57 citations as recorded by crossref.

1. Can streamflow observations constrain snow mass reconstructions? Lessons from two synthetic numerical experiments P. Wiersma et al. https://doi.org/10.5194/hess-30-3331-2026
2. Metamorphic testing of machine learning and conceptual hydrologic models P. Reichert et al. https://doi.org/10.5194/hess-28-2505-2024
3. 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
4. CAMELS-NZ: hydrometeorological time series and landscape attributes for New Zealand S. Bushra et al. https://doi.org/10.5194/essd-17-5745-2025
5. Comparing Frequency-Matched and Natural Data Approaches for Estimating the Curve Number from Rainfall-Runoff Data A. Brandão et al. https://doi.org/10.1061/JHYEFF.HEENG-6400
6. 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
7. 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
8. How well do hydrological models simulate streamflow extremes and drought-to-flood transitions? E. Muñoz-Castro et al. https://doi.org/10.5194/hess-30-825-2026
9. 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
10. 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
11. River temperature response to atmospheric heatwaves is modulated by discharge and meltwater A. van Hamel et al. https://doi.org/10.1038/s43247-026-03269-6
12. 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
13. 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
14. Streamflow elasticity as a function of aridity V. Andréassian et al. https://doi.org/10.5194/hess-30-1865-2026
15. 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
16. Can discharge be used to inversely correct precipitation? A. Manoj J et al. https://doi.org/10.5194/hess-29-6115-2025
17. LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland H. Helgason & B. Nijssen https://doi.org/10.5194/essd-16-2741-2024
18. 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
19. 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
20. Unveiling the limits of deep learning models in hydrological extrapolation tasks S. Baste et al. https://doi.org/10.5194/hess-29-5871-2025
21. Large-sample hydrology – a few camels or a whole caravan? F. Clerc-Schwarzenbach et al. https://doi.org/10.5194/hess-28-4219-2024
22. 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. https://doi.org/10.5194/hess-29-5791-2025
23. High-resolution hydrometeorological and snow data for the Dischma catchment in Switzerland J. Magnusson et al. https://doi.org/10.5194/essd-17-703-2025
24. 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
25. HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network T. Nguyen et al. https://doi.org/10.1016/j.ecoinf.2025.102994
26. FOCA: a new quality-controlled database of floods and catchment descriptors in Italy P. Claps et al. https://doi.org/10.5194/essd-16-1503-2024
27. 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
28. Suspended sediment concentrations in Alpine rivers: from annual regimes to sub-daily extreme events A. van Hamel et al. https://doi.org/10.5194/hess-29-2975-2025
29. Toward a better understanding of curve number and initial abstraction ratio values from a large sample of watersheds perspective A. Brandão et al. https://doi.org/10.1016/j.jhydrol.2025.132941
30. 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
31. A dataset of land surface characteristics and time-series hydrometeorological data for typical catchments in China (2003–2020) H. MA et al. https://doi.org/10.11922/11-6035.csd.2025.0144.zh
32. 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
33. 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
34. Comparison of high-resolution climate reanalysis datasets for hydro-climatic impact studies R. Wood et al. https://doi.org/10.5194/hess-29-4153-2025
35. 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
36. Runoff prediction in gauged and ungauged basins using Transformer-XAJ model H. Yin et al. https://doi.org/10.1016/j.jhydrol.2025.133954
37. Meteorological and hydrological dry-to-wet transition events are only weakly related over European catchments M. Brunner et al. https://doi.org/10.1088/1748-9326/ade72c
38. CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland B. Kraft et al. https://doi.org/10.5194/hess-29-1061-2025
39. 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
40. Assessing temporal and spatial generalization of LSTMs for streamflow modeling in French watersheds with and without European training data M. Puche et al. https://doi.org/10.1016/j.ejrh.2025.103022
41. 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
42. How extreme are transitions in streamflow? A conditional probability approach B. Anderson et al. https://doi.org/10.1088/1748-9326/ae6d19
43. Spatially resolved rainfall streamflow modeling in central Europe M. Vischer et al. https://doi.org/10.5194/hess-29-5233-2025
44. Deep learning foundation and pattern models: Challenges in hydrological time series J. He et al. https://doi.org/10.1177/10943420251380008
45. Impact of bias adjustment strategy on ensemble projections of hydrological extremes P. Astagneau et al. https://doi.org/10.5194/hess-29-5695-2025
46. 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
47. 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
48. FlowGATFormer: A streamflow prediction model based on spatiotemporal dual attention F. Liu et al. https://doi.org/10.1016/j.jhydrol.2026.135771
49. Catchment characterization: Current descriptors, knowledge gaps and future opportunities L. Tarasova et al. https://doi.org/10.1016/j.earscirev.2024.104739
50. 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
51. Hydrological modelling for water resource assessment in drylands of Sub-Saharan Africa under climate and land use change: a systematic review E. Meresa et al. https://doi.org/10.1080/27669645.2026.2678674
52. 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
53. 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
54. Swiss glacier mass loss during the 2022 drought: persistent streamflow contributions amid declining melt water volumes M. van Tiel et al. https://doi.org/10.5194/hess-30-23-2026
55. Snow drought propagation and its impacts on streamflow drought in the Alps C. Chartier-Rescan et al. https://doi.org/10.1088/1748-9326/adc824
56. 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
57. 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