Skip to main content
SpringerLink
Log in

Dominant processes concept, model simplification and classification framework in catchment hydrology

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

Technological and methodological advances have facilitated tremendous growth in hydrology during the last century; however, there are also concerns that these advances indirectly contribute to additional problems in our research. An insight into hydrologic literature reveals our tendency to develop more complex models than perhaps needed, and our increasing emphasis on individual mathematical techniques rather than general hydrologic issues. Some recent studies of diverse forms have suggested that simplification in modeling and development of a common framework may help alleviate these problems. The present study is intended to bring such studies together towards a more coherent approach to research in catchment hydrology. This is done by highlighting the need for model simplification and generalization and proposing some potential directions for achieving such. Through a discussion of difficulties in data measurements, the need for moving beyond the notion of “modeling everything” to the notion of “capturing the essential features” is explained; the concept of dominant processes in model simplification and the utility of integration of concepts for modeling improvement are discussed. Formulation of a catchment classification framework is advocated as a possible means for a common framework in hydrology, and the role of dominant processes in this formulation is presented; the problems due to adoption of different modeling terminologies are highlighted and potential ways to overcome such are also discussed.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

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

    Article  Google Scholar 

  • Amorocho J (1967) The nonlinear prediction problems in the study of the runoff cycle. Water Resour Res 3(3):861–880

    Article  Google Scholar 

  • Amorocho J, Brandstetter A (1971) Determination of nonlinear functional response functions in rainfall-runoff processes. Water Resour Res 7(5):1087–1101

    Article  Google Scholar 

  • Anagnostou EN (2004) Overview of overland satellite rainfall estimation for hydro-meteorological applications. Surv Geophys 25(5–6):511–537

    Article  Google Scholar 

  • Berndtsson R, Jinno K, Kawamura A, Olsson J, Xu S (1994) Dynamical systems theory applied to long-term temperature and precipitation time series. Trends Hydrol 1:291–297

    Google Scholar 

  • Beven KJ (1993) Prophecy, reality and uncertainty in distributed modeling. Adv Water Resour 16:41–51

    Article  Google Scholar 

  • Beven KJ (2001) On hypothesis testing in hydrology. Hydrol Processes 15:1655–1657

    Article  Google Scholar 

  • Beven KJ (2002) Uncertainty and the detection of structural change in models of environmental systems. In: Beck MB (ed) Environmental foresight and models: a manifesto. Elsevier, The Netherlands, pp 227–250

    Chapter  Google Scholar 

  • Beven KJ (2006) On undermining the science? Hydrol Processes 20:3141–3146

    Article  Google Scholar 

  • Beven KJ, Binley A (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrol Processes 6:279–298

    Article  Google Scholar 

  • Blöschl G, Sivapalan M (1995) Scale issues in hydrological modeling – a review. Hydrol Processes 9:251–290

    Article  Google Scholar 

  • Caine N (1990) The rainfall intensity-duration control of shallow landslides and debris flows. Geogr Ann 62A:23–27

    Google Scholar 

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

    Article  Google Scholar 

  • Coulibaly P, Bobee B, Anctil F (2001) Improving extreme hydrologic events forecasting using a new criterion for artificial neural network selection. Hydrol Processes 15:1533–1536

    Article  Google Scholar 

  • Croke B, Post D, Littlewood I, Pomeroy J (2006) Developing new hydrological models – towards merging top-down and bottom-up approaches. Technical Session at the 3rd Biennial Meeting of the International Environmental Modeling and Software Society, Burlington

  • Crozier M (1986) Landslides: causes, consequences and environment. Croom Helm, London

    Google Scholar 

  • Dawdy DR (2007) Prediction versus understanding (The 2007 Ven Te Chow Lecture). ASCE J Hydrol Eng 12:1–3

    Article  Google Scholar 

  • Dooge JCI (1986) Looking for hydrologic laws. Water Resour Res 22(9):46S–58S

    Article  Google Scholar 

  • Duan Q, Sorooshian S, Gupta VK (1992) Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour Res 28:1015–1031

    Article  Google Scholar 

  • Duan Q, Gupta HV, Sorooshian S, Rousseau AN, Turcotte R (2002) Calibration of watershed models, Water Science and Application Series. American Geophysical Union, Washington, vol. 6, pp 1–346

  • Dunne T (1998) Wolman lecture: hydrologic science … in landscapes … on a planet … in the future. In: Hydrologic sciences: taking stock and looking ahead. National Academy Press, Washington, pp 1–138

  • Farmer DJ, Sidorowich JJ (1987) Predicting chaotic time series. Phys Rev Lett 59:845–848

    Article  Google Scholar 

  • Fraedrich K (1986) Estimating the dimensions of weather and climate attractors. J Atmos Sci 43:419–432

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Grayson RB, Blöschl G (2000) Spatial patterns in catchment hydrology: observations and modelling. Cambridge University Press, Cambridge

    Google Scholar 

  • Gupta VK (2004) Emergence of statistical scaling in floods on channel networks from complex runoff dynamics. Chaos Solitons Fractals 19:357–365

    Article  Google Scholar 

  • Gupta VK, Dawdy DR (1995) Physical interpretation of regional variations in the scaling exponents in flood quantiles. Hydrol Processes 9(3–4):347–361

    Article  Google Scholar 

  • Gupta VK, Waymire E (1990) Multiscaling properties of spatial rainfall and river flow distributions. J Geophys Res 95(D3):1999–2009

    Article  Google Scholar 

  • Gupta VK, Waymire E (1998) Spatial variability and scale invariance in hydrologic regionalization. In: Sposito G (ed) Scale dependence and scale invariance in hydrology. Cambridge University Press, New York, pp 88–135

    Google Scholar 

  • Gupta VK, Mesa OJ, Dawdy D (1994) Multiscaling theory of floods: regional quantile analysis. Water Resour Res 30(12):3405–3421

    Article  Google Scholar 

  • Gupta VK, Castro S, Over TM (1996) On scaling exponents of spatial peak flows from rainfall and river network geometry. J Hydrol 187(1–2):81–104

    Article  Google Scholar 

  • Gupta HV, Sorooshian S, Yapo PO (1998) Toward improved calibration of hydrologic models: multiple and noncommensurable measures of information. Water Resour Res 34(4):751–764. doi:10.1029/97WR03495

    Article  Google Scholar 

  • Gupta VK, Duffy C, Grossman R, Krajewski W, Lall U, McCaffrey M, Milne B, Pielke R Sr, Reckow K, Swanson R (2000) A Framework for reassessment of basic research and educational priorities in hydrologic sciences. A Report to the U S National Science Foundation, pp 1–40

  • Hossain F, Sivakumar B (2006) 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

    Article  Google Scholar 

  • Hossain F, Anagnostou EN, Lee KH (2004) A non-linear and stochastic response surface method for Bayesian estimation of uncertainty in soil moisture simulation from a land surface model. Nonlinear Processes Geophys 11:427–440

    Google Scholar 

  • Hsu KL, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of the rainfall-runoff process. Water Resour Res 31(10):2517–2530

    Article  Google Scholar 

  • Hsu KL, Gupta HV, Gao X, Sorooshian S, Imam B (2002) Self-organizing linear output map (SOLO): an artificial neural network suitable for hydrologic modeling and analysis. Water Resour Res 38(12). doi:10.1029/2001WR000795

  • Izzard CF (1966) A mathematical model for nonlinear hydrologic systems. J Geophys Res 71(20):4811–4824

    Google Scholar 

  • Jakeman AJ, Hornberger GM (1993) How much complexity is warranted in a rainfall-runoff model? Water Resour Res 29(8):2637–2650

    Article  Google Scholar 

  • Jayawardena AW, Gurung AB (2000) Noise reduction and prediction of hydrometeorological time series: dynamical systems approach vs. stochastic approach. J Hydrol 228:242–264

    Article  Google Scholar 

  • Kalma JD, Sivapalan M (1996) Scale issues in hydrological modelling. Wiley, London

    Google Scholar 

  • Kennel MB, Brown R, Abarbanel HDI (1992) Determining embedding dimension for phase space reconstruction using a geometric method. Phys Rev A 45:3403–3411

    Article  Google Scholar 

  • Klemes V (1978) Physically based stochastic hydrologic analysis. Adv Hydrosci 11:285–352

    Google Scholar 

  • Konikow LF, Bredehoeft JD (1992) Ground-water models cannot be validated. Adv Water Resour 15:75–83

    Article  Google Scholar 

  • Lorenz EN (1963) Deterministic nonperiodic flow. J Atmos Sci 20:130–141

    Article  Google Scholar 

  • McDonnell JJ, Woods R (2004) On the need for catchment classification. J Hydrol 299:2–3

    Google Scholar 

  • Michaud J, Sorooshian S (1994) Comparison of simple versus complex distributed runoff models on a midsized semiarid watershed. Water Resour Res 30(3):593–606

    Article  Google Scholar 

  • Packard NH, Crutchfield JP, Farmer JD, Shaw RS (1980) Geometry from a time series. Phys Rev Lett 45(9):712–716

    Article  Google Scholar 

  • Parlange MB, Katul GG, Cuenca RH, Kavvas ML, Nielsen DR, Mata M (1992) Physical basis for a time series model of soil water content. Water Resour Res 28(9):2437–2446

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Refsgaard JC, Henriksen HJ (2004) Modeling guidelines—terminology and guiding principles. Adv Water Resour 27:71–82

    Article  Google Scholar 

  • Reichenbach P, Cardinali M, De Vita P, Guzzetti F (1998) Regional hydrological thresholds for landslides and floods in the Tiber River Basin (central Italy). Environ Geol 35(2–3):146–159

    Article  Google Scholar 

  • Rigon R, Rinaldo A, Rodriguez-Iturbe I (1994) On landscape self-organization. J Geophys Res 99(B6):11971–11993

    Article  Google Scholar 

  • Rodriguez-Iturbe I, Rinaldo A (1997) Fractal river basins: chance and self-organization. Cambridge University Press, Cambridge

    Google Scholar 

  • Rodriguez-Iturbe I, De Power FB, Sharifi MB, Georgakakos KP (1989) Chaos in rainfall. Water Resour Res 25(7):1667–1675

    Article  Google Scholar 

  • Rykiel ER (1996) Testing ecological models: the meaning of validation. Ecol Model 90:229–244

    Article  Google Scholar 

  • Salas JD, Smith RA (1981) Physical basis of stochastic models of annual flows. Water Resour Res 17(2):428–430

    Article  Google Scholar 

  • Sangoyomi TB, Lall U, Abarbanel HDI (1996) Nonlinear dynamics of the Great Salt Lake: dimension estimation. Water Resour Res 32(1):149–159

    Article  Google Scholar 

  • Savanije HHG (2001) Equifinality, a blessing in disguise? Hydrol Processes 15:2835–2838

    Article  Google Scholar 

  • Sevruk B (1996) Adjustment of tipping-bucket precipitation gage measurement. Atmos Res 42:237–246

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Sivakumar B (2003) Forecasting monthly streamflow dynamics in the western United States: a nonlinear dynamical approach. Environ Model Softw 18(8–9):721–728

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Sivakumar B (2004b) Dominant processes concept in hydrology: moving forward. Hydrol Processes 18(12):2349–2353

    Article  Google Scholar 

  • Sivakumar B (2005a) Hydrologic modeling and forecasting: role of thresholds. Environ Model Softw 20(5):515–519

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Sivakumar B, Persson M, Berndtsson R, Uvo CB (2002) Is correlation dimension a reliable indicator of low-dimensional chaos in short hydrological time series? Water Resour Res 38(2). doi:10.1029/2001WR000333

  • Sivakumar B, Wallender WW, Puente CE, Islam MN (2004) Streamflow disaggregation: a nonlinear deterministic approach. Nonlinear Processes Geophys 11:383–392

    Google Scholar 

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

    Google Scholar 

  • Sivakumar B, Jayawardena AW, Li WK (2007). Hydrologic complexity and classification: a simple data reconstruction approach. Hydrol Processes 21. doi:10.1002/hyp6362

  • Sivapalan M (2005) Pattern, process and function: elements of a unified theory of hydrology at the catchment scale. In: Anderson MG (ed) Encyclopedia of hydrological sciences. Wiley, London, pp 193–219

    Google Scholar 

  • Sivapalan M, Blöschl G, Zhang L, Vertessy R (2003) Downward approach to hydrological prediction. Hydrol Processes 17:2101–2111

    Article  Google Scholar 

  • Sorooshian S, Gupta VK (1983) Automatic calibration of conceptual rainfall-runoff models: the question of parameter observability and uniqueness. Water Resour Res 19(1):251–259

    Article  Google Scholar 

  • Spear RC, Grieb TM, Shang N (1994) Parameter uncertainty and interaction in complex environmental models. Water Resour Res 31(11):3159–3170

    Article  Google Scholar 

  • Takens F (1981) Detecting strange attractors in turbulence. In: Rand DA, Young LS (eds) Dynamical systems and turbulence, Lecture Notes in Mathematics, vol 898. Springer, Berlin, pp 366–381

  • Tsonis AA, Elsner JB (1988) The weather attractor over short timescales. Nature 333:545–547

    Article  Google Scholar 

  • UNDP (1997) United Nations Development Program. Human Development Report: Eradicating Poverty, Washington

  • UNESCO (1998) UNESCO conference on World Water Resources at the Dawn of the Twenty-first Century, Paris

  • Vrugt JA, Bouten W, Gupta HV, Sorooshian S (2002) Toward improved identifiability of hydrologic model parameters: the information content of experimental data. Water Resour Res 38(12):1312. doi:10.1029/2001WR001118

    Article  Google Scholar 

  • Wainwright W, Mulligan M (2004) Environmental modeling: finding simplicity in complexity. Wiley, London

    Google Scholar 

  • Wolf A, Swift JB, Swinney HL, Vastano A (1985) Determining Lyapunov exponents from a time series. Physica D 16:285–317

    Article  Google Scholar 

  • Woods R (2002) Seeing catchments with new eyes. Hydrol Processes 16:1111–1113

    Article  Google Scholar 

  • Yevjevich VM (1963) Fluctuations of wet and dry years, I. Research data assembly and mathematical models. Hydrological Paper 1. Colorado State University, Fort Collins, pp 1–55

  • Young PC, Beven KJ (1994) Database mechanistic modeling and rainfall-flow non-linearity. Environmetrics 5(3):335–363

    Article  Google Scholar 

  • Young PC, Parkinson SD (2002) Simplicity out of complexity. In: Beck MB (ed) Environmental foresight and models: a manifesto. Elsevier Science, The Netherlands, pp 251–294

    Chapter  Google Scholar 

  • Young PC, Parkinson SD, Lees M (1996) Simplicity out of complexity in environmental systems: Occam’s Razor revisited. Journal of Applied Statistics 23:165–210

    Article  Google Scholar 

  • Zehe E, Blöschl G (2004) Predictability of hydrologic response at the plot and catchment scales: role of initial conditions. Water Resources Research 40. doi:10.1029/2003WR002869

Download references

Acknowledgments

The author would like to thank the two anonymous reviewers for their positive comments and suggestions on an earlier version of this manuscript. The author is also thankful to Keith Beven and Vijay Gupta for their constructive comments on this study.

Author information

Authors and Affiliations

  1. Department of Land, Air and Water Resources, University of California, Davis, CA, 95616, USA

    Bellie Sivakumar

Authors
  1. Bellie Sivakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bellie Sivakumar.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sivakumar, B. Dominant processes concept, model simplification and classification framework in catchment hydrology. Stoch Environ Res Risk Assess 22, 737–748 (2008). https://doi.org/10.1007/s00477-007-0183-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-007-0183-5

Keywords

Search

Navigation