PHYSICALLY BASED EVALUATION OF SNOWPACK SENSITIVITY TO TEMPERATURE PERTURBATIONS IN EAST KAZAKHSTAN

Abstract

Seasonal snowpack is a critical regulator of water supply and flood risk in continental climates, yet its reliable assessment in Central Asia is constrained by sparse observations. This study applies the multilayer Snow Thermal Model, driven by ERA5-Land reanalysis, to simulate snowpack evolution in East Kazakhstan during the 2022-2023 season and evaluates its performance against snow depth and snow water equivalent observations from the Kazhydromet network.

The model reproduced snow accumulation, peak storage, and melt onset with high accuracy, achieving explained variance above 90%. Importantly, analysis of energy fluxes and stratigraphy revealed that more than half of simulated meltwater was produced under subfreezing air temperatures. Snowmelt is primarily controlled by positive surface energy balance dominated by net radiation and turbulent heat fluxes.

Perturbation experiments further highlight the disproportionate sensitivity of the snow regime to modest thermal changes. A uniform +2 °C warming reduced peak snow water equivalent by nearly one third and advanced melt onset by two to three weeks, while a −1 °C cooling increased snow storage and prolonged snow duration. These threshold-driven responses show that even small climatic deviations or biases in forcing data can shift runoff timing and seasonal water availability. For water managers, this implies that operational planning must explicitly account for temperature sensitivity, since minor departures from average conditions can trigger substantial changes in spring flood risk.

Overall, the study demonstrates that reanalysis-driven, physically based snow modeling provides robust diagnostics in data-scarce regions, surpassing empirical methods in both accuracy and explanatory power. The findings establish its importance for climate sensitivity analysis, flood preparedness, and water resource planning in snow-dominated basins.