Chapter 13 Simplicity out of complexity

Developments in Environmental Modelling

Volumn 22, Issue C, 2002, Pages 251-301

  Young, P C ^a   Parkinson, S ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77957071062     PISSN: 01678892     EISSN: None     Source Type: Book Series    
DOI: 10.1016/S0167-8892(02)80014-8     Document Type: Article

References (73)

1. Akaike H. A new look at statistical model identification. IEEE Trans. Auto. Control AC-19 (1974) 716-723
2. Beck M.B. Model structure identification from experimental data. In: Halfon E. (Ed). Theoretical Systems Ecology (1979), Academic Press, New York 259-289
3. Beck M.B. Uncertainty, system identification and the prediction of water quality. In: Beck M.B., and van Straten G. (Eds). Uncertainty and Forecasting of Water Quality (1983), Springer-Verlag, Berlin 3-68
4. Beck M.B. Water quality modeling: a review of the analysis of uncertainty. Water Resour. Res. 23 8 (1987) 1393-1442
5. Beck M.B., and Halfon E. Uncertainty, identifiability and the propagation of prediction errors: a case study of Lake Ontario. J. Forecasting 10 l&2 (1991) 135-161
6. Beck M.B., and Young P.C. A dynamic model for BOD-DO relationships in a non-tidal stream. Water Res. 9 (1975) 769-776
7. Beer T., and Young P.C. Longitudinal dispersion in natural streams. Proc. Am. Soc. Civil Eng., Jnl. Env. Eng. 109 (1983) 1049-1067
8. Beven K.J. Prophecy, reality and uncertainty in distributed hydrological modelling. Adv. Water Resour. 16 (1993) 41-51
9. Beven K.J., Freer J., Hankin B., and Schulz K. The use of generalised likelihood measures for uncertainty estimation in high order models of environmental systems. In: Fitzgerald W.J., Walden A., Smith R., and Young P.C. (Eds). Nonlinear and Nonstationary Signal Processing (2000), Cambridge University Press, Cambridge 115-151
10. Beven K.J., and Binley A. The future of distributed models: model calibration and uncertainty prediction. Hydrol. Proc. 6 (1992) 279-298
11. Box G.E.P., and Jenkins G.M. Time Series Analysis Forecasting and Control (1970), Holden-Day, San Francisco
12. CDIAC. In: Boden T.A., Serpanski R.J., and Farrell M.P. (Eds). Trends 91: A Compendium of Global Change (1991), Carbon Dioxide Information and Analysis Center, Oak Ridge, Tennessee
13. 2 Concentrations. Division of Atmospheric Research, Technical Paper 22 (1991), CSIRO, Melbourne, Australia
14. Enting, I.G., 1994. Personal Communication.
15. Enting I.G., and Pearman G.I. Refinements to a One-Dimensional Carbon Cycle Model. Division of Atmospheric Research, Technical Paper 3 (1983), CSIRO, Melbourne, Australia
16. 2. Division of Atmospheric Research, Technical Paper 27 (1993), CSIRO, Melbourne, Australia
17. Gamerman D. Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference (1997), Chapman and Hall, London
18. Goodwin G.C., and Payne R.L. Dynamic System Identification: Experiment Design and Data Analysis (1977), Academic Press, New York
19. Hasselmann K. Climate-change research after Kyoto. Nature 390 (1998) 225-226
20. Hasselmann K., Hasselmann S., Giering R., Ocana V., and Storch H.v. Sensitivity study of optimal emission paths using a simplified structural integrated assessment (SIAM). Climate Change 37 (1997) 345-386
21. Hornberger G.M., and Spear R.C. An approach to the preliminary analysis of environmental systems. J. Environ. Manage. 12 (1981) 7-18
22. Huggett R.J. Modelling the Human Impact on Nature: Systems Analysis of Environmental Problems (1993), Oxford University Press, Oxford
23. IPCC. Climate Change: The IPCC Scientific Assessment. In: Houghton J.T., Jenkins G.J., and Ephraums J.J. (Eds). Intergovernmental Panel on Climate Change (1990), Cambridge University Press, Cambridge
24. IPCC. Climate Change 1992: The Supplementary Report to the IPCC Scientific Assessment. In: Houghton J.T., Callender B.A., and Varley S.K. (Eds). Intergovernmental Panel on Climate Change (1992), Cambridge University Press, Cambridge
25. Joos F., Bruno M., Fink R., Siegenthaler U., Stocker T.F., Le Quere C., and Sarmiento J.L. An efficient and accurate representation of complex oceanic and biospheric models of anthropogenic carbon uptake. Tellus 48B (1996) 397-417
26. Kailath T. Linear Systems (1980), Prentice Hall, New York
27. Keesman K.J., and van Straten G. Set-membership approach to identification and prediction of lake eutrophication. Water Resour. Res. 26 11 (1990) 2643-2652
28. Konikow L.F., and Bredehoeft J.D. Ground water models cannot be validated. Adv. Water Resources 15 (1992) 75-83
29. Lees M.J., Young P.C., Ferguson S., Beven K.J., and Burns J. An adaptive flood warning scheme for the River Nith at Dumfries. In: White W.R., and Watts J. (Eds). Second International Conference on River Flood Hydraulics (1994), Wiley, Chichester 65-77
30. Ljung L., and Söderström T. Theory and Practice of Recursive Estimation (1983), MIT Press, Cambridge, Mass.
31. Mathworks, Inc. MATLAB Reference Guide (1994), Mathworks, Natick, MA
32. Moore B. Principle component analysis in linear systems: controllability, observability, and model reduction. IEEE Trans. Automatic Contr. 26 (1981) 17-32
33. Oeschger H., Siegenthaler U., Schottere U., and Gugelmann A. A box diffusion model to study the carbon dioxide exchange in nature. Tellus 27 (1975) 168-192
34. Oglesby R.J., and Saltzman B. Sensitivity to the equilibrium surface temperature of a GCM to systematic changes in atmospheric carbon dioxide. Geophys. Res. Lett. 17 (1990) 1089-1092
35. Oreskes N., Shrader-Frechette K., and Belitz K. Verification, validation, and confirmation of numerical models in the earth sciences. Science 263 (1994) 641-646
36. TPS-01-1. Osidele O.O., and Beck M.B. Analysis of uncertainty in model predictions for Lake Lanier, Georgia. In: Warwick J.J. (Ed). Proceedngs AWRA Annual Spring Specialty Conference (2001), American Water Resources Association, Middleburg, Virginia 133-137
37. Parkinson S. The Application of Stochastic Modelling Techniques to Global Climate Change. Ph.D Thesis (1995), Lancaster University, U.K.
38. Parkinson S., and Young P.C. Uncertainty and sensitivity in global carbon cycle modelling. Climate Res. 9 (1998) 157-174
39. Philip J.R. Some remarks on science and catchment prediction. In: Chapman T.G., and Dunin F.X. (Eds). Prediction in Catchment Hydrology (1975), Australian Academy of Science, Canberra, Australia
40. Price L.E., Young P.C., Berckmans D., Janssens K., and Taylor J. Data-based mechanistic modelling and control of mass and energy transfer in agricultural buildings. Annual Rev. Contr. 23 (1999) 71-82
41. In: Saltelli A., Chan K., and Scott M. (Eds). Sensitivity Analysis (2000), Wiley, Chichester
42. Shackley S., Young P.C., Parkinson S., and Wynne B. Uncertainty, complexity and concepts of good science in climate change modelling: are GCMs the best tools?. Climatic Change 38 (1998) 159-205
43. Spear R.C. The application of Kolmogorov-Renyi statistics to problems of parameter uncertainty in systems design. Int. J. Contr. 11 5 (1970) 771-778
44. Spear R.C. Regional sensitivity analysis in environmental systems. In: Young P.C. (Ed). Concise Encyclopaedia of Environmental Systems (1993), Pergamon Press, Oxford 476-479
45. Taylor J.A. Fossil fuel emissions required to achieve stabilisation using ANU-BACE: a box-diffusion carbon cycle model. Ecol. Modelling 86 (1996) 195-199
46. Wallis S.G., Young P.C., and Beven K.J. Experimental investigation of the aggregated dead zone model for longitudinal solute transport in stream channels. Proc. Inst. Civil Engrs, Part 2 87 (1989) 1-22
47. Wellstead P.E. An instrumental product moment test for model order estimation. Automatica 14 (1978) 89-91
48. West M., and Harrison J. Bayesian Forecasting and Dynamic Models (1989), Springer-Verlag, New York
49. Wigley T.M.L., and Raper S.C.B. Implications for climate and sea level of revised IPCC emissions scenarios. Nature 357 (1992) 293-300
50. Young P.C. A general theory of modelling for badly defined dynamic systems. In: Vansteenkiste G.C. (Ed). Modeling, Identification and Control in Environmental Systems (1978), North-Holland, Amsterdam 103-135
51. Young P.C. The validity and credibility of models for badly defined systems. In: Beck M.B., and van Straten G. (Eds). Uncertainty and Forecasting of Water Quality (1983), Springer-Verlag, Berlin
52. Young P.C. Recursive Estimation and Time-Series Analysis (1984), Springer-Verlag, Berlin
53. Young P.C. The instrumental variable method: a practical approach to identification and system parameter estimation. In: Barker H.A., and Young P.C. (Eds). Identification and System Parameter Estimation: Vol. 1 & 2 (1985), Pergamon Press, Oxford
54. Young P.C. Recursive estimation, forecasting and adaptive control. In: Leondes C.T. (Ed). Control and Dynamic Systems (1989), Academic Press, San Diego 119-166
55. Young P.C. Parallel processes in hydrology and water quality: a unified time series approach. J. Inst. Water Environ. Manage. 6 (1992) 598-612
56. Chapter 2. Young P.C. Parallel processes in hydrology and water quality: objective inference from hydrological data. In: Falconer R.A. (Ed). Water Quality Modelling (1992), Ashgate, Vermont 10-52
57. Chapter 26. Young P.C. Time variable and state dependent modelling of nonstationary and nonlinear time series. In: Rao T.S. (Ed). Developments in Time Series Analysis (1993), Chapman and Hall, London 374-413
58. In: Young P.C. (Ed). Concise Encyclopaedia of Environmental Systems (1993), Pergamon Press, Oxford
59. Young P.C. Data-based mechanistic modelling of environmental, ecological, economic and engineering systems. Environ. Modelling and Software 13 (1998) 105-122
60. Young P.C. Nonstationary time series analysis and forecasting. Progr. Environ. Sci. 1 (1999) 3-48
61. Young P.C. Data-based mechanistic modelling, generalised sensitivity and dominant mode analysis. Computer Phys. Commun. 117 (1999) 113-129
62. Young P.C. Stochastic, dynamic modelling and signal processing: time variable and state dependent parameter estimation. In: Fitzgerald W.J., Walden A., Smith R., and Young P.C. (Eds). Nonlinear and Nonstationary Signal Processing (2000), Cambridge University Press, Cambridge 74-114
63. Young P.C. Data-based mechanistic modelling and validation of rainfall-flow processes. In: Anderson M.G., and Bates P.D. (Eds). Model Validation: Perspectives in Hydrological Science (2001), Wiley, Chichester 117-161
64. Young P.C., and Beven K.J. Data-based mechanistic modelling and the rainfall-flow nonlinearity (special issue on Environmental Time Series Analysis). Environmetrics 5 (1994) 335-363
65. Young P.C., and Lees M. The active mixing volume: a new concept in modelling environmental systems. In: Barnett V., and Turkman R. (Eds). Statistics and the Environment (1992), Wiley, Chichester 3-43
66. Young P.C., and Minchin P.E.H. Environmetric time-series analysis: modelling natural systems from experimental time-series data. Int. J. Biol. Macromolecules 13 (1991) 190-201
67. Young P.C., and Tomlin C. Data-based mechanistic modelling and adaptive flow forecasting. In: Lees M.J., and Walsh P. (Eds). Flood Forecasting: What Does Current Research Offer the Practitioner?. British Hydrological Society (BHS) Occasional Paper No. 12 (2000), Centre for Ecology and Hydrology on behalf of BHS, Wallingford, U.K. 26-40
68. Young P.C., Chotai A., and Tych W. Identification, estimation and control of continuous-time systems described by delta operator models. In: Sinha N.K., and Rao G.P. (Eds). Identification of Continuous-Time Systems (1991), Kluwer, Dordrecht 363-418
69. Young P.C., Jakeman A.J., and McMurtrie R. An instrumental variable method for model order identification. Automatica 16 (1980) 281-294
70. Young P.C., Parkinson S., and Lees M.J. Simplicity out of complexity: Occam's razor revisited. J. Appl. Statistics 23 (1996) 165-210
71. Young P.C., Parkinson S., and Mcllveen J.F.R. Computer simulation. In: Young P.C. (Ed). Concise Encyclopedia of Environmental Systems (1993), Pergamon, Oxford 113-118
72. Young P.C., Spear R.C., and Hornberger G.M. Modeling badly defined systems: some further thoughts. Proc. SIMSIG Conference. Canberra, Australia (1978)
73. Young P.C., Behzadi M.A., Wang C.L., and Chotai A. Direct digital and adaptive control by input-output, state variable feedback. Int. J. Control 46 (1987) 1861-1881