Search

Snow water equivalent (SWE) is one of the important snow parameters, and is of great significance to climate change prediction and water resource management. GPS interferometric reflectometry (GPS-IR) is an effective technique for snow depth monitoring. Combined with the snow density model, the SWE can be estimated. An efficient framework to enhance the snow parameters estimation with GPS-IR dual-frequency data was presented. Firstly, by applying GPS-IR to the dual-frequency signal to noise ratio (SNR) data recorded by two Plate Boundary Observatory (PBO) GPS stations AB33 and P019 in water years of 2016 and 2020, the corresponding daily measurements of reflector heights were obtained. Secondly, based on the linear relationship between the L1 and L2 C signal reflector heights, a simple linear model to calibrate the L1 to L2 C signal results was proposed. Finally, the calibrated reflector heights were used to obtain the daily snow depth measurements, which were then converted to SWE by an empirical snow density model. By using in-situ observations from Snow Telemetry (SNOTEL) sites, the results show that the precision of L2 C signal snow depth measurement is better than that of L1 signal; the precision of the snow depth measurements from the enhanced method is moderate but closer to the precision of L2 C signal; the precision of SWE from the enhanced method is comparable to that of L2 C signal, and both are better than that of L1 signal; the dual-frequency enhanced method effectively fills 25.8% and 13.7% data gaps in the L2 C signal snow depth/SWE time series at stations AB33 and P019, respectively. The proposed method makes full use of the GPS dual-frequency SNR data resources, and can obtain high-continuity GPS snow depth and snow water equivalent, which provides data for the effective estimation of snow water equivalent and water environment monitoring in areas with poor equipment. [ABSTRACT FROM AUTHOR]