Drought is one of the most common natural events having a great negative impact on agriculture being associated with a deficit of water resources over large geographical areas. Drought severity is conventionally assessed by various drought indices, which depend on different types of data. Among them, the Reconnaissance Drought Index (RDI) exhibits significant advantages over the other indices in the calculation of the drought severity by using, simultaneously, the precipitation and the potential evapotranspiration cumulated on a reference time scale processing monthly, seasonal or annual data. The main objective of the study was to assess the drought severity in a southern Italy area (Calabria region) by using the RDI and to map its spatial distribution and uncertainty. Calculating RDI requires the availability of precipitation and temperature data covering the whole study area. Precipitation and temperature data can be treated as random variables and analyzed by geostatistical methods. Particularly, to take into account the errors propagation in computing RDI, the input variables (precipitation and temperature data) were simulated using a geostatistical simulation approach. A set of 500 alternative stochastic images of the variables were generated and the expected value and standard deviation for RDI values were mapped.
Drought assessment using the reconnaissance drought index (RDI) in a southern Italy region
Buttafuoco G;Caloiero T;Guagliardi I;Ricca N
2016
Abstract
Drought is one of the most common natural events having a great negative impact on agriculture being associated with a deficit of water resources over large geographical areas. Drought severity is conventionally assessed by various drought indices, which depend on different types of data. Among them, the Reconnaissance Drought Index (RDI) exhibits significant advantages over the other indices in the calculation of the drought severity by using, simultaneously, the precipitation and the potential evapotranspiration cumulated on a reference time scale processing monthly, seasonal or annual data. The main objective of the study was to assess the drought severity in a southern Italy area (Calabria region) by using the RDI and to map its spatial distribution and uncertainty. Calculating RDI requires the availability of precipitation and temperature data covering the whole study area. Precipitation and temperature data can be treated as random variables and analyzed by geostatistical methods. Particularly, to take into account the errors propagation in computing RDI, the input variables (precipitation and temperature data) were simulated using a geostatistical simulation approach. A set of 500 alternative stochastic images of the variables were generated and the expected value and standard deviation for RDI values were mapped.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.