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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]

Objectives: Snow water equivalent (SWE) plays a key role in climate change prediction, water resource management and agricultural production planning. GPS interferometric reflectometry (GPS IR) has been proven to be a powerful tool to monitor snow depth. An efficient framework is presented to rapidly estimate SWE from GPS IR derived snow depth. Methods: Firstly, the daily snow depth product is obtained using GPS observations with GPS IR technique. Secondly, an SWE conversion model is constructed using snow depth, SWE and climate observations from snow telemetry (SNOTEL) stations in the study region. Finally, using the climate forecast data provided by the historical and projected climate data for North America (Climate-NA) project as parameter constraints, the daily GPS snow depth product is converted into SWEs. Results: The application of the proposed framework to GPS data from the plate boundary observatory (PBO), USA shows that GPS IR derived snow depth product is reliable (R2 = 0.98, RMSE (root mean square error) = 11.1 cm, Bias =-3.7 cm).The daily GPS snow depth can be converted into SWE with high reliability (R² = 0.98, RMSE = 4.2 cm, Bias =-2.5 cm) and stability. The rapid conversion model has high spatiotemporal stability with most of the residuals lying with the range of [-5, 5] cm. The additional uncertainties introduced by the climate forecast data and the spatial variations of snow depths have limited impacts on the estimation of SWE. Conclusions: It is believed that the proposed framework can not only provide guidance to rapidly estimate SWE inregions lacking snow monitoring equipment, but also provide a reference to enhance existing snow observation network and improve accumulated snow products. [ABSTRACT FROM AUTHOR]