An assessment of the ecological state of the water area of the Volga-Kama reach of the Kuibyshev reservoir in the area of the Saralinsky section of the Volga-Kama Reserve were carried out. Thus, according to hydrobiological indicators, the water quality were estimate as “moderately polluted” – “polluted”, which corresponded to the third class.

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Introduction. Nonparametric Bayesian networks are a promising tool for analyzing, visualizing, interpreting and predicting the structural and dynamic characteristics of complex systems. Modern interdisciplinary research involves the complex processing of heterogeneous data obtained using sensors of various physical nature. In the study of the forest fund, both methods of direct dendrological measurements and methods of remote observation using unmanned aerial vehicles are widely used. Information obtained using these methods must be analyzed in conjunction with hydrometeorological monitoring data.Aim. Investigation of the possibility of automating the monitoring of the well-being of the forest fund based on the integration of ground survey data, remote multispectral measurements and hydrometeorological observations using the mathematical apparatus of nonparametric Bayesian networks.Materials and methods. To assess the long-term joint dynamics of natural and climatic indicators and the radial growth of trees, a modified method of multiscale cross-correlation analysis was used with the removal of the background trend described by the moving average model. Relationships between various indicators were estimated based on the unconditional and conditional nonparametric Spearman correlation coefficients, which were used to reconstruct and parameterize the nonparametric Bayesian network.Results. A multiscale nonparametric Bayesian network was constructed to characterize both unconditional and conditional statistical relationships between parameters obtained from remote sensing, hydroclimatic and dendrological measurements. The proposed model showed a good quality of the plant fund state forecasting. The correlation coefficients between the observed and predicted indicators exceed 0.6, with the correlation coefficient comprising 0.77 when predicting the growth trend of annual tree rings.Conclusion. The proposed nonparametric Bayesian network model reflects the relationship between various factors that affect the forest ecosystem. The Bayesian network can be used to assess risks and improve environmental management planning.

In a changing climate, forest ecosystems become increasingly vulnerable to the continuously exacerbating heat and drought stress conditions. Climate stress resilience is governed by a complex interplay of global, regional and local factors, with hydrological conditions among the key roles. Using a modified detrended partial cross-correlation analysis (DPCCA), we analyze the interconnections between long-term tree ring width (TRW) data and regional climate variations at various scales and time lags. By comparing dendrochronological series of Scots pine trees near the southern edge of the boreal ecotone, we investigate how local hydrological conditions affect heat and drought stress resilience of the forest ecosystem. While TRW are negatively correlated with spring and summer temperatures and positively correlated with the Palmer drought severity index (PDSI) in the same year indicating that heat waves and droughts represent the limiting factors, at inter-annual scales remarkable contrasts can be observed between areas with different local hydrological conditions. In particular, for the sphagnum bog area positive TRW trends over several consecutive years tend to follow negative PDSI trends and positive spring and summer temperature trends of the same duration with a time lag between one and three years, indicating that prolonged dry periods, as well as warmer springs and summers appear beneficial for the increased annual growth. In contrast, for the surrounding elevated dry land area a reversed tendency can be observed, with pronounced negative long-term correlations with temperature and positive correlations with PDSI. Moreover, by combining detrending models and partial correlation analysis, we show explicitly that the long-term temperature dependence could be partially attributed to the spurious correlations induced by coinciding trends of the trees ageing and climate warming, while contrasts in correlations between TRW and PDSI become only further highlighted, indicating the major impact of the local hydrological conditions on the drought stress resilience.