27 citations as recorded by crossref.

1. Comparison of the soil physical properties and hydrological processes in two different forest type catchments C. Zhang et al. https://doi.org/10.1134/S0097807816120034
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3. Karst water resources in a changing world: Review of hydrological modeling approaches A. Hartmann et al. https://doi.org/10.1002/2013RG000443
4. Parameter identification for conceptual modelling using combined behavioural knowledge S. Dunn et al. https://doi.org/10.1002/hyp.1127
5. Assessment of the adequacy of the regional relationships between catchment attributes and catchment response dynamics, calibrated by a multi‐objective approach H. Kim & S. Lee https://doi.org/10.1002/hyp.9942
6. Machine learning modeling of base flow generation potential: A case study of the combined application of BWM and Fallback bargaining algorithm A. Nasiri Khiavi https://doi.org/10.1016/j.jhydrol.2024.131220
7. Investigating the relationship between a soils classification and the spatial parameters of a conceptual catchment-scale hydrological model S. Dunn & A. Lilly https://doi.org/10.1016/S0022-1694(01)00462-0
8. Testing the realism of model structures to identify karst system processes using water quality and quantity signatures A. Hartmann et al. https://doi.org/10.1002/wrcr.20229
9. Using high resolution tracer data to constrain water storage, flux and age estimates in a spatially distributed rainfall‐runoff model M. van Huijgevoort et al. https://doi.org/10.1002/hyp.10902
10. Towards reduced uncertainty in conceptual rainfall‐runoff modelling: dynamic identifiability analysis T. Wagener et al. https://doi.org/10.1002/hyp.1135
11. Effects of Model Spatial Resolution on Ecohydrologic Predictions and Their Sensitivity to Inter-Annual Climate Variability K. Son et al. https://doi.org/10.3390/w8080321
12. Modélisation pluie-débit et classification hydroclimatique Z. Bargaoui et al. https://doi.org/10.7202/018468ar
13. A variable source area for groundwater evapotranspiration: impacts on modeling stream flow Y. Tsang et al. https://doi.org/10.1002/hyp.9811
14. Transit time distributions of a conceptual model: their characteristics and sensitivities S. Dunn et al. https://doi.org/10.1002/hyp.7560
15. Resolving structural errors in a spatially distributed hydrologic model using ensemble Kalman filter state updates J. Spaaks & W. Bouten https://doi.org/10.5194/hess-17-3455-2013
16. A joined multi-metric calibration of river discharge and nitrate loads with different performance measures M. Haas et al. https://doi.org/10.1016/j.jhydrol.2016.03.001
17. Developing the snow component of a distributed hydrological model: a step-wise approach based on multi-objective analysis S. Dunn & R. Colohan https://doi.org/10.1016/S0022-1694(99)00095-5
18. Towards integrating tracer studies in conceptual rainfall‐runoff models: recent insights from a sub‐arctic catchment in the Cairngorm Mountains, Scotland C. Soulsby & S. Dunn https://doi.org/10.1002/hyp.1132
19. Improving model structure and reducing parameter uncertainty in conceptual water balance models through the use of auxiliary data K. Son & M. Sivapalan https://doi.org/10.1029/2006WR005032
20. The role of hydrograph indices in parameter estimation of rainfall-runoff models E. Shamir et al. https://doi.org/10.1002/hyp.5676
21. Capability and limitations of a simple grid-based model for simulating land use influences on stream nitrate concentrations S. Dunn et al. https://doi.org/10.1016/j.jhydrol.2013.10.016
22. Experiences in calibrating and evaluating lumped karst hydrological models A. Hartmann https://doi.org/10.1144/SP466.18
23. On the use of spatial regularization strategies to improve calibration of distributed watershed models P. Pokhrel & H. Gupta https://doi.org/10.1029/2009WR008066
24. Parameter estimation and regionalization for continuous rainfall-runoff models including uncertainty T. Wagener & H. Wheater https://doi.org/10.1016/j.jhydrol.2005.07.015
25. Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology K. Beven & J. Freer https://doi.org/10.1016/S0022-1694(01)00421-8
26. Diagnostic analysis of water balance variability: A comparative modeling study of catchments in Perth, Newcastle, and Darwin, Australia J. Samuel et al. https://doi.org/10.1029/2007WR006694
27. Smart low flow signature metrics for an improved overall performance evaluation of hydrological models M. Pfannerstill et al. https://doi.org/10.1016/j.jhydrol.2013.12.044