RII Track-4: Canary in the Watershed: Concentration-Discharge Relationships as a Sentinel of Change

RII Track-4：分水岭中的金丝雀：浓度-排放关系作为变革的哨兵

• 批准号: 1929148
• 负责人: Adam Wymore
• 金额: $ 18.22万
• 依托单位: University of New Hampshire
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-01 至 2022-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1929148&HistoricalAwards=false
• 关键词: RII; Track; Canary; Watershed; Concentration

The goal of this project is to improve understanding of how climate and watershed characteristics interact to control solute and sediment export and ultimately water quality. The export of material from watersheds to river networks and downstream receiving ecosystems is influenced by a myriad of factors including climate, lithology, hydrology, soil type and porosity and vegetation, among others. These factors and their complex interactions make developing predictive models of watershed solute and sediment export difficult and synthesizing the required data for continental-scale analyses is wrought with computational challenges. The overarching vision of this EPSCoR Track 4 project is to synthesize and analyze spatially and temporally rich data sets to develop a more holistic understanding of how material export (expressed as concentration-discharge relationships: C-Q) changes across broad environmental gradients including climatic variability. Understanding these processes has direct implications for water quality and determining how watersheds and ecosystems will respond to climate-based perturbations. Results will be shared through peer-reviewed manuscripts, international conferences, and a series of departmental seminars. The proposed work offers unique training opportunities for an early-career faculty member and a female post-doctoral scholar.The analysis of concentration-discharge (C-Q) relationships provides a holistic and systems-level approach to assess where material is stored within a watershed and the internal and external controls that regulate the mobilization of this material from the landscape to river networks. Putting C-Q behavior into a predictive framework is extremely challenging however, as factors vary with space and time. Traditional approaches to data synthesis and analytics may be inadequate to fully take advantage of novel data resources and to quantify, predict and describe complex dynamics at the earth?s surface. This project will address this challenge by taking a big data and data synthesis approach to 1) build predictive continental-scale models of C-Q behavior and 2) to understanding how C-Q behavior responds to climate variability. This project will advance the field by examining the efficacy of multiple predictive algorithms including machine learning based approaches which may be able to handle the unique challenges associated with big environmental data. The outputs of this project will help to stimulate competitive research at the University of New Hampshire including the future use of high-frequency environmental sensor technology.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.

该项目的目标是提高对气候和流域特征如何相互作用以控制溶质和沉积物输出并最终控制水质的了解。从流域到河网和下游接收生态系统的物质出口受到各种因素的影响，包括气候、岩性、水文、土壤类型和孔隙度以及植被等。这些因素及其复杂的相互作用使得开发流域溶质和沉积物输出的预测模型变得困难，并且合成大陆尺度分析所需的数据具有计算挑战。EPSCoR Track 4项目的总体愿景是综合和分析空间和时间上丰富的数据集，以更全面地了解材料出口(表示为浓度-排放关系：C-Q)如何跨越包括气候变异性在内的广泛环境梯度的变化。了解这些过程对水质和确定流域和生态系统将如何应对基于气候的扰动具有直接影响。成果将通过同行评审的手稿、国际会议和一系列部门研讨会分享。这项拟议的工作为一名早期教师和一名女性博士后奖学金提供了独特的培训机会。集中-排放(C-Q)关系的分析提供了一种全面的和系统级的方法来评估分水岭内材料的存储位置，以及管理这些材料从景观到河流网络动员的内部和外部控制。然而，将C-Q行为纳入预测框架是极其具有挑战性的，因为因素会随着空间和时间的不同而变化。传统的数据合成和分析方法可能不足以充分利用新的数据资源，并量化、预测和描述地球表面的复杂动力学--S。该项目将通过采用大数据和数据综合的方法来应对这一挑战：1)建立C-Q行为的大陆尺度预测模型；2)了解C-Q行为如何响应气候变化。该项目将通过检查多种预测算法的有效性来推动这一领域的发展，其中包括基于机器学习的方法，这些方法可能能够应对与大数据相关的独特挑战。该项目的成果将有助于刺激新汉普郡大学的竞争性研究，包括未来高频环境传感器技术的使用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

Nitrogen wet deposition stoichiometry: the role of organic nitrogen, seasonality, and snow

• DOI: 10.1007/s10533-022-00966-0
• 发表时间: 2022-08
• 期刊: Biogeochemistry
• 影响因子: 4
• 作者: D. Murray;Michelle D. Shattuck;W. McDowell;A. Wymore

Quantifying the frequency of synchronous carbon and nitrogen export to the river network

量化同步碳氮输出到河网的频率

• DOI: 10.1007/s10533-020-00741-z
• 发表时间: 2021
• 期刊: Biogeochemistry
• 影响因子: 4
• 作者: Wymore, Adam S.;Fazekas, Hannah M.;McDowell, William H.
• 通讯作者: McDowell, William H.

The Lamprey River Hydrological Observatory: Suburbanization and changing seasonality

七鳃鳗河水文观测站：郊区化和季节性变化

• DOI: 10.1002/hyp.14131
• 发表时间: 2021
• 期刊: Hydrological Processes
• 影响因子: 3.2
• 作者: Wymore, Adam S.;Shattuck, Michelle D.;Potter, Jody D.;Snyder, Lisle;McDowell, William H.
• 通讯作者: McDowell, William H.

Priorities for synthesis research in ecology and environmental science

生态学和环境科学综合研究的重点

• DOI: 10.1002/ecs2.4342
• 发表时间: 2023
• 期刊: Ecosphere
• 影响因子: 2.7
• 作者: Halpern, Benjamin S.;Boettiger, Carl;Dietze, Michael C.;Gephart, Jessica A.;Gonzalez, Patrick;Grimm, Nancy B.;Groffman, Peter M.;Gurevitch, Jessica;Hobbie, Sarah E.;Komatsu, Kimberly J.
• 通讯作者: Komatsu, Kimberly J.

The Environmental Responsibility Framework: A Toolbox for Recognizing and Promoting Ecologically Conscious Research

环境责任框架：认可和促进生态意识研究的工具箱

• DOI: 10.1029/2022ef002964
• 发表时间: 2023
• 期刊: Earth's Future
• 作者: Murray, Desneiges S.;Cole, Isabel;Nunez, Nicholas;Parker, Eric M.;Mikulis, Anna;Herreid, Allison M.;Donovan, Mitchell;Fazekas, Hannah M.;Wymore, Adam S.
• 通讯作者: Wymore, Adam S.