Analyses of six daily rainfall series (Milano, Bergamo, Brescia, Como, Mantova and Sondrio) are used to assess trends in yearly precipitation behaviour (precipitation height and number of wet days; precipitation height and number of wet days for different classes of daily precipitation; precipitation height related to 1 wet day; number of dry days between wet days) over Lombardia, Northern Italy. The time series analysis was performed with two non-parametric rank based tests: the Mann Kendall test and Sen’s estimator of slope. Also a new and advantageous procedure that combines a time series model and Bayesian statistics through the Markov Chain Monte Carlo (MCMC) method of Gibbs sampling is adopted to model trends. Globally, the different methods detect the same tendencies. The results point toward a significant decreasing trend in the annual number of wet days and an increasing tendency in the mean and maximum precipitation height related to 1 day, with a generalized increasing tendency for the number of dry days between wet days. These trends affect the rainfall runoff process and the pollutant dynamics on urban areas and in drainage systems and could provoke a significant efficacy reduction of existing urban quantity and quality control systems.
Trends in long daily rainfall series of Lombardia (Northern Italy) affecting urban stormwater control
TODESCHINI, SARA
2012-01-01
Abstract
Analyses of six daily rainfall series (Milano, Bergamo, Brescia, Como, Mantova and Sondrio) are used to assess trends in yearly precipitation behaviour (precipitation height and number of wet days; precipitation height and number of wet days for different classes of daily precipitation; precipitation height related to 1 wet day; number of dry days between wet days) over Lombardia, Northern Italy. The time series analysis was performed with two non-parametric rank based tests: the Mann Kendall test and Sen’s estimator of slope. Also a new and advantageous procedure that combines a time series model and Bayesian statistics through the Markov Chain Monte Carlo (MCMC) method of Gibbs sampling is adopted to model trends. Globally, the different methods detect the same tendencies. The results point toward a significant decreasing trend in the annual number of wet days and an increasing tendency in the mean and maximum precipitation height related to 1 day, with a generalized increasing tendency for the number of dry days between wet days. These trends affect the rainfall runoff process and the pollutant dynamics on urban areas and in drainage systems and could provoke a significant efficacy reduction of existing urban quantity and quality control systems.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
