Hydropower (potential and kinetic energy) is one of the most important renewable energy sources in the world. This energy is directly dependent on water resources and the hydrological cycle. Ongoing climate changes are likely to influence the availability/amount of this energy resource. The present study explores the relationship between climate changes and river runoff, projects future runoff in both gauged and ungauged river catchments, and then assesses how these alterations may affect the future hydropower resources in Lithuania. Runoff projections of the gauged rivers were evaluated applying Swedish Department of Climate hydrological model, and runoff of ungauged river catchments were estimated by created isoline maps of specific runoff. According to an ensemble of three climate models and two Representative Concentration Pathway scenarios, runoff and hydroelectric energy projections were evaluated for two future periods (2021–2040, 2081–2100). The results demonstrated a decrease in future river runoff. Especially significant changes are expected according to the most pessimistic RCP8.5 scenario at the end of the century. The projected changes are likely to bring a negative effect on hydropower production in the country. These findings could help understand what kind of benefits and challenges water resource managers may face in the future.

Hydropower remains the most important and largest source of renewable energy. However, besides many additional benefits, such as dams for water supply, irrigation, flood control, recreation, navigation, etc., hydropower generation has a negative impact on the environment. This study aimed to investigate the hydrologic changes in Lithuanian lowland rivers caused by small hydropower plants (HPPs). Thirty-two indicators of hydrologic alteration (IHA) were studied in 11 rivers downstream of hydropower plants in the post-impact and pre-impact periods. The findings showed that HPPs and reservoirs considerably disturbed the primary flow of river ecosystems downstream. The largest changes in mean IHA values were found for low and high pulse characteristics (up to 57%) and the number of reversals (up to 44%). Only small or no deviations of the timing of annual extreme flows were found. The number of reversals, a low pulse count, and a fall rate were the flow characteristics that fell outside their historical ranges of variability most often. Six (out of 11) hydropower plants were identified that provoked hydrologic alterations of a moderate degree.

The Baltic Sea faces prolonged eutrophication due to nutrient pollution, with the Nemunas River regulating nutrient input via the Curonian Lagoon. In this study, we aimed to assess the seasonal variations and changes over the past two decades in nutrient concentrations within the Curonian Lagoon–Baltic Sea transitional zone, and to identify the main factors affecting these trends. We observed slightly reduced nutrient levels in the lagoon and the Klaipėda Strait and increased nitrogen loadings in the Baltic Sea nearshore over time. Between 2007 and 2009, the average total nitrogen (TN) concentrations in the Klaipeda Strait and the Baltic Sea were 1.60 ± 0.25 and 0.54 ± 0.04 mg/L, respectively, while the average total phosphorus (TP) concentrations in the Klaipeda Strait and the Baltic Sea were 0.061 ± 0.04 and 0.03 ± 0.01 mg/L, respectively. Between 2018 and 2020, TN concentrations in the Strait and the Sea were 1.2 ± 0.36 and 0.65 ± 0.32 mg/L, respectively, while the average TP concentrations in the Klaipeda Strait and the Baltic Sea were 0.025 ± 0.002 and 0.021 ± 0.002 mg/L, respectively. The average annual amount of TN and TP entering the Curonian Lagoon from the sea was 2736 t and 162 t, respectively. Significantly higher nutrient influx to the Baltic Sea was recorded reaching 32,302 t for TN and 1278 t for TP. Nutrient concentrations correlated with water temperature, salinity, and dissolved oxygen, influenced by seasonal runoff patterns and climate change. Over time, there have been noticeable shifts in environmental conditions, including rising temperatures, decreasing oxygen levels, salinity changes, increased evaporation, and reduced precipitation.

Hydromorphology of rivers assessed through direct measurements is a time-consuming and relatively expensive procedure. The rapid development of unmanned aerial vehicles and machine learning (ML) technologies enables the usage of aerial images to determine hydromorphological units (HMUs) automatically. The application of various direct and indirect data sources and their combinations for the determination of river HMUs from aerial images was the main aim of this research. Aerial images with and without the Sobel filter, a layer of boulders identified using Yolov5x6, and a layer of direct measurements of depth and streamflow velocity were used as data sources. Three ML models were constructed for the cases if one, two, or three data sources were used. The ML models for HMU segmentation were constructed of MobileNetV2 pre-trained on ImageNet data for the feature extraction part and U-net for the segmentation part. The stratified K-fold cross-validation with five folds was carried out to evaluate the performance of the model due to the limited dataset. The analysis of the ML results showed that the measured metrics of segmentation using direct measurements were close to the ones of the model trained only on the combination of boulder layer and aerial images with the Sobel filter. The obtained results demonstrated the potential of the applied approach for the determination of HMUs only from the aerial images, and provided a basis for further development to increase its accuracy.

Coastal lagoons are recognized as specific and complex water bodies vulnerable to climate change. The focus of this study was the Curonian Lagoon, the largest freshwater lagoon in the Baltic Sea and the whole of Europe. The changes in the hydrometeorological parameters of the lagoon over six decades were evaluated using two periods of climatological standard normal: the most recent 30-year period, i.e., 1991–2020, and the period of 1961–1990. Before statistical analysis, data were checked for homogeneity, and breakpoints were determined by Pettitt and Buishand tests. The Mann–Kendall test was used to determine trends in the data series. The analysis revealed substantial changes in the hydrometeorological parameters of the lagoon during two climate normal periods. An exceptionally high rise in air temperature was detected. A considerable increase was identified in the lagoon water temperature and water level data series. The duration of permanent ice cover on the lagoon declined, as did the ice thickness, whereas the ice breakup advanced. A downward trend in wind speed data was detected, while the change in precipitation had a positive direction. Air and water temperatures were highly correlated with the Arctic Oscillation (AO) index and the water level with the Scandinavia pattern (SCAND).