83 citations as recorded by crossref.

1. A Brief Analysis of Conceptual Model Structure Uncertainty Using 36 Models and 559 Catchments W. Knoben et al. 10.1029/2019WR025975
2. HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists R. Rigon et al. 10.5194/hess-26-4773-2022
3. Multi-model ensemble benchmark data for hydrological modeling in Japanese river basins Y. Sawada et al. 10.3178/hrl.16.73
4. Marching in step: The importance of matching model complexity to data availability in terrestrial biosphere models X. Feng 10.1111/gcb.15090
5. How Robust Is a Multi-Model Ensemble Mean of Conceptual Hydrological Models to Climate Change? T. Kimizuka & Y. Sawada 10.3390/w14182852
6. Contrasting hydrological responses to climate change in two adjacent catchments dominated by karst and nonkarst Y. Chang et al. 10.1016/j.jhydrol.2023.130013
7. On the use of streamflow transformations for hydrological model calibration G. Thirel et al. 10.5194/hess-28-4837-2024
8. Historical development of rainfall‐runoff modeling M. Peel & T. McMahon 10.1002/wat2.1471
9. Confidence intervals of the Kling-Gupta efficiency J. Vrugt & D. de Oliveira 10.1016/j.jhydrol.2022.127968
10. RavenR v2.1.4: an open-source R package to support flexible hydrologic modelling R. Chlumsky et al. 10.5194/gmd-15-7017-2022
11. Mimicry of a Conceptual Hydrological Model (HBV): What's in a Name? K. Jansen et al. 10.1029/2020WR029143
12. OpenForecast: An Assessment of the Operational Run in 2020–2021 G. Ayzel & D. Abramov 10.3390/geosciences12020067
13. Seamless Integration of Rainfall Spatial Variability and a Conceptual Hydrological Model Y. Zhou et al. 10.3390/su13063588
14. TATOO – Python Topographic Analysis Tool Library for semi-automated setup of high-resolution integrated hydrologic models J. Mitterer 10.1016/j.envsoft.2022.105406
15. Exploring parameter (dis)agreement due to calibration metric selection in conceptual rainfall–runoff models E. Muñoz-Castro et al. 10.1080/02626667.2023.2231434
16. The numerical error of the Xinanjiang model J. Zhao et al. 10.1016/j.jhydrol.2023.129324
17. Partitioning of Precipitation Into Terrestrial Water Balance Components Under a Drying Climate H. Gardiya Weligamage et al. 10.1029/2022WR033538
18. When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling Y. Song et al. 10.5194/hess-28-3051-2024
19. The PAVICS-Hydro platform: A virtual laboratory for hydroclimatic modelling and forecasting over North America R. Arsenault et al. 10.1016/j.envsoft.2023.105808
20. Experimental Coupling of TOPMODEL with the National Water Model: Effects of Coupling Interface Complexity on Model Performance D. Kim et al. 10.1111/1752-1688.12953
21. Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system A. Newman et al. 10.5194/hess-25-5603-2021
22. 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
23. Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling H. Herath et al. 10.5194/hess-25-4373-2021
24. A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting M. Jahangir et al. 10.1016/j.jhydrol.2023.129269
25. Modelling water quantity and quality for integrated water cycle management with the Water Systems Integrated Modelling framework (WSIMOD) software B. Dobson et al. 10.5194/gmd-17-4495-2024
26. Symptoms of Performance Degradation During Multi‐Annual Drought: A Large‐Sample, Multi‐Model Study L. Trotter et al. 10.1029/2021WR031845
27. Reliability of SM2RAIN precipitation datasets in comparison to gauge observations and hydrological modelling over arid regions F. Satgé et al. 10.1002/joc.6704
28. Automatic Model Structure Identification for Conceptual Hydrologic Models D. Spieler et al. 10.1029/2019WR027009
29. Teaching hydrological modelling: illustrating model structure uncertainty with a ready-to-use computational exercise W. Knoben & D. Spieler 10.5194/hess-26-3299-2022
30. Hydrological signatures describing the translation of climate seasonality into streamflow seasonality S. Gnann et al. 10.5194/hess-24-561-2020
31. Simple Catchments and Where to Find Them: The Storage-Discharge Relationship as a Proxy for Catchment Complexity F. Jehn et al. 10.3389/frwa.2021.631651
32. Unlocking the potential of stochastic simulation through Bluecat: Enhancing runoff predictions in arid and high‐altitude regions J. Jorquera & A. Pizarro 10.1002/hyp.15046
33. Ground truthing global-scale model estimates of groundwater recharge across Africa C. West et al. 10.1016/j.scitotenv.2022.159765
34. Lumped geohydrological modelling for long-term predictions of groundwater storage and depletion F. Ejaz et al. 10.1016/j.jhydrol.2021.127347
35. Disaggregated monthly hydrological models can outperform daily models in providing daily flow statistics and extrapolate well to a drying climate A. John et al. 10.1016/j.jhydrol.2021.126471
36. A multiple hydrograph separation technique for identifying hydrological model structures and an interpretation of dominant process controls on flow duration curves C. Leong & Y. Yokoo 10.1002/hyp.14569
37. Exploring the Implications of Modeling Choices on Prediction of Irrigation Water Savings C. Eluwa et al. 10.1029/2021WR031618
38. How to Tailor My Process‐Based Hydrological Model? Dynamic Identifiability Analysis of Flexible Model Structures T. Pilz et al. 10.1029/2020WR028042
39. Improving flood forecasting in Narmada river basin using hierarchical clustering and hydrological modelling D. Mehta et al. 10.1016/j.rineng.2023.101571
40. An Exploration of Bayesian Identification of Dominant Hydrological Mechanisms in Ungauged Catchments C. Prieto et al. 10.1029/2021WR030705
41. Assessing rainfall-runoff models for climate change: simple and differential split-sample tests for conceptual and artificial intelligence models N. Behfar et al. 10.1080/02626667.2024.2345151
42. A priori selection of hydrological model structures in modular modelling frameworks: application to Great Britain M. Kiraz et al. 10.1080/02626667.2023.2251968
43. Evaluating the parameter sensitivity and impact of hydrologic modeling decisions on flood simulations A. Alexander et al. 10.1016/j.advwatres.2023.104560
44. A history of TOPMODEL K. Beven et al. 10.5194/hess-25-527-2021
45. RoGeR v3.0.5 – a process-based hydrological toolbox model in Python R. Schwemmle et al. 10.5194/gmd-17-5249-2024
46. Simultaneous Calibration of Hydrologic Model Structure and Parameters Using a Blended Model R. Chlumsky et al. 10.1029/2020WR029229
47. A step toward global-scale applicability and transferability of flow duration curve studies: A flow duration curve review (2000–2020) C. Leong & Y. Yokoo 10.1016/j.jhydrol.2021.126984
48. Ground Truthing Global-Scale Model Estimates of Groundwater Recharge Across Africa C. West et al. 10.2139/ssrn.4184338
49. The Modeling Toolkit: how recruitment strategies for modeling positions influence model progress L. Melsen 10.3389/frwa.2023.1149590
50. The eWaterCycle platform for open and FAIR hydrological collaboration R. Hut et al. 10.5194/gmd-15-5371-2022
51. How well do the multi-satellite and atmospheric reanalysis products perform in hydrological modelling L. Gu et al. 10.1016/j.jhydrol.2022.128920
52. Profiling and Pairing Catchments and Hydrological Models With Latent Factor Model Y. Yang & T. Chui 10.1029/2022WR033684
53. Challenges in modeling and predicting floods and droughts: A review M. Brunner et al. 10.1002/wat2.1520
54. Can model structure families be inferred from model output? J. Remmers et al. 10.1016/j.envsoft.2020.104817
55. Identification of Dominant Hydrological Mechanisms Using Bayesian Inference, Multiple Statistical Hypothesis Testing, and Flexible Models C. Prieto et al. 10.1029/2020WR028338
56. Error correction method based on deep learning for improving the accuracy of conceptual rainfall-runoff model W. Wenchuan et al. 10.1016/j.jhydrol.2024.131992
57. Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models T. Lees et al. 10.5194/hess-25-5517-2021
58. Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data S. Gharari et al. 10.1029/2020WR027948
59. Nested Cross-Validation for HBV Conceptual Rainfall–Runoff Model Spatial Stability Analysis in a Semi-Arid Context M. El Garnaoui et al. 10.3390/rs16203756
60. Simulation of water flow management by the flood control facilities in the adjacent river basins V. Antonov et al. 10.1088/1742-6596/1400/7/077049
61. Changes in hydrological regime in High Arctic non-glaciated catchment in 1979–2020 using a multimodel approach M. Osuch et al. 10.1016/j.accre.2022.05.001
62. The Representation of Hydrological Dynamical Systems Using Extended Petri Nets (EPN) M. Bancheri et al. 10.1029/2019WR025099
63. TOSSH: A Toolbox for Streamflow Signatures in Hydrology S. Gnann et al. 10.1016/j.envsoft.2021.104983
64. Numerical daemons of hydrological models are summoned by extreme precipitation P. La Follette et al. 10.5194/hess-25-5425-2021
65. Improving performance of bucket-type hydrological models in high latitudes with multi-model combination methods: Can we wring water from a stone? A. Todorović et al. 10.1016/j.jhydrol.2024.130829
66. Technical note: Hydrology modelling R packages – a unified analysis of models and practicalities from a user perspective P. Astagneau et al. 10.5194/hess-25-3937-2021
67. Exploring Hydrologic Model Process Connectivity at the Continental Scale Through an Information Theory Approach G. Konapala et al. 10.1029/2020WR027340
68. Advancing objective functions in hydrological modelling: Integrating knowable moments for improved simulation accuracy A. Pizarro & J. Jorquera 10.1016/j.jhydrol.2024.131071
69. A multi‐objective approach to select hydrological models and constrain structural uncertainties for climate impact assessments D. Saavedra et al. 10.1002/hyp.14446
70. A self-identification Neuro-Fuzzy inference framework for modeling rainfall-runoff in a Chilean watershed Y. Morales et al. 10.1016/j.jhydrol.2020.125910
71. Are gridded precipitation datasets a good option for streamflow simulation across the Juruá river basin, Amazon? F. Satgé et al. 10.1016/j.jhydrol.2021.126773
72. Comprehensive uncertainty analysis for surface water and groundwater projections under climate change based on a lumped geo-hydrological model F. Ejaz et al. 10.1016/j.jhydrol.2023.130323
73. The impact of hydrological model structure on the simulation of extreme runoff events G. van Kempen et al. 10.5194/nhess-21-961-2021
74. Towards more realistic runoff projections by removing limits on simulated soil moisture deficit K. Fowler et al. 10.1016/j.jhydrol.2021.126505
75. Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder M. Jahangir & J. Quilty 10.1016/j.jhydrol.2023.130498
76. Impact of the Mean Daily Air Temperature Calculation on the Rainfall-Runoff Modelling N. Bezak et al. 10.3390/w12113175
77. Setting expectations for hydrologic model performance with an ensemble of simple benchmarks W. Knoben 10.1002/hyp.15288
78. Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1: an object-oriented implementation of 47 established hydrological models for improved speed and readability L. Trotter et al. 10.5194/gmd-15-6359-2022
79. Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model J. Aerts et al. 10.5194/hess-26-4407-2022
80. Why do we have so many different hydrological models? A review based on the case of Switzerland P. Horton et al. 10.1002/wat2.1574
81. Identifying Causal Interactions Between Groundwater and Streamflow Using Convergent Cross‐Mapping G. Bonotto et al. 10.1029/2021WR030231
82. Impacts of Uncontrolled Operator Splitting Methods on Parameter Identification, Prediction Uncertainty, and Subsurface Flux Representation in Conceptual Hydrological Models B. Woldegiorgis et al. 10.1029/2022WR033250
83. Estimating rainfall–runoff modeling using the rainfall prognostic model-based artificial framework with a well-ordered selective genetic algorithm S. Kumar et al. 10.2166/hydro.2022.009