Analysis of spatial and temporal rainfall trends in Sicily during the 1921–2012 period

Abstract

Precipitation patterns worldwide are changing under the effects of global warming. The impacts of these changes could dramatically affect the hydrological cycle and, consequently, the availability of water resources. In order to improve the quality and reliability of forecasting models, it is important to analyse historical precipitation data to account for possible future changes. For these reasons, a large number of studies have recently been carried out with the aim of investigating the existence of statistically significant trends in precipitation at different spatial and temporal scales. In this paper, the existence of statistically significant trends in rainfall from observational datasets, which were measured by 245 rain gauges over Sicily (Italy) during the 1921-2012 period, was investigated. Annual, seasonal and monthly time series were examined using the Mann-Kendall non-parametric statistical test to detect statistically significant trends at local and regional scales, and their significance levels were assessed. Prior to the application of the Mann-Kendall test, the historical dataset was completed using a geostatistical spatial interpolation technique, the residual ordinary kriging, and then processed to remove the influence of serial correlation on the test results, applying the procedure of trend-free pre-whitening. Once the trends at each site were identified, the spatial patterns of the detected trends were examined using spatial interpolation techniques. Furthermore, focusing on the 30 years from 1981 to 2012, the trend analysis was repeated with the aim of detecting short-term trends or possible changes in the direction of the trends. Finally, the effect of climate change on the seasonal distribution of rainfall during the year was investigated by analysing the trend in the precipitation concentration index. The application of the Mann-Kendall test to the rainfall data provided evidence of a general decrease in precipitation in Sicily during the 1921-2012 period. Downward trends frequently occurred during the autumn and winter months. However, an increase in total annual precipitation was detected during the period from 1981 to 2012.

Uncertainty Analysis in the Evaluation of Extreme Rainfall Trends and Its Implications on Urban Drainage System Design

“…In Italy, the change point of the annual average temperature has been identified at the beginning of the 1980s [39]. The evidence of a statistically significant increase in mean annual rainfall over the last 30 years in Sicily [40] further supports this assumption. Finally, the purpose of this study is the assessment of implications of climate change on urban drainage system design; thus, referring to long-term trends (more than 35-40 years) seems to be inadequate because the design return periods of drainage systems typically range between 5 and 10 years.…”

Flood frequency analysis for an urban watershed: comparison between several statistical methodologies simulating synthetic rainfall events

“…This type of flooding leads to huge economic and environmental losses due to recurrent and frequent episodes of inundation rather than to the impact of single devastating event. Unfortunately, urban pluvial flood risk is expected to increase significantly in the future as a result of climate changes (Liuzzo et al ., ; Luizzo and Freni, ; Notaro et al ., ) and demographic shifts: the former is likely to increase the magnitude and frequency of extreme storm events, the driving force of pluvial flooding, while the latter will increase exposure and hence, risk. This has attracted the attention of researchers and practitioners.…”

Linear Trend Detection in Serially Dependent Hydrometeorological Data Based on a Variance Correction Spearman Rho Method

“…The problem has attracted considerable attention in recent decades, along with an increasing concern related to the impact of intensifying climate change and human activity on the hydrologic cycle [4][5][6][7][8][9][10][11][12].…”

“…We computed the corrected rho variance var C pρq via the theoretical formula, Equation (8), or the empirical formula, Equation (12), by introducing HMR-ESS, Equation (14).…”