Efficient regularization and uncertainty analysis using a global optimization methodology

Water Resources Research

Volumn 46, Issue 8, 2010, Pages

  Moore, Catherine ^a,b   Wöhling, Thomas ^c   Doherty, John ^d,e  

^a ESR Christchurch Science Centre   (New Zealand)

^b Foresight Partners Pty Ltd   (Australia)

^c LINCOLN AGRITECH LTD   (New Zealand)

^d Watermark Numerical Computing   (Australia)

^e FLINDERS UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

CONSTRAINED OPTIMIZATION PROBLEMS; GLOBAL SEARCH; LOCAL OPTIMA; MODEL CALIBRATION; MODEL OUTPUTS; MODEL PARAMETERS; OBJECTIVE FUNCTIONS; OPTIMIZATION METHODOLOGY; OPTIMIZERS; PARAMETER SPACES; PARETO FRONT; PREDICTIVE UNCERTAINTY; REAL-WORLD; SYNTHETIC GROUNDWATER; THEORETICAL BASIS; TRADE-OFF POINT;

CALIBRATION; CONSTRAINED OPTIMIZATION; GLOBAL OPTIMIZATION; GROUNDWATER;

UNCERTAINTY ANALYSIS;

CALIBRATION; GROUNDWATER RESOURCE; HYDROLOGICAL MODELING; NUMERICAL MODEL; OPTIMIZATION; SURFACE WATER; TRADE-OFF; UNCERTAINTY ANALYSIS;

EID: 77955912827     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2009WR008627     Document Type: Article

References (52)

1. Anderman, E. R., and M. C. Hill (2001), MODFLOW-2000, The U.S. Geological Survey modular ground-water model-Documentation of the advective-transport observation (ADV2) package, version 2, U.S. Geol. Surv. Open-File Rep., 01-54, 69 p., U.S. Geol. Surv., Reston, Va.
2. Aster, R. C, B. Borchers, and C. H. Thurber (2005), Parameter Estimation and Inverse Problems, 301 pp., Elsevier, Amsterdam.
3. Baalousha, H., and H. Köngeter (2006), Stochastic modelling and risk analysis of groundwater pollution using FORM coupled with automatic differentiation, Adv. WaterResour, 29, 1815-1832.
4. Beven, K. (2009), Environmental Modelling: An Uncertain Future?, Routledge, New York.
5. Beven, K. J., P. J. Smith, and J. E. Freer (2008), So why would a modeller choose to be incoherent, J. Hydrol, 354, 15-32.
6. Bicknell, B. R., J. C. Imhoff, J. L. Kittle, T. H. Jobes, and A. S. Donigian (2001), HSPF User's Manual, Aqua Terra Consultants, Mountain View, California.
7. Box, G. E. P., and D. R. Cox (1964), An analysis of transformations, J. R. Stat. Soc. Ser. B, 26, 211-243.
8. Boyle, D. P., H. V. Gupta, and S. Sorooshian (2000), Toward improved calibration of hydrological models: Combining the strengths of manual and automatic methods, Water Resour. Res., 36(12), 3663-3674, doi:10.1029/ 2000WR900207. (Pubitemid 32039916)
9. Braak, C. J. F., and J. A. Vrugt (2008), Differential evolution Markov chain with snooker updater and fewer chains, Stat. Comput, 435-446.
10. Campbell, E. P., and B. C. Bates (2001), Regionalization of rainfall-runoff model parameters using Markov chain Monte Carlo samples, Water Resour. Res., 37(3), 731-739, doi:10.1029/2000WR900349. (Pubitemid 32526071)
11. Campbell, E. P., D. R. Fox, and B. C. Bates (1999), A Bayesian approach to parameter estimation and pooling in nonlinear flood event models, Water Resour. Res., 35(1), 211-220, doi:10.1029/1998WR900043. (Pubitemid 29054161)
12. Christensen, S., and R. L. Cooley (1999), Evaluation of confidence intervals for a steady state leaky aquifer model, Adv. Water Resour., 22(8), 807-819.
13. Cooley, R. L., and S. Christensen (2006), Bias and uncertainty in regression-calibrated models of groundwater flow in heterogeneous media, Adv. Water Resourc., 29, 639-656.
14. Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan (2002), A fast and elitist multiobjective genetic algorithm: NGSA-II, IEEE T. Evolut. Comput, 6, 182-197.
15. de Groot-Hedlin, C, and S. Constable (1990), Occam's inversion to generate smooth, two-dimensional models from magnetotelluric data, Geophysics, 55(12), 1613-1624.
16. Doherty, J. (2010), PEST: Model-Independent Parameter Estimation, Watermark Numerical Computing, Brisbane, Australia. (Downloadable from http:\\www.pesthomepage.org)
17. Doherty, J., and D. Welter (2010), A short exploration of structural noise, Water Resour. Res., 46, W05525, doi:10.1029/2009WR008377.
18. Dorigo, M., and T. Stützle (2004), Ant Colony Optimization, MIT Press, Cambridge, MA.
19. Duan, Q., S. Sorooshian, and V. Gupta (1992), Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resour. Res., 28(4), 1015-1031, doi:10.1029/91WR02985.
20. Eberhart, R. C, and J. Kennedy (1995), A new optimizer using particle swarm theory, in Proceedings of the Sixth International Symposium on Micromachine and Human Science, pp. 39-43, Nagoya, Japan.
21. Fienen, M. N., C. T. Muffles, and R. J. Hunt (2009), On constraining pilot point calibration with regularization in PEST, Ground Water, 47(6), 835-844.
22. Gallagher, M. R., and J. Doherty (2006), Parameter estimation and uncertainty analysis for a watershed model, Environmental Modelling and Software, 22, 1000-1020.
23. Gupta, H. V., S. Sorooshian, and P. O. Yapo (1998), Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information, Water Resour. Res., 34(4), 751-764, doi:10.1029/97WR03495.
24. Hansen, N., and A. Ostermeier (2001), Completely derandomized self-adaptation in evolution strategies, Evol. Comput, 9, 159-195.
25. Hansen, N., S. D. Muller, and P. Koumoutsakos (2003), Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMAES), Evol. Comput, 9, 159-195.
26. Harbaugh, A. W., E. R. Banta, M. C. Hill, and M. G. McDonald (2000), The U.S. Geological Survey Modular Ground-Water Model\User Guide to Modularization Concepts and the Ground-Water Flow process, in U.S. Geol. Surv. Open File Rep., 00-92, Reston, Virginia.
27. Harmon, R., and P. Challenor (1997), A Markov chain Monte Carlo method for estimation and assimilation into models, Ecol. Model, 101, 41-59.
28. Hassan, A. E., H. M. Bekhit, and J. B. Chapman (2008), Uncertainty assessment of a stochastic groundwater flow model using GLUE analysis, J. Hydrol, 362, 89-109.
29. Huisman, J. A., 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, J. Hydrol, 380, 62-73.
30. James, S. C, J. Doherty, and A.-A. Eddebarh (2009), Post-calibration uncertainty analysis: Yucca Mountain, Nevada, USA, Ground Water, 47(6), 851-869.
31. Journel, A. G., and Ch. J. Huijbregts (1978), Mining Geostatistics, Academic, London.
32. Kanso, A., M. C. Gromaire, E. Gaume, B. Tassin, and G. Ghebbo (2003), Bayesian approach for the calibration of models: application to an urban stormwater pollution model, Water Sci. Technol, 47(4), 77-84.
33. Kavetski, D., G. Kuczera, and S. W. Franks (2006), Calibration of conceptual hydro logical models revisited: 1. Overcoming numerical artefacts, J. Hydrol, 320, 173-186.
34. Koch, K. R. (1988), Parameter Estimation and Hypothesis Testing in Linear Models, XVI+378 pp., Springer, Berlin.
35. Kuczera, G., and E. Parent (1998), Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm, J. Hydrol, 211, 69-85.
36. Madsen, H. (2000), Automatic calibration of a conceptual rainfall-runoff model using multiple objectives, J. Hydrol, 235, 267-288.
37. Madsen, H. (2003), Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives, Adv. Water. Resour., 26, 205-216.
38. Makowski, D., D. Wallach, and M. Tremblay (2002), Using a Bayesian approach to parameter estimation; comparison of the GLUE and MCMC methods, Agronomie, 22, 191-203.
39. Moore, C. (2005), The Use of Regularized Inversion in Groundwater Model Calibration and Prediction Uncertainty Analysis, PhD thesis, University of Queensland, St. Lucia, Australia.
40. Moore, C, and J. Doherty (2005), The role of the calibration process in reducing model predictive error, Water Resour. Res., 41(5), W05020, doi:10.1029/2004WR003501. (Pubitemid 40856470)
41. Moore, C, and J. Doherty (2006), The cost of uniqueness in groundwater model calibration, Adv. Water Resour., 29(4), 605-623.
42. Pan, L. H., and L. S. Wu (1998), A hybrid global optimization method for inverse estimation of hydraulic parameters: annealing-simplex method, Water Resour. Res., 34(9), 2261-2269, doi:10.1029/98WR01672. (Pubitemid 28407895)
43. Qian, S. S., C. A. Stow, and M. E. Borsuk (2003), On Monte Carlo methods for Bayesian inference, Ecol. Model., 159, 269-277.
44. Skahill, B., and J. Doherty (2006), Efficient accommodation of local minima in watershed model calibration, J. Hydrol, 329, 122-139.
45. Tikhonov, A. N., and V. Y. Arsenin (1977), Solution of Ill-Posed Problems, VH Winston and Sons, Washington, D.C.
46. Tolson, B. A., and C. A. Shoemaker (2007), Dynamically dimensioned search algorithm for computationally efficient watershed model calibration, Water Resour. Res., 43, W01413, doi:10.1029/2005WR004723. (Pubitemid 46364291)
47. Tonkin, M. J., and J. Doherty (2009), Calibration-constrained Monte Carlo analysis of highly parameterized models using subspace techniques, Water Resour. Res., 45, W00B10, doi:10.1029/2007WR006678.
48. Tonkin, M., J. Doherty, and C. Moore (2007), Efficient nonlinear predictive error variance analysis for highly parameterized models, Water Resour. Res., 43, W07429, doi:10.1029/2006WR005348. (Pubitemid 350199324)
49. Vrugt, J. A., and B. A. Robinson (2007), Improved evolutionary optimization from genetically adaptive multimethod search, Proc. Natl. Acad. Sci., 104, 708-711.
50. Vrugt, J. A., H. V. Gupta, L. A. Bastidas, W. Bouten, and S. Sorooshian (2003), Effective and efficient algorithm for multiobjective optimization of hydrologic models, Water Resour. Res., 39(5), 1214, doi:10.1029/2002WR001746.
51. Vrugt, J. A., C. J. F. terBraak, M. P. Clark, J. M. Hyman, andB. A. Robinson (2008), Treatment of input uncertainty in hydrologic modeling: doing hydrology backward with Markov chain Monte Carlo simulation, Water Resour. Res., 44, W00B09, doi:10.1029/2007WR006720.
52. Vrugt, J. A., C. J. F. ter Braak, C. G. H. Diks, B. A. Robinson, J. M. Hyman, and D. Hidgon (2009), Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling, Int. J. Nonlinear Sci., 10(3), 273-290.