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First author names with an underscore indicate the work of a (co-advised) student

126. A. Wright, J.A. Vrugt, J. Walker, D. Robertson, and V. Pauwels (2016), Estimating temporal rainfall and model parameter distributions using model data reduction and inversion techniques, Water Resources Research, XX, XX–XX, doi:10.1002/wrcr.XXXX, In Review.
125. A. Wright, J.A. Vrugt, J. Walker, D. Robertson, and V. Pauwels (2016), A comparison of the discrete cosine and wavelet transforms for hydrologic model input data reduction, Hydrology and Earth System Sciences, XX, XX–XX, doi:10.5194/hess-2016-XXX, In Review.
124. M. Naeini, J.A. Vrugt, G.J.C> Gomes, and M. Sadegh (2016), Scaling and regionalization of flow duration curve across the contiguous United States, Advances in Water Resources, XX, XX–XX, doi:10.1016/j.advwatres.2016.XX.XXX, In Review.
123. E. Massoud, C. Xu, R. Fisher, N. McDowell, and J.A. Vrugt (2016), Global sensitivity analysis of the Community Land Model with Ecosystem Demography, CLM4.5(ED), Journal of Geophysical Research: Biogeosciences, XX, XX–XX, doi:10.1002/2016JG00XXXX, In Review.
122. E. Massoud, J. Huisman, and E. Benincà, W. Bouten, and J.A. Vrugt (2016), Predicting the unpredictable: data assimilation improves predictability of complex dynamics in ecosystems, Ecology Letters, XX, XX–XX, doi:10.1111/ele.XXXXX, In Review.
121. E. Massoud, A.J. Purdy, M. Miro, S. Hallerback, J.S. Famiglietti, and J.A. Vrugt (2016), Groundwater sustainability in California’s Central Valley – An empirical approach to estimate and project groundwater depletion and recharge, Journal of the American Water Resources Association, XX, XX–XX, doi:10.1111/1752-XXXX, In Review.
120. E. Volpi, J.A. Vrugt, G. Firmani, and G. Schoups (2016), Bayesian model selection with DREAM: Multi-dimensional integration of the evidence, Water Resources Research, XX, XX–XX, doi:10.1002/wrcr.XXXX, In Review.
119. E.A Parker, S.B. Grant, M.A. Rippy, A. Mehring, B. Winfrey, J.A. Vrugt, B. E. Hatt, M. Azizian, E. Gomez, and C. Patel (2016), Residence time matters: The impact of plants on the removal of fecal indicator bacteria in biofilters, Environmental Science and Technology, XX, XX–XX, doi:10.1021/acs.est.5b0XXXX, In Review.
118. G.J.C. Gomes, J.A. Vrugt, E.A. Vargas Jr., J.T. Camargo, Q. Velloso, and M.Th. van Genuchten (2016), The coordinated impact of soil hydraulic and bedrock depth uncertainty on the stability of variably saturated hillslopes, Computers and Geotechnics, XX, XX–XX, doi:10.1016/j.compgeo.2016.02.006, In Review.
117. H.J. Zhang, H.J. Hendricks Franssen, X.J. Han, J.A. Vrugt, and H. Vereecken (2016), Evaluation of state-parameter estimation with EnKF and PF for two LSMs at the TERENO-site Rollesbroich, Germany, Hydrology and Earth System Sciences, XX, XX–XX, doi:10.5194/hess-XX-XXXX-2016, In Review.
116. H. Post, J.A. Vrugt (2016), R. Baatz, H. Vereecken, and H.J. Hendricks-Franssen (2016), Estimation of community land model parameters for an improved assessment of net carbon fluxes at European sites, Journal of Geophysical Research – Biogeosciences, XX, XX–XX, doi:10.1002/2015jg003297, In Review.
115. J.A. Vrugt, and K.J. Beven (2016), To be coherently incoherent: GLUE limits of acceptability with DREAM, Journal of Hydrology, XX, XX–XX, doi:10.1016/j.jhydrol.2015.XX.XXX, In Review.
114. V. Yildiz, and J.A. Vrugt (2016), Run-of-river hydropower plants: HYPER numerical model, turbines, energy production, economic analysis, and global optimization, Environmental Modeling & Software, XX, XX–XX, doi:envsoft-d-15-00306, In Press.
113. C. Brunetti, N. Linde, and J.A. Vrugt (2016), Prediction of flood discharges using non-stationary extreme value analysis: Application, assessment, and recommendations, Water Resources Research, XX, XX–XX, doi:10.1002/wrcr.XXXX, In Press.
112. C. Brunetti, N. Linde, and J.A. Vrugt (2016), Bayesian model selection in geophysics: Application to conceptual subsurface models of the South Oyster Bacterial Transport Site, Advances in Water Resources, XX, XX–XX, doi:10.1016/j.advwatres.2016.XX.XXX, In Press.
111. H. Vereecken, A. Schnepf, J.W. Hopmans, M. Javaux, D. Or, T. Roose. J. Vanderborght, M. Young, W. Amelung, M. Aitkenhead, S. Allison, S. Assouline, P. Baveye, M. Berli, N. Brüggemann, P. Finke, M. Flury, T. Gaiser, G. Govers, T. Ghezzehei, P. Hallett, H.J. Hendricks-Franssen, J. Heppel, R. Horn, J.A. Huisman, D. Jacques, F. Jonard, S. Kollet, F. Lafolie, K. Lamorski, D. Leitner, A. McBratney, B. Minasny, C. Montzka, W. Nowak, Y. Pachepsky, J. Padarian, N. Romano, K. Roth, Y. Rothfuss, E.C. Rowe, A. Schwen, J. Šimůnek, J. van Dam, S.E.A.T.M. van der Zee, H.J. Vogel, J.A. Vrugt, T. Wöhling, and I. Young (2016), Modelling soil processes: Key challenges and new perspectives, Vadose Zone Journal, 15 (5), 1-57, doi:10.2136/vzj2015.09.0131.
110. H. Qin , X. Xie, J.A. Vrugt, K. Zeng, and G. Hong (2016), Underground structure defect detection and reconstruction using crosshole GPR and Bayesian waveform inversion, Automation in Construction, 68, 156-169, doi:10.1016/j.autcon.2016.03.011.
109. G.J.C. Gomes, J.A. Vrugt, and E.A. Vargas Jr. (2016), Towards improved prediction of the bedrock depth underneath hillslopes: Bayesian inference of the bottom-up control hypothesis using high-resolution topographic data, Water Resources Research, 52, 3085-3112, doi:10.1002/2015WR018147.
108. A.A. Ali, Xu, A. Rogers, R.A. Fisher, S.D. Wullschleger, E.C. Massoud, J.A. Vrugt, J.D. Muss, N.G. McDowell, J.B. Fisher, P.B. Reich, and C.J. Wilson (2016), A global scale mechanistic model of the photosynthetic capacity (LUNA V1.0), Geoscientific Model Development, 9, 587-606, doi:10.5194/gmd-9-587-2016.
107. M. Sadegh, J.A. Vrugt, H.V. Gupta, and C. Xu (2016), The soil water characteristic as new class of closed-form parametric expressions for the flow duration curve, Journal of Hydrology, 535, 438-456, doi:10.1016/j.jhydrol.2016.01.027.
106. J.A. Vrugt (2016), Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB Implementation, Environmental Modeling & Software, 75, 273–316, doi:10.1016/j.envsoft.2015.08.013.
105. M. Sadegh, J.A. Vrugt, C. Xu, and E. Volpi (2015), The stationarity paradigm revisited: Hypothesis testing using diagnostics, summary metrics, and DREAM(ABC), Water Resources Research, 51, 9207-9231, doi:10.1002/2014WR016805.
104. T. Skaggs, M.H. Young, and J.A. Vrugt (2015), Reproducible research in vadose zone sciences, Vadose Zone Journal, 10, 1–5, doi:10.2136/vzj2015.06.0088.
103. F.C. Sperna Weiland, J.A. Vrugt, R.L.P.H. van Beek, A.H. Weerts, and M.F.P. Bierkens (2015), Significant uncertainty in global scale hydrological modeling from precipitation data errors, Journal of Hydrology, 529 (3), 1095-1115, doi:10.1016/j.jhydrol.2015.08.061.
102. C.P. Kikuchi, T.P.A. Ferré, and J.A. Vrugt (2015), On the optimal design of experiments for conceptual and predictive discrimination of hydrologic system models, Water Resources Research, 51, 4454–4481, doi:10.1002/2014WR016795.
101. A. Askarizadeh, M.A. Rippy, T.D. Fletcher, D. Feldman, J. Peng, P. Bowler, A. Mehring, B. Winfrey, J.A. Vrugt, A. AghaKouchak, S.C. Jiang, B.F. Sanders, L. Levin, S. Taylor, and S.B. Grant (2015), From rain tanks to catchments: Use of low-impact development to address hydrologic symptoms of the urban stream syndrome, Environmental Science and Technology, 49, 11264–11280, doi:10.1021/acs.est.5b01635.
100. E. Laloy, N. Linde, D. Jacques, and J.A. Vrugt (2015), Probabilistic inference of multi-Gaussian fields from indirect hydrological data using circulant embedding and dimensionality reduction, Water Resources Research, 51, 4224–4243, doi:10.1002/2014WR016395.
99. A. Ali, C. Xu, A. Rogers, N.G. McDowell, B.E. Medlyn, R.A. Fisher, S.D. Wullschleger, P.B. Reich, J.A. Vrugt, W.L. Bauerle, L.S. Santiago, and C.J. Wilson (2015), Global scale environmental control of plant photosynthetic capacity, Ecological Applications, 25, 2349–2365, doi:10.1890/14-2111.1.
98. T. Lochbühler, J.A. Vrugt, and N. Linde (2015), Summary statistics from training images as prior information in probabilistic inversion, Geophysical Journal International, 201, 157–171, doi:10.1093/gji/ggv008.
97. A.C. Martiny, J.A. Vrugt, and M.W. Lomas (2015), Concentrations and ratios of particulate organic carbon, nitrogen, and phosphorus in the global ocean, Nature Scientific Data, 1:140048, doi:10.1038/sdata.2014.48.
96. J.A. Vrugt, and E. Laloy (2014), Reply to comment by Chu et al. on “High-dimensional posterior exploration of hydrologic models using multiple-try DREAM(ZS) and high-performance computing”, Water Resources Research, 50, 2781–2786, doi:10.1002/2013WR014425.
95. H.R. Maier, Z. Kapelan, J. Kasprzyk, J. Kollat, L.S. Matott, G.C. Dandy, M.S. Gibbs, E. Keedwell, A. Marchi, A. Ostfeld, D. Savic, D.P. Solomatine, J.A. Vrugt, A.C. Zecchin, B.S. Minsker, E.J. Barbour, G. Kuczera, F. Pasha, A. Castelletti, M. Giuliani, and P.M. Reed (2014), Evolutionary algorithms and other metaheuristics in water resources: current status, research challenges and future directions, Environmental Modeling & Software, 62, 271-299, doi:10.1016/j.envsoft.2014.09.013.
94. T. Lochbüehler, S.J. Breen. R.L. Detwiler, J.A. Vrugt, and N. Linde (2014), Probabilistic electrical resistivity tomography of a CO2 sequestration analog, Journal of Applied Geophysics, 107, 80-92, doi:10.1016/j.jappgeo.2014.05.013.
93. G.J.M. De Lannoy, R.H. Reichle, and J.A. Vrugt (2014), Uncertainty quantification of GEOS-5 L-Band radiative transfer model parameters using Bayesian inference and SMOS observations, Remote Sensing of Environment, 148, 146-157, doi:10.1016/j.rse.2014.03.030.
92. M. Sadegh, and J.A. Vrugt (2014), Approximation Bayesian computation using Markov chain Monte Carlo simulation: DREAM(ABC), Water Resources Research, 50, doi:10.1002/2014WR015386.
91. J.A. Vrugt, D. Or, and M.H. Young (2013), Vadose Zone Journal: The first ten years, Vadose Zone Journal, 12, 1-3, doi:10.2136/vzj2013.10.0186.
90. J. Rings, T. Kamai, M. Kandelous, P. Hartsough, J. Šimůnek, J.A. Vrugt, and J.W. Hopmans (2013), Bayesian inference of tree water relations using a Soil-Tree-Atmosphere Continuum model, Procedia Environmental Sciences, 19, 26-36.
89. A.C. Martiny, J.A. Vrugt, F.W. Primeau, and M.W. Lomas (2013), Regional variation in the particulate organic carbon to nitrogen ratio in the surface ocean, Global Biogeochemical Cycles, 27, 1-9, doi:10.1002/gbc.20061.
88. M.R. Carbajal, N. Linde, T. Kalscheuer, and J.A. Vrugt (2013), Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data, Geophysical Journal International, 196, 1508–1524, doi:10.1093/gji/ggt482.
87. M. Sadegh, and J.A. Vrugt (2013), Bridging the gap between GLUE and formal statistical approaches: Approximate Bayesian computation, Hydrology and Earth System Sciences, 17, 4831–4850, doi:10.5194/hess-17-4831-2013.
86. J.A. Vrugt, and M. Sadegh (2013), Towards diagnostic model calibration and evaluation: Approximate Bayesian Computation, Water Resources Research, 49, 4335–4345, doi:10.1002/wrcr.20354.
85. E. Laloy, B. Rogiers, J.A. Vrugt, D. Mallants, and D. Jacques (2013), Efficient posterior exploration of a high-dimensional groundwater model from two-stage MCMC simulation and polynomial chaos expansion, Water Resources Research, 49, 2664-2682, doi:10.1002/wrcr.20226.
84. P. Nasta, J.A. Vrugt, and N. Romano (2013), Prediction of the saturated hydraulic conductivity from Brooks and Corey’s water retention parameters, Water Resources Research, 49, 2918-2925, doi:10.1002/wrcr.20269.
83. P. Flombaum, J.L. Gallegos, R.A. Gordillo, J. Rincón, L.L. Zabala, N. Jiao, D.M. Karl, W.K.W. Li, M.W. Lomas, D. Veneziano, C.S. Vera, J.A. Vrugt, and A.C. Martiny (2013), Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus, Proceedings of the National Academy of Sciences of the United States of America, 110(24), 9824-9829, doi:10.1073/pnas.1307701110.
82. P. Nasta, N. Romano, S. Assouline, J.A. Vrugt, and J.W. Hopmans (2013), Prediction of spatially-variable unsaturated hydraulic conductivity using scaled particle-size distribution functions, Water Resources Research, 49, 4219-4229, doi:10.1002/wrcr.20255.
81. A.C. Martiny, C.T.A. Pham, F.W. Primeau, J.A. Vrugt, J.K. Moore, S.A. Levin, and M.W. Lomas (2013), Strong latitudinal patterns in the elemental ratios of marine plankton and organic matter, Nature Geoscience, 6(4), 279-283, doi:NGS-2012-07-01120B.
80. N. Linde, and J.A. Vrugt (2013), Distributed soil moisture from crosshole ground-penetrating radar travel times using stochastic inversion, Vadose Zone Journal, 12, doi:10.2136/vzj2012.0101.
79. J.A. Vrugt, C.J.F. ter Braak, C.G.H. Diks, and G. Schoups (2013), Advancing hydrologic data assimilation using particle Markov chain Monte Carlo simulation: theory, concepts and applications, Advances in Water Resources, Anniversary Issue – 35 Years, 51, 457-478, doi:10.1016/j.advwatres.2012.04.002.
78. J.A. Huisman, J.A. Vrugt, and T.P.A. Ferré (2012), Vadose zone model-data fusion: State of the art and future challenges, Vadose Zone Journal, 11, vzj2012.0140, doi:10.2136/vzj2012.0140.
77. H.V. Gupta, M.P. Clark, J.A. Vrugt, G. Abramowitz, and M. Ye (2012), Towards a comprehensive assessment of model structural adequacy, Water Resources Research, 48, W08301, doi:10.1029/2011WR011044.
76. E. Laloy, N. Linde, and J.A. Vrugt (2012), Mass conservative three-dimensional water tracer distribution from MCMC inversion of time-lapse GPR data, Water Resources Research, 48, W07510, doi:10.1029/2011WR011238.
75. J. Rings, J.A. Vrugt, G. Schoups, J.A. Huisman, and H. Vereecken (2012), Bayesian model averaging using particle filtering and Gaussian mixture modeling: theory, concepts, and simulation experimentsWater Resources Research, 48, W05520, doi:10.1029/2011WR011607.
74. J. Bikowski, J.A. Huisman, J.A. Vrugt, H. Vereecken, and J. van der Kruk (2012), Inversion and sensitivity analysis of ground penetrating radar data with waveguide dispersion using deterministic and Markov chain Monte Carlo methods, Near Surface Geophysics, Special issue “Physics-based integrated characterization”, 10, 641-652, doi:10.3997/1873-0604.2012041.
73. M. Kandelous, T. Kamai, J.A. Vrugt, J. Šimunek, B. Hanson, and J.W. Hopmans (2012), Evaluation of subsurface drip irrigation design and management parameters for alfalfa, Agricultural Water Management, 109, 81–93.
72. E. Laloy, and J.A. Vrugt (2012), High-dimensional posterior exploration of hydrologic models using multiple-try DREAM(ZS) and high-performance computing, Water Resources Research, 48, W01526, doi:10.1029/2011WR010608.
71. D.G. Partridge, J.A. Vrugt, P. Tunved, A.M.L. Ekman, H. Struthers, and A. Sorooshian (2012), Inverse modeling of cloud-aerosol interactions — Part 2: Sensitivity tests on liquid phase clouds using a Markov Chain Monte Carlo based simulation approach, Atmospheric Chemistry and Physics, 11, 20051-20105, doi:10.5194/acpd-11-20051-2011.
70. J.A. Vrugt, and C.J.F. ter Braak (2011), DREAM(D) An adaptive Markov chain Monte Carlo simulation algorithm to solve discrete, noncontinuous, and combinatorial posterior parameter estimation problems, Hydrology and Earth System Sciences, 15, 3701-3713, doi:10.5194/hess-15-3701-2011.
69. B. ScharnaglJ.A. Vrugt, H. Vereecken, and M. Herbst (2011), Bayesian inverse modelling of in situ soil water dynamics: using prior information about the soil hydraulic propertiesHydrology and Earth System Sciences Discussions, 8, 2019–2063.
68. D.G. Partridge, J.A. Vrugt, P. Tunved, A.M.L. Ekman, D. Gorea, and A. Sorooshian (2011), Inverse modeling of cloud-aerosol interactions — Part 1: Detailed response surface analysis, Atmospheric Chemistry and Physics, 11, 7269-7287, doi:10.5194/acp-11-7269-2011.
67. M. He, T.S. Hogue, K.J. Franz, S.A. Margulis, and J.A. Vrugt (2011), Corruption of parameter behavior and regionalization by model and forcing data errors: A Bayesian example using the SNOW17 model, Water Resources Research, 47, W07546, doi:10.1029/2010WR009753.
66. B.Minasny, J.A. Vrugt, and A.B. McBratney (2011), Treatment of uncertainty in model-based geostatistics using Markov chain Monte Carlo simulation, Geoderma, 163, 150–162.
65. M. He, T.S. Hogue, K.J. Franz, S.A. Margulis, and J.A. Vrugt (2011), Characterizing parameter sensitivity and uncertainty for a snow model across hydroclimatic regimes, Advances in Water Resources, 34, 114–127, doi:10.1016/j.advwatres.2010.10.002.
64. T. Wöhling, and J.A. Vrugt (2010), Multi-response multi-layer vadose zone model calibration using Markov chain Monte Carlo simulation and field water retention data, Water Resources Research, 47, W04510, doi:10.1029/2010WR009265.
63. J.H. Dane, J.A. Vrugt, and E. Unsal (2010), Soil hydraulic functions determined from measurements of air permeability, capillary modeling and high-dimensional AMALGAM parameter estimation. Vadose Zone Journal, 10, 1-7, doi:10.2136/vzj2010.0053.
62. S.C. Dekker, J.A. Vrugt, and R.J. Elkington (2010), Significant variation in vegetation characteristics and dynamics from ecohydrologic optimality of net carbon profit, Ecohydrology, 5, 1-18, doi: 10.1002/eco.17.
61. G. Schoups, and J.A. Vrugt (2010), A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic and non-Gaussian errors, Water Resources Research, 46, W10531, doi:10.1029/2009WR008933.
60. G. Schoups, J.A. Vrugt, F. Fenicia, and N.C. van de Giesen (2010), Corruption of accuracy and efficiency of Markov chain Monte Carlo simulation by inaccurate numerical implementation of conceptual hydrologic models, Water Resources Research, 46, W10530, doi:10.1029/2009WR008648.
59. E. Keating, J. Doherty, J.A. Vrugt, and Q. Kang (2010), Optimization and uncertainty assessment of strongly non-linear groundwater models with high parameter dimensionality, Water Resources Research, 46, W10517, doi:10.1029/2009WR008584.
58. J.A. Vrugt (2010), Comment on: “Multi-strategy ensemble evolutionary algorithm for dynamic multiobjective optimization” by Yu Wang and Bin Li, Memetic Computing, 2, 161-162, doi:10.1007/s12293-010-0046-2.
57. K.W. Blasch, T.P.A. Ferré, and J.A. Vrugt (2010), Environmental controls on drainage behavior of an ephemeral stream: An example of the limitations of simple correlative data analyses, Stochastic Environmental Research and Risk Assessment, 24 (7), 1077-1087, doi: 10.1007/s00477-010-0398-8.
56. J.J. Gourley, S. Giangrande, Y. Hong, Z.L. Flamig, T. Schuur, and J.A. Vrugt (2010), Impacts of polarimetric radar observations on hydrologic simulation, Journal of Hydrometeorology, 11(3), 781-796, doi:10.1175/2010JHM1218.1.
55. C.G.H. Diks, and J.A. Vrugt (2010), Comparison of point forecast accuracy of model averaging methods in hydrologic applications, Stochastic Environmental Research and Risk Assessment, 24(6), 809-821, doi:10.1007/s00477-010-0378-z.
54. G.J. Kluitenberg, T. Kamai, J.A. Vrugt, and J.W. Hopmans (2010), Effect of probe deflection on dual probe heat-pulse thermal conductivity measurements, Soil Science Society of America Journal, 74(5), doi:10.2136/sssaj2010.0016N.
53. A.W. Hinnell, T.P.A. Ferré, J.A. Vrugt, S. Moysey, J.A. Huisman, and M.B. Kowalsky (2010), Improved extraction of hydrologic information from geophysical data through coupled hydrogeophysical inversion, Water Resources Research, 46, W00D40, doi:10.1029/2008WR007060.
52. B. Scharnagl, J.A. Vrugt, H. Vereecken, and M. Herbst (2010), Information content of incubation experiments for inverse estimation of pools in the Rothamsted carbon model: a Bayesian perspective, Biogeosciences, 7, 763-776, 2010.
51. J.A. Huisman, J. Rings, J.A. Vrugt, J. Sorg, and H. Vereecken (2010), Hydraulic properties of a model dike from coupled Bayesian and multi-criteria hydrogeophysical inversion, Journal of Hydrology, 380(1-2), 62-73, doi:10.1016/j.jhydrol.2009.10.023.
50. J.A. Vrugt, C.J.F. ter Braak, H.V. Gupta, and B.A. Robinson (2009), Reply to Comment on: “Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?” by Keith Beven, Stochastic Environmental Research and Risk Assessment, 23(7), 1061-1062, doi:10.1007/s00477-008-0284-9.
49. J.A. Vrugt, C.J.F. ter Braak, H.V. Gupta, and B.A. Robinson (2009), Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?, Stochastic Environmental Research and Risk Assessment, 23(7), 1011-1026, doi:10.1007/s00477-008-0274-y.
48. P.H. Stauffer, J.A. Vrugt, H.J. Turin, C.W. Gable, and W.E. Soll (2009), Untangling diffusion from advection in unsaturated porous media: Experimental data, modeling and parameter uncertainty assessment, Vadose Zone Journal, 8(2), 510-522, doi:10.2136/vzj2008.0055.

Features on the cover (2009), Vadose Zone Journal, 8(2)

47. J.A. Vrugt, C.J.F. ter Braak, C.G.H. Diks, D. Higdon, B.A. Robinson, and J.M. Hyman (2009), Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling, International Journal of Nonlinear Sciences and Numerical Simulation, 10(3), 273-290.
46. J.A. Vrugt, B.A. Robinson, and J.M. Hyman (2009), Self-adaptive multimethod search for global optimization in real-parameter spaces, IEEE Transactions on Evolutionary Computation, 13(2), 243-259, doi:10.1109/TEVC.2008.924428.
45. A. Behrangi, B. Khakbaz, J.A. Vrugt, Q. Duan, and S. Sorooshian (2008), Comment on: “Dynamically dimensioned search algorithm for computationally efficient watershed model calibration”, Water Resources Research, 44, W12603, doi:10.1029/2007WR006429.
44. T. Wöhling, and J.A. Vrugt (2008), Combining multi-objective optimization and Bayesian model averaging to calibrate forecast ensembles of soil hydraulic models, Water Resources Research, 44, W12432, doi:10.1029/2008WR007154.
43. J.A. Vrugt, C.J.F. ter Braak, M.P. Clark, J.M. Hyman, and B.A. Robinson (2008), Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation, Water Resources Research, 44, W00B09, doi:10.1029/2007WR006720.
42. C.J.F. ter Braak, and J.A. Vrugt (2008), Differential evolution Markov chain with snooker updater and fewer chains, Statistics and Computing, 18(4), 435-446, doi:10.1007/s11222-008-9104-9.
41. J.A. Vrugt, C.G.H. Diks, and M.P. Clark (2008), Ensemble Bayesian model averaging using Markov chain Monte Carlo sampling, Environmental Fluid Mechanics, 8(5-6), 579-595, doi:10.1007/s10652-008-9106-3.
40. H. Vereecken, J.A. Huisman, H. Bogena, J. Vanderborght, J.A. Vrugt, and J.W. Hopmans (2008), On the value of soil moisture measurements in vadose zone hydrology: A review, Water Resources Research, 44, W00D06, doi:10.1029/2008WR006829.
39. M.P. Clark, A.G. Slater, D.E. Rupp, R.A. Woods, J.A. Vrugt, H. Gupta, T. Wagener, and L. Hay (2008), Framework for understanding structural errors (FUSE): A modular framework to diagnose differences between hydrological models, Water Resources Research, 44, W00B02, doi:10.1029/2007WR006735.
38. J.A. Vrugt, P.H. Stauffer, T. Wöhling, B.A. Robinson, and V.V. Vesselinov (2008), Inverse modeling of subsurface flow and transport properties: A review with new developments, Vadose Zone Journal, 7(2), 843-864, doi:10.2136/vzj2007.0078.
37. D.R. Harp, Z. Dai, A.V. Wolfsberg, J.A. Vrugt, B.A. Robinson, and V.V. Vesselinov (2008), Aquifer structure identification using stochastic inversion, Geophysical Research Letters, 35, L08404, doi:10.1029/2008GL033585.
36. L. Feyen, M. Khalas, and J.A. Vrugt (2008), Semi-distributed parameter optimization and uncertainty assessment for large-scale streamflow simulation using global optimization, Hydrological Sciences Journal, 53(2), 293-208.
35. R.S. Blasone, J.A. Vrugt, H. Madsen, D. Rosbjerg, G.A. Zyvoloski, and B.A. Robinson (2008), Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling, Advances in Water Resources, 31, 630-648, doi:10.1016/j.advwatres.2007.12.003.
34. T. Wöhling, J.A. Vrugt, and G.F. Barkle (2008), Comparison of three multiobjective optimization algorithms for inverse modeling of vadose zone hydraulic properties, Soil Science Society of America Journal, 72, 305-319, doi:10.2136/sssaj2007.0176.
33. P. Tittonell, M.T. van Wijk, M.C. Rufino, J.A. Vrugt, and K.E. Giller (2007), Analyzing trade-offs in resource and labor allocation by smallholder African farmers using inverse modeling techniques, Agricultural Systems, 95, 76-95.
32. J.A. Vrugt (2007), Comment on: “How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?”, Hydrology and Earth System Sciences, 11, 1435-1436.
31. J. Koller, Y. Chen, G. D. Reeves, R. H. W. Friedel, T. E. Cayton, and J.A. Vrugt (2007), Identifying the radiation belt source region by data assimilation, Journal of Geophysical Research – Space Physics, 112, A06244, doi:10.1029/2006JA012196.
30. J.A. Vrugt, J. van Belle, and W. Bouten (2007), Pareto front analysis of flight time and energy use in long distance bird migration, Journal of Avian Biology, 38, 432-442, doi:10.1111/j.2007.0908-8857.03909.

See also:

29. J.A. Vrugt, and B.A. Robinson (2007), Improved evolutionary optimization from genetically adaptive multimethod search, Proceedings of the National Academy of Sciences of the United States of America, 104, 708-711, doi:10.1073/pnas.0610471104.
28. J.A. Vrugt, and B.A. Robinson (2007), Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging, Water Resources Research, 43, W01411, doi:10.1029/2005WR004838.
27. L. Feyen, J.A. Vrugt, B. Ó Nualláin, J. van der Knijff, and A. de Roo (2007), Parameter optimization and uncertainty assessment for large-scale streamflow forecasting, Journal of Hydrology, 332, 276-289.
26. J.A. Vrugt, M.P. Clark, C.G.H. Diks, Q. Duan, and B.A. Robinson (2006), Multi-objective calibration of forecast ensembles using Bayesian Model Averaging, Geophysical Research Letters, 33, L19817, doi:10.1029/2006GL027126.
25. J.A. Vrugt, and Shlomo P. Neuman (2006), Introduction to special section on parameter estimation and uncertainty estimation in the unsaturated zone, Vadose Zone Journal, 5, 915-916, doi:10.2136/vzj2006.0098.
24. J.A. Vrugt, B. Ó Nualláin, B.A. Robinson, W. Bouten, S.C. Dekker, and P.M.A. Sloot (2006), Application of parallel computing to stochastic parameter estimation in environmental models, Computers & Geosciences, 32(8), 1139 – 1155, doi:10.1016/j.cageo.2005.10.015.
23. J.A. Vrugt, H.V. Gupta, S. Sorooshian, T. Wagener, and W. Bouten (2006), Application of stochastic parameter optimization to the Sacramento soil moisture accounting model, Journal of Hydrology, 325(1-4), 288 – 307, doi:10.1016/j.jhydrol.2005.10.041.
22. J.A. Vrugt, H.V. Gupta, B. Ó Nualláin, and W. Bouten (2006), Real-time data assimilation for operational ensemble streamflow forecasting, Journal of Hydrometeorology, 7(3), 548-565,
21. M.P. Clark, and J.A. Vrugt (2006), Unraveling uncertainties in hydrologic model calibration: Addressing the problem of compensatory parameters, Geophysical Research Letters, 33(6), L06406,
20. G. Schoups, J.W. Hopmans, C.A. Young, J.A. Vrugt, and W.W. Wallender (2005), Multi-objective optimization of a regional spatially-distributed subsurface waterflow model, Journal of Hydrology, 20-48, 311(1-4), doi:10.1016/j.jhydrol.2005.01.001.
19. G. Schoups, J.W. Hopmans, C.A. Young, J.A. Vrugt, and W.W. Wallender (2005), Sustainability of irrigated agriculture in the San Joaquin Valley, California, Proceedings of the National Academy of Sciences of the United States of America, 102 (43), 15352-15356, doi:10.1073/pnas.0507723102.

Features as Editor’s Choice in Science (2005), Science, 310, 593

18. J.A. Vrugt, B.A. Robinson, and V.V. Vesselinov (2005), Improved inverse modeling of flow and transport in subsurface media: Combined parameter and state estimation, Geophysical Research Letters, 32, L18408, doi:10.1029/2005GL023940.
17. J.A. Vrugt, C.G.H. Diks, W. Bouten, H.V. Gupta, and J.M. Verstraten (2005), Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation, Water Resources Research, 41(1), W01017, doi:10.1029/2004WR003059.
16. K.J. Raat, J.A. Vrugt, W. Bouten, and A. Tietema (2004), Towards reduced uncertainty in nitrogen catchment modeling: quantifying the effect of field observation uncertainty on model calibration, Hydrology and Earth Systems Sciences, 8(4), 751-763.
15. T.J. Heimovaara, J.A. Huisman, J.A. Vrugt, and W. Bouten (2004), Obtaining the spatial distribution of water content along a TDR probe using the SCEM-UA Bayesian inverse modeling scheme, Vadose Zone Journal, 3, 1128-1145.
14. J.A. Vrugt, G.H. Schoups, J.W. Hopmans, C.H. Young, W. Wallender, T. Harter, and W. Bouten (2004), Inverse modeling of large scale spatially distributed vadose zone properties using global optimization, Water Resources Research, 40(6), W06503, doi:10.1029/2003WR002706.
13. B. Jansen, K.G.J. Nierop, J.A. Vrugt, and J.M. Verstraten (2004), (Un)certainty of overall binding constants of Al with dissolved organic matter determined by the Scatchard approach, Water Research, 38, 1270-1280.
12. J.A. Huisman, W. Bouten, J.A. Vrugt, and P.A. Ferré (2004), Accuracy of frequency domain analysis scenarios for the determination of complex dielectric permittivity, Water Resources Research, W02401, doi:10.1029/2002WR001601.
11. J.A. Vrugt, W. Bouten, H.V. Gupta, and J.W. Hopmans (2003), Toward improved identifiability of soil hydraulic parameters: On the selection of a suitable parametric model, Vadose Zone Journal, 2, 98-113.
10. J.A. Vrugt, S.C. Dekker, and W. Bouten (2003), Identification of rainfall interception model parameters from measurements of throughfall and forest canopy storage, Water Resources Research, 39 (9), 1251, doi:10.1029/2003WR002013.
9. J.A. Vrugt, H.V. Gupta, L.A. Bastidas, W. Bouten, and S. Sorooshian (2003), Effective and efficient algorithm for multi-objective optimization of hydrologic models, Water Resources Research, 39 (8), 1214, doi:10.1029/2002WR001746.
8. J.A. Vrugt, H.V. Gupta, W. Bouten, and S. Sorooshian (2003), A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters, Water Resources Research, 39 (8), 1201, doi:10.1029/2002WR001642.
7. K.G.J. Nierop, B. Jansen, J.A. Vrugt, and J.M. Verstraten (2002), Copper complexation by dissolved organic matter and uncertainty assessment of their stability constants, Chemosphere, 49 (10), 1191-1200.
6. J.A. Vrugt, W. Bouten, H.V. Gupta, and S. Sorooshian (2002), Toward improved identifiability of hydrologic model parameters: The information content of experimental data, Water Resources Research, 38 (12), 1312, doi:10.1029/2001WR001118.
5. J.A. Vrugt, and W. Bouten (2002), Validity of first-order approximations to describe parameter uncertainty in soil hydrologic models, Soil Science Society of America Journal, 66 (6), 1740-1752.
4. J.A. Vrugt, W. Bouten, S.C. Dekker, and P.A.D. Musters (2002), Transpiration dynamics of an Austrian Pine stand and its forest floor: identifying controlling conditions using artificial neural networks, Advances in Water Resources, 25, 293-303.
3. J.A. Vrugt, M.T. van Wijk, J.W. Hopmans, and J. Šimůnek (2001), One, two, and three-dimensional root water uptake functions for transient modeling, Water Resources Research, 37 (10), 2457-2470.
2. J.A. Vrugt, J.W. Hopmans, and J. Šimůnek (2001), Calibration of a two-dimensional root water uptake model, Soil Science Society of America Journal, 65, 1027-1037.
1. J.A. Vrugt, A.H. Weerts, and W. Bouten (2001), Information content of data for identifying soil hydraulic parameters from outflow experiment, Soil Science Society of America Journal, 65, 19-27.