Latest News

Lecture Slides of CEE-290: Merging Models and Data (UPLOADED – June/2016)

Please click HERE or for access to syllabus and all lecture slides.

Lecture Slides of CEE-271: Soil Hydrology (UPLOADED – June/2016)

Please click HERE or for access to syllabus and all lecture slides.

DREAM Suite (RELEASED – April/2016)

DREAM Suite (DIRECT LINK or is a software package for Bayesian inference of numerical simulation models. The program has been developed by PC-Progress in cooperation with Dr. Jasper Vrugt and can be used for the rapid development of applications based on the theory of Markov chain Monte Carlo (MCMC) simulation and the DiffeRential Evolution Adaptive Metropolis (DREAM) method. The package includes more than twenty different examples illustrating the main capabilities and functionalities of DREAM Suite. These examples are available including C++ source code, are easy to adapt and can serve as templates for other inference problems. Plugin modules for new projects can be simply generated using the build-in C++ code generator and finished in Microsoft Visual Studio (free Community Edition 2015 or 2013).

Screenshot of website DREAM Suite.

As COM server DREAM Suite is readily combined with many other programming languages so that users can rapidly combine their models and data with the GUI and implement the solver and graphical interface for Bayesian inference. We hope that DREAM Suite facilitates the further growth of Bayesian methods in science, engineering, philosophy, medicine, economy, sport and law. New examples are continuously published in the peer-reviewed literature and added to the program. More information about DREAM Suite can be found at the following LINK or

Software Manuals (UPDATED – April/2016)

J.A. Vrugt, Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation, Environmental Modeling & Software, 75, pp. 273-316, doi:10.1016/j.envsoft.2015.08.013, 2016. ( MATLAB Code )

J.A. Vrugt, Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation, Latest version manual, pp. 1-106, 2016.

J.A. Vrugt, Multi-criteria optimization using the AMALGAM software package: Theory, concepts, and MATLAB implementation, Manual, Version 1, pp. 1-69, 2016. ( MATLAB Code )

J.A. Vrugt, MODELAVG: A MATLAB toolbox for postprocessing of model ensembles, Manual, Version 1, pp. 1-69, 2016. ( MATLAB Code )

J.A. Vrugt, and M. Sadegh, FDCFIT: A MATLAB toolbox of parametric expressions of the flow duration curve, Manual, Version 1, pp. 1-37, 2016. ( MATLAB Code )

A list with different software codes is found here. For codes other than those listed above, please fill out the electronic WUFOO document.

Recent talks (UPDATED – April/2016)

At Computer Science at UC-Irvine, April 25, 2016.

New publications (UPDATED – April/2016)

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, XX, XX–XX, doi:10.2136/vzj2015.XX.XXXX, In Press.
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, XX, XX–XX, doi:10.1016/j.autcon.2016.03.011, In Press.
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, XX, XX–XX, doi:10.1002/wrcr.XXXX, In Press.
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, XX–XX, 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.