51 citations as recorded by crossref.

1. Quantile-Based Hydrological Modelling H. Tyralis & G. Papacharalampous 10.3390/w13233420
2. Bayesian flood forecasting methods: A review S. Han & P. Coulibaly 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. 10.1016/j.jhydrol.2020.125908
4. Merging Satellite and Gauge-Measured Precipitation Using LightGBM With an Emphasis on Extreme Quantiles H. Tyralis et al. 10.1109/JSTARS.2023.3297013
5. 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
6. 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
7. Temporally varied error modelling for improving simulations and quantifying uncertainty L. Liu et al. 10.1016/j.jhydrol.2020.124914
8. 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
9. Severe floods predictive ability: A proxy based probabilistic assessment of the Italian early warning system F. Silvestro et al. 10.1111/jfr3.12970
10. 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
11. Impact of Dataset Size on the Signature-Based Calibration of a Hydrological Model S. Mohammed et al. 10.3390/w13070970
12. 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
13. 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
14. 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
15. A stochastic deep-learning-based approach for improved streamflow simulation N. Dolatabadi & B. Zahraie 10.1007/s00477-023-02567-1
16. 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
17. Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting O. Wani et al. 10.5194/hess-21-4021-2017
18. Skill of ensemble flood inundation forecasts at short- to medium-range timescales M. Gomez et al. 10.1016/j.jhydrol.2018.10.063
19. 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
20. A framework for optimizing environmental covariates to support model interpretability in digital soil mapping B. Kasraei et al. 10.1016/j.geoderma.2024.116873
21. 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
22. Probabilistic Water Demand Forecasting Using Quantile Regression Algorithms G. Papacharalampous & A. Langousis 10.1029/2021WR030216
23. A Framework for Recalibrating Pedotransfer Functions Using Nonlinear Least Squares and Estimating Uncertainty Using Quantile Regression B. Heung et al. 10.2139/ssrn.4352027
24. Post-Processing of Stream Flows in Switzerland with an Emphasis on Low Flows and Floods K. Bogner et al. 10.3390/w8040115
25. Quantifying uncertainty in future sea level projections downscaled from CMIP5 global climate models S. Sithara et al. 10.1007/s00477-024-02669-4
26. Performance Assessment of Hydrological Models Considering Acceptable Forecast Error Threshold Q. Dong & F. Lu 10.3390/w7116173
27. Dynamic quantile regression for trend analysis of streamflow time series L. Lima et al. 10.1002/rra.3983
28. Spatial mode-based calibration (SMoC) of forecast precipitation fields from numerical weather prediction models P. Zhao et al. 10.1016/j.jhydrol.2022.128432
29. Ensemble Learning for Blending Gridded Satellite and Gauge-Measured Precipitation Data G. Papacharalampous et al. 10.3390/rs15204912
30. 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
31. Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system S. Sharma et al. 10.5194/hess-22-1831-2018
32. 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
33. Hydrological post-processing for predicting extreme quantiles H. Tyralis & G. Papacharalampous 10.1016/j.jhydrol.2023.129082
34. 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
35. Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning L. Knoll et al. 10.1088/1748-9326/ab7d5c
36. 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
37. 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
38. Technical note: Combining quantile forecasts and predictive distributions of streamflows K. Bogner et al. 10.5194/hess-21-5493-2017
39. A framework for recalibrating pedotransfer functions using nonlinear least squares and estimating uncertainty using quantile regression A. Arbor et al. 10.1016/j.geoderma.2023.116674
40. 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
41. Comparative Study of Two State-of-the-Art Semi-Distributed Hydrological Models P. Paul et al. 10.3390/w11050871
42. Bayesian LSTM With Stochastic Variational Inference for Estimating Model Uncertainty in Process‐Based Hydrological Models D. Li et al. 10.1029/2021WR029772
43. Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms G. Papacharalampous et al. 10.3390/w11102126
44. Skill of Hydrological Extended Range Forecasts for Water Resources Management in Switzerland K. Bogner et al. 10.1007/s11269-017-1849-5
45. 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
46. Diagnosing Credibility of a Large-Scale Conceptual Hydrological Model in Simulating Streamflow P. Paul et al. 10.1061/(ASCE)HE.1943-5584.0001766
47. 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
48. 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
49. A review on statistical postprocessing methods for hydrometeorological ensemble forecasting W. Li et al. 10.1002/wat2.1246
50. Characterizing distributed hydrological model residual errors using a probabilistic long short-term memory network D. Li et al. 10.1016/j.jhydrol.2021.126888
51. 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