Detection of overparameterization and overfitting in an automatic calibration of SWAT

Transactions of the ASABE

Volumn 53, Issue 5, 2010, Pages 1487-1499

  Whittaker, G ^a   Confesor Jr , R ^b   Di Luzio, M ^c   Arnold, J G ^a  

^a U S DEPARTMENT OF AGRICULTURE   (United States)

^b HEIDELBERG UNIVERSITY   (United States)

^c BLACKLAND RESEARCH AND EXTENSION CENTER   (United States)

Author keywords

Automatic calibration; Distributed hydrologic model; NSGA II; Overfitting; Overparameterization; Regularization

Indexed keywords

GENETIC ALGORITHMS; INVERSE PROBLEMS;

AUTOMATIC CALIBRATION; DISTRIBUTED HYDROLOGIC MODEL; NSGA-II; OVERFITTING; OVERPARAMETERIZATION; REGULARIZATION;

PARAMETER ESTIMATION;

AUTOMATION; CALIBRATION; GENETIC ALGORITHM; HETEROGENEITY; HYDROLOGICAL MODELING; INVERSE PROBLEM; MODEL VALIDATION; NUMERICAL MODEL; PARAMETERIZATION; WATERSHED;

BLUE RIVER [OKLAHOMA]; OKLAHOMA [UNITED STATES]; UNITED STATES;

EID: 78149389861     PISSN: 21510032     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (46)

1. Arnold, J. G., R. Srinivasan, R. S. Muttiah, and J. R. Williams. 1998. Large-area hydrologic modeling and assessment part I: Model development. J. American Water Resources Assoc. 34(1): 73.
2. Baumer, O., P. Kenyon, and J. Bettis. 1994. MUUF v2.14 User's Manual. Lincoln, Neb.: USDA-NRCS National Soil Survey Center.
3. Beven, K. 1993. Prophecy, reality, and uncertainty in distributed hydrological modeling. Advances in Water Resources 16(1): 41-51.
4. Beven, K. 2000. Rainfall-Runoff Modeling: The Primer. New York, N.Y.: John Wiley and Sons.
5. Beven, K. 2006. A manifesto for the equifinality thesis. J. Hydrol. 320(1-2): 18-36.
6. Boyle, D. P., H. V. Gupta, and S. Sorooshian. 2000. Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods. Water Resources Res. 36(12): 3663.
7. Boyle, D. P., H. V. Gupta, S. Sorooshian, V. Koren, Z. Zhang, and M. Smith. 2001. Toward improved streamflow forecasts: Value of semidistributed modeling. Water Resources Res. 37(11): 2749-2759.
8. Confesor, R. B., and G. Whittaker. 2007. Automatic calibration of hydrologic models with multi-objective evolutionary algorithm and Pareto optimization. J. American Water Resources Assoc. 43(4): 1:9.
9. Deb, K. 2001. Multi-Objective Optimization Using Evolutionary Algorithms. Chichester, U.K.: John Wiley and Sons.
10. Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan. 2002a. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Computation 6(2): 182-197.
11. Deb, K., A. Anand, and D. Joshi. 2002b. A computationally efficient evolutionary algorithm for real-parameter optimization. Evol. Comput. 10(4): 371-395.
12. Di Luzio, M., and J. G. Arnold. 2004a. Formulation of a hybrid calibration approach for a physically based distributed model with NEXRAD data input. J. Hydrol. 298: 136-154.
13. Di Luzio, M., and J. G. Arnold. 2004b. Multi resources GIS hydrologic framework for TMDL assessment. Poster presentation at the 2004 AWRA Spring Specialty Conf.: Geographic Information Systems (GIS) and Water Resources III. Middleburg, Va.: American Water Resources Association.
14. Di Luzio, M., R. Srinivasan, and J. G. Arnold. 2004. A GIS-coupled hydrological model system for the watershed assessment of agricultural nonpoint and point sources of pollution. Trans. GIS 8(1): 113-136.
15. Doherty, J., and R. J. Hunt. 2009. Two statistics for evaluating parameter identifiability and error reduction. J. Hydrol. 366: 119-127.
16. Doherty, J., and B. E. Skahill. 2006. An advanced regularization methodology for use in watershed model calibration. J. Hydrol. 327(3-4): 564-577.
17. Faber, N. M., and R. Rajko. 2007. How to avoid over-fitting in multivariate calibration: The conventional validation approach and an alternative. Analytica Chimica Acta 595(1-2): 98-106.
18. Gesch, D. B. 2007. The national elevation dataset. In Digital Elevation Model Technologies and Applications: The DEM Users Manual, 99-118. 2nd ed. D. Maune, ed. Bethesda, Md.: American Society for Photogrammetry and Remote Sensing.
19. Gesch, D., M. Oimoen, S. Greenlee, C. Nelson, M. Steuck, and D. Tyler. 2002. The national elevation dataset. Photogram. Eng. and Remote Sensing 68(1): 5-11.
20. Gupta, H. V., S. Sorooshian, and P. O. Yapo. 1999. Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. J. Hydrol. Eng. 4(2): 135.
21. Hinnell, A. C., T. P. A. Ferre, J. A. Vrugt, J. A. Huisman, S. Moysey, J. Rings, and M. B. Kowalsky. 2009. Improved extraction of hydrologic information from geophysical data through coupled hydrogeophysical inversion. Water Resources Res. 46(11): W00D40, DOI: 10.1029/2009WR008288.
22. Jakeman, A. J., and G. M. Hornberger. 1993. How much complexity is warranted in a rainfall-runoff model? Water Resources Res. 29(8): 2637-2649.
23. Marseguerra, M., E. Zio, and L. Podofillini. 2003. Model parameters estimation and sensitivity by genetic algorithms. Annals of Nuclear Energy 30(14): 1437-1456.
24. Moriasi, D. N., J. G. Arnold, M. W. Van Liew, R. L. Bingner, R. D. Harmel, and T. L. Veith. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE 50(3): 885-900.
25. Nash, J. E., and J. V. Sutcliffe. 1970. River flow forecasting through conceptual models: Part I. A discussion of principles. J. Hydrol. 10(3): 282-290.
26. Neitsch, S. L., J. G. Arnold, J. R. Kiniry, and J. R. Williams. 2005. Soil and Water Assessment Tool Theoretical Documentation, Version 2005. Temple, Tex.: USDA-ARS Grassland, Soil and Water Research Laboratory.
27. Pokhrel, P., and H. V. Gupta. 2009. On the use of spatialregularization strategies to improve calibration of distributed watershed models. Water Resources Res. 46(1): W01505, DOI: 10.1029/2009WR008066, in press.
28. Pokhrel, P., H. V. Gupta, and T. Wagener. 2008. A spatial regularization approach to parameter estimation for a distributed watershed model. Water Resources Res. 44(12): W12419, DOI: 12410.11029/12007WR006615.
29. Pokhrel, P., K. K. Yilmaz, and H. V. Gupta. 2009. Multiple-criteria calibration of a distributed watershed model using spatial regularization and response signatures. J. Hydrol. DOI: 10.1016/j.jhydrol.2008.12.004 (in press).
30. R Development Core Team. 2007. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available at: www.R-project.org.
31. Radtke, P. V. W. and T. Wong. 2006. An evaluation of over-fit control strategies for multi-objective evolutionary optimization. In Proc. IEEE Intl. Joint Conf. on Neural Networks, 3327-3334. Piscataway, N.J.: IEEE.
32. Reed, S., and D. Maidment. 1999. Coordinate transformations for using NEXRAD data in GIS-based hydrologic modeling. J. Hydrol. Eng. 4(2): 174-182.
33. Reed, S., V. Koren, M. Smith, Z. Zhang, F. Moreda, D. Seo, and DMIP participants. 2004. Overall distributed model intercomparison project results. J. Hydrol. 298: 27-60. (Pubitemid 39098767)
34. Reiczigel, J., I. Zakariás, and L. Rózsa. 2005. A bootstrap test of stochastic equality of two populations. American Statistician 59(2): 1-6.
35. Schoups, G., N. C. van de Giesen, and H. H. G. Savenije. 2008. Model complexity control for hydrologic prediction. Water Resources Res. 44: W00B03, DOI: 10.1029/2008WR006836.
36. Scott, D. W. 1992. Multivariate Density Estimation: Theory, Practice, and Visualization. New York, N.Y.: John Wiley and Sons.
37. Smith, M. B., D.-J. Seo, V. I. Koren, S. Reed, Z. Zhang, Q.-Y. Duan, F. Moreda, and S. Cong. 2004. The distributed model intercomparison project (DMIP): Motivation and experiment design. J. Hydrol. 298: 4-26.
38. Smith, M., V. Koren, S. Reed, Z. Zhang, D. J. Seo, F. Moreda, and Z. Cui. 2006. The distributed model intercomparison project: Phase 2. Science plan. Silver Spring, Md.: National Weather Service, Hydrology Laboratory. Available at: www.nws.noaa. gov/oh/hrl/dmip/2/docs/dmip-2-plan-march10-06-update.pdf. Accessed 16 September 2010.
39. Tikhonov, A. N., and V. Y. Arsenin. 1977. Solutions of Ill-Posed Problems. New York, N.Y.: V. H. Winston, distributed by Wiley.
40. Tonkin, M. J., and J. Doherty. 2005. A hybrid regularized inversion methodology for highly parameterized environmental models. Water Resources Res. 41(10): W10412, DOI: 10410.11029/12005WR003995.
41. USDA. 1992. State Soil Geographic Database (STATSGO) Data Users' Guide. Publication 1492. Washington, D.C.: USDANRCS.
42. van Werkhoven, K., T. Wagener, P. Reed, and Y. Tang. 2009. Sensitivity-guided reduction of parametric dimensionality for multi-objective calibration of watershed models. Advances in Water Resources 32(8): 1154-1169.
43. Venables, W. N. and B. D. Ripley. 2002. Modern Applied Statistics with S-Plus. 4th ed. New York, N.Y.: Springer.
44. Vogelmann, J. E., S. M. Howard, L. Yang, C. R. Larson, B. K. Wylie, and N. Van Driel. 2001. Completion of the 1990s National Land Cover Dataset for the conterminous United States from Landsat Thematic Mapper data and ancillary data sources. J. American Soc. for Photogrammetry and Remote Sensing 67(6): 650-662.
45. Vrugt, J. A., 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 Environ. Research and Risk Assessment 23(7): 1011-1026.
46. Wagener, T., M. Sivapalan, P. A. Troch, and R. Woods. 2007. Catchment classification and hydrologic similarity. Geogr. Compass 1(4): 901-931.