WANG Jiatong, HU Yufeng, LI Zhenhong, ZHANG Chenglong, ZHANG Miaomiao, YANG Jing, JIANG Wandong. Rapid Estimation of Snow Water Equivalent Using GPS-IR Observations[J]. Geomatics and Information Science of Wuhan University, 2021, 46(11): 1666-1676. DOI: 10.13203/j.whugis20210199
Citation: WANG Jiatong, HU Yufeng, LI Zhenhong, ZHANG Chenglong, ZHANG Miaomiao, YANG Jing, JIANG Wandong. Rapid Estimation of Snow Water Equivalent Using GPS-IR Observations[J]. Geomatics and Information Science of Wuhan University, 2021, 46(11): 1666-1676. DOI: 10.13203/j.whugis20210199

Rapid Estimation of Snow Water Equivalent Using GPS-IR Observations

  •   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 (ClimateNA) 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.
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