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

在美国，水是一种战略资源，影响着农村和城市社区的可持续性和宜居性。因此，建立水文事件监测和早期预警系统至关重要，该系统可以对水资源进行清查，并在多个尺度上准确模拟水资源的使用及其对社区的影响。从“沙尘暴”年代起，干旱就一直与大平原的环境联系在一起。许多干旱指标，如帕尔默干旱严重指数和标准化降水指数，已经被开发出来模拟气象干旱的强度（严重程度）。然而，干旱是一个复杂的过程，也有水文、农业和社会经济因素。水文干旱指标通常被视为流域、水库、湖泊和溪流的点观测，这些指标尚未扩展到时空干旱区域，反映自然景观。我们提出了一个综合框架，通过各种窗口来观察干旱，可以提供更高的分辨率，更好地检测事件的出现和关闭，以及它们的时空影响。该框架基于一种称为数据和信息智能联合演化（IJEDI）的创新方法，该方法在多个时间（窗口）和空间尺度上演化数据和信息，以获得支持决策。在这种方法中，数据和信息分别被视为原材料和产品，并以此计算数据质量（QoD）和信息质量（qi）的概念。这项研究将开发一个基于信息科学的框架，以模拟干旱从气象事件演变为农业和水文事件的过程。

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.