260 citations as recorded by crossref.

1. What has Global Sensitivity Analysis ever done for us? A systematic review to support scientific advancement and to inform policy-making in earth system modelling T. Wagener & F. Pianosi https://doi.org/10.1016/j.earscirev.2019.04.006
2. An experimental approach to reduce the parametric dimensionality for rainfall–runoff models J. Chen et al. https://doi.org/10.2166/nh.2016.145
3. Assessment of combined sewer overflows impacts under flooding in coastal cities H. Abbasi et al. https://doi.org/10.2166/wcc.2021.322
4. Temporal dynamics of model parameter sensitivity for computationally expensive models with the Fourier amplitude sensitivity test D. Reusser et al. https://doi.org/10.1029/2010WR009947
5. A comprehensive framework for HSPF hydrological parameter sensitivity, optimization and uncertainty evaluation based on SVM surrogate model- A case study in Qinglong River watershed, China L. Xingpo et al. https://doi.org/10.1016/j.envsoft.2021.105126
6. Issues of experimental design for comparing the performance of hydrologic models R. Clarke https://doi.org/10.1029/2007WR005927
7. Global sensitivity analysis of an energy–economy model of the residential building sector F. Branger et al. https://doi.org/10.1016/j.envsoft.2015.03.021
8. Determination of the saturated film conductivity to improve the EMFX model in describing the soil hydraulic properties over the entire moisture range Y. Wang et al. https://doi.org/10.1016/j.jhydrol.2017.03.063
9. An Urban Sociohydrologic Model for Exploration of Beijing's Water Sustainability Challenges and Solution Spaces B. Li et al. https://doi.org/10.1029/2018WR023816
10. Identifying sensitive sources and key control handles for the reduction of greenhouse gas emissions from wastewater treatment C. Sweetapple et al. https://doi.org/10.1016/j.watres.2014.06.002
11. Sensitivity analysis of Takagi-Sugeno-Kang rainfall-runoff fuzzy models A. Jacquin & A. Shamseldin https://doi.org/10.5194/hess-13-41-2009
12. Assessment of the parameter sensitivity for the ORYZA model at the regional scale - A case study in the Yangtze River Basin Q. Yu et al. https://doi.org/10.1016/j.envsoft.2022.105575
13. Parameter sensitivity analysis of a sea ice melt pond parametrisation and its emulation using neural networks S. Driscoll et al. https://doi.org/10.1016/j.jocs.2024.102231
14. Variance‐Based Global Sensitivity Analysis: A Methodological Framework and Case Study for Microkinetic Modeling B. van den Boorn et al. https://doi.org/10.1002/adts.202200615
15. Retrofitting low-performance units to abate sewer overflow pollution based on mathematical model and Sobol algorithm Z. Zhao et al. https://doi.org/10.1007/s42241-023-0048-5
16. Validation of a microalgal growth model accounting with inorganic carbon and nutrient kinetics for wastewater treatment B. Decostere et al. https://doi.org/10.1016/j.cej.2015.09.111
17. Advancing Diagnostic Model Evaluation to Better Understand Water Shortage Mechanisms in Institutionally Complex River Basins A. Hadjimichael et al. https://doi.org/10.1029/2020WR028079
18. Changes of potential evapotranspiration and its sensitivity across China under future climate scenarios P. Zeng et al. https://doi.org/10.1016/j.atmosres.2021.105763
19. Global Sensitivity Analysis of environmental models: Convergence and validation F. Sarrazin et al. https://doi.org/10.1016/j.envsoft.2016.02.005
20. Seasonal Hydrologic Forecasting: Do Multimodel Ensemble Averages Always Yield Improvements in Forecast Skill? T. Bohn et al. https://doi.org/10.1175/2010JHM1267.1
21. Evaluation of five dry particle deposition parameterizations for incorporation into atmospheric transport models T. Khan & J. Perlinger https://doi.org/10.5194/gmd-10-3861-2017
22. A framework for identifying influential factors in cost-benefit analysis of building-applied photovoltaics systems S. Sharbaf et al. https://doi.org/10.1016/j.apenergy.2025.126542
23. How much can we gain with increasing model complexity with the same model concepts? H. Li et al. https://doi.org/10.1016/j.jhydrol.2015.05.044
24. A Process‐Based Model Integrating Remote Sensing Data for Evaluating Ecosystem Services Z. Niu et al. https://doi.org/10.1029/2020MS002451
25. Evaluation of water quality of Chahnimeh as natural reservoirs from Sistan region in southwestern Iran: a Monte Carlo simulation and Sobol sensitivity assessment H. Kamani et al. https://doi.org/10.1007/s11356-023-26879-5
26. A modified Sobol′ sensitivity analysis method for decision-making in environmental problems A. Ganji et al. https://doi.org/10.1016/j.envsoft.2015.10.001
27. Quantifying the impacts of land cover change on hydrological responses in the Mahanadi river basin in India S. Naha et al. https://doi.org/10.5194/hess-25-6339-2021
28. Sensitivity of permissible aquifer recharge rate to hydrogeological and well design parameters R. Kumar et al. https://doi.org/10.1016/j.jhydrol.2025.133438
29. Parameter sensitivity of a distributed enhanced temperature-index melt model M. Heynen et al. https://doi.org/10.3189/2013AoG63A537
30. Learning from model improvement: On the contribution of complementary data to process understanding F. Fenicia et al. https://doi.org/10.1029/2007WR006386
31. Isolating parameter sensitivity in reach scale transient storage modeling N. Schmadel et al. https://doi.org/10.1016/j.advwatres.2015.12.020
32. Enhancing the performance of machine learning models for simulation of river flows in data-scarce catchments using bootstrapping and physics-based data augmentation J. Murungi et al. https://doi.org/10.1016/j.engappai.2026.115603
33. Assessing global parameters of slope stability model using Earth data observations for forecasting rainfall – induced shallow landslides J. Thomas et al. https://doi.org/10.1016/j.jappgeo.2023.104994
34. Bioaccessibility-integrated probabilistic risk assessment for poly-aromatic hydrocarbons in Chinese soils Y. Zhu et al. https://doi.org/10.1016/j.ecoenv.2026.120143
35. Experimental and Model-Based Analysis to Optimize Microalgal Biomass Productivity in a Pilot-Scale Tubular Photobioreactor T. Weise et al. https://doi.org/10.3389/fbioe.2020.00453
36. Parameter dimensionality reduction of a conceptual model for streamflow prediction in Canadian, snowmelt dominated ungauged basins R. Arsenault et al. https://doi.org/10.1016/j.advwatres.2015.08.014
37. Improving structure identifiability of hydrological processes by temporal sensitivity with a flexible modeling framework L. Zhou et al. https://doi.org/10.1016/j.jhydrol.2022.128843
38. Third-Party Funding in Arbitration: A Game Theory Approach to Analyze Its Impact on the Construction Industry A. Hassan et al. https://doi.org/10.1061/JLADAH.LADR-1514
39. Are we unnecessarily constraining the agility of complex process-based models? P. Mendoza et al. https://doi.org/10.1002/2014WR015820
40. Advances and visions in large-scale hydrological modelling: findings from the 11th Workshop on Large-Scale Hydrological Modelling P. Döll et al. https://doi.org/10.5194/adgeo-18-51-2008
41. A downward structural sensitivity analysis of hydrological models to improve low-flow simulation R. Pushpalatha et al. https://doi.org/10.1016/j.jhydrol.2011.09.034
42. A post audit and inverse modeling in reactive transport: 50years of artificial recharge in the Amsterdam Water Supply Dunes R. Karlsen et al. https://doi.org/10.1016/j.jhydrol.2012.05.019
43. Global sensitivity analysis for urban water quality modelling: Terminology, convergence and comparison of different methods P. Vanrolleghem et al. https://doi.org/10.1016/j.jhydrol.2014.12.056
44. Fugitive methane emissions from full-scale wastewater treatment plants: A plant-wide model A. Ismail et al. https://doi.org/10.1016/j.cej.2026.176290
45. Three complementary methods for sensitivity analysis of a water quality model X. Sun et al. https://doi.org/10.1016/j.envsoft.2012.04.010
46. Parameter sensitivity analysis and optimization for a satellite‐based evapotranspiration model across multiple sites using Moderate Resolution Imaging Spectroradiometer and flux data K. Zhang et al. https://doi.org/10.1002/2016JD025768
47. Global sensitivity analysis of correlated uncertainties in life cycle assessment A. Kim et al. https://doi.org/10.1111/jiec.70036
48. Application of a pseudo simulator to evaluate the sensitivity of parameters in complex watershed models K. Sudheer et al. https://doi.org/10.1016/j.envsoft.2010.07.007
49. Monte Carlo Sensitivity Analysis for a Carbon Capture, Utilization, and Storage Whole-Process System Z. Han et al. https://doi.org/10.3390/pr13051356
50. Sensitivity analysis, calibration and validation of EPIC for modelling soil phosphorus dynamics in Swiss agro-ecosystems R. Della Peruta et al. https://doi.org/10.1016/j.envsoft.2014.08.018
51. On selection of the optimal data time interval for real-time hydrological forecasting J. Liu & D. Han https://doi.org/10.5194/hess-17-3639-2013
52. Assessing Uncertainty in Multicriteria Evaluation of Centralized and Decentralized Dual Water Supply Strategies J. Cole et al. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001572
53. Coupled hydrologic and hydraulic modeling for a lowland river basin in China J. Zhang et al. https://doi.org/10.1016/j.jhydrol.2024.132470
54. Time scale dependent sensitivities of the XinAnJiang model parameters M. Lu & X. Li https://doi.org/10.3178/hrl.8.51
55. A Quantitative Approach to Guiding Target Antigen Selection and Antibody Optimization in Recycling Antibody Discovery H. Ghaffari et al. https://doi.org/10.1208/s12248-025-01144-4
56. How is a global sensitivity analysis of a catchment-scale, distributed pesticide transfer model performed? Application to the PESHMELBA model E. Rouzies et al. https://doi.org/10.5194/gmd-16-3137-2023
57. Integrating UAV and Freely Available Space-Borne Data to Describe Tree Decline Across Semi-arid Mountainous Forests M. Ghasemi et al. https://doi.org/10.1007/s10666-023-09911-3
58. A two-step sensitivity analysis for hydrological signatures in Jinhua River Basin, East China S. Pan et al. https://doi.org/10.1080/02626667.2017.1388917
59. Estimating the Increase in Regional Evaporative Water Consumption as a Result of Vegetation Restoration Over the Loess Plateau, China R. Shao et al. https://doi.org/10.1029/2019JD031295
60. Characterization of watershed model behavior across a hydroclimatic gradient K. van Werkhoven et al. https://doi.org/10.1029/2007WR006271
61. Estimation of soil loss by the WATEM/SEDEM model using an automatic parameter estimation procedure N. Bezak et al. https://doi.org/10.1007/s12665-015-4534-0
62. Enhancement of the SWAT+ model for simulating paddy rice cultivation and irrigation management in agricultural watersheds J. Jeong et al. https://doi.org/10.1016/j.jhydrol.2025.133288
63. Free vibration analysis and multi-objective robust optimization of three-dimensional pyramidal truss core sandwich plates with interval uncertain parameters Y. Chen et al. https://doi.org/10.1016/j.euromechsol.2024.105401
64. An empirical workflow to integrate uncertainty and sensitivity analysis to evaluate agent-based simulation outputs C. Abreu & C. Ralha https://doi.org/10.1016/j.envsoft.2018.06.013
65. Evaluation of an urban drainage system and its resilience using remote sensing and GIS G. Guptha et al. https://doi.org/10.1016/j.rsase.2021.100601
66. Foodweb modeling for polychlorinated biphenyls (PCBs) in the Twelvemile Creek Arm of Lake Hartwell, South Carolina, USA B. Rashleigh et al. https://doi.org/10.1016/j.ecolmodel.2008.09.007
67. Parameter regionalization of a monthly water balance model for the conterminous United States A. Bock et al. https://doi.org/10.5194/hess-20-2861-2016
68. Assessing effects of model complexity and structure on predictions of hydrological responses using serial and parallel model design H. Chien & D. Mackay https://doi.org/10.1002/hyp.13594
69. Sources of uncertainty in modeling the glaciohydrological response of a Karakoram watershed to climate change S. Ragettli et al. https://doi.org/10.1002/wrcr.20450
70. Parameter Sensitivity of a Surface Water Quality Model of the Lower South Saskatchewan River—Comparison Between Ice-On and Ice-Off Periods N. Hosseini et al. https://doi.org/10.1007/s10666-016-9541-3
71. Two improved shuttleworth-wallace models for estimating consecutive daily evapotranspiration J. Zhao et al. https://doi.org/10.1016/j.agrformet.2025.110498
72. Advancing parameter uncertainty quantification in hydrology models through integration of variational inference with a differentiable hydrology framework G. V. Nguyen et al. https://doi.org/10.1007/s00477-025-02954-w
73. Calibration and validation of fatigue lifetime model in complex structures based on multi-level data X. Liu et al. https://doi.org/10.1016/j.ijfatigue.2022.106783
74. Sensitivity and Uncertainty of Ground‐Water Discharge Estimates for Semiarid Shrublands1 J. Zhu & M. Young https://doi.org/10.1111/j.1752-1688.2009.00312.x
75. Parametric sensitivity analysis of precipitation and temperature based on multi-uncertainty quantification methods in the Weather Research and Forecasting model Z. Di et al. https://doi.org/10.1007/s11430-016-9021-6
76. Multiscale evaluation of three remote sensing-based evapotranspiration models across the humid to arid tropics: a study over India K. Athira et al. https://doi.org/10.1080/02626667.2025.2541749
77. Impacts of Climate Change on the Water Quality of a Regulated Prairie River N. Hosseini et al. https://doi.org/10.3390/w9030199
78. How to Tailor My Process‐Based Hydrological Model? Dynamic Identifiability Analysis of Flexible Model Structures T. Pilz et al. https://doi.org/10.1029/2020WR028042
79. A new multimedia contaminant fate model for China: How important are environmental parameters in influencing chemical persistence and long-range transport potential? Y. Zhu et al. https://doi.org/10.1016/j.envint.2014.03.020
80. Quantification of predictive uncertainty with a metamodel: toward more efficient hydrologic simulations V. Tran & J. Kim https://doi.org/10.1007/s00477-019-01703-0
81. A simple and efficient method for global sensitivity analysis based on cumulative distribution functions F. Pianosi & T. Wagener https://doi.org/10.1016/j.envsoft.2015.01.004
82. Model‐based analysis of the influence of catchment properties on hydrologic partitioning across five mountain headwater subcatchments C. Kelleher et al. https://doi.org/10.1002/2014WR016147
83. Socio-hydrologic modeling to understand and mediate the competition for water between agriculture development and environmental health: Murrumbidgee River basin, Australia T. van Emmerik et al. https://doi.org/10.5194/hess-18-4239-2014
84. Quantifying plasticity in simulation models R. Confalonieri et al. https://doi.org/10.1016/j.ecolmodel.2011.11.022
85. A general framework for Dynamic Emulation Modelling in environmental problems A. Castelletti et al. https://doi.org/10.1016/j.envsoft.2012.01.002
86. Modified DNDC model to improve performance of soil temperature simulation under plastic film mulching and snow cover W. Zhao et al. https://doi.org/10.1016/j.compag.2023.108354
87. Calibration of Rainfall-Runoff Model in Urban Watersheds for Stormwater Management Assessment N. Mancipe-Munoz et al. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000382
88. Distribution-based sensitivity analysis from a generic input-output sample F. Pianosi & T. Wagener https://doi.org/10.1016/j.envsoft.2018.07.019
89. Characterization of process-oriented hydrologic model behavior with temporal sensitivity analysis for flash floods in Mediterranean catchments P. Garambois et al. https://doi.org/10.5194/hess-17-2305-2013
90. Sensitivity analysis of effective population size to demographic parameters in house sparrow populations M. Stubberud et al. https://doi.org/10.1111/mec.14057
91. Simulating typhoon-induced storm hydrographs in subtropical mountainous watershed: an integrated 3-layer TOPMODEL J. Huang et al. https://doi.org/10.5194/hess-13-27-2009
92. 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
93. Simulation of layered soil water transport in the semi-arid region based on Hydrus-3D Y. Wang et al. https://doi.org/10.1371/journal.pone.0321537
94. Advancing the calibration of macroscale hydrological models by integrating sensitivity analysis, adaptive pseudo-modeling optimization and multi-site calibration C. Thakur et al. https://doi.org/10.1016/j.jhydrol.2026.135840
95. Assessing parameter importance of the Common Land Model based on qualitative and quantitative sensitivity analysis J. Li et al. https://doi.org/10.5194/hess-17-3279-2013
96. A General Probabilistic Framework for uncertainty and global sensitivity analysis of deterministic models: A hydrological case study G. Baroni & S. Tarantola https://doi.org/10.1016/j.envsoft.2013.09.022
97. Sensitivity analysis of the DeNitrification and Decomposition model for simulating regional carbon budget at the wetland-grassland area on the Zoige Plateau, china J. Wang et al. https://doi.org/10.1007/s11629-015-3520-z
98. Modeling the Compression Stiffness Degradation in Circular Elastomeric Bearings due to Fatigue P. Deng & G. Warn https://doi.org/10.1061/(ASCE)EM.1943-7889.0000947
99. A Matlab toolbox for Global Sensitivity Analysis F. Pianosi et al. https://doi.org/10.1016/j.envsoft.2015.04.009
100. Estimating parameters of the variable infiltration capacity model using ant colony optimization J. Yue et al. https://doi.org/10.2166/wst.2016.282
101. Implementation and sensitivity analysis of the Dam-Reservoir OPeration model (DROP v1.0) over Spain M. Sadki et al. https://doi.org/10.5194/gmd-16-427-2023
102. An efficient integrated approach for global sensitivity analysis of hydrological model parameters C. Zhan et al. https://doi.org/10.1016/j.envsoft.2012.10.009
103. Sensitivity analysis of spatial models L. Lilburne & S. Tarantola https://doi.org/10.1080/13658810802094995
104. Comparison of variance-based and moment-independent global sensitivity analysis approaches by application to the SWAT model F. Khorashadi Zadeh et al. https://doi.org/10.1016/j.envsoft.2017.02.001
105. Characterizing parameter sensitivity and uncertainty for a snow model across hydroclimatic regimes M. He et al. https://doi.org/10.1016/j.advwatres.2010.10.002
106. Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding C. Kelleher et al. https://doi.org/10.5194/hess-21-3325-2017
107. Screening‐Level Sensitivity Analysis for the Design of Pump‐and‐Treat Systems L. Matott https://doi.org/10.1111/j.1745-6592.2011.01375.x
108. Development of a calibration approach using DNDC and PEST for improving estimates of management impacts on water and nutrient dynamics in an agricultural system A. Bhattarai et al. https://doi.org/10.1016/j.envsoft.2022.105494
109. Understanding hydrological flow paths in conceptual catchment models using uncertainty and sensitivity analysis E. Mockler et al. https://doi.org/10.1016/j.cageo.2015.08.015
110. Improvements to the hydrological processes of the Town Energy Balance model (TEB-Veg, SURFEX v7.3) for urban modelling and impact assessment X. Stavropulos-Laffaille et al. https://doi.org/10.5194/gmd-11-4175-2018
111. Impact of temporal data resolution on parameter inference and model identification in conceptual hydrological modeling: Insights from an experimental catchment D. Kavetski et al. https://doi.org/10.1029/2010WR009525
112. Significant variables affecting the performance of concrete panels impacted by wind-borne projectiles: A global sensitivity analysis S. Zamanian et al. https://doi.org/10.1016/j.ijimpeng.2020.103650
113. Variance-based sensitivity analysis of a forest growth model X. Song et al. https://doi.org/10.1016/j.ecolmodel.2012.08.005
114. Parameter sensitivity analysis and optimization of Noah land surface model with field measurements from Huaihe River Basin, China T. Hou et al. https://doi.org/10.1007/s00477-015-1033-5
115. A comprehensive sensitivity and uncertainty analysis for discharge and nitrate-nitrogen loads involving multiple discrete model inputs under future changing conditions C. Schürz et al. https://doi.org/10.5194/hess-23-1211-2019
116. Stepwise sensitivity analysis from qualitative to quantitative: Application to the terrestrial hydrological modeling of a Conjunctive Surface‐Subsurface Process (CSSP) land surface model Y. Gan et al. https://doi.org/10.1002/2014MS000406
117. An Improved Approach for Estimating Pan Evaporation Using a New Aerodynamic Mechanism Model G. Qiu et al. https://doi.org/10.1029/2020WR027870
118. Assimilation of AMSR-E snow water equivalent data in a spatially-lumped snow model D. Dziubanski & K. Franz https://doi.org/10.1016/j.jhydrol.2016.05.046
119. Numerical model of the irradiance field surrounding a UV disinfection robot L. De Matteis et al. https://doi.org/10.1088/2057-1976/ac7e6e
120. Innovative Digital Stochastic Methods for Multidimensional Sensitivity Analysis in Air Pollution Modelling V. Todorov & I. Dimov https://doi.org/10.3390/math10122146
121. Modeling pH-dependent growth and photosynthetic activity of turbidostatically cultivated microalgae L. Ringleben & M. Pfaff https://doi.org/10.1016/j.algal.2026.104728
122. Unraveling the hydrology of a Himalayan catchment through integration of high resolution in situ data and remote sensing with an advanced simulation model S. Ragettli et al. https://doi.org/10.1016/j.advwatres.2015.01.013
123. Evaluation of TOPLATS on three Mediterranean catchments J. Loizu et al. https://doi.org/10.1016/j.jhydrol.2016.05.025
124. A Bayesian-based multilevel factorial analysis method for analyzing parameter uncertainty of hydrological model Y. Liu et al. https://doi.org/10.1016/j.jhydrol.2017.08.048
125. Future potential evapotranspiration changes and contribution analysis in Zhejiang Province, East China Y. Xu et al. https://doi.org/10.1002/2013JD021245
126. Technical Note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models J. Herman et al. https://doi.org/10.5194/hess-17-2893-2013
127. Improving multi-objective reservoir operation optimization with sensitivity-informed dimension reduction J. Chu et al. https://doi.org/10.5194/hess-19-3557-2015
128. An approach for global sensitivity analysis of a complex environmental model to spatial inputs and parameters: A case study of an agro-hydrological model P. Moreau et al. https://doi.org/10.1016/j.envsoft.2013.04.006
129. MODIS-Based Potential Evapotranspiration Demand Curves for the Sacramento Soil Moisture Accounting Model A. Bowman et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001261
130. Sensitivity analysis and estimation using a hierarchical Bayesian method for the parameters of the FvCB biochemical photosynthetic model T. Han et al. https://doi.org/10.1007/s11120-019-00684-z
131. Modeling farmer behavior in adopting agroecological practices using AI tools in shea butter agroforestry systems D. Gbemavo et al. https://doi.org/10.1016/j.heliyon.2024.e40600
132. Modified calibration strategies and parameter regionalization potential for streamflow estimation using a hydrological model S. Guniganti et al. https://doi.org/10.1080/02626667.2024.2335294
133. Uncertainty, sensitivity analysis and the role of data based mechanistic modeling in hydrology M. Ratto et al. https://doi.org/10.5194/hess-11-1249-2007
134. Integration of Remote Sensing Evapotranspiration into Multi-Objective Calibration of Distributed Hydrology–Soil–Vegetation Model (DHSVM) in a Humid Region of China S. Pan et al. https://doi.org/10.3390/w10121841
135. Importance estimation of hyperparameters in reinforcement learning D. Weller & M. Moll https://doi.org/10.1016/j.neucom.2025.131770
136. 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
137. The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support S. Razavi et al. https://doi.org/10.1016/j.envsoft.2020.104954
138. An increasing trend in the ratio of transpiration to total terrestrial evapotranspiration in China from 1982 to 2015 caused by greening and warming Z. Niu et al. https://doi.org/10.1016/j.agrformet.2019.107701
139. Stability mechanism and control of the pumpable supports in longwall recovery room D. Zhang et al. https://doi.org/10.1016/j.ijmst.2024.07.006
140. Global sensitivity analysis for a prediction model of soil solute transfer into surface runoff C. Huang et al. https://doi.org/10.1016/j.jhydrol.2021.126342
141. Many-objective de Novo water supply portfolio planning under deep uncertainty J. Kasprzyk et al. https://doi.org/10.1016/j.envsoft.2011.04.003
142. Developing a parsimonious canopy model (PCM v1.0) to predict forest gross primary productivity and leaf area index of deciduous broad-leaved forest B. Bahrami et al. https://doi.org/10.5194/gmd-15-6957-2022
143. Global sensitivity analysis of a model related to memory formation in synapses: Model reduction based on epistemic parameter uncertainties and related issues D. Kulasiri et al. https://doi.org/10.1016/j.jtbi.2017.02.003
144. Evaluation of a new observationally based channel parameterization for the National Water Model A. Heldmyer et al. https://doi.org/10.5194/hess-26-6121-2022
145. Climate change expected to amplify land-use change impacts on nitrogen export from a subtropical catchment in China S. Jiang et al. https://doi.org/10.1016/j.jhydrol.2025.134888
146. An approach to measure parameter sensitivity in watershed hydrological modelling T. Ranatunga et al. https://doi.org/10.1080/02626667.2016.1174335
147. Now or later? Exploring the potential of step-by-step passive retrofitting approach in buildings S. Sharbaf et al. https://doi.org/10.1016/j.jobe.2026.115448
148. Impact of input forcings variability on the global sensitivity analysis of a hydrological model K. Radišić et al. https://doi.org/10.1016/j.envsoft.2025.106522
149. Regional analysis of parameter sensitivity for simulation of streamflow and hydrological fingerprints S. Höllering et al. https://doi.org/10.5194/hess-22-203-2018
150. Sobol′’s sensitivity analysis for a distributed hydrological model of Yichun River Basin, China C. Zhang et al. https://doi.org/10.1016/j.jhydrol.2012.12.005
151. Sensitivity analysis and calibration of a dynamic physically based slope stability model T. Zieher et al. https://doi.org/10.5194/nhess-17-971-2017
152. Modeling and uncertainty analysis of carbon and water fluxes in a broad-leaved Korean pine mixed forest based on model-data fusion X. Ren et al. https://doi.org/10.1016/j.ecolmodel.2018.03.013
153. Auswahl der Modellkomplexität für die Beschreibung der Sauerstoffverhältnisse in Fließgewässern und Poldern während Hochwasserereignissen I. Pech et al. https://doi.org/10.1007/s00506-008-0020-1
154. A systematic assessment and reduction of parametric uncertainties for a distributed hydrological model Y. Gan et al. https://doi.org/10.1016/j.jhydrol.2018.07.055
155. Identification of the Controlling Mechanism for Predicting Critical Loads in Elastomeric Bearings X. Han et al. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000811
156. Use of a scenario-neutral approach to identify the key hydro-meteorological attributes that impact runoff from a natural catchment D. Guo et al. https://doi.org/10.1016/j.jhydrol.2017.09.021
157. Application of distributed hydrological models for predictions in ungauged basins: a method to quantify predictive uncertainty R. Cibin et al. https://doi.org/10.1002/hyp.9721
158. Temporal Scale‐Dependent Sensitivity Analysis for Hydrological Model Parameters Using the Discrete Wavelet Transform and Active Subspaces D. Bittner et al. https://doi.org/10.1029/2020WR028511
159. Lumped parameter sensitivity analysis of a distributed hydrological model within tropical and temperate catchments L. Cuo et al. https://doi.org/10.1002/hyp.8017
160. Variance Based Sensitivity Analysis of FLake Lake Model for Global Land Surface Modeling A. Bernus et al. https://doi.org/10.1029/2019JD031928
161. Identifiability of transient storage model parameters along a mountain stream C. Kelleher et al. https://doi.org/10.1002/wrcr.20413
162. Parameter Analysis and Estimates for the MODIS Evapotranspiration Algorithm and Multiscale Verification K. Zhang et al. https://doi.org/10.1029/2018WR023485
163. Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework M. Raleigh et al. https://doi.org/10.5194/hess-19-3153-2015
164. Transferability of a Conceptual Hydrological Model across Different Temporal Scales and Basin Sizes S. Sheng et al. https://doi.org/10.1007/s11269-020-02594-5
165. Practical Use of Computationally Frugal Model Analysis Methods M. Hill et al. https://doi.org/10.1111/gwat.12330
166. Data-Driven Sensitivity Analysis for Static Mechanical Properties of Additively Manufactured Ti–6Al–4V A. Sharma et al. https://doi.org/10.1115/1.4051799
167. Proposal for the Introduction of the Spatial Perspective in the Application of Global Sensitivity Analysis W. Plata-Rocha et al. https://doi.org/10.4236/jgis.2012.46055
168. On the accuracy requirement of surrogate models for adequate global sensitivity analysis of urban low-impact development model K. Yi et al. https://doi.org/10.1016/j.jhydrol.2025.133102
169. Exploring the potential for parameter transfer from daily to hourly time step in the HYPE model for Sweden D. Fuentes-Andino et al. https://doi.org/10.1080/02626667.2022.2121165
170. Diagnostic Framework for Evaluating How Parametric Uncertainty Influences Agro‐Hydrologic Model Projections of Crop Yields Under Climate Change T. Karimi et al. https://doi.org/10.1029/2021WR031249
171. Sensitivity analysis and auto-calibration of ORYZA2000 using simulation-optimization framework B. Soundharajan & K. Sudheer https://doi.org/10.1007/s10333-011-0293-z
172. DGSA: A Matlab toolbox for distance-based generalized sensitivity analysis of geoscientific computer experiments J. Park et al. https://doi.org/10.1016/j.cageo.2016.08.021
173. Estimation of daily net radiation based on the continuous meteorological data observed from different underlying surfaces R. Bao et al. https://doi.org/10.1007/s00382-026-08115-1
174. A comprehensive assessment and comparison of the impacts of storage parameters on solute transport in streams using a novel framework S. Khodambashi Emami & M. Mazaheri https://doi.org/10.1016/j.jhydrol.2024.130873
175. Potential evapotranspiration changes in Lancang River Basin and Yarlung Zangbo River Basin, southwest China D. Ma et al. https://doi.org/10.1080/02626667.2018.1524147
176. Modelling bidirectional modulations in synaptic plasticity: A biochemical pathway model to understand the emergence of long term potentiation (LTP) and long term depression (LTD) Y. He et al. https://doi.org/10.1016/j.jtbi.2016.05.015
177. Bioenergetic and water quality modeling for eutrophication assessment of El Pañe Reservoir, Peru A. Mamani Larico et al. https://doi.org/10.1016/j.ecohyd.2020.08.004
178. Exploring snow model parameter sensitivity using Sobol' variance decomposition E. Houle et al. https://doi.org/10.1016/j.envsoft.2016.11.024
179. Data-driven multi-objective affective product design integrating three-dimensional form and color Z. Wang et al. https://doi.org/10.1007/s00521-022-07232-2
180. Hydrological Simulation and Parameter Optimization Based on the Distributed Xin’anjiang Model and the Particle Swarm Optimization Algorithm: A Case Study of Xunhe Watershed in Shandong, China Z. Wang et al. https://doi.org/10.3390/w16223168
181. Assessing the predictive impact of factor fixing with an adaptive uncertainty-based approach Q. Wang et al. https://doi.org/10.1016/j.envsoft.2021.105290
182. Sensitivity-guided reduction of parametric dimensionality for multi-objective calibration of watershed models K. van Werkhoven et al. https://doi.org/10.1016/j.advwatres.2009.03.002
183. Evaluation of parameter interaction effect of hydrological models using the sparse polynomial chaos (SPC) method H. Wang et al. https://doi.org/10.1016/j.envsoft.2019.104612
184. Flood hazard mapping for data-scarce and ungauged coastal river basins using advanced hydrodynamic models, high temporal-spatial resolution remote sensing precipitation data, and satellite imageries M. Trinh & F. Molkenthin https://doi.org/10.1007/s11069-021-04843-1
185. A source-oriented precise risk assessment of trace metal(loid)s in sediments of the Yangtze River Basin K. Pang et al. https://doi.org/10.1016/j.envpol.2025.126896
186. Improved Understanding of River Ice Processes Using Global Sensitivity Analysis Approaches R. Sheikholeslami et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001574
187. A non-stationary cost-benefit analysis approach for extreme flood estimation to explore the nexus of ‘Risk, Cost and Non-stationarity’ W. Qi https://doi.org/10.1016/j.jhydrol.2017.09.009
188. Increasing parameter identifiability through clustered time-varying sensitivity analysis L. Wang et al. https://doi.org/10.1016/j.envsoft.2024.106189
189. Drainmod-urban for bioretention hydrology: a comprehensive sensitivity analysis G. Diab et al. https://doi.org/10.1016/j.jhydrol.2025.134138
190. Decoding parameter sensitivities across scales: a Wavelet–Sobol framework for hydrological modelling V. Sai Suswara et al. https://doi.org/10.1016/j.jhydrol.2026.135482
191. Global Sensitivity of Penman–Monteith Reference Evapotranspiration to Climatic Variables in Mato Grosso, Brazil M. Sabino & A. de Souza https://doi.org/10.3390/earth4030038
192. Quantifying uncertainty in urban flooding analysis considering hydro-climatic projection and urban development effects I. Jung et al. https://doi.org/10.5194/hess-15-617-2011
193. Sensitivity and Interaction Analysis Based on Sobol’ Method and Its Application in a Distributed Flood Forecasting Model H. Wan et al. https://doi.org/10.3390/w7062924
194. Evaluating the fate of six common pharmaceuticals using a reactive transport model: Insights from a stream tracer test J. Riml et al. https://doi.org/10.1016/j.scitotenv.2013.03.077
195. Local and global sensitivity analysis for a prediction model of nitrogen loss in Southern China’s paddy fields via HYDRUS-1D S. Wang et al. https://doi.org/10.1038/s41598-025-09858-3
196. Sobol's sensitivity analysis for a fuel cell stack assembly model with the aid of structure-selection techniques W. Zhang et al. https://doi.org/10.1016/j.jpowsour.2015.08.076
197. How can expert knowledge increase the realism of conceptual hydrological models? A case study based on the concept of dominant runoff process in the Swiss Pre-Alps M. Antonetti & M. Zappa https://doi.org/10.5194/hess-22-4425-2018
198. Environmental risk of dissolved oxygen depletion of diverted flood waters in river polder systems – A quasi-2D flood modelling approach K. Lindenschmidt et al. https://doi.org/10.1016/j.scitotenv.2008.11.024
199. Evaluating Hydrologic Behavior of Hydrology Simulation using Time Area (HYSTAR) Model through Sensitivity Analysis Y. Her https://doi.org/10.5389/KSAE.2015.57.3.041
200. Parameters Sensitivity Analysis of the Xin’anjiang Model under Different Temporal and Spatial Scales 晟. 盛 https://doi.org/10.12677/JWRR.2018.76061
201. Evaluation of statistical models for forecast errors from the HBV model K. Engeland et al. https://doi.org/10.1016/j.jhydrol.2010.01.018
202. Diagnostic Assessment of Search Controls and Failure Modes in Many-Objective Evolutionary Optimization D. Hadka & P. Reed https://doi.org/10.1162/EVCO_a_00053
203. Understanding catchment behavior through stepwise model concept improvement F. Fenicia et al. https://doi.org/10.1029/2006WR005563
204. A primer on thermodynamic-based models for deciphering transcriptional regulatory logic J. Dresch et al. https://doi.org/10.1016/j.bbagrm.2013.04.011
205. Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis J. Yang https://doi.org/10.1016/j.envsoft.2010.10.007
206. Inferring model structural deficits by analyzing temporal dynamics of model performance and parameter sensitivity D. Reusser & E. Zehe https://doi.org/10.1029/2010WR009946
207. Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models O. Rakovec et al. https://doi.org/10.1002/2013WR014063
208. Historical pan evaporation changes in the Qiantang River Basin, East China Y. Xu et al. https://doi.org/10.1002/joc.4470
209. Development and application of a hybrid artificial neural network model for simulating future stream flows in catchments with limited in situ observed data S. Mugume et al. https://doi.org/10.2166/hydro.2024.066
210. Epistemic uncertainties and natural hazard risk assessment – Part 2: What should constitute good practice? K. Beven et al. https://doi.org/10.5194/nhess-18-2769-2018
211. Modeling risk analysis for forecasting peak discharge during flooding prevention and warning operation S. Wu et al. https://doi.org/10.1007/s00477-010-0436-6
212. Robust high-dimensional screening A. Kim et al. https://doi.org/10.1016/j.envsoft.2021.105270
213. Modeling the relationship between air temperature and grapefruit quality traits C. Zhang et al. https://doi.org/10.1002/jsfa.12219
214. Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications X. Song et al. https://doi.org/10.1016/j.jhydrol.2015.02.013
215. Synergy between satellite observations of soil moisture and water storage anomalies for runoff estimation S. Camici et al. https://doi.org/10.5194/gmd-15-6935-2022
216. Fuzzy-Based Model for Predicting Failure of Oil Pipelines A. Senouci et al. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000181
217. Human Health Implication of Major and Trace Elements Present in Commercial Crustaceans of a Traditional Seafood Marketing Region, Egypt G. El-Said et al. https://doi.org/10.1007/s12011-020-02126-7
218. Contribution of the satellite-data driven snow routine to a karst hydrological model S. Çallı et al. https://doi.org/10.1016/j.jhydrol.2022.127511
219. Identifying key sources of uncertainty in the modelling of greenhouse gas emissions from wastewater treatment C. Sweetapple et al. https://doi.org/10.1016/j.watres.2013.05.021
220. What drives uncertainty surrounding riverine flood risks? I. Hosseini-Shakib et al. https://doi.org/10.1016/j.jhydrol.2024.131055
221. Analysis of the behavior of a rainfall–runoff model using three global sensitivity analysis methods evaluated at different temporal scales C. Massmann & H. Holzmann https://doi.org/10.1016/j.jhydrol.2012.09.026
222. Evaluation of the Parameter Sensitivities of a Coupled Land Surface Hydrologic Model at a Critical Zone Observatory Y. Shi et al. https://doi.org/10.1175/JHM-D-12-0177.1
223. Sensitivity and identifiability of stream flow generation parameters of the SWAT model R. Cibin et al. https://doi.org/10.1002/hyp.7568
224. Time‐varying sensitivity analysis clarifies the effects of watershed model formulation on model behavior J. Herman et al. https://doi.org/10.1002/wrcr.20124
225. Modeling Soil Organic Carbon Changes under Alternative Climatic Scenarios and Soil Properties Using DNDC Model at a Semi-Arid Mediterranean Environment I. Lembaid et al. https://doi.org/10.3390/cli10020023
226. Identification of key controlling factors of groundwater chemistry and probabilistic health risk assessment using kernel density estimation X. Wang et al. https://doi.org/10.1016/j.ecoenv.2026.120270
227. Sobol’ sensitivity analysis of a complex environmental model J. Nossent et al. https://doi.org/10.1016/j.envsoft.2011.08.010
228. Iterative integration of deep learning in hybrid Earth surface system modelling M. Chen et al. https://doi.org/10.1038/s43017-023-00452-7
229. Valuing carbon assets for high-tech with application to the wind energy industry L. Han et al. https://doi.org/10.1016/j.enpol.2015.09.025
230. Deep Uncertainties in Sea‐Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management P. Oddo et al. https://doi.org/10.1111/risa.12888
231. Sobol sensitivity approach for the appraisal of geomedical health risks associated with oral intake and dermal pathways of groundwater fluoride in a semi-arid region of south India D. Karunanidhi et al. https://doi.org/10.1016/j.ecoenv.2020.110438
232. Parameter sensitivity and inversion errors in a leaky aquifer system: the impact of neglecting aquitard nonlinear consolidation X. Meng et al. https://doi.org/10.1016/j.advwatres.2026.105423
233. Cloud computing for integrated stochastic groundwater uncertainty analysis Y. Liu et al. https://doi.org/10.1080/17538947.2012.687778
234. Evaluation of global sensitivity analysis methods for computational structural mechanics problems C. Crusenberry et al. https://doi.org/10.1017/dce.2023.23
235. Perceptions of how occupants adopt water conservation behaviors under psychosocial processes: A complementary dual-stage SEM-ANN perspective S. Shahangian et al. https://doi.org/10.1016/j.scs.2024.105354
236. Local and global sensitivity analyses on the overflow probability of Abadia de Goiás radioactive near surface repository D. Teixeira et al. https://doi.org/10.1016/j.nucengdes.2025.114090
237. Evaluation of Sentinel-1, SMAP and SMOS surface soil moisture products for distributed eco-hydrological modelling in Mediterranean forest basins J. Gomis-Cebolla et al. https://doi.org/10.1016/j.jhydrol.2022.127569
238. A novel linear spectral unmixing-based method for tree decline monitoring by fusing UAV-RGB and optical space-borne data M. Ghasemi et al. https://doi.org/10.1080/01431161.2024.2305630
239. Improving risk assessment in financial feasibility of international engineering projects: A risk driver perspective J. Liu et al. https://doi.org/10.1016/j.ijproman.2016.11.004
240. Sensitivity of Simulating Hydrologic Processes to Gauge and Radar Rainfall Data in Subtropical Coastal Catchments J. Diaz-Ramirez et al. https://doi.org/10.1007/s11269-012-0088-z
241. About the trustworthiness of physics-based machine learning – considerations for geomechanical applications D. Degen et al. https://doi.org/10.5194/se-16-477-2025
242. An analysis of parameter sensitivities of preference-inspired co-evolutionary algorithms R. Wang et al. https://doi.org/10.1080/00207721.2015.1008600
243. Vegetation controls on surface energy partitioning and water budget over China X. Lan et al. https://doi.org/10.1016/j.jhydrol.2020.125646
244. Inaction and climate stabilization uncertainties lead to severe economic risks M. Butler et al. https://doi.org/10.1007/s10584-014-1283-0
245. Towards More Reliable Aircraft Emission Inventories for Local Air Quality Assessment K. Sanajou & O. Tchepel https://doi.org/10.3390/aerospace13010088
246. Global sensitivity analysis for an integrated model for simulation of nitrogen dynamics under the irrigation with treated wastewater H. Sun et al. https://doi.org/10.1007/s11356-015-4860-5
247. A dynamic parameter identification method for the 5-DOF hybrid robot based on sensitivity analysis Z. Luo et al. https://doi.org/10.1108/IR-08-2023-0178
248. Multi-method global sensitivity analysis of flood inundation models F. Pappenberger et al. https://doi.org/10.1016/j.advwatres.2007.04.009
249. In-depth sensitivity analysis of heating demand and overheating in Dutch terraced houses using interpretable machine learning A. Cvetkov-Iliev et al. https://doi.org/10.1016/j.enbuild.2025.115611
250. Identifying parametric controls and dependencies in integrated assessment models using global sensitivity analysis M. Butler et al. https://doi.org/10.1016/j.envsoft.2014.05.001
251. Time-dependent global sensitivity analysis of the C-RIVE biogeochemical model in contrasted hydrological and trophic contexts S. Wang et al. https://doi.org/10.1016/j.watres.2018.07.033
252. Sensitivity of potential evapotranspiration to changes in climate variables for different Australian climatic zones D. Guo et al. https://doi.org/10.5194/hess-21-2107-2017
253. A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model Y. Gan et al. https://doi.org/10.1016/j.envsoft.2013.09.031
254. Analysis of parameter sensitivity on surface heat exchange in the Noah land surface model at a temperate desert steppe site in China G. Zhang et al. https://doi.org/10.1007/s13351-017-7050-1
255. Emulation techniques for the reduction and sensitivity analysis of complex environmental models M. Ratto et al. https://doi.org/10.1016/j.envsoft.2011.11.003
256. Identification of the dominant hydrological process and appropriate model structure of a karst catchment through stepwise simplification of a complex conceptual model Y. Chang et al. https://doi.org/10.1016/j.jhydrol.2017.02.050
257. Uncertainty analysis of modeled carbon and water fluxes in a subtropical coniferous plantation X. Ren et al. https://doi.org/10.1002/2013JG002402
258. Gross primary production-coupled evapotranspiration in the global arid and semi-arid regions based on the NIRv index Y. Su et al. https://doi.org/10.1016/j.jhydrol.2024.132012
259. Sensitivity Analysis‐Based Automatic Parameter Calibration of the VIC Model for Streamflow Simulations Over China J. Gou et al. https://doi.org/10.1029/2019WR025968
260. Refined River Discharge Estimation Through Integrating Multisource Satellite Remote Sensing and Aerial Photogrammetry H. Long et al. https://doi.org/10.1109/JSTARS.2025.3641233