54 citations as recorded by crossref.

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3. A Model Combination Approach for Improving Streamflow Prediction A. Kadu & B. Biswal https://doi.org/10.1007/s11269-022-03336-5
4. Catchment response to intense rainfall: Evaluating modelling hypotheses P. Astagneau et al. https://doi.org/10.1002/hyp.14676
5. A multi-step strategy for enhancing the rainfall-runoff modeling: combination of lumped and artificial intelligence-based hydrological models B. Mohammadi et al. https://doi.org/10.1007/s10668-025-06743-x
6. Assessing Fourier and Latin hypercube sampling methods as new multi-model methods for hydrological simulations A. Singh et al. https://doi.org/10.1007/s00477-023-02627-6
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9. Analysis of Detailed Lake Variations and Associated Hydrologic Driving Factors in a Semi-Arid Ungauged Closed Watershed N. Wang et al. https://doi.org/10.3390/su15086535
10. Improving performance of bucket-type hydrological models in high latitudes with multi-model combination methods: Can we wring water from a stone? A. Todorović et al. https://doi.org/10.1016/j.jhydrol.2024.130829
11. Rainfall-Runoff Modeling to Predict Maximum Daily Flow under Climate Change Conditions M. Bashirgonbad https://doi.org/10.52547/jwmr.13.26.115
12. Etude de sensibilité globale comparée des modèles hydrologiques GRP et MORDOR-TS A. Tiberi-Wadier et al. https://doi.org/10.1080/27678490.2021.1977014
13. Evaluating surface and subsurface fluxes in hydrological models to advance basin-scale operational water supply forecasting S. Shin et al. https://doi.org/10.1080/02626667.2024.2378100
14. PREMHYCE : un outil opérationnel pour la prévision des étiages F. Tilmant et al. https://doi.org/10.1051/lhb/2020043
15. Extreme Low Flow Estimation under Climate Change S. Parey & J. Gailhard https://doi.org/10.3390/atmos13020164
16. Evaluating SUDS Efficiency in Urban Environments: A Dual-Scale Methodology Applied to the City of Madrid A. Monachese et al. https://doi.org/10.3390/w18111268
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18. A large transient multi-scenario multi-model ensemble of future streamflow and groundwater projections in France E. Sauquet et al. https://doi.org/10.5194/hess-30-2277-2026
19. Évolution de la ressource en eau et de la demande en eau dans une station de sports d’hiver dans le contexte du changement climatique A. Valéry et al. https://doi.org/10.1080/27678490.2025.2473589
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21. Precipitation-elevation relationship: Non-linearity and space–time variability prevail in the Swiss Alps L. Benoit et al. https://doi.org/10.1016/j.hydroa.2024.100186
22. Structural differences across hydrological models affect certainty of predictions of nature-based solution benefits A. Rebelo et al. https://doi.org/10.1016/j.ecolmodel.2024.110940
23. Optimal timescale of antecedent precipitation index for flood peak prediction in data-scarce mountain river basins S. Li et al. https://doi.org/10.1016/j.ejrh.2026.103624
24. Hydrological modeling of three rivers under Mediterranean climate in Chile, Greece, and Morocco: study of high flow trends by indicator calculation A. Bouadila et al. https://doi.org/10.1007/s12517-020-06013-2
25. A process-based flood frequency analysis within a trivariate statistical framework. Application to a semi-arid Mediterranean case study S. Salazar-Galán et al. https://doi.org/10.1016/j.jhydrol.2021.127081
26. Evaluation of hydrological models on small mountainous catchments: impact of the meteorological forcings G. Evin et al. https://doi.org/10.5194/hess-28-261-2024
27. Using altimetry observations combined with GRACE to select parameter sets of a hydrological model in a data-scarce region P. Hulsman et al. https://doi.org/10.5194/hess-24-3331-2020
28. Inter-comparison of lumped hydrological models in data-scarce watersheds using different precipitation forcing data sets: Case study of Northern Ontario, Canada P. Darbandsari & P. Coulibaly https://doi.org/10.1016/j.ejrh.2020.100730
29. Representing subgrid precipitation variability in snowpack and hydrological modeling: Is adding complexity worth it? F. Givovich et al. https://doi.org/10.1016/j.jhydrol.2025.134038
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31. Evaluating the Potential of Different Evapotranspiration Datasets for Distributed Hydrological Model Calibration X. Guo et al. https://doi.org/10.3390/rs14030629
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33. Potential of snow data to improve the consistency and robustness of a semi-distributed hydrological model using the SAFRAN input dataset D. Ruelland https://doi.org/10.1016/j.jhydrol.2024.130820
34. Investigating hydrological model versatility to simulate extreme flood events D. Peredo et al. https://doi.org/10.1080/02626667.2022.2030864
35. A multi-variable calibration framework at the grid scale for integrating streamflow with evapotranspiration data to improve the simulation of distributed hydrological model X. Guo et al. https://doi.org/10.1016/j.ejrh.2024.101944
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37. The S2M meteorological and snow cover reanalysis over the French mountainous areas: description and evaluation (1958–2021) M. Vernay et al. https://doi.org/10.5194/essd-14-1707-2022
38. Development of the snow- and ice-accounting routine (SIAR) D. Ruelland https://doi.org/10.1016/j.jhydrol.2023.129867
39. Assessing water system vulnerabilities under changing climate conditions using different representations of a hydrological system A. Sharifinejad et al. https://doi.org/10.1080/02626667.2021.2014057
40. An empirical investigation into the effect of antecedent precipitation on flood volume B. Bennett et al. https://doi.org/10.1016/j.jhydrol.2018.10.025
41. A comprehensive intercomparison study between a lumped and a fully distributed hydrological model across a set of 50 catchments in the United Kingdom S. Sinha et al. https://doi.org/10.1002/hyp.14544
42. Snow data assimilation for seasonal streamflow supply prediction in mountainous basins S. Metref et al. https://doi.org/10.5194/hess-27-2283-2023
43. Should altitudinal gradients of temperature and precipitation inputs be inferred from key parameters in snow-hydrological models? D. Ruelland https://doi.org/10.5194/hess-24-2609-2020
44. Strategies for hydrologic ensemble generation and calibration: On the merits of using model-based predictors A. Tiberi-Wadier et al. https://doi.org/10.1016/j.jhydrol.2021.126233
45. Prévision hydrométéorologique opérationnelle à EDF-DTG – Progrès récents et état des lieux en 2018 L. Moulin et al. https://doi.org/10.1051/lhb/2019014
46. Is hillslope-based catchment decomposition approach superior to hydrologic response unit (HRU) for stream-aquifer interaction modelling: Inference from two process-based coupled models S. Sahoo & B. Sahoo https://doi.org/10.1016/j.jhydrol.2020.125588
47. Future streamflow along the French part of the Meuse River – a closer look at uncertainties G. Thirel et al. https://doi.org/10.1080/27678490.2025.2484192
48. Multi-objective calibration by combination of stochastic and gradient-like parameter generation rules – the caRamel algorithm C. Monteil et al. https://doi.org/10.5194/hess-24-3189-2020
49. Spatial variability in the seasonal precipitation lapse rates in complex topographical regions – application in France V. Dura et al. https://doi.org/10.5194/hess-28-2579-2024
50. A semi-distributed model with physically-derived HRUs and time-dependent parameters for event-based flood simulation Y. Xu et al. https://doi.org/10.1016/j.jhydrol.2026.135587
51. Learning from satellite observations: increased understanding of catchment processes through stepwise model improvement P. Hulsman et al. https://doi.org/10.5194/hess-25-957-2021
52. Error covariance tuning in variational data assimilation: application to an operating hydrological model S. Cheng et al. https://doi.org/10.1007/s00477-020-01933-7
53. Hydrological climate change impact assessment in high latitudes: are bucket-type models up to the task? A. Todorović et al. https://doi.org/10.1016/j.envsoft.2026.106964
54. Impact of mesoscale spatial variability of climatic inputs and parameters on the hydrological response L. Rouhier et al. https://doi.org/10.1016/j.jhydrol.2017.07.037