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You are here : eLibrary : IAHR World Congress Proceedings : 36th Congress - The Hague (2015) ALL CONTENT : Extreme events, natural variability and climate change : Derived distribution of annual precipitation: efects of record length and rainfall data aggregation
Derived distribution of annual precipitation: efects of record length and rainfall data aggregation
Author : CLAUDIO I. MEIER(1), GERI PRANZINI(1), J. SEBASTI¨˘N MORAGA(1) & PETER MOLN¨˘R(2)
ABSTRACT
The interannual variability of precipitation is given by the probability distribution (pdf) of annual rainfall, which in
temperate zones is typically obtained by fitting a normal (or sometimes a lognormal) distribution to a series of annual
rainfall data. There are three problems with this approach: (i) a long record (n > 25 ~ 30) is required in order to fit the
model; (ii) years with missing data cannot be used for the analysis; and (iii) trends or jumps (non-stationarities) can
happen over the rather long period required for adequately fitting a probabilistic model. Therefore, a methodology is
needed that would allow for better estimation of the pdf of annual rainfall, without requiring long records.
In 1978, Eagleson proposed using derived distributions to obtain the pdf of annual rainfall, by combining the marginal
pdfs for storm depth and inter-arrival time. Our aim in this paper is to assess the differences between Eaglesonˇäs
approach and the fitting of a normal distribution, looking at two effects: length of the record and level of aggregation of
the rainfall data. We quantified the first effect by randomly subsampling the rainfall series, in order to create multiple
synthetic records of different lengths, to which we applied both methods. We then re-did our analyses for precipitation
data aggregated over 12 and 24-h long periods, the typical information available at most weather stations. We used
records for Concepci¨®n, Chile (25 yr), and for Lugano, Switzerland (32 yr), to compare two different humid climates,
where a normal distribution would be typically used for fitting annual rainfall data.
Annual rainfall is uncorrelated in time, at both sites. Both approaches yield very similar pdfs when using all data
available. In Concepci¨®n, totalising rainfall every 12 h results in a pdf that is undistinguishable from that obtained for
continuous data, but aggregating over 24 h introduces bias. In Lugano, any level of aggregation introduces a bias. The
proposed method is a much more consistent way of estimating the pdf of annual rainfall when only a few years of data
are available, allowing for a robust estimation even for records as short as 5 years. Thus, derived distributions are a
powerful tool for describing interannual variability in rainfall, in the case of short records or when climate might be nonstationary.
File Size : 535,264 bytes
File Type : Adobe Acrobat Document
Chapter : IAHR World Congress Proceedings
Category : 36th Congress - The Hague (2015) ALL CONTENT
Article : Extreme events, natural variability and climate change
Date Published : 14/08/2015
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