Design impact and significance of non-stationarity of variance in extreme rainfall
Stationarity in hydro-meteorological records is often investigated through an assessment of the mean value of the tested parameter. This is arguably insufficient for capturing fully the non-stationarity signal, and parameter variance is an equally important indicator. This study applied the Mann...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2015-06-01
|
| Series: | Proceedings of the International Association of Hydrological Sciences |
| Online Access: | https://www.proc-iahs.net/371/117/2015/piahs-371-117-2015.pdf |
| Summary: | Stationarity in hydro-meteorological records is often investigated through an
assessment of the mean value of the tested parameter. This is arguably
insufficient for capturing fully the non-stationarity signal, and parameter
variance is an equally important indicator. This study applied the
Mann-Kendall linear and Mann-Whitney-Wilcoxon step change trend detection
techniques to investigate the changes in the mean and variance of annual
maximum daily rainfalls at eight stations in Dublin, Ireland, where long and
high quality daily rainfall records were available. The eight stations are located
in a geographically similar and spatially compact region (< 950 km<sup>2</sup>)
and their rainfalls were shown to be well correlated. Results indicate that
while significant positive step changes were observed in mean annual maximum daily rainfalls (1961
and 1997) at only two of the eight stations, a significant and consistent
shift in the variance was observed at all eight stations during the 1980's.
This period saw a widely noted positive shift in the winter North Atlantic
Oscillation that greatly influences rainfall patterns in Northern Europe.
<br><br>
Design estimates were obtained from a frequency analysis of annual maximum
daily rainfalls (AM series) using the Generalised Extreme Value distribution,
identified through application of the Modified Anderson Darling Goodness of
Fit criterion. To evaluate the impact of the observed non-stationarity in
variance on rainfall design estimates, two sets of depth-frequency
relationships at each station for return periods from 5 to 100-years were
constructed. The first was constructed with bootstrapped confidence intervals
based on the full AM series assuming stationarity and the second was based on
a partial AM series commencing in the year that followed the observed shift
in variance. Confidence intervals distinguish climate signals from natural
variability. Increases in design daily rainfall estimates obtained from the
depth-frequency relationship developed from the truncated AM series, as
opposed to those using the full series, ranged from 5 to 16% for the
5-year event and from 20 to 41% for the 100-year event. Results indicate
that the observed trends exceed the envelopes of natural climate variability
and suggest that the non-stationarity in variance is associated with a
climate change signal. Results also illustrate the importance of considering
trends in higher order moments (e.g. variance) of hydro-meteorological
variables in assessing non-stationarity influences. |
|---|---|
| ISSN: | 2199-8981 2199-899X |