Erin M.K. Haacker
Date of this Version
Dowell, B., 2022: Using Field Scale Electrical Data to Understand Real-Time Agricultural Water Delivery. M.S. thesis, Dept. of Earth and Atmospheric Sciences, University of Nebraska, 94 pp.
Areas across the High Plains (Ogallala) Aquifer region are experiencing unsustainable groundwater level declines and impacts to streamflow due to increasing human influence, posing challenges for sustaining future agricultural economies and groundwater resources. State and local agencies manage water using groundwater models, which are not at the same temporal and spatial scale as water management on farms. Well-informed agricultural water usage cannot be achieved without reliable and cost-effective water use at farm scale. Water meters are expensive and rarely installed unless required by the state or other regulatory agency; however, most center pivots have their own power supply, which reports real-time electricity consumption. Thus, finding novel ways of measuring real-time water usage from center pivot irrigation provides essential information to farmers and watershed managers balancing economic, sustainability, and governance decisions. This study leverages data gathered across the food-energy-water nexus by translating electrical measurements gathered in 15-minute time intervals on 10 center pivot agricultural production wells in western Nebraska into estimates of water delivery. Water delivery estimated using an electrical run-time algorithm and ultrasonic flow tests is found to be within 6.60% when compared to water delivery measured taken independently with calibrated flow meters. Translating electrical measurements from wells is an accurate way to estimate water withdrawals relative to the costs, but faces uncertainty arising from ultrasonic flow tests, field topography, and variable water delivery. Hydrologic modeling runs using the COHYST regulatory model for the Platte Basin demonstrated that errors in pumping on the scale of field-level estimated uncertainties can have a meaningful effect on estimated streamflow in the Platte River during peak pumping months, but that the model is constructed in a way that prevents assessment of the effects of the spatial distribution of pumping error. This novel data approach takes advantage of reliable and cost-effective data gathering across the rural electric smart grid to provide cost-effective food-energy-water solutions—supporting well-informed and economic use of water resources and models.
Advisor: Erin Haacker