88 citations as recorded by crossref.

1. Incremental model breakdown to assess the multi-hypotheses problem F. Jehn et al. 10.5194/hess-22-4565-2018
2. Hydrological modelling at multiple sub-daily time steps: Model improvement via flux-matching A. Ficchì et al. 10.1016/j.jhydrol.2019.05.084
3. Assessment of global reanalysis precipitation for hydrological modelling in data-scarce regions: A case study of Kenya M. Wanzala et al. 10.1016/j.ejrh.2022.101105
4. Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration M. Demirel et al. 10.3390/w11102083
5. Investigation of Low- and High-Flow Characteristics of Karst Catchments under Climate Change K. Sapač et al. 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 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. 10.1016/j.jhydrol.2017.07.002
8. Climate change impacts on the streamflow in Spanish basins monitored under near-natural conditions D. Pulido-Velazquez et al. 10.1016/j.ejrh.2021.100937
9. Development of a national 7-day ensemble streamflow forecasting service for Australia H. Hapuarachchi et al. 10.5194/hess-26-4801-2022
10. 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. 10.3390/w13223191
11. 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 10.1007/s00477-023-02607-w
12. Exploring parsimonious daily rainfall-runoff model structure using the hyperbolic tangent function and Tank model J. Song et al. 10.1016/j.jhydrol.2019.04.054
13. Assessing the performance and robustness of two conceptual rainfall-runoff models on a worldwide sample of watersheds T. Mathevet et al. 10.1016/j.jhydrol.2020.124698
14. SuperflexPy 1.3.0: an open-source Python framework for building, testing, and improving conceptual hydrological models M. Dal Molin et al. 10.5194/gmd-14-7047-2021
15. Multi-model approach in a variable spatial framework for streamflow simulation C. Thébault et al. 10.5194/hess-28-1539-2024
16. Scale-dependence of observational and modelling uncertainties in forensic flash flood analysis W. Amponsah et al. 10.1016/j.jhydrol.2022.127502
17. 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. 10.5194/gmd-11-1591-2018
18. 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 10.1016/j.cageo.2021.104708
19. Rainfall-Runoff Modeling of the Nested Non-Homogeneous Sava River Sub-Catchments in Slovenia K. Lavtar et al. 10.3390/w12010128
20. 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 10.1007/s11600-022-00746-2
21. 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. 10.1016/j.ejrh.2023.101544
22. 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 10.3390/hydrology6020032
23. 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. 10.5194/gmd-12-2463-2019
24. Two integrated conceptual–wavelet-based data-driven model approaches for daily rainfall–runoff modelling C. Sezen & T. Partal 10.2166/hydro.2022.171
25. Hydrological modelling of karst catchment using lumped conceptual and data mining models C. Sezen et al. 10.1016/j.jhydrol.2019.06.036
26. Evaluating the effects of alternative model structures on dynamic storage simulation in heterogeneous boreal catchments S. Karimi et al. 10.2166/nh.2022.121
27. From spatially variable streamflow to distributed hydrological models: Analysis of key modeling decisions F. Fenicia et al. 10.1002/2015WR017398
28. Impact of correcting sub-daily climate model biases for hydrological studies M. Faghih et al. 10.5194/hess-26-1545-2022
29. The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models R. Nijzink et al. 10.5194/hess-20-1151-2016
30. An Exploration of Bayesian Identification of Dominant Hydrological Mechanisms in Ungauged Catchments C. Prieto et al. 10.1029/2021WR030705
31. The Quantile Solidarity approach for the parsimonious regionalization of flow duration curves C. Poncelet et al. 10.1080/02626667.2017.1335399
32. Teaching hydrological modelling: illustrating model structure uncertainty with a ready-to-use computational exercise W. Knoben & D. Spieler 10.5194/hess-26-3299-2022
33. Crossing the rural–urban boundary in hydrological modelling: How do conceptual rainfall–runoff models handle the specificities of urbanized catchments? M. Saadi et al. 10.1002/hyp.13808
34. Improving structure identifiability of hydrological processes by temporal sensitivity with a flexible modeling framework L. Zhou et al. 10.1016/j.jhydrol.2022.128843
35. Catchment model regionalization approach based on spatial proximity: Does a neighbor catchment-based rainfall input strengthen the method? G. Drogue & W. Ben Khediri 10.1016/j.ejrh.2016.07.002
36. Review and comparison of performance indices for automatic model induction J. Chadalawada & V. Babovic 10.2166/hydro.2017.078
37. When does a parsimonious model fail to simulate floods? Learning from the seasonality of model bias P. Astagneau et al. 10.1080/02626667.2021.1923720
38. Improving the simulations of the hydrological model in the karst catchment by integrating the conceptual model with machine learning models C. Sezen & M. Šraj 10.1016/j.scitotenv.2024.171684
39. Factors controlling alterations in the performance of a runoff model in changing climate conditions P. Sleziak et al. 10.2478/johh-2018-0031
40. Identification of Dominant Hydrological Mechanisms Using Bayesian Inference, Multiple Statistical Hypothesis Testing, and Flexible Models C. Prieto et al. 10.1029/2020WR028338
41. Regionalization of a Rainfall-Runoff Model: Limitations and Potentials J. Song et al. 10.3390/w11112257
42. Is Catchment Classification Possible by Means of Multiple Model Structures? A Case Study Based on 99 Catchments in Germany R. Ley et al. 10.3390/hydrology3020022
43. Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods C. Broderick et al. 10.1002/2016WR018850
44. Correspondence Between Model Structures and Hydrological Signatures: A Large‐Sample Case Study Using 508 Brazilian Catchments P. David et al. 10.1029/2021WR030619
45. Variable storage behavior controlled by rainfall intensity and profile structure upon saturation excess overland flow generation J. Zhang et al. 10.1016/j.jhydrol.2022.127860
46. Development and hydrometeorological evaluation of a new stochastic daily rainfall model: Coupling Markov chain with rainfall event model C. Gao et al. 10.1016/j.jhydrol.2020.125337
47. Automatic Model Structure Identification for Conceptual Hydrologic Models D. Spieler et al. 10.1029/2019WR027009
48. Simulation of Gauged and Ungauged Streamflow of Coastal Catchments across Australia M. Bari et al. 10.3390/w16040527
49. Systematic increase in model complexity helps to identify dominant streamflow mechanisms in two small forested basins P. David et al. 10.1080/02626667.2019.1585858
50. 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. 10.5194/hess-23-4011-2019
51. Analysing spatio-temporal process and parameter dynamics in models to characterise contrasting catchments B. Guse et al. 10.1016/j.jhydrol.2018.12.050
52. Identification of factors influencing hydrologic model performance using a top‐down approach in a large number of U.S. catchments C. Massmann 10.1002/hyp.13566
53. Assessment of current and future trends in water resources in the Gambia River Basin in a context of climate change S. Séne et al. 10.1186/s12302-024-00848-2
54. Impact of temporal resolution of inputs on hydrological model performance: An analysis based on 2400 flood events A. Ficchì et al. 10.1016/j.jhydrol.2016.04.016
55. Using a multi-hypothesis framework to improve the understanding of flow dynamics during flash floods A. Douinot et al. 10.5194/hess-22-5317-2018
56. Process‐based interpretation of conceptual hydrological model performance using a multinational catchment set C. Poncelet et al. 10.1002/2016WR019991
57. The 3DNet-Catch hydrologic model: Development and evaluation A. Todorović et al. 10.1016/j.jhydrol.2018.10.040
58. Conceptual Models and Calibration Performance—Investigating Catchment Bias A. Buzacott et al. 10.3390/w11112424
59. The robustness of conceptual rainfall-runoff modelling under climate variability – A review H. Ji et al. 10.1016/j.jhydrol.2023.129666
60. 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. 10.5194/hess-24-3251-2020
61. 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. 10.2166/nh.2015.221
62. Comparison of Calibration Strategies for Daily Streamflow Simulations in Semi-Arid Basins K. Ait Naceur et al. 10.1007/s11269-024-04007-3
63. 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. 10.5194/hess-24-1319-2020
64. Structural calibration of an semi-distributed hydrological model of the Liard River basin G. Brown & J. Craig 10.1080/07011784.2020.1803143
65. Investigating hydrological model versatility to simulate extreme flood events D. Peredo et al. 10.1080/02626667.2022.2030864
66. Reproducing different types of changes in hydrological indicators with rainfall-runoff models C. Massmann 10.2166/nh.2020.073
67. 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. 10.3390/rs15194741
68. 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. 10.1016/j.jhydrol.2017.02.050
69. A novel framework for discharge uncertainty quantification applied to 500 UK gauging stations G. Coxon et al. 10.1002/2014WR016532
70. Assessment of hydrological model performance in Morocco in relation to model structure and catchment characteristics O. Jaffar et al. 10.1016/j.ejrh.2024.101899
71. 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. 10.3390/hydrology9080150
72. Suitability of a lumped rainfall–runoff model for flashy tropical watersheds in New Caledonia T. Desclaux et al. 10.1080/02626667.2018.1523613
73. Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling H. Herath et al. 10.5194/hess-25-4373-2021
74. Impact of suspicious streamflow data on the efficiency and parameter estimates of rainfall–runoff models C. Thébault et al. 10.1080/02626667.2023.2234893
75. Hillslope-scale experiment demonstrates the role of convergence during two-step saturation A. Gevaert et al. 10.5194/hess-18-3681-2014
76. Random Forest Ability in Regionalizing Hourly Hydrological Model Parameters M. Saadi et al. 10.3390/w11081540
77. Catchment response to intense rainfall: Evaluating modelling hypotheses P. Astagneau et al. 10.1002/hyp.14676
78. A Brief Analysis of Conceptual Model Structure Uncertainty Using 36 Models and 559 Catchments W. Knoben et al. 10.1029/2019WR025975
79. Identifying and Interpreting Hydrological Model Structural Nonstationarity Using the Bayesian Model Averaging Method Z. Gui et al. 10.3390/w16081126
80. Evaluating Hydrological Models for Deriving Water Resources in Peninsular Spain J. Pérez-Sánchez et al. 10.3390/su11102872
81. A priori selection of hydrological model structures in modular modelling frameworks: application to Great Britain M. Kiraz et al. 10.1080/02626667.2023.2251968
82. Physically consistent conceptual rainfall–runoff model for urbanized catchments M. Saadi et al. 10.1016/j.jhydrol.2021.126394
83. Simulink Implementation of a Hydrologic Model: A Tank Model Case Study J. Song et al. 10.3390/w9090639
84. Efficiency of Artificial Groundwater Recharge, Quantification Through Conceptual Modelling M. Zammouri & N. Brini 10.1007/s11269-020-02617-1
85. Assessing multidecadal runoff (1970–2010) using regional hydrological modelling under data and water scarcity conditions in Peruvian Pacific catchments P. Rau et al. 10.1002/hyp.13318
86. Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction J. Chadalawada et al. 10.1029/2019WR026933
87. Comparison of performance of twelve monthly water balance models in different climatic catchments of China P. Bai et al. 10.1016/j.jhydrol.2015.09.015
88. A System for Continuous Hydrological Ensemble Forecasting (SCHEF) to lead times of 9 days J. Bennett et al. 10.1016/j.jhydrol.2014.08.010