Abstract
This study presents a comprehensive quantitative analysis of the interplay between meteorological variables and soil conditions over the period 2018–2023 in the Almaty region. The investigation is grounded in high-resolution meteorological observations encompassing air temperature (mean annual variations ranging from -5°C to +30°C), atmospheric pressure (average values between 681 and 685 hPa), and wind velocity (0.5–12 m/s). Additionally, a systematic evaluation of soil characteristics was conducted to assess seasonal fluctuations in soil composition and physicochemical properties across spring, summer, and autumn. By employing advanced statistical methodologies, significant correlations between meteorological dynamics and soil parameters were elucidated. The findings reveal that the most pronounced deviations in soil conditions occur during the spring season, exhibiting a deviation coefficient of 0.7896, whereas the summer season demonstrates the most substantial negative deviation at -0.9566. Furthermore, the predictive model developed within this study exhibits high precision, yielding a coefficient of determination (R²) of 0.85, thereby enabling not only the real-time assessment of ecosystem status but also the reliable forecasting of its temporal evolution. The novelty of this research lies in the integration of classical Navier-Stokes equations with contemporary big data analytics, facilitating a sophisticated representation of atmospheric flow dynamics and their consequential impact on soil properties. This interdisciplinary approach enhances the accuracy of predictive environmental modeling, offering a robust framework for ecosystem monitoring and management.