SCC-CIVIC-PG Track B: Developing Low Power Wide Area Sensor Networks to Improve Cold Region Disaster Prediction and Management in the Fairbanks North Star Borough

北极和亚北极正在经历由快速气候变化引发的气候变暖带来的重大环境变化。该项目的目标是将阿拉斯加州费尔班克斯北星区从事土地管理、保护和备灾的当地组织与来自阿拉斯加大学费尔班克斯的研究人员聚集在一起，开发基于地点、数据驱动的社区复原力指标和干预措施，使阿拉斯加人能够更好地预测和应对与融化有关的洪水、融化土壤下沉和野火等天气和气候驱动事件。这些危害对当地生态系统来说是自然的，但最近这些事件的频率、强度和持续时间的增加挑战了缓解或预防战略的弹性。传统的传感器网络需要高能耗和大天线系统来实现远程安装所需的范围，这导致了稀疏的覆盖，特别是在人口较少的地区。较新的技术，如低功耗广域(LPWA)网络，提供了一种解决方案来克服成本和覆盖范围问题，从而大幅改进了观测网络。该项目旨在确定使用LPWA的可行性，支持收集社区最感兴趣和最有用的数据类型，并改进建模、规划和决策。在覆盖范围稀少的地区提供与气候相关的关键变量的一致、定期观测，从而能够更好地为灾害管理、政策和决策机制提供信息。探索LPWA测量传感器网络将扩大对在寒冷地区规划与气候变化相关的威胁的实际应用至关重要的科学和技术知识，例如极端降雪和洪水以及冻土融化造成的基础设施破坏。学生和社区成员将在网络发展过程中接受培训，并将学习如何应对寒冷地区常见的气候挑战。长期成果将被用作STEM教育的材料，并与直接受益的利益攸关方分享。预计新的LPWA网络将成为农村和郊区社区收集数据的替代方法，从而在世界各地寒冷地区实现高效的资源消耗、灾害管理和更好的土地利用。在费尔班克斯北星区测试这一应用程序，在那里可以进行数据验证，最终将使许多更偏远、服务不足的地区受益。该项目是对公民创新挑战计划的回应，B轨道-自然灾害的恢复力-是NSF和国土安全部的合作。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。

The Arctic and subarctic are experiencing significant environmental shifts from rapid climate change induced warming. The goal of this project is to bring together local organizations engaged in land management and protection and disaster preparedness in the Fairbanks North Star Borough, Alaska, with researchers from the University of Alaska, Fairbanks to develop place-based, data-driven community resilience indicators and interventions that will better enable Alaskans to predict and respond to weather and climate driven events such as thaw-related flooding, subsidence of thawed soils, and wildfire. These hazards are natural to the local ecosystem, but recent increases in the frequency, intensity, and duration of these events have challenged the resilience of mitigation or prevention strategies. Conventional sensor networks require high energy consumption and large antenna systems to achieve the range required for remote installations, which has resulted in sparse coverage, especially in low population areas. Newer technologies like Low Power Wide Area (LPWA) networks provide a solution to overcome both cost and range issues, allowing for substantial improvements in observation networks. This project aims to determine the feasibility of using LPWA, support the collection of data types of greatest interest and utility to the community, and improve modeling, planning, and decision making.Providing consistent, regular observations of crucial climate-related variables in areas of sparse coverage allows for the ability to better inform disaster management, policy, and decision-making mechanisms. Exploring LPWA networks of measurement sensors will expand scientific and technical knowledge crucial for practical applications in planning for climate-change-related threats in cold regions, such as extreme snowfall and flooding and infrastructure damage due to frozen ground thaw. Students and community members will be trained during the course of the development of the network, and will learn to address climate challenges common to cold regions. Long-term outcomes will be used as materials for STEM education and shared with stakeholders who will directly benefit. The new LPWA networks are expected to serve as an alternative method for rural and suburban communities to collect data, leading to efficient resource consumption, disaster management, and better land use in world-wide cold regions. Testing this application in the Fairbanks North Star Borough, where data validation is possible, will ultimately benefit many regions that are even more remote and underserved. This project is in response to the Civic Innovation Challenge program, Track B—Resilience to Natural Disasters—and is a collaboration between NSF and the Department of Homeland Security.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.