Innovative trend analysis method for drought indicators and pattern detection of the Urmia lake basin, Iran

Effective drought and water-resource management is a fundamental challenge worldwide. In recent decades, the intensification of drought has become a serious challenge in northwestern Iran, particularly in the Lake Urmia basin, where rising temperatures and declining heavy rainfall have accelerated water scarcity. Therefore, monitoring drought and studying its trends is crucial.

This study evaluates drought patterns at seven meteorological stations using the Standardized Precipitation Index (SPI) and the Standardized Precipitation-Evapotranspiration Index (SPEI) at 3, 12, and 24-month time scales. The Innovative Trend Analysis (ITA) method, supported by the Seasonal Kendall test, was used to identify and assess drought behavior.

The ITA method clearly showed drought trends, whereas the Seasonal Kendall test often failed to detect any trends in short-term data. The results showed that the stations of Tabriz and Urmia have more dry and normal periods, while wet periods have reduced, indicating a reduction in overall moisture. Mahabad, Saqqez, Maragheh, and Sarab had a decrease in all categories (dry, normal, and wet), which demonstrates severe and persistent drought. SPEI also identified short-term droughts in Mahabad and Tekab, which SPI was unable to capture.

Frequency analysis using McKee’s classification showed that most months fall within the normal range; however, ITA trends indicated that the intensity and persistence of normal periods are decreasing in many stations. These results indicate that ITA trends can identify which stations enter drought rapidly, retain moisture stability, and is critical for water storage planning and early warning systems.

Overall, the integration of SPI and SPEI with statistical and trend methods provides a comprehensive framework for drought monitoring in semi-arid regions. The findings suggest that the use of ITA is highly effective for water resource management, long-term change prediction, and strengthening adaptation strategies in the sensitive and critical Lake Urmia basin.