50 citations as recorded by crossref.

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4. Stochastic inversion for soil hydraulic parameters in the presence of model error: An example involving ground-penetrating radar monitoring of infiltration C. Köpke et al. 10.1016/j.jhydrol.2018.12.016
5. On the use of distribution-adaptive likelihood functions: Generalized and universal likelihood functions, scoring rules and multi-criteria ranking J. Vrugt et al. 10.1016/j.jhydrol.2022.128542
6. Using maximum likelihood to derive various distance-based goodness-of-fit indicators for hydrologic modeling assessment Q. Cheng et al. 10.1007/s00477-017-1507-8
7. Uncertainty quantification in watershed hydrology: Which method to use? A. Gupta & R. Govindaraju 10.1016/j.jhydrol.2022.128749
8. Comparison of GLUE and DREAM for the estimation of cultivar parameters in the APSIM-maize model M. Sheng et al. 10.1016/j.agrformet.2019.107659
9. Estimating the parameters of a monthly hydrological model using hydrological signatures A. Matos et al. 10.1590/2318-0331.292420230121
10. Relative model score: a scoring rule for evaluating ensemble simulations with application to microbial soil respiration modeling A. Elshall et al. 10.1007/s00477-018-1592-3
11. Revising tortuosity and multi-fractal assumptions of unsaturated hydraulic conductivity from critical path analysis of percolation theory A. Rad et al. 10.1016/j.geoderma.2019.06.002
12. Robust and efficient uncertainty quantification for extreme events that deviate significantly from the training dataset using polynomial chaos-kriging V. Tran & J. Kim 10.1016/j.jhydrol.2022.127716
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15. A game theoretical low impact development optimization model for urban storm water management M. Latifi et al. 10.1016/j.jclepro.2019.118323
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17. Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale G. Papacharalampous et al. 10.1016/j.advwatres.2019.103470
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19. Multi-objective conflict resolution optimization model for reservoir’s selective depth water withdrawal considering water quality M. Haghighat et al. 10.1007/s11356-020-10475-y
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21. Copula‐Based Convection‐Permitting Projections of Future Changes in Multivariate Drought Characteristics B. Zhang et al. 10.1029/2019JD030686
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36. Improving probabilistic hydroclimatic projections through high-resolution convection-permitting climate modeling and Markov chain Monte Carlo simulations S. Wang & Y. Wang 10.1007/s00382-019-04702-7
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38. Embracing equifinality with efficiency: Limits of Acceptability sampling using the DREAM(LOA) algorithm J. Vrugt & K. Beven 10.1016/j.jhydrol.2018.02.026
39. Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre‐ and Post‐Processing in Sequential Data Assimilation S. Wang et al. 10.1002/2018WR022546
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41. Parameter estimation for complex thermal-fluid flows using approximate Bayesian computation J. Christopher et al. 10.1103/PhysRevFluids.3.104602
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45. Considering rating curve uncertainty in water level predictions A. Sikorska et al. 10.5194/hess-17-4415-2013
46. Stochastic inversion for soil hydraulic parameters in the presence of model error: An example involving ground-penetrating radar monitoring of infiltration C. Köpke et al. 10.1016/j.jhydrol.2018.12.016
47. An Improved Bayesian Structural Identification Using the First Two Derivatives of Log-Likelihood Measure J. Zhou et al. 10.1155/2015/236475
48. Estimating hydrologic model uncertainty in the presence of complex residual error structures S. Samadi et al. 10.1007/s00477-017-1489-6
49. Experimental Coupling of TOPMODEL with the National Water Model: Effects of Coupling Interface Complexity on Model Performance D. Kim et al. 10.1111/1752-1688.12953
50. Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation J. Vrugt 10.1016/j.envsoft.2015.08.013