This investigation endeavors to demarcate the boundaries of small- and medium-sized river catchments within the Caspian Sea drainage basin, with a specific focus on the Northeastern Caucasus, Azerbaijan, and Iran regions. A multi-faceted approach was employed, incorporating various remote sensing methods to select key areas, including the catchments of the Sunzha, Sulak, Ulluchay, Karachay, Atachay, Haraz, and Gorgan rivers. Subsequently, geoinformation systems (GISs) and topographic maps were utilized to determine the morphometric characteristics of these catchments, accompanied by an assessment of the accuracy of remote sensing data. The aim of this study is to evaluate the accuracy and suitability of digital elevation models (DEMs) with a spatial resolution of 30 m per pixel (including ASTER DEM, ALOS DEM, NASADEM, Copernicus 30 m DEM, and SRTM 30 m DEM) and 90 m per pixel (Copernicus 90 m DEM and SRTM 90 m DEM) for delineating small- and medium-sized Caspian Sea basin river catchments. For the DEMs that successfully and accurately delineated watershed boundaries, the morphometric characteristics of the river basins were calculated. This research has yielded novel findings regarding the morphometric characteristics (area, perimeter, ruggedness of the catchment line (roundness coefficient), maximum height, minimum height, average height of the river basin, maximum slope of the surface, average slope of the surface, length of the main watercourse, basin shape parameter (catchment elongation coefficient), shape coefficient, length of the river basin, average river basin slope, and average width of the basin) of individual mountainous small- and medium-sized rivers in the Northeastern Caucasus, Azerbaijan, and Iran, with the catchments of the aforementioned rivers serving as exemplars. The practical significance of these results lies in the fact that such detailed morphometric characteristics of catchments have been obtained for the first time, and their boundaries have been clarified (burned out according to various DEMs), which can serve as a basis for decision-making processes and contribute to the development of operational environmental monitoring of the state of rivers and their catchments.

The Caspian region represents a complex and unique system of terrestrial, coastal, and aquatic environments, marked by an exceptional landscape and biological diversity. This diversity, however, is increasingly threatened by substantial anthropogenic pressures. One notable impact of this human influence is the rising concentration of pollutants atypical for the atmosphere. Advances in science and technology now make it possible to detect certain atmospheric pollutants using remote Earth observation techniques, specifically through data from the Sentinel-5 satellite, which provides continuous insights into atmospheric contamination. This article investigates the dynamics of atmospheric pollution in the southwestern part of the Caspian Sea basin using Sentinel-5P satellite data and the cloud-computing capabilities of the Google Earth Engine (GEE) platform. The study encompasses an analysis of concentrations of seven key pollutants: nitrogen dioxide (NO₂), formaldehyde (HCHO), carbon monoxide (CO), ozone (O₃), sulfur dioxide (SO₂), methane (CH₄), and the Aerosol Index (AI). Spatial and temporal variations in pollution fields were examined for the Caspian region and the basins of the seven rivers (key areas) flowing into the Caspian Sea: Sunzha, Sulak, Ulluchay, Karachay, Atachay, Haraz, and Gorgan. The research methodology is based on the use of data from the Sentinel-5 satellite, SRTM DEM data on absolute elevations, surface temperature data, and population density data. Data processing is performed using the Google Earth Engine cloud-computing platform and the ArcGIS software suite. The main aim of this study is to evaluate the spatiotemporal variability of pollutant concentration fields in these regions from 2018 to 2023 and to identify the primary factors influencing pollution distribution. The study’s findings reveal that the Heraz and Gorgan River basins have the highest concentrations of nitrogen dioxide and Aerosol Index levels, marking these basins as the most vulnerable to atmospheric pollution among those assessed. Additionally, the Gorgan basin exhibited elevated carbon monoxide levels, while the highest ozone concentrations were detected in the Sunzha basin. Our temporal analysis demonstrated a substantial influence of the COVID-19 pandemic on pollutant dispersion patterns. Our correlation analysis identified absolute elevation as a key factor affecting pollutant distribution, particularly for carbon monoxide, ozone, and aerosol indices. Population density showed the strongest correlation with nitrogen dioxide distribution. Other pollutants exhibited more complex distribution patterns, influenced by diverse mechanisms associated with local emission sources and atmospheric dynamics.

This study explores the potential of using Unmanned Aircraft Vehicles (UAVs) as a measurement platform for estimating greenhouse gas (GHG) fluxes over complex terrain. We proposed and tested an inverse modeling approach for retrieving GHG fluxes based on two-level measurements of GHG concentrations and airflow properties over complex terrain with high spatial resolution. Our approach is based on a three-dimensional hydrodynamic model capable of determining the airflow parameters that affect the spatial distribution of GHG concentrations within the atmospheric boundary layer. The model is primarily designed to solve the forward problem of calculating the steady-state distribution of GHG concentrations and fluxes at different levels over an inhomogeneous land surface within the model domain. The inverse problem deals with determining the unknown surface GHG fluxes by minimizing the difference between measured and modeled GHG concentrations at two selected levels above the land surface. Several numerical experiments were conducted using surrogate data that mimicked UAV observations of varying accuracies and density of GHG concentration measurements to test the robustness of the approach. Our primary modeling target was a 6 km² forested area in the foothills of the Greater Caucasus Mountains in Russia, characterized by complex topography and mosaic vegetation. The numerical experiments show that the proposed inverse modeling approach can effectively solve the inverse problem, with the resulting flux distribution having the same spatial pattern as the required flux. However, the approach tends to overestimate the mean value of the required flux over the domain, with the maximum errors in flux estimation associated with areas of maximum steepness in the surface topography. The accuracy of flux estimates improves as the number of points and the accuracy of the concentration measurements increase. Therefore, the density of UAV measurements should be adjusted according to the complexity of the terrain to improve the accuracy of the modeling results.

This study investigates the anthropogenic transformation of small- and medium-sized river basins within the Caspian Sea catchment. The basins of seven rivers—Sunzha, Sulak, Ulluchay, Karachay, Atachay, Haraz, and Gorgan—were selected as key study areas. For both the broader Caspian region, particularly its southwestern sector, and the selected study sites, trends in land cover types were analyzed, natural resource use practices were assessed, and population density dynamics were examined. Furthermore, a range of indices were calculated to quantify the degree of anthropogenic transformation, including the coefficient of anthropogenic transformation, the land degradation index, the urbanity index, the degree of anthropogenic transformation, coefficients of absolute and relative tension of the ecological and economic balance, and the natural protection coefficient. The study was conducted using geoinformation research methods and sets of geodata databases—the global LandScan population density database, the GHS Population Grid database, the ESRI land cover type dynamics database, and OpenStreetMap (OSM) data. The analysis was performed using the geoinformation programs QGIS and ArcGIS, and a large amount of literary and statistical data was additionally analyzed. It is shown that within the studied region, there has been a decrease in the number and density of the population, as a result of which the territories of river basins are experiencing an increasing anthropogenic impact, the woody type of land cover is decreasing, and the agricultural type is increasing. The most anthropogenically transformed river basins are Karachay, Haraz, and Gorgan.

Применение программного комплекса «Коскад 3D» для анализа потенциальных полей Терско-Каспийского прогиба

This study provides a comprehensive assessment of the geoecological status of selected mountain rivers in the North-Eastern Caucasus—specifically, the Sunzha, Sulak, Ulluchay, Karachay, and Atachay—through an analysis of chemical element concentrations, including heavy metals (HMs), in surface water, suspended particulate matter (SPM), and bottom sediments. The elemental composition was determined using inductively coupled plasma mass spectrometry (ICP-MS) on a PlasmaQuant MS Elite instrument (Analytik Jena, Germany), enabling high-precision quantification of 70 chemical elements. Element concentrations in surface water were compared against regulatory limits (e.g., maximum permissible concentrations (MPCs)) defined in international and national guidelines; concentrations in SPM were assessed relative to global average riverine values; and those in bottom sediments were evaluated with reference to average upper continental crust abundances (Clarke values). To trace potential sources of heavy metals entering the riverine systems, enrichment factors (EFs) were calculated for bottom sediments. The results indicate that surface water, suspended particulate matter, and bottom sediments in the investigated rivers exhibit enrichment in numerous chemical elements to levels exceeding their respective reference values (MPCs, global river means, or crustal Clarke values). Significant regional variations in abiotic parameters were observed. Water temperature ranges were 4.6–28 °C (Russian rivers) and 6.9–13.6 °C (Azerbaijan rivers). The pH of Russian rivers was circumneutral to mildly alkaline (7.12–8.83), whereas Azerbaijani rivers were distinctly alkaline, with values reaching 9.88. Reducing conditions in sediments (Eh as low as −206 mV) were prevalent at several stations across both regions. This enrichment reflects an overall unfavorable geoecological status of the studied river systems. Elevated concentrations of several rare earth elements (REEs), observed across multiple sampling locations, suggest a substantial lithogenic contribution linked to the geological structure of the catchments, including the composition of the drained rocks and the presence of ore-bearing formations. Furthermore, localized increases in the concentrations of key heavy metals—such as copper, zinc, cadmium, arsenic, and mercury—point to anthropogenic inputs, most likely associated with mining operations, industrial activities, or other human-induced sources.