Skip to main content

Part of the book series: NATO ASI Series ((NSSE,volume 237))

Abstract

Time series modelling has already taken place in hydrological technology. ARMA, AR models, etc. are devoted to preserving statistical properties from the stochastic process underlying a given sample, to generate long undistinguishable synthetic samples to provide for better analysis or derived processes. These models are characterized by the use of information from the analyzed series.

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

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Akaike, H. (1974) ‘A new look at the statistical model identification’, IEEE Transactions on Automatic Control, AC-19, pp 716–723.

    Article  Google Scholar 

  • Anselmo, V. and Ubertini, L. (1979) ‘Transfer function-noise model applied to flow forecasting’, Hydro. Sci. Bull, 24, 3, pp 353–359.

    Article  Google Scholar 

  • Anselmo, V., Melone, F. and Ubertini, L. (1981) ‘Application of multiple stochastic models to rainfall-flow relationship of the Toce River’, Proc. Ins. Con. Rainfall-Runoff Modeling, V. Singh, ed.

    Google Scholar 

  • Box, G.E.P. and Jenkins, G.M. (1970) ‘Time series analysis forecasting and control’, Holden Day, Inc., San Francisco, California.

    Google Scholar 

  • Box, G.E.P. and Tiao, G.C. (1975) ‘Intervention analysis with applications to economic and environmental problems’, Journal Amer. Statis. Assoc. 70, pp 70–79.

    Article  Google Scholar 

  • Box, G.E.P. and Tiao, G.C. (1977) ‘A canonical analysis of multiple time series’, Biometrika, 64, 2, pp 355–365.

    Article  Google Scholar 

  • Budzianowski, R.J. and Strupczewski, W.G. (1981) ‘On the structure of the linear stochastic forecasting models’, Proc. Int. Conf. Rainfall-Runoff Modeling, V. Singh, ed.

    Google Scholar 

  • Burn, D.H. and McBean, E.A. (1985) ‘River flow forecasting model for Sturgeon River’, ASCE J. of Hyd. Eng. Vol. III, No 2.

    Google Scholar 

  • Cluckie, I.E. (1980) Hydrological Forecasting, IAHS Publication No 129.

    Google Scholar 

  • Cooper, D.M. and Wood, E.F. (1982a) ‘Identification of multivariate time series and multivariate input-output models’, Water Resour. Res. 18 (4), pp 937–946.

    Article  Google Scholar 

  • Cooper, D.M. and Wood, E.F. (1982b) ‘Parameter estimation of multiple input-output time series models: application to rainfall-runoff processes’, Water Resour. Res. (18(5), pp 1352–1364.

    Article  Google Scholar 

  • Damsleth, E. (1978) ‘Analysis of hydrologic data with linear transfer models’, Publ. 602 Norwegian Computing Centre, Oslo, Norway.

    Google Scholar 

  • Demareé, G. (1981) ‘Hybrid conceptual-stochastic modeling of rainfall-runoff processes applied to the Dijle catchment’, Proc. Int. Conf. Rainfall-Runoff Modeling, V. Singh, Ed.

    Google Scholar 

  • Estrela, T. and Sahuquillo, A. (1985) ‘Modeling the response hydrograph of subsurface flow’, in Multivariate Analysis of Hydrologic Processes, M.W. Shen et al., Eds. Fort Collins, pp 141–152.

    Google Scholar 

  • Ganendra, T. (1979) ‘Real-time forecasting and control in the operation of water resources systems’, Ph D. Thesis, University of London.

    Google Scholar 

  • Haltiner, J.P. (1985) ‘Stochastic modeling of season and daily streamflow’, Ph D. Dissertation, Colorado State University, Fort Collins, Colorado.

    Google Scholar 

  • Haltiner, J.P. and Salas J.D. (1988) ‘Short-term forecasting of snowmelt runoff using ARMAX models’, Water Res. Bull., 24(5), pp 1083–1089.

    Article  Google Scholar 

  • Hannan, E.J. (1970) Multiple time series, J. Wiley, New York.

    Book  Google Scholar 

  • Hannan, E.J. and Kavalieris, L. (1984) ‘Multivariate linear time series models’, Adv. Appl. Prob. 16, pp 292–561.

    Article  Google Scholar 

  • Hino, M. (1970) ‘Runoff forecast by linear predictive filter’, ASCE H. Hydraulics, 96 (HY3), pp 681–707.

    Google Scholar 

  • Hipel, K.W., McLeod, A.I. and McBean (1977) ‘Stochastic modelling of the effects of reservoir operation’, J. of Hydrol. 31, pp 97–113.

    Article  Google Scholar 

  • Hipel, K.W. and McLeod, A.I. (1985) Time Series Modelling for Water Resources and Environmental Engineers, Elsevier, Amsterdam.

    Google Scholar 

  • Hipel, K.W. (1985) ‘Time series analysis in perspective’, Water Res. Bull. 21(4), pp 609–624.

    Article  Google Scholar 

  • Kashyap, R.L. and Rao, A.R. (1973) ‘Real time recursive prediction of river flows’, Automatica 9, pp 179–183.

    Article  Google Scholar 

  • Kashyap, R.L. and Rao, A.R. (1976) ‘Dynamic-stochastic models from empirical data’, Academic Press, New York.

    Google Scholar 

  • Katayama, T. (1976) ‘Application of maximum likelihood identification to river flow prediction’, IIASA Workshop on real time forecasting/control of water resources systems, E. Wood (Ed.), Pergamon Press.

    Google Scholar 

  • Lawrence, A.J. and Kottegoda, N.T. (1977) ‘Stochastic modeling of river-flow time series’, J.R. Statist. Soc. Ser. A, 140, pp 1–27.

    Article  Google Scholar 

  • Ledolter, J. and Abraham, B. (1981) ‘Parsimony and its importance in times series forecasting’, Technometrics 23(4), pp 411–414.

    Article  Google Scholar 

  • Lorent, B. (1975) ‘Test of different river flow predictors’, in Modelling and Simulation of Water Resources Systems, G.C. Vansteenkiste (Ed.), North Holland, Amsterdam.

    Google Scholar 

  • Marco, J. and Yevjevich, V. (1985) ‘Stochastic modelling of ground-water recharge’, 21st Congress IAHR, Proceedings, Melbourne, Australia.

    Google Scholar 

  • Miller, R.B., Bell, W., Ferreiro, O and Wang R.Y. (1981) ‘Modeling daily river flows with precipitation input’, Water Resour. Res. 17(1), pp 209–215.

    Article  Google Scholar 

  • Mizumura, K. and Chiu, C.L. (1985) ‘Prediction of combined snowmelt and rainfall-runoff’, ASCE J. of Hyd. Eng. 2, pp 179–193.

    Article  Google Scholar 

  • Moore, R.J. (1981) ‘Transfer functions, noise predictors and the forecasting of flood events in real time’, Prec. Int. Conf. Rainfall-Runoff Modelling.

    Google Scholar 

  • Natale, L. and Todini, E. (1976) ‘A stable estimator for linear models’, Water Resour. Res. 12(4), pp 667–676.

    Article  Google Scholar 

  • Natural Environment Research Council, UK (1975) Flood Studies Report, Vol. 1, chap. 7, pp 513–531, London.

    Google Scholar 

  • O’Connell, P.E. and Clarke, R.T. (1981) ‘Adaptative hydrological forecasting, a Review’, Hydrol. Sci. Bull., 26(2), pp 179–205.

    Article  Google Scholar 

  • Oliver, J. and Marco, J.B. (1985) ‘Real time management of an irrigation water resources system’, Multivariate Analysis of Hydrologic Processes, Shen et al., Eds., Colorado State University, Fort Collins, pp 703–715.

    Google Scholar 

  • Patry, G.G. and Marino, M.A. (1984) ‘Parameter identification of time varying noise difference equations for real-time urban runoff forecasting’, J. of Hydrol. 72, pp 25–55.

    Article  Google Scholar 

  • Piccolo, D. and Ubertini, L. (1979) ‘Flood forecasting by intervention transfer stochastic models’, Proc. of IAHR 18th Congress, Vol. 5, pp 319–326.

    Google Scholar 

  • Rissanen, J. (1974) ‘Basis of invariants and canonical forms for linear dynamic systems’, Automatica 10, pp 175–182.

    Article  Google Scholar 

  • Thompstone, R.M., Hipel, K.W. and McLeod, A.I. (1985) ‘Forecasting quarter-monthly riverflow’, Water Res. Bull., 21(5), pp 731–741.

    Article  Google Scholar 

  • Todini, E. and Bouillot, D. (1975) ‘A rainfall-runoff Kalman filter model’, in: System Simulation in Water Resources, G.C. Vansteenkiste, Ed. North Holland, Amsterdam.

    Google Scholar 

  • Todini, E. (1975) ‘The Arno River model. Problems, methodologies and techniques’, in: Modelling and Simulation of Water Resources Systems, G.C. Vansteenkiste, Ed. North Holland, Amsterdam.

    Google Scholar 

  • Todini, E. and Wallis, J.R. (1977) ‘Using CLS for daily or longer period rainfall-runoff modelling’, in Mathematical Models in Surface Water Hydrology, (Ed. by Ciriani, Maiore and Wallis), pp 148–168, J. Wiley, London.

    Google Scholar 

  • Whitehead, P.G. and Young, P.C. (1975) ‘A dynamic stochastic model for water quality in part of the Bedford-Ouse River System’, in: Computer Simulation of Water Resources Systems, G.C. Vansteenkiste, Ed. North Holland, Amsterdam.

    Google Scholar 

  • Whitfield, P.H. and Woods, P.F. (1984) ‘Intervention analysis of water quality records’, Water Res. Bull., 20(5), pp 657–667.

    Article  Google Scholar 

  • Yevjevich, V. (1972) ‘Stochastic processes in hydrology’, Water Resources Publications, Fort Collins, Colorado.

    Google Scholar 

  • Young, P.C. (1974) ‘Recursive approaches to time series analysis’, Bull Inst. Math. Appl. 10, pp 209–224.

    Google Scholar 

  • Young, P.C. (1986) ‘Time series methods and recursive estimation in hydrological systems analysis’, in: River Flow Modelling and Forecasting, D.A. Kraijenholl and J.R. Moll, Eds. Reide, The Netherlands.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1993 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Marco, J.B. (1993). Armax and Transfer Function Modelling in Hydrology. In: Marco, J.B., Harboe, R., Salas, J.D. (eds) Stochastic Hydrology and its Use in Water Resources Systems Simulation and Optimization. NATO ASI Series, vol 237. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-1697-8_6

Download citation

  • DOI: https://doi.org/10.1007/978-94-011-1697-8_6

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-4743-2

  • Online ISBN: 978-94-011-1697-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics