Statistical Modelling of Annual Maxima in Hydrology
DOI:
https://doi.org/10.17713/ajs.v35i1.345Abstract
In this paper conditional modelling of annual maxima for predicting flood water is considered. The aim is to predict flood water of rivers, where no data about discharge but data about properties of the catchment of the rivers are available. A generalized linear mixed model is used to model the annual maxima depending on properties of the catchment and to take the correlation among measurements of one year into account. The fitted means and variances according to this model are plugged into the method of moment estimates of the parameters of the Gumbel distribution to obtain some extreme quantiles. These quantiles are commonly used to predict flood water of rivers. This approach is applied to data from Styria (Austria). The result is a satisfactory model for predicting flood water for rivers, where no data about the discharge are available.
References
Breslow, N., und Clayton, D. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association, 88, 9–25.
Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. London: Springer.
Coles, S., und Twan, J. (1991). Modelling extreme multivariate events. Journal of the Royal Statistical Society, Series B, 53, 377–392.
Coles, S., und Twan, J. (1996). Modelling extremes of areal rainfall process. Journal of the Royal Statistical Society, Series B, 58, 392–347.
Davison, A., und Smith, R. (1990). Models for exceedances over high thresholds. Journal of the Royal Statistical Society, Series B, 52, 393–442.
Dempster, A., Laird, N., und Rubin, D. (1977). Maximum likelihood with incomplete data via the E-M algorithm. Journal of the Royal Statistical Society, Series B, 39,
–38.
Fitzmaurice, G. M., Laird, N. M., undWare, J. H. (2004). Applied Longitudinal Analysis. Wiley.
Gumbel, E. (1958). Statistics of Extremes. New York: Columbia University Press.
Hosking, J. (1984). Testing wether the shape parameter is zero in the generalized extremevalue distribution. Biometrica, 71(2), 367–374.
McCullagh, P., und Nelder, J. (1989). Generalized Linear Models. Chapman and Hall.
Wedderburn, R. (1974). Quasilikelihood functions, generalized linear models and the Gauss-Newton method. Biometrica, 61, 439–447.
Downloads
Published
How to Cite
Issue
Section
License
The Austrian Journal of Statistics publish open access articles under the terms of the Creative Commons Attribution (CC BY) License.
The Creative Commons Attribution License (CC-BY) allows users to copy, distribute and transmit an article, adapt the article and make commercial use of the article. The CC BY license permits commercial and non-commercial re-use of an open access article, as long as the author is properly attributed.
Copyright on any research article published by the Austrian Journal of Statistics is retained by the author(s). Authors grant the Austrian Journal of Statistics a license to publish the article and identify itself as the original publisher. Authors also grant any third party the right to use the article freely as long as its original authors, citation details and publisher are identified.
Manuscripts should be unpublished and not be under consideration for publication elsewhere. By submitting an article, the author(s) certify that the article is their original work, that they have the right to submit the article for publication, and that they can grant the above license.