56 citations as recorded by crossref.

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2. Variability in flow and tracer-based performance metric sensitivities reveal regional differences in dominant hydrological processes across the Athabasca River basin T. Holmes et al. https://doi.org/10.1016/j.ejrh.2022.101088
3. Revisiting the Basis of Sensitivity Analysis for Dynamical Earth System Models H. Gupta & S. Razavi https://doi.org/10.1029/2018WR022668
4. Time‐Varying Sensitivity Analysis Reveals Relationships Between Watershed Climate and Variations in Annual Parameter Importance in Regions With Strong Interannual Variability R. Basijokaite & C. Kelleher https://doi.org/10.1029/2020WR028544
5. Effectiveness of Adjacent and Bivariate Maps in Communicating Global Sensitivity Analysis for Geodiversity Assessment P. Jankowski et al. https://doi.org/10.3390/ijgi13060199
6. Hydrological Stability and Sensitivity Analysis of the Cahaba River Basin: A Combined Review and Simulation Study P. Preetha et al. https://doi.org/10.3390/w18080894
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8. Analysing the Sub-processes of a Conceptual Rainfall-Runoff Model Using Information About the Parameter Sensitivity and Variance C. Massmann & H. Holzmann https://doi.org/10.1007/s10666-014-9414-6
9. Moment-based metrics for global sensitivity analysis of hydrological systems A. Dell'Oca et al. https://doi.org/10.5194/hess-21-6219-2017
10. A Parallel Computation Tool to Enable Dynamic Sensitivity and Model Performance Analysis of APEX: Evapotranspiration Modeling M. Talebizadeh et al. https://doi.org/10.1111/1752-1688.12758
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13. Ten strategies towards successful calibration of environmental models J. Mai https://doi.org/10.1016/j.jhydrol.2023.129414
14. Hydrologic model parameter estimation in snow-dominated headwater catchments using multiple observation datasets L. North et al. https://doi.org/10.5194/hess-30-5195-2026
15. On characterizing the temporal dominance patterns of model parameters and processes B. Guse et al. https://doi.org/10.1002/hyp.10764
16. Rainstorm Magnitude Likely Regulates Event Water Fraction and Its Transit Time in Mesoscale Mountainous Catchments: Implication for Modelling Parameterization J. Lee et al. https://doi.org/10.3390/w12041169
17. The dilemma of objective function selection for sensitivity and uncertainty analyses of semi-distributed hydrologic models across spatial and temporal scales J. Acero Triana et al. https://doi.org/10.1016/j.jhydrol.2024.132482
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20. A streamlined approach to uncertainty and sensitivity analysis for models with spatial outputs with an example from geodiversity assessment A. Ligmann-Zielinska et al. https://doi.org/10.1080/13658816.2024.2348065
21. Understanding the influence of hydrologic parameter uncertainty on Community Water Model predictions: a diagnostic assessment through extensive ensemble simulations J. Kim & K. Ahn https://doi.org/10.1088/1748-9326/add27e
22. A multi-method Generalized Global Sensitivity Matrix approach to accounting for the dynamical nature of earth and environmental systems models S. Razavi & H. Gupta https://doi.org/10.1016/j.envsoft.2018.12.002
23. Simultaneously determining global sensitivities of model parameters and model structure J. Mai et al. https://doi.org/10.5194/hess-24-5835-2020
24. Parameter Estimation and Uncertainty Analysis: A Comparison between Continuous and Event-Based Modeling of Streamflow Based on the Hydrological Simulation Program–Fortran (HSPF) Model H. Xie et al. https://doi.org/10.3390/w11010171
25. Spatiotemporal sensitivity analysis of vertical transport of pesticides in soil T. Hong & S. Purucker https://doi.org/10.1016/j.envsoft.2018.03.018
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27. Sensitivity analysis of hydrological models: review and way forward M. Devak & C. Dhanya https://doi.org/10.2166/wcc.2017.149
28. A Simple Time-Varying Sensitivity Analysis (TVSA) for Assessment of Temporal Variability of Hydrological Processes Y. Medina & E. Muñoz https://doi.org/10.3390/w12092463
29. Sensitivity analysis of environmental models: A systematic review with practical workflow F. Pianosi et al. https://doi.org/10.1016/j.envsoft.2016.02.008
30. Coordination and control – limits in standard representations of multi-reservoir operations in hydrological modeling C. Rougé et al. https://doi.org/10.5194/hess-25-1365-2021
31. Identifying dominant parameters in SWAT across subbasin and HRU scales using a two-step deep learning-assisted spatial sensitivity analysis J. Yang et al. https://doi.org/10.5194/hess-30-4095-2026
32. Global sensitivity analysis of a three-dimensional nutrients-algae dynamic model for a large shallow lake X. Yi et al. https://doi.org/10.1016/j.ecolmodel.2016.01.005
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34. Global Sensitivity Analysis for Multiple Interpretive Models With Uncertain Parameters A. Dell'Oca et al. https://doi.org/10.1029/2019WR025754
35. Evaluating the significance of wetland representation in isotope-enabled distributed hydrologic modeling in mesoscale Precambrian shield watersheds A. Tafvizi et al. https://doi.org/10.1016/j.jhydrol.2024.131377
36. Demasking the integrated information of discharge: Advancing sensitivity analysis to consider different hydrological components and their rates of change B. Guse et al. https://doi.org/10.1002/2016WR018894
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38. The sensitivity of simulated streamflow to individual hydrologic processes across North America J. Mai et al. https://doi.org/10.1038/s41467-022-28010-7
39. Multivariate global sensitivity analysis for dynamic models based on wavelet analysis S. Xiao et al. https://doi.org/10.1016/j.ress.2017.10.007
40. Ecological vulnerability analysis of Beidagang National Park, China X. Yu et al. https://doi.org/10.1007/s11707-018-0726-8
41. What Is Controlling Our Control Rules? Opening the Black Box of Multireservoir Operating Policies Using Time‐Varying Sensitivity Analysis J. Quinn et al. https://doi.org/10.1029/2018WR024177
42. Improving Information Extraction From Simulated Discharge Using Sensitivity‐Weighted Performance Criteria B. Guse et al. https://doi.org/10.1029/2019WR025605
43. A multi-criteria trajectory-based parameter sampling strategy for the screening method of elementary effects Y. Khare et al. https://doi.org/10.1016/j.envsoft.2014.11.013
44. How Should Robustness Be Defined for Water Systems Planning under Change? J. Herman et al. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000509
45. A new approach to evaluate spatiotemporal dynamics of controlling parameters in distributed environmental models Z. Chen et al. https://doi.org/10.1016/j.envsoft.2016.10.005
46. Supporting M5 model trees with sensitivity information derived from conceptual hydrological models C. Massmann https://doi.org/10.2166/hydro.2015.111
47. Flood and drought hydrologic monitoring: the role of model parameter uncertainty N. Chaney et al. https://doi.org/10.5194/hess-19-3239-2015
48. Improved understanding of vegetation dynamics and wetland ecohydrology via monthly UAV‐based classification S. Wu et al. https://doi.org/10.1002/hyp.14988
49. Analysing spatio-temporal process and parameter dynamics in models to characterise contrasting catchments B. Guse et al. https://doi.org/10.1016/j.jhydrol.2018.12.050
50. Sensitivity Analysis of Fully Distributed Parameterization Reveals Insights Into Heterogeneous Catchment Responses for Water Quality Modeling X. Yang et al. https://doi.org/10.1029/2019WR025575
51. Identifying Dominant Processes in Time and Space: Time‐Varying Spatial Sensitivity Analysis for a Grid‐Based Nitrate Model S. Wu et al. https://doi.org/10.1029/2021WR031149
52. Parameter uncertainty and sensitivity analysis of water quality model in Lake Taihu, China L. Jiang et al. https://doi.org/10.1016/j.ecolmodel.2018.02.014
53. Understanding the time‐varying importance of different uncertainty sources in hydrological modelling using global sensitivity analysis F. Pianosi & T. Wagener https://doi.org/10.1002/hyp.10968
54. Simulation and sensitivity analysis of DNAPL migration in low-permeability media Y. Pan et al. https://doi.org/10.1007/s10040-026-03027-0
55. Time-varying sensitivity analysis of hydrologic and sediment parameters at multiple timescales: Implications for conservation practices H. Xie et al. https://doi.org/10.1016/j.scitotenv.2017.04.074
56. Multivariate sensitivity analysis based on the direction of eigen space through principal component analysis S. Xiao et al. https://doi.org/10.1016/j.ress.2017.03.011