CAREER: Understanding the dynamics and predictability of land-to-aquatic nitrogen loading under climate extremes by combining deep learning with process-based modeling

全球作物产量的增加在很大程度上依赖于氮肥的投入。然而，在陆地上过量的N的应用已经深刻地影响了水生生态系统，导致富营养化，缺氧（死区），并在河口和近岸海域有害藻华（HABs）。尽管在了解水文系统中的氮运输方面取得了很大进展，但在极端水文气候（主要是干旱和洪水）下有效管理水质方面仍然存在挑战。该项目解决了水文学和地球系统科学中的一个基本问题：河流氮负荷对极端气候的反应如何变化，为什么？该项目的重点是密西西比河上游-俄亥俄河流域（UMORB），该地区贡献了美国50%以上的玉米和大豆产量，以及从密西西比河-阿查法拉亚河流域到墨西哥湾的45%的氮通量。该项目的研究成果将提高对上游陆地生态系统中自然扰动和人类管理如何改变N命运的理解，并将为未来更频繁的极端气候下减少从陆地到河流到沿海海洋的N负荷带来新的见解。这将导致开发新的地球系统模型，并为“气候智能型”水管理奠定坚实的基础。该项目将弥合科学与实践之间的差距，并向公众传播地球系统建模的最新知识。该研究团队将开发一个流域到建模（M2M）的学习平台，以“选择与机遇”的在线流域游戏为特色，使下一代科学家、土地管理者、政策制定者和选民能够更具体地了解流域管理的复杂概念。该研究的总体目标是了解、量化和预测陆地到水生氮负荷如何应对水文气候极端情况。该项目将数据驱动的深度学习方法与基于过程的水文生态建模相结合，以表征和表示氮负荷的跨尺度气候敏感性，并预测整个景观中水文氮损失的缓解范围。研究人员将综合广泛的高频水质监测数据，遥感图像，以及美国中西部土地利用和管理历史的时间序列地理空间数据，以揭示氮流对各种极端事件的恢复机制。基于混合深度学习过程的建模框架将建立我们对人类和自然耦合系统中水文N运动动态的预测能力。混合模型，然后将被应用于评估如何有效的流域管理的做法，什么是一个合理的N负荷减少的目标，以寻求在该领域达到减少缺氧程度的目标，在考虑极端气候事件的海湾，以及在“漏桶”的漏洞。该项目由水文科学和促进竞争性研究的既定计划（EPSCoR）共同资助。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。

Increases in global crop yield largely rely on nitrogen (N) fertilizer input. However, excessive N applications on land have profoundly impacted aquatic ecosystems, leading to eutrophication, hypoxia (dead zones), and harmful algal blooms (HABs) in estuarine and near-shore seas. Despite great progress in understanding N transport in hydrologic systems, challenges still exist in effectively managing water quality under hydroclimate extremes (mainly drought and floods). This project addresses a fundamental issue in hydrology and Earth system science: How varied are river N loads in response to climate extremes, and why? The project focuses on the Upper Mississippi-Ohio River Basin (UMORB), a region contributing over 50% of the U.S. corn and soybean production, and 45% of the N flux from the Mississippi-Atchafalaya River Basin to the Gulf of Mexico. Research outcomes from this project will improve understanding of how the fate of N is altered by natural perturbation and human management in upstream land ecosystems and will bring new insights for reducing N loads from land to rivers to coastal oceans under more frequent climate extremes in the future. It will result in the development of novel Earth system models and lay a solid foundation for “climate-smart” water management. The project will bridge a gap between science and practice and disseminate the most current knowledge of Earth system modeling to the public. The team will develop a Monitoring-to-Modeling (M2M) learning platform, featuring an online watershed game of “choice and chance,” to make the complex concept of watershed management more concrete for the next-generation of scientists, land managers, policy makers, and voters.The overarching goal of the research is to understand, quantify and predict how land-to-aquatic N loadings respond to hydroclimate extremes. This project will blend data-driven deep learning approaches with process-based Hydro-Ecological modeling to characterize and represent cross- scale climate sensitivity of N loadings and predict the mitigation range of hydrological N loss across the landscape. The investigators will synthesize extensive high-frequency water quality monitoring data, remote sensing images, as well as the time-series geospatial data of land use and management history in the Midwestern U.S. to unravel the mechanisms underlying N flow resilience to various extreme events. The hybrid deep learning-process based modeling framework will build up our predictive capability for the dynamics of hydrological N movement in a coupled human and natural system. The hybrid model will then be applied to assess how effective watershed management practices are, what is a reasonable N load reduction goal to be sought in the field to reach the goal of reducing hypoxia extent in the Gulf with consideration of extreme climate events, and where are holes in “the leaky bucket.”This project is jointly funded by Hydrologic Sciences and the Established Program to Stimulate Competitive Research (EPSCoR).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.