J. Vrugt

发表

Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation

C. Diks, W. Bouten, J. Vrugt, 2005 .

Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation

Cajo J. F. ter Braak, Martyn P. Clark, Jasper A. Vrugt, 2008 .

Real-Time Data Assimilation for Operational Ensemble Streamflow Forecasting

H. Gupta, W. Bouten, J. Vrugt, 2006 .

A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters

S. Sorooshian, W. Bouten, J. Vrugt, 2002 .

Hydrologic data assimilation using particle Markov chain Monte Carlo simulation: Theory, concepts and applications (online first)

Jasper A. Vrugt, Cees Diks, F BraakterC.J., 2012 .

A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non‐Gaussian errors

J. Vrugt, G. Schoups, 2010 .

Unraveling uncertainties in hydrologic model calibration: Addressing the problem of compensatory parameters

M. Clark, J. Vrugt, 2006 .

Corruption of accuracy and efficiency of Markov chain Monte Carlo simulation by inaccurate numerical implementation of conceptual hydrologic models

J. Vrugt, G. Schoups, F. Fenicia, 2010 .

Effective and efficient algorithm for multiobjective optimization of hydrologic models

S. Sorooshian, W. Bouten, J. Vrugt, 2003 .

Identification of rainfall interception model parameters from measurements of throughfall and forest canopy storage

S. Dekker, W. Bouten, J. Vrugt, 2003 .

Optimization and uncertainty assessment of strongly nonlinear groundwater models with high parameter dimensionality

Q. Kang, J. Doherty, J. Vrugt, 2010 .

Self-Adaptive Multimethod Search for Global Optimization in Real-Parameter Spaces

Bruce A. Robinson, Jasper A. Vrugt, James M. Hyman, 2009, IEEE Transactions on Evolutionary Computation.

1 Joint State and Parameter Estimation of Two Land Surface Models Using the Ensemble Kalman Filter and Particle Filter

Xujun Han, J. Vrugt, Hongjuan Zhang, 2016 .

State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter

Harrie-Jan Hendricks Franssen, Harry Vereecken, Jasper A. Vrugt, 2016 .

On the value of soil moisture measurements in vadose zone hydrology: A review

J. Vrugt, H. Vereecken, J. Huisman, 2008 .

Bayesian analysis of the impact of rainfall data product on simulated slope failure for North Carolina locations

J. Godt, R. Baum, J. Vrugt, 2019, Computational Geosciences.

The role of uncertainty in bedrock depth and hydraulic properties on the stability of a variably-saturated slope

J. Vrugt, E. Vargas, G. J. Gomes, 2017 .

Title The role of uncertainty in bedrock depth and hydraulic properties on stability of a variably-saturated slope Permalink

J. Vrugt, E. Vargas, G. J. Gomes, 2017 .

From Rain Tanks to Catchments: Use of Low-Impact Development To Address Hydrologic Symptoms of the Urban Stream Syndrome.

Sunny C. Jiang, S. Grant, B. Sanders, 2015, Environmental science & technology.

Inverse Modeling of Subsurface Flow and Transport Properties: A Review with New Developments

J. Vrugt, B. Robinson, P. Stauffer, 2008 .

U A L DREAM Suite : Technical manual

J. Vrugt, M. Šejna, 2016 .

A global scale mechanistic model of photosynthetic capacity (LUNA V1.0)

Ashehad A. Ali, N. McDowell, J. Muss, 2016 .

Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES)

N. McDowell, J. Chambers, Chonggang Xu, 2019, Geoscientific Model Development.

Toward diagnostic model calibration and evaluation: Approximate Bayesian computation

J. Vrugt, M. Sadegh, 2013 .

Summary statistics from training images as prior information in probabilistic inversion

J. Vrugt, M. Sadegh, T. Lochbuhler, 2015, 1701.01376.

Multiple Criteria Global Optimization for Watershed Model Calibration

S. Sorooshian, H. Gupta, J. Vrugt, 2013 .

On the optimal design of experiments for conceptual and predictive discrimination of hydrologic system models

J. Vrugt, T. Ferré, C. Kikuchi, 2015 .

Information content of incubation experiments for inverse estimation of pools in the Rothamsted carbon model: a Bayesian perspective

J. Vrugt, H. Vereecken, M. Herbst, 2009 .

Comparison of Three Multiobjective Optimization Algorithms for Inverse Modeling of Vadose Zone Hydraulic Properties

J. Vrugt, T. Wöhling, G. Barkle, 2008 .

UC Irvine UC Irvine Previously Published Works Title Evolutionary algorithms and other metaheuristics in water resources : Current status , research challenges and future directions

M. C. Cunha, A. Castelletti, F. Pasha, 2014 .

Strong latitudinal patterns in the elemental ratios of marine plankton and organic matter

Jasper A. Vrugt, Michael W. Lomas, Simon A. Levin, 2013 .

Toward improved prediction of the bedrock depth underneath hillslopes: Bayesian inference of the bottom‐up control hypothesis using high‐resolution topographic data

J. Vrugt, E. Vargas, G. J. Gomes, 2016 .

Sustainability of irrigated agriculture in the San Joaquin Valley, California

Gerrit Schoups, Sorab Panday, Jasper A Vrugt, 2005, Proceedings of the National Academy of Sciences of the United States of America.

Confronting uncertainty in model-based geostatistics using Markov Chain Monte Carlo simulation

Budiman Minasny, Jasper A. Vrugt, Alex B. McBratney, 2011 .

Dual-domain mixing cell modelling and uncertainty analysis for unsaturated bromide and chloride transport

J. Vrugt, V. Bidwell, G. Barkle, 2011 .

Multiresponse multilayer vadose zone model calibration using Markov chain Monte Carlo simulation and field water retention data

Jasper A. Vrugt, Thomas Wöhling, J. Vrugt, 2011 .

Inverse Modeling of Subsurface Flow and Transport Properties Using Recent Advances in Global Optimization, Parallel Computing and Sequential Data Assimilation

J. Vrugt, B. Robinson, P. Stauffer, 2007 .

Significant uncertainty in global scale hydrological modeling from precipitation data errors

A. Weerts, M. Bierkens, J. Vrugt, 2015 .

Model Calibration in Watershed Hydrology

Soroosh Sorooshian, Hoshin V. Gupta, Jasper A. Vrugt, 2009 .

Corruption of parameter behavior and regionalization by model and forcing data errors: A Bayesian example using the SNOW17 model

Minxue He, K. Franz, J. Vrugt, 2011 .

The soil water characteristic as new class of closed-form parametric expressions for the flow duration curve

Hoshin Vijai Gupta, Jasper A. Vrugt, Chonggang Xu, 2016 .

Parameter optimisation and uncertainty assessment for large-scale streamflow simulation with the LISFLOOD model

Breanndán Ó Nualláin, Jasper A. Vrugt, Ad de Roo, 2007 .

Semi-distributed parameter optimization and uncertainty assessment for large-scale streamflow simulation using global optimization / Optimisation de paramètres semi-distribués et évaluation de l'incertitude pour la simulation de débits à grande échelle par l'utilisation d'une optimisation globale

Jasper A. Vrugt, Luc Feyen, Milan Kalas, 2008 .

Mass conservative three‐dimensional water tracer distribution from Markov chain Monte Carlo inversion of time‐lapse ground‐penetrating radar data

Eric Laloy, Niklas Linde, Jasper A. Vrugt, 2012 .

A Shuffled Complex Evolution Metropolis algorithm for estimating posterior distribution of watershed model parameters.

Soroosh Sorooshian, Willem Bouten, Hoshin Vijai Gupta, 2013 .

Towards improved treatment of parameter uncertainty in hydrologic modeling

J. Vrugt, 2004 .

Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation

Jasper A. Vrugt, Mojtaba Sadegh, J. Vrugt, 2013 .

Hydraulic properties of a model dike from coupled Bayesian and multi-criteria hydrogeophysical inversion

Johan Alexander Huisman, Jasper A. Vrugt, H. Vereecken, 2010 .

Inverse modeling of cloud-aerosol interactions - Part 2: Sensitivity tests on liquid phase clouds using a Markov Chain Monte Carlo based simulation approach

Jasper A. Vrugt, Armin Sorooshian, Annica M. L. Ekman, 2011 .

Inverse modelling of cloud-aerosol interactions – Part 2: Sensitivity tests on liquid phase clouds using a Markov chain Monte Carlo based simulation approach

Jasper A. Vrugt, Armin Sorooshian, Annica M. L. Ekman, 2011 .

Sworn testimony of the model evidence: Gaussian Mixture Importance (GAME) sampling

Elena Volpi, Jasper A. Vrugt, Gerrit Schoups, 2017 .

Mass conservative three-dimensional water tracer 1 distribution from MCMC inversion of time-lapse 2 GPR data

Eric Laloy, Niklas Linde, Jasper A. Vrugt, 2012 .

Spatially Distributed Soil Moisture From Traveltime Observations of Crosshole Ground Penetrating Radar using Markov chain Monte Carlo Simulation

J. Vrugt, N. Linde, 2017 .

A numerical method to account for distance in a farmer’s willingness to pay for land

G. Heuvelink, J. Vrugt, M. Bakker, 2018, Spatial Statistics.

Joint probabilistic inference of multi-Gaussian conductivity fields and their associated variograms from indirect hydrological data

J. Vrugt, N. Linde, D. Jacques, 2015 .

Uncertainty quantification of GEOS-5 L-band radiative transfer model parameters using Bayesian inference and SMOS observations

Jasper A. Vrugt, Gabrielle De Lannoy, Rolf H. Reichle, 2014 .

Parameter and Predictive Uncertainty Analysis of a Spatially Distributed Watershed 1 Simulation Model Using DREAM ( ZS ) and a Formal , Generalized Likelihood Function 2

Misgana K. Muleta, J. Vrugt, G. Schoups, 2012 .

Integrated analysis of waveguide dispersed GPR pulses using deterministic and Bayesian inversion methods

Johan Alexander Huisman, Harry Vereecken, Jasper A. Vrugt, 2012 .

Inversion and sensitivity analysis of Ground Penetrating Radar data with waveguide dispersion using deterministic and Markov Chain Monte Carlo methods

Johan A. Huisman, Jasper A. Vrugt, Jutta Bikowski, 2011 .

Explicit Consideration of Measurement Uncertainty

J. Vrugt, H. Vereecken, J. Kruk, 2011 .

Probabilistic electrical resistivity tomography of a CO2 sequestration analog

J. Vrugt, N. Linde, R. Detwiler, 2014 .

Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling

D. Higdon, J. Hyman, C. Diks, 2009 .

Bayesian Inference of Tree Water Relations Using a Soil-Tree-Atmosphere Continuum Model☆

J. A. Vrugt, Jirka Simunek, Jan W. Hopmans, 2013 .

Approximate Bayesian Computation in hydrologic modeling: equifinality of formal and informal approaches

J. A. Vrugt, M. Sadegh, J. Vrugt, 2013 .

Towards inverse modeling of cloud-aerosol interactions - Part 1: A detailed response surface analysis

J. A. Vrugt, D. Gorea, J. Vrugt, 2011 .

Soil hydraulic properties estimation from one‐dimensional infiltration experiments using characteristic time concept

J. Vrugt, H. Vereecken, J. Šimůnek, 2020, Vadose Zone Journal.

Comment on "How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?" by Y. Tang et al., Hydrol. Earth Syst. Sci., 10, 289–307, 2006

Jasper A. Vrugt, J. Vrugt, 2007 .

Probabilistic electrical resistivity tomography of a CO2 sequestration analog

J. Vrugt, N. Linde, R. Detwiler, 2014 .

Regional variation in the particulate organic carbon to nitrogen ratio in the surface ocean

M. Lomas, J. Vrugt, A. Martiny, 2013 .

Concentrations and ratios of particulate organic carbon, nitrogen, and phosphorus in the global ocean

M. Lomas, J. Vrugt, A. Martiny, 2014, Scientific Data.

One‐, two‐, and three‐dimensional root water uptake functions for transient modeling

Jasper A. Vrugt, Jirka Simunek, Jan W. Hopmans, 2001 .

Confidence intervals of the Kling-Gupta efficiency

J. Vrugt, Debora Y. de Oliveira, 2022, Journal of Hydrology.

Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States

B. Sanders, J. Vrugt, A. Aghakouchak, 2017 .

Advances in automatic methods for identification of hydrologic models

Hoshin Vijai Gupta, J. A. Vrugt, Thorsten Wagener, 2004 .

A dynamic approach to the identification of conceptual hydrological models

Soroosh Sorooshian, Hoshin Vijai Gupta, Thorsten Wagener, 2002 .

for predictive discrimination of

J. Vrugt, T. Ferré, C. Kikuchi, 2022 .

L-Band Microwave Satellite Data and Model Simulations Over the Dry Chaco to Estimate Soil Moisture, Soil Temperature, Vegetation, and Soil Salinity

R. Reichle, J. Wigneron, J. Vrugt, 2022, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

UvA-DARE (Digital Academic Repository) Towards diagnostic model calibration and evaluation: Approximate Bayesian computation

J. Vrugt, M. Sadegh, Adrian Marie Legendre, 2013 .

State and parameter estimation of two land surface models using the ensemble Kalman ﬁlter and the particle ﬁlter

H. Franssen, Xujun Han, J. Vrugt, 2017 .

Special Section: Model-Data Fusion in the Vadose Zone

J. Vrugt, J. Huisman, 2012 .

Prediction of the saturated hydraulic conductivity from Brooks and Corey's water retention parameters

J. Vrugt, N. Romano, P. Nasta, 2013 .

Efficient posterior exploration of a high‐dimensional groundwater model from two‐stage Markov chain Monte Carlo simulation and polynomial chaos expansion

D. Mallants, J. Vrugt, D. Jacques, 2013 .

Improved treatment of uncertainty in hydrologic modeling

C. Diks, W. Bouten, J. Vrugt, 2004 .

On the use of distribution-adaptive likelihood functions: Generalized and universal likelihood functions, scoring rules and multi-criteria ranking

C. Diks, J. Vrugt, G. Schoups, 2022, Journal of Hydrology.

The Treatment of Uncertainty in Hydrometric Observations: A Probabilistic Description of Streamflow Records

J. Vrugt, Debora Y. de Oliveira, 2022, Water Resources Research.

Environmental controls on drainage behavior of an ephemeral stream

J. Vrugt, T. Ferré, K. Blasch, 2010 .

Global-scale environmental control of plant photosynthetic capacity.

Ashehad A. Ali, N. McDowell, Chonggang Xu, 2015, Ecological applications : a publication of the Ecological Society of America.

A global scale mechanistic model of the photosynthetic capacity

Joshua B. Fisher, Jasper A. Vrugt, Nate G. McDowell, 2015 .

Appendix G. Standardization of Vc,max25 and Jmax25.

Jasper A. Vrugt, Nate G. McDowell, William L. Bauerle, 2016 .

Vadose Zone Journal: The First Ten Years

Michael H. Young, Jasper A. Vrugt, Dani Or, 2013 .

CHAPTER 3 MODEL CALIBRATION IN WATERSHED HYDROLOGY

S. Sorooshian, H. Gupta, J. Vrugt, 2009 .

Parasite inversion for determining the coefficients and time‐validity of Philip's two‐term infiltration equation

J. Vrugt, H. Vereecken, M. Rahmati, 2022, Vadose Zone Journal.

Bayesian model selection in hydrogeophysics: Application to conceptual subsurface models of the South Oyster Bacterial Transport Site, Virginia, USA

J. Vrugt, N. Linde, C. Brunetti, 2017 .

Probing the limits of predictability: data assimilation of chaotic dynamics in complex food webs.

Willem Bouten, Jef Huisman, Michael C Dietze, 2018, Ecology letters.

Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus

Jasper A. Vrugt, David M. Karl, Michael W. Lomas, 2013, Proceedings of the National Academy of Sciences.

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Avi Ostfeld, Andrea Castelletti, Joseph R. Kasprzyk, 2014, Environ. Model. Softw..

Improved evolutionary optimization from genetically adaptive multimethod search

Jasper A Vrugt, Bruce A Robinson, J. Vrugt, 2007, Proceedings of the National Academy of Sciences.

Differential Evolution Markov Chain with snooker updater and fewer chains

Cajo J. F. ter Braak, Jasper A. Vrugt, C. Braak, 2008, Stat. Comput..

Analysing trade-offs in resource and labour allocation by smallholder farmers using inverse modelling techniques: A case-study from Kakamega district, western Kenya

J. Vrugt, M. Rufino, M. Wijk, 2007 .

Embracing equifinality with efficiency : Limits of Acceptability sampling using the DREAM(LOA) algorithm

Jasper A. Vrugt, Keith Beven, Keith Beven, 2018 .

Approximate Bayesian Computation using Markov Chain Monte Carlo simulation: DREAM(ABC)

J. Vrugt, M. Sadegh, 2014 .

Pareto front analysis of flight time and energy use in long-distance bird migration

Willem Bouten, Jasper A. Vrugt, W. Bouten, 2007 .

Toward Improved Identifiability of Soil Hydraulic Parameters: On the Selection of a Suitable Parametric Model

Willem Bouten, Hoshin Vijai Gupta, Jasper A. Vrugt, 2003 .

Information content of data for identifying soil hydraulic parameters from outflow experiments

Willem Bouten, Jasper A. Vrugt, Albrecht H. Weerts, 2001 .

Toward improved identifiability of hydrologic model parameters: The information content of experimental data

Soroosh Sorooshian, Willem Bouten, Hoshin Vijai Gupta, 2002 .

Can Smart Stormwater Systems Outsmart the Weather? Stormwater Capture with Real-Time Control in Southern California

S. Grant, J. Vrugt, Emily A. Parker, 2021, ACS ES&T Water.

Modeling Soil Processes: Review, Key Challenges, and New Perspectives

Hans-Jörg Vogel, Jan Vanderborght, Mathieu Javaux, 2016 .

Joint State and Parameter Estimation for Two Land Surface Models Using the Ensemble Kalman Filter and Particle Filter

Xujun Han, J. Vrugt, H. Vereecken, 2016 .

FDCFIT: A MATLAB Toolbox of Closed-form Parametric Expressions of the Flow Duration Curve

Jasper A. Vrugt, Mojtaba Sadegh, J. Vrugt, 2015 .

Soil Hydraulic Functions Determined from Measurements of Air Permeability, Capillary Modeling, and High‐Dimensional Parameter Estimation

J. H. Dane, J. Vrugt, E. Unsal, 2011 .

Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models

Martyn P. Clark, Hoshin Vijai Gupta, Thorsten Wagener, 2008 .

Ensemble Bayesian model averaging using Markov Chain Monte Carlo sampling

Martyn P. Clark, Jasper A. Vrugt, Cees Diks, 2008 .

Multi‐objective calibration of forecast ensembles using Bayesian model averaging

Martyn P. Clark, Bruce A. Robinson, Jasper A. Vrugt, 2006 .

Combining multiobjective optimization and Bayesian model averaging to calibrate forecast ensembles of soil hydraulic models

Jasper A. Vrugt, Thomas Wöhling, J. Vrugt, 2008 .

Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging

Bruce A. Robinson, Jasper A. Vrugt, J. Vrugt, 2007 .

Evaluation of subsurface drip irrigation design and management parameters for alfalfa

Jasper A. Vrugt, Jan W. Hopmans, Tamir Kamai, 2012 .

Calibration of a two-dimensional root water uptake model

Jasper A. Vrugt, Jan W. Hopmans, J. Vrugt, 2001 .

Uncertainty Quantification of Complex System Models: Bayesian Analysis

Jasper A. Vrugt, Elias C. Massoud, J. Vrugt, 2018 .

Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation

Jasper A. Vrugt, J. Vrugt, 2016, Environ. Model. Softw..

High‐dimensional posterior exploration of hydrologic models using multiple‐try DREAM(ZS) and high‐performance computing

Jasper A. Vrugt, Eric Laloy, J. Vrugt, 2012 .

Copper complexation by dissolved organic matter and uncertainty assessment of their stability constants.

J. Vrugt, J. Verstraten, B. Jansen, 2002, Chemosphere.

Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES)

Liang Wei, Jasper A. Vrugt, Daniel M. Ricciuto, 2019, Geoscientific Model Development.

A hybrid multi-step sensitivity-driven evolutionary polynomial regression enables robust model structure selection

J. Vrugt, G. Gomes, Ruan G. S. Gomes, 2022, Engineering applications of artificial intelligence.

Regional variation in the particulate organic carbon to nitrogen ratio in the surface ocean

M. Lomas, J. Vrugt, A. Martiny, 2013 .

Improving Simulation Efficiency of MCMC for Inverse Modeling of Hydrologic Systems With a Kalman‐Inspired Proposal Distribution

Jasper A. Vrugt, Guang Lin, Lingzao Zeng, 2017, Water Resources Research.

Towards a comprehensive assessment of model structural adequacy

Ming Ye, Martyn P. Clark, Hoshin V. Gupta, 2012 .

Estimation of Community Land Model parameters for an improved assessment of net carbon fluxes at European sites

H. Hendricks Franssen, J. Vrugt, H. Vereecken, 2017 .

Application of parallel computing to stochastic parameter estimation in environmental models

Peter M. A. Sloot, Willem Bouten, Breanndán Ó Nualláin, 2006, Comput. Geosci..

Comparison of point forecast accuracy of model averaging methods in hydrologic applications

Jasper A. Vrugt, Cees Diks, C. Diks, 2010 .

Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?

Cajo J. F. ter Braak, Hoshin Vijai Gupta, Bruce A. Robinson, 2009 .

A toolbox for the optimal design of run-of-river hydropower plants

Jasper A. Vrugt, Veysel Yildiz, J. Vrugt, 2019, Environ. Model. Softw..

Probabilistic inference of multi‐Gaussian fields from indirect hydrological data using circulant embedding and dimensionality reduction

Eric Laloy, Diederik Jacques, Niklas Linde, 2015 .

UvA-DARE ( Digital Academic Repository ) DREAM ( D ) : An adaptive Markov chain Monte Carlo simulation algorithm to solve discrete , noncontinuous , and combinatorial posterior parameter estimation problems

Jasper A. Vrugt, F BraakterC.J., J. Vrugt, 2011 .

Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data

J. A. Vrugt, N. Linde, T. Kalscheuer, 2014, 1701.02540.

Improved extraction of hydrologic information from geophysical data through coupled hydrogeophysical inversion

A. C. Hinnell, M. Kowalsky, J. Vrugt, 2010 .

Obtaining the Spatial Distribution of Water Content along a TDR Probe Using the SCEM‐UA Bayesian Inverse Modeling Scheme

Willem Bouten, Johan Alexander Huisman, Jasper A. Vrugt, 2004 .

Towards reduced uncertainty in catchment nitrogen modelling: quantifying the effect of field observation uncertainty on model calibration

W. Bouten, J. Vrugt, A. Tietema, 2004 .

Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling

Henrik Madsen, Dan Rosbjerg, Bruce A. Robinson, 2008 .

On the three‐parameter infiltration equation of Parlange et al. (1982): Numerical solution, experimental design, and parameter estimation

J. Vrugt, Yifu Gao, 2022, Vadose Zone Journal.

UvA-DARE ( Digital Academic Repository ) Inverse modeling of cloud-aerosol interactions-- Part 1 : Detailed response surface analysis

J. Vrugt, P. Tunved, A. Sorooshian, 2011 .

Towards inverse modeling of cloud-aerosol interactions -- Part 2

J. Vrugt, P. Tunved, A. Ekman, 2011 .

UvA-DARE ( Digital Academic Repository ) Inverse modelling of in situ soil water dynamics : investigating the effect of different prior distributions of the soil hydraulic parameters

Michael Herbst, Jasper A. Vrugt, B. Scharnagl, 2011 .

Characterizing parameter sensitivity and uncertainty for a snow model across hydroclimatic regimes

Steven A. Margulis, Terri S. Hogue, Jasper A. Vrugt, 2011 .

Improved inverse modeling for flow and transport in subsurface media: Combined parameter and state estimation

Velimir V. Vesselinov, Jasper A. Vrugt, Bruce A. Robinson, 2005 .

Probabilistic Sensitivity Analysis With Dependent Variables: Covariance‐Based Decomposition of Hydrologic Models

J. Vrugt, Yifu Gao, Abdullah Sahin, 2023, Water Resources Research.

Accuracy of frequency domain analysis scenarios for the determination of complex dielectric permittivity

Willem Bouten, Jasper A. Vrugt, Jef Huisman, 2004 .

Impacts of Polarimetric Radar Observations on Hydrologic Simulation

Y. Hong, J. Gourley, J. Vrugt, 2010 .

Identifying the radiation belt source region by data assimilation

J. A. Vrugt, Josef Koller, Geoff D. Reeves, 2007 .

Application of stochastic parameter optimization to the Sacramento Soil Moisture Accounting model

S. Sorooshian, H. Gupta, S. Dekker, 2006, Journal of Hydrology.

PyDREAM: high-dimensional parameter inference for biological models in python

Jasper A. Vrugt, Carlos F. Lopez, Carlos F. Lopez, 2017, Bioinform..

The Influence of Geostatistical Prior Modeling on the Solution of DCT-Based Bayesian Inversion: A Case Study from Chicken Creek Catchment

Davood Moghadas, Jasper A. Vrugt, J. Vrugt, 2019, Remote. Sens..

Information content of incubation experiments for inverse estimation of pools sizes in the Rothamsted carbon model : a Bayesian approach

J. Vrugt, H. Vereecken, M. Herbst, 2009 .

Bayesian inverse modelling of in situ soil water dynamics: using prior information about the soil hydraulic properties

Harry Vereecken, Jasper A. Vrugt, Michael Herbst, 2011 .

Prediction of spatially variable unsaturated hydraulic conductivity using scaled particle‐size distribution functions

J. Vrugt, N. Romano, P. Nasta, 2013 .

Untangling Diffusion from Advection in Unsaturated Porous Media: Experimental Data, Modeling, and Parameter Uncertainty

W. E. Soll, J. Vrugt, C. Gable, 2009 .

Improved characterization of underground structure defects from two-stage Bayesian inversion using crosshole GPR data

Jasper A. Vrugt, Xiongyao Xie, Hui Qin, 2018, Automation in Construction.

Underground structure defect detection and reconstruction using crosshole GPR and Bayesian waveform inversion

Hui Qin, Kun Zeng, Jasper A. Vrugt, 2016 .

Distributed Soil Moisture from Crosshole Ground‐Penetrating Radar Travel Times using Stochastic Inversion

J. A. Vrugt, J. Vrugt, N. Linde, 2013, 1701.01634.

(Un)certainty of overall binding constants of Al with dissolved organic matter determined by the Scatchard approach.

J. Vrugt, J. Verstraten, B. Jansen, 2004, Water research.

Hydrology and Earth System Sciences Discussions

J. A. Vrugt, J. Vrugt, 2022 .

Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States

Jasper A. Vrugt, Adam Luke, Amir AghaKouchak, 2017 .

Validity of First-Order Approximations to Describe Parameter Uncertainty in Soil Hydrologic Models

W. Bouten, J. Vrugt, 2002 .

Speed-up of posterior inference of highly-parameterized environmental models from a Kalman proposal distribution: DREAM(KZS)

Guang Lin, Jasper A. Vrugt, Lingzao Zeng, 2017 .

The stationarity paradigm revisited: Hypothesis testing using diagnostics, summary metrics, and DREAM(ABC)

Elena Volpi, Jasper A. Vrugt, Chonggang Xu, 2015 .

A MATLAB Toolbox of Parametric Expressions of the Flow Duration Curve

Jasper A. Vrugt, Mojtaba Sadegh, J. Vrugt, 2016 .

MODELAVG: A MATLAB Toolbox for Postprocessing of Model Ensembles

Jasper A. Vrugt, J. Vrugt, 2015 .

Bayesian model averaging using particle filtering and Gaussian mixture modeling: Theory, concepts, and simulation experiments

Johan Alexander Huisman, Jasper A. Vrugt, Gerrit Schoups, 2012 .

Multi-criteria Optimization Using the AMALGAM Software Package: Theory, Concepts, and MATLAB Implementation

Jasper A. Vrugt, J. Vrugt, 2016 .

A shuffled Complex Evolution Metropolis algorithm for confronting parameter uncertainty in hydrologic modeling

Soroosh Sorooshian, Willem Bouten, Hoshin Vijai Gupta, 2004 .

Significant variation in vegetation characteristics and dynamics from ecohydrological optimality of net carbon profit

Jasper A. Vrugt, Stefan C. Dekker, S. Dekker, 2009 .

Posterior Exploration for Computationally Intensive Forward Models

Colin Fox, J. David Moulton, David Higdon, 2011 .

Probing the limits of predictability: data assimilation of chaotic dynamics in complex food webs.

J. Huisman, W. Bouten, J. Vrugt, 2018, Ecology letters.

Uncertainty quantification of GEOS-5 L-band radiative transfer model parameters using Bayesian inference and SMOS observations

G. Lannoy, R. Reichle, J. Vrugt, 2014 .

Introduction to the Special Section in Vadose Zone Journal: Parameter Identification and Uncertainty Assessment in the Unsaturated Zone

S. P. Neuman, Jasper A. Vrugt, Shlomo P. Neuman, 2006 .

16 Posterior Exploration for Computationally Intensive Forward Models

D. Higdon, C. Fox, J. Vrugt, 2011 .

Bayesian inference of rock strength anisotropy: Uncertainty analysis of the Hoek–Brown failure criterion

J. Vrugt, E. A. Vargas, J. H. Forero, 2021, International Journal of Rock Mechanics and Mining Sciences.

Four Decades of Progress in Monitoring and Modeling of Processes in the Soil-Plant- Atmosphere System: Applications and Challenges Bayesian inference of tree water relations using a Soil-Tree- Atmosphere Continuum model

J. Vrugt, J. Rings, J. Šimůnek, 2013 .

Posterior exploration for computationally intensive forward models

D. Higdon, C. Fox, J. Vrugt, 2024, 2405.00397.

A toolbox for the optimal design of run-of-river hydropower plants

J. Vrugt, V. Yildiz, 2019, Environ. Model. Softw..

Aquifer structure identification using stochastic inversion

Velimir V. Vesselinov, Dylan R. Harp, Jasper A. Vrugt, 2008 .