Flexible automated parameterization of hydrologic models using fuzzy logic

Water Resources Research

Volumn 39, Issue 1, 2003, Pages SWC-1-1-SWC-1-13

  Samanta, Sudeep ^a   Mackay, D Scott ^a  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

Automated parameter estimation; Fuzzy logic; Hydrologic models; Monte Carlo sampling

Indexed keywords

CALIBRATION; COMPUTATIONAL METHODS; ECOSYSTEMS; FUNCTIONS; FUZZY SETS; ITERATIVE METHODS; STREAM FLOW;

HYDROLOGIC MODELS;

HYDROLOGY;

FUZZY MATHEMATICS; HYDROLOGICAL MODELING; MONTE CARLO ANALYSIS; PARAMETERIZATION; STREAMFLOW;

EID: 0038128165     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2002wr001349     Document Type: Article

References (43)

1. Band, L. E., P. Patterson, R. Nemani, and S. W. Running, Forest ecosystem processes at the watershed scale: Incorporating hillslope hydrology, Agric. For. Meteorol., 63, 93-126, 1993.
2. 2, Water Resour. Res., 36(1), 89-99, 2000.
3. Beldring, S., Multi-criteria validation of a precipitation-runoff model, J. Hydrol., 257, 189-211, 2002.
4. Beven, K. J., Runoff production and flood frequency in catchments of order n: An alternative approach, in Scale Problems in Hydrology, edited by V. K. Gupta, I. Rodriguez-Iturbe, and E. F. Wood, pp. 107-131, D. Reidel, Norwell, Mass., 1986.
5. Beven, K. J., Changing ideas in hydrology-The case of physically based models, J. Hydrol., 105, 157-172, 1989.
6. Beven, K. J., Prophecy, reality and uncertainty in distributed hydrological modeling, Adv. Water Resour., 16, 41-55, 1993.
7. Beven, K. J., and A. M. Binley, The future of distributed models: Model calibration and uncertainty prediction, Hydrol. Processes, 6, 279-298, 1992.
8. Beven, K. J., and M. J. Kirkby, A physically based variable contributing area model of basin hydrology, Hydrol. Sci. Bull., 24(1), 43-69, 1979.
9. Binley, A. M., and K. J. Beven, Physically based modelling of catchment hydrology: A likelihood approach to reduce predictive uncertainty, in Computer Modeling in Environmental Sciences, edited by D. G. Farmer and M. J. Rycroft, pp. 75-88, Clarendon, Oxford, UK, 1991.
10. Boyle, D. P., H. V. Gupta, and S. Sorooshian, Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods, Water Resour. Res., 36(12), 3663-3674, 2000.
11. Duan, Q., S. Sorooshian, and H. V. Gupta, Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resour. Res., 28(4), 1015-1031, 1992.
12. Franks, S. W., and K. J. Beven, Estimation of evapotranspiration at the landscape scale: A fuzzy disaggregation approach, Water Resour. Res., 33(12), 2929-2938, 1997.
13. Franks, S. W., and K. J. Beven, Conditioning a multiple-patch SVAT model using uncertain time-space estimates of latent heat fluxes as inferred from remotely sensed data, Water Resour. Res., 35(9), 2751-2761, 1999.
14. Franks, S. W., P. Gineste, K. J. Beven, and P. Merot, On constraining the predictions of a distributed model: The incorporation of fuzzy estimates of saturated areas into calibration process, Water Resour. Res., 34(4), 787-797, 1998.
15. Franks, S. W., K. J. Beven, and J. H. C. Gash, Multi-objective conditioning of a simple SVAT model, Hydrol. Earth Syst. Sci., 3(4), 477-489, 1999.
16. Gupta, H. V., S. Sorooshian, and P. O. Yapo, Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information, Water Resour. Res., 34(4), 751-763, 1998.
17. Gupta, H. V., L. A. Bastidas, S. Sorooshian, W. J. Shuttleworth, and Z. L. Yang, Parameter estimation of a land surface scheme using multicriteria methods, J. Geophys. Res., 104(D16), 19,491-19,503, 1999.
18. Hankin, B. G., and K. J. Beven, Modelling dispersion in complex open channel flows: Fuzzy calibration (2), Stochastic Hydrol. Hydraul., 12(6), 397-412, 1998.
19. Hartley, R. V. L., Transmission of information, Bell Syst. Tech. J., 7(3), 535-563, 1928.
20. Higashi, M., and G. J. Klir, Measures of uncertainty and information based on possibility distributions, Int. J. Gen. Syst., 9, 43-58, 1982.
21. Klepper, O., H. Scholten, and J. P. G. Van De Kamer, Prediction uncertainty in an ecological model of the Oosterschelde estuary, J. Forecasting, 10, 191-209, 1991.
22. Klir, G. J., and M. J. Wierman, Uncertainty-Based Information: Elements of Generalized Information Theory, Studies in Fuzziness and Soft Computing, vol. 15, edited by J. Kacprzyk, Physica, New York, 1998.
23. Kuczera, G., Improved parameter inference in catchment models, 1, Evaluating parameter uncertainty, Water Resour. Res., 19(5), 1151-1162, 1983.
24. Kuczera, G., and E. Parent, Monte Carlo assessment of parameter uncertainty in conceptual catchment models: The Metropolis algorithm, J. Hydrol., 211, 69-85, 1998.
25. Legates, D. R., and G. J. McCabe, Jr., Evaluating the use of "goodness of fit" measures in hydrologic and hydroclimatic model validation, Water Resour. Res., 35(1), 233-241, 1999.
26. Mackay, D. S., Evaluation of hydrologic equilibrium in a mountainous watershed: Incorporating forest canopy spatial adjustment to soil biogeochemical processes, Adv. Water Resour., 24, 1211-1227, 2001.
27. Mackay, D. S., and L. E. Band, Forest ecosystem processes at the watershed scale: Dynamic coupling of distributed hydrology and canopy growth, Hydrol. Processes, 11, 1197-1217, 1997.
28. Madsen, H., Automatic calibration of a conceptual rainfall-runoff model using multiple objectives, J. Hydrol., 235, 276-288, 2000.
29. Melching, C. S., Reliability estimation, in Computer Models of Watershed Hydrology, edited by V. P. Singh, pp. 69-118, Water Resour. Publ., Highlands Ranch, Colo., 1995.
30. Quinn, P. F., K. J. Beven, and R. Lamb, The ln(a/tanβ) index: How to calculate it and how to use it within the TOPMODEL framework, Hydrol. Processes, 9, 161-182, 1995.
31. See, L., and S. Openshaw, A hybrid multi-model approach to river level forecasting, Hydrol. Sci. J., 45(4), 523-536, 2000.
32. Sivapalan, M., K. J. Beven, and E. F. Wood, On hydrologic similarity, 2, A scaled model of storm water runoff production, Water Resour. Res., 23(12), 2266-2278, 1987.
33. Sorooshian, S., and V. K. Gupta, Model calibration, in Computer Models of Watershed Hydrology, edited by V. P. Singh, pp. 23-68, Water Resour. Publ., Highlands Ranch, Colo., 1995.
34. Spear, R. C., and G. M. Hornberger, Eutrophication in Peel Inlet, II, Identification of critical uncertainties via generalized sensitivity analysis, Water Res., 14, 43-49, 1980.
35. U.S. Department of Agriculture, Map of H. J. Andrews Experimental Forest, Blue River Ranger District, Willamatte Natl. For., Oreg., 1986.
36. Van Stratten, G., and K. J. Keesman, Uncertainty propagation and speculation in projective forecasts of environmental change: A lake-eutrophication example, J. Forecasting, 10, 163-190, 1991.
37. Watson, G. R., R. A. Vertessy, and L. E. Band, Distributed parameterization of a large scale water balance model for an Australian forested region, in Hydrogis 96: Application of Geographic Information Systems in Hydrology and Water Resources Management (Proceedings of Vienna Conference), IAHS Publ., 235, 157-166, 1996.
38. White, J. D., S. W. Running, P. E. Thornton, R. E. Keane, K. C. Ryan, D. B. Farge, and C. H. Key, Assessing simulated ecosystem processes for climate variability research at Glacier National Park, USA, Ecol. Appl., 8(3), 805-823, 1998.
39. Yapo, P. O., H. V. Gupta, and S. Sorooshian, Automatic calibration of conceptual rainfall-runoff models: Sensitivity to calibration data, J. Hydrol., 181, 23-48, 1996.
40. Yapo, P. O., H. V. Gupta, and S. Sorooshian, Multi-objective global optimization for hydrologic models, J. Hydrol., 204, 83-97, 1998.
41. Zadeh, L. A., Fuzzy sets, Inf. Control, 8(3), 338-353, 1965.
42. Zadeh, L. A., Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets Syst., 1, 3-28, 1978.
43. Zhang, X., and G. Lindström, Development of an automatic calibration scheme for the HBV hydrological model, Hydrol. Processes, 11, 1671-1682, 1997.