57 citations as recorded by crossref.

1. Quantile-Based Hydrological Modelling H. Tyralis & G. Papacharalampous https://doi.org/10.3390/w13233420
2. Bayesian flood forecasting methods: A review S. Han & P. Coulibaly https://doi.org/10.1016/j.jhydrol.2017.06.004
3. Multi-source error correction for flood forecasting based on dynamic system response curve method Z. Liang et al. https://doi.org/10.1016/j.jhydrol.2020.125908
4. Machine learning approaches to predict significant wave height and assessment of model uncertainty S. Sithara et al. https://doi.org/10.1016/j.oceaneng.2025.121039
5. Merging Satellite and Gauge-Measured Precipitation Using LightGBM With an Emphasis on Extreme Quantiles H. Tyralis et al. https://doi.org/10.1109/JSTARS.2023.3297013
6. Quantile regression as a generic approach for estimating uncertainty of digital soil maps produced from machine-learning B. Kasraei et al. https://doi.org/10.1016/j.envsoft.2021.105139
7. Parametric uncertainty assessment of hydrological models: coupling UNEEC-P and a fuzzy general regression neural network A. Ahmadi et al. https://doi.org/10.1080/02626667.2019.1610565
8. Temporally varied error modelling for improving simulations and quantifying uncertainty L. Liu et al. https://doi.org/10.1016/j.jhydrol.2020.124914
9. Performance Evaluation and Uncertainty Quantification of Deep Learning Models for Agricultural Drought Prediction S. Das K. & N. Chithra https://doi.org/10.1061/JHYEFF.HEENG-6751
10. Uncertainty Estimation Using the Glue and Bayesian Approaches in Flood Estimation: A case Study—Ba River, Vietnam P. Cu Thi et al. https://doi.org/10.3390/w10111641
11. Severe floods predictive ability: A proxy based probabilistic assessment of the Italian early warning system F. Silvestro et al. https://doi.org/10.1111/jfr3.12970
12. 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. https://doi.org/10.1016/j.advwatres.2019.103471
13. Enhancing drought monitoring with a multivariate hydrometeorological index and machine learning-based prediction in the south of Iran H. Zamani et al. https://doi.org/10.1007/s11356-025-36049-4
14. Impact of Dataset Size on the Signature-Based Calibration of a Hydrological Model S. Mohammed et al. https://doi.org/10.3390/w13070970
15. Comparison of Tree-Based Ensemble Algorithms for Merging Satellite and Earth-Observed Precipitation Data at the Daily Time Scale G. Papacharalampous et al. https://doi.org/10.3390/hydrology10020050
16. Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods O. Rahmati et al. https://doi.org/10.1016/j.scitotenv.2019.06.320
17. Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models R. Taghizadeh-Mehrjardi et al. https://doi.org/10.1016/j.geoderma.2020.114793
18. A stochastic deep-learning-based approach for improved streamflow simulation N. Dolatabadi & B. Zahraie https://doi.org/10.1007/s00477-023-02567-1
19. Quantifying climate and anthropogenic impacts on runoff using the SWAT model, a Budyko-based approach and empirical methods R. Xu et al. https://doi.org/10.1080/02626667.2023.2218551
20. 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. https://doi.org/10.1016/j.catena.2024.108310
21. Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting O. Wani et al. https://doi.org/10.5194/hess-21-4021-2017
22. Skill of ensemble flood inundation forecasts at short- to medium-range timescales M. Gomez et al. https://doi.org/10.1016/j.jhydrol.2018.10.063
23. Exploring a copula-based alternative to additive error models—for non-negative and autocorrelated time series in hydrology O. Wani et al. https://doi.org/10.1016/j.jhydrol.2019.06.006
24. A framework for optimizing environmental covariates to support model interpretability in digital soil mapping B. Kasraei et al. https://doi.org/10.1016/j.geoderma.2024.116873
25. 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. https://doi.org/10.1016/j.ecoenv.2023.114665
26. Probabilistic Water Demand Forecasting Using Quantile Regression Algorithms G. Papacharalampous & A. Langousis https://doi.org/10.1029/2021WR030216
27. A Framework for Recalibrating Pedotransfer Functions Using Nonlinear Least Squares and Estimating Uncertainty Using Quantile Regression B. Heung et al. https://doi.org/10.2139/ssrn.4352027
28. Explicit Prediction and Uncertainty Analysis of Slurry Density during Slurry Shield Tunneling in Sand Y. Liu et al. https://doi.org/10.1061/IJGNAI.GMENG-12431
29. Post-Processing of Stream Flows in Switzerland with an Emphasis on Low Flows and Floods K. Bogner et al. https://doi.org/10.3390/w8040115
30. Quantifying uncertainty in future sea level projections downscaled from CMIP5 global climate models S. Sithara et al. https://doi.org/10.1007/s00477-024-02669-4
31. Performance Assessment of Hydrological Models Considering Acceptable Forecast Error Threshold Q. Dong & F. Lu https://doi.org/10.3390/w7116173
32. Dynamic quantile regression for trend analysis of streamflow time series L. Lima et al. https://doi.org/10.1002/rra.3983
33. Spatial mode-based calibration (SMoC) of forecast precipitation fields from numerical weather prediction models P. Zhao et al. https://doi.org/10.1016/j.jhydrol.2022.128432
34. Ensemble Learning for Blending Gridded Satellite and Gauge-Measured Precipitation Data G. Papacharalampous et al. https://doi.org/10.3390/rs15204912
35. Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS H. Tyralis et al. https://doi.org/10.1016/j.jhydrol.2019.123957
36. Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system S. Sharma et al. https://doi.org/10.5194/hess-22-1831-2018
37. Assessing future rainfall uncertainties of climate change in Taiwan with a bootstrapped neural network‐based downscaling model C. Li et al. https://doi.org/10.1111/wej.12443
38. Hydrological post-processing for predicting extreme quantiles H. Tyralis & G. Papacharalampous https://doi.org/10.1016/j.jhydrol.2023.129082
39. A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting G. Papacharalampous & H. Tyralis https://doi.org/10.3389/frwa.2022.961954
40. Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning L. Knoll et al. https://doi.org/10.1088/1748-9326/ab7d5c
41. Extending a joint probability modelling approach for post-processing ensemble precipitation forecasts from numerical weather prediction models P. Zhao et al. https://doi.org/10.1016/j.jhydrol.2021.127285
42. Comparison of different quantile regression methods to estimate predictive hydrological uncertainty in the Upper Chao Phraya River Basin, Thailand S. Acharya et al. https://doi.org/10.1111/jfr3.12585
43. Technical note: Combining quantile forecasts and predictive distributions of streamflows K. Bogner et al. https://doi.org/10.5194/hess-21-5493-2017
44. A framework for recalibrating pedotransfer functions using nonlinear least squares and estimating uncertainty using quantile regression A. Arbor et al. https://doi.org/10.1016/j.geoderma.2023.116674
45. Adjusting for Conditional Bias in Process Model Simulations of Hydrological Extremes: An Experiment Using the North Wyke Farm Platform S. Curceac et al. https://doi.org/10.3389/frai.2020.565859
46. Comparative Study of Two State-of-the-Art Semi-Distributed Hydrological Models P. Paul et al. https://doi.org/10.3390/w11050871
47. Bayesian LSTM With Stochastic Variational Inference for Estimating Model Uncertainty in Process‐Based Hydrological Models D. Li et al. https://doi.org/10.1029/2021WR029772
48. Quantile deep learning models for multi-step ahead time series prediction J. Cheung et al. https://doi.org/10.1016/j.asoc.2025.114043
49. Explainable deeply-fused nets electricity demand prediction model: Factoring climate predictors for accuracy and deeper insights with probabilistic confidence interval and point-based forecasts S. Ghimire et al. https://doi.org/10.1016/j.apenergy.2024.124763
50. Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms G. Papacharalampous et al. https://doi.org/10.3390/w11102126
51. Skill of Hydrological Extended Range Forecasts for Water Resources Management in Switzerland K. Bogner et al. https://doi.org/10.1007/s11269-017-1849-5
52. 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. https://doi.org/10.1016/j.advwatres.2019.103470
53. Diagnosing Credibility of a Large-Scale Conceptual Hydrological Model in Simulating Streamflow P. Paul et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001766
54. A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context L. Berthet et al. https://doi.org/10.5194/hess-24-2017-2020
55. Changes in extreme rainfall and its implications for design rainfall using a Bayesian quantile regression approach S. Uranchimeg et al. https://doi.org/10.2166/nh.2020.003
56. A review on statistical postprocessing methods for hydrometeorological ensemble forecasting W. Li et al. https://doi.org/10.1002/wat2.1246
57. Characterizing distributed hydrological model residual errors using a probabilistic long short-term memory network D. Li et al. https://doi.org/10.1016/j.jhydrol.2021.126888