246-6
- 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
- Diagnosing Credibility of a Large-Scale Conceptual Hydrological Model in Simulating Streamflow P. Paul et al. 10.1061/(ASCE)HE.1943-5584.0001766
- A hybrid model enhancing streamflow forecasts in paddy land use-dominated catchments with numerical weather prediction model-based meteorological forcings A. Mohanty et al. 10.1016/j.jhydrol.2024.131225
- Probabilistic runoff volume forecasting in risk-based optimization for RTC of urban drainage systems R. Löwe et al. 10.1016/j.envsoft.2016.02.027
- Real-Time Probabilistic Flood Forecasting Using Multiple Machine Learning Methods D. Nguyen & S. Chen 10.3390/w12030787
- A parsimonious post-processor for uncertainty evaluation of ensemble precipitation forecasts: an application to quantitative precipitation forecasts for civil protection purposes D. Biondi et al. 10.2166/nh.2021.045
- Prediction of heat waves in Pakistan using quantile regression forests N. Khan et al. 10.1016/j.atmosres.2019.01.024
- Identification of the best multi-model combination for simulating river discharge A. Kumar et al. 10.1016/j.jhydrol.2015.03.060
- Predictive Uncertainty Estimation of Hydrological Multi-Model Ensembles Using Pair-Copula Construction B. Klein et al. 10.3390/w8040125
- Local vs. integrated control of a variable refrigerant flow system using artificial neural networks K. Ahn et al. 10.1080/23744731.2020.1760636
- Skill of Hydrological Extended Range Forecasts for Water Resources Management in Switzerland K. Bogner et al. 10.1007/s11269-017-1849-5
- Quantile hydrologic model selection and model structure deficiency assessment: 2. Applications S. Pande 10.1002/wrcr.20422
- Evolutionary and ensemble machine learning predictive models for evaluation of water quality A. Aldrees et al. 10.1016/j.ejrh.2023.101331
- Post‐processing hydrological ensemble predictions intercomparison experiment S. van Andel et al. 10.1002/hyp.9595
- Bayesian flood forecasting methods: A review S. Han & P. Coulibaly 10.1016/j.jhydrol.2017.06.004
- Towards an Extension of the Model Conditional Processor: Predictive Uncertainty Quantification of Monthly Streamflow via Gaussian Mixture Models and Clusters J. Romero-Cuellar et al. 10.3390/w14081261
- Adaptive correction of deterministic models to produce probabilistic forecasts P. Smith et al. 10.5194/hess-16-2783-2012
- 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
- Spatiotemporal modeling of hydrological return levels: A quantile regression approach M. Franco‐Villoria et al. 10.1002/env.2522
- Explainable machine learning for predicting stomatal conductance across multiple plant functional types S. Gaur & D. Drewry 10.1016/j.agrformet.2024.109955
- Hydrological post-processing based on approximate Bayesian computation (ABC) J. Romero-Cuellar et al. 10.1007/s00477-019-01694-y
- Application of data-based mechanistic modelling for flood forecasting at multiple locations in the Eden catchment in the National Flood Forecasting System (England and Wales) D. Leedal et al. 10.5194/hess-17-177-2013
- A runoff probability density prediction method based on B-spline quantile regression and kernel density estimation Y. He et al. 10.1016/j.apm.2020.12.043
- Runoff Prediction Based on Hybrid Clustering with WOA Intervals Mapping Model X. Yuan et al. 10.1061/(ASCE)HE.1943-5584.0002087
6 citations as recorded by crossref.
- Real-time correction of water stage forecast during rainstorm events using combination of forecast errors S. Wu et al. 10.1007/s00477-011-0514-4
- Model uncertainty analysis by variance decomposition P. Willems 10.1016/j.pce.2011.07.003
- Real-time correction of water stage forecast using combination of forecasted errors by time series models and Kalman filter method J. Shen et al. 10.1007/s00477-015-1074-9
- Trend Analysis of Extreme Precipitation Using Quantile Regression B. So et al. 10.3741/JKWRA.2012.45.8.815
- Development of a New Quantile-Based Method for the Assessment of Regional Water Resources in a Highly-Regulated River Basin S. Abbas & Y. Xuan 10.1007/s11269-019-02290-z
- Real-time error correction of two-dimensional flood-inundation simulations during rainstorm events S. Wu et al. 10.1007/s00477-020-01792-2
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