53 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
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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
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11. Rainfall-Runoff Modeling to Predict Maximum Daily Flow under Climate Change Conditions M. Bashirgonbad https://doi.org/10.52547/jwmr.13.26.115
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14. PREMHYCE : un outil opérationnel pour la prévision des étiages F. Tilmant et al. https://doi.org/10.1051/lhb/2020043
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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
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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. 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
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25. 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
26. 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
27. 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
28. 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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30. 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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34. 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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38. 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
39. 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
40. 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
41. Snow data assimilation for seasonal streamflow supply prediction in mountainous basins S. Metref et al. https://doi.org/10.5194/hess-27-2283-2023
42. 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
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45. 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
46. 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
47. 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
48. 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
49. 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
50. 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
51. 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
52. 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
53. 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