79 citations as recorded by crossref.

1. Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling H. Beck et al. https://doi.org/10.5194/hess-21-6201-2017
2. Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale G. Papacharalampous et al. https://doi.org/10.1016/j.advwatres.2019.103470
3. Control of climate and physiography on runoff response behavior through use of catchment classification and machine learning S. Du et al. https://doi.org/10.1016/j.scitotenv.2023.166422
4. Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case I. Pechlivanidis & B. Arheimer https://doi.org/10.5194/hess-19-4559-2015
5. Assessing watershed hydrological response to climate change based on signature indices A. Fatehifar et al. https://doi.org/10.2166/wcc.2021.293
6. Accelerating advances in continental domain hydrologic modeling S. Archfield et al. https://doi.org/10.1002/2015WR017498
7. On the consistency of scale among experiments, theory, and simulation J. McClure et al. https://doi.org/10.5194/hess-21-1063-2017
8. Hyperresolution information and hyperresolution ignorance in modelling the hydrology of the land surface K. Beven et al. https://doi.org/10.1007/s11430-014-5003-4
9. Diagnosing Credibility of a Large-Scale Conceptual Hydrological Model in Simulating Streamflow P. Paul et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001766
10. Hydrological Alteration Index as an Indicator of the Calibration Complexity of Water Quantity and Quality Modeling in the Context of Global Change R. Cakir et al. https://doi.org/10.3390/w12010115
11. Climate change impacts on floods in West Africa: new insight from two large-scale hydrological models S. Diop et al. https://doi.org/10.5194/nhess-25-3161-2025
12. Towards observation-based gridded runoff estimates for Europe L. Gudmundsson & S. Seneviratne https://doi.org/10.5194/hess-19-2859-2015
13. Global‐scale regionalization of hydrologic model parameters H. Beck et al. https://doi.org/10.1002/2015WR018247
14. MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data H. Beck et al. https://doi.org/10.5194/hess-21-589-2017
15. A review of hydrologic signatures and their applications H. McMillan https://doi.org/10.1002/wat2.1499
16. The legacy of STAHY: milestones, achievements, challenges, and open problems in statistical hydrology E. Volpi et al. https://doi.org/10.1080/02626667.2024.2385686
17. Time Distributed Deep Learning Models for Purely Exogenous Forecasting: Application to Water Table Depth Predictions Using Weather Image Time Series M. Salis et al. https://doi.org/10.1016/j.envsoft.2025.106568
18. HESS Opinions: The complementary merits of competing modelling philosophies in hydrology M. Hrachowitz & M. Clark https://doi.org/10.5194/hess-21-3953-2017
19. Falsification‐Oriented Signature‐Based Evaluation for Guiding the Development of Land Surface Models and the Enhancement of Observations H. Zheng et al. https://doi.org/10.1029/2020MS002132
20. Imbalanced land surface water budgets in a numerical weather prediction system A. Kauffeldt et al. https://doi.org/10.1002/2015GL064230
21. How to make advances in hydrological modelling K. Beven https://doi.org/10.2166/nh.2019.134
22. Hydrological Climate Change Impact Assessment at Small and Large Scales: Key Messages from Recent Progress in Sweden J. Olsson et al. https://doi.org/10.3390/cli4030039
23. Curating 62 Years of Walnut Gulch Experimental Watershed Data: Improving the Quality of Long-Term Rainfall and Runoff Datasets M. Meles et al. https://doi.org/10.3390/w14142198
24. Diagnosis of insidious data disasters J. Lundquist et al. https://doi.org/10.1002/2014WR016585
25. Facets of uncertainty: epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication K. Beven https://doi.org/10.1080/02626667.2015.1031761
26. Surface Depression and Wetland Water Storage Improves Major River Basin Hydrologic Predictions A. Rajib et al. https://doi.org/10.1029/2019WR026561
27. On the importance of discharge observation uncertainty when interpreting hydrological model performance J. Aerts et al. https://doi.org/10.5194/hess-28-5011-2024
28. Uncertainties in predicting impacts of climate change on hydrology in basin scale: a review D. Jose & G. Dwarakish https://doi.org/10.1007/s12517-020-06071-6
29. Global patterns and key drivers of stream nitrogen concentration: A machine learning approach R. Sheikholeslami & J. Hall https://doi.org/10.1016/j.scitotenv.2023.161623
30. Global evaluation of runoff from 10 state-of-the-art hydrological models H. Beck et al. https://doi.org/10.5194/hess-21-2881-2017
31. Assessment of the Value of Remotely Sensed Surface Water Extent Data for the Calibration of a Lumped Hydrological Model A. Meyer Oliveira et al. https://doi.org/10.1029/2023WR034875
32. Quo vadis, hydrology? Z. Kundzewicz https://doi.org/10.1080/02626667.2018.1489597
33. Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS H. Beck et al. https://doi.org/10.5194/hess-23-207-2019
34. A large dataset of fluvial hydraulic and geometry attributes derived from USGS field measurement records S. Erfani et al. https://doi.org/10.1016/j.envsoft.2024.106136
35. Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance A. Newman et al. https://doi.org/10.5194/hess-19-209-2015
36. A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies Z. Yin et al. https://doi.org/10.5194/essd-16-1559-2024
37. Understanding hydrologic variability across Europe through catchment classification A. Kuentz et al. https://doi.org/10.5194/hess-21-2863-2017
38. From threat to opportunity: Hydrologic uncertainty regionalization across large domains S. Pokorny et al. https://doi.org/10.1016/j.ejrh.2024.101819
39. On (in)validating environmental models. 2. Implementation of a Turing‐like test to modelling hydrological processes K. Beven et al. https://doi.org/10.1002/hyp.14703
40. Bias Correction of Global High-Resolution Precipitation Climatologies Using Streamflow Observations from 9372 Catchments H. Beck et al. https://doi.org/10.1175/JCLI-D-19-0332.1
41. Fuzzy Postprocessing to Advance the Quality of Continental Seasonal Hydrological Forecasts for River Basin Management H. Macian-Sorribes et al. https://doi.org/10.1175/JHM-D-19-0266.1
42. Towards improved Euro-Mediterranean discharge simulations in regional coupled climate models: a comparative assessment of hydrologic performance M. Hamitouche et al. https://doi.org/10.5194/gmd-19-2881-2026
43. How the performance of hydrological models relates to credibility of projections under climate change V. Krysanova et al. https://doi.org/10.1080/02626667.2018.1446214
44. On (in)validating environmental models. 1. Principles for formulating a Turing‐like Test for determining when a model is fit‐for purpose K. Beven & S. Lane https://doi.org/10.1002/hyp.14704
45. Application of a newly developed large‐scale conceptual hydrological model in simulating streamflow for credibility testing in data scarce condition P. Paul et al. https://doi.org/10.1111/nrm.12283
46. Achieving water budget closure through physical hydrological process modelling: insights from a large-sample study X. Zheng et al. https://doi.org/10.5194/hess-29-627-2025
47. Assessing global water mass transfers from continents to oceans over the period 1948–2016 D. Cáceres et al. https://doi.org/10.5194/hess-24-4831-2020
48. Evaluation of distributed process-based hydrologic model performance using only a priori information to define model inputs S. Bhanja et al. https://doi.org/10.1016/j.jhydrol.2023.129176
49. DHI-GHM: Real-time and forecasted hydrology for the entire planet A. Murray et al. https://doi.org/10.1016/j.jhydrol.2023.129431
50. Development of Machine Learning Flood Model Using Artificial Neural Network (ANN) at Var River M. Ahmad et al. https://doi.org/10.3390/liquids2030010
51. Regional water balance modelling using flow-duration curves with observational uncertainties I. Westerberg et al. https://doi.org/10.5194/hess-18-2993-2014
52. Impacts of observational uncertainty on analysis and modelling of hydrological processes: Preface H. McMillan et al. https://doi.org/10.1002/hyp.14481
53. Can Continental Models Convey Useful Seasonal Hydrologic Information at the Catchment Scale? L. Crochemore et al. https://doi.org/10.1029/2019WR025700
54. Debates—The future of hydrological sciences: A (common) path forward? A call to action aimed at understanding velocities, celerities and residence time distributions of the headwater hydrograph J. McDonnell & K. Beven https://doi.org/10.1002/2013WR015141
55. Evaluation of large-scale precipitation data sets for water resources modelling in Central Asia M. Malsy et al. https://doi.org/10.1007/s12665-014-3107-y
56. Global catchment modelling using World-Wide HYPE (WWH), open data, and stepwise parameter estimation B. Arheimer et al. https://doi.org/10.5194/hess-24-535-2020
57. Development of a 60-year high-resolution water body evaporation dataset in China P. Bai & X. Guo https://doi.org/10.1016/j.agrformet.2023.109428
58. Uncertainty in hydrological signatures I. Westerberg & H. McMillan https://doi.org/10.5194/hess-19-3951-2015
59. A worldwide evaluation of basin-scale evapotranspiration estimates against the water balance method W. Liu et al. https://doi.org/10.1016/j.jhydrol.2016.04.006
60. Multi-Basin Modelling of Future Hydrological Fluxes in the Indian Subcontinent I. Pechlivanidis et al. https://doi.org/10.3390/w8050177
61. A worldwide analysis of trends in water-balance evapotranspiration A. Ukkola & I. Prentice https://doi.org/10.5194/hess-17-4177-2013
62. Regionalization of catchment hydrological model parameters for global water resources simulations W. Qi et al. https://doi.org/10.2166/nh.2022.118
63. A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven https://doi.org/10.1002/wat2.1761
64. 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
65. Hydrological data uncertainty and its implications H. McMillan et al. https://doi.org/10.1002/wat2.1319
66. Global hydrological models: a review A. Sood & V. Smakhtin https://doi.org/10.1080/02626667.2014.950580
67. Challenges of classifying and mapping perennial freshwater systems within highly variable climate zones: A case study in the Murray Darling Basin, Australia D. Verdon-Kidd et al. https://doi.org/10.1016/j.scitotenv.2023.167260
68. Global Fully Distributed Parameter Regionalization Based on Observed Streamflow From 4,229 Headwater Catchments H. Beck et al. https://doi.org/10.1029/2019JD031485
69. MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment H. Beck et al. https://doi.org/10.1175/BAMS-D-17-0138.1
70. How do geological map details influence the identification of geology-streamflow relationships in large-sample hydrology studies? T. do Nascimento et al. https://doi.org/10.5194/hess-29-7173-2025
71. On the value of water quality data and informative flow states in karst modelling A. Hartmann et al. https://doi.org/10.5194/hess-21-5971-2017
72. Satellite-Based Evapotranspiration in Hydrological Model Calibration L. Jiang et al. https://doi.org/10.3390/rs12030428
73. Customizing large-scale hydrological models: Harnessing the open data realm for impactful local applications I. Pechlivanidis & J. Musuuza https://doi.org/10.1016/j.ejrh.2025.102390
74. Estimating virtual water content and yield of wheat using machine learning tools A. Muratoglu et al. https://doi.org/10.1016/j.jhydrol.2024.132526
75. Effect of Observation Errors on the Timing of the Most Informative Isotope Samples for Event-Based Model Calibration L. Wang et al. https://doi.org/10.3390/hydrology5010004
76. Evaluation of high-resolution meteorological data products using flux tower observations across Brazil J. Brown et al. https://doi.org/10.5194/hess-30-141-2026
77. Observational uncertainties in hypothesis testing: investigating the hydrological functioning of a tropical catchment I. Westerberg & C. Birkel https://doi.org/10.1002/hyp.10533
78. A regional parameter estimation scheme for a pan-European multi-basin model Y. Hundecha et al. https://doi.org/10.1016/j.ejrh.2016.04.002
79. On doing hydrology with dragons: Realizing the value of perceptual models and knowledge accumulation T. Wagener et al. https://doi.org/10.1002/wat2.1550