Skip to main content

Chaos Theory for Modeling Environmental Systems: Philosophy and Pragmatism

  • Chapter

Abstract

The last two decades have witnessed a significant momentum in chaos theory applications to environmental systems. The outcomes are certainly encouraging, especially considering the still fairly exploratory stage of the theory in the field. Nevertheless, there have also been persistent skepticisms and criticisms on these studies, motivated by the potential limitations in chaos identification methods. The goal of this chapter is to offer a balanced perspective of chaos studies in environmental systems: between the philosophy of chaos theory and the down-to-earth pragmatism needed in its applications. After a presentation of the development of chaos theory, some basic identification methods are described and their reliability for determining system properties demonstrated. A brief review of chaos theory studies in environmental systems as well as the progress and pitfalls is then made. Analysis of four river flow series presents support to the contention that environmental systems are neither deterministic nor stochastic, but a combination of the two, and that chaos theory can offer a middle-ground approach to our extreme deterministic and stochastic views.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Abarbanel, H.D.I.: Analysis of Observed Chaotic Data. Springer, New York (1996)

    Book  MATH  Google Scholar 

  2. Abarbanel, H.D.I., Lall, U.: Nonlinear dynamics of the Great Salt Lake: system identification and prediction. Clim. Dyn. 12, 287–297 (1996)

    Article  Google Scholar 

  3. Casdagli, M.: Nonlinear prediction of chaotic time series. Physica D 35, 335–356 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  4. Casdagli, M.: Chaos and deterministic versus stochastic nonlinear modeling. J. R. Stat. Soc. B 54(2), 303–328 (1992)

    MathSciNet  Google Scholar 

  5. Dodov, B., Foufoula-Georgiou, E.: Incorporating the spatiotemporal distribution of rainfall and basin geomorphology into nonlinear analysis of streamflow dynamics. Adv. Water Resour. 28(7), 711–728 (2005)

    Article  Google Scholar 

  6. Elshorbagy, A., Simonovic, S.P., Panu, U.S.: Estimation of missing streamflow data using principles of chaos theory. J. Hydrol. 255, 123–133 (2002)

    Article  Google Scholar 

  7. Farmer, D.J., Sidorowich, J.J.: Predicting chaotic time series. Phys. Rev. Lett. 59, 845–848 (1987)

    Article  MathSciNet  Google Scholar 

  8. Feigenbaum, M.J.: Quantitative universality for a class of nonlinear transformations. J. Stat. Phys. 19, 25–52 (1987)

    Article  MathSciNet  Google Scholar 

  9. Gelhar, L.W.: Stochastic Subsurface Hydrology. Prentice-Hall, Englewood Cliffs (1993)

    Google Scholar 

  10. Gleick, J.: Chaos: Making of a New Science. Penguin Books, New York (1987)

    MATH  Google Scholar 

  11. Goerner, S.J.: Chaos and the Evolving Ecological Universe. Gordon and Breach, Langhorne (1994)

    Google Scholar 

  12. Grassberger, P., Procaccia, I.: Measuring the strangeness of strange attractors. Physica D 9, 189–208 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  13. Grassberger, P., Procaccia, I.: Estimation of the Kolmogorov entropy from a chaotic signal. Phys. Rev. A 28, 2591–2593 (1983)

    Article  Google Scholar 

  14. Henon, M.: A two-dimensional mapping with a strange attractor. Commun. Math. Phys. 50, 69–77 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  15. Hossain, F., Sivakumar, B.: Spatial pattern of arsenic contamination in shallow wells of Bangladesh: regional geology and nonlinear dynamics. Stoch. Environ. Res. Risk Assess. 20(1–2), 66–76 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  16. Hossain, F., Anagnostou, E.N., Lee, K.H.: A non-linear and stochastic response surface method for Bayesian estimation of uncertainty in soil moisture simulation from a land surface model. Nonlinear Process. Geophys. 11, 427–440 (2004)

    Article  Google Scholar 

  17. Jayawardena, A.W., Gurung, A.B.: Noise reduction and prediction of hydrometeorological time series: dynamical systems approach vs. stochastic approach. J. Hydrol. 228, 242–264 (2000)

    Article  Google Scholar 

  18. Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis. Cambridge University Press, Cambridge (1997)

    MATH  Google Scholar 

  19. Kennel, M.B., Brown, R., Abarbanel, H.D.I.: Determining embedding dimension for phase space reconstruction using a geometric method. Phys. Rev. A 45, 3403–3411 (1992)

    Article  Google Scholar 

  20. Koutsoyiannis, D.: On the quest for chaotic attractors in hydrological processes. Hydrol. Sci. J. 51(6), 1065–1091 (2006)

    Article  Google Scholar 

  21. Kyoung, M.S., Kim, H.S., Sivakumar, B., Singh, V.P., Ahn, K.S.: Dynamic characteristics of monthly rainfall in the Korean Peninsula under climate change. Stoch. Environ. Res. Risk Assess. 25(4), 613–625 (2011)

    Article  Google Scholar 

  22. Linsay, P.S.: Period doubling and chaotic behaviour in a driven anharmonic oscillator. Phys. Rev. Lett. 47, 1349–1352 (1981)

    Article  Google Scholar 

  23. Lisi, F., Villi, V.: Chaotic forecasting of discharge time series: a case study. J. Am. Water Resour. Assoc. 37(2), 271–279 (2001)

    Article  Google Scholar 

  24. Lorenz, E.N.: Deterministic nonperiodic flow. J. Atmos. Sci. 20, 130–141 (1963)

    Article  Google Scholar 

  25. May, R.M.: Simple mathematical models with very complicated dynamics. Nature 261, 459–467 (1976)

    Article  Google Scholar 

  26. Nerenberg, M.A.H., Essex, C.: Correlation dimension and systematic geometric effects. Phys. Rev. A 42(12), 7065–7074 (1990)

    Article  MathSciNet  Google Scholar 

  27. Packard, N.H., Crutchfield, J.P., Farmer, J.D., Shaw, R.S.: Geometry from a time series. Phys. Rev. Lett. 45(9), 712–716 (1980)

    Article  Google Scholar 

  28. Phillips, J.D.: Sources of nonlinearity and complexity in geomorphic systems. Prog. Phys. Geogr. 26, 339–361 (2003)

    Google Scholar 

  29. Phillips, J.D.: Deterministic chaos and historical geomorphology: a review and look forward. Geomorphology 76, 109–121 (2006)

    Article  Google Scholar 

  30. Phoon, K.K., Islam, M.N., Liaw, C.Y., Liong, S.Y.: A practical inverse approach for forecasting of nonlinear time series analysis. J. Hydrol. Eng. 7(2), 116–128 (2002)

    Article  Google Scholar 

  31. Porporato, A., Ridolfi, R.: Nonlinear analysis of river flow time sequences. Water Resour. Res. 33(6), 1353–1367 (1997)

    Article  Google Scholar 

  32. Puente, C.E., Obregon, N.: A deterministic geometric representation of temporal rainfall. Results for a storm in Boston. Water Resour. Res. 32(9), 2825–2839 (1996)

    Article  Google Scholar 

  33. Regonda, S., Sivakumar, B., Jain, A.: Temporal scaling in river flow: can it be chaotic? Hydrol. Sci. J. 49(3), 373–385 (2004)

    Article  Google Scholar 

  34. Rodriguez-Iturbe, I., De Power, F.B., Sharifi, M.B., Georgakakos, K.P.: Chaos in rainfall. Water Resour. Res. 25(7), 1667–1675 (1989)

    Article  Google Scholar 

  35. Rossler, O.E.: An equation for continuous chaos. Phys. Lett. A 57, 397–398 (1976)

    Article  Google Scholar 

  36. Ruelle, D., Takens, F.: On the nature of turbulence. Commun. Math. Phys. 20, 167–192 (1971)

    Article  MathSciNet  MATH  Google Scholar 

  37. Schertzer, D., Tchiguirinskaia, I., Lovejoy, S., Hubert, P., Bendjoudi, H.: Which chaos in the rainfall-runoff process? A discussion on ‘Evidence of chaos in the rainfall-runoff process’ by Sivakumar et al. Hydrol. Sci. J. 47(1), 139–147 (2002)

    Article  Google Scholar 

  38. Schreiber, T., Kantz, H.: Observing and predicting chaotic signals: is 2 percent noise too much. In: Kravtsov, Yu.A., Kadtke, J.B. (eds.) Predictability of Complex Dynamical Systems. Springer Series in Synergetics, pp. 43–65. Springer, Berlin (1996)

    Google Scholar 

  39. Sivakumar, B.: Chaos theory in hydrology: important issues and interpretations. J. Hydrol. 227(1–4), 1–20 (2000)

    Article  Google Scholar 

  40. Sivakumar, B.: Rainfall dynamics at different temporal scales: a chaotic perspective. Hydrol. Earth Syst. Sci. 5(4), 645–651 (2001)

    Article  Google Scholar 

  41. Sivakumar, B.: A phase-space reconstruction approach to prediction of suspended sediment concentration in rivers. J. Hydrol. 258, 149–162 (2002)

    Article  Google Scholar 

  42. Sivakumar, B.: Chaos theory in geophysics: past, present and future. Chaos Solitons Fractals 19(2), 441–462 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  43. Sivakumar, B.: Correlation dimension estimation of hydrologic series and data size requirement: myth and reality. Hydrol. Sci. J. 50(4), 591–604 (2005)

    Article  MathSciNet  Google Scholar 

  44. Sivakumar, B.: Nonlinear dynamics and chaos in hydrologic systems: latest developments and a look forward. Stoch. Environ. Res. Risk Assess. 23, 1027–1036 (2009)

    Article  Google Scholar 

  45. Sivakumar, B., Jayawardena, A.W.: An investigation of the presence of low-dimensional chaotic behavior in the sediment transport phenomenon. Hydrol. Sci. J. 47(3), 405–416 (2002)

    Article  Google Scholar 

  46. Sivakumar, B., Liong, S.Y., Liaw, C.Y., Phoon, K.K.: Singapore rainfall behavior: chaotic? J. Hydrol. Eng. 4(1), 38–48 (1999)

    Article  Google Scholar 

  47. Sivakumar, B., Phoon, K.K., Liong, S.Y., Liaw, C.Y.: A systematic approach to noise reduction in chaotic hydrological time series. J. Hydrol. 219(3/4), 103–135 (1999)

    Article  Google Scholar 

  48. Sivakumar, B., Berndttson, R., Olsson, J., Jinno, K.: Evidence of chaos in the rainfall-runoff process. Hydrol. Sci. J. 46(1), 131–145 (2001)

    Article  Google Scholar 

  49. Sivakumar, B., Sorooshian, S., Gupta, H.V., Gao, X.: A chaotic approach to rainfall disaggregation. Water Resour. Res. 37(1), 61–72 (2001)

    Article  Google Scholar 

  50. Sivakumar, B., Berndtsson, R., Olsson, J., Jinno, K.: Reply to ‘Which chaos in the rainfall-runoff process?’ by Schertzer et al. Hydrol. Sci. J. 47(1), 149–158 (2002)

    Article  Google Scholar 

  51. Sivakumar, B., Jayawardena, A.W., Fernando, T.M.G.H.: River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches. J. Hydrol. 265(1–4), 225–245 (2000)

    Google Scholar 

  52. Sivakumar, B., Persson, M., Berndtsson, R., Uvo, C.B.: Is correlation dimension a reliable indicator of low-dimensional chaos in short hydrological time series? Water Resour. Res. (2002). doi:10.1029/2001WR000333

    Google Scholar 

  53. Sivakumar, B., Wallender, W.W., Puente, C.E., Islam, M.N.: Streamflow disaggregation: a nonlinear deterministic approach. Nonlinear Process. Geophys. 11, 383–392 (2004)

    Article  Google Scholar 

  54. Sivakumar, B., Harter, T., Zhang, H.: Solute transport in a heterogeneous aquifer: a search for nonlinear deterministic dynamics. Nonlinear Process. Geophys. 12, 211–218 (2005)

    Article  Google Scholar 

  55. Sivakumar, B., Berndtsson, R., Lall, U.: Nonlinear deterministic dynamics in hydrologic systems: present activities and future challenges. Nonlinear Processes. Geophys. (2006)

    Google Scholar 

  56. Sivakumar, B., Jayawardena, A.W., Li, W.K.: Hydrologic complexity and classification: a simple data reconstruction approach. Hydrol. Process. 21(20), 2713–2728 (2007)

    Article  Google Scholar 

  57. Strogatz, S.H.: Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering. Perseus Books, Cambridge (1994)

    Google Scholar 

  58. Swinney, H.L., Gollub, J.P.: Hydrodynamic instabilities and the transition to turbulence. Phys. Today 31(8), 41–49 (1998)

    Article  Google Scholar 

  59. Takens, F.: Detecting strange attractors in turbulence. In: Rand, D.A., Young, L.S. (eds.) Dynamical Systems and Turbulence. Lecture Notes in Mathematics, vol. 898, pp. 366–381. Springer, Berlin (1981)

    Chapter  Google Scholar 

  60. Teitsworth, S.W., Westervelt, R.M.: Chaos and broad-band noise in extrinsic photoconductors. Phys. Rev. Lett. 53(27), 2587–2590 (1984)

    Article  Google Scholar 

  61. Theiler, J., Eubank, S., Longtin, A., Galdrikian, B., Farmer, J.D.: Testing for nonlinearity in time series: the method of surrogate data. Physica D 58, 77–94 (1992)

    Article  MATH  Google Scholar 

  62. Tsonis, A.A.: Chaos: From Theory to Applications. Plenum Press, New York (1992)

    Google Scholar 

  63. Tsonis, A.A., Triantafyllou, G.N., Elsner, J.B., Holdzkom, J.J. II, Kirwan, A.D. Jr.: An investigation on the ability of nonlinear methods to infer dynamics from observables. Bull. Am. Meteorol. Soc. 75, 1623–1633 (1994)

    Article  Google Scholar 

  64. Wang, Q., Gan, T.Y.: Biases of correlation dimension estimates of streamflow data in the Canadian prairies. Water Resour. Res. 34(9), 2329–2339 (1998)

    Article  Google Scholar 

  65. Wilcox, B.P., Seyfried, M.S., Matison, T.M.: Searching for chaotic dynamics in snowmelt runoff. Water Resour. Res. 27(6), 1005–1010 (1991)

    Article  Google Scholar 

  66. Wolf, A., Swift, J.B., Swinney, H.L., Vastano, A.: Determining Lyapunov exponents from a time series. Physica D 16, 285–317 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  67. Yevjevich, V.M.: Misconceptions in hydrology and their consequences. Water Resour. Res. 4(2), 225–232 (1968)

    Article  Google Scholar 

  68. Young, P.C.: Recursive approaches to time-series analysis. Bull. Inst. Math. Appl. 10, 209–224 (1974)

    Google Scholar 

  69. Young, P.C.: Data-based mechanistic modeling of environmental, ecological, economic and engineering systems. Environ. Model. Softw. 13, 105–122 (1998)

    Article  Google Scholar 

  70. Young, P.C.: The data-based mechanistic approach to the modelling, forecasting and control of environmental systems. Annu. Rev. Control 30, 169–182 (2006)

    Article  Google Scholar 

  71. Young, P.C., Beck, M.B.: The modelling and control of water quality in a river system. Automatica 10, 455–468 (1974)

    Article  Google Scholar 

  72. Young, P.C., Beven, K.J.: Data-based mechanistic modeling and rainfall-flow non-linearity. EnvironMetrics 5(3), 335–363 (1994)

    Article  Google Scholar 

  73. Young, P.C., Lees, M.J.: The active mixing volume: a new concept in modelling environmental systems. In: Barnett, V., Turkman, K. (eds.) Statistics for the Environment, pp. 3–43. Wiley, Chichester (1993)

    Google Scholar 

  74. Young, P.C., Parkinson, S.: Simplicity out of complexity. In: Beck, M.B. (ed.) Environmental Foresight and Models: A Manifesto, pp. 251–294. Elsevier, Oxford (2002)

    Chapter  Google Scholar 

  75. Young, P.C., Ratto, M.: A unified approach to environmental systems modeling. Stoch. Environ. Res. Risk Assess. 23, 1037–1057 (2009)

    Article  Google Scholar 

  76. Young, P.C., Parkinson, S.D., Lees, M.: Simplicity out of complexity: Occam’s razor revisited. J. Appl. Stat. 23, 165–210 (1996)

    Article  Google Scholar 

  77. Zhou, Y., Ma, Z., Wang, L.: Chaotic dynamics of the flood series in the Huaihe River Basin for the last 500 years. J. Hydrol. 258, 100–110 (2002)

    Article  Google Scholar 

Download references

Acknowledgements

This work was financially supported in part by the Korean Research Foundation funded by the Korean Government (MEST) (KRF-2009-D0010).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bellie Sivakumar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag London Limited

About this chapter

Cite this chapter

Sivakumar, B. (2012). Chaos Theory for Modeling Environmental Systems: Philosophy and Pragmatism. In: Wang, L., Garnier, H. (eds) System Identification, Environmental Modelling, and Control System Design. Springer, London. https://doi.org/10.1007/978-0-85729-974-1_26

Download citation

  • DOI: https://doi.org/10.1007/978-0-85729-974-1_26

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-973-4

  • Online ISBN: 978-0-85729-974-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics