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.

湿地景观是河流和湿地相互连接的挂毯，对于维持水质标准至关重要，但由于开发和农业扩张，其覆盖范围在最近几个世纪急剧减少。这些系统去除污染物（如硝酸盐）的程度可以极大地影响下游水生系统。在理解动态连接的作用，以拦截，保留，并通过湿地传输硝酸盐和自然和人类特征控制这些连接的强度的程度存在重大差距。这些差距限制了预测这些景观的功能并有效管理它们的能力。这项研究将提供一个机会，终身助理教授和一名研究生，通过与俄亥俄州辛辛那提的USEPA环境测量和建模中心（CEMM）合作，推动在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科学家合作，他们已经建立了基于科学过程的模型，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。PI的家乡堪萨斯已经失去了48%的湿地覆盖率。因此，这项工作将改善基础设施的建模，管理和维护这些景观功能，通过教育学生和开放获取工具的开发与流域科学家，管理人员和利益相关者共享。这个研究基础设施改善轨道-4 EPSCoR研究员（RII轨道-4：NSF）项目将提供奖学金，以助理教授和培训的研究生在堪萨斯大学。这项工作将与美国环保局环境测量和建模中心的研究人员合作进行。从历史上看，湿地连通性和污染物去除效率一直是通过静态透镜来看待的，由于数据和计算的限制，经常提出的问题是"在季节或年度期间硝酸盐的净去除量是多少？"。然而，河流和湿地是高度动态的，存在不成比例的去除发生的时刻。此外，人们往往认为，湿地景观的功效是由水文，这决定了硝酸盐的停留时间，但最近的工作表明，在个别湿地规模，去除出现高，相比，整体去除减少的集水区规模的功效之间的脱节。存在两个知识差距，拟议的研究旨在关闭。第一个是超越静态的连通性评估，第二个是自然和人类控制的湿地去除效率的评价。该研究小组将与美国环保署环境测量和建模中心（CEMM）的研究人员合作，他们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，但它们通常在单一位置高度参数化，难以跨尺度转移。因此，该项目的研究目标是（1）使用深度学习模型和基准来改善动态湿地景观连通性的表示，该模型的性能与现有的基于过程的模型相比较;（2）使用跨区域和尺度的深度学习建模来量化水文，人为和地貌对硝酸盐去除效率的控制。这个RII Track-4：NSF奖学金将为PI提供一个机会，使其在识别、量化和预测湿地景观行为方面取得根本性进展，并开发利益相关者可以用于改善土地管理成果的工具。该奖项反映了NSF的法定使命，并被认为值得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来支持。