Abstract1. The natural environment of the Arctic is changing rapidly owing to climate change. At the same time in many countries including Russia the region is attracting growing attention of decisionmakers and business communities. In light of the above it is necessary to protect the biodiversity of the regional marine ecosystems in the most effective way possible, namely by establishing a network of marine protected areas.2. Identifying conservation priority areas is a key step towards this goal. To achieve it, a study based on a systematic conservation planning approach was conducted. An expanded group of experts used the MARXAN algorithm to produce initial results, then discussed and refined them to select 47 conservation priority areas in the Russian Arctic seas.3. The resulting network covers nearly 25% of the Russian Arctic seas, which guarantees proportional representation of their biodiversity as well as achieving connectivity, sustainability and naturalness. This was largely made possible by the selected methodology, based on the MARXAN decision support tool supplemented by extensive post-analysis that helped fill any gaps inevitable in the formal approach.4. Although available data were sparse, and of varying quality and a single regionalization scheme could not be used (as is often the case for such areas), the selected approach has proven successful for such a large area that covers both the coastal zone and parts of the High Seas. Such an approach could be used further to identify marine protected areas throughout the Arctic Ocean. Kudersky, 2004;Pavlov & Sundet, 2011;Spiridonov & Zalota, 2017) and sea ice habitat loss (Amstrup, Marcot, & Douglas, 2008;Moore & Huntington, 2008). Perhaps equally important, these changes lead to greater human presence in the region (Huettmann, 2012;Jørgensen et al., 2016;Wenzel et al., 2016). This could take many forms from increased oil and gas exploration and production, intensified shipping, fishing, aquaculture and tourism as well as greater military presence.In recent years serious efforts to protect marine biodiversity have been undertaken worldwide and the Russian Arctic seas are no exception. The Arctic is receiving growing attention in Russia as politicians, investors, media and the general public are pushing for a comeback after the country's withdrawal from the region in the 1990s. There are two approaches to conservation that prevail in the world today. One is based on industries regulations that are introduced alongside measures to protect or manage particular species or stocks (Roff & Zacharias, 2011). The other centres on areabased conservation measures and is widely regarded as effective (Roff & Zacharias, 2011;Spiridonov et al., 2012). In the Russian Arctic the latter remains less common. The region has seven Strictly Protected Natural Reserves, or zapovedniks (IUCN Ia), three National Parks (IUCN II), four Preserves (IUCN IV/VI), one Natural Monument (IUCN III) and 41 Regional Protected Areas (IUCN Ib), but their primary purpose is to protect terr...

Abstract. Geographic Information System (GIS)and Geoportal with open access «River basins of the European Russia» were implemented. GIS and Geoportal are based on the map of basins of small rivers of the European Russia with information about natural and anthropogenic characteristics, namely geomorphometry of basins relief; climatic parameters, representing averages, variation, seasonal variation, extreme values of temperature and precipitation; land cover types; soil characteristics; type and subtype of landscape; population density. The GIS includes results of spatial analysis and modelling, in particular, assessment of anthropogenic impact on river basins; evaluation of water runoff and sediment runoff; climatic, geomorphological and landscape zoning for the European part of Russia. IntroductionCurrently, in Russia, there is no integrated geospatial database or geographic information system tied to the basins of small rivers. The urgency of creating such a complete coverage GIS that is capable to accumulate large volumes of spatial information on natural systems and integrated information on the state of small river basins is in relevancy of studying the results of increasing anthropogenic impact, as well as climatic changes observed in different landscape areas on basin geographic systems. The large region of Russia -its European part which is some 4 million square kilometres with the most part of population and industrial and agricultural potential of the country -is being fundamentally studied in terms of geographical analysis of minor river basins and river runoff while creating designated geographic information system (GIS) "River basins of the European Russia". The designed GIS is considered to be a base for modern data and knowledge on geographic, hydroclimatic, geoecological and other characteristics of natural resource potential of tens thousands river basins. The thematic information sources are the data from long-term monitoring's, Earth's remote sensing, and accumulated corpus of cartographic materials from state surveys. The GIS includes not only actual information, but also the results of its comprehensive spatial analysis and modelling, assessments of anthropogenic load on river basins, the results of studying the patterns of water runoff formation depending on the landscape and geographical conditions in the European part of Russia.The information accumulated in the GIS is located on the geoportal "River basins of the European Russia" with public (open) access, which will enable a wide range of representatives of a scientific community and experts in the field of environmental management and protection to obtain thematic

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The paper describes river runoff modeling for a plains region of the European territory of Russia (ETR), as well as a prediction for ungauged drainage basins. The study of river runoff is one of the key research objectives in determining the patterns of sediment yield formation. Among many other zonal factors, river runoff is considered to be the main factor in sediment yield formation in a humid climate. In this study, modeling results for the entire European territory of Russia and various landscape zones are presented via the use of multiple regression methods. Multiple regression methods do not require the mathematical description of the main physical processes of runoff formation in terms of their spatial heterogeneity. At the same time, such methods can be distinguished by their simplicity in terms of determining parameters and providing clear interpretations of the results. The research methodology in this work is based on a drainage basin approach. Initial data for the river runoff and its formation factors are presented in the open-access geoinformation database "Drainage basins of the European territory of Russia", which has been created earlier by the authors. The river runoff geodatabase was formed with results from 1440 gauging stations. The independent variables, such as the relief morphometric characteristics, climatic indicators reflecting average values, scale, seasonal variations, extreme values of temperature and precipitation, percentage of forest and swamp cover, plowing, percentage of meadows, assessment of the anthropogenic impact on the drainage basin, geographical coordinates of the centroid, prevailing soil type, type of soil-forming rock, and class of pre-Quaternary deposits are used for modeling here. Data processing and model development is conducted using the R software environment. Models obtained by linear and nonlinear methods explain about 85-88% of data variability and are well interpreted in terms of the water balance equation. It is found here that the most significant predictors in the model are annual precipitation, the sum of the active temperatures (characterizing runoff losses via evaporation), average slope gradient, and the forest cover of the catchment. For Environmental Resources Management, it is required that data for river runoff are collected at the local (municipal) level. The results for the extrapolation of the river runoff values to ungauged river basins in a plains region of the European territory of Russia are presented here. Calculations of predicted values for the river runoff are given based on the obtained discharge per unit area logarithm model. The model and its cartographic representation reflect the patterns of the spatial distribution of river runoff for the level of spatial detail accepted in the study. The methods applied in this study and the results obtained could be used for similar studies of plains territories across the world.

The analysis of the geoecological state of basin geosystems was carried out by evaluation of the anthropogenic pressure on the basin. As indicators that directly or indirectly reflect the anthropogenic impact, the following were used: population density in the basin, density of the road network, and agricultural development of the basin territory. The spatial and statistical distributions of indicators were analyzed after the indicators were brought to a unified scale (transformation, normalization). The integral indicator of anthropogenic pressure, calculated as a linear combination of individual variables, was ranked to six categories of anthropogenic pressure: “absent”, “very low”, “low”, “moderate”, “high”, and “very high”. Using the developed methodology and prepared geodata, for the first time at scale of 1:200,000, the territory of the Volga Federal District was zoned according to the anthropogenic pressure on each river basin. Basins with a high and very high pressure are concentrated around large cities. Most of the basins belonging to the categories of low and moderate anthropogenic pressure are located in the forest-steppe and steppe zones with maximal agricultural development. Basins with zero and very low pressure lie in the north of the study area, in the forest zone, and in the southern Ural.

Evaluation of the vegetation and agricultural-management factor (C-factor) is an important task, the solution of which affects the correct assessment of the intensity of soil erosion. For the vast area of the European part of Russia (EPR), this task is particularly relevant since no products allow taking into account the C-factor. An approach based on automated interpretation of the main crop groups based on MODIS satellite imaging data from Terra and Aqua satellites with the LSTM machine-learning method was used to achieve this goal. The accuracy of crop group recognition compared to the open data of the Federal State Statistics Service of Russia was 94%. The resulting crop maps were used to calculate the C-factor for each month of a particular year from 2014 to 2019. After that, summaries were made at the regional and landscape levels. The average C-factor value for the EPR was 0.401, for the forest landscape zone 0.262, for the forest-steppe zone 0.362, and for the steppe zone 0.454. The obtained results are in good correlation with the results of previous field studies and provide up-to-date (based on 2014–2019 data) estimates of C-factor for rainfall erosion (monthly, annual) with high spatial detail (250 m).

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