I-Corps: A low cost GPS snow sensor

I-Corps：低成本 GPS 雪传感器

• 批准号: 1249009
• 负责人: Dennis Akos
• 金额: $ 5万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1249009&HistoricalAwards=false
• 关键词: Corps; low; cost; GPS; snow

Snow is an important component of both regional and global climate systems, as well as a critical storage component in the hydrologic cycle. Of particular interest is the snow water equivalent (SWE) which considers both depth and density. However, the methods and sensors currently available to measure SWE, or even just snow depth, are either restricted to a single point or expensive (e.g., aircraft based methods.) An emerging option for remotely determining snow depth is the utilization of existing networks of GPS base stations, as is currently being investigated by CU professors Kristine M. Larson (PI), Eric E. Small (co-PI), Mark W. Williams and Dennis M. Akos under NSF grants. Multipath, the reflection of signals off the ground and other nearby surfaces, is here used to estimate the snow depth over a comparably large area. However, this method has two drawbacks. A survey-grade basestation including receivers and antennas is expensive, around $20k, and since geodesy-type antennas are designed to mitigate low elevation signals the resulting quality of the estimates suffers compared to what could be offered by a custom solution. To overcome this, this project proposes the development and marketing of a snow sensor based on a mass-market GPS receiver coupled with a custom antenna, that potentially can be produced at low cost in reasonable quantities. The sensor also has the potential to be used to estimate soil moisture content and vegetation growth.At present, available snow depth information is temporally and/or spatially limited, the knowledge about the current state of the snow water equivalent potentially available for runoff is similarly limited. Thus, a widely-deployed network of affordable and accurate wide area snow sensors will increase the knowledge and understanding of the hydrologic cycle and potentially improve both climate and weather models. Furthermore, it may also prove valuable for water management facilities where snow levels are an important source of information when predicting droughts. Also, farmers and the tourism industry are also likely to benefit from the information this sensor can provide. It is possible that soil moisture and vegetation growth can also be measured with the snow sensor, however additional research effort will be required to assess the full capabilities. The prototypes and production units will "call home" with the raw data rather than provide measurements directly. The users can then access processed estimates online. This has several benefits; it offers a convenient method of accessing data, it allows for refinement of the algorithms as more data and insight becomes available and it is also possible, at the users' discretion, to make the data available to the scientific community.

雪是区域和全球气候系统的重要组成部分，也是水文循环中的关键储存组成部分。特别令人感兴趣的是考虑了深度和密度的雪水当量（SWE）。然而，目前可用于测量 SWE 甚至雪深的方法和传感器要么仅限于单点，要么价格昂贵（例如基于飞机的方法）。远程确定雪深的一个新兴选择是利用现有的 GPS 基站网络，CU 教授 Kristine M. Larson (PI)、Eric E. Small (co-PI)、Mark W. Williams 和 Dennis M. Akos 目前正在研究这一点。 在国家科学基金会的资助下。多路径，即信号离开地面和其他附近表面的反射，在这里用于估计相对较大区域的积雪深度。然而，这种方法有两个缺点。包括接收器和天线的勘测级基站价格昂贵，约为 2 万美元，而且由于大地测量型天线旨在减轻低海拔信号，因此与定制解决方案提供的估计质量相比，结果质量会受到影响。为了克服这个问题，该项目建议开发和营销基于大众市场 GPS 接收器和定制天线的雪传感器，该传感器可以以低成本、合理的数量进行生产。该传感器还有可能用于估计土壤湿度和植被生长。目前，可用的雪深信息在时间和/或空间上是有限的，有关潜在可用于径流的雪水当量当前状态的知识也同样有限。因此，广泛部署的经济实惠且准确的广域雪传感器网络将增加对水文循环的认识和理解，并有可能改善气候和天气模型。此外，它对于水管理设施也可能很有价值，因为雪位是预测干旱时的重要信息来源。此外，农民和旅游业也可能从该传感器提供的信息中受益。 土壤湿度和植被生长也可以用雪传感器测量，但是需要额外的研究工作来评估全部功能。原型和生产单位将使用原始数据“打电话回家”，而不是直接提供测量结果。然后，用户可以在线访问经过处理的估算。这有几个好处；它提供了一种访问数据的便捷方法，随着更多数据和洞察力的出现，它允许改进算法，并且根据用户的判断，还可以将数据提供给科学界。