Modern features of the annual flow distribution of the Volga Basin were analyzed. Statistical analysis of the annual flow data for the spring, summer-autumn, and winter seasons were carried out to study the changes of the water regime of the Volga Basin Rivers in 1946–2010, 1946–1977, and 1978–2010 observation years. Using the data of 207 stations, new maps of seasonal flow were created and changes in the natural river flow regulation were shown. In recent decades, the water regime of the rivers in European Russia and their annual flow distribution has changed greatly. A statistically significant decrease in the river flow irregularity after 1946 has been revealed for the most of the Volga Basin Rivers. It resulted in the increase in the natural regulation index φ, change of the seasonal flow, primarily in low-water period, and especially in winter. The increase in the φ index during the last 30 years is approximately 30%, compared to the similar previous period. Such a change of the φ value took place due to the reduction of the spring flood flow and the increased significance of the groundwater flow. In the second half of the 1970s, the rivers of the Volga Basin were characterized as “snowfeeding”; at the end of the XXth century, they became the rivers with “mixed feeding” or even “mixed feeding with prevailing groundwater feeding” according to B.D. Zaikov’s river classification. This has resulted in the significant increase in the natural flow regulation comparable to the effect of seasonal storage reservoirs.

Within the joint Russian-Austrian monitoring programme “REFCOND_VOLGA (2006 – 20XX)”, monitoring sites were established in the headwaters of the Volga (Tver Region). River Tudovka, a right tributary to the Volga River, was included within this monitoring programme as its catchment is partly protected and has only few anthropogenic activities. The monitoring activities include physico-chemical and hydraulic parameters as well as biota with a focus is on benthic organisms (diatoms and macrozoobenthos). In this work, the longitudinal patterns in community structure are classified in the lowland river Tudovka using a novel feature-based approach taken from signal processing theory. The method first clusters field sampling data into longitudinal classes (upper, middle, lower course). Community features based on the relative frequency of individual species occurring per class are then generated. We apply both generative and discriminative classification methods. The application of generative methods provides data models which predict the probability of a new sample to belong to an existing class. In contrast, discriminative approaches search for differences between classes and allocate new data accordingly. Leveraging both methods allows for the creation of stable classifications. On this basis we show how the community features can be used to predict the longitudinal class. The community features approach also allows for a robust cross-comparison of investigation reaches over time. In cases where suitable long-term data set are available, predictive models using this approach can also be developed.

Improper land management, such as over-grazing in arid areas, has negative effects on the local ecosystems for both the short and the long term time periods. An effective rehabilitation scheme requires human interference by introducing ecosystem engineering organisms together with activities that encourage the spreading and the reproduction of the local plant and animal species. Most of the former studies in arid lands focused on shrubs as engineering species, and much less on other organisms. The major focus of this study was on assessing the impact of Messor ebeninus and M. arenarius on the micro-topographic patterns of arid areas using unique spatial statistical tools designed solely for this purpose. As a case study, the nests’ sizes and their distribution were compared between two adjacent shrublands with similar geographic outlines during 2008 and 2015.One of the shrublands was moderately grazed for the last 20 years (at the far past it was exposed to over-grazing), while the other one is still exposed to over-grazing. The results collected in 2014 at the shrublands and at the adjacent loess area demonstrate the spatial ecosystem ability of the Messor sp. to engineer and beneficially modify their environment by enlarging the water conserving area, increasing the soil fertility and vegetative productivity, and finally accelerating the whole area rehabilitation.

This paper assessed the vagaries of climatic elements on crop yield in Kwara State with a view to predicting the future climatic suitability level for selected crops in the state. Descriptive and infrential statistics analytical methods were used to examine the pattern of climatic elements for a period of 30 years. Analysis of variance was used to examine the variations in crop yield and also to determine whether or not significant differences in the harvests of the period under investigation. Correlation analysis was used to determine the relationship between climatic elements and crop yield while multiple regression analysis was used to determine the contribution of each climatic elements to crop yield. Time series analysis was used to project crop yield from 2014 to 2025. GAEZ model was adopted to determine the climatic suitability for the selected crops over time 1960 - 2050 and ArcGIS 10.3 software was used to produce the crop suitability maps. The result revealed that cassava, yam, maize and cowpea would be less suitable for production with the rate at which the climate is changing. The result also revealed that the climatic suitability level for cassava, yam, maize and cowpea would reduce drastically with time. The prediction shows severe impacts of changes in the selected climatic elements on both overall climatic suitability and crop the selected crops yield for by 2050.

Sharp differences in natural environment and resources and demographic and economic characteristics in Russia place special importance on analysis of regional models of transition to “green economy.” Regional model of transition to“green economy” for the Khanty-Mansi Autonomous Okrug-Ugra is discussed. This region is situated in Siberia and is leading in hydrocarbons extraction (57% of the country’s total). The existing nature management pattern is closely connected with poor modern ecological situation. System analysis of preconditions for the transition of the Khanty-Mansi Autonomous Okrug-Ugra to green economy is presented. It includes description of limiting and promoting natural and social-economic factors and consideration of the ecological situation. Economic, institutional, and nature management tools for regional transition to “green economy” and sustainable development are demonstrated. These tools reflect regional pattern of “green economy” movement, though common mechanisms play the leading role (e.g., energy efficiency, climatically neutral production, environmental accounting, etc.). Regional pattern includes specific measures for forests preservation, ethnic-culture tourism development, organic agriculture based on reindeer meet, wild berries and mushrooms production, etc.