Abstract
Understanding the relationship between droughts and drought awareness is vital towards decision making and policy for water management and conservation strategies, and socioeconomic outcomes. We used computer vision (UNet models) to analyze nonlinear, heterogeneous, lagged correlations between Standardized Precipitation Evapotranspiration Index (SPEI) and Google Trends Search Interest within the Continental United States (CONUS). The most important drivers of the relationship between drought occurrence and drought awareness are the variability and ranges of drought trends and severity, as well as extreme drought conditions. This relationship was the strongest for Western states, followed by Northeastern, Southeastern, and Central regions. Search interest tends to lag droughts by a period of̃1-3 months. We also found evidence that reductionist linear approaches, such as a Principal Component Analysis, might not be as effective as UNet models in capturing the nuanced relationship between droughts and drought awareness at various dimensions and scales.
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