43 citations as recorded by crossref.

1. A framework for seasonal variations of hydrological model parameters: impact on model results and response to dynamic catchment characteristics T. Lan et al. https://doi.org/10.5194/hess-24-5859-2020
2. Physical process-based attention encoder-decoder LSTM model to improve global soil moisture prediction Q. Li et al. https://doi.org/10.1016/j.agrformet.2026.111161
3. A Comparative Study of a Two-Dimensional Slope Hydrodynamic Model (TDSHM), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) Models for Runoff Prediction Y. Zhou et al. https://doi.org/10.3390/w17091380
4. Comparison of LSTM and conceptual hydrological models for runoff simulation under changing climatic conditions in Iran A. Jahanshahi & M. Booij https://doi.org/10.1016/j.jaridenv.2026.105679
5. Simulating runoff under changing climatic conditions: A comparison of the long short-term memory network with two conceptual hydrologic models P. Bai et al. https://doi.org/10.1016/j.jhydrol.2020.125779
6. Effects of characteristics of calibration periods on building hydrologic models in the upper basins of South Korea J. Park et al. https://doi.org/10.1016/j.ejrh.2025.102636
7. Lack of robustness of hydrological models: a large-sample diagnosis and an attempt to identify hydrological and climatic drivers L. Santos et al. https://doi.org/10.5194/hess-29-683-2025
8. Exploring controls on precipitation-runoff dependencies: Implications for non-stationary and spatially heterogeneous analyses T. Lan et al. https://doi.org/10.1016/j.jhydrol.2025.133333
9. BiGRU-dXAJ: A hybrid hydrologic model enabling differentiable parameter learning for runoff simulation M. Sun et al. https://doi.org/10.1016/j.envsoft.2026.107136
10. Multi-model simulation performance of monthly water balance models for global catchments: Thresholds and structural sensitivity Z. Ning et al. https://doi.org/10.1016/j.jhydrol.2025.134852
11. Modelling phosphorus and potassium dynamics in drip-irrigated potato systems using coupled agro-hydrological model M. Rezaei et al. https://doi.org/10.1016/j.agwat.2025.109920
12. Dissolved organic carbon response to hydrological drought characteristics: Based on long-term measurements of headwater streams J. Wu et al. https://doi.org/10.1016/j.watres.2022.118252
13. Identification of time-varying parameters of a monthly Budyko function in US MOPEX catchments and its implications W. Liu et al. https://doi.org/10.1016/j.ejrh.2025.102348
14. Event-aware SWAT+ calibration for stormflows with monthly nutrient data in the Qingjiang River Basin R. Wang et al. https://doi.org/10.1007/s10661-026-15187-3
15. Improving streamflow predictions in data-scarce nested basins through residual transfer and post-processing K. Kim et al. https://doi.org/10.1038/s41598-026-49028-7
16. Multi-Spatial Resolution Rainfall-Runoff Modelling—A Case Study of Sabari River Basin, India V. Sharma & S. Regonda https://doi.org/10.3390/w13091224
17. Spatial modeling for detection of water retention capacity in technosols developed on carboniferous spoil heap after hard coal mining P. Singh et al. https://doi.org/10.1016/j.ecoinf.2024.102751
18. Improving the hydrological consistency of a process-based solute-transport model by simultaneous calibration of streamflow and stream concentrations J. Salmon-Monviola et al. https://doi.org/10.5194/hess-29-127-2025
19. Investigating streamflow dynamics in heterogeneous catchments through sensitivity function analysis S. Rusjan et al. https://doi.org/10.1016/j.jhydrol.2026.136409
20. A time-varying parameter estimation approach using split-sample calibration based on dynamic programming X. Zhang & P. Liu https://doi.org/10.5194/hess-25-711-2021
21. Predicting Ice Phenomena in a River Using the Artificial Neural Network and Extreme Gradient Boosting R. Graf et al. https://doi.org/10.3390/resources11020012
22. Simulating Runoff and Actual Evapotranspiration via Time-Variant Parameter Method: The Effects of Hydrological Model Structures Z. Pan et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002220
23. Hydrological balance and runoff from a montane peat bog traced by water temperature K. Falatkova et al. https://doi.org/10.1080/02626667.2024.2320392
24. Long-term hydrological budget over urban areas: approaches and challenges Y. Lin Xu et al. https://doi.org/10.1016/j.jhydrol.2025.134000
25. Identification of dynamic drought propagation from a nonstationary perspective and its application to drought warnings T. Zhang et al. https://doi.org/10.1016/j.jhydrol.2023.130372
26. DBv2: an improved climate-centric calibration-free model for runoff-generation simulation P. Istalkar & B. Biswal https://doi.org/10.1080/02626667.2025.2581263
27. Faster increase in evapotranspiration in permafrost-dominated basins in the warming Pan-Arctic Q. Huang et al. https://doi.org/10.1016/j.jhydrol.2022.128678
28. 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
29. Satellite-derived evapotranspiration for calibrating a spatially distributed hydrological model in a highly regulated Iranian river basin A. Jahanshahi https://doi.org/10.1016/j.jaridenv.2026.105627
30. Hydrodynamic Modelling of Semi-Enclosed Coastal Systems: A Stepwise Assessment of Key Forcing Factors B. Chen et al. https://doi.org/10.3390/jmse14111058
31. Testing a low-complexity spatially distributed model to simulate the intra-annual dynamics of soil erosion and sediment delivery F. Matthews et al. https://doi.org/10.1016/j.catena.2025.109054
32. A novel hybrid deep learning model with dynamic parameterization for accurate flood simulation B. Sun et al. https://doi.org/10.1016/j.jhydrol.2026.135182
33. Parameter optimization method of hydrological model based on neural ordinary differential equations Q. Xiangzhao et al. https://doi.org/10.18307/2025.0342
34. 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
35. Integrating machine learning ensembles and flood classification for enhanced flood forecasting with dynamic parameter weighting H. Oppel et al. https://doi.org/10.2166/hydro.2025.030
36. An Adaptive Process-Wise Fitting Approach for Hydrological Modeling Based on Streamflow and Remote Sensing Evapotranspiration C. Wang et al. https://doi.org/10.3390/w16233446
37. Streamflow prediction in ungauged basins: How dissimilar are drainage basins? P. Istalkar & B. Biswal https://doi.org/10.1016/j.jhydrol.2024.131357
38. Quantifying uncertainty contributions of hydrologic modeling process to hydrologic drought projection in South Korea J. Park et al. https://doi.org/10.1016/j.ejrh.2025.102991
39. Quantifying Baseflow Changes Due to Irrigation Expansion Using SWAT+gwflow R. Navas et al. https://doi.org/10.3390/w17111680
40. Sensitivity and uncertainty of baseflow and discharge simulations using WetSpass-M in the Akaki catchment, central highlands of Ethiopia G. Nigussie et al. https://doi.org/10.1016/j.watcyc.2026.03.007
41. Estimation of spatiotemporally varying parameters for grid-based distributed hydrologic models X. Zhang et al. https://doi.org/10.1016/j.ejrh.2025.102536
42. Increasing parameter identifiability through clustered time-varying sensitivity analysis L. Wang et al. https://doi.org/10.1016/j.envsoft.2024.106189
43. Enhancing representation of data-scarce reservoir-regulated river basins using a hybrid DL-process based approach L. Deng et al. https://doi.org/10.1016/j.jhydrol.2025.132895