Enjoying a hike in the Swiss Alps. Lauterbrunnen to Mannlichen via Wengen.
Enjoying a hike in the Swiss Alps. Lauterbrunnen to Mannlichen via Wengen

My research group combines numerical modeling (deterministic, stochastic) and/or analytic solutions with small and large-scale measurement (direct and indirect observations), and inverse modeling (parameter estimation, data assimilation, model averaging, etc.) to improve theory, understanding and predictability of complex Earth systems. We engage in all aspects of the iterative research cycle (see figure below) and regularly develop new numerical, computational, statistical, and optimization approaches to reconcile complex system models with observations for the purpose of learning and scientific discovery and, thereby, enhancing the growth of environmental knowledge. We use distributed computing to permit inference of CPU-intensive forward models.

Our papers appear in a wide variety of different scientific journals, and describe methodological advances, and their application to problem solving in (alphabetic order) agriculture, avian biology, ecohydrology, ecology, fluid mechanics, geomorphology, groundwater, hydrogeophysics, hydrogeology, geophysics, geostatistics, surface hydrology, soil physics, vadose zone hydrology, and water resources.

Our current methodological work focuses on, (i) a new paradigm of process-based model evaluation (to help diagnose which components of the model are malfunctioning), (ii) likelihood-free inference (use of summary metrics in geophysics as a powerful and “objective” alternative to the rather “subjective” deterministic penalized least-squares inversions), (iii) Bayesian model selection (inference of marginal likelihood through multi-dimensional integration of the posterior distribution), (iv) emulation of CPU-intensive models (to permit inference of computationally demanding models), and (v) monitoring network design (real-time measurement selection to help discriminate among conceptual models).

More application oriented work includes (amongst others), (a) investigation of the environmental controls of photosynthetic capacity (ecology), (b) global-scale hydrologic modeling (hydrology), (c) uncertainty quantification of GEOS-5- L-Band radiative transfer parameters (remote sensing), (d) joint inference of multi-Gaussian permeability fields and their geostatistical properties (hydrogeology), (e) the biogeography and composition (particulate ratios) of marine plankton (ecology), (f) two- and three-dimensional subsurface characterization (geophysics), (g) scaling and prediction of soil hydraulic parameters (soil physics), and (h) geomorphological modeling of soil depths (geomorphology / sediment transport). Publications on these different topics are forthcoming.

We share freely all our work with others, and provide short-courses for those interested in numerical modeling and model-data analysis.

The iterative research cycle (scientific method) for a soil-water-atmosphere-transport model. The initial hypothesis is that the system can be described with a simple axi-symmetrical numerical model that uses Richards’ equation to describe water flow through the soil and tree trunk (plant) continuum. Experimentation then involves standard meteorological measurements such as precipitation, and other variables (temperature, vapor pressure deficit, global (net) radiation, wind speed) that determine the atmospheric moisture demand, and measurements of spatially distributed soil moisture and matric head, and the sapflux through the xylem, and tree trunk potential. The conceptual model is subsequently calibrated against these observations using methods such as DREAM and model-data analysis proceeds by analyzing the error residuals. This last step has proven to be the most difficult, and often ad-hoc decisions are being made on model improvement. Much emphasis in our work is on how to diagnose, detect, and resolve model structural errors. This is key to refining existing hypotheses, scientific discovery and learning. Note: in science and engineering the hypothesis often constitutes some numerical model which summarizes, in algebraic and differential equations, state variables and fluxes, all our knowledge of the system of interest, and the unknown parameter values are subject to inference using the data.

NEW: Software Manuals

J.A. Vrugt, Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation, Environmental Modeling & Software, XX, pp. 1-82, doi:10.1016/j.envsoft.2014.XX.XXX, 2015.

J.A. Vrugt, Multi-criteria optimization using the AMALGAM software package: Theory, concepts, and MATLAB implementation, Manual, Version 1, pp. 1-53, 2015.

J.A. Vrugt, and M. Sadegh, FDCFIT: A MATLAB toolbox of closed-form parametric expressions of the flow duration curve, Manual, Version 1, pp. 1-37, 2015.

J.A. Vrugt, MODELAVG: A MATLAB toolbox for postprocessing of model ensembles, Manual, Version 1, pp. 1-44, 2015.

Software can now be downloaded directly from here

For other news please visit the “Latest News” tab

Selection of Recent Publications

A.C. Martiny, J.A. Vrugt, and M.W. Lomas (2014), 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.

H.R. Maier, Z. Kapelan, J. Kasprzyk, J. Kollat, L.S. Matott, C.M. da Conceição, G.C. Dandy, M.S. Gibbs, E. Keedwell, A. Marchi, A. Ostfeld, D. Savic, D. Solomatine, J.A. Vrugt, A.C. Zecchin, B.S. Minsker, E. Barbour, D. Kang, G. Kuczera, and F. Pasha (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.

M. Sadegh, and J.A. Vrugt, (2014), Approximate Bayesian computation using Markov chain Monte Carlo simulation: DREAM(ABC), Water Resources Research, 50, doi:10.1002/2014WR015386.

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.

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.

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.

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.

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.

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.

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.

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, 279–283, doi:NGS-2012-07-01120B.

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.

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.

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.

Short CV

2011 – 2014: Associate Professor (0.2 FTE), Faculty of Science (CGE), University of AmsterdamNetherlands

2010 – present: Assistant Professor, Civil and Environmental Engineering (CEE), University of California IrvineUSA

2010 – present: Assistant Professor (Joint Appointment), Earth System Science (ESS), University of California, Irvine, USA. 

2006 – 2009: J. Robert Oppenheimer Distinguished Postdoctoral Fellow, LANL, USA

2005 – 2006: Directors Funded Postdoctoral Fellow, LANL, USA

2000 – 2004: PhD in Science, University of Amsterdam, Netherlands

1994-1999: MS in Physical Geography, University of Amsterdam, Netherlands


UC Irvine

2011 – present CEE-20: Problem Solving in Engineering using MATLAB: Undergraduate

2010 – present CEE-271: Unsaturated Zone Hydrology: Graduate

2007 – present CEE-290: Merging Models and Data: Graduate


Main Lecturer for a week long short-course on “Bayesian inverse modeling and data assimilation methods in the Earth Sciences“. Once or twice a year, at a different University worldwide. Look for more information under “Teaching / Outreach”

Editorial Boards

2008 – present Vadose Zone Journal

2008 – present Hydrology and Earth Systems Sciences

2009 – present Environmental Modeling & Software

2010 – present Water Resources Research

Other Professional Activities

2004 – present Chair and Organizer of 20+ sessions at (inter)National conferences

2004 – present About 60 invited talks and seminars at Universities, Inter(National) Meetings, and Labs.