CAREER: Understanding the dynamics and predictability of land-to-aquatic nitrogen loading under climate extremes by combining deep learning with process-based modeling
职业:通过将深度学习与基于过程的建模相结合,了解极端气候下陆地到水生氮负荷的动态和可预测性
基本信息
- 批准号:1945036
- 负责人:
- 金额:$ 61.03万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-05-15 至 2025-04-30
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
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.
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。
项目成果
期刊论文数量(6)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Increased extreme precipitation challenges nitrogen load management to the Gulf of Mexico
极端降水增加对墨西哥湾的氮负荷管理提出了挑战
- DOI:10.1038/s43247-020-00020-7
- 发表时间:2020
- 期刊:
- 影响因子:7.9
- 作者:Lu, Chaoqun;Zhang, Jien;Tian, Hanqin;Crumpton, William G.;Helmers, Mathew J.;Cai, Wei-Jun;Hopkinson, Charles S.;Lohrenz, Steven E.
- 通讯作者:Lohrenz, Steven E.
Half‐Century History of Crop Nitrogen Budget in the Conterminous United States: Variations Over Time, Space and Crop Types
美国本土作物氮收支半个世纪的历史:随时间、空间和作物类型的变化
- DOI:10.1029/2020gb006876
- 发表时间:2021
- 期刊:
- 影响因子:5.2
- 作者:Zhang, Jien;Cao, Peiyu;Lu, Chaoqun
- 通讯作者:Lu, Chaoqun
Heavy Precipitation Impacts on Nitrogen Loading to the Gulf of Mexico in the 21st Century: Model Projections Under Future Climate Scenarios
21世纪强降水对墨西哥湾氮负荷的影响:未来气候情景下的模型预测
- DOI:10.1029/2021ef002141
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Zhang, Jien;Lu, Chaoqun;Crumpton, William;Jones, Christopher;Tian, Hanqin;Villarini, Gabriele;Schilling, Keith;Green, David
- 通讯作者:Green, David
Extreme climate increased crop nitrogen surplus in the United States
- DOI:10.1016/j.agrformet.2021.108632
- 发表时间:2021-11
- 期刊:
- 影响因子:6.2
- 作者:Jien Zhang;Chaoqun Lu;H. Feng;D. Hennessy;Yong Guan;M. Wright
- 通讯作者:Jien Zhang;Chaoqun Lu;H. Feng;D. Hennessy;Yong Guan;M. Wright
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Chaoqun Lu其他文献
AgBIS: A Blockchain-enabled Crop Insurance Platform Against Adverse Selection, Moral Hazard, and Insurance Frauds
AgBIS:基于区块链的农作物保险平台,防止逆向选择、道德风险和保险欺诈
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Zhonghao Liao;Chaoqun Lu;Mark Mba Wright;Hongli Feng;Yong Guan - 通讯作者:
Yong Guan
Biochar stimulates nitrogen loss in anoxic soil through ammonium oxidation coupled with iron reduction
生物炭通过与铁还原偶联的铵氧化作用刺激缺氧土壤中的氮损失
- DOI:
10.1016/j.geoderma.2025.117372 - 发表时间:
2025-07-01 - 期刊:
- 影响因子:6.600
- 作者:
Bo Yi;Qichun Zhang;Steven J. Hall;Xiang Zou;Wenjuan Huang;Wenjuan Yu;Qinsi He;Peiyu Cao;Jing Hou;Jiuwei Song;Hongjie Di;Chaoqun Lu - 通讯作者:
Chaoqun Lu
Pre-crystallisation applied in sequential deposition approaches to improve the photovoltaic performance of perovskite solar cells
预结晶应用于顺序沉积方法以提高钙钛矿太阳能电池的光伏性能
- DOI:
10.1016/j.jallcom.2019.153616 - 发表时间:
2020-08 - 期刊:
- 影响因子:6.2
- 作者:
Yulong Zhang;Zhaoyi Jiang;Weijia Zhang;Lanqin Yan;Chaoqun Lu;Cong Ni - 通讯作者:
Cong Ni
Manure nitrogen production and application in cropland and rangeland during1860–2014: A 5-minute gridded global data set for Earth system modeling
1860-2014 年农田和牧场粪肥氮的生产和应用:用于地球系统建模的 5 分钟网格全球数据集
- DOI:
- 发表时间:
2017 - 期刊:
- 影响因子:0
- 作者:
Bowen Zhang;H. Tian;Chaoqun Lu;S. Dangal;Jia Yang;S. Pan - 通讯作者:
S. Pan
Riverine nitrogen footprint of agriculture in the Mississippi–Atchafalaya River Basin: do we trade water quality for crop production?
密西西比-阿查法拉亚河流域农业的河流氮足迹:我们是否以水质换取农作物生产?
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:6.7
- 作者:
Chaoqun Lu;Jien Zhang;Bo Yi;Ignacio Calderon;Hongli Feng;R. Miao;David Hennessy;S. Pan;Hanqin Tian - 通讯作者:
Hanqin Tian
Chaoqun Lu的其他文献
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{{ truncateString('Chaoqun Lu', 18)}}的其他基金
Collaborative Research: A Physics-Informed Flood Early Warning System for Agricultural Watersheds with Explainable Deep Learning and Process-Based Modeling
合作研究:基于物理的农业流域洪水预警系统,具有可解释的深度学习和基于过程的建模
- 批准号:
2243775 - 财政年份:2023
- 资助金额:
$ 61.03万 - 项目类别:
Standard Grant
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