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  • 标题:A geostatistical exploratory analysis of precipitation extremes in southern Portugal
  • 本地全文:下载
  • 作者:Ana Cristina Costa ; Rita Durão ; Amílcar Soares
  • 期刊名称:RevStat : Statistical Journal
  • 印刷版ISSN:1645-6726
  • 出版年度:2008
  • 卷号:6
  • 期号:1
  • 页码:21-32
  • 出版社:Instituto Nacional de Estatística
  • 摘要:In Mediterranean climate regions, prolonged periods of unusually dry conditions re- duce the availability of water resources and affect vegetation cover; while other areas can be affected by an increase in the number of heavy precipitation events, with an increase in the flood risk. Issues such as drought and erosive rainfall have been raising concern about the risks of land degradation and desertification. The main objective of this paper is to provide an insight of the geographic distribution of extreme precip- itation events in the Southern region of continental Portugal, as a basis for a future study of the relationships between extreme rainfall patterns, both spatial and tem- poral, and desertification processes. The data used in this study are a set of 105 station records with daily precipitation observations for the period 1940–1999. This 60-year period was chosen to optimize data availability across the region, taking into consideration the quality control analysis performed. Among the numerous indices of extreme precipitation described in the literature, we selected three of them for an exploratory analysis: one index representing dry conditions, another one represent- ing extremely heavy precipitation events and another index representing flood events. For each of these three indices, yearly trends and decadal space-time patterns are investigated. The results show no significant trends in the regional extreme indices. The geostatistical study concluded that the spatial patterns are more continuous in the last decade than the other ones before. The preliminary results of this study agree with other similar studies of the same region reported in the literature.
  • 关键词:indices of precipitation extremes; geostatistical approach; southern Portugal.
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