175 citations as recorded by crossref.

1. From empirical to physical constraints: Revisiting the structure of monthly water balance models with global evaluation Z. Ning et al. https://doi.org/10.1016/j.jhydrol.2026.134916
2. Revealing the importance of groundwater for potable private supplies in Wales G. Farr et al. https://doi.org/10.1144/qjegh2021-078
3. Co-developing frameworks towards environmentally directed pharmaceutical prescribing in Scotland – A mixed methods study L. Niemi et al. https://doi.org/10.1016/j.scitotenv.2024.176929
4. Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models R. Arsenault et al. https://doi.org/10.5194/hess-27-139-2023
5. Technical note: High Nash–Sutcliffe Efficiencies conceal poor simulations of interannual variance in seasonal regimes S. Ruzzante et al. https://doi.org/10.5194/hess-30-2337-2026
6. EStreams: An integrated dataset and catalogue of streamflow, hydro-climatic and landscape variables for Europe T. do Nascimento et al. https://doi.org/10.1038/s41597-024-03706-1
7. Linking explainable artificial intelligence and soil moisture dynamics in a machine learning streamflow model A. Ley et al. https://doi.org/10.2166/nh.2024.003
8. Agriculture’s impact on water–energy balance varies across climates M. Zaerpour et al. https://doi.org/10.1073/pnas.2410521122
9. Improving trans-regional hydrological modelling by combining LSTM with big hydrological data S. Tang et al. https://doi.org/10.1016/j.ejrh.2025.102257
10. CAMELS-FR dataset: a large-sample hydroclimatic dataset for France to explore hydrological diversity and support model benchmarking O. Delaigue et al. https://doi.org/10.5194/essd-17-1461-2025
11. Towards Universal Runoff Forecasting: A KAN-WLSTM Framework for Robust Multi-Basin Hydrological Modeling F. Sai et al. https://doi.org/10.3390/w17213152
12. Hydrological concept formation inside long short-term memory (LSTM) networks T. Lees et al. https://doi.org/10.5194/hess-26-3079-2022
13. Contribution of urbanisation to non-stationary river flow in the UK S. Han et al. https://doi.org/10.1016/j.jhydrol.2022.128417
14. Location, location, location – Considering relative catchment location to understand subsurface losses M. Kiraz-Safari et al. https://doi.org/10.1016/j.jhydrol.2024.132328
15. 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. https://doi.org/10.5194/hess-28-2871-2024
16. Hybrid process-based and deep learning for river nutrient prediction under limited monitoring data J. Tang et al. https://doi.org/10.1016/j.jhydrol.2026.135098
17. CAMELS-DE: hydro-meteorological time series and attributes for 1582 catchments in Germany R. Loritz et al. https://doi.org/10.5194/essd-16-5625-2024
18. Evaluation of GPM IMERG satellite precipitation for rainfall–runoff modelling in Great Britain J. Gautam et al. https://doi.org/10.1080/02626667.2024.2394172
19. Catchments do not strictly follow Budyko curves over multiple decades, but deviations are minor and predictable M. Ibrahim et al. https://doi.org/10.5194/hess-29-1703-2025
20. CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia K. Fowler et al. https://doi.org/10.5194/essd-13-3847-2021
21. CAMELSH: A Large-Sample Hourly Hydrometeorological Dataset and Attributes at Watershed-Scale for CONUS V. Tran et al. https://doi.org/10.1038/s41597-025-05612-6
22. Diving into AI? Exploring the Potential for AI to Tackle Complex Water Quality Challenges E. Borgomeo et al. https://doi.org/10.1021/acs.est.5c15991
23. ML4FF: A machine-learning framework for flash flood forecasting applied to a Brazilian watershed J. Soares et al. https://doi.org/10.1016/j.jhydrol.2025.132674
24. CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India N. Mangukiya et al. https://doi.org/10.5194/essd-17-461-2025
25. Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale G. Papacharalampous et al. https://doi.org/10.1016/j.gsf.2022.101349
26. What is the near-natural catchment? An application of hydrological signatures assessment H. Xu et al. https://doi.org/10.1016/j.ecolind.2025.113209
27. What makes a robust calibration period? Insights into the effects of data properties O. Jaffar et al. https://doi.org/10.1080/02626667.2026.2619031
28. A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments P. Yeste et al. https://doi.org/10.5194/hess-28-5331-2024
29. CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations J. Liu et al. https://doi.org/10.5194/essd-17-1551-2025
30. A Global Benchmark of the Vector-Based Routing Model MizuRoute: Similarities and Divergent Patterns in Simulated River Discharge S. Xu et al. https://doi.org/10.3390/w18040485
31. Using century-long reanalysis and a rainfall-runoff model to explore multi-decadal variability in catchment hydrology at the European scale P. Brigode & L. Oudin https://doi.org/10.5194/hess-29-5535-2025
32. 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
33. A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022) X. Chen et al. https://doi.org/10.5194/essd-15-4463-2023
34. Temporal hydrological drought clustering varies with climate and land-surface processes M. Brunner & K. Stahl https://doi.org/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. https://doi.org/10.1088/1748-9326/acd655
36. Evaluating E-OBS forcing data for large-sample hydrology using model performance diagnostics F. Clerc-Schwarzenbach & T. do Nascimento https://doi.org/10.5194/hess-30-119-2026
37. A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting G. Papacharalampous & H. Tyralis https://doi.org/10.3389/frwa.2022.961954
38. Hybrid forecasting: blending climate predictions with AI models L. Slater et al. https://doi.org/10.5194/hess-27-1865-2023
39. A Framework for Estimating the Probability Distribution of Event Runoff Coefficient in Ungauged Catchments Y. Zheng et al. https://doi.org/10.1029/2022WR033227
40. Panta Rhei: a decade of progress in research on change in hydrology and society H. Kreibich et al. https://doi.org/10.1080/02626667.2025.2469762
41. MacroSheds: A synthesis of long‐term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies M. Vlah et al. https://doi.org/10.1002/lol2.10325
42. 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. https://doi.org/10.1016/j.advwatres.2024.104694
43. Are LSTM and conceptual rainfall-runoff models able to cope with limited training datasets under diverse hydrometeorological conditions? F. Boodoo et al. https://doi.org/10.1007/s40808-025-02316-z
44. DECIPHeR-GW v1: a coupled hydrological model with improved representation of surface–groundwater interactions Y. Zheng et al. https://doi.org/10.5194/gmd-18-4247-2025
45. A theoretical appraisal of the GR4J rainfall-runoff modelling framework S. Mathias et al. https://doi.org/10.1016/j.jhydrol.2025.134393
46. Have river flow droughts become more severe? A review of the evidence from the UK – a data-rich, temperate environment J. Hannaford et al. https://doi.org/10.5194/hess-29-4371-2025
47. Comprehensive Global Assessment of 24 Gridded Precipitation Datasets Across 18 428 Catchments Using Hydrological Modeling A. Abbas et al. https://doi.org/10.5194/hess-30-3399-2026
48. Impact of climate change on hydropower potential in the UK and Ireland R. Dallison & S. Patil https://doi.org/10.1016/j.renene.2023.03.021
49. Impacts of agriculture and snow dynamics on catchment water balance in the U.S. and Great Britain M. Zaerpour et al. https://doi.org/10.1038/s43247-024-01891-w
50. EARLS: a runoff reconstruction dataset for Europe D. Klotz et al. https://doi.org/10.5194/essd-18-5485-2026
51. PISCO_HyM_GR2M: A Model of Monthly Water Balance in Peru (1981–2020) H. Llauca et al. https://doi.org/10.3390/w13081048
52. Global patterns in observed hydrologic processes H. McMillan et al. https://doi.org/10.1038/s44221-025-00407-w
53. Caravan-Qual: A global scale integration of stream water quality observations into a large-sample hydrology dataset E. Jones et al. https://doi.org/10.1038/s41597-026-07352-7
54. Spatially resolved meteorological and ancillary data in Central Europe for rainfall streamflow modeling M. Vischer et al. https://doi.org/10.5194/essd-18-3099-2026
55. Streamflow droughts aggravated by human activities despite management A. Van Loon et al. https://doi.org/10.1088/1748-9326/ac5def
56. Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al. https://doi.org/10.1016/j.jhydrol.2024.131598
57. LamaH | Large-Sample Data for Hydrology: Big data für die Hydrologie und Umweltwissenschaften C. Klingler et al. https://doi.org/10.1007/s00506-021-00769-x
58. How is Baseflow Index (BFI) impacted by water resource management practices? J. Bloomfield et al. https://doi.org/10.5194/hess-25-5355-2021
59. Skilful probabilistic predictions of UK flood risk months ahead using a large-sample machine learning model trained on multimodel ensemble climate forecasts S. Moulds et al. https://doi.org/10.5194/hess-29-2393-2025
60. Spatially resolved rainfall streamflow modeling in central Europe M. Vischer et al. https://doi.org/10.5194/hess-29-5233-2025
61. Multi-step regional rainfall-runoff modeling using pyramidal transformer H. Yin et al. https://doi.org/10.1016/j.jhydrol.2025.132935
62. CCAM: China Catchment Attributes and Meteorology dataset Z. Hao et al. https://doi.org/10.5194/essd-13-5591-2021
63. LCM2021 – the UK Land Cover Map 2021 C. Marston et al. https://doi.org/10.5194/essd-15-4631-2023
64. Time shift between precipitation and evaporation has more impact on annual streamflow variability than the elasticity of potential evaporation V. Andréassian et al. https://doi.org/10.5194/hess-29-5477-2025
65. Evidence-based requirements for perceptualising intercatchment groundwater flow in hydrological models L. Oldham et al. https://doi.org/10.5194/hess-27-761-2023
66. Can discharge be used to inversely correct precipitation? A. Manoj J et al. https://doi.org/10.5194/hess-29-6115-2025
67. Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management L. Slater et al. https://doi.org/10.5194/hess-25-3897-2021
68. Regional significance of historical trends and step changes in Australian streamflow G. Amirthanathan et al. https://doi.org/10.5194/hess-27-229-2023
69. CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland M. Höge et al. https://doi.org/10.5194/essd-15-5755-2023
70. QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany P. Ebeling et al. https://doi.org/10.5194/essd-14-3715-2022
71. A comparative assessment of cluster-based regionalization approaches using conceptual rainfall–runoff models J. Ougahi & J. Rowan https://doi.org/10.1038/s41598-026-49424-z
72. Data‐Driven Worldwide Quantification of Large‐Scale Hydroclimatic Covariation Patterns and Comparison With Reanalysis and Earth System Modeling N. Ghajarnia et al. https://doi.org/10.1029/2020WR029377
73. Intercomparison of joint bias correction methods for precipitation and flow from a hydrological perspective K. Kim et al. https://doi.org/10.1016/j.jhydrol.2021.127261
74. Controls on the Spatial and Temporal Patterns of Rainfall‐Runoff Event Characteristics—A Large Sample of Catchments Across Great Britain Y. Zheng et al. https://doi.org/10.1029/2022WR033226
75. Climate shapes baseflows, influencing drought severity M. Zaerpour et al. https://doi.org/10.1088/1748-9326/ad975a
76. Deep learning for cross-region streamflow and flood forecasting at a global scale B. Zhang et al. https://doi.org/10.1016/j.xinn.2024.100617
77. Large-sample hydrology – a few camels or a whole caravan? F. Clerc-Schwarzenbach et al. https://doi.org/10.5194/hess-28-4219-2024
78. Wastewater discharges and urban land cover dominate urban hydrology signals across England and Wales G. Coxon et al. https://doi.org/10.1088/1748-9326/ad5bf2
79. CAMELS-AUS v2: updated hydrometeorological time series and landscape attributes for an enlarged set of catchments in Australia K. Fowler et al. https://doi.org/10.5194/essd-17-4079-2025
80. GRQA: Global River Water Quality Archive H. Virro et al. https://doi.org/10.5194/essd-13-5483-2021
81. Streamflow elasticity as a function of aridity V. Andréassian et al. https://doi.org/10.5194/hess-30-1865-2026
82. Climate change impact assessment on a German lowland river using long short-term memory and conceptual hydrological models A. Ley et al. https://doi.org/10.1016/j.ejrh.2025.102426
83. CAMELS-FI: hydrometeorological time series and landscape properties for 320 catchments in Finland I. Seppä et al. https://doi.org/10.5194/essd-18-4745-2026
84. Setting expectations for hydrologic model performance with an ensemble of simple benchmarks W. Knoben https://doi.org/10.1002/hyp.15288
85. CAMELS-GB v2: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain G. Coxon et al. https://doi.org/10.5194/essd-18-4345-2026
86. Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes H. McMillan et al. https://doi.org/10.1029/2021WR031751
87. Drivers of wastewater dynamics: a statistical analysis of England's large wastewater treatment works R. Maes-Prior et al. https://doi.org/10.2166/wst.2026.295
88. RFM_Trans: Runoff forecasting model for catchment flood protection using strategies optimized Transformer N. Bao et al. https://doi.org/10.1016/j.eswa.2025.127228
89. BULL Database – Spanish Basin attributes for Unravelling Learning in Large-sample hydrology J. Senent-Aparicio et al. https://doi.org/10.1038/s41597-024-03594-5
90. Deep learning for the probabilistic prediction of semi-continuous hydrological variables – An application to streamflow prediction across CONUS J. Quilty & M. Jahangir https://doi.org/10.1016/j.jhydrol.2026.134986
91. A century long ensemble streamflow dataset in the Pacific Northwest to support water security assessments N. Mizukami et al. https://doi.org/10.1038/s41597-026-06865-5
92. National-scale geodatabase of catchment characteristics in the Philippines for river management applications R. Boothroyd et al. https://doi.org/10.1371/journal.pone.0281933
93. Swiss data quality: augmenting CAMELS-CH with isotopes, water quality, agricultural and atmospheric data T. do Nascimento et al. https://doi.org/10.1038/s41597-025-05625-1
94. Teaching hydrological modelling: illustrating model structure uncertainty with a ready-to-use computational exercise W. Knoben & D. Spieler https://doi.org/10.5194/hess-26-3299-2022
95. Multivariate calibration can increase simulated discharge uncertainty and model equifinality S. Pool et al. https://doi.org/10.5194/hess-30-2797-2026
96. Impacts of observational uncertainty on analysis and modelling of hydrological processes: Preface H. McMillan et al. https://doi.org/10.1002/hyp.14481
97. Interpretable feature incorporation machine-learning framework for flood magnitude estimation E. Ford et al. https://doi.org/10.5194/hess-30-2135-2026
98. CAMELS-NZ: hydrometeorological time series and landscape attributes for New Zealand S. Bushra et al. https://doi.org/10.5194/essd-17-5745-2025
99. BCUB – a large-sample ungauged basin attribute dataset for British Columbia, Canada D. Kovacek & S. Weijs https://doi.org/10.5194/essd-17-259-2025
100. 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
101. When physics gets in the way: an entropy-based evaluation of conceptual constraints in hybrid hydrological models M. Álvarez Chaves et al. https://doi.org/10.5194/hess-30-629-2026
102. A region of influence approach for attributing fluvial climate change allowances A. Hammond https://doi.org/10.1080/02626667.2022.2153596
103. Advancing flood warning procedures in ungauged basins with machine learning Z. Rasheed et al. https://doi.org/10.1016/j.jhydrol.2022.127736
104. To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization E. Acuña Espinoza et al. https://doi.org/10.5194/hess-28-2705-2024
105. A dataset of lake-catchment characteristics for the Tibetan Plateau J. Liu et al. https://doi.org/10.5194/essd-14-3791-2022
106. Theoretical and empirical evidence against the Budyko catchment trajectory conjecture N. Reaver et al. https://doi.org/10.5194/hess-26-1507-2022
107. Co-Occurring Wintertime Flooding and Extreme Wind Over Europe, from Daily to Seasonal Timescales H. Bloomfield et al. https://doi.org/10.2139/ssrn.4197062
108. How to out-perform default random forest regression: choosing hyperparameters for applications in large-sample hydrology D. Bilolikar et al. https://doi.org/10.2166/nh.2025.075
109. A review of hydrologic signatures and their applications H. McMillan https://doi.org/10.1002/wat2.1499
110. A hybrid Budyko-type regression framework for estimating baseflow from climate and catchment attributes S. Chen & X. Ruan https://doi.org/10.1016/j.jhydrol.2023.129118
111. Survey of Time Series Data Generation in IoT C. Hu et al. https://doi.org/10.3390/s23156976
112. Predicting the dispersal of SARS-CoV-2 RNA from the wastewater treatment plant to the coast P. Robins et al. https://doi.org/10.1016/j.heliyon.2022.e10547
113. Co-occurring wintertime flooding and extreme wind over Europe, from daily to seasonal timescales B. H.C. et al. https://doi.org/10.1016/j.wace.2023.100550
114. Challenges in modeling and predicting floods and droughts: A review M. Brunner et al. https://doi.org/10.1002/wat2.1520
115. Hybrid modeling for daily streamflow forecasting: A study over the contiguous United States F. Zeng et al. https://doi.org/10.1016/j.jhydrol.2025.134477
116. Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs Y. Shen et al. https://doi.org/10.5194/essd-15-2781-2023
117. LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe C. Klingler et al. https://doi.org/10.5194/essd-13-4529-2021
118. A Signature‐Based Hydrologic Efficiency Metric for Model Calibration and Evaluation in Gauged and Ungauged Catchments M. Kiraz et al. https://doi.org/10.1029/2023WR035321
119. Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks A. Sun et al. https://doi.org/10.1029/2021WR030394
120. Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application X. Chen et al. https://doi.org/10.1016/j.envsoft.2025.106350
121. Catchment characterization: Current descriptors, knowledge gaps and future opportunities L. Tarasova et al. https://doi.org/10.1016/j.earscirev.2024.104739
122. 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. https://doi.org/10.1002/hyp.14544
123. Uncertainty, temporal variability, and influencing factors of empirical streamflow sensitivities S. Gnann et al. https://doi.org/10.5194/hess-30-779-2026
124. Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. https://doi.org/10.1029/2022WR034352
125. Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff D. Althoff & G. Destouni https://doi.org/10.1016/j.oneear.2023.08.002
126. AIFL: A global daily streamflow forecasting model using deterministic a LSTM pre-trained on ERA5-land and fine-tuned on IFS M. Taccari et al. https://doi.org/10.1016/j.jhydrol.2026.136064
127. 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
128. Hydro-PE: gridded datasets of historical and future Penman–Monteith potential evaporation for the United Kingdom E. Robinson et al. https://doi.org/10.5194/essd-15-4433-2023
129. Regionalizing hydrologic information for runoff predictions beyond continental boundaries using machine learning M. Fathi & A. Awadallah https://doi.org/10.1016/j.advwatres.2025.105162
130. Co-Occurring Wintertime Flooding and Extreme Wind Over Europe, from Daily to Seasonal Timescales H. Bloomfield et al. https://doi.org/10.2139/ssrn.4174051
131. 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
132. Multi-model hydrological reference dataset over continental Europe and an African basin B. Droppers et al. https://doi.org/10.1038/s41597-024-03825-9
133. Catchment response to climatic variability: implications for root zone storage and streamflow predictions N. Tempel et al. https://doi.org/10.5194/hess-28-4577-2024
134. Assessing the impact of process simulation order in hydrological modeling X. Lei et al. https://doi.org/10.1016/j.jhydrol.2026.135873
135. Knowledge gaps in our perceptual model of Great Britain's hydrology T. Wagener et al. https://doi.org/10.1002/hyp.14288
136. A large-sample investigation into uncertain climate change impacts on high flows across Great Britain R. Lane et al. https://doi.org/10.5194/hess-26-5535-2022
137. Simbi: historical hydro-meteorological time series and signatures for 24 catchments in Haiti R. Bathelemy et al. https://doi.org/10.5194/essd-16-2073-2024
138. Varying performance of eight evapotranspiration products with aridity and vegetation greenness across the globe H. Wang et al. https://doi.org/10.3389/fenvs.2023.1079520
139. Are UK Rivers Getting Saltier and More Alkaline? S. Jiang et al. https://doi.org/10.3390/w14182813
140. Use of streamflow indices to identify the catchment drivers of hydrographs J. Mathai & P. Mujumdar https://doi.org/10.5194/hess-26-2019-2022
141. Deep learning foundation and pattern models: Challenges in hydrological time series J. He et al. https://doi.org/10.1177/10943420251380008
142. A physics-driven hybrid transformer model for hydrologic simulation under nonstationary environmental conditions H. Zhang et al. https://doi.org/10.1016/j.jhydrol.2026.135133
143. WITHDRAWN: Intercomparison of joint bias correction methods for precipitation and flow from a hydrological perspective K. Kim et al. https://doi.org/10.1016/j.hydroa.2021.100109
144. Caravan - A global community dataset for large-sample hydrology F. Kratzert et al. https://doi.org/10.1038/s41597-023-01975-w
145. Evaluation of a socio-hydrological water resource model for drought management in groundwater-rich areas D. Wendt et al. https://doi.org/10.5194/hess-30-2837-2026
146. PatagoniaMet: A multi-source hydrometeorological dataset for Western Patagonia R. Aguayo et al. https://doi.org/10.1038/s41597-023-02828-2
147. 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. https://doi.org/10.5194/hess-25-5517-2021
148. A priori selection of hydrological model structures in modular modelling frameworks: application to Great Britain M. Kiraz et al. https://doi.org/10.1080/02626667.2023.2251968
149. Functional data analysis to investigate controls on and changes in the seasonality of UK baseflow K. Leeming et al. https://doi.org/10.1080/02626667.2024.2434714
150. QUADICA v2: extending the large-sample data set for water QUAlity, DIscharge and Catchment Attributes in Germany P. Ebeling et al. https://doi.org/10.5194/essd-18-691-2026
151. Continental-scale streamflow modeling of basins with reservoirs: Towards a coherent deep-learning-based strategy W. Ouyang et al. https://doi.org/10.1016/j.jhydrol.2021.126455
152. Discharge-based classifications of spatio-temporal patterns of potentially gaining and losing subcatchments in the Bode River catchment, Central Germany C. Lei et al. https://doi.org/10.1016/j.ejrh.2026.103161
153. Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios M. Buechel et al. https://doi.org/10.1038/s43247-021-00334-0
154. Interacting Effects of Precipitation and Potential Evapotranspiration Biases on Hydrological Modeling J. Wang et al. https://doi.org/10.1029/2022WR033323
155. Improving the Applicability of Lumped Hydrological Models by Integrating the Generalized Complementary Relationship X. Lei et al. https://doi.org/10.1029/2023WR035567
156. Building Cross-Site and Cross-Network collaborations in critical zone science B. Arora et al. https://doi.org/10.1016/j.jhydrol.2023.129248
157. Combining global precipitation data and machine learning to predict flood peaks in ungauged areas with similar climate Z. Rasheed et al. https://doi.org/10.1016/j.advwatres.2024.104781
158. GRDC-Caravan: extending Caravan with data from the Global Runoff Data Centre C. Färber et al. https://doi.org/10.5194/essd-17-4613-2025
159. Investigate the rainfall-runoff relationship and hydrological concepts inside LSTM Y. Hu et al. https://doi.org/10.1016/j.envsoft.2025.106527
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