54 citations as recorded by crossref.

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3. Downscaling the Z–R relationship and bias correction solution for flash flood assessment in a data-scarce basin, Thailand P. Mapiam et al. https://doi.org/10.2166/wst.2023.056
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6. Remote Sensed and/or Global Datasets for Distributed Hydrological Modelling: A Review M. Ali et al. https://doi.org/10.3390/rs15061642
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9. RNN-Based Monthly Inflow Prediction for Dez Dam in Iran Considering the Effect of Wavelet Pre-Processing and Uncertainty Analysis A. Adib et al. https://doi.org/10.3390/hydrology11090155
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12. Modeling oasis dynamics driven by ecological water diversion and implications for oasis restoration in arid endorheic basins F. Huang et al. https://doi.org/10.1016/j.jhydrol.2020.125774
13. Monitoring water storage decline over the Middle East M. Khaki & I. Hoteit https://doi.org/10.1016/j.jhydrol.2021.127166
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17. Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis E. Moges et al. https://doi.org/10.3390/w13010028
18. Trade-offs between parameter constraints and model realism: a case study F. Jehn et al. https://doi.org/10.1038/s41598-019-46963-6
19. Improving the Informational Value of MODIS Fractional Snow Cover Area Using Fuzzy Logic Based Ensemble Smoother Data Assimilation Frameworks A. Teweldebrhan et al. https://doi.org/10.3390/rs11010028
20. Hydrologic multi-model ensemble predictions using variational Bayesian deep learning D. Li et al. https://doi.org/10.1016/j.jhydrol.2021.127221
21. Assessment of the structural uncertainty of hydrological models and its impact on flood inundation mapping I. Dasari & V. Vema https://doi.org/10.1080/02626667.2023.2271456
22. Impact of land use land cover changes on flow uncertainty in Siliana watershed of northwestern Tunisia I. El Ghoul et al. https://doi.org/10.1016/j.catena.2022.106733
23. State updating in a distributed hydrological model by ensemble Kalman filtering with error estimation J. Gong et al. https://doi.org/10.1016/j.jhydrol.2023.129450
24. Copula function with Variational Bayesian Monte Carlo for unveiling uncertainty impacts on meteorological and agricultural drought propagation Q. Zhang et al. https://doi.org/10.1016/j.jhydrol.2023.129669
25. Diagnosis of Model Errors With a Sliding Time‐Window Bayesian Analysis H. Hsueh et al. https://doi.org/10.1029/2021WR030590
26. AR-GARCH with Exogenous Variables as a Postprocessing Model for Improving Streamflow Forecasts X. Zha et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001955
27. Revealing the positive influence of young water fractions derived from stable isotopes on the robustness of karst water resources predictions K. Çallı et al. https://doi.org/10.1016/j.jhydrol.2023.129549
28. Crops as sensors: Using crop yield data to increase the robustness of hydrologic and biogeochemical models I. Ilampooranan et al. https://doi.org/10.1016/j.jhydrol.2020.125599
29. Eco-Compensation Schemes for Controlling Agricultural Non-Point Source Pollution in Maoli Lake Watershed Y. Zheng et al. https://doi.org/10.3390/w13111536
30. Hydrological data and modeling to combine and validate precipitation datasets relevant to hydrological applications A. Reis et al. https://doi.org/10.1016/j.ejrh.2022.101200
31. Comparison of MODIS and Model-Derived Snow-Covered Areas: Impact of Land Use and Solar Illumination Conditions N. Di Marco et al. https://doi.org/10.3390/geosciences10040134
32. Multi-parameter approaches for improved ensemble prediction accuracy in hydrology and water quality modeling S. Shin et al. https://doi.org/10.1016/j.jhydrol.2023.129458
33. Reducing hydrological uncertainty in large mountainous basins: the role of isotope, snow cover, and glacier dynamics in capturing streamflow seasonality D. Avesani et al. https://doi.org/10.5194/hess-29-5755-2025
34. Reducing hydrological modelling uncertainty by using MODIS snow cover data and a topography-based distribution function snowmelt model N. Di Marco et al. https://doi.org/10.1016/j.jhydrol.2021.126020
35. Assessing Spatio-Temporal Hydrological Impacts of Climate Change in the Siliana Watershed, Northwestern Tunisia I. El Ghoul et al. https://doi.org/10.3390/atmos15101209
36. Impact of Catchment Discretization and Imputed Radiation on Model Response: A Case Study from Central Himalayan Catchment B. Bhattarai et al. https://doi.org/10.3390/w12092339
37. Accounting for model parameter uncertainty provides more robust projections of dissolved organic carbon dynamics to aid drinking water management R. Paíz et al. https://doi.org/10.1016/j.watres.2025.123238
38. Correlation among parameters and boundary conditions in river ice models P. Rokaya & K. Lindenschmidt https://doi.org/10.1007/s40808-019-00696-7
39. Assessing the hydrological and geomorphic behaviour of a landscape evolution model within a limits‐of‐acceptability uncertainty analysis framework J. Wong et al. https://doi.org/10.1002/esp.5140
40. Assessing the potential value of the regionalised input constraint indices for constraining hydrological model simulations in the Congo River Basin P. Kabuya et al. https://doi.org/10.1016/j.advwatres.2021.104093
41. Quantifying Uncertainty in Hydrological Drought Index Using Calibrated SWAT Model J. Kim et al. https://doi.org/10.1007/s12205-024-1029-0
42. Evaluation of Uncertainty in Stream Flow Prediction Using Monte Carlo Simulation for Watershed-Scale Hydrological Modeling P. Dey et al. https://doi.org/10.1061/JHYEFF.HEENG-6008
43. Uncertainties in future monsoon flow predictions in the context of projected climate change: A study of the Chaliyar River Basin S. Ansa Thasneem et al. https://doi.org/10.1016/j.envres.2023.115301
44. DREAM(LoAX): Simultaneous Calibration and Diagnosis for Tracer‐Aided Ecohydrological Models Under the Equifinality Thesis S. Wu et al. https://doi.org/10.1029/2024WR038779
45. Equifinality and Flux Mapping: A New Approach to Model Evaluation and Process Representation Under Uncertainty S. Khatami et al. https://doi.org/10.1029/2018WR023750
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47. On (in)validating environmental models. 2. Implementation of a Turing‐like test to modelling hydrological processes K. Beven et al. https://doi.org/10.1002/hyp.14703
48. Evaluation of global forcing datasets for hydropower inflow simulation in Nepal B. Bhattarai et al. https://doi.org/10.2166/nh.2020.079
49. Calibration and Evaluation of the SIMPLE Crop Growth Model Applied to the Common Bean under Irrigation M. Servín-Palestina et al. https://doi.org/10.3390/agronomy14050917
50. Investigating the spatiotemporal behavior of VIC model parameters over the Tibetan plateau via global sensitivity analysis and machine learning Y. Li et al. https://doi.org/10.1080/17538947.2026.2625537
51. Application of Generalized Likelihood Uncertainty Estimation (GLUE) at different temporal scales to reduce the uncertainty level in modelled river flows R. Ragab et al. https://doi.org/10.1080/02626667.2020.1764961
52. Evaluating Operational Risk in Environmental Modeling: Assessment of Reliability and Sharpness for Ensemble Selection S. Pokorny et al. https://doi.org/10.1061/JHYEFF.HEENG-5833
53. Improvement of City Rainfall Model Subcatchment Structure Based on Urban Hydrology Process J. Li et al. https://doi.org/10.1061/JHYEFF.HEENG-6084
54. Developing an integrated PCE-ANOVA-RF method for uncertainty quantification of hydrological model – The Amu Darya River Basin in Central Asia Z. Xu et al. https://doi.org/10.1016/j.jhydrol.2023.129941