WWW.WATERPOWERMAGAZINE.COM OCTOBER 2010 15
INSIGHT
marises periodicities from examples in the
Middle East, China, North America, Africa
and northern Europe.
Various groupings of periodicity are evident
in Table 1. Those of between 3 and 3.8 years
almost certainly correspond to El Nino. Those
of 18.6 years could correspond to that of
known lunar tidal effects. It has been argued
that those close to 11, 22, 90 and 200 years
match known periodicities in solar variation.
Lessons for the future
Cyclic variability in weather patterns has been
explored and debated in the past but for practi-
cal engineering purposes it has generally been
ignored. Conventional analyses for hydropow-
er yield and ood assessment generally assume
any records to be random. One problem with
periodicities is that unless one is actively look-
ing for them one is unlikely to nd them. Cyclic
trends are often intermittent and discontinuous
while their causal mechanisms are also often
unclear and without that understanding there
is natural caution against relying on them.
However with increased availability of data
and the increased ease with which such data
can be processed surely it is about time the pro-
fession expanded its horizons. As demonstrat-
ed in the preceding sections, an understanding
of periodicity in the record can improve invest-
ment timing. It can also improve an under-
standing of required flood design capacity.
The assessment of design oods based on a
hydrological record is meaningless unless one
knows whether the basic data set corresponds
to a dry, average or wet phase in terms of long
term hydrology. This is not only true for nal
works but also for planning the capacity and
timing of river diversion during construction.
In the wider context an improved understand-
ing of apparent periodicities in the natural record
would seem to offer at least one planning sce-
nario to be considered in terms of investment
and even for the long term planning of aid and
famine relief. It can only be hoped that as cli-
matic data sets continue to be expanded, their
analyses will include not only statistical prob-
abilities but also cyclic variability as an aid to the
long term planning of those aspects and also of
major civil infrastructure.
Dr Peter Mason can be contacted at
peter.j.mason@mwhglobal.com
IWP& DC
2
1
0
-1
-2
1100.0
900.0
700.0
500.0
300.0
100.0
–100.0
30.0
20.0
10.0
0.0
-10.0
-20.0
-30.0
19401920 1980 20001960
Year
1881
1885
1889
1893
1897
1901
1905
1909
1913
1917
1921
1925
1929
1933
1937
1941
1945
1949
1953
1957
1961
1965
1969
1973
1977
1981
1985
1989
1993
1997
2001
2005
Year
Precipitation in mm
Annual precipitation Cumulative departure
fromMAP (scale x 10)
Sunspot number:
Cumulative departure from mean
Dublin: Annual precipitation 1881-2006
2
1
0
-1
-2
1100.0
900.0
700.0
500.0
300.0
100.0
–100.0
30.0
20.0
10.0
0.0
-10.0
-20.0
-30.0
19401920 1980 20001960
Year
1881
1885
1889
1893
1897
1901
1905
1909
1913
1917
1921
1925
1929
1933
1937
1941
1945
1949
1953
1957
1961
1965
1969
1973
1977
1981
1985
1989
1993
1997
2001
2005
Year
Precipitation in mm
Annual precipitation Cumulative departure
fromMAP (scale x 10)
Sunspot number:
Cumulative departure from mean
Dublin: Annual precipitation 1881-2006
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Above: Figure 6 – De-trended time series for Parana flows (in black) and similarly de-trended sunspot numbers (in red). Both series were normalised by subtract-
ing the means and dividing by the standard deviations; Figure 7 – Comparison between the cumulative departure of annual rainfall recorded in Dublin and the
cumulative departure of annual sunspot numbers