Sergey A. Lebedev

The paper’s aim is to study the impact of global and regional climate change on the state of the Caspian Sea. Determining the most probable climate scenarios for different coastal zones is an important step in predicting the change in the Caspian Sea level and developing plans for the region's adaptation to future climate change. This study assessed the Shared Socioeconomic Pathways (SSP) climate scenarios obtained using the CMIP6 global climate models (GCMs) by comparing the simulated data with actual measurements of surface air temperature (tas) at 72 weather stations in a 200-km buffer zone of the Caspian Sea. The analysis covered the period from 2015 to 2025. The research focuses on air temperature as the most reliably predicted parameter. The study used four main SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) calculated based on six climate models (AWI-CM-1-1-MR, CMCC-CM2-SR5, CNRM-CM6-1-HR, EC-Earth3, MPI-ESM1-2-HR and INM-CM5-0). The quality of the modeling was assessed using a number of indicators: mean error (ME), correlation coefficient (r), coefficient of determination ( 2), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE) and Kling–Gupta efficiency (KGE). The results showed a high degree of agreement between the models and observations for temperature, with correlation coefficients ranging from 0.85 to 0.96. However, systematic biases are observed (from −12.9 to +6.5 °C), with modeled data generally overestimated for lowland areas and underestimated for mountainous areas. The choice of the “best” SSP scenario varies depending on the metric used: based on RMSE and NSE, the most consistent with the actual mean temperature data (2015–2025) is the SSP5-8.5 scenario, while based on KGE, the SSP3-7.0 scenario better reproduces the variability and structure of the time series.

The paper is devoted to the study of the Barents Sea wave climate, which was built on the basis of satellite altimetry data provided by ERS-1/2, GFO-1, Envisat, Cryosat-2, SARAL, Sentinel-3a/3b. A special statistical procedure has been applied to obtain a single dataset of Significant Wave Height (SWH) variability relative to ERS-1 satellite altimetry measurements for 1991–2021. This is the first time such a wind wave database has been created for the Barents Sea. For this time period a monthly mean SWH interannual trend was equal to +0.10 ± 0.06 m/decade. Seasonal variability of SWH interannual trends varies from +0.05 m/decade in March to +0.15 m/decade in September. Values of SWH trends over +0.12 m/decade are observed from July to November which is explained by steady interannual reduction of ice cover in the Barents Sea and by a significant increase in storm activity in the region. The obtained results show that, on the one hand, significant reduction in ice cover in the Barents Sea allows ice-free navigation for a longer time during a year, especially in Autumn, but on the other hand it increases wind fetch, which along with a significant increase in storm activity in the Barents Sea will lead to an increase of wind wave height in the Barents Sea. This will represent potential difficulties (and even danger) to navigation of ships designed for navigation in ice conditions and not intended for navigation in open waters of the world's oceans where large waves may be observed (e.g., icebreakers, barges, and other vessels).

This study is devoted to assessing the accuracy of determining the Caspian Sea surface temperature (SST) using MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data. The authors used in situ temperature measurements obtained from Surface Velocity Program Barometer (SVPB) drifters deployed in the Caspian Sea in 2006 – 2007 and 2008 as part of the MACE Project. For comparison, daily MODIS LST products with a spatial resolution of 1 km, available on the Google Earth Engine platform, were used. Products obtained using the Split-Window (MOD11A1 and MYD11A1) and Temperature Emissivity Separation (TES) algorithms (MOD21A1D, MYD21A1D, MOD21A1N, MYD21A1N) for day and night surveys were analyzed. The results showed a significant linear relationship between the MODIS satellite data and drifter measurements for all considered products. For the products based on the Split-Window Algorithm, the correlation coefficients were 0.94 – 0.96, and the determination coefficients were 0.73 – 0.78. The average error for daytime data was -1.23 – -1.52 °C, and for nighttime data -2.14 – -2.16 °C. It was noted that for daytime data of the MOD11A1 and MYD11A1 products, the deviation increases with increasing water temperature. The products based on the TES algorithm showed higher accuracy. For daytime products MOD21A1D and MYD21A1D, the correlation coefficient was 0.91 – 0.93, the determination coefficient was 0.83 – 0.84, and the average error was 0.37 – 0.64 °C. For the nighttime MOD21A1N and MYD21A1N products, the correlation coefficient was 0.96, the determination coefficient was 0.91 – 0.92, and the average error was 0.03 – 0.25 °C. The MODIS data were also compared with Landsat Level-2 data. The determination coefficient was 0.92 in both cases (using MODIS Split-Window and TES products). On average, the Landsat temperature was higher by 0.5 °C when compared with the MYD11A1 and MOD11A1 products (daytime) and lower by 0.85 °C when compared with the MYD21A1D and MOD21A1D products. Overall, the study showed that MODIS products demonstrate high accuracy in reproducing the Caspian Sea surface temperature, with products using the TES algorithm having higher accuracy compared to products based on the Split-Window algorithm.

On the basis of statistical analysis, studies of the features of the distribution of typhoons in the Spratly Archipelago (the South China Sea) for the period 1884–2020 were carried out using satellite information. As a result, it was revealed that 229 typhoons were observed over the water area of the Spratly Islands during this period. Of these, 27 had a maximum speed of over 33 m/s. A clear trend is identified towards an increasing number of typhoons, which could be a major source of regional climate risks in the future. This is probably due to the intense development of the East Asian Monsoon with a strong tropical convergence zone under current climatic conditions.

The Arctic Zone of the Russian Federation is characterized by the rapid growth of the mining industry, aimed at the extraction of oil, gas, coal, and ores, including rare earth metals. Railways are essential in the transportation of these resources to different regions of Russia for processing and export. Part of the cargo delivery is performed via the Arctic ports connected to the railway network. Rapid climate change, including regional climate warming, is among the compromising factors for the operation of the Arctic transport infrastructure. System analysis of climatic processes and assessment of potential hazards that they may induce requires adequate geoinformation support. This paper presents the results of spatio-temporal variability analysis of various hydrometeorological parameters for selected railway mainlines within the Arctic region. For this purpose, a new geoinformation method based on the Hovmöller diagrams was elaborated. This tool is useful for representing climate dynamics along the specified railway mainlines over several decades. It allows us to determine railway sections affected by anomalous climatic conditions on the variable time scale. The presented Hovmöller diagrams proved to be an efficient instrument for the regional climate change representation. It might be quite useful for railway infrastructure maintenance, planning, operation, and adaptation.

The Ivankovskoye Reservoir is a shallow reservoir of the valley-basin type. Low banks and overgrowing of shallow waters cause difficulties in determining its coastline and water surface area. In the Order of Rosvodresursy dated 31.05.2019 no. 125, the area of the reservoir is indicated as 316 km2, while the earlier publications gave the value of 327 km2. However, even a preliminary analysis of remote sensing data proves that the actual area of the Ivankovskoye Reservoir differs significantly from those mentioned in publications. The work is devoted to clarifying the water surface area of the reservoir using satellite data. The available cartographic materials and remote sensing data, starting from the filling of the reservoir to the present were assessed. According to retrospective data, it was established that the area of the reservoir on the maps compiled in the first years after filling was significantly larger than on later maps, which show a significant decrease of the part of the Shoshinsky reach. The total area of the reservoir is equal to or less than the reference values. The first available satellite images of the Ivankovskoye Reservoir water area date back to the early 1970s, which allows estimating the area of the reservoir from that period to the present. The Sentinel-2 satellite images were used for interpretation and analysis. Based on remote sensing data, the values of the actual area of the Ivankovskoye Reservoir water surface were identified at different water levels during the period of no ice formation. According to the obtained data, the actual area of the reservoir at levels close to the normal backwater is in most cases within 220-240 km2, which is 22-31% less than in the interpolation table of the surface area of the Ivankovskoye Reservoir at different water levels given in the “Rules for the Use of Water Resources of the Ivankovskoye Reservoir on the Volga River” (Order of Rosvodresursy dated 31.05.2019 no. 125). The use of an overestimated water surface area of the reservoir in research and design projects distorts the assessment of the reservoir’s impact on the environment and other ecological and hydrological characteristics. The paper provides an example of calculating methane emissions from the Ivankovskoye Reservoir, which turned out to be 20-25% lower based on the adjusted area. It is recommended to clarify the morphometric characteristics of the Ivankovskoye Reservoir in the coming years.

The global marine gravity anomaly model is predominantly recovered from along-track radar altimeter data. Despite significant advances in gravity anomaly recovery, the improvement of the gravity anomaly model remains constrained by the absence of cross-track geoid gradients and the reduction in radar altimeter data, especially in coastal and high-latitude regions. ICESat-2 laser altimetry, with a three-pair laser beam configuration, a small footprint, and a near-polar orbit, facilitates the determination of cross-track geoid gradients and provides valid observations in certain regions. We present an ICESat-2 altimeter data processing strategy that includes the determination of cross-track geoid gradients and the combination of along-track and cross-track geoid gradients. Utilizing these methods, we developed a new global marine gravity model, SDUST2022GRA, from radar and laser altimeter data. Different weight determination methods were applied to each type of altimeter datum. The precision and spatial resolution of SDUST2022GRA were assessed against published altimeter-derived global gravity anomaly models (DTU17, V32.1, NSOAS22) and shipborne gravity measurements. SDUST2022GRA achieved a global precision of 4.43 mGal, representing an improvement of approximately 0.22 mGal over existing altimeter-derived models. In local coastal and high-latitude regions, SDUST2022GRA showed an enhancement of 0.16–0.24 mGal compared to the other models. The spatial resolution of SDUST2022GRA is approximately 20 km in certain regions, which is slightly superior to the other models. The percentage contribution of ICESat-2 to the improvement of the gravity anomaly model is 4.3 % in low- to mid-latitude regions by comparing SDUST2022GRA with ICESat-2 to SDUST2021GRA without ICESat-2, and this is increasing in coastal regions. These assessments suggest that SDUST2022GRA is a reliable global marine gravity anomaly model. The SDUST2022GRA data are freely available at https://doi.org/10.5281/zenodo.8337387 (Li et al., 2023).

The Central Research Institute for Machine Building JSC has created a software package for processing primary satellite altimetry data of the “GEO-IK-2” space geodetic system, adapted for high-performance computing systems. The software package performs step-by-step multi-level processing of measuring and auxiliary information with sequential recording of intermediate and final results in a database. The technology of processing level 0 data received from the spacecraft up to and including level 2 has been implemented. The article provides information about the input data used at different levels of processing, about the algorithms and functionality of the software package. In the process of debugging the software package on high-performance computing systems all the valid data of the “GEO-IK-2” space geodetic system from 2018 to 2022 were processed for the first time. The developed software package should serve as a prototype of the satellite altimetry data processing subsystem of the ground-based special complex of the promising new generation space geodetic system