Long-term climatic rainfall trends over the central Mediterranean Region

Abstract

The increased awareness on climate change has boosted researchers to conduct studies related to climatic trends relevant indices. This study examines the presence of any precipitation trend for the period 1962-2013, using six official precipitation indices collected by four, WMO-compliant climatological stations. The geographical area for this study is the central Mediterranean region, for which climatological information is very scanty. The climatological stations are: Trapani Birgi and Pantelleria on the western part and Catania Sigonella and Malta on the eastern part of this region. Data was brought from an official online archive consisting of meteorological and climatological data sets. All the stations, except for Trapani Birgi's station which had no missing data, had a small percentage of missing values. This study is relevant mainly for the socioeconomic and environmental aspects, and to identify relevant adaptive measures against negative effects from climatic rainfall patterns. The null hypothesis (no precipitation trend) was tested for rejection by the Mann-Kendall test, with a 0.05 level of significance, for which the only statistically significant trends found are Matta (positive) and Trapani Birgi (negative) for the Consecutive Dry Days index, Malta (positive) for the Consecutive Wet Days index and Pantelleria (positive) for the Heavy Precipitation Days (≥10mm) index. The Precipitation Sum index is a very important index that is commonly used in precipitation researches. With regards to this study, no statistically significant trends for the Precipitation Sum Index for the four stations were detected. Due to the relatively short climatic period analysed, and due to the high spatial variability, it is difficult to evaluate precipitation trends. A much more detailed research would be therefore required to likely obtain more significant results. This would help in identifying precipitation trends and thus implementing suitable adaptive measures to the relevant location.