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In this study, a hybrid index called the aggregated drought index (ADI) is developed for drought assessment integrating various meteorological, hydrological, and agricultural features of the region. To this end, precipitation, reservoir storage, discharge, temperature, potential evapotranspiration, and the degree of vegetation based on remote sensing images are utilized to quantify ADI. Then, the performance of two models, auto-regressive integrated moving average (ARIMA) and adaptive neuro-fuzzy inference system (ANFIS), is investigated in predicting the developed drought index. The proposed framework is applied to the Aharchay watershed located in East Azarbaijan Province, Iran. The results demonstrate that the region has experienced normal and mild drought conditions during the investigated time period. ADI is also applied in another watershed (Ajichay) to show ADI’s capability in different climatic settings. Regarding the predictive capability, the ANFIS model outperforms ARIMA in drought prediction, particularly in severe weather conditions. The ADI benefits planning for drought mitigation and preparedness by incorporating several different aspects of the region.

Application of meteorological, hydrological and remote sensing data to develop a hybrid index for drought assessment