An Enhanced Run Theory for Agricultural Drought Characterization using Satellite Soil Moisture Data.

India, being an agriculture-dependent country, experiences recurrent droughts that significantly impact agricultural productivity. Assessing agricultural droughts and accurately identifying their onset is essential for effective planning and mitigation strategies. Soil Moisture (SM)-based drought indices, often paired with the run theory, are commonly used to identify the agricultural drought onsets. However, traditional run theory approaches rely on a single, uniform threshold to detect drought events, which may inadequately represent long-term drought patterns and oversimplify spatial variability in SM conditions. This study addresses these limitations by proposing an enhanced run theory approach that uses multiple dynamic grid-specific thresholds. The southern plateau and hills region of India was chosen as the study area. The thresholds are derived based on the standard deviation of the Standardized Soil Moisture Index (SSI) time series for each grid, ensuring adaptability to spatial heterogeneity of SM conditions. The SSI is calculated using European Space Agency Climate Change Initiative (ESA CCI) SM data. The enhanced run theory is then applied to compute key agricultural drought characteristics including duration, peak, frequency, and intensity.
The results reveal that the computed dynamic SSI thresholds capture subtle but notable spatial variations, reflecting the influence of grid-specific factors such as soil types and land cover. This approach enhances the accuracy of drought detection and characterization. The analysis of drought metrics reveals that drought duration and frequency share similar spatial distributions, suggesting that areas experiencing frequent droughts are also prone to prolonged drought periods. This spatial congruence highlights the consistent vulnerability of certain regions to both drought initiation and sustained impacts. Furthermore, the analysis of drought peak and intensity demonstrates a predominance of moderate drought conditions, with severe droughts occurring less frequently and extreme droughts being rare. The findings underscore the importance of dynamic, location-specific thresholds for improving drought assessment. By capturing spatial variability in SM conditions, the proposed enhanced run theory provides a robust framework for characterizing agricultural droughts.