Topic Areas: Government and social policy; Drought Risk Management; Data integration and statistics

Abstract

We are developing an advanced Geospatial Decision Support System (GDSS) to improve the quality and accessibility of drought related data for drought risk management. This is part of a Digital Government project aimed at developing and integrating new information technologies for improved government services in the USDA Risk Management Agency (RMA) and the Natural Resources Conservation Service (NRCS). Our overall goal is to substantially improve RMA's delivery of risk management services in the near-term and provide a foundation and directions for the future. We integrate spatio-temporal knowledge discovery techniques into our GDSS using a combination of data mining techniques applied to rich, geospatial, time-series data. Our data mining objectives are to: 1) find relationships between user-specified target episodes and other climatic events and 2) predict the target episodes. Understanding relationships between changes in soil moisture regimes and global climatic events such as El Niño could provide a reasonable drought mitigation strategy for farmers to adjust planting dates, hybrid selection, plant populations, tillage practices or crop rotations. This work highlights the innovative data mining approaches integral to our project's success and provides preliminary results that indicate our system’s potential to substantially improve RMA's delivery of drought risk management services