Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-29T08:18:05.319Z Has data issue: false hasContentIssue false

Analysing Daily Rainfall Measurements to Give Agronomically Useful Results. II. A Modelling Approach

Published online by Cambridge University Press:  03 October 2008

R. D. Stern
Affiliation:
Departments of Applied Statistics and Agricultural Botany, University of Reading, England, RG6 2AN
M. D. Dennett
Affiliation:
Departments of Applied Statistics and Agricultural Botany, University of Reading, England, RG6 2AN
I. C. Dale
Affiliation:
Departments of Applied Statistics and Agricultural Botany, University of Reading, England, RG6 2AN

Summary

A probabilistic model of daily rainfall can be used to derive results of potential value to agriculture. A simple example shows how this model works, but more realistic models are also fitted, using standard statistical computer packages, and examples of the results are presented graphically. The modelling approach is compared and contrasted with direct methods of analysing daily rainfall.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1982

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Baker, R. J. & Nelder, J. A. (1978). The GLIM Manual – Release 3. Oxford: Numerical Algorithms Group.Google Scholar
Buishand, T. A. (1977). Stochastic Modelling of Daily Rainfall Sequences. Wageningen, Netherlands: Veenman and Zonen.Google Scholar
Coe, R. D. & Stern, R. D. (1982). Fitting models to daily rainfall data. Journal of Applied Meteorology (in press).Google Scholar
Fitzpatrick, E. A. & Krishnan, A. (1967). A first order Markov model for assessing rainfall discontinuity in central Australia. Archives for Meteorology, Geophysics and Bioclimatology Series B 15:242259.Google Scholar
Gabriel, K. R. & Neumann, J. (1962). A Markov chain model for daily rainfall occurrence at Tel Aviv. Quarterly Journal of the Royal Meteorological Society 88:9095.CrossRefGoogle Scholar
Garbutt, D. J., Stern, R. D., Dennett, M. D. & Elston, J. (1981). A comparison of the rainfall climate of eleven places in West Africa using a two-part model for daily rainfall. Archives for Meteorology, Geophysics and Bioclimatology Series B 29:137155.Google Scholar
Gates, P. & Tong, H. (1976). On Markov chain modelling to some weather data. Journal of Applied Meteorology 15:11451151.Google Scholar
Haan, C. T. (1977). Statistical Methods in Hydrology. Ames, USA: Iowa State University Press.Google Scholar
Katz, R. W. (1977). Precipitation as a chain-dependent process. Journal of Applied Meteorology 16: 671676.2.0.CO;2>CrossRefGoogle Scholar
Nelder, J. A. & Wedderburn, R. W. M. (1972). Generalised linear models. Journal of the Royal Statistical Society (A) 135:370384.CrossRefGoogle Scholar
Stern, R. D. (1980). The calculation of probability distributions from models of daily precipitation. Archives for Meteorology, Geophysics and Bioclimatology Series B 28:137147.Google Scholar
Stern, R. D. & Coe, R. (1982). The use of rainfall models in agricultural planning. Agricultural Meteorology 26:(in press).Google Scholar
Stern, R. D., Dennett, M. D. & Dale, I. C. (1982). Methods for analysing daily rainfall measurements to give useful agronomic results. I. Direct methods. Experimental Agriculture 18:223236.CrossRefGoogle Scholar
Stern, R. D., Dennett, M. D. & Garbutt, D. J. (1981). The start of the rains in West Africa. Journal of Climatology 1:5968.Google Scholar
Todorovic, P. & Woolhiser, D. A. (1975). A stochastic model of n-day precipitation. Journal of Applied Meteorology 14:1724.2.0.CO;2>CrossRefGoogle Scholar
White, J., Yeats, A. & Skipworth, G. (1972). Tables for Statisticians. London: Stanley Thornes.Google Scholar