Filtering properties and local energy distribution in different classes of periodic micro-structured elastic systems are analysed in this work. Out-of-plane wave propagation is considered in continuous and discrete elastic systems arranged in series and parallel. Filtering properties are determined from the analysis of dispersion diagrams and energy distribution within different phases in the representative unit cell. These are determined analytically by implementing a transfer matrix formalism. The analysis given in the work indicates quantitatively how to couple phases, having discrete and continuous nature, in order to tune wave propagation and energy localisation.

Based on data regarding the snow water equivalent, daily average air temperatures, and daily precipitation values obtained from meteorological stations in the East Kazakhstan Region of the Republic of Kazakhstan, calculations of the temperature coefficients of snowmelt have been performed. The study was based on long-term observations, which increases the reliability of the identified patterns. The analysis allowed for the identification of the features of both spatial and temporal differentiation of this coefficient, as well as the establishment of possible causes for its variations. Probable errors arising during the calculations were identified, and the prospects for using the obtained results to determine the maximum snow water reserves in the study basin were considered. It was established that the nature of snowmelt is influenced not only by daily average temperatures and precipitation amounts. This underlines the need to further expand the range of parameters considered and to improve the methodological approaches to estimating snow water reserves. The conclusions drawn may prove useful for water resource planning and flood forecasting under changing climatic conditions, as well as for optimizing the region’s hydrological models.

Existing hydrologic landscape classifications, such as Hydrologic Landscape Regions (HLRs) and Hydrologic Response Units (HRUs), group catchments by similarity of climate, geology, and topography, but do not directly link this classification to runoff-generation function, limiting their use for ranking hydrological activity in data-sparse mountain regions. The concept of a landscape–hydrological background, integrating climatic–hydrological, soil–hydrological, and topo–hydrological components, was used as the basis for a classification framework applied to 15 model catchments in the East Kazakhstan Region. The proposed approach provides a functional representation of landscape organization by linking environmental characteristics directly to runoff-generation processes and hydrological activity. The analysis revealed two distinct hydrological domains: a runoff-generating mountain domain (Bukhtarma, Uba, and Ulba), where highly active hydrological landscapes occupy 55.2–63.9% of the catchment area, and a transitional mountain-foothill domain (Narym, Kurchum, and Kalzhyr), where their share decreases to 18.0–25.4%. The highest landscape diversity, with Shannon indices of 1.31–1.53, occurs in the transitional catchments, reflecting the most balanced combination of functional groups. This study demonstrates that hydrological activity is controlled by the combined influences of catchment size, relief differentiation, and the spatial extent of high-mountain environments rather than by elevation alone. The proposed framework has practical applications for watershed management, water resources planning, environmental monitoring, and climate change adaptation, and is particularly valuable for data-sparse mountain regions.

Snowpack dynamics in continental climates are important for water-resource monitoring and snow water equivalent (SWE) estimation, yet the response of the snow depth–snow water equivalent (SD-SWE) relationship to changing thermodynamic and accumulation forcing remains insufficiently understood. This study develops a process-based framework to evaluate how moderate perturbations in air temperature and precipitation influence snowpack evolution and depth–mass coupling in representative snow regimes of northeastern Kazakhstan. SNTHERM (the Snow Thermal Model) simulations were combined with regression analysis, ANCOVA diagnostics, and bulk-density evaluation under controlled delta-change perturbations of air temperature (±1–2 °C) and precipitation (±5–10%). The results show that the SD-SWE relationship remains approximately linear within the tested perturbation range (R² ≈ 0.78–0.84), although its parameters are partially sensitive to precipitation-driven accumulation. Temperature perturbations mainly affect melt timing, seasonal persistence, and snow-density redistribution, whereas precipitation modifies snowpack mass and overburden, enhancing mechanical compaction and increasing the regression slope. These findings indicate that snow density is a key integrative state variable linking energy balance, phase change, and compaction processes. Under the tested conditions, snow depth remains a physically consistent proxy for SWE, although the conclusions are limited by the one-dimensional model structure, reanalysis-based forcing, and restricted observational coverage.

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.

We consider a microstructural lattice model consisting of two springs of different stiffness and identical mass. The dynamic behavior of the elastic system is investigated analytically and compared with numerical solutions. The results for the dispersion relation of the model have demonstrated obvious advantages associated with the manipulation of elastic wave propagation. Based on the model, a non-reciprocal device design is introduced by incorporating a gyroscopic spinner into one of the isolated masses. A particular non-reciprocal effect for transmitted waves due to the coupling effect is verified based on the theoretical concept supported by the computation of two mutual work between the applied forces.