Collaborative Research: EAGER: IMPRESS-U: Groundwater Resilience Assessment through iNtegrated Data Exploration for Ukraine (GRANDE-U)

IMPRESS-U项目由美国国家科学基金会、爱沙尼亚研究委员会（ETAG）、拉脱维亚科学委员会（LCS）、立陶宛研究委员会（LMT）、波兰国家科学中心（NCN）、美国国家科学院和美国海军全球研究办公室（DoD）联合资助。这项研究将在一个多边国际伙伴关系中进行，该伙伴关系将联合加利福尼亚大学圣地亚哥分校、新墨西哥州立大学（美国）、乌克兰水文气象研究所和基辅（乌克兰）塔拉斯舍甫琴科国立大学、波兰地质研究所和华沙（波兰）波兰科学院空间研究中心、拉脱维亚大学里加（拉脱维亚）、维尔纽斯大学维尔纽斯（立陶宛）和塔尔图大学塔尔图（爱沙尼亚）。美国部分的合作工作将由NSF （ise /OD、GEO/RISE和GEO/EAR）共同资助。地下水是全世界一半人口饮用水的重要来源，而这一资源正面临枯竭的威胁。准确评估其数量和质量是全球和国家长期面临的挑战，特别是在跨界区域。该项目旨在提高我们对地下水动力学的理解，并在精细的时空尺度上实现水平衡的综合参数化。这些结果对乌克兰特别有用，因为乌克兰地面观测网的局限性需要基于遥感的更有效的评估技术。为了建立准确和及时的含水层评估算法和模型，该项目将汇集来自六个国家的研究人员：美国、乌克兰、波兰、拉脱维亚、立陶宛和爱沙尼亚。这一努力的核心是强有力的国际合作，连接不同的跨界人类系统和相互联系的自然含水层系统，将加速任何单一学科或国家都无法单独取得的进展。同时，该项目将培养下一代研究领导者，并支持乌克兰现代创新生态系统的共同发展。该项目将开创一种全面的跨学科方法，对跨界环境下地表水和地下水相互作用进行精细建模。我们提出的技术解决方案将水文地质模型与卫星和地面数据相结合，使水资源评估能够在不确定的情况下进行决策。项目的主要组成部分包括：(1)为乌克兰和该区域的伙伴国家的跨界地区建立卫星和原位地下水和地表水观测资料和其他有关数据层的综合空间数据库；(2)创建新的算法，利用更高分辨率的地质、原位、地形和土地覆盖数据，在地面观测足够密集的地区缩小重力恢复和气候实验后续任务（GRACE-FO）卫星遥感数据的比例；(3)训练人工智能模型，将水平衡参数化扩展到GRACE-FO数据与原位观测数据不相关的地区；(4)利用参与式建模方法，形成概念化和模拟，以评估不同水文地质和水利用情景下的含水层恢复力；(5)利用该项目的研究设计，建立一个国际参与的劳动力发展计划，以实现乌克兰最先进的创新基础设施。虽然研究任务的相互依赖性、创新性和探索性使项目具有高风险，但它在改变我们对跨界地下水评价和管理的认识和实践方面具有很大的潜在影响。该奖项反映了美国国家科学基金会的法定使命，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。

This IMPRESS-U project is jointly funded by NSF, Estonian Research Council (ETAG), Latvian Council of Science (LCS), Research Council of Lithuania (LMT), National Science Center of Poland (NCN), US National Academy of Sciences, and Office of Naval Research Global (DoD). The research will be performed in a multilateral international partnership that unites the University of California-San Diego, New Mexico State University (US), Ukrainian Hydrometeorological Institute and Taras Shevchenko National University, Kyiv (Ukraine), Polish Geological Institute and Space Research Center of the Polish Academy of Sciences, Warsaw (Poland), University of Latvia, Riga (Latvia), Vilnius University, Vilnius (Lithuania), and University of Tartu, Tartu (Estonia). US portion of the collaborative effort will be co-funded by NSF OISE/OD, GEO/RISE, and GEO/EAR).Groundwater is a critical source of drinking water for half of the population worldwide, and this resource is under threat of depletion. An accurate assessment of its quantity and quality is a persistent global and national challenge, particularly in transboundary regions. This project seeks to improve our understanding of groundwater dynamics and achieve integrated water balance parameterization at fine spatial and temporal scales. The results will be especially useful to Ukraine, where the limitations of the ground observation network require more efficient assessment techniques based on remote sensing. To build algorithms and models for accurate and timely aquifer assessment, this project will bring together researchers from six countries: the United States, Ukraine, Poland, Latvia, Lithuania, and Estonia. The robust international collaboration at the core of this effort, bridging distinct transboundary human systems and connected natural aquifer systems, will accelerate progress that no single discipline or nation could achieve alone. Simultaneously, this project will foster the next generation of research leaders and support the co-development of a modern innovation ecosystem in Ukraine.This project will pioneer a comprehensive transdisciplinary approach to fine-scale modeling of surface water and groundwater interactions in a transboundary setting. Our proposed technical solution integrates hydrogeologic models with satellite and ground-based data to enable water resource assessment for decision-making under uncertainty. The main project components include: (1) Developing a consolidated spatial database of satellite and in-situ groundwater and surface water observations and other relevant data layers for transboundary areas in Ukraine and the partner countries in the region; (2) Creating novel algorithms to downscale satellite remote sensing data from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission using higher-resolution geologic, in-situ, topographic, and land cover data, over areas with sufficiently dense ground observations; (3) Training artificial intelligence models to extend water balance parameterization to areas where GRACE-FO data have not been correlated with in-situ observations; (4) Generating conceptualizations and simulations to assess aquifer resilience under different hydrogeologic and water use scenarios, using participatory modeling approaches; and (5) Building an internationally-engaged workforce development program leveraging the project's research design to enable state-of-the-art innovation infrastructure in Ukraine. While the research tasks' interdependent, innovative and exploratory nature make the project high-risk, it promises a high potential impact in transforming our understanding and the practice of transboundary groundwater assessment and management.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.