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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
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.
Anselmo, V. and Ubertini, L. (1979) ‘Transfer function-noise model applied to flow forecasting’, Hydro. Sci. Bull, 24, 3, pp 353–359.
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.
Box, G.E.P. and Jenkins, G.M. (1970) ‘Time series analysis forecasting and control’, Holden Day, Inc., San Francisco, California.
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.
Box, G.E.P. and Tiao, G.C. (1977) ‘A canonical analysis of multiple time series’, Biometrika, 64, 2, pp 355–365.
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.
Burn, D.H. and McBean, E.A. (1985) ‘River flow forecasting model for Sturgeon River’, ASCE J. of Hyd. Eng. Vol. III, No 2.
Cluckie, I.E. (1980) Hydrological Forecasting, IAHS Publication No 129.
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.
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.
Damsleth, E. (1978) ‘Analysis of hydrologic data with linear transfer models’, Publ. 602 Norwegian Computing Centre, Oslo, Norway.
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.
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.
Ganendra, T. (1979) ‘Real-time forecasting and control in the operation of water resources systems’, Ph D. Thesis, University of London.
Haltiner, J.P. (1985) ‘Stochastic modeling of season and daily streamflow’, Ph D. Dissertation, Colorado State University, Fort Collins, Colorado.
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.
Hannan, E.J. (1970) Multiple time series, J. Wiley, New York.
Hannan, E.J. and Kavalieris, L. (1984) ‘Multivariate linear time series models’, Adv. Appl. Prob. 16, pp 292–561.
Hino, M. (1970) ‘Runoff forecast by linear predictive filter’, ASCE H. Hydraulics, 96 (HY3), pp 681–707.
Hipel, K.W., McLeod, A.I. and McBean (1977) ‘Stochastic modelling of the effects of reservoir operation’, J. of Hydrol. 31, pp 97–113.
Hipel, K.W. and McLeod, A.I. (1985) Time Series Modelling for Water Resources and Environmental Engineers, Elsevier, Amsterdam.
Hipel, K.W. (1985) ‘Time series analysis in perspective’, Water Res. Bull. 21(4), pp 609–624.
Kashyap, R.L. and Rao, A.R. (1973) ‘Real time recursive prediction of river flows’, Automatica 9, pp 179–183.
Kashyap, R.L. and Rao, A.R. (1976) ‘Dynamic-stochastic models from empirical data’, Academic Press, New York.
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.
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.
Ledolter, J. and Abraham, B. (1981) ‘Parsimony and its importance in times series forecasting’, Technometrics 23(4), pp 411–414.
Lorent, B. (1975) ‘Test of different river flow predictors’, in Modelling and Simulation of Water Resources Systems, G.C. Vansteenkiste (Ed.), North Holland, Amsterdam.
Marco, J. and Yevjevich, V. (1985) ‘Stochastic modelling of ground-water recharge’, 21st Congress IAHR, Proceedings, Melbourne, Australia.
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.
Mizumura, K. and Chiu, C.L. (1985) ‘Prediction of combined snowmelt and rainfall-runoff’, ASCE J. of Hyd. Eng. 2, pp 179–193.
Moore, R.J. (1981) ‘Transfer functions, noise predictors and the forecasting of flood events in real time’, Prec. Int. Conf. Rainfall-Runoff Modelling.
Natale, L. and Todini, E. (1976) ‘A stable estimator for linear models’, Water Resour. Res. 12(4), pp 667–676.
Natural Environment Research Council, UK (1975) Flood Studies Report, Vol. 1, chap. 7, pp 513–531, London.
O’Connell, P.E. and Clarke, R.T. (1981) ‘Adaptative hydrological forecasting, a Review’, Hydrol. Sci. Bull., 26(2), pp 179–205.
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.
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.
Piccolo, D. and Ubertini, L. (1979) ‘Flood forecasting by intervention transfer stochastic models’, Proc. of IAHR 18th Congress, Vol. 5, pp 319–326.
Rissanen, J. (1974) ‘Basis of invariants and canonical forms for linear dynamic systems’, Automatica 10, pp 175–182.
Thompstone, R.M., Hipel, K.W. and McLeod, A.I. (1985) ‘Forecasting quarter-monthly riverflow’, Water Res. Bull., 21(5), pp 731–741.
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.
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.
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.
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.
Whitfield, P.H. and Woods, P.F. (1984) ‘Intervention analysis of water quality records’, Water Res. Bull., 20(5), pp 657–667.
Yevjevich, V. (1972) ‘Stochastic processes in hydrology’, Water Resources Publications, Fort Collins, Colorado.
Young, P.C. (1974) ‘Recursive approaches to time series analysis’, Bull Inst. Math. Appl. 10, pp 209–224.
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights 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