The Atlantic walrus, Odobenus rosmarus rosmarus, forms a herd of nearly 4,000 heads in the Pechora Sea (south‐eastern Barents Sea). The Near Threatened status of O. rosmarus rosmarus and the relative isolation of the Pechora Sea population, as well as the potential impacts of human activities in the area, make it important to characterize key habitats, including feeding grounds, in order to protect the species. The aim of the present study was to integrate multiple sources of environmental and biological data collected by satellite telemetry, remotely operated vehicle (ROV), and benthic grab sampling to examine the distribution and diversity of benthic foraging resources used by walrus in the Pechora Sea. Analysis of satellite telemetry data from seven males tagged on Vaigach Island helped to identify areas of high use by walruses near haulout sites on Matveev and Vaigach islands, and in between. Field data were collected from those feeding grounds in July 2016 using ROV video recordings and bottom grab sampling. Analysis of 19 grab stations revealed a heterogeneous macrobenthic community of 133 taxa with a mean biomass of 147.11 ± 7.35 g/m2. Bivalve molluscs, particularly Astarte borealis, Astarte montagui, and Ciliatocardium ciliatum, dominated the overall macrobenthic biomass, making up two‐thirds of the total. Analysis of 16 ROV video transects showed high occurrences of mobile benthic decapods (3.03 ± 2.74 ind./min) and provided the first direct evidence that areas actively used by walrus in the Pechora Sea overlap with the distribution of the non‐native omnivorous snow crab, Chionoecetes opilio. Integrating multiple data sources provides an early foundation for the kinds of ecosystem‐based approaches needed to improve Pechora Sea resource management and to underpin Russia’s nascent marine spatial planning initiatives. Factors that need to be considered in marine spatial planning include impacts on benthic feeding grounds from offshore oil and gas development and the spread of the snow crab.
The Don River is the largest river in the southwestern part of European Russia and the second largest river system in European Russia. The Don River basin is one of the most water deficient regions in Russia and the long term average water usage in the basin amounts to 45%. The period 2007-2016 was the longest long-term low-flow period observed, with an estimated total water resources deficit of 40.4 km 3 over 8 years. The main reason for this deficit were anomalously warm winters (2-4 degrees over average) with a low degree of soil frost penetration. This resulted in low spring flood volume (37% of the average) due to heavy seepage losses combined with thin snow cover. A similar low-flow situation was observed in 2014, when the drought caused great damage to ecosystem of Tsimlianskoye water reservoir and the River Don. Most of the fish breeding grounds had dried up by May 2014. This caused the number of round fish whitebait to drop 5-10 times below the 2002-2014 average. Inland shipping and hydropower industry also sustained losses of 42 million euro (according to interview from State Shipping company) due to low water level. This study shows that the main reasons for the 2007-2016 extreme hydrological drought are exceptional hydro-climatic conditions and anthropogenic transformations in the watershed, such as urbanisation growth and afforestation. The analysis shows that the main cause in water deficit is associated with the left tributaries of Don -Khoper and Medveditsa, while the flow in Upper Don remained more or less normal. The results can be interpreted as a "warning sign" to reduce water consumption in these sub-basins to avoid similar drought situations in future.
Currently, the improvement of numerical models of weather forecasting allows using them for hydrological problems, including calculations of snow water equivalent (SWE) or snow storage. In this paper, we discuss the applicability of daily precipitation forecasts for three global atmospheric models: GFS (USA), GEM (Canada) and PL-AV (Russia) for calculating snow storage (SWE) in the Kama river basin for the cold season of 2017–2018. As the main components of the balance of snow storages the following parameters were taken into account: precipitation (with regard for the phase); snow melting during thaws; evaporation from the surface of the snow cover; interception of solid precipitation by forest vegetation. The calculation of snow accumulation and melting was based on empirical methods and performed with the GIS technologies. The degree-day factor was used to calculate snowmelt intensity, and snow sublimation was estimated by P.P. Kuz’min formula. The accuracy of numerical precipitation forecasts was estimated by comparing the results with the data of 101 weather stations. Materials of 40 field and 27 forest snow-measuring routes were taken into account to assess the reliability of the calculation of snow storages (SWE). During the snowmelt period, the part of the snow-covered area of the basin was also calculated using satellite images of Terra/Aqua MODIS on the basis of the NDFSI index. The most important result is that under conditions of 2017/18 the mean square error of calculating the maximum snow storage by the GFS, GEM and PL-AB models was less than 25% of its measured values. It is difficult to determine which model provides the maximum accuracy of the snow storage calculation since each one has individual limitations. According to the PL-AV model, the mean square error of snow storage calculation was minimal, but there was a significant underestimation of snow accumulation in the mountainous part of the basin. According to the GEM model, snow storages were overestimated by 10–25%. When calculating with use of the GFS model data, a lot of local maximums and minimums are detected in the field of snow storages, which are not confirmed by the data of weather stations. The main sources of uncertainty in the calculation are possible systematic errors in the numerical forecasts of precipitation, as well as the empirical coefficients used in the calculation of the intensity of snowmelt and evaporation from the snow cover surface.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.