The climate continentality or oceanity is one of the main characteristics of the local climatic conditions, which varies with global and regional climate change. This paper analyzes indexes of continentality and oceanity, as well as their variations in the middle and high latitudes of the Northern Hemisphere in the period 1950–2015. Climatology and changes in continentality and oceanity are examined using Conrad’s Continentality Index (CCI) and Kerner’s Oceanity Index (KOI). The impact of Northern Hemisphere teleconnection patterns on continentality/oceanity conditions was also evaluated. According to CCI, continentality is more significant in Northeast Siberia and lower along the Pacific coast of North America as well as in coastal areas in the northern part of the Atlantic Ocean. However, according to KOI, areas of high continentality do not precisely correspond with those of low oceanity, appearing to the south and west of those identified by CCI. The spatial patterns of changes in continentality thus seem to be different. According to CCI, a statistically significant increase in continentality has only been found in Northeast Siberia. In contrast, in the western part of North America and the majority of Asia, continentality has weakened. According to KOI, the climate has become increasingly continental in Northern Europe and the majority of North America and East Asia. Oceanity has increased in the Canadian Arctic Archipelago and in some parts of the Mediterranean region. Changes in continentality were primarily related to the increased temperature of the coldest month as a consequence of changes in atmospheric circulation: the positive phase of North Atlantic Oscillation (NAO) and East Atlantic (EA) patterns has dominated in winter in recent decades. Trends in oceanity may be connected with the diminishing extent of seasonal sea ice and an associated increase in sea surface temperature.

Climate change leads to more variable meteorological conditions. In many Northern Hemisphere temperate regions, cold seasons have become more variable and unpredictable, necessitating frequent river ice observations over long sections of rivers. Satellite SAR (Synthetic Aperture Radar)-based river ice detection models have been successfully applied and tested, but different hydrological, morphological and climatological conditions can affect their skill. In this study, we developed and tested Sentinel-1 SAR-based ice detection models in 525 km sections of the Nemunas and Neris Rivers. We analyzed three binary classification models based on VV, VH backscatter and logistic regression. The model sensitivity and specificity were used to determine the optimal threshold between ice and water classes. We used in situ observations and Sentinel-2 Sen2Cor ice mask to validate models in different ice conditions. In most cases, SAR-based ice detection models outperformed Sen2Cor classification because Sen2Cor misclassified pixels as ice in areas with translucent clouds, undetected by the scene classification algorithm, and misclassified pixels as water in cloud or river valley shadow. SAR models were less accurate in river sections where river flow and ice formation conditions were affected by large valley-dammed reservoirs. Sen2Cor and SAR models accurately detected border and consolidated ice but were less accurate in moving ice conditions. The skill of models depended on how dense the moving ice was. With a lowered classification threshold and increased model sensitivity, SAR models detected sparse frazil ice. In most cases, the VV polarization-based model was more accurate than the VH polarization-based model. The results of logistic and VV models were highly correlated, and the use of VV was more constructive due to its simpler algorithm.

Due to climate change, extreme floods are projected to increase in the 21st century in Europe. As a result, flood risk and flood-related losses might increase. It is therefore essential to simulate potential floods not only relying on historical but also future projecting data. Such simulations can provide necessary information for the development of flood protection measures and spatial planning. This paper analyzes the risk of compound flooding in the Danė River under different river discharge and Klaipėda Strait water level probabilities. Additionally, we examine how a water level rise of 1 m in the Klaipėda Strait could impact Danė River floods in Klaipėda city. Flood extent was estimated with the Hydrologic Engineering Center’s River Analysis System (HEC-RAS) and visualized with ArcGIS Pro. Research results show that a rise in the water level in the Klaipėda Strait has a greater impact on the central part of Klaipėda city, while that of the maximum discharge rates of the river affected the northern upstream part of the analyzed river section. A sea level rise of 1 m could lead to an increase in areas affected by Danė floods by up to three times. Floods can cause significant damage to the infrastructure of Klaipėda port city, urbanized territories in the city center, and residential areas in the northern part of the city. Our results confirm that, in the long run, sea level rise will significantly impact the urban areas of the Klaipėda city situated near the Baltic Sea coast.

One of the largest uncertainties in remote sensing data comes from atmospheric influence. This research aims to explain the uncertainties emanating from atmospheric correction (AC) product selection and how they influence chlorophyll α concentration retrieval in lakes in eastern Lithuania. We tested seven products from six AC processors (Acolite, Acolite Rayleigh, iCOR, Sen2Cor, C2RCC, C2X, and POLYMER) and 10 chlorophyll α retrieval algorithms with different architectures. The uncertainty of AC products transferred to chlorophyll α concentrations, and large differences in the chlorophyll α concentrations retrieved using different AC products were observed. The match-up analysis showed that chlorophyll α algorithms based on band difference performed best in terms of a high coefficient of determination and the lowest median bias when used with image-based, Sen2Cor, and TOA data. The results of this study highlight the uncertainties of AC products as well as how the selection of the chlorophyll α retrieval algorithm can mitigate the influence of AC selection.