69 citations as recorded by crossref.

1. Calibration and validation of a physically distributed hydrological model, MIKE SHE, to predict streamflow at high frequency in a flashy mountainous Hawaii stream G. Sahoo et al. https://doi.org/10.1016/j.jhydrol.2005.11.012
2. Different modelling approaches to evaluate nitrogen transport and turnover at the watershed scale A. Epelde et al. https://doi.org/10.1016/j.jhydrol.2016.05.066
3. Modeling water discharge and nitrate leaching using DRAINMOD–GIS technology at small catchment scale A. El‐Sadek et al. https://doi.org/10.1002/ird.89
4. Parameter estimation in semi‐distributed hydrological catchment modelling using a multi‐criteria objective function H. Rouhani et al. https://doi.org/10.1002/hyp.6527
5. Potential Evapotranspiration for the Distributed Modeling of Belgian Catchments R. Vázquez & J. Feyen https://doi.org/10.1061/(ASCE)0733-9437(2004)130:1(1)
6. Current and future groundwater withdrawals: Effects, management and energy policy options for a semi-arid Indian watershed R. Sishodia et al. https://doi.org/10.1016/j.advwatres.2017.05.014
7. Análisis de sensibilidad con MIKE SHE variando topografía y ET R. Vázquez et al. https://doi.org/10.4995/ia.2004.2540
8. Assessment of uncertainty in estimating future flood return levels under climate change J. Das & N. Umamahesh https://doi.org/10.1007/s11069-018-3291-2
9. Performance evaluation of spatial lumped model and spatial distributed travel time model using event based rainfall for hydrological simulation M. Rosli et al. https://doi.org/10.1007/s12517-022-11068-4
10. Climate change impact on river flows and catchment hydrology: a comparison of two spatially distributed models T. Vansteenkiste et al. https://doi.org/10.1002/hyp.9480
11. Continuous Contour Trench (CCT): Understandings of hydrological processes after standardisation of dimensions and development of a user-friendly software M. Nagdeve et al. https://doi.org/10.1016/j.still.2020.104792
12. Improving the predictions of a MIKE SHE catchment‐scale application by using a multi‐criteria approach R. Vázquez et al. https://doi.org/10.1002/hyp.6815
13. Temperature Modeling, a Key to Assessing Impact on Rivers Due to Urbanization and Climate Change E. McBean et al. https://doi.org/10.3390/w14131994
14. Physics‐based continuous simulation of long‐term near‐surface hydrologic response for the Coos Bay experimental catchment B. Ebel et al. https://doi.org/10.1029/2007WR006442
15. MIKE SHE modeling of ecohydrological processes: Merits, applications, and challenges L. Ma et al. https://doi.org/10.1016/j.ecoleng.2016.01.008
16. Calibration of a physically based, spatially distributed hydrological model in a glacierized basin: On the use of knowledge from glaciometeorological processes to constrain model parameters S. Ragettli & F. Pellicciotti https://doi.org/10.1029/2011WR010559
17. Applicability of Urban Water Simulation Models for Estimating Urban Water Balance of Kabul City: A Review F. Shinwari et al. https://doi.org/10.3390/w18111307
18. Integrated hydrological modeling of the North China Plain: Options for sustainable groundwater use in the alluvial plain of Mt. Taihang Y. Shu et al. https://doi.org/10.1016/j.jhydrol.2012.06.048
19. Assessment of the effects of DEM gridding on the predictions of basin runoff using MIKE SHE and a modelling resolution of 600m R. Vázquez & J. Feyen https://doi.org/10.1016/j.jhydrol.2006.10.001
20. Modeling future hydrological responses through parameter optimization and climate change scenarios in Dirima Watershed, Ethiopia S. Atanaw et al. https://doi.org/10.1007/s40808-023-01817-z
21. On constraining a lumped hydrological model with both piezometry and streamflow: results of a large sample evaluation A. Pelletier & V. Andréassian https://doi.org/10.5194/hess-26-2733-2022
22. Validation of catchment models for predicting land-use and climate change impacts. 3. Blind validation for internal and outlet responses J. Bathurst et al. https://doi.org/10.1016/j.jhydrol.2003.09.021
23. Modelling groundwater/surface water interaction in a managed riparian chalk valley wetland A. House et al. https://doi.org/10.1002/hyp.10625
24. Impact of land use change on hydrological systems: A review of current modeling approaches G. Dwarakish et al. https://doi.org/10.1080/23312041.2015.1115691
25. Impact of different hydrological models on hydroelectric operation planning J. Páez Mendieta et al. https://doi.org/10.1016/j.renene.2024.120975
26. Coupling the Xinanjiang model and wavelet-based random forests method for improved daily streamflow simulation J. Wang et al. https://doi.org/10.2166/hydro.2021.111
27. Improving hydrological modelling of urban drainage swales through use of soil water content observations I. Broekhuizen et al. https://doi.org/10.1080/1573062X.2026.2715094
28. Modelling the hydrology of a catchment using a distributed and a semi‐distributed model A. El‐Nasr et al. https://doi.org/10.1002/hyp.5610
29. A fuzzy‐based simulation method for modelling hydrological processes under uncertainty Y. Huang et al. https://doi.org/10.1002/hyp.7790
30. A Multiobjective Decision Framework for River Basin Management X. Zhang et al. https://doi.org/10.4028/www.scientific.net/AMM.238.288
31. An approach for predicting groundwater recharge in mountainous watersheds B. Smerdon et al. https://doi.org/10.1016/j.jhydrol.2008.11.023
32. A hybrid model coupling process-driven and data-driven models for improved real-time flood forecasting C. Xu et al. https://doi.org/10.1016/j.jhydrol.2024.131494
33. Improving the streamflow simulations of a data-poor mountainous watershed using parameter transfer based on similar land use and land cover M. Kavya et al. https://doi.org/10.1080/02626667.2026.2621070
34. Effects of Urbanization on Watershed Evapotranspiration and Its Components in Southern China Q. Zheng et al. https://doi.org/10.3390/w12030645
35. Water displacement by sewer infrastructure in the Grote Nete catchment, Belgium, and its hydrological regime effects D. Vrebos et al. https://doi.org/10.5194/hess-18-1119-2014
36. Distributed modelling of future changes in hydrological processes of Spencer Creek watershed Z. Sultana & P. Coulibaly https://doi.org/10.1002/hyp.7891
37. Current awareness https://doi.org/10.1002/hyp.439
38. A Study on Runoff Properties of Non-point Pollutant in Nakdong watershed by using SWAT model E. Lee et al. https://doi.org/10.11001/jksww.2012.26.6.815
39. Suitability of a Coupled Hydrologic and Hydraulic Model to Simulate Surface Water and Groundwater Hydrology in a Typical North-Eastern Germany Lowland Catchment M. Waseem et al. https://doi.org/10.3390/app10041281
40. HESS Opinions: Advocating process modeling and de-emphasizing parameter estimation A. Bahremand https://doi.org/10.5194/hess-20-1433-2016
41. Effects of Fertilizer Types on Pollutant Loadings from Rice Paddy Fields S. Chang et al. https://doi.org/10.5389/KSAE.2011.53.6.111
42. Examining the challenges of simulating surface water–groundwater interactions in a post-glacial environment F. Turgeon et al. https://doi.org/10.1080/07011784.2017.1414635
43. On the performance of conceptual and physically based modelling approach to simulate a headwater catchment in Brazil P. Melo et al. https://doi.org/10.1016/j.jsames.2021.103683
44. Hydrologic process simulation of a semiarid, endoreic catchment in Sahelian West Niger. 2. Model calibration and uncertainty characterization B. Cappelaere et al. https://doi.org/10.1016/S0022-1694(03)00182-3
45. GLUE Based Assessment on the Overall Predictions of a MIKE SHE Application R. Vázquez et al. https://doi.org/10.1007/s11269-008-9329-6
46. Dealing with sub-pixel landscape elements in distributed rainfall-runoff modelling in agricultural catchments I. Rosier et al. https://doi.org/10.1016/j.iswcr.2025.02.009
47. Probabilistic Analysis of Vertical Drains using Hasofer-Lind Reliability Index S. Kim et al. https://doi.org/10.5389/KSAE.2011.53.6.001
48. Potential evapotranspiration-related uncertainty in climate change impacts on river flow: An assessment for the Mekong River basin J. Thompson et al. https://doi.org/10.1016/j.jhydrol.2013.12.010
49. Modelling water-level options for ecosystem services and assessment of climate change: Loktak Lake, northeast India C. Singh et al. https://doi.org/10.1080/02626667.2011.631012
50. Performance Evaluation of a Distributed Hydrological Model Using Satellite Data over the Lake Kastoria Catchment, Greece D. Papadimos & D. Papamichail https://doi.org/10.3390/hydrology13010002
51. A time series tool to support the multi-criteria performance evaluation of rainfall-runoff models P. Willems https://doi.org/10.1016/j.envsoft.2008.09.005
52. Multi-site identification of a distributed hydrological nitrogen model using Bayesian uncertainty analysis S. Jiang et al. https://doi.org/10.1016/j.jhydrol.2015.09.009
53. SWAT-Based Streamflow Estimation and Its Responses to Climate Change in the Kadongjia River Watershed, Southern Tibet R. Sun et al. https://doi.org/10.1175/JHM-D-12-0159.1
54. Assessing the impacts of land use changes on watershed hydrology using MIKE SHE S. Im et al. https://doi.org/10.1007/s00254-008-1303-3
55. Application of a Distributed Hydrologic Model to Flood Forecasting in Catchments of Different Conditions Z. Liu et al. https://doi.org/10.1061/(ASCE)1084-0699(2008)13:5(378)
56. Improve streamflow simulations by combining machine learning pre-processing and post-processing Y. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.132904
57. Multicriteria analysis on rock moisture and streamflow in a rainfall‐runoff model improves accuracy of model results P. La Follette et al. https://doi.org/10.1002/hyp.14536
58. Upscaling Field Scale Hydrology and Water Quality Modelling to Catchment Scale A. El-Sadek https://doi.org/10.1007/s11269-006-9046-y
59. Sensitivity Analysis and Calibration of Hydrological Modeling of the Watershed Northeast Brazil M. da Silva et al. https://doi.org/10.4236/jep.2015.68076
60. Effect of grid size on effective parameters and model performance of the MIKE‐SHE code R. Vázquez et al. https://doi.org/10.1002/hyp.334
61. Effect of potential evapotranspiration estimates on effective parameters and performance of the MIKE SHE-code applied to a medium-size catchment R. Vázquez https://doi.org/10.1016/S0022-1694(02)00308-6
62. Multi-site calibration, validation, and sensitivity analysis of the MIKE SHE Model for a large watershed in northern China S. Wang et al. https://doi.org/10.5194/hess-16-4621-2012
63. Prediction limits of a catchment hydrological model using different estimates of ETp R. Vázquez & H. Hampel https://doi.org/10.1016/j.jhydrol.2014.03.051
64. Importance of including small‐scale tile drain discharge in the calibration of a coupled groundwater‐surface water catchment model A. Hansen et al. https://doi.org/10.1029/2011WR011783
65. Optimising training data for ANNs with Genetic Algorithms R. Kamp & H. Savenije https://doi.org/10.5194/hess-10-603-2006
66. A data-driven and expert flood knowledge model based on the development of the Huber loss function for flood forecasting H. Malik et al. https://doi.org/10.1016/j.watres.2025.124632
67. Assessment of the Performance of a Distributed Code in Relation to the ETp Estimates R. Vázquez Z & J. Feyen https://doi.org/10.1023/A:1021903211213
68. Assessment of uncertainty in river flow projections for the Mekong River using multiple GCMs and hydrological models J. Thompson et al. https://doi.org/10.1016/j.jhydrol.2013.01.029
69. Assessing the Performance of SHETRAN Simulating a Geologically Complex Catchment R. Vázquez et al. https://doi.org/10.3390/w14203334