Drought Forecasting in a Semi-arid Watershed Using Climate Signals: a Neuro-fuzzy Modeling Approach

Large-scale annual climate indices were used to forecast annual drought conditions in the Maharlu-Bakhtegan watershed, located in Iran, using a neuro-fuzzy model. The Standardized Precipitation Index (SPI) was used as a proxy for drought conditions. Among the 45 climate indices considered, eight identified as most relevant were the Atlantic Multidecadal Oscillation (AMO), Atlantic Meridional Mode (AMM), the Bivariate ENSO Time series (BEST), the East Central Tropical Pacific Surface Temperature (NINO 3.4), the Central Tropical Pacific Surface Temperature (NINO 4), the North Tropical Atlantic Index (NTA), the Southern Oscillation Index (SOI), and the Tropical Northern Atlantic Index (TNA). These indices accounted for 81% of the variance in the Principal Components Analysis (PCA) method. The Atlantic surface temperature (SST: Atlantic) had an inverse relationship with SPI, and the AMM index had the highest correlation. Drought forecasts of neuro-fuzzy model demonstrate better prediction at a two-year lag compared to a stepwise regression model.