98 citations as recorded by crossref.

1. Incremental model breakdown to assess the multi-hypotheses problem F. Jehn et al. https://doi.org/10.5194/hess-22-4565-2018
2. Hydrological modelling at multiple sub-daily time steps: Model improvement via flux-matching A. Ficchì et al. https://doi.org/10.1016/j.jhydrol.2019.05.084
3. Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration M. Demirel et al. https://doi.org/10.3390/w11102083
4. Comparative analysis of runoff simulations using multiple hydrological models in the Ganjiang River Basin X. Jiang et al. https://doi.org/10.2166/nh.2025.020
5. Investigation of Low- and High-Flow Characteristics of Karst Catchments under Climate Change K. Sapač et al. https://doi.org/10.3390/w11050925
6. Which range of streamflow data is most informative in the calibration of an hourly hydrological model? M. Saadi & C. Furusho-Percot https://doi.org/10.1080/02626667.2023.2277835
7. Design and experimentation of an empirical multistructure framework for accurate, sharp and reliable hydrological ensembles G. Seiller et al. https://doi.org/10.1016/j.jhydrol.2017.07.002
8. Exploring parsimonious daily rainfall-runoff model structure using the hyperbolic tangent function and Tank model J. Song et al. https://doi.org/10.1016/j.jhydrol.2019.04.054
9. SuperflexPy 1.3.0: an open-source Python framework for building, testing, and improving conceptual hydrological models M. Dal Molin et al. https://doi.org/10.5194/gmd-14-7047-2021
10. Scale-dependence of observational and modelling uncertainties in forensic flash flood analysis W. Amponsah et al. https://doi.org/10.1016/j.jhydrol.2022.127502
11. Continuous state-space representation of a bucket-type rainfall-runoff model: a case study with the GR4 model using state-space GR4 (version 1.0) L. Santos et al. https://doi.org/10.5194/gmd-11-1591-2018
12. Can a Calibration-Free Dynamic Rainfall‒Runoff Model Predict FDCs in Data-Scarce Regions? Comparing the IDW Model with the Dynamic Budyko Model in South India A. Nag & B. Biswal https://doi.org/10.3390/hydrology6020032
13. Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v1.2: an open-source, extendable framework providing implementations of 46 conceptual hydrologic models as continuous state-space formulations W. Knoben et al. https://doi.org/10.5194/gmd-12-2463-2019
14. Two integrated conceptual–wavelet-based data-driven model approaches for daily rainfall–runoff modelling C. Sezen & T. Partal https://doi.org/10.2166/hydro.2022.171
15. Impact of correcting sub-daily climate model biases for hydrological studies M. Faghih et al. https://doi.org/10.5194/hess-26-1545-2022
16. The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models R. Nijzink et al. https://doi.org/10.5194/hess-20-1151-2016
17. Crossing the rural–urban boundary in hydrological modelling: How do conceptual rainfall–runoff models handle the specificities of urbanized catchments? M. Saadi et al. https://doi.org/10.1002/hyp.13808
18. Partitioning uncertainties of extreme flood estimates using long continuous simulations E. Kritidou et al. https://doi.org/10.1016/j.jhydrol.2025.134804
19. A spatially hybrid hydrological modeling approach using subbasin-specific model structures Y. Wang et al. https://doi.org/10.1016/j.envsoft.2026.106944
20. Improving structure identifiability of hydrological processes by temporal sensitivity with a flexible modeling framework L. Zhou et al. https://doi.org/10.1016/j.jhydrol.2022.128843
21. When does a parsimonious model fail to simulate floods? Learning from the seasonality of model bias P. Astagneau et al. https://doi.org/10.1080/02626667.2021.1923720
22. Improving the simulations of the hydrological model in the karst catchment by integrating the conceptual model with machine learning models C. Sezen & M. Šraj https://doi.org/10.1016/j.scitotenv.2024.171684
23. Identification of Dominant Hydrological Mechanisms Using Bayesian Inference, Multiple Statistical Hypothesis Testing, and Flexible Models C. Prieto et al. https://doi.org/10.1029/2020WR028338
24. Regionalization of a Rainfall-Runoff Model: Limitations and Potentials J. Song et al. https://doi.org/10.3390/w11112257
25. Is Catchment Classification Possible by Means of Multiple Model Structures? A Case Study Based on 99 Catchments in Germany R. Ley et al. https://doi.org/10.3390/hydrology3020022
26. Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods C. Broderick et al. https://doi.org/10.1002/2016WR018850
27. Variable storage behavior controlled by rainfall intensity and profile structure upon saturation excess overland flow generation J. Zhang et al. https://doi.org/10.1016/j.jhydrol.2022.127860
28. Simulation of Gauged and Ungauged Streamflow of Coastal Catchments across Australia M. Bari et al. https://doi.org/10.3390/w16040527
29. Identification of factors influencing hydrologic model performance using a top‐down approach in a large number of U.S. catchments C. Massmann https://doi.org/10.1002/hyp.13566
30. Assessment of current and future trends in water resources in the Gambia River Basin in a context of climate change S. Séne et al. https://doi.org/10.1186/s12302-024-00848-2
31. Process‐based interpretation of conceptual hydrological model performance using a multinational catchment set C. Poncelet et al. https://doi.org/10.1002/2016WR019991
32. Conceptual Models and Calibration Performance—Investigating Catchment Bias A. Buzacott et al. https://doi.org/10.3390/w11112424
33. Structural calibration of an semi-distributed hydrological model of the Liard River basin G. Brown & J. Craig https://doi.org/10.1080/07011784.2020.1803143
34. Applicability Assessment of GPM IMERG Satellite Heavy-Rainfall-Informed Reservoir Short-Term Inflow Forecast and Optimal Operation: A Case Study of Wan’an Reservoir in China Q. Ma et al. https://doi.org/10.3390/rs15194741
35. Identification of the dominant hydrological process and appropriate model structure of a karst catchment through stepwise simplification of a complex conceptual model Y. Chang et al. https://doi.org/10.1016/j.jhydrol.2017.02.050
36. A novel framework for discharge uncertainty quantification applied to 500 UK gauging stations G. Coxon et al. https://doi.org/10.1002/2014WR016532
37. Assessment of hydrological model performance in Morocco in relation to model structure and catchment characteristics O. Jaffar et al. https://doi.org/10.1016/j.ejrh.2024.101899
38. Prediction at Ungauged Catchments through Parameter Optimization and Uncertainty Estimation to Quantify the Regional Water Balance of the Ethiopian Rift Valley Lake Basin T. Abraham et al. https://doi.org/10.3390/hydrology9080150
39. Challenges of modelling climate change impacts on hydrology and water resources: AI is the game changer—a review C. Onyutha https://doi.org/10.1088/2752-5295/ae2a60
40. Random Forest Ability in Regionalizing Hourly Hydrological Model Parameters M. Saadi et al. https://doi.org/10.3390/w11081540
41. A priori selection of hydrological model structures in modular modelling frameworks: application to Great Britain M. Kiraz et al. https://doi.org/10.1080/02626667.2023.2251968
42. Physically consistent conceptual rainfall–runoff model for urbanized catchments M. Saadi et al. https://doi.org/10.1016/j.jhydrol.2021.126394
43. Assessing multidecadal runoff (1970–2010) using regional hydrological modelling under data and water scarcity conditions in Peruvian Pacific catchments P. Rau et al. https://doi.org/10.1002/hyp.13318
44. Assessment of global reanalysis precipitation for hydrological modelling in data-scarce regions: A case study of Kenya M. Wanzala et al. https://doi.org/10.1016/j.ejrh.2022.101105
45. Effects of Model Spatial Structure and Basin Characteristics on the Performance of Three Hydrologic Models F. Kacar et al. https://doi.org/10.1007/s11269-025-04308-1
46. A controlled model experiment for the global hydrological model WaterGAP3: Understanding recent and new advances in the model structure J. Kupzig & M. Flörke https://doi.org/10.1016/j.envsoft.2025.106703
47. Climate change impacts on the streamflow in Spanish basins monitored under near-natural conditions D. Pulido-Velazquez et al. https://doi.org/10.1016/j.ejrh.2021.100937
48. Development of a national 7-day ensemble streamflow forecasting service for Australia H. Hapuarachchi et al. https://doi.org/10.5194/hess-26-4801-2022
49. A multi-hybrid model approach optimizing discharge forecasts in karst catchment under climate change C. Sezen & M. Šraj https://doi.org/10.1016/j.jhydrol.2025.134620
50. Comparison of Three Daily Rainfall-Runoff Hydrological Models Using Four Evapotranspiration Models in Four Small Forested Watersheds with Different Land Cover in South-Central Chile N. Flores et al. https://doi.org/10.3390/w13223191
51. Hourly rainfall-runoff modelling by combining the conceptual model with machine learning models in mostly karst Ljubljanica River catchment in Slovenia C. Sezen & M. Šraj https://doi.org/10.1007/s00477-023-02607-w
52. Performance of different reanalysis rainfall datasets and hydrological models in gap-filling for Mpologoma catchment in Eastern Uganda M. Twinomuhangi et al. https://doi.org/10.1016/j.ejrh.2026.103416
53. Assessing the performance and robustness of two conceptual rainfall-runoff models on a worldwide sample of watersheds T. Mathevet et al. https://doi.org/10.1016/j.jhydrol.2020.124698
54. Multi-model approach in a variable spatial framework for streamflow simulation C. Thébault et al. https://doi.org/10.5194/hess-28-1539-2024
55. The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU: A case study for six basins from the CAMELS dataset G. Ayzel & M. Heistermann https://doi.org/10.1016/j.cageo.2021.104708
56. Rainfall-Runoff Modeling of the Nested Non-Homogeneous Sava River Sub-Catchments in Slovenia K. Lavtar et al. https://doi.org/10.3390/w12010128
57. The utilisation of conceptual and data-driven models for hydrological modelling in semi-arid and humid areas of the Antalya basin in Turkey C. Sezen & T. Partal https://doi.org/10.1007/s11600-022-00746-2
58. To tame a land: Limiting factors in model performance for the multi-objective calibration of a pan-European, semi-distributed hydrological model for discharge and sediments C. Brendel et al. https://doi.org/10.1016/j.ejrh.2023.101544
59. Distributed and semi-distributed hydrological modelling for catchments draining to the Great Barrier Reef coastline U. Khan et al. https://doi.org/10.2166/nh.2025.156
60. Hydrological modelling of karst catchment using lumped conceptual and data mining models C. Sezen et al. https://doi.org/10.1016/j.jhydrol.2019.06.036
61. Evaluating the effects of alternative model structures on dynamic storage simulation in heterogeneous boreal catchments S. Karimi et al. https://doi.org/10.2166/nh.2022.121
62. From spatially variable streamflow to distributed hydrological models: Analysis of key modeling decisions F. Fenicia et al. https://doi.org/10.1002/2015WR017398
63. An Exploration of Bayesian Identification of Dominant Hydrological Mechanisms in Ungauged Catchments C. Prieto et al. https://doi.org/10.1029/2021WR030705
64. The Quantile Solidarity approach for the parsimonious regionalization of flow duration curves C. Poncelet et al. https://doi.org/10.1080/02626667.2017.1335399
65. Teaching hydrological modelling: illustrating model structure uncertainty with a ready-to-use computational exercise W. Knoben & D. Spieler https://doi.org/10.5194/hess-26-3299-2022
66. Catchment model regionalization approach based on spatial proximity: Does a neighbor catchment-based rainfall input strengthen the method? G. Drogue & W. Ben Khediri https://doi.org/10.1016/j.ejrh.2016.07.002
67. Review and comparison of performance indices for automatic model induction J. Chadalawada & V. Babovic https://doi.org/10.2166/hydro.2017.078
68. Factors controlling alterations in the performance of a runoff model in changing climate conditions P. Sleziak et al. https://doi.org/10.2478/johh-2018-0031
69. Correspondence Between Model Structures and Hydrological Signatures: A Large‐Sample Case Study Using 508 Brazilian Catchments P. David et al. https://doi.org/10.1029/2021WR030619
70. Development and hydrometeorological evaluation of a new stochastic daily rainfall model: Coupling Markov chain with rainfall event model C. Gao et al. https://doi.org/10.1016/j.jhydrol.2020.125337
71. Automatic Model Structure Identification for Conceptual Hydrologic Models D. Spieler et al. https://doi.org/10.1029/2019WR027009
72. Systematic increase in model complexity helps to identify dominant streamflow mechanisms in two small forested basins P. David et al. https://doi.org/10.1080/02626667.2019.1585858
73. Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across over 1000 catchments in Great Britain R. Lane et al. https://doi.org/10.5194/hess-23-4011-2019
74. Analysing spatio-temporal process and parameter dynamics in models to characterise contrasting catchments B. Guse et al. https://doi.org/10.1016/j.jhydrol.2018.12.050
75. Impact of temporal resolution of inputs on hydrological model performance: An analysis based on 2400 flood events A. Ficchì et al. https://doi.org/10.1016/j.jhydrol.2016.04.016
76. Using a multi-hypothesis framework to improve the understanding of flow dynamics during flash floods A. Douinot et al. https://doi.org/10.5194/hess-22-5317-2018
77. The 3DNet-Catch hydrologic model: Development and evaluation A. Todorović et al. https://doi.org/10.1016/j.jhydrol.2018.10.040
78. The robustness of conceptual rainfall-runoff modelling under climate variability – A review H. Ji et al. https://doi.org/10.1016/j.jhydrol.2023.129666
79. Applicability of the daily hydrological models (GR4J, GR5J, and GR6J) in the South Korean basins J. Park et al. https://doi.org/10.1038/s41598-025-16429-z
80. Assessment of extreme flows and uncertainty under climate change: disentangling the uncertainty contribution of representative concentration pathways, global climate models and internal climate variability C. Gao et al. https://doi.org/10.5194/hess-24-3251-2020
81. Comparing classical performance measures with signature indices derived from flow duration curves to assess model structures as tools for catchment classification R. Ley et al. https://doi.org/10.2166/nh.2015.221
82. Comparison of Calibration Strategies for Daily Streamflow Simulations in Semi-Arid Basins K. Ait Naceur et al. https://doi.org/10.1007/s11269-024-04007-3
83. Understanding dominant controls on streamflow spatial variability to set up a semi-distributed hydrological model: the case study of the Thur catchment M. Dal Molin et al. https://doi.org/10.5194/hess-24-1319-2020
84. Investigating hydrological model versatility to simulate extreme flood events D. Peredo et al. https://doi.org/10.1080/02626667.2022.2030864
85. Reproducing different types of changes in hydrological indicators with rainfall-runoff models C. Massmann https://doi.org/10.2166/nh.2020.073
86. Suitability of a lumped rainfall–runoff model for flashy tropical watersheds in New Caledonia T. Desclaux et al. https://doi.org/10.1080/02626667.2018.1523613
87. Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling H. Herath et al. https://doi.org/10.5194/hess-25-4373-2021
88. Impact of suspicious streamflow data on the efficiency and parameter estimates of rainfall–runoff models C. Thébault et al. https://doi.org/10.1080/02626667.2023.2234893
89. Hillslope-scale experiment demonstrates the role of convergence during two-step saturation A. Gevaert et al. https://doi.org/10.5194/hess-18-3681-2014
90. Catchment response to intense rainfall: Evaluating modelling hypotheses P. Astagneau et al. https://doi.org/10.1002/hyp.14676
91. Catchment features-based interpretation of performance of the conceptual hydrological and deep learning models using large sample hydrologic data D. Sourya et al. https://doi.org/10.1016/j.jhydrol.2025.134270
92. A Brief Analysis of Conceptual Model Structure Uncertainty Using 36 Models and 559 Catchments W. Knoben et al. https://doi.org/10.1029/2019WR025975
93. Technical note: How many models do we need to simulate hydrologic processes across large geographical domains? W. Knoben et al. https://doi.org/10.5194/hess-29-2361-2025
94. Identifying and Interpreting Hydrological Model Structural Nonstationarity Using the Bayesian Model Averaging Method Z. Gui et al. https://doi.org/10.3390/w16081126
95. Evaluating Hydrological Models for Deriving Water Resources in Peninsular Spain J. Pérez-Sánchez et al. https://doi.org/10.3390/su11102872
96. Multi-model simulation performance of monthly water balance models for global catchments: Thresholds and structural sensitivity Z. Ning et al. https://doi.org/10.1016/j.jhydrol.2025.134852
97. Simulink Implementation of a Hydrologic Model: A Tank Model Case Study J. Song et al. https://doi.org/10.3390/w9090639
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