PBO H2O: GPS Reflection-Based Climate Products for 2007-2017

该项目的目标是制作一个长达十年的土壤湿度、雪深、雪水当量（SWE，从雪深得出）和地球镜板块边界观测站（PBO）许多站点的植被含水量数据集。 PBO是一个覆盖美国西部的1,100个GPS站的大地测量网络，旨在研究太平洋和北美构造板块之间边界区的地质。 虽然安装GPS接收器是为了监测板块运动，但PI在早期的一项裁决（1144221）中表明，通过一种称为GPS干涉反射测量法（GPS-IR）的方法，测量结果也可用于估算上述地表水文变量。 GPS-IR利用直接从发射卫星接收的GPS信号与在到达接收器之前从地面反射的信号之间的干扰。 反射信号对土壤和地表植被的含水量以及积雪引起的有效地表高度变化很敏感。 上一个奖项下的工作产生了一个数据集，数据始于2011年。本奖项下的工作通过将记录期延长到2007年并将每个站点的采样率提高到每日或亚每日频率来增强现有数据集。实现这些目标的关键是使用L2 P“传统”GPS信号。 由于L2 P信号是加密的，因此更难以使用，因此现有的数据集是使用未加密的L2 C信号开发的，这些信号自2011年以来一直在PBO站点进行跟踪。 数据集的向后扩展将通过开发更复杂的算法来完成，这些算法可用于2007年开始的PBO存档中的L2 P信号。 另一个问题是，PI最初开发的算法仅限于从接收器以南的天空中有点狭窄的楔形的卫星位置传输的信号。算法的改进现在允许使用来自更广泛方向的传输，从而能够将采样率增加到每日或亚每日频率。 该奖项下的资金支持开发和应用新的算法来增强数据集，验证增强的数据与现场测量数据，比较L2 P和L2 C信号生成的数据，生成长达十年的气候学，以及在整个奖项期间持续收集数据。资助这项工作的主要动机是数据集对研究人员，政策制定者，和与地表水文学有关的利益相关者。 地表水文变量，如土壤湿度，积雪深度和植被含水量是了解水循环的必要条件，但它们很难制造，只有稀疏可用。 鉴于和平与建设组织已经完全建立起来，并为其他目的提供资金，和平与建设组织的全球定位系统台站是一个难得的机会。 因此，水文数据大大提高了网络的价值，而增加的实地费用最少。 除了其科学价值外，鉴于西部各州对水资源的担忧，这些数据具有高度的社会相关性。 此外，该项目将支持一名研究生和一名博士后，并将通过NSF支持的学生固体地球科学研究经验（RESSES）暑期项目支持学生实习生。在该项目下开发的算法和数据将在网上免费提供。

The goal of this project is to produce a decade-long dataset of soil moisture, snow depth, snow water equivalent (SWE, derived from snow depth), and vegetation water content at many of the stations of the EarthScope Plate Boundary Observatory (PBO). The PBO is a geodetic network with 1,100 GPS stations covering the Western US, designed to study the geology of the boundary zone between the Pacific and North American tectonic plates. Although the GPS receivers were installed to monitor plate movements, the PIs showed under an earlier award (EAR-1144221) that the measurements can also be used to produce estimates for the above surface hydrology variables through a method called GPS Interferometric Reflectometry (GPS-IR). GPS-IR uses interference between GPS signals received directly from the transmitting satellite and signals which are reflected from the ground before arriving at the receiver. The reflected signal is sensitive to the moisture content of the soil and surface vegetation, as well as the change in effective surface height due to snow cover. Work under the previous award produced a dataset with data beginning in 2011.Work under this award augments the existing dataset by extending the period of record back to 2007 and by increasing the sampling rate at each site to daily or sub-daily frequency. The key to accomplishing these goals is the use of the L2P "legacy" GPS signals. Because the L2P signals are encrypted and thus more difficult to use, the existing dataset was developed using the unencrypted L2C signals, which have been tracked at the PBO sites since 2011. The backward extension of the dataset will be accomplished by developing more sophisticated algorithms which can be used with the L2P signals, available in the PBO archive starting in 2007. A further issue is that the algorithms initially developed by the PIs were limited to use on signals transmitted from satellite positions in a somewhat narrow wedge of the sky to the south of the receiver. Improvements to the algorithms now allow the use of transmissions from a much wider range of directions, enabling an increase of the sampling rate to daily or sub-daily frequency. Funds under this award support the development and application of new algorithms to augment the dataset, validation of the augmented data against in-situ measurements, comparisons between data generated from the L2P and L2C signals, generation of decade-long climatologies, and continued data collection throughout the period of the award.The primary motivation for funding this work is the value of the dataset for researchers, policy makers, and stakeholders concerned with surface hydrology. Surface hydrological variables like soil moisture, snow depth, and vegetation water content are essential for understanding the water cycle, yet they are difficult to make and only sparsely available. The GPS stations of the PBO constitute a remarkable opportunity, given that the PBO is already fully established and funded for other purposes. The hydrological data thus substantially enhances the value of the network with minimal additional field costs. Beyond its scientific value, the data has high societal relevance given concerns in the western states regarding water resources. In addition, the project will support a graduate student and a postdoctoral associate, and will also support student interns through the NSF-supported Research Experiences in Solid Earth Sciences for Students (RESSES) summer program. Algorithms and data developed under the project will be freely accessible online.