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.
Research article
| 
23 Jul 2015
Research article |
| 23 Jul 2015
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
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54 citations as recorded by crossref.
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- 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
- Machine learning approaches to predict significant wave height and assessment of model uncertainty S. Sithara et al. 10.1016/j.oceaneng.2025.121039
- 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
- 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
- Severe floods predictive ability: A proxy based probabilistic assessment of the Italian early warning system F. Silvestro et al. 10.1111/jfr3.12970
- 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
- Enhancing drought monitoring with a multivariate hydrometeorological index and machine learning-based prediction in the south of Iran H. Zamani et al. 10.1007/s11356-025-36049-4
- 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
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- A stochastic deep-learning-based approach for improved streamflow simulation N. Dolatabadi & B. Zahraie 10.1007/s00477-023-02567-1
- 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
- Filling the gaps in soil data: A multi-model framework for addressing data gaps using pedotransfer functions and machine-learning with uncertainty estimates to estimate bulk density A. Arbor et al. 10.1016/j.catena.2024.108310
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- 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
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- Probabilistic Water Demand Forecasting Using Quantile Regression Algorithms G. Papacharalampous & A. Langousis 10.1029/2021WR030216
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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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- Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning L. Knoll et al. 10.1088/1748-9326/ab7d5c
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- 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
- 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
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2 citations as recorded by crossref.
- Enhancing drought monitoring with a multivariate hydrometeorological index and machine learning-based prediction in the south of Iran H. Zamani et al. 10.1007/s11356-025-36049-4
- 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
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