Relationship between annual variations in GNSS data and snow depth: a case study of Tateyama Murodo, Toyama, from 2006 to 2023

We herein investigated the relationship between vertical displacement derived from Global Navigation Satellite System (GNSS) and snow depth in the Tateyama Murodo area, one of Japan’s heaviest snowfall regions. Previous studies assumed that annual snow loading is constant; however, snow depth in reality varies significantly from year to year, introducing interannual variability that may obscure tectonic signals. Using GNSS data from GNSS Earth Observation Network System (GEONET) station 041138 during the period from 2006 through 2023, we calculated elevation changes through Precise Point Positioning with Ambiguity Resolution (PPP-AR) analysis with GipsyX software. We then removed deformation caused by atmosphere, land water storage other than snow, and non-tidal ocean loadings using modeled surface mass loading. We also removed tectonic uplift (~ 4 cm over 17 years) using a piecewise linear approximation based on average elevation of snow-free periods (every August). Residual elevation changes showed annual uplift and subsidence of approximately 2–3 cm. Comparing the resulting deformation pattern with snow depth observations taken near the GNSS station by the Tateyama Caldera Sabo Museum and the University of Toyama, we found a clear negative relationship: greater snow depth led to increased subsidence. Model selection using Akaike Information Criterion (AIC) indicated little difference between linear and quadratic fits. Thus, we adopted a simpler linear relationship, which allows estimation of daily snow depth from GNSS elevation data. Remaining discrepancies are likely due to unaccounted variations in snow properties (e.g., density). While our study focuses on a single site with limited snow surveys, it demonstrates that GNSS-derived snow load effects can be quantitatively linked to seasonal signals. Future improvements may be achieved by incorporating satellite snow observations and advanced statistical modeling. Our findings highlight the utility of GNSS for improving geophysical monitoring accuracy and for contributing to snow hydrology and climate research.