HR: 0800h
AN: GC21A-0160    [Abstracts]
TI: Tendency and Long Memory Detection in Precipitation Indexes in Tuscany
AU: * CAPORALI, E
EM: enrica.caporali@unifi.it
AF: Department of Civil and Environmental Engineering, University of Florence, Italy., Via S. Marta 3, Firenze, 50139, Italy
AU: FATICHI, S
EM: simone.fatichi@dicea.unifi.it
AF: Department of Civil and Environmental Engineering, University of Florence, Italy., Via S. Marta 3, Firenze, 50139, Italy
AB: The climate change issues are becoming everyday more central, not only for scientist and specialist but for a large part of the public opinion. An important issue involved in this science is covered by the modification of precipitation regime, since droughts and water resources management are great problems in several countries. Citizens, stakeholders and managers have to know if the amount and the distribution of the available water could be different in the future. The analyses of climatic events like long periods of water shortage are made more difficult by the general lack of long sequences of data. Here the authors analyzed 5 indexes of precipitation regime: the annual precipitation, the number of wet days (precipitation > 1 mm), the Precipitation Concentration Index PCI, the number of days with more than 10 mm of precipitation and the maximum number of consecutive dry days (precipitation < 1 mm). The region analyzed is the Tuscany with a dataset of 785 rain gauges, cover the period 1916-2003. A methodology to use more data than usual, including the gauges with very short time series, even only 1 year, is purposed, basing on time variable spatial interpolation techniques. Both a distributed and lumped trends analysis of the indexes calculated has been performed by mean of the Mann-Kendall test. The time series of regional value of the monthly precipitation and monthly number of wet days has been detected to present long memory, i.e. to reveal the presence of a not negligible dependence between distant observations in the time series. The implication of long memory or long term persistency LTP can led to a dramatic increase of uncertainty in statistical estimation. The results do not show any evident signals of changes in the amount of water precipitated in Tuscany during the last century even in the more restrictive hypothesis of absence of long term persistency.
DE: 1616 Climate variability (1635, 3305, 3309, 4215, 4513)
DE: 1854 Precipitation (3354)
DE: 1872 Time series analysis (3270, 4277, 4475)
DE: 3235 Persistence, memory, correlations, clustering (3265, 7857)
SC: Global Environmental Change [GC]
MN: 2007 Fall Meeting