Current Status of Time Series Analysis in Hydrological Sciences - Hydrologic Time Series Analysis: Theory and Practice - page 106

amount per wet day and dividing the precipitation into heavy and non-heavy
classes. In addition, the return period of extreme events was calculated for the
30-years and its variations were examined. The results indicated a negative
trend in the number of wet days associated with an increase in the contribution
of heavy rainfall events to the total precipitation. This finding is in agreement
with the reality (i.e., a reduction in the return period of extreme events since
1920).
Kripalani and Kulkarni (2001) prepared regional rainfall anomaly time
series using the 118-year (1881 to 1998) data of three regions, India, northern
China and southern Japan. All the three series (India, China and Japan) were
subjected to selected statistical tests. The analysis of the results revealed that
though there are year-to-year fluctuations in rainfalls, the Mann-Kendall rank
statistic suggested no significant long-term trends. However, the application
of the Cramer's statistic to study the short-term climate variability depicts
decadal variability with certain epochs of above and below normal rainfall
over each region. The epochs tend to last for about three decades over India
and China, and about five decades over Japan. The turning points for China
follow those of India after about a decade.
Adamowski and Bougadis (2003) estimated trends for different durations
of annual extreme rainfall by using the regional average Mann-Kendall S
trend test. The method of L-moments was employed to delineate homogeneous
regions. The trend test was modified to account for the observed autocorrelation,
and the bootstrap method was used to account for the observed spatial
correlation. Numerical analysis was performed for 44 rainfall stations in
Ontario, Canada for a 20-year time frame using the data from homogeneous
regions. Depending on the rainfall durations, four and five homogeneous
regions were delineated. At the 5% significance level, approximately 23% of
the regions tested had a significant trend, predominantly for short-duration
storms. The serial dependency was observed in 2-3% of datasets and the
spatial correlation was found in 18% of the regions. The presence of serial and
spatial correlation was found to have significant impacts on trend determination.
Xu et al. (2003) detected long-term trends in the spatially averaged Japanese
precipitation time series by applying the parametric t -test and the nonparametric
Mann-Kendall and Mann-Whitney tests. The results indicated that despite
several step changes in the Japanese precipitation, the time series did not
exhibit significant evidence of monotonic trend during the past century. Further,
it was found that if the magnitude of the step change reaches one or two times
of its standard deviation, the previous 50-year records together with five years
or more new data will be available for detecting the possible trend. This
finding is useful for the detection of step changes in the regions where the
precipitation has near-normal distributions.
Oguntunde et al. (2006) investigated hydrological variability and trends
in the Volta River basin in West Africa over the period 1901-2002. Potential
( E p) and actual evaporation ( E ), rainfall variability index ( d ), Budyko's aridity