Forecasting Palmer Index Using Neural Networks and Climatic Indexes

In recent years, several drought monitoring indexes have found application to describe and compare droughts among different time periods and regions as well as to forecast the evolution of ongoing droughts, in order to select appropriate mitigation measures and policies for water resources management under shortage risk conditions. However, limited efforts have been made to investigate the possibilities of using information conveyed by large-scale climatic indexes to improve the forecasting ability of drought indexes, provided they exert some influence on the climatic variability in a region. The aim of the paper is to develop models for forecasting Palmer Hydrological Drought Index series in Sicily (Italy) based on artificial neural networks, and to extend such models in order to include information from large-scale climatic indexes. First, the influence of North Atlantic Oscillation (NAO) and European Blocking (EB) indexes on Palmer index series, computed on areal monthly precipitation from 1955 until 1999 in Sicily, has been investigated by means of a correlation analysis. Results indicate that NAO and EB series are significantly correlated with Palmer index series for winter and autumn months, with special reference to the last decades. Then, forecasting models based on neural networks have been developed, using different approaches. The comparison between the prediction for winter and autumn months obtained by either including or not including the NAO and EB indexes within the forecasting model indicates some improvements in terms of R-2 when NAO and especially EB are considered. A different model behavior has been observed for the spring and summer predictions for which no significant improvements in terms of model predictive capability, due to the introduction of the climatic indexes as input variables, have been observed.