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

GSTDTAP

项目编号	1449554
	PBO H2O: GPS Reflection-Based Climate Products for 2007-2017
	Kristine Larson
主持机构	University of Colorado at Boulder
项目开始年	2015
	2015-06-01
项目结束日期	2018-05-31
资助机构	US-NSF
项目类别	Continuing grant
项目经费	465007(USD)
国家	美国
语种	英语
英文摘要	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.
来源学科分类	Geosciences - Atmospheric and Geospace Sciences
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/68018
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Kristine Larson.PBO H2O: GPS Reflection-Based Climate Products for 2007-2017.2015.