A deficit of precipitation has different impacts on the ground water, reservoir storage, soil moisture, snowpack, and streamflow. In this study spatial and temporal patterns of drought in a region of southern Italy (Calabria region) have been analysed. First, the original database was homogenised and the gaps filled in for 129 daily rain gauges for the 1916-2006 period. Then both the short-time (3, 6 and 9 months) and long-time (12 and 24 months) Standardized Precipitation Index (SPI) was estimated to analyse drought especially from the agricultural point of view. A time series analysis was performed with the Mann-Kendall nonparametric test to detect possible trends. A generally negative trend has been detected. Running trend analysis, carried out for long-time SPI, revealed that the previously discussed tendencies were not persistent throughout the series length, but depended on the period examined.SPI data were interpolated and mapped using a geostatistical approach. Moreover, both the short- and long-time SPI data were analysed by using Factor Kriging Analysis (FKA) to identify and map regionalized factors at different spatial scales from the point of view of SPI.
Modelling drought severity at different timescales using Standardized Precipitation Index and Geostatistics: an application in southern Italy
Gabriele Buttafuoco;Tommaso Caloiero;Roberto Coscarelli
2013
Abstract
A deficit of precipitation has different impacts on the ground water, reservoir storage, soil moisture, snowpack, and streamflow. In this study spatial and temporal patterns of drought in a region of southern Italy (Calabria region) have been analysed. First, the original database was homogenised and the gaps filled in for 129 daily rain gauges for the 1916-2006 period. Then both the short-time (3, 6 and 9 months) and long-time (12 and 24 months) Standardized Precipitation Index (SPI) was estimated to analyse drought especially from the agricultural point of view. A time series analysis was performed with the Mann-Kendall nonparametric test to detect possible trends. A generally negative trend has been detected. Running trend analysis, carried out for long-time SPI, revealed that the previously discussed tendencies were not persistent throughout the series length, but depended on the period examined.SPI data were interpolated and mapped using a geostatistical approach. Moreover, both the short- and long-time SPI data were analysed by using Factor Kriging Analysis (FKA) to identify and map regionalized factors at different spatial scales from the point of view of SPI.File | Dimensione | Formato | |
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