30 citations as recorded by crossref.

1. Global‐scale regionalization of hydrologic model parameters H. Beck et al. https://doi.org/10.1002/2015WR018247
2. 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
3. Characterizing Uncertainty in Daily Streamflow Estimates at Ungauged Locations for the Massachusetts Sustainable Yield Estimator W. Farmer & S. Levin https://doi.org/10.1111/1752-1688.12603
4. Regional Parameter Estimation of the SWAT Model: Methodology and Application to River Basins in the Peruvian Pacific Drainage F. Asurza-Véliz & W. Lavado-Casimiro https://doi.org/10.3390/w12113198
5. 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
6. EvalHyd v0.1.2: a polyglot tool for the evaluation of deterministic and probabilistic streamflow predictions T. Hallouin et al. https://doi.org/10.5194/gmd-17-4561-2024
7. On the choice of calibration metrics for “high-flow” estimation using hydrologic models N. Mizukami et al. https://doi.org/10.5194/hess-23-2601-2019
8. Assessment of spatial transferability of process‐based hydrological model parameters in two neighbouring catchments in the Himalayan Region S. Nepal et al. https://doi.org/10.1002/hyp.11199
9. Quantifying uncertainty in simulated streamflow and runoff from a continental-scale monthly water balance model A. Bock et al. https://doi.org/10.1016/j.advwatres.2018.10.005
10. Leveraging ensemble meteorological forcing data to improve parameter estimation of hydrologic models H. Liu et al. https://doi.org/10.1002/hyp.14410
11. Random Forest Ability in Regionalizing Hourly Hydrological Model Parameters M. Saadi et al. https://doi.org/10.3390/w11081540
12. On the deterministic and stochastic use of hydrologic models W. Farmer & R. Vogel https://doi.org/10.1002/2016WR019129
13. A space-time geostatistical approach for ensemble rainfall nowcasting A. Caseri et al. https://doi.org/10.1051/e3sconf/20160718001
14. Evaluation of Uncertainty Intervals for Daily, Statistically Derived Streamflow Estimates at Ungaged Basins across the Continental U.S. S. Levin & W. Farmer https://doi.org/10.3390/w12051390
15. Evaluating Hydrological Models for Deriving Water Resources in Peninsular Spain J. Pérez-Sánchez et al. https://doi.org/10.3390/su11102872
16. Multi-site learning for hydrological uncertainty prediction: the case of quantile random forests T. El Ouahabi et al. https://doi.org/10.5194/hess-30-3549-2026
17. Cumulative Effects of Uncertainty on Simulated Streamflow in a Hydrologic Modeling Environment S. Pokorny et al. https://doi.org/10.1525/elementa.431
18. Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms H. Tyralis et al. https://doi.org/10.3390/rs13030333
19. Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis E. Moges et al. https://doi.org/10.3390/w13010028
20. 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
21. How to explain and predict the shape parameter of the generalized extreme value distribution of streamflow extremes using a big dataset H. Tyralis et al. https://doi.org/10.1016/j.jhydrol.2019.04.070
22. Estimation of uncertainty in flood forecasts—A comparison of methods L. Boelee et al. https://doi.org/10.1111/jfr3.12516
23. A stochastic wavelet-based data-driven framework for forecasting uncertain multiscale hydrological and water resources processes J. Quilty & J. Adamowski https://doi.org/10.1016/j.envsoft.2020.104718
24. 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
25. 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
26. A stochastic conceptual-data-driven approach for improved hydrological simulations J. Quilty et al. https://doi.org/10.1016/j.envsoft.2022.105326
27. Climate change impacts on the streamflow in Spanish basins monitored under near-natural conditions D. Pulido-Velazquez et al. https://doi.org/10.1016/j.ejrh.2021.100937
28. Vers une production en temps réel d'intervalles prédictifs associés aux prévisions de crue dans Vigicrues en France J. Viatgé et al. https://doi.org/10.1051/lhb/2019016
29. 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
30. Controls on lag time in Philippine catchments identified using rainfall–runoff modelling and a generalized additive model (GAM) P. Tolentino et al. https://doi.org/10.1080/02626667.2025.2608147