RII Track-4: NSF:Assessing Dynamic Connectivity of Streams and Wetlands across Spatial and Human Gradients with Deep Learning

RII Track-4：NSF：利用深度学习评估跨空间和人类梯度的溪流和湿地的动态连通性

• 批准号: 2229616
• 负责人: Admin Husic
• 金额: $ 25.95万
• 依托单位: University of Kansas Center for Research Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229616&HistoricalAwards=false
• 关键词: RII; Track; NSF; Assessing; Dynamic

Wetlandscapes, the interconnected tapestries of streams and wetlands, are crucial for maintaining water quality standards, yet their coverage has dramatically decreased in recent centuries due to development and agricultural expansion. The extent to which these systems remove pollutants, such as nitrate, can greatly impact downstream aquatic systems. There exist major gaps in understanding the dynamic connections that act to intercept, retain, and transmit nitrate through wetlandscapes and the extent to which natural and human features control the strength of these connections. These gaps have limited ability to predict the function of these landscapes and manage them effectively. This research will provide an opportunity for a tenure-track assistant professor and a graduate student to propel research in the area of modeling wetlandscape connectivity and water quality through collaborations with the USEPA Center for Environmental Measurement and Modeling (CEMM) in Cincinnati, Ohio. The PI and a student will work with CEMM scientists, who have established state-of-the-science process-based models, to advance the modeling frontier with a deep learning modeling framework that can overcome the limitations of existing approaches. The PI’s home jurisdiction, Kansas, has lost 48% of its wetland coverage. Thus, this effort will improve infrastructure for modeling, managing, and maintaining these landscape features through the education of students and the development of open access tools to be shared with watershed scientists, managers, and stakeholders. This Research Infrastructure Improvement Track-4 EPSCoR Research Fellows (RII Track-4:NSF) project would provide a fellowship to an Assistant Professor and training for a graduate student at the University of Kansas. This work would be conducted in collaboration with researchers at the USEPA Center for Environmental Measurement and Modeling (CEMM). Historically, wetlandscape connectivity and contaminant removal efficiency has been viewed through a static lens whereby, due to data and computational limitations, the question often posed is “What is the net removal of nitrate over seasonal or annual periods?”. However, streams and wetlands are highly dynamic and there exist moments where disproportionate removal occurs. Further, it is often assumed that wetlandscape efficacy is controlled by hydrology, which determines the residence time of nitrate, but recent work shows a disconnect between efficacy at the individual wetland-scale, where removal appears high, compared to the catchment scale where overall removal decreases. Two knowledge gaps exist that the proposed research aims to close. The first is a move beyond a static assessment of connectivity and the second is an evaluation of the natural and human controls of wetlandscape removal efficiency. The research team will collaborate with researchers at the USEPA Center for Environmental Measurement and Modeling (CEMM) who have developed state-of-the-science, process-based wetland models. While process-based models can be effective tools, they are often highly parameterized to a single location and difficult to transfer across scales. Thus, the research objectives of this project are to (1) improve the representation of dynamic wetlandscape connectivity with a deep learning model and benchmark that model performance against existing process-based models and (2) quantify the hydrologic, anthropogenic, and geomorphic controls on nitrate removal efficacy using deep learning modeling across regions and scales. This RII Track-4:NSF fellowship will provide an opportunity for the PI to generate fundamental advances to identify, quantify, and predict wetlandscape behavior and to develop tools that stakeholders can use for improved land management outcomes.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.

湿地景观是相互联系的溪流和湿地的织锦，对维持水质标准至关重要，但由于发展和农业扩张，它们的覆盖率在最近几个世纪大幅下降。这些系统去除硝酸盐等污染物的程度可能会对下游水生系统产生很大影响。在理解通过湿地截留、保留和传输硝酸盐的动态联系以及自然和人为特征控制这些联系强度的程度方面，存在着重大差距。这些差距对预测这些景观的功能和有效管理它们的能力有限。这项研究将为终身教职助理教授和研究生提供机会，通过与位于俄亥俄州辛辛那提的美国环保局环境测量和建模中心(CEMM)的合作，推动湿地景观连通性和水质建模领域的研究。PI和一名学生将与CeMM的科学家合作，他们已经建立了基于科学过程的最先进的模型，通过可以克服现有方法局限性的深度学习建模框架来推进建模前沿。少年派的家乡堪萨斯州已经失去了48%的湿地覆盖率。因此，这项工作将通过教育学生和开发开放获取工具，与分水岭科学家、管理人员和利益攸关方共享，改善建模、管理和维护这些景观特征的基础设施。研究基础设施改进Track-4 EPSCoR研究学者(RII Track-4：NSF)项目将为堪萨斯大学的一名助理教授提供奖学金，并为一名研究生提供培训。这项工作将与美国环保局环境测量和建模中心(CeMM)的研究人员合作进行。在历史上，湿地景观的连通性和污染物去除效率一直是通过静态的视角来看待的，因此，由于数据和计算的限制，经常提出的问题是“季节或年度期间硝酸盐的净去除是多少？”然而，溪流和湿地是高度动态的，存在发生不成比例的去除的时刻。此外，人们通常认为湿地景观的功效是由水文学控制的，而水文决定了硝酸盐的停留时间，但最近的研究表明，与总去除减少的集水尺度相比，个别湿地尺度的功效之间存在脱节，在湿地尺度上，硝酸盐的去除似乎很高。拟议中的研究旨在弥合两个知识差距。第一个是超越对连通性的静态评估，第二个是对湿地景观移除效率的自然和人为控制的评估。研究小组将与美国环保局环境测量与建模中心(CeMM)的研究人员合作，后者开发了最先进的、基于过程的湿地模型。虽然基于流程的模型可能是有效的工具，但它们通常高度参数化到单个位置，难以跨比例传输。因此，本项目的研究目标是(1)通过深度学习模型和基准改进动态湿地景观连通性的表示，并对照现有的基于过程的模型对性能进行建模；(2)使用跨区域和跨尺度的深度学习模型来量化水文、人为和地貌对硝酸盐去除效果的控制。RII Track-4：NSF奖学金将为PI提供机会，以产生识别、量化和预测湿地景观行为的根本进展，并开发利益相关者可用于改善土地管理结果的工具。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。