116 citations as recorded by crossref.

1. What makes a robust calibration period? Insights into the effects of data properties O. Jaffar et al.
2. Climate and land management accelerate the Brazilian water cycle V. Chagas et al.
3. Large-sample hydrology – a few camels or a whole caravan? F. Clerc-Schwarzenbach et al.
4. Deep learning foundation and pattern models: Challenges in hydrological time series J. He et al.
5. Synergistic and nonlinear effects of fertilizer use on stream nitrogen dynamics in agricultural watersheds: An ensemble machine learning approach H. Yan et al.
6. How is Baseflow Index (BFI) impacted by water resource management practices? J. Bloomfield et al.
7. Are gridded precipitation datasets a good option for streamflow simulation across the Juruá river basin, Amazon? F. Satgé et al.
8. Multi-step regional rainfall-runoff modeling using pyramidal transformer H. Yin et al.
9. Multivariate Geostatistics for Mapping of Transmissivity and Uncertainty in Karst Aquifers T. Gonçalves et al.
10. The design of the Brazilian Sub-Daily Rainfall dataset (BR-SDR): two decades of high-time-resolution data in Brazil C. Das Neves Almeida et al.
11. Spatially resolved rainfall streamflow modeling in central Europe M. Vischer et al.
12. PatagoniaMet: A multi-source hydrometeorological dataset for Western Patagonia R. Aguayo et al.
13. Correspondence Between Model Structures and Hydrological Signatures: A Large‐Sample Case Study Using 508 Brazilian Catchments P. David et al.
14. Metamorphic testing of machine learning and conceptual hydrologic models P. Reichert et al.
15. Multi-model ensemble benchmark data for hydrological modeling in Japanese river basins Y. Sawada et al.
16. Calibrated Eckhardt’s filter versus alternative baseflow separation methods: A silica-based approach in a Brazilian catchment F. Helfer et al.
17. Functional data analysis to investigate controls on and changes in the seasonality of UK baseflow K. Leeming et al.
18. CAMELS-FR dataset: a large-sample hydroclimatic dataset for France to explore hydrological diversity and support model benchmarking O. Delaigue et al.
19. Enhanced baseflow separation in rural catchments: event-specific calibration of recursive digital filters with tracer-derived data F. Helfer et al.
20. Findings from a National Survey of Canadian perspectives on predicting river channel migration and river bank erosion C. Kupferschmidt & A. Binns
21. Data‐Driven Worldwide Quantification of Large‐Scale Hydroclimatic Covariation Patterns and Comparison With Reanalysis and Earth System Modeling N. Ghajarnia et al.
22. Technical note: High Nash–Sutcliffe Efficiencies conceal poor simulations of interannual variance in seasonal regimes S. Ruzzante et al.
23. QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany P. Ebeling et al.
24. Hydrological responses to land use changes and precipitation variability in Southern Brazil A. Avcıoğlu et al.
25. CABra: a novel large-sample dataset for Brazilian catchments A. Almagro et al.
26. EStreams: An integrated dataset and catalogue of streamflow, hydro-climatic and landscape variables for Europe T. do Nascimento et al.
27. Simbi: historical hydro-meteorological time series and signatures for 24 catchments in Haiti R. Bathelemy et al.
28. A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting G. Papacharalampous & H. Tyralis
29. Improving trans-regional hydrological modelling by combining LSTM with big hydrological data S. Tang et al.
30. CAMELS-AUS v2: updated hydrometeorological time series and landscape attributes for an enlarged set of catchments in Australia K. Fowler et al.
31. Hydroclimatic drivers of at‐a‐station hydraulic geometry of Brazilian rivers A. Perez et al.
32. Catchment Attributes and MEteorology for Large-Sample SPATially distributed analysis (CAMELS-SPAT): streamflow observations, forcing data and geospatial data for hydrologic studies across North America W. Knoben et al.
33. LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland H. Helgason & B. Nijssen
34. Scalable, adaptive and risk-informed design of hydrological sensor networks J. Oh & M. Bartos
35. Spatial and temporal patterns of propagation from meteorological to hydrological droughts in Brazil A. Bevacqua et al.
36. CCAM: China Catchment Attributes and Meteorology dataset Z. Hao et al.
37. Evaluation of Brazilian irrigated agriculture: what to expect? F. Matheus
38. GRDC-Caravan: extending Caravan with data from the Global Runoff Data Centre C. Färber et al.
39. How do geological map details influence the identification of geology-streamflow relationships in large-sample hydrology studies? T. do Nascimento et al.
40. What is the near-natural catchment? An application of hydrological signatures assessment H. Xu et al.
41. rabpro: global watershed boundaries, river elevation profiles, and catchment statistics J. Schwenk et al.
42. CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India N. Mangukiya et al.
43. 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.
44. A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments P. Yeste et al.
45. CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations J. Liu et al.
46. A Global Benchmark of the Vector-Based Routing Model MizuRoute: Similarities and Divergent Patterns in Simulated River Discharge S. Xu et al.
47. Caravan - A global community dataset for large-sample hydrology F. Kratzert et al.
48. CAMELSH: A Large-Sample Hourly Hydrometeorological Dataset and Attributes at Watershed-Scale for CONUS V. Tran et al.
49. QUADICA v2: extending the large-sample data set for water QUAlity, DIscharge and Catchment Attributes in Germany P. Ebeling et al.
50. Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs Y. Shen et al.
51. Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil L. Kuana et al.
52. CAMELS-Chem: augmenting CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) with atmospheric and stream water chemistry data G. Sterle et al.
53. Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff D. Althoff & G. Destouni
54. HESS Opinions: Towards a common vision for the future of hydrological observatories P. Nasta et al.
55. GRQA: Global River Water Quality Archive H. Virro et al.
56. Discharge-based classifications of spatio-temporal patterns of potentially gaining and losing subcatchments in the Bode River catchment, Central Germany C. Lei et al.
57. Catchment characterization: Current descriptors, knowledge gaps and future opportunities L. Tarasova et al.
58. Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application X. Chen et al.
59. Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al.
60. Recent fire occurrence and associated emissions in Southern Brazil N. Teixeira et al.
61. DAM-IN: Comprehensive Dam Catchment Attributes for Dam Safety Studies in India M. Tiwari & S. Aadhar
62. CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland M. Höge et al.
63. MacroSheds: A synthesis of long‐term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies M. Vlah et al.
64. 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.
65. Global dataset combining open-source hydropower plant and reservoir data J. Shah et al.
66. Uneven shifts in flood and drought flows in a Brazilian water supply catchment T. Mattos et al.
67. Varying performance of eight evapotranspiration products with aridity and vegetation greenness across the globe H. Wang et al.
68. Efficiency of global precipitation datasets in tropical and subtropical catchments revealed by large sampling hydrological modelling J. Andrade et al.
69. LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe C. Klingler et al.
70. PISCO_HyM_GR2M: A Model of Monthly Water Balance in Peru (1981–2020) H. Llauca et al.
71. Geostatistical modeling and traditional approaches for streamflow regionalization in a Brazilian Southeast watershed R. Ferreira et al.
72. How to out-perform default random forest regression: choosing hyperparameters for applications in large-sample hydrology D. Bilolikar et al.
73. Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes H. McMillan et al.
74. Climate shapes baseflows, influencing drought severity M. Zaerpour et al.
75. Diverse wildfire impacts on river flows across the globe M. Grillakis & A. Voulgarakis
76. A dataset of lake-catchment characteristics for the Tibetan Plateau J. Liu et al.
77. A dataset of land surface characteristics and time-series hydrometeorological data for typical catchments in China (2003–2020) H. MA et al.
78. A hybrid Budyko-type regression framework for estimating baseflow from climate and catchment attributes S. Chen & X. Ruan
79. FOCA: a new quality-controlled database of floods and catchment descriptors in Italy P. Claps et al.
80. CAMELS-NZ: hydrometeorological time series and landscape attributes for New Zealand S. Bushra et al.
81. Combining global precipitation data and machine learning to predict flood peaks in ungauged areas with similar climate Z. Rasheed et al.
82. Temporal Fusion Transformers for streamflow Prediction: Value of combining attention with recurrence S. Rasiya Koya & T. Roy
83. National-scale geodatabase of catchment characteristics in the Philippines for river management applications R. Boothroyd et al.
84. Machine learning models for streamflow regionalization in a tropical watershed R. Ferreira et al.
85. How GPM IMERG and GSMaP advance hydrological applications: A global perspective Z. Huang et al.
86. Setting expectations for hydrologic model performance with an ensemble of simple benchmarks W. Knoben
87. A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies Z. Yin et al.
88. Spatial heterogeneity in rainfall extremetrends across Santa Catarina, Brazil J. Bernardo et al.
89. BULL Database – Spanish Basin attributes for Unravelling Learning in Large-sample hydrology J. Senent-Aparicio et al.
90. A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al.
91. CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia K. Fowler et al.
92. Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management L. Slater et al.
93. Improving the Applicability of Lumped Hydrological Models by Integrating the Generalized Complementary Relationship X. Lei et al.
94. Swiss data quality: augmenting CAMELS-CH with isotopes, water quality, agricultural and atmospheric data T. do Nascimento et al.
95. Streamflow depletion time scales and well distancing criteria from parallel rivers for improved aquifer management A. Oliveira et al.
96. Deep learning for the probabilistic prediction of semi-continuous hydrological variables – An application to streamflow prediction across CONUS J. Quilty & M. Jahangir
97. Teaching hydrological modelling: illustrating model structure uncertainty with a ready-to-use computational exercise W. Knoben & D. Spieler
98. A Simple Model of Flood Peak Attenuation R. Paiva & S. Lima
99. Using climate information as covariates to improve nonstationary flood frequency analysis in Brazil G. Anzolin et al.
100. A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022) X. Chen et al.
101. Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale G. Papacharalampous et al.
102. Spatial and temporal analysis of changes in hydrological fluxes and their relation to deforestation in the Madeira River basin E. Torres et al.
103. ML4FF: A machine-learning framework for flash flood forecasting applied to a Brazilian watershed J. Soares et al.
104. BCUB – a large-sample ungauged basin attribute dataset for British Columbia, Canada D. Kovacek & S. Weijs
105. LamaH | Large-Sample Data for Hydrology: Big data für die Hydrologie und Umweltwissenschaften C. Klingler et al.
106. A Continental Assessment of Reservoir Storage and Water Availability in South America B. Paredes-Beltran et al.
107. Causes, seasonality and climate variability of floods in southern Brazil V. Kuchinski & R. Cauduro Dias de Paiva
108. Nonstationary frequency analysis of extreme precipitation: Embracing trends in observations G. Anzolin et al.
109. Comparative analysis of GEOGloWS and PM tank models for river flow forecasting in data-Sparse regions P. Santana et al.
110. Evaluating E-OBS forcing data for large-sample hydrology using model performance diagnostics F. Clerc-Schwarzenbach & T. do Nascimento
111. Process Controls on Flood Seasonality in Brazil V. Chagas et al.
112. Panta Rhei: a decade of progress in research on change in hydrology and society H. Kreibich et al.
113. Assessment of the Value of Remotely Sensed Surface Water Extent Data for the Calibration of a Lumped Hydrological Model A. Meyer Oliveira et al.
114. The influence of topography on floods in southern Brazil V. Kuchinski & R. Paiva
115. Assessment of synthetic series to design the capacity of flow regulation reservoirs F. Heitor et al.
116. Climate Change and Hydrological Extremes J. Xiong & Y. Yang