A non parametric approach for drought forecasting through the Standardized Precipitation Index

Due to a slow evolution in time, drought is a phenomenon whose consequences take a significant amount of time with respect to its inception in order to be perceived by the socioeconomic systems. Due to this feature, an effective mitigation of drought impacts is possible, more than in the case of other extreme events (e.g. floods, earthquakes, hurricanes, etc.), provided a timely monitoring and/or forecasting of an incoming drought is available. Thus, an accurate selection of indices, able to provide drought forecast in addition to a synthetic and objective description of historical droughts, is essential to help decision makers to implement appropriate mitigation measures. The objective of this study is to contribute to the development of drought forecasting methodologies based on stochastic techniques, with particular reference to the Standardized Precipitation Index (SPI, McKee et al., 1993), which is one of the most widely applied index for drought monitoring. According to the proposed methodology, SPI forecasts at a generic time horizon M are analytically determined, in terms of conditional expectation, as a function of a finite number of past observations of SPI. The forecasting accuracy is estimated through an expression of the Mean Square Error (MSE), which allows to derive confidence intervals of prediction. Validation of the derived expressions, carried out by hindcasting observed SPI values computed on monthly precipitation series observed in Sicily region, indicates the validity of the proposed approach.