Abstract
Abstract. 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 km2) 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. 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.
Highlights
Climate induced non-stationarity in hydro-meteorological variables has significant impacts on the engineering design of water systems
The positive step changes in variance could be attributed to the increased frequency of extreme events that has been observed in more recent times
The stationarity in mean and variance of extreme annual daily rainfall of eight historical records in the Greater Dublin region was investigated in this paper and its impact on hydraulic design was evaluated
Summary
Climate induced non-stationarity in hydro-meteorological variables has significant impacts on the engineering design of water systems. Failure to correctly identify and account for non-stationarity influences can compromise the reliability of design estimates which are typically obtained from frequency analyses of historical time series of extreme events (Coles, 2001; Klein Tank et al, 2009). Such frequency analyses are commonly underpinned by the assumption of stationarity, i.e. observations are independent and identically distributed (Stedinger, 1993). Al Saji et al.: Design impact and significance of non-stationarity of variance
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More From: Proceedings of the International Association of Hydrological Sciences
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