Institute: Colorado
Year Established: 2025 Start Date: 2026-01-01 End Date: 2026-12-31
Total Federal Funds: $10,000 Total Non-Federal Funds: $10,000
Principal Investigators: Ryan Smith
Project Summary: Groundwater is a crucial natural resource in arid Colorado, where environmental and human activities have increasingly strained this subsurface freshwater supply. Assessing these impacts requires long-term groundwater use records, which remain scarce. This study will leverage a satellite data and physics-informed machine learning method to estimate effective precipitation ? a key flux in irrigation dynamics ? and consumptive groundwater use for irrigation in Colorado from 1985 to 2024, generating the first long-term, annual-scale high-resolution (~2 km) datasets of this kind for the region. Using these datasets, a Bayesian Probabilistic Approach (BPA) will be developed to quantify how weather, economic, and policy factors have historically influenced farmers' decision of groundwater use across Colorado. Next, building on this framework, the BPA will be extended to simulate how changes in weather patterns, economic factors, and policy decisions may impact groundwater use for irrigation in the future. Findings from this study will provide data-driven insights to support sustainable groundwater management, helping policymakers, water agencies, and farmers make informed decisions to safeguard Colorado's water future.