One of the consequences of the aggression of the russian federation in Ukraine is a change in the properties of the surface layers of some regions of the soil. Construction of structures in such areas must be carried out considering the above. In order to create prerequisites for taking into account the artificially created layering of the soil, an algorithm for analytical determination of the stress-strain state of a two-layered soil was developed in a linear setting within the limits of plane deformation. Layers are considered as linear elastic bodies of limited dimensions in the plan. The algorithm is based on the Ery stress function with arbitrary coefficients, on the dependence of the indicators of the stress-strain state of the soil layers on it and on the mechanical indicators of the material of the layers, the thickness of the artificially formed surface layer. The algorithm provides for the formulation of the load conditions by the normally distributed force of part of the soil surface, the conditions of the interaction of the layers, and the unlimited thickness of the main soil layer.The listed conditions and features of the layers constitute a system of linear algebraic equations. The solution of the system of levels provides an opportunity to determine the coefficients of the stress function and, accordingly, to determine the indicators of the stress-strain state of the two-layer soil support. The generalization of the results, carried out by planning the experiment for the selected limits of possible realizations of the mechanical properties of the soil layers, allows for determining the deflections of the surfaces of the layers depending on individual factors.The following is established. The characteristics of the dependences of the deflections of the layer surfaces on other parameters are similar. Maximum deflections decrease with increasing surface layer thickness. Deflections of the interaction surface of the soil layers are linearly dependent on the Poisson ratio of the main soil layer and decrease as the ratio increases. The results obtained within the limits of the linear formulation can be considered sufficiently reliable because they are obtained analytically and generalized by the methods of the linear theory of elasticity and the method of planning the experiment.

Canopy resistance (rc) is a critical parameter for estimating vegetation transpiration. The site-specific rc can be calculated using the inversed Penman-Monteith (PM) equation with the effective leaf area index (LAI), which requires meteorological and turbulent flux data. The spatial distribution of rc is difficult to characterize due to the harsh environment of the Tibetan Plateau. The Jarvis-type model for modeling rc, described as a multiplicative function of environmental variables, has been widely used. However, the differences and optimization of different Jarvis-type models for alpine meadows have not been fully addressed. Consequently, our overall objective was to determine the appropriate functions for rc estimation and improve its accuracy for the alpine meadow ecosystem. Twelve Jarvis-type models composed of different stress functions were examined and compared with the observed rc calculated using PM equation at the Arou site in the northeastern Tibetan Plateau. The results suggest that the proper air temperature function and vapor pressure deficit function could improve model performance obviously. There was no obvious difference between the two different stress functions of downward shortwave radiation. The best model (M10), which was composed of an asymptotical function of downward shortwave radiation, a linear function of air temperature, an exponential function of vapor pressure deficit and a piecewise function of soil water content, had best performance with coefficient of determination of 0.93, root mean square error of 60.2 s m-1 and Nash-Sutcliffe efficiency coefficient of 0.92. The selection of proper stress functions is important for rc modeling. Models that considered the air temperature for rc calculations produced better results than those without temperature. The sensitivity analysis of rc to environmental variables indicated that rc was most sensitive to vapor pressure deficit, followed by LAI and downward shortwave radiation, whereas rc was less sensitive to soil water content. For all optimized parameters, rc was the most sensitive to kT (a fitting parameter for temperature), followed by kD (a fitting parameter for vapor pressure deficit) and rcmin (minimum rc under the optimal physiological condition). This study addresses the selection of proper stress functions in modeling rc for the alpine meadow site, which can also provide a reference for other ecosystems.