38 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. 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
5. 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
6. 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
7. 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
8. 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
9. 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
10. 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
11. 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
12. 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
13. Multi-Spatial Resolution Rainfall-Runoff Modelling—A Case Study of Sabari River Basin, India V. Sharma & S. Regonda https://doi.org/10.3390/w13091224
14. 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
15. 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
16. 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
17. 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
18. 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
19. 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
20. Long-term hydrological budget over urban areas: approaches and challenges Y. Lin Xu et al. https://doi.org/10.1016/j.jhydrol.2025.134000
21. 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
22. DBv2: an improved climate-centric calibration-free model for runoff-generation simulation P. Istalkar & B. Biswal https://doi.org/10.1080/02626667.2025.2581263
23. 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
24. 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
25. 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
26. 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
27. 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
28. Parameter optimization method of hydrological model based on neural ordinary differential equations Q. Xiangzhao et al. https://doi.org/10.18307/2025.0342
29. 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
30. 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
31. 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
32. Streamflow prediction in ungauged basins: How dissimilar are drainage basins? P. Istalkar & B. Biswal https://doi.org/10.1016/j.jhydrol.2024.131357
33. 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
34. Quantifying Baseflow Changes Due to Irrigation Expansion Using SWAT+gwflow R. Navas et al. https://doi.org/10.3390/w17111680
35. 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
36. Estimation of spatiotemporally varying parameters for grid-based distributed hydrologic models X. Zhang et al. https://doi.org/10.1016/j.ejrh.2025.102536
37. Increasing parameter identifiability through clustered time-varying sensitivity analysis L. Wang et al. https://doi.org/10.1016/j.envsoft.2024.106189
38. 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