Assessment of drought risk using multi-sensor drought indices and vulnerability factors: a case study of semi-arid region in Iran.

Drought is an unpleasant climatic phenomenon that directly impacts the different dimensions of human societies. To pry-awareness and choice optimum management decision, design and development of an integrated approach is needed to control this phenomenon more effectively and provide early warnings. This study used twelve remotely sensed indices of the Moderate Resolution Imaging Spectroradiometer (MODIS) and digital elevation model (DEM) to monitor drought during the 2000–2018 growing season. Standardized Precipitation Index (SPI) with 1 to 12 months time scales was used as reference data. A machine learning approach modulated the relations between thirteen indices and SPI with different time scales. The random forest technique was used to construct a comprehensive drought monitoring model in Ilam Province. Validation data were provided based on relative soil moisture, Standardized Precipitation Evapotranspiration Index (SPEI), and crop yield data. It was observed that random forest produced good applicability (R² = 0.88) for SPI prediction. In the next step, the Drought Hazard Index (DHI) was generated based on the probability occurrences of drought using the comprehensive drought model made in the previous step. The Drought Vulnerability Index (DVI) was calculated by using 7 socioeconomic indices. Finally, the Drought Risk Index (DRI) was obtained by multiplying DHI and DVI for Ilam province. The result of the DRI map showed that two counties are at very high risk of drought, 4 counties are at high risk, and 4 counties are at moderate and low risk of drought. Overall, the result of our study provides a comprehensive method for the assessment of regional drought. Also based on this model, counties with high vulnerability can be identified to provide timely management programs to help improve the situation. [ABSTRACT FROM AUTHOR]