Extended Abstract
 Introduction
Drought, as one of the most important climate hazards with widespread impacts on environmental sustainability and human livelihoods, requires a multidimensional approach for monitoring and assessment. In this study, with the aim of providing a comprehensive model for assessing drought in northwest Iran, a new composite index was developed based on the integration of four perspectives: meteorology, agriculture, hydrology, and remote sensing. The data used included synoptic observations of the Iran Meteorological Organization, Landsat image time series, and MODIS data for the period 2000 to 2024. All processing and calculations of the indices were performed in the Google Earth Engine environment. The resulting indices were normalized using the Analytic Hierarchy Process (AHP) method and optimized weights, and six-month drought maps were produced.  The combined results showed that the severity of drought has increased significantly after 2015, especially in the Lake Urmia basin, while higher mountainous areas show a more stable pattern of moisture. So that the area of ​​severe drought areas in the first half of 2015 reached an area of ​​49.68 km² and in the second half it reached 34.50 km², which is almost more than half of the area of ​​the study area. This amount reached 58.70 km² in the first half of 2019 and 35.20 km² in the second half, which reached the peak of drought in 2021, so that the first half of this number reached 42.36 km² and in the second half it reached 41 km². Also, the combined results of drought maps emphasize that the most droughts occurred in the areas around Lake Urmia and these areas are under serious threat.  Finally, the combined method presented in this study provides an efficient method for more accurate spatial and temporal identification of critical areas and provides an effective decision-making tool for managing water resources and agriculture in arid and semi-arid regions.
Materials & Methods
This study introduces a novel hybrid methodology for drought monitoring by integrating four distinct perspectives—meteorological, agricultural, hydrological, and remote sensing—to achieve a more comprehensive and accurate assessment. Focusing on the drought-stricken region of Lake Urmia in the provinces of East and West Azerbaijan, the research combined multi-source data within a Geographic Information System (GIS) environment. To manage the computationally intensive workload, key indices from each perspective were calculated over a 25-year period, segmented into six-month intervals, using the Google Earth Engine platform. The process first synthesized indices within each perspective using the Analytic Hierarchy Process (AHP) algorithm to generate individual drought severity maps. These four distinct maps were then integrated into a single, comprehensive six-monthly drought map through an overlay analysis with equal weighting, effectively transforming qualitative, multi-domain assessments into a quantifiable, spatially explicit drought severity index. The accuracy of this integrated index was validated against meteorological maps derived from reliable in-situ data, offering a refined tool for precise drought monitoring.
Results and Discussion
Spatio-temporal analysis of composite drought maps reveals a critical trend of intensifying drought severity in the Lake Urmia basin, particularly from 2000 onwards. The quantitative evidence demonstrates a dramatic expansion of areas classified under "severe dryness," which escalated from approximately 73 km² in the first half of 2015 to 154 km² in the second half, and further soared to 300 km² and 615 km² in the respective halves of 2020. By 2024, the affected area remained persistently high at 310 km² and 200 km² for the first and second halves, marking increases of approximately 24% and 25% compared to the corresponding periods in 2015. Spatially, the results confirm that the most severe drought conditions are concentrated in the lands immediately surrounding Lake Urmia. This spatial pattern suggests a vicious cycle whereby the lake's desiccation exacerbates local agricultural and ecological drought through feedback mechanisms such as salt-dust storms, thereby rendering these areas acutely vulnerable and threatening their long-term habitability if the current trend persists.
Conclusions
This study conclusively demonstrates that the proposed multi-perspective, index-based methodology is a powerful and efficient tool for the comprehensive spatio-temporal monitoring and assessment of drought. Empirical findings confirm a significant intensification of drought within the Lake Urmia basin following 2015, successfully identifying the lake's periphery as the critical epicenter of this environmental crisis. By providing a robust model for monitoring agricultural and ecological drought, this integrated approach equips policymakers and environmental managers with a precise mechanism to pinpoint critical areas at risk. Consequently, the identified regions on the resulting maps offer essential information for planners to implement timely, targeted mitigation and adaptation strategies, thereby enabling proactive measures to combat the devastating effects of drought in vulnerable ecosystems globally.