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 1) 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.
NEW: Slides of JPL Seminar (July 16, 2014) (Link)
NEW: Video lectures of CEE-20 (Introduction to Engineering Problem Solving Using MATLAB) are found here (Link)
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.
2011 – 2014: Associate Professor (0.2 FTE), Faculty of Science (CGE), University of Amsterdam, Netherlands
2010 – present: Assistant Professor, Civil and Environmental Engineering (CEE), University of California Irvine, USA
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
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”
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.