Abstract
Accurate modeling of snowpack dynamics is essential for understanding seasonal water balance and mitigating flood risks in snow-dominated regions. This study presents the first documented application of the physically based SNTHERM model in East Kazakhstan, focusing on the Shemonaikha district, a flood-prone area characterized by a temperate continental climate and significant seasonal snow accumulation. The SNTHERM model has been forced with ERA5-Land reanalysis data and validated against in situ observations of snow depth and Snow Water Equivalent (SWE) from the Kazhydromet network. Results based on statistical evaluation indicate that SNTHERM effectively replicates the seasonal evolution of snow depth (R2 = 0.949; RMSE = 0.07 m), although it tends to overestimate SWE, which may be attributed to systematic biases in the reanalysis input data, including elevation mismatches and limited representation of local conditions in ERA5-Land. Despite these discrepancies, the results demonstrate that reanalysis-driven simulations can yield physically realistic snowpack estimates in under-monitored continental regions. The findings support the broader application of SNTHERM for snowmelt forecasting and flood risk reduction, offering transferable insights for other cold-climate basins across Central Asia.