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Home / About us / Personal Pages / Jasper Vrugt

Jasper Vrugt

Jasper Vrugt Associate Professor
Department of Civil and Environmental Engineering
University of California, Irvine
Irvine, CA, USA
Web: UCI Samueli
Phone: +1-949-824-4515 (Office)
Phone: +1-505-231-2698 (Cell)
Fax: +1-949-824-3672
e-mail: jasper@uci.edu


Other WEB sites:

  • Faculty Website
  • Software

Academic Degrees:

  • Ph.D. Faculty of Science, University of Amsterdam, 2004, Cum Laude
  • M.S. Faculty of Social and Behavioral Sciences, University of Amsterdam, 1999, Cum Laude

Short CV:

  • 2015 - present: Associate Professor, Civil and Environmental Engineering (CEE), Uiversity of California Irvine, USA
  • 2011 - 2014: Associate Professor (0.2 FTE), Faculty of Science (CGE), University of Amsterdam, The Netherlands
  • 2010 - present: Assistant Professor, Civil and Environmental Engineering, 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: Director's Funded Postdoctoral Fellow, LANL, USA
  • 2000 - 2004: PhD in Science, University of Amsterdam, The Netherlands
  • 1994-1999: MS in Physical Geography, University of Amsterdam, The Netherlands

Selected Honors:

  • 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

Other Professional Activities:

  • 2004 - present Chair and Organizer of 25+ sessions at (inter)National conferences
  • 2004 - present About 80 invited talks and seminars at Universities, Inter(National) Meetings, and Labs.

Research Interests:

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, geophysics, groundwater, hydrogeophysics, hydrogeology, geophysics, geostatistics, structural engineering, soil physics, surface hydrology, 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), (h) geomorphologic modeling of the depth to unweathered bedrock, (i) detection of structure defects in concrete buildings, and (j) and probabilistic analysis of slope stability. 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.

Editorial Appointments:

  • Associate editor of the Vadose Zone Journal (2008 - present).
  • Associate editor of the Environmental Modeling & Software (2009 - present).
  • Associate editor of the Hydrology and Earth Systems Sciences (2008 - 2015).
  • Associate editor of the Water Resources Research (2010 - 2015).

Publications

  1. J.A. Vrugt, A.H. Weerts, and W. Bouten (2001), Information content of data for identifying soil hydraulic parameters from outflow experiments, Soil Science Society of America Journal, 65, 19-27.
  2. J.A. Vrugt, J.W. Hopmans, and J. Simunek (2001), Calibration of a two-dimensional root water uptake model, Soil Science Society of America Journal, 65, 1027-1037.
  3. J.A. Vrugt, M.T. van Wijk, J.W. Hopmans, and J. Simunek (2001), One, two, and three-dimensional root water uptake functions for transient modeling, Water Resources Research, 37 (10), 2457-2470.
  4. J.A. Vrugt, W. Bouten, S.C. Dekker, and P.A.D. Musters (2002), Transpiration dynamics of an Austrian Pine stand and its forest floor: identifying controlling conditions using artificial neural networks, Advances in Water Resources, 25, 293-303.
  5. J.A. Vrugt, and W. Bouten (2002), Validity of first-order approximations to describe parameter uncertainty in soil hydrologic models, Soil Science Society of America Journal, 66 (6), 1740-1752.
  6. J.A. Vrugt, W. Bouten, H.V. Gupta, and S. Sorooshian (2002), Toward improved identifiability of hydrologic model parameters: The information content of experimental data, Water Resources Research, 38 (12), art. no. 1312, doi:10.1029/2001WR001118
  7. K.G.J. Nierop, B. Jansen, J.A. Vrugt, , and J.M. Verstraten (2002), Copper complexation by dissolved organic matter and uncertainty assessment of their stability constants, Chemosphere, 49 (10), 1191- 1200.
  8. J.A. Vrugt, H.V. Gupta, W. Bouten, and S. Sorooshian (2003), A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters, Water Resources Research, 39 (8), art. No. 1201, doi:10.1029/2002WR001642.
  9. J.A. Vrugt, H.V. Gupta, L.A. Bastidas, W. Bouten, and S. Sorooshian (2003), Effective and efficient algorithm for multi-objective optimization of hydrologic models, Water Resources Research, 39 (8), art. No. 1214, doi:10.1029/2002WR001746.
  10. J.A. Vrugt, S.C. Dekker, and W. Bouten (2003), Identification of rainfall interception model parameters from measurements of throughfall and forest canopy storage, Water Resources Research, 39 (9), art. No. 1251, doi:10.1029/2003WR002013.
  11. J.A. Vrugt, W. Bouten, H.V. Gupta, and J.W. Hopmans (2003), Toward improved identifiability of soil hydraulic parameters: On the selection of a suitable parametric model, Vadose Zone Journal, 2, 98-113.
  12. J.A. Huisman, W. Bouten, J.A. Vrugt, and P.A. Ferré (2004), Accuracy of frequency domain analysis scenarios for the determination of complex dielectric permittivity, Water Resources Research, W02401, doi:10.1029/2002WR001601.
  13. B. Jansen, K.G.J. Nierop, J.A. Vrugt, and J.M. Verstraten (2004), (Un)certainty of overall binding constants of Al with dissolved organic matter determined by the Scatchard approach, Water Research, 38, 1270-1280.
  14. J.A. Vrugt, G.H. Schoups, J.W. Hopmans, C.H. Young, W. Wallender, T. Harter, and W. Bouten (2004), Inverse modeling of large scale spatially distributed vadose zone properties using global optimization, Water Resources Research, 40(6), W06503, doi:10.1029/2003WR002706.
  15. T.J. Heimovaara, J.A. Huisman, J.A. Vrugt, and W. Bouten (2004), Obtaining the spatial distribution of water content along a TDR probe using the SCEM-UA Bayesian inverse modeling scheme, Vadose Zone Journal, 3, 1128-1145.
  16. K.J. Raat, J.A. Vrugt, W. Bouten, and A. Tietema (2004), Towards reduced uncertainty in nitrogen catchment modeling: quantifying the effect of field observation uncertainty on model calibration, Hydrology and Earth Systems Sciences, 8(4), 751-763.
  17. J.A. Vrugt, C.G.H. Diks, W. Bouten, H.V. Gupta, and J.M. Verstraten (2005), Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation, Water Resources Research, 41(1), W01017, doi:10.1029/2004WR003059.
  18. J.A. Vrugt, B.A. Robinson, and V.V. Vesselinov (2005), Improved inverse modeling of flow and transport in subsurface media: Combined parameter and state estimation, Geophysical Research Letters, 32, L18408, doi:10.1029/2005GL023940.
  19. G. Schoups, J.W. Hopmans, C.A. Young, J.A. Vrugt, and W.W. Wallender (2005), Sustainability of irrigated agriculture in the San Joaquin Valley, California, Proceedings of the National Academy of Sciences of the United States of America, 102 (43), 15352-15356, doi:10.1073/pnas.0507723102. Features as Editor’s Choice in Science (2005), Science, 310, 593
  20. G. Schoups, J.W. Hopmans, C.A. Young, J.A. Vrugt, and W.W. Wallender (2005), Multi-objective optimization of a regional spatially-distributed subsurface waterflow model, Journal of Hydrology, 20 - 48, 311(1-4), doi:10.1016/j.jhydrol.2005.01.001.
  21. M.P. Clark, and J.A. Vrugt (2006), Unraveling uncertainties in hydrologic model calibration: Addressing the problem of compensatory parameters, Geophysical Research Letters, 33(6), L06406, doi:10.1029/2005GL025604.
  22. J.A. Vrugt, H.V. Gupta, B. Ó Nualláin, and W. Bouten (2006), Real-time data assimilation for operational ensemble streamflow forecasting, Journal of Hydrometeorology, 7(3), 548-565, doi:10.1175/JHM504.1.
  23. J.A. Vrugt, H.V. Gupta, S. Sorooshian, T. Wagener, and W. Bouten (2006), Application of stochastic parameter optimization to the Sacramento soil moisture accounting model, Journal of Hydrology, 325(1-4), 288 - 307, doi:10.1016/j.jhydrol.2005.10.041.
  24. J.A. Vrugt, B. Ó Nualláin, B.A. Robinson, W. Bouten, S.C. Dekker, and P.M.A. Sloot (2006), Application of parallel computing to stochastic parameter estimation in environmental models, Computers & Geosciences, 32(8), 1139 - 1155, doi:10.1016/j.cageo.2005.10.015.
  25. J.A. Vrugt, and Shlomo P. Neuman (2006), Introduction to special section on parameter estimation and uncertainty estimation in the unsaturated zone, Vadose Zone Journal, 5, 915-916, doi:10.2136/vzj2006.0098.
  26. J.A. Vrugt, M.P. Clark, C.G.H. Diks, Q. Duan, and B.A. Robinson (2006), Multi-objective calibration of forecast ensembles using Bayesian Model Averaging, Geophysical Research Letters, 33, L19817, doi:10.1029/2006GL027126.
  27. L. Feyen, J.A. Vrugt, B. Ó Nualláin, J. van der Knijff, and A. de Roo (2007), Parameter optimization and uncertainty assessment for large-scale streamflow forecasting, Journal of Hydrology, 332, 276-289.
  28. J.A. Vrugt, and B.A. Robinson (2007), Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging, Water Resources Research, 43, W01411, doi:10.1029/2005WR004838.
  29. J.A. Vrugt, and B.A. Robinson (2007), Improved evolutionary optimization from genetically adaptive multimethod search, Proceedings of the National Academy of Sciences of the United States of America, 104, 708-711, doi:10.1073/pnas.0610471104.
  30. J.A. Vrugt, J. van Belle, and W. Bouten (2007), Pareto front analysis of flight time and energy use in long distance bird migration, Journal of Avian Biology, 38, 432-442, doi:10.1111/j.2007.0908-8857.03909. See also: http://openwetware.org/wiki/Optimality_In_Biology
  31. J. Koller, Y. Chen, G. D. Reeves, R. H. W. Friedel, T. E. Cayton, and J.A. Vrugt (2007), Identifying the radiation belt source region by data assimilation, Journal of Geophysical Research - Space Physics, 112, A06244, doi:10.1029/2006JA012196.
  32. J.A. Vrugt (2007), Comment on: "How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?", Hydrology and Earth System Sciences, 11, 1435-1436.
  33. P. Tittonell, M.T. van Wijk, M.C. Rufino, J.A. Vrugt, and K.E. Giller (2007), Analyzing trade-offs in resource and labor allocation by smallholder African farmers using inverse modeling techniques, Agricultural Systems, 95, 76-95.
  34. T. Wöhling, J.A. Vrugt, and G.F. Barkle (2008), Comparison of three multiobjective optimization algorithms for inverse modeling of vadose zone hydraulic properties, Soil Science Society of America Journal, 72, 305-319, doi:10.2136/sssaj2007.0176.
  35. R.S. Blasone, J.A. Vrugt, H. Madsen, D. Rosbjerg, G.A. Zyvoloski, and B.A. Robinson (2008), Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling, Advances in Water Resources, 31, 630-648, doi:10.1016/j.advwatres.2007.12.003.
  36. L. Feyen, M. Khalas, and J.A. Vrugt (2008), Semi-distributed parameter optimization and uncertainty assessment for large-scale streamflow simulation using global optimization, Hydrological Sciences Journal, 53(2), 293-208.
  37. D.R. Harp, Z. Dai, A.V. Wolfsberg, J.A. Vrugt, B.A. Robinson, and V.V. Vesselinov (2008), Aquifer structure identification using stochastic inversion, Geophysical Research Letters, 35, L08404, doi:10.1029/2008GL033585.
  38. J.A. Vrugt, P.H. Stauffer, T. Wöhling, B.A. Robinson, and V.V. Vesselinov (2008), Inverse modeling of subsurface flow and transport properties: A review with new developments, Vadose Zone Journal, 7(2), 843-864, doi:10.2136/vzj2007.0078.
  39. M.P. Clark, A.G. Slater, D.E. Rupp, R.A. Woods, J.A. Vrugt, H. Gupta, T. Wagener, and L. Hay (2008), Framework for understanding structural errors (FUSE): A modular framework to diagnose differences between hydrological models, Water Resources Research, 44, W00B02, doi:10.1029/2007WR006735.
  40. H. Vereecken, J.A. Huisman, H. Bogena, J. Vanderborght, J.A. Vrugt, and J.W. Hopmans (2008), On the value of soil moisture measurements in vadose zone hydrology: A review, Water Resources Research, 44, W00D06, doi:10.1029/2008WR006829.
  41. J.A. Vrugt, C.G.H. Diks, and M.P. Clark (2008), Ensemble Bayesian model averaging using Markov chain Monte Carlo sampling, Environmental Fluid Mechanics, 8(5-6), 579-595, doi:10.1007/s10652-008- 9106-3.
  42. C.J.F. ter Braak, and J.A. Vrugt (2008), Differential evolution Markov chain with snooker updater and fewer chains, Statistics and Computing, 18(4), 435-446, doi:10.1007/s11222-008-9104-9.
  43. J.A. Vrugt, C.J.F. ter Braak, M.P. Clark, J.M. Hyman, and B.A. Robinson (2008), Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation, Water Resources Research, 44, W00B09, doi:10.1029/2007WR006720.
  44. T. Wöhling, and J.A. Vrugt (2008), Combining multi-objective optimization and Bayesian model averaging to calibrate forecast ensembles of soil hydraulic models, Water Resources Research, 44, W12432, doi:10.1029/2008WR007154.
  45. A. Behrangi, B. Khakbaz, J.A. Vrugt, Q. Duan, and S. Sorooshian (2008), Comment on: "Dynamically dimensioned search algorithm for computationally efficient watershed model calibration", Water Resources Research, 44, W12603, doi:10.1029/2007WR006429.
  46. J.A. Vrugt, B.A. Robinson, and J.M. Hyman (2009), Self-adaptive multimethod search for global optimization in real-parameter spaces, IEEE Transactions on Evolutionary Computation, 13(2), 243-259, doi:10.1109/TEVC.2008.924428.
  47. J.A. Vrugt, C.J.F. ter Braak, C.G.H. Diks, D. Higdon, B.A. Robinson, and J.M. Hyman (2009), Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling, International Journal of Nonlinear Sciences and Numerical Simulation, 10(3), 273-290.
  48. P.H. Stauffer, J.A. Vrugt, H.J. Turin, C.W. Gable, and W.E. Soll (2009), Untangling diffusion from advection in unsaturated porous media: Experimental data, modeling and parameter uncertainty assessment, Vadose Zone Journal, 8(2), 510-522, doi:10.2136/vzj2008.0055. Features on the cover (2009), Vadose Zone Journal, 8(2)
  49. J.A. Vrugt, C.J.F. ter Braak, H.V. Gupta, and B.A. Robinson (2009), Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?, Stochastic Environmental Research and Risk Assessment, 23(7), 1011-1026, doi:10.1007/s00477-008-0274-y.
  50. J.A. Vrugt, C.J.F. ter Braak, H.V. Gupta, and B.A. Robinson (2009), Reply to Comment on: "Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?" by Keith Beven, Stochastic Environmental Research and Risk Assessment, 23(7), 1061-1062, doi:10.1007/s00477-008-0284-9.
  51. J.A. Huisman, J. Rings, J.A. Vrugt, J. Sorg, and H. Vereecken (2010), Hydraulic properties of a model dike from coupled Bayesian and multi-criteria hydrogeophysical inversion, Journal of Hydrology, 380(1-2), 62-73, doi:10.1016/j.jhydrol.2009.10.023.
  52. B. Scharnagl, J.A. Vrugt, H. Vereecken, and M. Herbst (2010), Information content of incubation experiments for inverse estimation of pools in the Rothamsted carbon model: a Bayesian perspective, Biogeosciences, 7, 763-776.
  53. A.W. Hinnell, T.P.A. Ferré, J.A. Vrugt, S. Moysey, J.A. Huisman, and M.B. Kowalsky (2010), Improved extraction of hydrologic information from geophysical data through coupled hydrogeophysical inversion, Water Resources Research, 46, W00D40, doi:10.1029/2008WR007060.
  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.
  55. C.G.H. Diks, and J.A. Vrugt (2010), Comparison of point forecast accuracy of model averaging methods in hydrologic applications, Stochastic Environmental Research and Risk Assessment, 24(6), 809-820, doi:10.1007/s00477-010-0378-z.
  56. J.J. Gourley, S. Giangrande, Y. Hong, Z.L. Flamig, T. Schuur, and J.A. Vrugt (2010), Impacts of polarimetric radar observations on hydrologic simulation, Journal of Hydrometeorology, 11(3), 781-796, doi:10.1175/2010JHM1218.1.
  57. K.W. Blasch, T.P.A. Ferré, and J.A. Vrugt (2010), Environmental controls on drainage behavior of an ephemeral stream: An example of the limitations of simple correlative data analyses, Stochastic Environmental Research and Risk Assessment, 24(7), 1077-1087, doi:10.1007/s00477-010-0398-8.
  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.
  59. E. Keating, J. Doherty, J.A. Vrugt, and Q. Kang (2010), Optimization and uncertainty assessment of strongly non-linear groundwater models with high parameter dimensionality, Water Resources Research, 46, W10517, doi:10.1029/2009WR008584.
  60. G. Schoups, J.A. Vrugt, F. Fenicia, and N.C. van de Giesen (2010), Corruption of accuracy and efficiency of Markov Chain Monte Carlo simulation by inaccurate numerical implementation of conceptual hydrologic models, Water Resources Research, 46, W10530, doi:10.1029/2009WR008648.
  61. G. Schoups, and J.A. Vrugt (2010), A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic and non-Gaussian errors, Water Resources Research, 46, W10531, doi:10.1029/2009WR008933.
  62. S.C. Dekker, J.A. Vrugt, and R.J. Elkington (2010), Significant variation in vegetation characteristics and dynamics from ecohydrologic optimality of net carbon profit, Ecohydrology, 5, 1-18, doi:10.1002/eco.177.
  63. J.H. Dane, J.A. Vrugt, and E. Unsal (2010), Soil hydraulic functions determined from measurements of air permeability, capillary modeling and high-dimensional AMALGAM parameter estimation. Vadose Zone Journal, 10, 1-7, doi:10.2136/vzj2010.0053.
  64. T. Wöhling, and J.A. Vrugt (2011), Multi-response multi- layer vadose zone model calibration using Markov chain Monte Carlo simulation and field water retention data, Water Resources Research, 47, W04510, doi:10.1029/2010WR009265.
  65. M. He, T.S. Hogue, K.J. Franz, S.A. Margulis, and J.A. Vrugt (2010), Characterizing parameter sensitivity and uncertainty for a snow model across hydroclimatic regimes, Advances in Water Resources, 34, 114--127, doi:10.1016/j.advwatres.2010.10.002.
  66. B. Minasny, J.A. Vrugt, and A.B. McBratney (2011), Confronting uncertainty in model-based geostatistics using Markov chain Monte Carlo simulation, Geoderma, 163, 150-622, doi:10.1016/j.geoderma.2011.03.011.
  67. M. He, T.S. Hogue, K.J. Franz, S.A. Margulis, and J.A. Vrugt (2011), Corruption of parameter behavior and regionalization by model and forcing data errors: A Bayesian example using the SNOW17 model, Water Resources Research, 47, W07546, doi:10.1029/2010WR009753.
  68. D.G. Partridge, J.A. Vrugt, P. Tunved, A.M.L. Ekman, D. Gorea, and A. Sorooshian (2011), Inverse modeling of cloud-aerosol interactions - Part I: Detailed response surface analysis, Atmospheric Chemistry and Physics, 11, 4749-4806, doi:10.5194/acpd-11-4749-2011.
  69. B. Scharnagl, J.A. Vrugt, H. Vereecken, and M. Herbst (2011), Inverse modelling of in situ soil water dynamics: investigating the effect of different prior distributions of the soil hydraulic parameters, Hydrology and Earth System Sciences, 15, 3043-3059, doi:10.5194/hess-15-3043-2011.
  70. J.A. Vrugt, and C.J.F. ter Braak (2011), DREAM_(D): An adaptive Markov chain Monte Carlo simulation algorithm to solve discrete, noncontinuous, and combinatorial posterior parameter estimation problems, Hydrology and Earth System Sciences, 15, 3701-3713, doi:10.5194/hess-15-3701-2011.
  71. D.G. Partridge, J.A. Vrugt, P. Tunved, A.M.L. Ekman, H. Struthers, and A. Sorooshian (2012), Inverse modeling of cloud-aerosol interactions - Part II: Sensitivity tests on liquid phase clouds using Markov chain Monte Carlo simulation approach, Atmospheric Chemistry and Physics, 12, 2823-2847, doi:10.5194/acp-12-2823-2012.
  72. E. Laloy, and J.A. Vrugt (2012), High-dimensional posterior exploration of hydrologic models using multiple-try DREAM_(ABC) and high-performance computing, Water Resources Research, 48, W01526, doi:10.1029/2011WR010608.
  73. M.M. Kandelous, T. Kamai, J.A. Vrugt, J. Simunek, B. Hanson, and J.W. Hopmans (2012), Evaluation of subsurface drip irrigation design and management parameters for alfalfa, Agricultural Water Management, 109, 81-93, doi:10.1016/j.agwat.2012.02.009.
  74. J. Bikowski, J.A. Huisman, J.A. Vrugt, H. Vereecken, and J. van der Kruk (2012), Inversion and sensitivity analysis of ground penetrating radar data with waveguide dispersion using deterministic and Markov chain Monte Carlo methods, Near Surface Geophysics, Special issue "Physics-based integrated characterization", 10(6), 641-652, doi:10.3997/1873-0604.2012041.
  75. J. Rings, J.A. Vrugt, G. Schoups, J.A. Huisman, and H. Vereecken (2012), Bayesian model averaging using particle filtering and Gaussian mixture modeling: Theory, concepts, and simulation experiments, Water Resources Research, 48, W05520, doi:10.1029/2011WR011607.
  76. E. Laloy, N. Linde, and J.A. Vrugt (2012), Mass conservative three-dimensional water tracer distribution from MCMC inversion of time-lapse GPR data, Water Resources Research, 48, W07510, doi:10.1029/2011WR011238.
  77. H.V. Gupta, M.P. Clark, J.A. Vrugt, G. Abramowitz, and M. Ye (2012), Towards a comprehensive assessment of model structural adequacy, Water Resources Research, 48, W08301, doi:10.1029/2011WR011044.
  78. J.A. Huisman, J.A. Vrugt, and T.P.A. Ferré (2012), Vadose zone model-data fusion: State of the art and future challenges, Vadose Zone Journal, 11, doi:10.2136/vzj2012.0140.
  79. J.A. Vrugt, C.J.F. ter Braak, C.G.H. Diks, and G. Schoups (2013), Advancing hydrologic data assimilation using particle Markov chain Monte Carlo simulation: theory, concepts and applications, Advances in Water Resources, Anniversary Issue - 35 Years, 51, 457-478, doi:10.1016/j.advwatres.2012.04.002.
  80. N. Linde, and J.A. Vrugt (2013), Distributed soil moisture from crosshole ground-penetrating radar travel times using stochastic inversion, Vadose Zone Journal, 12(1), doi:10.2136/vzj2012.0101.
  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.
  82. P. Nasta, N. Romano, S. Assouline, J.A. Vrugt, and J.W. Hopmans (2013), Prediction of spatially variable unsaturated hydraulic conductivity using scaled particle-size distribution functions, Water Resources Research, 49(7), 4219-4229, doi:10.1002/wrcr.20255.
  83. P. Flombaum, J.L. Gallegos, R.A. Gordillo, J. Rincon, 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.
  84. P. Nasta, J.A. Vrugt, and N. Romano (2013), Prediction of the saturated hydraulic conductivity from Brooks and Corey's water retention parameters, Water Resources Research, 49, 2918-2925, doi:10.1002/wrcr.20269.
  85. E. Laloy, B. Rogiers, J.A. Vrugt, D. Jacques, and D. Mallants (2013), Efficient posterior exploration of a high-dimensional groundwater model from two-stage Markov chain Monte Carlo simulation and polynomial chaos expansion, Water Resources Research, 49(5), 2664-2682, doi:10.1002/wrcr.20226.
  86. J.A. Vrugt, and M. Sadegh (2013), Towards diagnostic model calibration and evaluation: Approximate Bayesian computation, Water Resources Research, 49, 4335-4345, doi:10.1002/wrcr.20354.
  87. M. Sadegh, J.A. Vrugt (2013), Bridging the gap between GLUE and formal statistical approaches: Approximation Bayesian computation, Hydrology and Earth System Sciences, 17, 4831-4850, doi:10.5194/hess-17-4831-2013.
  88. M.R. Carbajal, N. Linde, T. Kalscheuer, and J.A. Vrugt (2014), Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data, Geophysical Journal International, 196(3), 1508-1524, doi: 10.1093/gji/ggt482.
  89. A. Martiny, J.A. Vrugt, F.W. Primeau, and M.W. Lomas (2013), Regional variation in the particulate organic carbon to nitrogen ratio in the surface ocean, Global Biogeochemical Cycles, 27, 1-9, doi:10.1002/gbc.20061.
  90. J. Rings, T. Kamai, M. Kandelous, P. Hartsough, J. \v{Sim\r unek, J.A. Vrugt, and J.W. Hopmans (2013), Bayesian inference of tree water relations using a soil-tree-atmosphere continuum model, Procedia Environmental Sciences, 19, 26-36.
  91. M. Sadegh, J.A. Vrugt (2014), Approximation Bayesian computation using Markov chain Monte Carlo simulation: DREAM_(ABC), Water Resources Research, 50, doi:10.1002/2014WR015386.
  92. 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.
  93. G.J.M. De Lannoy, R.H. Reichle, and J.A. Vrugt (2014), Uncertainty quantification of GEOS-5 L-Band radiative transfer model parameters using Bayesian inference and SMOS observations, Remote Sensing of Environment, 148, 146-157, doi:10.1016/j.rse.2014.03.030.
  94. T. Lochbuehler, S.J. Breen, R.L. Detwiler, J.A. Vrugt, and N. Linde (2014), Probabilistic electrical resistivity tomography for a CO2 sequestration analog, Journal of Applied Geophysics, 107, 80-92, doi:10.1016/j.jappgeo.2014.05.013.
  95. J.A. Vrugt (2014), Soroosh Sorooshian receives 2013 Robert E. Horton Medal: Citation, Eos, Transactions American Geophysical Union, 95(1), 7, doi:10.1002/2014EO010017.
  96. H.R. Maier, Z. Kapelan, J. Kasprzyk, J. Kollat, L.S. Matott, M.C. Cunha, G.C. Dandy, M.S. Gibbs, E. Keedwell, A. Marchi, A. Ostfeld, D. Savic, D.P. Solomatine, J.A. Vrugt, A.C. Zecchin, B.S. Minsker, E.J. Barbour, G. Kuczera, F. Pasha, A. Castelletti, M. Giuliani, and P.M. Reed (2014), Evolutionary algorithms and other metaheuristics in waterresources: Current status, research challenges and future directions, Environmental Modelling & Software, 62, 271-299, doi:10.1016/j.envsoft.2014.09.013.
  97. J.A. Vrugt, and E. Laloy (2014), Reply to comment by Chu et al. on "High-dimensional posterior exploration of hydrologic models using multiple-try DREAM_(ZS) and high-performance computing, Water Resources Research, 50, 2781-2786, doi:10.1002/2013WR014425.
  98. A.C. Martiny, J.A.Vrugt, and M.W. Lomas (2015), Concentrations and ratios of particulate organic carbon, nitrogen, and phosphorus in the global ocean, Nature Scientific Data, 1:140048, doi:10.1038/sdata.2014.48.
  99. T. Lochbuehler, J.A. Vrugt, M. Sadegh, and N. Linde (2015), Summary statistics from training images as prior information in probabilistic inversion, Geophysical Journal International, 201, 157-171, doi:10.1093/gji/ggv008.
  100. J. Ball, E. Davidson, T. Holloway, M.A. Holmes, J.A. McKenzie, S. Mukasa, B. Paredes, C. Pieters, M. Sivapalan, and J.A. Vrugt (2015), Improving your success in AGU honors, Eos, Transactions American Geophysical Union, 96, doi:10.1029/2015EO026143.
  101. A.A. Ali, C. Xu, A. Rogers, N.G. McDowell, B.E. Medlyn, R.A. Fisher, S.D. Wullschleger, P.B. Reich, J.A. Vrugt, W.L. Bauerle, L.S. Santiago, and C.J. Wilson (2015), Global scale environmental control of plant photosynthetic capacity, Ecological Applications, 25, 2349-2365, doi:10.1890/14-2111.1, 2015, 2015.
  102. E. Laloy, N. Linde, D. Jacques, and J.A. Vrugt (2015), Probabilistic inference of multi-Gaussian fields from indirect hydrological data using circulant embedding and dimensionality reduction, Water Resources Research, 51, 4224-4243, doi:10.1002/2014WR016395.
  103. A. Askarizadeh, M.A. Rippy, T.D. Fletcher, D. Feldman, J. Peng, P. Bowler, A. Mehring, B. Winfrey, J.A Vrugt, A. AghaKouchak, S.C. Jiang, B.F. Sanders, L. Levin, S. Taylor, S.B. Grant (2015), From rain tanks to catchments: Use of low-impact development to address hydrologic symptoms of the urban stream syndrome, Environmental Science and Technology, 49, 11264-11280, doi:10.1021/acs.est.5b01635.
  104. C.P. Kikuchi, T.P.A. Ferré, and J.A. Vrugt (2015), On the optimal design of experiments for conceptual and predictive discrimination of hydrologic system models, Water Resources Research, 51, 4454-4481, doi:10.1002/2014WR016795.
  105. F.C. Sperna Weiland, J.A. Vrugt, R.L.P.H. van Beek, A.H. Weerts, and M.F.P. Bierkens (2015), Significant uncertainty in global scale hydrological modeling from precipitation data errors, Journal of Hydrology, 529 (3), 1095-1115, doi:10.1016/j.jhydrol.2015.08.061.
  106. T. Skaggs, M.H. Young, and J.A. Vrugt (2015), Reproducible research in vadose zone sciences, Vadose Zone Journal, 10, XX-XX, doi:10.2136/vzj2015.06.0088.
  107. M. Sadegh, J.A. Vrugt, C. Xu, and E. Volpi (2015), The stationarity paradigm revisited: Hypothesis testing using diagnostics, summary metrics, and DREAM_(ABC), Water Resources Research, 51, 9207-9231, doi:10.1002/2014WR016805.
  108. J.A. Vrugt (2016), Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB Implementation, Environmental Modelling & Software, 75, 273-316, doi:10.1016/j.envsoft.2015.08.013.
  109. V. Yildiz, and J.A. Vrugt (2016), Run-of-river hydropower plants: HYPER numerical model, turbines, energy production, economic analysis, and global optimization, Environmental Modeling & Software, XX, XX-XX, doi:envsoft-d-15-00306, In Press.
  110. M. Sadegh, J.A.Vrugt, H.V. Gupta, and C. Xu (2016), The soil water characteristic as new class of closed-form parametric expressions for the flow duration curve, Journal of Hydrology, 535, 438–456, doi:10.1016/j.jhydrol.2016.01.027.
  111. A.A. Ali, C. Xu, A. Rogers, R.A. Fisher, S.D. Wullschleger, E.C. Massoud, J.A. Vrugt, J.D. Muss, N.G. McDowell, J.B. Fisher, P.B. Reich, and C.J. Wilson (2016), A global scale mechanistic model of the photosynthetic capacity (LUNA V1.0) Geoscientific Model Development, 9, 587-606, doi:10.5194/gmd-9-587-2016.
  112. H. Vereecken, A, Schnepf, J.W. Hopmans, M. Javaux M., D. Or, T. Roose. J. Vanderborght, M. Young, W. Amelung, M. Aitkenhead, S. Allison, S. Assouline, P. Baveye, M. Berli, N. Bruggemann, P. Finke, M. Flury, T. Gaiser, G. Govers, T. Ghezzehei, P. Hallett, H.J. Hendricks-Franssen, J. Heppel, R. Horn, J.A. Huisman, D. Jacques, F. Jonard, S. Kollet, F. Lafolie, K. Lamorski, D. Leitner, A. McBratney, B. Minasny, C. Montzka, W. Nowak, Y. Pachepsky, J. Padarian, N. Romano, K. Roth, Y. Rothfuss, E.C. Rowe, A. Schwen, J. Simunek, J. van Dam, S.E.A.T.M. van der Zee, H.J. Vogel, J.A. Vrugt, T. Wöhling, and I. Young (2016), Modelling soil processes: Key challenges and new perspectives, Vadose Zone Journal, XX, XX-XX, doi:10.2136/vzj2015.XX.XXXX, In Press.
  113. E. Volpi, J.A. Vrugt, and G. Schoups (2016), The evidence: Theory, practical implementation and numerical integration, Water Resources Research, XX, XX-XX, doi:10.1002/wrcr.XXXX.
  114. A. Mehran, A. Aghakouchak, J.A. Vrugt, M.C. Peel, S.B. Grant, M.J. Stewardson, and J.K. Ravalico (2016), Climate change impacts on water resources accounting for local resilience, In Review.
  115. J.A. Vrugt (2016), The scientific method, Bayes theorem, diagnostic model evaluation, and summary metrics as prior information, Water Resources Research, XX, XX-XX, doi:10.1002/wrcr.XXXX.
  116. E. Massoud, J. Huisman, E. Beninc\`{a, and J.A. Vrugt (2016), Predicting the unpredictable: data assimilation improves predictability of complex dynamics in ecosystems, Ecology Letters, XX, XX-XX, doi:10.1111/ele.XXXXX.
  117. J.A. Vrugt, and K. Beven (2016), To be coherently incoherent: GLUE limits of acceptability with DREAM, Journal of Hydrology, XX, XX-XX, doi:10.1016/j.jhydrol.2015.XX.XXX.
  118. G.J.C. Gomes, J.A. Vrugt, and E.A. Vargas Jr. (2016), Towards Improved Prediction of the Bedrock Depth Underneath Hillslopes: Bayesian Inference of the Bottom-up Control Hypothesis using High-Resolution Topographic Data, Water Resources Research, XX, XX-XX, doi:10.1002/wrcr.XXXX, In Press.
  119. M. Naeini, J.A. Vrugt, and M. Sadegh (2016), Miller similarity and scaling of flow duration curves: Theory, numerical implementation and regionalization, Water Resources Research, XX, XX-XX, doi:10.1002/wrcr.XXXX.
  120. H. Post, J.A. Vrugt, A. Fox, H. Vereecken, and H.J. Hendricks Franssen (2016), Estimation of Community Land Model parameters for an improved assessment of net carbon fluxes at European sites, Journal of Geophysical Research - Biogeosciences, XX-XX, doi:10.1002/2015jg003297.
  121. H.J. Zhang, H.J. Hendricks Franssen, X.J. Han, J.A. Vrugt, and H. Vereecken (2016), Evaluation of state-parameter estimation with EnKF and PF for two LSMs at the TERENO-site Rollesbroich, Germany, Water Resources Research, XX, XX-XX, doi:10.1002/wrcr.XXXX.
  122. H. Qin, X. Xie, J.A. Vrugt, K. Zeng, and G. Hong (2016), Underground structure defect detection and reconstruction using crosshole GPR and Bayesian waveform inversion, Automation in Construction, XX, XX-XX, doi:10.1016/j.autcon.2015.XX.XXX, In Press.
  123. A. Wright, J.A. Vrugt, V. Pauwels, and J. Walker (2016), Hydrology backwards: Rainfall reconstruction from wavelets, Water Resources Research, XX, XX-XX, doi:10.1002/wrcr.XXXX.
  124. G.J.C. Gomes, J.A. Vrugt, E.A. Vargas Jr., J.T. Camargo, and Q. Velloso (2016), The coordinated impact of soil hydraulic and bedrock depth uncertainty on the stability of variably saturated hillslopes, Computers and Geotechnics, XX, XX-XX, doi:10.1016/j.compgeo.2016.02.006.
 

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