PFI-TT: Gravity Satellite Observation System for Water Resource Management

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The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project aims at the commercialization of a novel satellite observational technology, which would enable better management of groundwater resources in the United States and other climate-stressed regions in the world. The project focuses on the State of California as its agricultural productions exceeded $50 billion in 2017 - the largest in the United States. The technology translation would reduce the expenses of California’s Department of Water Resources and 537 Special Water Districts by 10–20%, which could net a savings of up to $10 million a year and positively impact its economic development. The team will train a underrepresented students, enabling them to realize their aspiration as entrepreneurs and enriching people’s lives with their innovative inventions. The technology is likely to develop a range of outputs to provide user-friendly local information for timely monitoring of natural hazards, formulating sustainable programs, and protecting vulnerable areas and populations in the United States and the world. The project seeks to capitalize on the market need to efficiently and efficiently monitor increasingly scarce water resources in the State of California and other climate-stressed regions in the United States and the world. Sixty percent of California’s water supply is from groundwater, which is the main irrigation source. Intensifying extreme weather exacerbates the scarcity of water due to excessive pumping due to frequent droughts. The intellectual merits of this project include the demonstration of a novel technology, its prototyping, and commercialization, to enable all-weather and timely quantification of groundwater storage in California using data from NASA’s/GFZ’s Gravity Recovery And Climate Experiment Followon (GRACE-FO) gravimetry satellites. The team will ameliorate large spatial data gap limitations and improve temporal resolutions by employing machine learning-enabled algorithms using an ensemble of additional datasets to effectively downscale timely groundwater estimates. The technology may significantly reduce the expense of water management and usage by closing the data gap and enabling efficient compliance with laws pertaining to water usage.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.