Our team is involved in the Water Intelligence thrust of this project. Wise stewardship and management of water resources can be facilitated by answering two key questions where human factors are pronounced. How does precipitation translate to spatiotemporal water availability? How can we optimize water allocation to make it available when and where it's most needed?
We have begun multiple projects to approach these questions from several directions. We are developing models to improve streamflow forecasting to understand the spatiotemporal availability of water, machine learning methods to predict which fields are followed to gain insight into water needs and decisions, and developing new satellite-imagery based spatially explicit data products of snowmelt period, snow off data, snow water equivalent and soil moisture. These will be critical inputs for models.
In other projects, we are analyzing the VIC-CropSyst model to understand how climatic changes might trigger phase shifts in water availability and crop yields. We are working to evaluate the accuracy of seasonal forecasts available in the IBM PAIRS platform for our region of interest and reviewing the uses of AI and agent-based modeling for agricultural water use.
Ag-AID Website
