Jasper A. Vrugt

University of California, IrvineDept. of Civil and Environmental Engineering4130 Engineering GatewayIrvine, CA 92697-2175

Voice: (949) 824-4515

Fax: (949) 824-3672

Email: jasper@uci.edu

WWW: faculty.sites.uci.edu/jasper

Citizenship: Dutch, US Resident (Green Card Holder)

Research Interests

My research group combines measurement and modeling to investigate, understand, and predict thebehavior of (Earth) systems. We study all aspects of the iterative research cycle (see Figure 1) and continu-ously develop new numerical and statistical approaches to systematically engage complex system modelswith observations for the purpose of learning and scientific discovery and, thereby, enhancing the growthof environmental knowledge.

HYPOTHESIS FORMULATION

EXPERIMENTAL DESIGN

DATA COLLECTION

MODEL-DATA ANALYSIS

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Figure 1: Example of the iterative research cycle for asoil-water-atmosphere-continuum model.

In the past years, we have developed theory,concepts, and algorithms for measurement net-work design, (spatial) scaling and geostatistics, dis-tributed computation, model-data fusion, ensem-ble simulation, process-based model evaluation,emulation, and treatment of uncertainty. The con-cepts and methods we develop are published inmore theoretically oriented journals and are sub-sequently used to solve old and emerging prob-lems in a wide variety of fields including (but notlimited to) agriculture, atmospheric chemistry andphysics, avian biology, ecohydrology, ecology, fluidmechanics, hydrogeology, hydrogeophysics, geo-physics, pedometrics, remote sensing, soil physics,surface hydrology, and water resources manage-ment.

My students and I are currently working on anew paradigm of process-based model identification and evaluation which has much better prospectsthan classical residual-based fitting methods to detect epistemic errors (behavior observed in the data butnot represented in our models). We are also focusing on model-data fusion problems in ecology, withparticular interest in modeling the behavior of chaotic systems using population models.

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

Education

Ph.D. Faculty of Science, University of Amsterdam, 2004, Cum Laude.

Dissertation: Towards Improved Treatment of Parameter Uncertainty in Hydrologic Modeling

M.S. Faculty of Social and Behavioral Sciences, University of Amsterdam, 1999, Cum Laude.

Employment

University of California Irvine, USA

Assistant Professor, Civil and Environmental Engineering (CEE), 2010–Present

Assistant Professor (Joint Appointment), Earth System Science (ESS), 2010–Present

University of Amsterdam, The Netherlands

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

Los Alamos National Laboratory

J. Robert Oppenheimer (JRO) Distinguished Postdoctoral Fellowship, 12/06–12/09

Director’s Postdoctoral Fellowship, 3/05–11/06

University of Amsterdam, University of Arizona, University of California (Davis)

(visiting) M.S. Student, 9/94 – 12/99

(visiting) Ph.D. Student, 1/00 – 5/04

Honors, Awards, & Fellowships

Sir Frederick McMaster Fellowship, Australia (CSIRO), 2012

Fellow, Geological Society of America (GSA), 2012

Editors’ Choice Award, Water Resources Research (AGU), 2011

Donath Medal, Geological Society of America (GSA), 2011

James B. Macelwane Medal, American Geophysical Union (AGU), 2010

Outstanding Young Scientist Award, European Geosciences Union (EGU), 2010

Fellow, American Geophysical Union (AGU), 2010

Top 50 of Most Talented Young People From the Netherlands (Elsevier), 2009

Early Career Award in Soil Physics, Soil Science Society of America (SSSA), 2007

Hydrology Prize 2004 - 2006, Dutch Hydrological Society (NHV), 2007

J. Robert Oppenheimer Distinguished Postdoctoral Fellowship (LANL), 2006

Director’s Postdoctoral Fellowship (LANL), 2005

Graduated with Cum Laude for Ph.D. degree (UvA), 2004

Dutch National Science Foundation Travel Grant (NWO), 2001 & 2002

Graduated with Cum Laude for M.S. degree (UvA), 1999

Professional Activities

Memberships

American Geophysical Union (AGU), Dutch Hydrologic Society (NHV), European Geophysical Union(EGU), Geological Society of America (GSA), Soil Science Society of America (SSSA), Society for In-dustrial and Applied Mathematics (SIAM)

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Editorial

Guest-editor (with D. Or) "Vadose Zone Journal: The First Ten Years", Vadose Zone Journal, 12(4), 2013

Guest-editor (with J.A. Huisman, and T.P.A. Ferré) "Vadose Zone Model-Data Fusion: State of the Artand Future Challenges", Vadose Zone Journal, 11(4), 2012

Reviewer for Department of Defence (DOD), 2011–present

Associate Editor of Water Resources Research (WRR), 2010–present

Editorial Board of Environmental Modeling & Software (EMS), 2009–present

Reviewer for DOE Office of Science - Applied Mathematics, 2009–present

Reviewer for U.S. Army Engineer Research and Development Center (ERDC), 2009–present

Associate Editor of Hydrology and Earth Systems Sciences (HESS), 2008–present

Associate Editor of Vadose Zone Journal (VZJ), 2008–present

Guest-editor (with Shlomo P. Neuman) "Parameter Identification and Uncertainty Assessment in theUnsaturated Zone", Vadose Zone Journal, 5(3), 2006

Reviewer for National Science Foundations of many different countries, 2006–present

Reviewer for 50+ peer-reviewed scientific journals (about 20 per year), 2002–present

Elected Positions

Member, Advisory Board "Quantifying Uncertainty in Integrated Catchment Studies", European Com-mission: Marie Curie Initial Training Networks, Europe, 2014–present

Partner, Helmholtz Research School on High-Performance Computing, Germany, 2013–present

Secretary-Elect, Hydrology Section, AGU, 2012

Member, Selection Committee Dutch Hydrology Prize, NHV, 2011–present

Teaching Experience at University of California Irvine

CEE-21: Problem Solving in Engineering using MATLAB (SOPHOMORES): Review of first year, func-tions, interpolation, ODE solvers, numerical integration, and functions, 2014–present

CEE-20: Introduction to Problem Solving in Engineering using MATLAB (FRESHMEN): Scalar, vec-tors, and matrices, strings, for and while loops, matrix algebra, plotting, linear and nonlinear systemsof equations, root finding, least-squares, Gaussian elimination, model fitting, functions, 2014–present

CEE-20: Problem Solving in Engineering using MATLAB (FRESHMEN): Scalar, vectors, and matrices,linear equation solver, root finding, Newton’s and bisection method, structures, strings, for and whileloops, interpolation, ODE solvers, numerical integration, and functions, 2011–2014

CEE-271: Vadose Zone Hydrology (MS AND PHD): Physical properties of soils, saturated and unsatu-rated water flow, infiltration and drainage, steady-state and non-steady state flow, potential diagrams,and methods of measurement (direct and indirect), 2010–present

CEE-276: Hydrology (MS and PHD): Given few invited lectures on model identification, time-seriesanalysis, parameter estimation, data assimilation, and high performance computing

ESS 132/232: Terrestrial Hydrology: Lecture on soil hydraulic properties, Darcy’s Law, soil texture,soil structure, potential diagrams, and Richards’ equation

CEE-290: Merging Models and Data (MS and PhD): Concepts, algorithms and numerical implementa-tion of single and multiple objective optimization methods (local and global), Bayesian statistics, prior

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and posterior distribution, (particle) Markov chain Monte Carlo simulation, data assimilation, param-eter and state estimation, and high performance computing. Concepts in class are coded in homeworkassignments and tested using several toy and earth system modeling problems, 2011–present

Teaching Experience (Inter)national

Main Lecturer for a weekly short-course on Environmental modeling: Optimization and Bayesian analysis.Organized by Ben-Gurion University of the Negev, Beer-Sheva, Israel. PARTICIPANTS: XX; July XX-XX, 2014

Lecturer for a weekly short course on Environmental systems analysis. Organized by Eawag: SwissFederal Institute of Aquatic Science and Technology, Dübendorf, Switzerland. PARTICIPANTS: XX;June 16-20, 2014

Main Lecturer for a weekly training course on Uncertainty Quantification of Environmental Models usingDREAM simulation. Organized by CSIRO Land & Water, Adelaide, Australia. PARTICIPANTS: 42;Sept. 24-28, 2012

Main Lecturer for a weekly short course on Bayesian Inference in the Earth Sciences. Organized at KULeuven, Belgium. PARTICIPANTS: 26; July 30-Aug. 3, 2012

Main Lecturer for a pre-conference workshop on Bayesian Inverse Modeling in the Earth Sciences: Theory,Concepts, and Applications. Czech University of Life Sciences, Prague. PARTICIPANTS: 20; Aug. 28-29,2011

Main Lecturer for a weekly short-course on Bayesian inverse modeling and data assimilation methods toimprove environmental and ecological models. Institute for Biodiversity and Ecosystem Dynamics (IBED),University of Amsterdam, The Netherlands. PARTICIPANTS: 24; June 27 - July 1, 2011

Main lecturer for a weekly short-course on Model Calibration in the Earth Sciences. Organized at KULeuven, Belgium. PARTICIPANTS: 32; July 26-30, 2010

Main lecturer for a weekly short-course on Inverse Modeling for Improving Environmental and EcologicalModels. Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, TheNetherlands. PARTICIPANTS 37; July 6-10, 2009

Invited Lecturer on Parameter Estimation in Subsurface Hydrology: Algorithms, Numerical Approaches andApplications at the Hydrology Program, University of California, Davis. May 26, 2009.

Main lecturer for a weekly short-course on Model Calibration in the Earth Sciences. Organized at KatholiekeUniversiteit Leuven, Belgium. PARTICIPANTS: 40; Aug. 4-8, 2008

Invited Lecturer on Parameter estimation in subsurface hydrology: algorithms, numerical approaches andapplications at the Hydrology Program, University of California, Davis, June 7-8, 2007

Invited Lecturer at International Summer School on Atmospheric and Oceanic Sciences (ISSAOS),L’Aquila, Italy, August 29-September 2, 2005

Graduate Teaching Assistant, University of Amsterdam, Netherlands. Ten lectures on soil physics forundergraduates in Physical Geography and Soil Science. Prepared lectures for lab session exercises,and graded laboratory assignments, homework and final papers, 2000

Teaching Assistant, University of Amsterdam, Netherlands. Ten lectures on soil physics for under-graduates in Physical Geography. Prepared lectures for lab session exercises, graded laboratory as-signments, homework and final papers, 1999

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Committee Assignments

National

Los Alamos National laboratory: Laboratory Directed Research and Development, 2008–2009

American Geophysical Union: Surface Hydrology Committee, 2005–present

InterNational

International Federation of Automatic Control: Governing Board and Member of Technical Committee"Modeling and Control of Environmental Systems", 2012–present

Project Leader Uncertainty: Hydrologic Ensemble Prediction Experiment, 2005–present

European Geophysical Union: Unsaturated Zone Committee, 2004–present

European Geophysical Union: Surface Hydrological Processes Committee, 2004–present

International Working Group on Uncertainty Analysis, 2004–present

University of California, Irvine

Chair, Honorifics Committee, CEE Dept., 2013–present

Member, Executive Committee, Henri Samueli School of Engineering, 2012–present

Member, UCI Academic Senate, Engineering Representative, 2012–present

Member, Academic Planning Group, CEE Dept., 2012–present

Member, Search Committee for Tenure-Track Faculty Position in Hydrometeorology, CEE Dept., 2011

Advised Students at University of California Irvine

Mojtaba Sadegh, "Quantification of epistemic uncertainty in environmental modeling: ApproximateBayesian computation", PhD expected 2014

Elias Massoud, "Model-data fusion in ecology", PhD expected 2017

Veysel Yildiz, "Optimization of hydro power treatment plants using Bayesian decision analysis", MSexpected 2015

Hao Guo, "Precipitation estimation and error reconstruction from an ensemble of hydrologic models",MS expected 2015

Cyrus Wan, Undergraduate student, BS expected 2016

Co-Advised Students - (Inter)national

Guilherme Gomes, "The geomorphology of hillslope watersheds: Data collection, three-dimensionalmodeling, and model-data synthesis", PhD, Catholic University of Rio de Janeiro (PUC-RJ), Rio deJaneiro, Brazil, 2016 (expected)

Hanna Post, "Catchment scale water and carbon flux simulations using the Community Land Model(CLM)", PhD, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany, 2016 (expected)

Tobias Luchbuehler†1, "Approximate Bayesian inversion of geophysical Data: Nonlinear expansion,summary metrics, and likelihood functions", PhD, University of Lausanne, Switzerland, 2015 (awardedposthumous)

1Deceased

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Lotte de Vries, "Review of model-data fusion in ecology", MS Thesis, University of Amsterdam, Ams-terdam, The Netherlands, 2014 (expected)

Jiri Nossent, "Improved regionalization with the generalized likelihood function", Postdoctoral project,Free University of Brussel, Brussel, Belgium, 2013

Jurriaan Spaaks, "Hillslope model error reconstruction using joint parameter and state estimation",PhD, University of Amsterdam, 2013

Frederiek Sperna Weiland, "Global assessment of hydrological effects of climate change", PhD, UtrechtUniversity, The Netherlands

Benedikt Scharnagl, "Spatio-temporal patterns of water and C-fluxes at the field scale", PhD, Universityof Bonn, Germany, 2011

Dan Partridge, "Inverse modeling of cloud-aerosol interactions", PhD, Stockholm University, Sweden,2011

Paolo Nasta, "Scaling of soil hydraulic properties using primary and secondary data", Postdoctoralproject, UC-Davis, 2010

Minxue (Kevin) He, "Data assimilation in watershed models for improved hydrologic forecasting",PhD, UCLA, 2010

Roberta Blasone, "Parameter estimation and uncertainty assessment in hydrologic modeling", PhD,Technical University of Denmark (DTU), Denmark, 2007

Sabbatical visitors

Elena Volpi, Assistant Professor, Department of Engineering, University of Roma Tre, Rome, Italy, 2012.

Awards of (co-)Advised Students

Mojtaba Sadegh (University of California, Irvine), Henri Samueli Endowed Fellowship, 2010

Hao Guo (University of California, Irvine), Environmental Engineering Summer Fellowship, 2014

Current Positions of (co-)Advised Students

Jurriaan Spaaks, Dutch E-Science Center, Amsterdam, The Netherlands, 2012

Dan Partridge, Postdoctoral student, Oxford University, UK, 2012

Benedikt Scharnagl, Postdoctoral student, Technical University Braunschweig, Germany, 2011

Eric Laloy, Staff member Belgian Nuclear Research Center, Mol, Belgium, 2011

Paolo Nasta, Postdoctoral student, University of Nebraska, Lincoln, USA, 2010

Minxue (Kevin) He, Senior scientist at Riverside technology & NOAA NWS, 2010

External PhD examinator

Colin P. Kikuchi (University of Arizona, Tucson), June, 2014

Jiri Nossent (Free University Brussel, Belgium), March, 2012

Frederiek Sperna Weiland (Utrecht University, The Netherlands), December, 2011

Ying Jang (Institute of Aquatic Science and Technology, Switzerland), June, 2006

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Reza Entezarolmahdi (University of Trento, Italy), March, 2006

Preliminary Exam

Luis Herrera (University of California Irvine, CEE Dept.), July, 2014

Ricardo Medina (University of California Irvine, CEE Dept.), June, 2014

Aryan Safaie (University of California Irvine, CEE Dept.), April, 2014

Riccardo Cappa (University of California Irvine, CEE Dept.), March, 2013

Asal Bardsiri (University of California Irvine, CEE Dept.), September, 2011

Mojtaba Sadegh (University of California Irvine, CEE Dept.), August, 2011

Scott Sellars (University of California Irvine, CEE Dept.), May, 2011

Qualifying Exam

W. Wang, "The role of clouds in Greenland melt", Department of Earth System Science, University ofCalifornia Irvine, June 2014

A. Gansmiller, "Developing selections for Enzyme turnovers through substrate-wrapped phage display", De-partment of Chemistry, University of California Irvine, May 2014

N. Oldenhuis, "Ru-Macho catalyzed transformations: Acceptorless dehydrogenative amidation and chiral aminesynthesis", Department of Chemistry, University of California Irvine, May 2014

S. Hiew, "A hemoglobin for CO2: Designing small molecules that bind carbon dioxide cooperatively", Depart-ment of Chemistry, University of California Irvine, May 2014

C. Arnold, "Nanocrystalline aluminum stabilized by diamantane (diamond cubic hydrocarbon)", Departmentof Chemical Engineering and Materials Science, University of California Irvine, April 2014

A.M. Hollas, "Synthesis and reactivity of tantalum-oxo complexes supported by a redox-active ligand", Depart-ment of Chemistry, University of California Irvine, November 2013

D. Tao, "Selective intramolecular oxocarbenium ion trapping as a study towards chromodorolides A, B, C, andD", Department of Chemistry, University of California Irvine, May 2013

M. Sadegh, "Approximate Bayesian computation in hydrologic modeling: Concepts, algorithms and case stud-ies", Department of Civil and Environmental Engineering, University of California Irvine, April 2012

Hao Liu, "A dynamic model for satellite remote sensing precipitation uncertainty estimation", Department ofCivil and Environmental Engineering, University of California Irvine, March 2012

Janice Wong, "Iron complexes containing the redox-active [ONO] ligand", Department of Chemistry, Uni-versity of California Irvine, November 2011

Alys Thomas, "Characterizing regional drought using GRACE terrestrial water storage and climate datasets",Department of Earth System Science, University of California Irvine, May 2011

Anne Kelly, "Controlling factors of biomass and forest productivity across an elevation gradient in California",Department of Earth System Science, University of California Irvine, April 2011

Salman Jabri, "Progress towards total synthesis of (+)-Gliocladin C", Department of Chemistry, Universityof California Irvine, June 2010

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Grants

7. Center for Comprehensive, optimaL, and Effective Abatement of Nutrients; PI: M. Arabi, lead PIs: S.Sharvelle, B. Bledsoe, D. Hoag; W. Hunt, D. Osmond; J.A. Vrugt, and J. Silverstein; Amount;$2,500,000, EPA-G2012-STAR-H1: Centers for Water Research on National Priorities Related to aSystems View of Nutrient Management, 2013 - 2017.

6. Low Energy Options for Making Water from Wastewater; PI: S. Grant, Co-PIs: A. AghaKouchak, R.Ambrose, P. Bowler, B. Cooper, R. Detwiler, S. Elghobashi, D. Feldman, S. Jiang, R. Lejano, L. Levin,M. McBride, M. Prather, J.D. Saphores, D. Rosso, B. Sanders, A. Sengupta, E. Stein, M. Sutula, W.Tang, K. Treseder, J.A. Vrugt, R. Brown, P. Cook, A. Deletic, T. Fletcher, A. Hamilton, I. Marusic, D.McCarthy, M. Stewardson, and A. Western; Amount; $5,000,000, National Science Foundation, PIREprogram, 2012 - 2017.

5. Towards Improved Prediction of Chaos in Ecology: A Model-Data Fusion Approach; PI: J.A. Vrugt, Co-PI:A.C. Martiny; Amount: $30,000, UCI Environment Institute, 2012 - 2013.

4. Next Generation Dynamic Carbon-Nitrogen Model; PI: C. Xu, Co-PI: J.A. Vrugt; Amount: $1,114,000,UC Lab Research Program, 2012 - 2015.

3. Multilevel Adaptive Sampling for Multiscale Inverse Problems; PI: D. Moulton, Co-PIs: D. Higdon, C.Fox, and J.A. Vrugt; Amount: $900,000, LANL, 2007 - 2010.

2. Creating a Mathematical Foundation for High-dimensional Search and Optimization Algorithms to SolveNonlinear Models; PI: J.A. Vrugt, Co-PIs: B.A. Robinson, and J.M. Hyman; Amount: $1,000,000,LANL, 2006 - 2009.

1. Subsurface Transport Parameter Estimation with Multiscale, Multiobjective Optimization; Principal Investi-gator PI: A. Wolfsberg, Co-PIs: Z. Dai, Z. Lu, P. Reimus, and J.A. Vrugt; Amount: $645,000, LANL,2006 - 2009.

Conferences and Symposia Activities

25. Co-chair and co-organizer of the American Geophysical Union Fall meeting session on Understandingthe Interface Between Models and Data, San Francisco, 2014.

24. Co-chair and organizer of the American Geophysical Union Fall meeting (Union) session on Frontiersin Uncertainty Quantification for Geophysical Modeling, San Francisco, 2014.

23. Co-organizer of the European Geophysical Union session on Parameter Estimation, Inverse Modelingand Data Assimilation in Subsurface Hydrology, Vienna, 2012.

22. Co-organizer of the European Geophysical Union session on Parameter Estimation, Inverse Modelingand Data Assimilation in Subsurface Hydrology, Vienna, 2011.

21. Co-chair and co-organizer of the American Geophysical Union Fall meeting session on Using Data toDetect and Resolve Model Structural Errors, San Francisco, 2010.

20. Co-chair and co-organizer of the American Geophysical Union session on Challenges in HydrologicModeling and Forecasting at the Western Pacific Geophysics Meeting, Taipei, Taiwan, 2010.

19. Co-chair and co-organizer of the European Geophysical Union session on Combining Modeling andMeasuring to Improve Understanding of Subsurface Flow and Transport Systems, Vienna, Austria, 2010.

18. Co-chair and co-organizer of the European Geophysical Union session on Reconciling Theory, Simula-tion, and Observations in Subsurface Flow and Transport Modeling, Vienna, Austria, 2009.

17. Co-chair and co-organizer of the American Geophysical Union Fall meeting session on Joint InversionMethods in Hydrogeophysics, San Francisco, 2008.

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16. Chair and organizer of the Soil Science Society of America (SSSA) Symposium on Measurement andModeling of Flow and Transport Processes in the Unsaturated Zone, in honor of the retirement of Dr. JacobDane, Houston, 2008.

15. Chair and organizer of the European Geophysical Union session on Reconciling Theory, Simulation,and Observations in Subsurface Flow and Transport Modeling, Vienna, Austria, 2008.

14. Chair and organizer (by invitation) of the Computational Methods in Water Resources (CMWR)meeting session on Ensemble Forecasting in Environmental Modeling, San Francisco, 2008.

13. Co-chair and organizer of the American Geophysical Union Fall meeting session on Advancing DataAssimilation and Uncertainty Assessment for Improved Hydrologic Predictions, San Francisco, 2007.

12. Co-chair and organizer of the American Geophysical Union Fall meeting session on Parameter Esti-mation in Hydrology: Theoretical Developments and Applications, San Francisco, 2007.

11. Co-chair of the AAPG Hedberg Research Conference session on Data Quality, Inversion and Uncer-tainty Estimation, The Hague, The Netherlands, 2007.

10. Co-chair and organizer of the European Geophysical Union session on Calibration of Spatially Dis-tributed Models, Vienna, Austria, 2007.

9. Chair and co-organizer of the American Geophysical Union Fall meeting session on Calibration andUncertainty Assessment of Spatially Distributed Hydrologic Models, Methods, Applications and Strategies,San Francisco, 2006.

8. Chair and organizer of the American Geophysical Union session on Improved Hydrologic ModelingThrough Ensemble Forecasting: Strategies, Concepts, and Applications, at Western Pacific GeophysicsMeeting, Beijing, China, 2006.

7. Chair and organizer of the workshop Advances in Parameter Estimation in Computational Science: Strate-gies, Concepts, and Applications at the International Conference on Computational Science 2006 (ICCS2006), Reading, UK, 2006.

6. Chair and organizer of the European Geophysical Union session on Advances in Uncertainty Assess-ment of Hydrologic Models, Vienna, Austria, 2006.

5. Chair and organizer of the European Geophysical Union session on Effective processes and parameteridentification in the unsaturated zone, Vienna, Austria, 2006.

4. Chair and organizer of the American Geophysical Union Fall meeting session on Calibration andUncertainty Assessment of Spatially Distributed Hydrologic Models, San Francisco, 2005.

3. Co-chair and co-convener European Geophysical Union session on Quantification of Structural Error,Parameter Estimation and Uncertainty Assessment in Groundwater and Hydrological Catchment Modeling,Vienna, Austria, 2005.

2. Chair and organizer of the European Geophysical Union session on Effective Processes and ParameterIdentification in the Unsaturated Zone, Vienna, Austria, 2005.

1. Chair and organizer of the European Geophysical Union session on Model Calibration and UncertaintyAssessment of Unsaturated Flow and Transport Processes Across Spatial Scales, Nice, France, 2004.

Publications

First authors with a low dash denote a (co-advised) student. 7/18/2014: 3,294 ISI citations, h-factor = 31

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105. E. Laloy, N. Linde, D. Jacques, and J.A. Vrugt (2014), Joint probabilistic inference of multi-Gaussianconductivity fields and their associated variograms from indirect hydrological data, Water ResourcesResearch, xx, xx–xx, doi:10.1002/2014wrcr.XXXX.

104. M. Sadegh, H.V. Gupta, and J.A.Vrugt (2014), Towards model structural improvement: Process-based model calibration using approximate Bayesian computation, Water Resources Research, xx, xx–xx, doi:10.1002/2014wrcr.XXXX.

103. A.C. Martiny, J.A.Vrugt et al. (2014), Concentrations and ratios of particulate organic carbon, nitro-gen, and phosphorus in the global ocean, Nature Scientific Data, XX, XX-XX, doi:10.1038/sdata.XX.XX.

102. M. Sadegh, J.A. Vrugt (2014), Detecting non-stationarity in hydrologic modeling: Process-basedmodel calibration, Water Resources Research, xx, xx–xx, doi:10.1002/2014wrcr.XXXX.

101. J.A. Vrugt, and E. Laloy (2014), Reply to comment by Chu et al. on "High-dimensional posteriorexploration of hydrologic models using multiple-try DREAM(ZS) and high-performance computing,Water Resources Research, 50, 2781–2786, doi:10.1002/2013WR014425.

100. H.R. Maier, Z. Kapelan, J. Kasprzyk, J. Kollat, L.S. Matott, C.M. da Conceiçao, 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 othermetaheuristics in water resources: current status, research challenges and future directions, Environ-mental Modeling & Software, XX, XX–XX, doi:10.1016/j.envsoft.XXXX.

99. J.A. Vrugt (2014), Soroosh Sorooshian receives 2013 Robert E. Horton Medal: Citation, Eos, Transac-tions American Geophysical Union, 95(1), 7, doi:10.1002/2014EO010017.

98. T. Lochbühler, S.J. Breen, R.L. Detwiler, J.A. Vrugt, and N. Linde (2014), Probabilistic electricalresistivity tomography for a CO2 sequestration analog, Journal of Applied Geophysics, XX, XX–XX,doi:10.1016/j.jappgeo.2014.XX.XXX, In Press.

97. C.P. Kikuchi, T.P.A. Ferré, and J.A. Vrugt (2014), A data discrimination index (DDI) for hydrologicmonitoring network design, Water Resources Research, XX, XX–XX, doi:10.1002/wrcr.XXXX.

96. F.C. Sperna Weiland, J.A. Vrugt, R.L.P.H. van Beek, A.H. Weerts, and M.F.P. Bierkens (2014), Signifi-cant uncertainty in global scale hydrological modeling from precipitation data errors, Water ResourcesResearch, XX, XX–XX, doi:10.1002/wrcr.XXXX.

95. 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, RemoteSensing of Environment, 148, 146–157, doi:10.1016/j.rse.2014.03.030.

94. J.A. Vrugt, D. Or, and M.H. Young (2013), Vadose Zone Journal: The first ten years, Vadose ZoneJournal, 12, 1–3, doi:10.2136/vzj2013.10.0186.

93. M. Sadegh, J.A. Vrugt (2014), Approximation Bayesian computation using Markov chain MonteCarlo simulation: DREAM(ABC), Water Resources Research, XX, XX–XX.

92. T. Lochbühler†2, J.A. Vrugt, M. Sadegh, and N. Linde (2014), Summary statistics from training im-ages as prior information in probabilistic inversion, Geophysical Journal International, XX, XX–XX,doi:GJI-S-13-0278.

91. A.A. Ali, C. Xu, A. Rogers, N.G. McDowell, R. Fisher, S.D. Wullschleger, P.P. Reich, B.E. Medlyn, andJ.A. Vrugt, W.L. Bauerle, and C.J. Wilson (2014), The environmental controls of plant photosyntheticcapacity at the global scale, Nature Communications, XX, XX–XX.

2Deceased after tragic accident in the Swiss Alps

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90. J. Rings, T. Kamai, M. Kandelous, P. Hartsough, J. Šimunek, J.A. Vrugt, and J.W. Hopmans (2013),Bayesian inference of tree water relations using a soil-tree-atmosphere continuum model, ProcediaEnvironmental Sciences, 19, 26–36.

89. A. Martiny, J.A. Vrugt, F.W. Primeau, and M.W. Lomas (2013), Regional variation in the partic-ulate 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 (2014), Two-dimensional probabilistic inver-sion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion withelectrical resistivity data, Geophysical Journal International, 196(3), 1508–1524, doi: 10.1093/gji/ggt482.

87. M. Sadegh, J.A. Vrugt (2013), Bridging the gap between GLUE and formal statistical approaches: Ap-proximation 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: Approxi-mate Bayesian Computation, Water Resources Research, 49, 4335–4345, doi:10.1002/wrcr.20354.

85. E. Laloy, B. Rogiers, J.A. Vrugt, D. Jacques, and D. Mallants (2013), Efficient posterior exploration ofa high-dimensional groundwater model from two-stage Markov chain Monte Carlo simulation andpolynomial chaos expansion, Water Resources Research, 49(5), 2664–2682, doi:10.1002/wrcr.20226.

84. P. Nasta, J.A. Vrugt, and N. Romano (2013), Prediction of the saturated hydraulic conductivity fromBrooks 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 distri-butions of the marine Cyanobacteria Prochlorococcus and Synechococcus, Proceedings of the NationalAcademy 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 spatiallyvariable unsaturated hydraulic conductivity using scaled particle-size distribution functions, WaterResources Research, 49(7), 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:10.1038/ngeo1757.

80. N. Linde, and J.A. Vrugt (2013), Distributed soil moisture from crosshole ground-penetrating radartravel times using stochastic inversion, Vadose Zone Journal, 12(1), 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 assim-ilation using particle Markov chain Monte Carlo simulation: theory, concepts and applications, Ad-vances in Water Resources, Anniversary Issue - 35 Years, 51, 457-478, 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 artand 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 as-sessment 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 dis-tribution from MCMC inversion of time-lapse GPR data, Water Resources Research, 48, W07510,doi:10.1029/2011WR011238.

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75. J. Rings, J.A. Vrugt, G. Schoups, J.A. Huisman, and H. Vereecken (2012), Bayesian model averagingusing particle filtering and Gaussian mixture modeling: Theory, concepts, and simulation experi-ments, Water 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 sensi-tivity analysis of ground penetrating radar data with waveguide dispersion using deterministic andMarkov chain Monte Carlo methods, Near Surface Geophysics, Special issue "Physics-based integratedcharacterization", 10(6), 641-652, doi:10.3997/1873-0604.2012041.

73. M.M. Kandelous, T. Kamai, J.A. Vrugt, J. Šimunek, B. Hanson, and J.W. Hopmans (2012), Evalua-tion of subsurface drip irrigation design and management parameters for alfalfa, Agricultural WaterManagement, 109, 81–93, doi:10.1016/j.agwat.2012.02.009.

72. E. Laloy, and J.A. Vrugt (2012), High-dimensional posterior exploration of hydrologic models usingmultiple-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), In-verse modeling of cloud-aerosol interactions - Part II: Sensitivity tests on liquid phase clouds usingMarkov chain Monte Carlo simulation approach, Atmospheric Chemistry and Physics, 12, 2823-2847,doi:10.5194/acp-12-2823-2012.

70. J.A. Vrugt, and C.J.F. ter Braak (2011), DREAM(D): An adaptive Markov chain Monte Carlo simu-lation algorithm to solve discrete, noncontinuous, and combinatorial posterior parameter estimationproblems, Hydrology and Earth System Sciences, 15, 3701-3713, doi:10.5194/hess-15-3701-2011.

69. B. Scharnagl, J.A. Vrugt, H. Vereecken, and M. Herbst (2011), Bayesian inverse modeling of soil waterdynamics at the field scale: using prior information about the soil hydraulic properties, Hydrologyand Earth System Sciences, 15, 3043-3059, doi:10.5194/hess-15-3043-2011.

68. D.G. Partridge, J.A. Vrugt, P. Tunved, A.M.L. Ekman, D. Gorea, and A. Sorooshian (2011), Inversemodeling of cloud-aerosol interactions - Part I: Detailed response surface analysis, Atmospheric Chem-istry and Physics, 11, 4749-4806, doi:10.5194/acpd-11-4749-2011.

67. M. He, T.S. Hogue, K.J. Franz, S.A. Margulis, and J.A. Vrugt (2011), Corruption of parameter behav-ior 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), Confronting uncertainty in model-based geo-statistics using Markov chain Monte Carlo simulation, Geoderma, 163, 150–622,doi:10.1016/j.geoderma.2011.03.011.

65. M. He, T.S. Hogue, K.J. Franz, S.A. Margulis, and J.A. Vrugt (2010), Characterizing parameter sensi-tivity 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 (2011), Multi-response multi-layer vadose zone model calibration usingMarkov 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 measurementsof 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 characteristicsand dynamics from ecohydrologic optimality of net carbon profit, Ecohydrology, 5, 1–18,doi:10.1002/eco.177.

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61. G. Schoups, and J.A. Vrugt (2010), A formal likelihood function for parameter and predictive infer-ence of hydrologic models with correlated, heteroscedastic and non-Gaussian errors, Water ResourcesResearch, 46, W10531, doi:10.1029/2009WR008933.

60. G. Schoups, J.A. Vrugt, F. Fenicia, and N.C. van de Giesen (2010), Corruption of accuracy andefficiency of Markov Chain Monte Carlo simulation by inaccurate numerical implementation of con-ceptual 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 assessmentof strongly non-linear groundwater models with high parameter dimensionality, Water ResourcesResearch, 46, W10517, doi:10.1029/2009WR008584.

58. J.A. Vrugt (2010), Comment on: "Multi-strategy ensemble evolutionary algorithm for dynamic multi-objective optimization" by Yu Wang and Bin Li, Memetic Computing, 2, 161-162, doi:10.1007/s12293-010-0041-8.

57. K.W. Blasch, T.P.A. Ferré, and J.A. Vrugt (2010), Environmental controls on drainage behavior ofan ephemeral stream: An example of the limitations of simple correlative data analyses, StochasticEnvironmental 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 ofpolarimetric 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 meth-ods in hydrologic applications, Stochastic Environmental Research and Risk Assessment, 24(6), 809-820,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), Improvedextraction of hydrologic information from geophysical data through coupled hydrogeophysical in-version, Water Resources Research, 46, W00D40, doi:10.1029/2008WR007060.

52. B. Scharnagl, J.A. Vrugt, H. Vereecken, and M. Herbst (2010), Information content of incubationexperiments for inverse estimation of pools in the Rothamsted carbon model: a Bayesian perspective,Biogeosciences, 7, 763-776.

51. J.A. Huisman, J. Rings, J.A. Vrugt, J. Sorg, and H. Vereecken (2010), Hydraulic properties of amodel dike from coupled Bayesian and multi-criteria hydrogeophysical inversion, Journal of Hydrol-ogy, 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: "Equifinal-ity of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?" by KeithBeven, 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 Researchand 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 fromadvection in unsaturated porous media: Experimental data, modeling and parameter uncertaintyassessment, Vadose Zone Journal, 8(2), 510-522, doi:10.2136/vzj2008.0055.

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

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47. J.A. Vrugt, C.J.F. ter Braak, C.G.H. Diks, D. Higdon, B.A. Robinson, and J.M. Hyman (2009), Acceler-ating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomizedsubspace 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 globaloptimization 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: "Dynamicallydimensioned search algorithm for computationally efficient watershed model calibration", WaterResources Research, 44, W12603, doi:10.1029/2007WR006429.

44. T. Wöhling, and J.A. Vrugt (2008), Combining multi-objective optimization and Bayesian model aver-aging 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 inputuncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlosimulation, 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 updaterand 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 Markovchain 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 ResourcesResearch, 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 differ-ences 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 modelingof 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), Aquiferstructure 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 uncertaintyassessment for large-scale streamflow simulation using global optimization, Hydrological SciencesJournal, 53(2), 293-208.

35. R.S. Blasone, J.A. Vrugt, H. Madsen, D. Rosbjerg, G.A. Zyvoloski, and B.A. Robinson (2008), General-ized 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 optimizationalgorithms for inverse modeling of vadose zone hydraulic properties, Soil Science Society of AmericaJournal, 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 inresource and labor allocation by smallholder African farmers using inverse modeling techniques,Agricultural Systems, 95, 76-95.

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32. J.A. Vrugt (2007), Comment on: "How effective and efficient are multiobjective evolutionary algo-rithms 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 theradiation 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 inlong distance bird migration, Journal of Avian Biology, 38, 432-442, doi:10.1111/j.2007.0908-8857.03909.

See also: http://openwetware.org/wiki/Optimality_In_Biology

29. J.A. Vrugt, and B.A. Robinson (2007), Improved evolutionary optimization from genetically adaptivemultimethod 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: Com-parison 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 optimizationand 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 calibra-tion 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 estimationand 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), Applica-tion 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 stochasticparameter 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 opera-tional ensemble streamflow forecasting, Journal of Hydrometeorology, 7(3), 548-565,doi:10.1175/JHM504.1.

21. M.P. Clark, and J.A. Vrugt (2006), Unraveling uncertainties in hydrologic model calibration: Address-ing the problem of compensatory parameters, Geophysical Research Letters, 33(6), L06406,doi:10.1029/2005GL025604.

20. G. Schoups, J.W. Hopmans, C.A. Young, J.A. Vrugt, and W.W. Wallender (2005), Multi-objectiveoptimization 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 ir-rigated agriculture in the San Joaquin Valley, California, Proceedings of the National Academy of Sciencesof 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 trans-port in subsurface media: Combined parameter and state estimation, Geophysical Research Letters, 32,L18408, doi:10.1029/2005GL023940.

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17. J.A. Vrugt, C.G.H. Diks, W. Bouten, H.V. Gupta, and J.M. Verstraten (2005), Improved treatmentof uncertainty in hydrologic modeling: Combining the strengths of global optimization and dataassimilation, 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 nitrogencatchment 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 distributionof water content along a TDR probe using the SCEM-UA Bayesian inverse modeling scheme, VadoseZone 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 globaloptimization, 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 bindingconstants 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 analysisscenarios 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 soilhydraulic 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 param-eters 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 efficientalgorithm 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 Metropo-lis algorithm for optimization and uncertainty assessment of hydrologic model parameters, WaterResources 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 dissolvedorganic 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 hy-drologic 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 un-certainty 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 AustrianPine 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. Šimunek (2001), One, two, and three-dimensionalroot water uptake functions for transient modeling, Water Resources Research, 37 (10), 2457-2470.

2. J.A. Vrugt, J.W. Hopmans, and J. Šimunek (2001), Calibration of a two-dimensional root water uptakemodel, 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 soilhydraulic parameters from outflow experiments, Soil Science Society of America Journal, 65, 19-27.

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Contributions in Books

5. K. Yilmaz, and J.A. Vrugt, H.V. Gupta, and S. Sorooshian (2010), Model calibration in watershed hy-drology, Chapter 3 in Advances in Data-based Approaches for Hydrologic Modeling and Forecasting(Eds: B. Sivakumar, and R. Berndtsson), World Scientific.

4. D. Higdon, C.S. Reese, J.D. Moulton, J.A. Vrugt, and C. Fox (2009), Posterior exploration for com-putationally intensive forward models, pp. xx-xx in The Handbook of Markov Chain Monte Carlo(Eds: X.L. Meng, A. Gelman, and G. Jones), Chapman & Hall/CRC Press.

3. J.A. Vrugt, and J.H. Dane (2005), Inverse modeling of soil hydraulic properties, pp. 1003-1120 inEncyclopedia of Hydrological Sciences (Eds: M.G. Anderson, and J.J. McDonnell), John Wiley &Sons Ltd., Chichester, UK.

2. H.V. Gupta, L. Bastidas, J.A. Vrugt, and S. Sorooshian (2002), Multiple criteria global optimizationfor watershed model calibration, pp. 125-132 in Monograph on Advances in Automatic Calibrationof Watershed Models, American Geophysical Union (Eds: Q. Duan, H.V. Gupta, S. Sorooshian, A.N.Rousseau, and R. Turcotte).

1. J.A. Vrugt, H.V. Gupta, W. Bouten, and S. Sorooshian (2002), A shuffled complex evolution Metropo-lis algorithm for estimating the posterior distribution of watershed model parameters, pp. 105-112

in Monograph on Advances in Automatic Calibration of Watershed Models, American GeophysicalUnion (Eds: Q. Duan, H.V. Gupta, S. Sorooshian, A.N. Rousseau, and R. Turcotte).

Conference Proceedings

8. T. Wöhling, S. Gayler, J. Ingwersen, T. Streck, J.A. Vrugt, and E. Priesack (2011), Multi-objectivecalibration of coupled soil-vegetation-atmosphere models, ModelCARE2011: Models - Repositoriesof Knowledge, Proceedings of ModelCARE2011, Leipzig, Germany, September 18-22.

7. T. Wöhling, G.F. Barkle, V.J. Bidwell, R. Dann, A. Wall, B. Moorhead, J. Clague, and J.A. Vrugt (2011),Dual-domain mixing cell modeling and uncertainty analysis for unsaturated bromide and chloridetransport, MODSIM 2011, Conference proceedings, pp. 662-668.

6. J. Bikowski, J. van der Kruk, J.A. Huisman, H. Vereecken, and J.A. Vrugt (2010), Inversion and sensi-tivity analysis of GPR data with waveguide dispersion using Markov chain Monte Carlo simulation,Proceedings of the XIII International Conference on ground penetrating radar, pp. 1-5, Lecce, Italy,June 21-25, doi:10.1109/ICGPR.2010.5550147.

5. T. Wöhling, and J.A. Vrugt (2007), Multiobjective inverse parameter estimation for modeling va-dose zone water movement, MODSIM 2007 International congress on modeling and simulation(Ed: L. Oxley, and D. Kulasiri), Modeling and simulation society of Australia and New Zealand,Christchurch, New Zealand, December 10-13, ISBN: 978-0-9758400-4-7.

4. J.A. Vrugt (2007), Markov chain Monte Carlo sampling using multiple-chain differential evolutionwith adaptive proposal updating, Proceedings of the Fifth International Symposium on Environ-mental Hydraulics, Arizona State University, Tempe, December.

3. W. Bouten, J. van Belle, J.R. van Gasteren, J.A. Vrugt, J. Shamoun-Baranes (2005), Predicting birdmigration: data-driven versus concept-driven models, Bird strike committee European proceedingsand working papers (Ed: A. Anagnostopoulos), pp. 193, Athens international airport, Athens.

2. J.A. Vrugt, C.G.H. Diks, W. Bouten, and J.M. Verstraten (2004), Improved treatment of uncertaintyin hydrologic modeling, Proceedings of the British Hydrological Society International Conference,Volume 1, pp. 389-397, Imperial College, London, England.

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1. J.A. Vrugt (2003), Merging the strengths of global optimization and data-assimilation to simultane-ously estimate parameters and state variables in hydrologic models, Proceedings of the ESF/LESCExploratory Workshop on "Hydrological risk: recent advances in peak river flow modeling, predic-tion and real time forecasting, Assessment of the impacts of land-use and climate change", Bologna,Italy, October, 23-25.

Invited Presentations

66. J.A. Vrugt, Joint inference of geostatistics and hydrogeology, Presented at American Geophysical UnionFall Meeting, San Francisco, USA, Dec. 15-19, 2014.

65. J.A. Vrugt, The iterative research cycle: Process-based model-data evaluation, Presented at American Geo-physical Union Fall Meeting, San Francisco, USA, Dec. 15-19, 2014.

64. J.A. Vrugt, The ins and outs of scientific publication: It is all in the writing, Presented at AmericanGeophysical Union Fall Meeting, San Francisco, USA, Dec. 15-19, 2014.

63. J.A. Vrugt, On diagnostic model evaluation, Department of Earth and Environmental Engineering,Columbia University, USA, TBD.

62. J.A. Vrugt, Particle Markov Chain Monte Carlo simulation, Catchment-based hydrological Model DataAssimilation (CAHMDA VI) workshop, Austin, USA, Sept. 10, 2014.

61. J.A. Vrugt, Recent advances in parameter and state estimation, Mathematics and Engineering in Marineand Earth Problems (MEME’2014), Aveiro, Portugal, July 22, 2014.

60. J.A. Vrugt, The iterative research cycle: Process-based model-data evaluation, Jet Propulsion Laboratory,Pasadena, USA, July 16, 2014.

59. J.A. Vrugt, The iterative research cycle: On model structural errors, Department of Environmental Sci-ences, Wageningen University, Wageningen, The Netherlands, Dec. 20, 2013.

58. J.A. Vrugt, Towards diagnostic model evaluation: Model malfunctioning, Department of Earth SystemScience, University of California Irvine, Irvine, USA, Oct. 9, 2013.

57. J.A. Vrugt, An information-based approach to model evaluation: On the detection of model structural errors,Meteorological Institute, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany, Sept. 20,2013.

56. J.A. Vrugt, An information-based approach to model evaluation, Forschungszentrum Jülich, IBG-3, Jülich,Germany, Sept. 16, 2013.

55. J.A. Vrugt, Bayesian analysis in environmental modeling: MCMC simulation, particle MCMC and parallelcomputing, NASA Global Modeling and Assimilation Office (GMAO), NASA Goddard Space FlightCenter, Greenbelt, MD, USA, Dec. 11, 2012.

54. J.A. Vrugt, Error reconstruction using Bayesian analysis, Presented at American Geophysical Union FallMeeting, San Francisco, USA, Dec. 3-7, 2012.

53. J.A. Vrugt, Uncertainty modeling in water resource, ecosystem and landscape management, Seminar pre-sented at CSIRO Dutton Park, Brisbane, Australia, Oct. 5, 2012.

52. J.A. Vrugt, Uncertainty modeling in water resource, ecosystem and landscape management, Seminar pre-sented at CSIRO Black Mountain, Canberra, Australia, Oct. 4, 2012.

51. J.A. Vrugt, Uncertainty modeling in water resource, ecosystem and landscape management, Seminar pre-sented at CSIRO Center for Environment & Life Sciences, Perth, Australia, Oct. 2, 2012.

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50. J.A. Vrugt, Uncertainty modeling in water resource, ecosystem and landscape management, Seminar pre-sented at CSIRO Groundwater Hydrology program, Land and Water, Adelaide, Australia, Sept. 21,2012.

49. J.A. Vrugt, An alternative blueprint for environmental modeling, Keynote presented at ComputationalMethods in Water Resources, XIX International Conference, Urbana-Champaign, USA, June 17-21,2012.

48. J.A. Vrugt, Uncertainty quantification of CPU-intensive models, Heiland and van Thyl lecture, Depart-ment of Geophysics, Colorado School of Mines, Golden, CO, USA, Feb. 9, 2012.

47. J.A. Vrugt, Particle Markov chain Monte Carlo simulation: theory, concepts and applications, Departmentof Mechanical and Aerospace Engineering, University of California San Diego, San Diego, USA, Nov.14, 2011.

46. J.A. Vrugt, Lost between two shores, Donath Medal Lecture at Annual Meeting of the Geological Societyof America, Minneapolis, USA, Oct. 9-12, 2011.

45. J.A. Vrugt, Bayesian treatment of uncertainty in environmental modeling: theory and applications, Depart-ment of Environmental Sciences, Wageningen University, Wageningen, The Netherlands, Sept. 15,2011.

44. J.A. Vrugt, A blueprint for Bayesian analysis in spatial statistics, Pedometrics 2011, University of LifeSciences, Prague, Czech Republic, Aug. 30 - Sept. 3, 2011.

43. J.A. Vrugt, Model - data synthesis using parameter estimation and uncertainty quantification, Departmentof Chemistry, School of Natural Sciences, University of California Irvine, Irvine, USA, May. 24, 2011.

42. J.A. Vrugt, Model - data synthesis using parameter estimation and uncertainty quantification, School ofEngineering, University of California at Irvine, Irvine, USA, Feb. 11, 2011.

41. J.A. Vrugt, Model calibration revisited: uncertainty quantification, Department of International Meteoro-logical Institute, Stockholm University, Stockholm, Sweden, Nov. 9, 2010.

40. J.A. Vrugt, Markov chain Monte Carlo simulation: efficiency and parallel computation, Workshop onBayesian inference in Econometrics, Finance and Earth System Science, University of Amsterdam,Amsterdam, The Netherlands, Nov. 8, 2010.

39. J.A. Vrugt, Calibration of environmental models: treatment of uncertainty, Department of Earth SystemScience, University of California Irvine, Irvine, USA, Nov. 3, 2010.

38. J.A. Vrugt, Model calibration revisited, Medal Lecture at General Assembly of the European Geophys-ical Union, Vienna, Austria, May 2-7, 2010.

37. J.A. Vrugt, Self adaptive learning in global optimization and filtering, First PEST Users Conference, Wash-ington DC, USA, Nov. 2-4, 2009.

36. J.A. Vrugt, Solving environmental models using parameter exploration and high performance computing,Presented at Earth and Environmental Sciences Division (Frontiers in Geosciences Seminar Series),Los Alamos National Laboratory, Los Alamos, USA, Sept. 28, 2009.

35. J.A. Vrugt, Parameter exploration using self-adaptive sampling and optimization algorithms to solve envi-ronmental models, Presented at Faculty of Geosciences, Utrecht University, Utrecht, The Netherlands,July 15, 2009.

34. J.A. Vrugt, Parameter exploration using self-adaptive sampling and optimization algorithms to solve envi-ronmental models, Presented at Faculty of Civil Engineering and Geosciences (48th Colloquium onRecent Advances in Water Resources), Delft University of Technology, Delft, The Netherlands, July2, 2009.

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33. J.A. Vrugt, Toward a systematic framework for model evaluation in catchment hydrology, Presented atGeneral Assembly of the European Geophysical Union, Vienna, Austria, Apr. 19-24, 2009.

32. J.A. Vrugt, Parameter exploration using self-adaptive sampling and optimization algorithms to solve envi-ronmental models, Presented at Henri Samueli School of Engineering, University of California Irvine,Irvine, USA, Apr. 6, 2009.

31. J.A. Vrugt, Uncertainty estimation using adaptive Markov chain Monte Carlo simulation and particle fil-tering, Presented at SIAM Conference on Computational Science and Engineering (CSE09), Miami,USA, Mar. 2-6, 2009.

30. J.A. Vrugt, Treatment of rainfall error using Markov chain Monte Carlo simulation, Presented at AnnualMeeting of the American Meteorological Society (AMS), Phoenix, USA, Jan. 11-15, 2009.

29. J.A. Vrugt, Inverse modeling to improve environmental models, Presented at Institute of Biodiversity andEcosystem Dynamics (IBED), University of Amsterdam, Amsterdam, The Netherlands, Nov. 13,2008.

28. J.A. Vrugt, Nonlinear parameter estimation using self-adaptive global optimization and Monte Carlo sam-pling, Presented at Mathematisches Institut der Universität Basel, Basel, Switzerland, Nov. 7, 2008.

27. J.A. Vrugt, Adaptive Markov chain Monte Carlo sampling and high performance computing for estimatingparameters in high-resolution three-dimensional flow and transport models, Presented at ComputationalMethods in Water Resources, XVII International Conference, San Francisco, USA, July 6-10, 2008.

26. J.A. Vrugt, Adaptive Markov chain Monte Carlo sampling for estimating parameters in high-resolution three-dimensional flow and transport models, Presented at New Mexico Institute of Technology, Socorro, USA,Mar. 24, 2008.

25. J.A. Vrugt, Treatment of hydrologic parameter uncertainty using Markov chain Monte Carlo sampling andhigh performance computing, Presented at Department of Hydrology and Water Resources, The Uni-versity of Arizona, Tucson, USA, Mar. 12, 2008.

24. J.A. Vrugt, Improved treatment of uncertainty in hydrologic modeling, Presented at Department of Civiland Environmental Engineering, University of California Berkeley, Berkeley, USA, Feb. 25, 2008.

23. J.A. Vrugt, and T. Wöhling, Upscaling soil hydraulic properties using field-scale inverse modeling andBayesian model averaging, Presented at American Geophysical Union Fall Meeting, San Francisco,USA, Dec. 10-14, 2007.

22. J.A. Vrugt, Self-adaptive Markov chain Monte Carlo simulation: methodological development and appli-cations, Presented at Fifth International Symposium on Environmental Hydraulics, Arizona StateUniversity, Phoenix, USA, Dec. 6, 2007.

21. J.A. Vrugt, Self-adaptive multimethod optimization, and Bayesian model averaging for calibration and uncer-tainty estimation, Presented at Chevron, San Ramon, USA, Oct. 9, 2007.

20. J.A. Vrugt, AMALGAM: A general-purpose multimethod evolutionary search algorithm for inverse modeling,Presented at Department of Geophysics, Stanford University, Stanford, USA, June 6, 2007.

19. J.A. Vrugt, Improved model calibration from genetically adaptive multimethod search, Presented at AAPGHedberg Conference on Basin Modeling Perspectives: Innovative Developments and Novel Applica-tions, The Hague, The Netherlands, May 9, 2007.

18. J.A. Vrugt, Uncertainty estimation in environmental modeling: from parameter to state estimation, Pre-sented at University of Amsterdam, Amsterdam, The Netherlands, May 7, 2007.

17. J.A. Vrugt, Confronting uncertainty in environmental modeling: Methods and applications, Presented atSwiss Federal Institute of Aquatic Science and Technology, Duebendorf, Switzerland, Sept. 19, 2006.

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16. J.A. Vrugt, Confronting uncertainty in environmental modeling: Methods and applications, Presented atKickoff Workshop on Development, Assessment and Utilization of Complex Computer Models, Sta-tistical and Applied Mathematical Sciences Institute (SAMSI), Durham, USA, Sept. 13, 2006.

15. J.A. Vrugt, Ensemble prediction strategies in environmental modeling, Presented at Workshop on Use ofLong-range Hydrologic Forecasts for Reservoir Operations, Beijing, China, July 28, 2006.

14. J.A. Vrugt, Development of a hydrologic analysis framework for improved treatment of uncertainty, Presentedat Earth and Environmental Sciences Division Review, Los Alamos, USA, May 17, 2006.

13. J.A. Vrugt, Uncertainty estimation in environmental models, Presented at Department of Civil and En-vironmental Engineering, University of Trento, Trento, Italy, Feb. 23, 2006.

12. J.A. Vrugt, and B.A. Robinson, Multi-objective parameter and state estimation for improved analysis ofmulti-tracer experiments in subsurface media, Presented at the General Assembly of the European Geo-physical Union, Vienna, Austria, Apr. 5, 2006.

11. J.A. Vrugt, and B.A. Robinson, Spatially distributed modeling of root water uptake using global optimiza-tion, Presented at workshop on Modeling Vadose Zone Flow and Transport Processes in RadioactiveWaste Management, Mol, Belgium, Feb. 23, 2006.

10. J.A. Vrugt, and B.A. Robinson, Development of a hydrologic analysis framework for improved treatmentof uncertainty, Presented at American Geophysical Union Fall Meeting, San Francisco, USA, Dec. 5,2005.

9. J.A. Vrugt, Hydrologic uncertainty assessment, Recursive calibration and data assimilation, Presented atInternational Summer School on Atmospheric and Oceanic Sciences (ISSAOS), L’Aquila, Italy, Sep.1, 2005.

8. J.A. Vrugt, Hydrologic model calibration, Concepts, strategies and applications, Presented at ISSAOS Sum-mer School, L’Aquila, Italy, Aug. 31, 2005.

7. J.A. Vrugt, Calibration of finite element models using combined parameter and state estimation, Presentedat Workshop on Community Finite Element Models for Fault Systems and Tectonic Studies, LosAlamos, USA, July 13, 2005.

6. J.A. Vrugt, Multi-criteria optimization of long-distance bird migration: analyzing the trade-off between flighttime and energy-use, Presented at 2nd EuroBAM Network Meeting, Amsterdam, Netherlands, Nov. 8,2004.

5. J.A. Vrugt, Large scale spatially distributed vadose zone modeling using global optimization, Presented atSubsurface Flow and Transport Modeling Team, Los Alamos National Laboratory, Los Alamos, USA,Aug. 24, 2004.

4. J.A. Vrugt, J.W. Hopmans, and P. Fisher, Assessment of multi-dimensional root water uptake distributions:combining measuring and modeling, Presented at University of Adelaide, Adelaide, Australia, Aug. 16,2004.

3. J.A. Vrugt, C.G.H. Diks, W. Bouten, and J.M. Verstraten, Advanced parameter sampling strategies forenvironmental modeling, Presented at 1st Meeting of the International Working Group on UncertaintyAnalysis in Hydrologic Modeling, Lugano, Switzerland, July 5-8, 2004.

2. W. Bouten, and J.A. Vrugt, Distributed modeling of catchments: Balancing modeling objectives, modelcomplexity and data availability, Presented at the General Assembly of the European GeophysicalUnion, Nice, France, Apr. 25-30, 2004.

1. J.A. Vrugt, Merging the strengths of global optimization and data-assimilation to simultaneously estimateparameters and state variables in hydrologic models, Presented at the ESF/LESC Exploratory Workshopon Hydrological Risk: Recent Advances in Peak River Flow Modeling, Prediction and Real-timeForecasting, Assessment of the Impacts of Land-use and Climate Change, Bologna, Italy, Oct. 23-25,2003.

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Other Presentations

For convenience, I only list a few of my own presentations. Over 100 co-authored talks of (co)-advisedstudents and collaborators.

22. J.A. Vrugt, Mojtaba Sadegh (2013), Approximate Bayesian Computation for Diagnostic Model Calibrationand Evaluation, Presented at American Geophysical Union Fall Meeting, San Francisco, Dec. 11, 2013.

21. J.A. Vrugt, Hydrologic data assimilation using particle Markov chain Monte Carlo simulation, Presented atAmerican Geophysical Union Fall Meeting, San Francisco, Dec. 3-7, 2012.

20. J.A. Vrugt, A differential evolution adaptive Metropolis (DREAM) particle filter for environmental modeldiagnostics, Presented at General Assembly of the European Geophysical Union, Vienna, April 19-24,2009.

19. J.A. Vrugt, C.J.F. ter Braak, M.P. Clark, J.M. Hyman, and B.A. Robinson, Bayesian treatment of forc-ing error using adaptive Markov chain Monte Carlo sampling, Presented at General Assembly of theEuropean Geophysical Union, Nice, France, April 13-18, 2008.

18. J.A. Vrugt, B.A. Robinson, and J.M. Hyman, Self-adaptive multimethod search for global optimizationof hydrologic model parameters, Presented at General Assembly of the European Geophysical Union,Nice, France, April 13-18, 2008.

17. J.A. Vrugt, A universal multimethod search strategy for computationally efficient global optimization, Pre-sented at Geological Society of America Annual Meeting, Denver, CO, USA, October 28-31, 2007.

16. J.A. Vrugt, Inverse modeling of subsurface flow and transport parameters using recent advances in globaloptimization and parallel computing, Presented at Unsaturated Zone Interest Group (UZIG) meeting,Los Alamos, NM, USA, August 27-30, 2007.

15. J.A. Vrugt, Self-adaptive multimethod search for improved calibration of hydrologic models, Presented atWater Research Symposium, Socorro, NM, USA, August 14, 2007.

14. J.A. Vrugt, and B.A Robinson, Improved evolutionary optimization from genetically adaptive multi-methodsearch, Presented at AGU fall meeting, San Francisco, CA, USA, December 10-15, 2006.

13. J.A. Vrugt, and B.A Robinson, Improved interpretation of multi-tracer experiments in subsurface media:multi-objective parameter and state estimation, Presented at 2006 international annual meeting of theAmerican Society of Agronomy, Crop Science Society of America, and Soil Science Society of Amer-ica, Indianapolis, IN, USA, Nov 12-16, 2006.

12. J.A. Vrugt, and B.A. Robinson, On the value of sequential data assimilation and Bayesian model averag-ing for probabilistic ensemble streamflow forecasting, Presented at Western Pacific Geophysics Meeting,Beijing, China, July 23-27, 2006.

11. J.A. Vrugt, and H.V. Gupta, Real-time data assimilation for operational ensemble streamflow forecasting,Presented at 2nd HEPEX workshop, Boulder, CO, USA, July 19-22, 2006.

10. J.A. Vrugt, Improved treatment of uncertainty in hydrologic modeling, Presented at New Mexico waterresources symposium, Socorro, NM, USA, August 16, 2005.

9. J.A. Vrugt, C.G.H. Diks, W. Bouten, H.V. Gupta, and J.M. Verstraten, Improved treatment of uncertaintyin hydrologic modeling: combining the strengths of global optimization and data assimilation, Presented atGeneral Assembly of the European Geophysical Union, Nice, France, April 25-30, 2004.

8. J.A. Vrugt, Improved treatment of uncertainty in hydrologic modeling, Presented at British HydrologicalSociety International Conference on Hydrology: Science & Practice for the 21st Century, ImperialCollege, London, England, July 12-16, 2004.

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7. J.A. Vrugt, C.G.H. Diks, W. Bouten, H.V. Gupta, and J.M. Verstraten, Towards a complete treatment ofuncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation,Presented at 7th Netherlands Earth Sciences conference, April 5-6, Veldhoven, 2004.

6. J.A. Vrugt, G.H. Schoups, J.W. Hopmans, C.A. Young, W.W. Wallender, T. Harter, and W. Bouten,Identification of spatially distributed soil hydraulic properties in hydrologic modeling using global optimiza-tion, Presented at AGU fall meeting, San Francisco, CA, USA, December 8-12, 2003.

5. J.A. Vrugt, H.V. Gupta, W. Bouten, and S. Sorooshian, A Shuffled Complex Evolution Metropolis algo-rithm for confronting parameter uncertainty in hydrologic modeling, Presented at General Assembly of theEuropean Geophysical Society, Nice, France, April 6-11, 2003.

4. J.A. Vrugt, H.V. Gupta, W. Bouten, and S. Sorooshian, Confronting uncertainty in hydrologic modeling,Presented at International Study Group on Inverse Modeling (ISGIM), Thurnau, Germany, April 3-5,2003.

3. J.A. Vrugt, H.V. Gupta, W. Bouten, and S. Sorooshian, A Shuffled Complex Evolution Metropolis algo-rithm for optimization and uncertainty assessment of hydrological model parameters, Presented at AGU fallmeeting, San Francisco, CA, USA, December 6-10, 2002.

2. J.A. Vrugt, J.W. Hopmans, and J. Šimunek, Application of one, two and three-dimensional root water uptakein transient flow modeling, Presented at International Study Group on Inverse Modeling (ISGIM),Orange Coast, AL, USA, November 1-3, 2000.

1. J.A. Vrugt, and W. Bouten, Is the best fit to experimental data what we are really looking for?, Presentedat International Study Group on Inverse Modeling (ISGIM), Orange Coast, AL, USA, November 1-3,2000.

Posters

9. E. Laloy, N. Linde, and J.A. Vrugt, Probabilistic inference of high-dimensional multiGaussian per-meability fields and their associated variograms from flow and/or transport data, ComputationalMethods in Water Resources (CMWR), Stuttgart, June 10 - 13, 2014.

8. G.J. De Lannoy, R.H. Reichle, and J.A. Vrugt, Posterior uncertainty of GEOS-5 L-band radiativetransfer model parameters and brightness temperatures after calibration with SMOS observations,AGU fall meeting, San Francisco, December 3 - 7, 2012.

7. G. Schoups, J.A. Vrugt, F. Fenicia, and N. van de Giesen, Identification of accurate nonlinear rainfall-runoff models with unique parameters, EGU meeting, Vienna, April 2009.

6. T.P.A. Ferré, J.A. Vrugt, and A.C. Hinnell, Hydrogeophysical estimation of soil hydraulic propertiesduring one-step outflow, AGU fall meeting, San Francisco, December, 2007.

5. P.H. Stauffer, and J.A. Vrugt, The unsaturated zone transport test, Busted Butte, NV: Phase 1B:Experimental results and model validation Unsaturated Zone Interest Group (UZIG) meeting, LosAlamos, NM, USA, August 27 - 30, 2007.

4. J.A. Vrugt, H.V. Gupta, B. Ó Nualláin, H.V. Gupta, and W. Bouten, Real-time data assimilation foroperational ensemble streamflow forecasting, EGU meeting, Vienna, April 2005.

3. J.W. Hopmans, G.H. Schoups, J.A. Vrugt, C. Young, T. Harter, and W.W. Wallender, Parameteridentification of large-scale spatially distributed vadose zone properties using global optimization,Gordon Conference on Flow and Transport in Porous Media, Oxford, England, July 11-16, 2004.

2. J.A. Vrugt, G.H. Schoups, J.W. Hopmans, C.A. Young, W.W. Wallender, T. Harter, and W. Bouten, In-verse modeling of large-scale spatially distributed vadose zone properties using global optimization,General Assembly of the European Geophysical Union, Nice, France, April 25-30, 2004.

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1. T. Wagener, J.A. Vrugt, H.S. Wheater, H.V. Gupta, and S. Sorooshian, A dynamic approach to theidentification of conceptual hydrological models, AGU fall meeting, San Francisco, CA, USA, De-cember 6-10, 2002.

Computer/ Programming Skills

Excellent in MATLAB/OCTAVE, proficient in Fortran, Unix/shell script programming, LaTeX, MPI, par-allel computing, and all aspects of Windows platform (Word, PowerPoint, Excel, WordPerfect). Familiarwith GIS software and C programming.

Language Skills

Dutch (native), English (fluent), German (fluent), Farsi (Basic), French (Basic).

References

Available upon request.

Last updated: July 24, 2014

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