ITR: Intelligent Joint Evolution of Data and Information:An Integrated Framework for Drought Monitoring and Mitigation

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Water is a strategic resource in the U.S. and impacts the sustainability and livability of both rural and urban communities. It is therefore critical to build monitoring and early warning systems for hydrologic events that inventory water resources and accurately model its usage and its impact on communities across multiple scales. Droughts have long been associated with Great Plain's environments from the "Dust Bowl" years. Many drought indicators such as the Palmer Drought Severity Index and Standardized Precipitation Index have been developed to model the intensity (severity) of meteorological drought. However, drought is a complex process that also has hydrological, agricultural, and socioeconomic components. Hydrological drought indicators are often treated as point observations in watersheds, reservoirs, lakes, and streams and these indicators have not been extended to spatio-temporal drought regions, reflecting the natural landscape. We propose an integrated framework that views droughts through various windows that can provide higher resolution, better detect emergence and closure of events, as well as their spatio-temporal impacts. The framework is based on an innovative methodology called the Intelligent Joint Evolution of Data and Information (IJEDI) that evolves data and information at multiple temporal (windows) and spatial scales to derive support decisions. In this methodology, data and information are considered as raw materials and products, respectively, with which the concepts of Quality of Data (QoD) and Quality of Information (QoI) are computed. This research will develop an information science-based framework to model droughts as they evolve from meteorological episodes to agricultural and hydrologic events.