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

PFI-TT：水资源管理重力卫星观测系统

• 批准号: 2044704
• 负责人: Yu Zhang
• 金额: $ 25万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2044704&HistoricalAwards=false
• 关键词: PFI; TT; Gravity; Satellite; Observation

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.

这一创新-技术转化伙伴关系项目具有更广泛的影响/商业潜力，其目的是使一种新的卫星观测技术商业化，从而能够更好地管理美国和世界上其他气候紧张地区的地下水资源。该项目的重点是加州州，因为其农业生产在2017年超过500亿美元，是美国最大的。技术转化将使加州水资源部和537个特殊水域的开支减少10- 20%，每年可节省高达1000万美元，并对其经济发展产生积极影响。该团队将培养一个代表性不足的学生，使他们能够实现他们作为企业家的愿望，并通过他们的创新发明丰富人们的生活。 该技术有可能开发一系列输出，提供用户友好的本地信息，用于及时监测自然灾害，制定可持续方案，保护美国和世界的脆弱地区和人口。该项目旨在利用市场需求，高效和有效地监测加州州和美国和世界其他气候紧张地区日益稀缺的水资源。加州60%的供水来自地下水，地下水是主要的灌溉水源。极端天气的加剧了干旱频繁造成的过度抽水造成的水资源短缺。该项目的智力价值包括一项新技术的示范、原型制作和商业化，利用美国宇航局/GFZ重力恢复和气候实验后续（GRACE-FO）重力测量卫星的数据，能够全天候和及时地量化加州的地下水储存。该团队将通过采用机器学习算法，使用额外的数据集集合来有效地缩小及时的地下水估计，从而改善大的空间数据差距限制并提高时间分辨率。该技术可以通过缩小数据差距和有效遵守与用水有关的法律，显著降低水管理和使用的费用。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Global groundwater droughts are more severe than they appear in hydrological models: An investigation through a Bayesian merging of GRACE and GRACE-FO data with a water balance model

• DOI: 10.1016/j.scitotenv.2023.169476
• 发表时间: 2023-12-29
• 期刊: SCIENCE OF THE TOTAL ENVIRONMENT
• 影响因子: 9.8
• 作者: Forootan，Ehsan;Mehrnegar，Nooshin;Shum，C. K.
• 通讯作者: Shum，C. K.

Bridging the gap between GRACE and GRACE-FO missions with deep learning aided water storage simulations

通过深度学习辅助储水模拟缩小 GRACE 和 GRACE-FO 任务之间的差距

• DOI: 10.1016/j.scitotenv.2022.154701
• 发表时间: 2022
• 期刊: Science of The Total Environment
• 影响因子: 9.8
• 作者: Uz, Metehan;Atman, Kazım Gökhan;Akyilmaz, Orhan;Shum, C.K.;Keleş, Merve;Ay, Tuğçe;Tandoğdu, Bihter;Zhang, Yu;Mercan, Hüseyin
• 通讯作者: Mercan, Hüseyin