CAREER: UAV-Based Radar Suite for Bulk-Snow Characterization and Risk Management

雪是地球气候系统的重要组成部分，因为它具有广泛的地表覆盖范围，并且作为冰冻圈的一部分发挥着关键作用。季节性积雪径流深刻地影响着流域融水的分布，融水约占全球淡水需求的六分之一，占山区水资源的大部分。此外，雪在大气和下层冰之间起着热毯的作用，这会对淡水湖生态系统产生影响，并在交通（如冰路持续时间）和冰塞洪水中发挥关键作用，因此具有社会经济影响。因此，研究这种水文循环是至关重要的，特别是模型预测表明，由于温室气体的持续排放和全球气温上升，未来将出现低至零降雪。然而，在捕捉这个循环的细节和水文后果方面存在很大的不确定性。这在一定程度上是因为在一些重要地区，如难以到达的山区，对分布的大块积雪特征没有足够的测量。这些观测差距限制了我们准确开发稳健的建模和预测技术以改进风险管理和减灾建议的能力。CAREER项目通过为小型无人机（UAV）配备先进的雷达套件，对积雪特性（如雪深、雪水当量、相对密度和液态水含量）进行精细的时空分辨率测量，满足了这一数据需求。综合研究和教育项目还包括准备、培训和激励下一代遥感工程师从事多学科职业。除了为本科生和研究生提供研究机会外，该项目还将把研究概念融入教学和实验课程，提供学术和体验学习机会。这个CAREER项目将包括开发一种新的基于无人机的定制穿雪传感器技术，以及对淡水湖泊和河流冰的雪水文和雪负荷进行研究。该技术研究将使用智能监测功能生成精细的时空数据，通过三维回波图形成淡水湖泊和河流的雪载荷和冰厚度，以及使用相干变化检测的雪的风重分布。这些数据将支持可操作的风险管理策略，从而大大改善居民的生活和社会经济弹性。CAREER项目还将把这项研究的各个方面纳入研究生和本科课程，包括通过设计-建造项目进行的体验式实验室练习，并且作为RISING学生大使研究（RISING STAR）计划的一部分，将涉及美国原住民和第一代学生，以增加对STEM职业的多样性和兴趣。该奖项反映了美国国家科学基金会的法定使命，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。

Snow is a crucial component of the earth’s climate system due to its extensive surface coverage and the critical role it plays as part of the cryosphere. Seasonal snowpack runoff profoundly influences the distribution of meltwater in watersheds, which constitutes roughly one-sixth of the freshwater needs globally, and the majority of water resources in mountainous regions. Moreover, snow acts as a thermal blanket between the atmosphere and underlying ice, that leads to impacts on freshwater lake ecosystems and has socioeconomic implications given its critical role in transportation (e.g., ice-road duration) and ice-jam flooding. Therefore, studying this hydrological cycle is crucial, especially with model projections suggesting a low-to-no snow future due to persistent emissions of greenhouse gases and globally rising temperatures. However, there are large uncertainties in capturing the fine details of this cycle and the hydrological consequences. This is partly because there are not enough measurements of distributed bulk-snow characteristics in important areas such as inaccessible mountainous regions. These observational gaps limit our ability to accurately develop robust modeling and forecasting techniques for improved risk management and hazard mitigation proposes. This CAREER project addresses this data need by equipping a small unmanned aerial vehicle (UAV) with an advanced radar suite to produce fine spatial and temporal resolution measurements of snowpack properties such as snow depth, snow water equivalent, relative density, and liquid water content. The integrated research and education project also involves preparing, training, and exciting the next generation of remote sensing engineers to engage in multi-disciplinary careers. Beyond providing research opportunities to undergraduate and graduate students, this project will integrate research concepts into the teaching and laboratory curriculum, providing academic and experiential learning opportunities.This CAREER project would encompass both the development of a new UAV-based custom snow-penetrating sensor technology and enable research on the snow hydrology and snow-loading on freshwater lakes and river ice. The technological research will produce fine spatiotemporal data using intelligent monitoring capabilities that allow mapping of snow loading and ice thickness on freshwater lakes and rivers through 3D echogram formation and wind redistribution of snow using coherent change detection. These data would support actionable risk management strategies that drastically improve residents’ lives and socioeconomic resiliency. The CAREER project will also incorporate aspects of this research in graduate and undergraduate courses, includes experiential laboratory exercises through design-build projects, and as part of the RISING STudent Ambassador Research (RISING STAR) program will involve Native American and first-generation students to increase diversity and interest in STEM careers.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.