Enhancing drought monitoring through regional adaptation: Performance and calibration of drought indices across varied climatic zones of Iran

Study region

Iran.

Study focus

This study evaluates the performance of various drought indices, including SPEI (Standardized Precipitation Evapotranspiration Index), Standardized Soil Moisture Index of the top two layers (SSI₁ and SSI₂), and the Multivariate Standardized Drought Indices (MSDI₁ (P&ET_ref), MSDI₂ (P&SM₁), and MSDI₃ (P&SM₂)) models, across six distinct climatic zones using data from 30 basins with 621 gridded points (1979–2022). The analysis covers three time scales—1, 3, and 12 ∼ months—and assesses the drought characteristics and criteria in diverse climate regions.

New hydrological insights for the region

The MSDI models exhibited superior performance across all climatic zones, achieving an overall precision rate of 85 % and consistently outperforming the SPEI and SSI models in both short-term (1- and 3-month) and long-term (12-month) drought predictions. In coastal wet and mountain regions, the MSDI models demonstrated exceptional precision rates of 90 % and 85 %, respectively, with robust Taylor skill scores of 0.92 and 0.89, significantly surpassing the accuracy of the SPEI and SSI models. In semi desert and desert regions, the MSDI models maintained a precision rate of 77 %, with a slight decline at the 12-month scale. Despite this decrease, they continued to outperform the SPEI and SSI models, particularly in short-term (3-month) drought assessments. These findings underscore the necessity of selecting and calibrating drought indices to enhance monitoring accuracy, with the MSDI models proving particularly reliable in semi-desert and mountainous regions. The study advocates for region-specific drought indices to better capture local climatic variations and emphasizes the importance of improved model calibration in regions exhibiting lower performance. Policymakers are urged to implement tailored drought management strategies to enhance water resource sustainability, strengthen agricultural resilience, and mitigate the adverse impacts of drought. Further research is essential to refine these models and integrate advanced methodologies, such as machine learning (ML), to enhance drought prediction accuracy and support climate adaptation efforts.