A Framework for Global Multicategory and Multiscalar Drought Characterization Accounting for Snow Processes

Drought indices do not always provide the most relevant information for water resources management as most of them neglect the role of snow in the land surface water balance. In this study, a physically based drought index, the Standardized Moisture Anomaly Index (SZI), was modified and improved by incorporating the effects of snow dynamics for drought characterization at multiple time scales. The new version of the SZI, called SZI_snow, includes snow in both the water supply and demand in drought characterization by using the water-energy budgets from the Global Land Data Assimilation Systems product. We compared and evaluated the performance of SZI_snow and SZI in drought identification globally across various time scales using observed multicategory drought evidences from several sources. Results show that the SZI_snow agrees better with the observed changes in hydrological and agricultural droughts than the SZI, particularly over basins with high snow accumulation. Furthermore, the SZI_snow is more consistent with the residual water-energy ratio than the SZI over snow-influenced regions. Overall, the SZI_snow can be either a complement or an improvement over the SZI for identifying, monitoring, and characterizing hydrological and agricultural droughts at various scales (e.g., 1-48 months) over high-latitude and high-elevation regions that receive snow.