PEST - Model-Independent Parameter Estimation and Uncertainty Analysis

Some References on PEST and Related Topics


Brunner, P., Doherty, J. and Simmons, C.T. 2012. Uncertainty assessment and implications for data acquisition in support of integrated hydrologic models. Water Resourc. Res. doi:10.1029/2011WR011342

Burrows, W. and Doherty, J., 2016. Gradient-based model calibration with proxy-model assistance. Journal of Hydrology, 533, 114-127.

Burrows, W. and Doherty, J., 2014. Efficient calibration/uncertainty analysis using paired complex/surrogate models. Groundwater, 53(4), pp531-541.

Christensen, S. and Doherty, J., 2008. Predictive error dependencies when using pilot points and singular value decomposition in groundwater model calibration. Advances in Water Resources. 31, 674-700.

Dausman, A.M., Doherty, J., Langevin, C.D., and Sukop, M.C., 2010. Quantifying data worth toward reducing predictive uncertainty. Groundwater, 48 (5), 729-740.

Dausman, A.M, Doherty, J., Langevin, C.D. and Dixon, J., 2010. Hypothesis testing of buoyant plume migration using a highly parameterized variable-density groundwater model. Hydrogeology Journal. DOI 10.1007/s10040-009-0511-6.

Doherty, J., 2003. Groundwater model calibration using pilot points and regularisation. Ground Water. 41 (2): 170-177.

Doherty, J. and Christensen, S., 2011. Use of paired simple and complex models in reducing predictive bias and quantifying uncertainty. Water Resourc. Res doi:10.1029/2011WR010763.

Doherty, J. and Hunt, R.J., 2009. Two easily calculated statistics for evaluating parameter identifiability and error reduction. Journal of Hydrology. 366, 119-127.

Doherty, J., and Hunt, R.J., 2010. Response to comment on “Two statistics for evaluating parameter identifiability and error reduction”. Journal of Hydrology. 380, 489-496.

Doherty, J. and Johnston, J.M., 2003.   Methodologies for calibration and predictive analysis of a watershed model. Journal of the American Water Resources Association. 39(2):251-265.

Doherty, J. and Simmons, C.T., 2013. Groundwater modelling in decision support: reflections on a unified conceptual framework. Hydrogeology Journal 21: 1531–1537

Doherty, J. and Skahill, B., 2006. An advanced regularization methodology for use in watershed model calibration. Journal of Hydrology. 327 (3-4), 564-577.

Doherty, J. and Vogwill, R., 2015. Models, Decision-Making and Science. In Solving the Groundwater Challenges of the 21st Century. Vogwill, R. editor. CRC Press.

Doherty, J., and Welter, D., 2010. A short exploration of structural noise. Water Resour. Res. In Press.#id2009WRR008377.

Gallagher, M.R. and Doherty, J., 2006. Parameter estimation and uncertainty analysis for a watershed model. Environmental Modelling and Software. 22, 1000-1020.

Gallagher, M.R. and Doherty, J., 2007. Predictive error analysis for a water resource management model. Journal of Hydrology. 34(3-4), 513-533.

Gallagher, M.R., and Doherty, J., 2007. Parameter interdependence and uncertainty induced by lumping in a hydrologic model. Water Resources Research. 43, W05421, doi:10.1029/2006WR005347.

Herckenrath, D., Langevin, C.D., and Doherty, J., 2011. Predictive uncertainty analysis of a salt water intrusion model using null space Monte Carlo. Water Resour. Res., 47, W05504. doi:10.1029/2010WR009342.

Hunt, R.J., Doherty, J, and Tonkin, M.J., 2007. Are models too simple? Arguments for increased parameterisation. Ground Water. 45 (3), 254–262.

Hunt, R.J., Luchette, J., Shreuder, W.A., Rumbaugh, J., Doherty, J., Tonkin, M.J. and Rumbaugh, D., 2010. Using the cloud to replenish parched groundwater modeling efforts. Rapid Communication for Ground Water, doi: 10.1111/j.1745-6584.2010.00699

James, S.C., Doherty, J. and Eddebarh, A.-A., 2009. Post-calibration uncertainty analysis: Yucca Mountain, Nevada, USA. Ground Water. 47 (6), 851-869.

Keating, E., Doherty, J., Vrugt, J.A. and Kang, Q., 2010. Optimization and uncertainty assessment of strongly nonlinear groundwater models with high parameterization dimensionality. Water Resources Research, Vol 46, W10517, 18 pp., 2010 doi:10.1029/2009WR008584.

McKenna, S.A., Doherty, J. and Hart, D.B., 2003. Non-Uniqueness of Inverse Transmissivity Field Calibration and Predictive Transport Modeling. Journal of Hydrology, 281(4) 265-282.

Moore, C. and Doherty, J., 2005. The role of the calibration process in reducing model predictive error. Water Resources Research. Vol 41, No 5. W05050.

Moore, C. and Doherty, J., 2006. The cost of uniqueness in groundwater model calibration. Advances in Water Resources. 29, 4, 605–623

Nolan, B.T., Malone, R.W., Doherty, J., Barbash, J.E., Ma, L., SHhner, D.L., 2014. Data worth and prediction uncertainty for pesticide transport fate in Nebraska and Maryland, USA. Pest Management Science. DOI 10.1002/ps.3875.

Rossi, P.K., Ala-aho, P., Doherty, J. and Klove, B., 2014. Impact of peatland draining and restoration on esker groundwater resources – modelling future scenarios for management. Hydrogeology Journal. DOI: 10.1007/s10040-014-1127-z

Schilling, O., Doherty, J., Kinzelbach, W. Wang H., Yang, P.N., and Brunner, P., 2014.  Using tree ring data as a proxy for transpiration to reduce predictive uncertainty of a model simulating groundwater-surface water-vegetation interactions.. Journal or Hydrology. 519B, 2258-2271.

Sepulveda, N., and Doherty, J.,2014. Uncertainty analysis of a groundwater flow model in East-Central Florida. Groundwater: 53 (3), 464-474.

Skahill, B. and Doherty, J., 2006. Efficient accommodation of local minima in watershed model calibration. Journal of Hydrology. 329 (1-2), pp122-139.

Tonkin, M. and Doherty, J., 2005. A hybrid regularised inversion methodology for highly parameterised models. Water Resources Research. 41, W10412, doi:10.1029/2005WR003995, 2005.

Tonkin, M., Doherty, J. and Moore, C., 2007. Efficient nonlinear predictive error variance analysis for highly parameterized models. Water Resources Research. 43, W07429, doi:10.1029/2006WR005348.

Tonkin, M., and Doherty, J., 2008. Calibration-constrained Monte Carlo analysis of highly-parameterized models using subspace techniques. Water Resources Research. 45, W00B10, doi:10.1029/2007WR006678.

Watson, T.A., Doherty, J.E. and Christensen, S., 2013. Parameter and predictive outcomes of mdel simplification. Water Resourc. Res. 49 (7), 3952-3977. DOI: 10.1002/wrcr.20145

White, J.T., Fienen, M.N., and Doherty, J.E., 2016. pyEMU: A Python framework for environmental model uncertainty analysis. Environ Modell Softw, 85, 217-228.

White, J.T., Doherty, J.E. and Hughes, J.D., 2014. Quantifying the predictive consequences of model error with linear subspace analysis. Water Resour. Res, 50 (2): 1152-1173. DOI: 10.1002/2013WR014767



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