Summary
Annual and monthly streamflow time series are mostly modelled using linear models of the ARMA class. Model identification is usually performed through statistical procedures (e.g. Noakes et al., 1985) or, sometimes, by describing the processes as linear conceptual models whose equations can be rearranged in order to assume AR or ARMA representation (e.g. Salas and Smith (1981), Moss and Bryson (1974).
The aim of the present work is to reproduce streamflow time series at both annual and monthly scales by mean of ARMA models whose order is identified through simple conceptual models of the processes. Explicit correspondences between conceptual and stochastic parameters result from the identification procedure.
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References
Moss, M.E. and Bryson, M.C. (1974) ‘Autocorrelation structure of monthly streamflows’, Water Resources Research, 10(4), pp 737–744.
Noakes, D.J., McLeod, A.I. and Hipel, K.W. (1985) ‘Forecasting monthly riverflow time series’, Int. J. Forecast. 1, pp 179–190.
Salas, J.D. and Smith, R.A. (1981) ‘Physical basis of stochastic models of annual flows’, Water Resources Research, 17(2), pp 428–430.
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© 1993 Springer Science+Business Media Dordrecht
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Claps, P. (1993). Conceptual Basis of Stochastic Models of Monthly Streamflows. 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_13
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DOI: https://doi.org/10.1007/978-94-011-1697-8_13
Publisher Name: Springer, Dordrecht
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