Recently, the number and intensity of hydrological droughts have been increasing; thus, it is necessary to identify and respond to them quickly. Since the primary hydrological data in Lithuania are water levels, and converting these data into discharge takes additional time, there is a need to develop a methodology or adapt these data to analyze and detect hydrological droughts. This paper examines the concept of the standardized water level index (SWLI) calculation, which is based on the standardized precipitation index (SPI) and streamflow drought index (SDI) methods. SDI and SWLI data were compared; SWLI was used to analyze the situation in the past and future. A total of 15 main sub-basins were considered, and the future discharge of three rivers was estimated; SWLI showed good compatibility with SDI. To better analyze droughts, the use of severe drought threshold values (SDTV) was suggested as some river data (especially those for small rivers) needed to be corrected due to dense riverine flora. The dry years and trends identified by SWLI are consistent with previous studies.

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.

The problem of droughts is acute due to climate change. The study aims to assess the temporal and spatial drought patterns in Lithuanian lowland rivers in the past and to project these phenomena according to climate scenarios and models. Drought analysis was based on Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI) and Streamflow Drought Index (SDI). To evaluate the past patterns, the hydrometeorological data of 17 rivers were used from 1961–2020. Future drought changes were analyzed in 2021–2100 according to the selected RCPs (Representative Concentration Pathways) using the hydrological model HBV. There were different patterns of droughts in three hydrological regions of Lithuania (Western, Central and Southeastern). The Southeastern region was more prone to extreme summer hydrological droughts, and they had a shorter accumulation period compared to the other two regions. SPI and RDI indices showed that the number of dry months and the minimum value of the index increased, extending the accumulation period. The highest correlation was recorded between RDI-12/SPI-12 and SDI-12. The amplitude between extremely wet and dry values of river runoff will increase according to RCP8.5. The projections indicated that hydrological drought intensity in the Central region is expected to increase under both analyzed RCPs.

At the beginning of the 21st century, ongoing climate change led to research into extreme streamflow phenomena. This study aimed to assess the patterns of low-flow changes in different hydrological regions of Lithuania using selected hydrological indices (the annual minimum 30-day flow (m³ s⁻¹) of the warm period—30Q), its duration, and deficit volume (below the 80th and 95th percentile flow: 30Q80 and 30Q95). Differences in low-flow indices in separate hydrological regions and over different periods (1961–2020, 1961–1990, 1991–2020) were analyzed, applying the HydroOffice tool, the TREND software package, and mapping using the Kriging interpolation. The highest specific indices of 30Q were estimated in the Southeastern hydrological region (3.97 L/s·km²) and the lowest in the Central hydrological region (1.47 L/s·km²). In general, the 30Q values in the periods 1961–2020 and 1991–2020 had no trends. In 1961–1990, trends in 30Q data were significantly positive, and positive in most investigated rivers of the Western and Central hydrological regions. The average number of dry days at both thresholds decreased in the Western and Southeastern hydrological regions and increased in the Central hydrological region comparing two subperiods.

The paper deals with the question of establishing dependencies between the data of night satellite monitoring and indicators of sustainable development on the example of the territory of Ukraine. The method of obtaining zonal statistics data for administrative units of different levels is described, based on the estimation of illumination in the night in the longterm period. The use of local correlation and regression indicators is grounded to establish spatial differences in the tightness of the dependence between nightlight luminocity and indicators of sustainable development.

The scientific and practical principles of data analysis are proposed with the help of methodology of spacetime cube construction as one of the types of data mining with spatial-temporal distribution. The use of this method on the example of information analysis from subscribers of one of the major mobile operator networks allows to carry out statistical analysis and to detect statistically significant spatio-temporal clusters in the data that can be used during data structuring in order to provide safety and react quickly to hazardous situations.