50 citations as recorded by crossref.

1. Is comprehensive event sampling necessary for constraining process models of water quality? A comparison of high and low frequency phosphorus sampling programs for constraining the HYPE water quality model A. Phillips et al. 10.1016/j.jhydrol.2024.131502
2. State updating in a distributed hydrological model by ensemble Kalman filtering with error estimation J. Gong et al. 10.1016/j.jhydrol.2023.129450
3. Tradeoffs Between Temporal and Spatial Pattern Calibration and Their Impacts on Robustness and Transferability of Hydrologic Model Parameters to Ungauged Basins M. Demirel et al. 10.1029/2022WR034193
4. Temporal Fusion Transformers for streamflow Prediction: Value of combining attention with recurrence S. Rasiya Koya & T. Roy 10.1016/j.jhydrol.2024.131301
5. In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance M. Gauch et al. 10.1029/2022WR033918
6. Technical Note: The divide and measure nonconformity – how metrics can mislead when we evaluate on different data partitions D. Klotz et al. 10.5194/hess-28-3665-2024
7. Benchmarking hydrological models for an uncertain future K. Beven 10.1002/hyp.14882
8. 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
9. Advancement of a Blended Hydrologic Model for Robust Model Performance R. Chlumsky et al. 10.1061/JHYEFF.HEENG-6246
10. Structural differences across hydrological models affect certainty of predictions of nature-based solution benefits A. Rebelo et al. 10.1016/j.ecolmodel.2024.110940
11. Applicability of a flood forecasting system for Nebraska watersheds S. Rasiya Koya et al. 10.1016/j.envsoft.2023.105693
12. Learning from hydrological models’ challenges: A case study from the Nelson basin model intercomparison project M. Ahmed et al. 10.1016/j.jhydrol.2023.129820
13. On how data are partitioned in model development and evaluation: Confronting the elephant in the room to enhance model generalization H. Maier et al. 10.1016/j.envsoft.2023.105779
14. Projected seasonal flooding in Canada under climate change with statistical and machine learning M. Grenier et al. 10.1016/j.ejrh.2024.101754
15. Assessing the impact of distributed snow water equivalent calibration and assimilation of Copernicus snow water equivalent on modelled snow and streamflow performance A. Beaton et al. 10.1002/hyp.15075
16. Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al. 10.5194/hess-28-2107-2024
17. Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application X. Chen et al. 10.1016/j.envsoft.2025.106350
18. Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations Y. Zhang et al. 10.5194/hess-27-4529-2023
19. Land surface hydrological modelling of the Mackenzie River Basin: Parametrization to simulate streamflow and permafrost dynamics M. Elshamy et al. 10.1016/j.jhydrol.2025.133134
20. Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL) D. Feng et al. 10.5194/gmd-17-7181-2024
21. Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
22. Reconstructing 42 Years (1979–2020) of Great Lakes Surface Temperature through a Deep Learning Approach M. Kayastha et al. 10.3390/rs15174253
23. Technical note: Testing the connection between hillslope-scale runoff fluctuations and streamflow hydrographs at the outlet of large river basins R. Mantilla et al. 10.5194/hess-28-1373-2024
24. Technical note: How many models do we need to simulate hydrologic processes across large geographical domains? W. Knoben et al. 10.5194/hess-29-2361-2025
25. BasinMaker 3.0: A GIS toolbox for distributed watershed delineation of complex lake-river routing networks M. Han et al. 10.1016/j.envsoft.2023.105688
26. CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations J. Liu et al. 10.5194/essd-17-1551-2025
27. Evaluating surface and subsurface fluxes in hydrological models to advance basin-scale operational water supply forecasting S. Shin et al. 10.1080/02626667.2024.2378100
28. A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting E. Snieder & U. Khan 10.5194/hess-29-785-2025
29. Pooling local climate and donor gauges with deep learning for improved reconstructions of streamflow in ungauged and partially gauged basins S. Wi et al. 10.1016/j.jhydrol.2025.133764
30. Evaluating the parameter sensitivity and impact of hydrologic modeling decisions on flood simulations A. Alexander et al. 10.1016/j.advwatres.2023.104560
31. Can Hydrological Models Benefit From Using Global Soil Moisture, Evapotranspiration, and Runoff Products as Calibration Targets? Y. Mei et al. 10.1029/2022WR032064
32. Intercomparison of Runoff and River Discharge Reanalysis Datasets at the Upper Jinsha River, an Alpine River on the Eastern Edge of the Tibetan Plateau S. Chen et al. 10.3390/w17060871
33. Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks G. Nearing et al. 10.5194/hess-26-5493-2022
34. Long-term hydroclimate trends in the Great Lakes basin: Are there hotspots of regional change? S. Shin et al. 10.1016/j.ejrh.2025.102347
35. Setting expectations for hydrologic model performance with an ensemble of simple benchmarks W. Knoben 10.1002/hyp.15288
36. Performance and projections of the NEX‐GDDP‐CMIP6 in simulating precipitation in the Brazilian Amazon and Cerrado biomes L. de Mendonça et al. 10.1002/joc.8547
37. Benchmarking high-resolution hydrologic model performance of long-term retrospective streamflow simulations in the contiguous United States E. Towler et al. 10.5194/hess-27-1809-2023
38. Simulating hydrological extremes for different warming levels–combining large scale climate ensembles with local observation based machine learning models S. Hauswirth et al. 10.3389/frwa.2023.1108108
39. Hydrologic outputs generated over the Great Lakes with a calibrated version of the GEM-Hydro model É. Gaborit et al. 10.1038/s41597-025-04409-x
40. Ten strategies towards successful calibration of environmental models J. Mai 10.1016/j.jhydrol.2023.129414
41. On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration S. Wi & S. Steinschneider 10.5194/hess-28-479-2024
42. A data-centric perspective on the information needed for hydrological uncertainty predictions A. Auer et al. 10.5194/hess-28-4099-2024
43. Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al. 10.3389/frwa.2023.1166124
44. Using a physics-based hydrological model and storm transposition to investigate machine-learning algorithms for streamflow prediction F. Gurbuz et al. 10.1016/j.jhydrol.2023.130504
45. A multi-model evaluation of probabilistic streamflow predictions via residual error modelling J. Romero-Cuellar et al. 10.1016/j.jhydrol.2024.131152
46. Optimizing parameter estimation in hydrological models with convolutional neural network guided dynamically dimensioned search approach A. Alexander & D. Kumar 10.1016/j.advwatres.2024.104842
47. The Impacts of Climate Change on Land Hydroclimatology of the Laurentian Great Lakes Basin N. Shrestha et al. 10.3389/frwa.2022.801134
48. On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration S. Wi & S. Steinschneider 10.5194/hess-28-479-2024
49. Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al. 10.5194/hess-28-2107-2024
50. The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) J. Mai et al. 10.5194/hess-26-3537-2022