A Bayesian Approach to Estimating the Parameters of a Hydrological Forecasting System

Sylvia Schnatter, Dieter Gutknecht and Robert Kirnbauer
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Sylvia Schnatter: Technische Universität Wien, Institut für Hydraulik, Gewässerkunde und Wasserwirtschaft
Dieter Gutknecht: Technische Universität Wien, Institut für Hydraulik, Gewässerkunde und Wasserwirtschaft
Robert Kirnbauer: Technische Universität Wien, Institut für Hydraulik, Gewässerkunde und Wasserwirtschaft

A chapter in Probability and Bayesian Statistics, 1987, pp 415-421 from Springer

Abstract: Summary In this paper certain aspects of estimating model parameters in hydrological short term forecasting are dealt with. In estimating parameters of hydrological flood routing models difficulties arise when the input-output relationship of the model is affected by violations of the mass condition. In this paper an estimation procedure is presented which can handle this problem. The procedure is based upon a Bayesian algorithm for recursive estimation of the parameters of a dynamic linear model. The unknown volume increase is dealt with a volume correction coefficient which is estimated using the Kaiman Filter. Finally, an application of the model to a real world example is given.

Keywords: Marginal Posterior; Unit Hydrograph; Short Term Forecast; Transfer Function Model; Bayesian Algorithm (search for similar items in EconPapers)
Date: 1987
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4613-1885-9_42

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DOI: 10.1007/978-1-4613-1885-9_42

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