57 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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8. Multi-objective assessment of hydrological model performances using Nash–Sutcliffe and Kling–Gupta efficiencies on a worldwide large sample of watersheds T. Mathevet et al. https://doi.org/10.5802/crgeos.189
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. Future runoff simulation based on different typical climate models: a case study in the Yalong River basin W. Feng et al. https://doi.org/10.1038/s41598-026-51969-y
12. Rainfall-Runoff Modeling to Predict Maximum Daily Flow under Climate Change Conditions M. Bashirgonbad https://doi.org/10.52547/jwmr.13.26.115
13. 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
14. 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
15. PREMHYCE : un outil opérationnel pour la prévision des étiages F. Tilmant et al. https://doi.org/10.1051/lhb/2020043
16. Extreme Low Flow Estimation under Climate Change S. Parey & J. Gailhard https://doi.org/10.3390/atmos13020164
17. Impact of seasonal snow on the recharge of a mountain karst aquifer under climate change: the Dévoluy case study (Southern Alps, France) N. Rispal et al. https://doi.org/10.5194/hess-30-4699-2026
18. 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
19. A hydro-climatic approach for extreme flood estimation in mountainous catchments M. Bashirgonbad et al. https://doi.org/10.1007/s13201-024-02149-8
20. 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
21. 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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23. 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
24. 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
25. 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
26. 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
27. 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
28. 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
29. 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
30. 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
31. 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
32. Future nuclear power outages in a changing climate - A case study on two contrasted French power plants L. Collet et al. https://doi.org/10.1016/j.energy.2025.135207
33. Evaluating the Potential of Different Evapotranspiration Datasets for Distributed Hydrological Model Calibration X. Guo et al. https://doi.org/10.3390/rs14030629
34. Modeling approaches to enhance the accuracy of streamflow and hydrological signature simulations in data-scarce Mediterranean regions: insights from Oued Issen, Morocco I. Bouizrou et al. https://doi.org/10.1007/s42990-025-00159-5
35. 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
36. Investigating hydrological model versatility to simulate extreme flood events D. Peredo et al. https://doi.org/10.1080/02626667.2022.2030864
37. 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
38. Cohérence des prévisions et place de l'expertise : les nouveaux défis pour la prévision des crues L. Berthet et al. https://doi.org/10.1051/lhb/2019001
39. Potential glacier contributions to the 2024 La Bérarde flood C. Ogier et al. https://doi.org/10.5194/nhess-26-3901-2026
40. 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
41. Development of the snow- and ice-accounting routine (SIAR) D. Ruelland https://doi.org/10.1016/j.jhydrol.2023.129867
42. 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
43. 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
44. 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
45. Snow data assimilation for seasonal streamflow supply prediction in mountainous basins S. Metref et al. https://doi.org/10.5194/hess-27-2283-2023
46. 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
47. 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
48. 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
49. 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
50. 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
51. 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
52. 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
53. 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
54. 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
55. 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
56. 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
57. 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