85 citations as recorded by crossref.

1. National-scale geodatabase of catchment characteristics in the Philippines for river management applications R. Boothroyd et al. 10.1371/journal.pone.0281933
2. Revealing the importance of groundwater for potable private supplies in Wales G. Farr et al. 10.1144/qjegh2021-078
3. Teaching hydrological modelling: illustrating model structure uncertainty with a ready-to-use computational exercise W. Knoben & D. Spieler 10.5194/hess-26-3299-2022
4. Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models R. Arsenault et al. 10.5194/hess-27-139-2023
5. Impacts of observational uncertainty on analysis and modelling of hydrological processes: Preface H. McMillan et al. 10.1002/hyp.14481
6. Linking explainable artificial intelligence and soil moisture dynamics in a machine learning streamflow model A. Ley et al. 10.2166/nh.2024.003
7. A region of influence approach for attributing fluvial climate change allowances A. Hammond 10.1080/02626667.2022.2153596
8. Advancing flood warning procedures in ungauged basins with machine learning Z. Rasheed et al. 10.1016/j.jhydrol.2022.127736
9. A dataset of lake-catchment characteristics for the Tibetan Plateau J. Liu et al. 10.5194/essd-14-3791-2022
10. Theoretical and empirical evidence against the Budyko catchment trajectory conjecture N. Reaver et al. 10.5194/hess-26-1507-2022
11. Hydrological concept formation inside long short-term memory (LSTM) networks T. Lees et al. 10.5194/hess-26-3079-2022
12. Co-Occurring Wintertime Flooding and Extreme Wind Over Europe, from Daily to Seasonal Timescales H. Bloomfield et al. 10.2139/ssrn.4197062
13. A review of hydrologic signatures and their applications H. McMillan 10.1002/wat2.1499
14. Contribution of urbanisation to non-stationary river flow in the UK S. Han et al. 10.1016/j.jhydrol.2022.128417
15. A hybrid Budyko-type regression framework for estimating baseflow from climate and catchment attributes S. Chen & X. Ruan 10.1016/j.jhydrol.2023.129118
16. Survey of Time Series Data Generation in IoT C. Hu et al. 10.3390/s23156976
17. Predicting the dispersal of SARS-CoV-2 RNA from the wastewater treatment plant to the coast P. Robins et al. 10.1016/j.heliyon.2022.e10547
18. Co-occurring wintertime flooding and extreme wind over Europe, from daily to seasonal timescales B. H.C. et al. 10.1016/j.wace.2023.100550
19. Challenges in modeling and predicting floods and droughts: A review M. Brunner et al. 10.1002/wat2.1520
20. CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia K. Fowler et al. 10.5194/essd-13-3847-2021
21. Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs Y. Shen et al. 10.5194/essd-15-2781-2023
22. LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe C. Klingler et al. 10.5194/essd-13-4529-2021
23. A Signature‐Based Hydrologic Efficiency Metric for Model Calibration and Evaluation in Gauged and Ungauged Catchments M. Kiraz et al. 10.1029/2023WR035321
24. Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks A. Sun et al. 10.1029/2021WR030394
25. Catchment characterization: Current descriptors, knowledge gaps and future opportunities L. Tarasova et al. 10.1016/j.earscirev.2024.104739
26. A comprehensive intercomparison study between a lumped and a fully distributed hydrological model across a set of 50 catchments in the United Kingdom S. Sinha et al. 10.1002/hyp.14544
27. Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. 10.1029/2022WR034352
28. Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale G. Papacharalampous et al. 10.1016/j.gsf.2022.101349
29. Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff D. Althoff & G. Destouni 10.1016/j.oneear.2023.08.002
30. A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies Z. Yin et al. 10.5194/essd-16-1559-2024
31. Hydro-PE: gridded datasets of historical and future Penman–Monteith potential evaporation for the United Kingdom E. Robinson et al. 10.5194/essd-15-4433-2023
32. Co-Occurring Wintertime Flooding and Extreme Wind Over Europe, from Daily to Seasonal Timescales H. Bloomfield et al. 10.2139/ssrn.4174051
33. A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022) X. Chen et al. 10.5194/essd-15-4463-2023
34. Temporal hydrological drought clustering varies with climate and land-surface processes M. Brunner & K. Stahl 10.1088/1748-9326/acb8ca
35. How will climate change affect the spatial coherence of streamflow and groundwater droughts in Great Britain? M. Tanguy et al. 10.1088/1748-9326/acd655
36. Knowledge gaps in our perceptual model of Great Britain's hydrology T. Wagener et al. 10.1002/hyp.14288
37. A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting G. Papacharalampous & H. Tyralis 10.3389/frwa.2022.961954
38. Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
39. A large-sample investigation into uncertain climate change impacts on high flows across Great Britain R. Lane et al. 10.5194/hess-26-5535-2022
40. A Framework for Estimating the Probability Distribution of Event Runoff Coefficient in Ungauged Catchments Y. Zheng et al. 10.1029/2022WR033227
41. Simbi: historical hydro-meteorological time series and signatures for 24 catchments in Haiti R. Bathelemy et al. 10.5194/essd-16-2073-2024
42. MacroSheds: A synthesis of long‐term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies M. Vlah et al. 10.1002/lol2.10325
43. A framework on utilizing of publicly availability stream gauges datasets and deep learning in estimating monthly basin-scale runoff in ungauged regions M. Le et al. 10.1016/j.advwatres.2024.104694
44. Varying performance of eight evapotranspiration products with aridity and vegetation greenness across the globe H. Wang et al. 10.3389/fenvs.2023.1079520
45. Are UK Rivers Getting Saltier and More Alkaline? S. Jiang et al. 10.3390/w14182813
46. Use of streamflow indices to identify the catchment drivers of hydrographs J. Mathai & P. Mujumdar 10.5194/hess-26-2019-2022
47. Impact of climate change on hydropower potential in the UK and Ireland R. Dallison & S. Patil 10.1016/j.renene.2023.03.021
48. PISCO_HyM_GR2M: A Model of Monthly Water Balance in Peru (1981–2020) H. Llauca et al. 10.3390/w13081048
49. WITHDRAWN: Intercomparison of joint bias correction methods for precipitation and flow from a hydrological perspective K. Kim et al. 10.1016/j.hydroa.2021.100109
50. Caravan - A global community dataset for large-sample hydrology F. Kratzert et al. 10.1038/s41597-023-01975-w
51. Streamflow droughts aggravated by human activities despite management A. Van Loon et al. 10.1088/1748-9326/ac5def
52. PatagoniaMet: A multi-source hydrometeorological dataset for Western Patagonia R. Aguayo et al. 10.1038/s41597-023-02828-2
53. LamaH | Large-Sample Data for Hydrology: Big data für die Hydrologie und Umweltwissenschaften C. Klingler et al. 10.1007/s00506-021-00769-x
54. How is Baseflow Index (BFI) impacted by water resource management practices? J. Bloomfield et al. 10.5194/hess-25-5355-2021
55. 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
56. A priori selection of hydrological model structures in modular modelling frameworks: application to Great Britain M. Kiraz et al. 10.1080/02626667.2023.2251968
57. Continental-scale streamflow modeling of basins with reservoirs: Towards a coherent deep-learning-based strategy W. Ouyang et al. 10.1016/j.jhydrol.2021.126455
58. CCAM: China Catchment Attributes and Meteorology dataset Z. Hao et al. 10.5194/essd-13-5591-2021
59. LCM2021 – the UK Land Cover Map 2021 C. Marston et al. 10.5194/essd-15-4631-2023
60. Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios M. Buechel et al. 10.1038/s43247-021-00334-0
61. Evidence-based requirements for perceptualising intercatchment groundwater flow in hydrological models L. Oldham et al. 10.5194/hess-27-761-2023
62. Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management L. Slater et al. 10.5194/hess-25-3897-2021
63. Regional significance of historical trends and step changes in Australian streamflow G. Amirthanathan et al. 10.5194/hess-27-229-2023
64. Interacting Effects of Precipitation and Potential Evapotranspiration Biases on Hydrological Modeling J. Wang et al. 10.1029/2022WR033323
65. CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland M. Höge et al. 10.5194/essd-15-5755-2023
66. Improving the Applicability of Lumped Hydrological Models by Integrating the Generalized Complementary Relationship X. Lei et al. 10.1029/2023WR035567
67. QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany P. Ebeling et al. 10.5194/essd-14-3715-2022
68. Building Cross-Site and Cross-Network collaborations in critical zone science B. Arora et al. 10.1016/j.jhydrol.2023.129248
69. Data‐Driven Worldwide Quantification of Large‐Scale Hydroclimatic Covariation Patterns and Comparison With Reanalysis and Earth System Modeling N. Ghajarnia et al. 10.1029/2020WR029377
70. Intercomparison of joint bias correction methods for precipitation and flow from a hydrological perspective K. Kim et al. 10.1016/j.jhydrol.2021.127261
71. Controls on the Spatial and Temporal Patterns of Rainfall‐Runoff Event Characteristics—A Large Sample of Catchments Across Great Britain Y. Zheng et al. 10.1029/2022WR033226
72. Deep learning for cross-region streamflow and flood forecasting at a global scale B. Zhang et al. 10.1016/j.xinn.2024.100617
73. GRQA: Global River Water Quality Archive H. Virro et al. 10.5194/essd-13-5483-2021
74. AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods A. Abbas et al. 10.5194/gmd-15-3021-2022
75. TOSSH: A Toolbox for Streamflow Signatures in Hydrology S. Gnann et al. 10.1016/j.envsoft.2021.104983
76. LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland H. Helgason & B. Nijssen 10.5194/essd-16-2741-2024
77. Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes H. McMillan et al. 10.1029/2021WR031751
78. Run-of-river hydropower in the UK and Ireland: the case for abstraction licences based on future flows R. Dallison & S. Patil 10.1088/2634-4505/ad064c
79. A quality-control framework for sub-daily flow and level data for hydrological modelling in Great Britain F. Fileni et al. 10.2166/nh.2023.045
80. River reach-level machine learning estimation of nutrient concentrations in Great Britain C. Tso et al. 10.3389/frwa.2023.1244024
81. Temporal Fusion Transformers for streamflow Prediction: Value of combining attention with recurrence S. Rasiya Koya & T. Roy 10.1016/j.jhydrol.2024.131301
82. Simulating spatial variability of groundwater table in England and Wales M. Rahman et al. 10.1002/hyp.14849
83. FOCA: a new quality-controlled database of floods and catchment descriptors in Italy P. Claps et al. 10.5194/essd-16-1503-2024
84. CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil V. Chagas et al. 10.5194/essd-12-2075-2020
85. Incorporating Uncertainty Into Multiscale Parameter Regionalization to Evaluate the Performance of Nationally Consistent Parameter Fields for a Hydrological Model R. Lane et al. 10.1029/2020WR028393