Water body feature extraction using a remote sensing technique represents an important tool in the investigation of water resources and hydrological drought assessment. Nuntasi-Tuzla Lake, a component of the Danube Delta Natural Reserve, is located on the Romanian Black Sea littoral. On account of an event in summer 2020, when the lake surface water decreased significantly, this study aims to identify the variation of the Nuntasi-Tuzla Lake surface water over a long-term period in correlation with human intervention and climate change. To this end, it provides an analysis in the period 1965–2021 via hydrological drought indices and data mining classification. The latter approach is based on several water indices derived from Landsat TM/ETM+/OLI and MODIS full-time series datasets: Normalized Difference Vegetation Index (NDVI), Normalized Difference Vegetation Index (NDVI), Modified NDWI (MNDWI), Weighted Normalized Difference Water Index (WNDWI), and Water Ratio Index (WRI). The experimental results indicate that the proposed classification methods can extract relevant features from waterbodies using remote sensing imagery with a high accuracy. Moreover, the study shows a similarity in the evolution of surface water cover identified with the data mining classification and the drought periods detected in the flow data series for the Nuntasi and Sacele Rivers that supply the Nuntasi-Tuzla Lake. Overall, the results of our investigation show that human intervention and hydrological drought had an extensive impact on the long-term changes in surface water of the Nuntasi-Tuzla Lake.

This article proposes a new approach for determining the optimal parameter (β) in the Inverse Distance Weighted Method (IDW) for spatial interpolation of hydrological data series. This is based on a genetic algorithm (GA) and finds a unique β for the entire study region, while the classical one determines different βs for different interpolated series. The algorithm is proposed in four scenarios crossover/mutation: single-point/uniform, single-point/swap, two-point/uniform, and two-point swap. Its performances are evaluated on data series collected for 41 years at ten observation sites, in terms of mean absolute error (MAE) and mean standard error (MSE). The smallest errors are obtained in the two-point swap scenario. Comparisons of the results with those of the ordinary kriging (KG), classical IDW (with β = 2 and the optimum beta found by our algorithm), and the Optimized IDW with Particle Swarm Optimization (OIDW) for each study data series show that the present approach better performs in 70% (80%) cases.

Drought raises significant challenges and consequences in the socioeconomic environment in Dobrogea, Romania. This research aimed to assess the spatiotemporal dynamics of agrometeorological droughts from 2001 to 2021 using a multi-index approach that includes the Vegetation Health Index (VHI) and Standardized Precipitation Evapotranspiration Index (SPEI). Severe-to-extreme drought events were detected in 2001, 2007, 2012, 2015, 2016, 2019, and 2020, when temperatures in the area reached as high as 40.91 °C. Regarding area coverage, 2012 and 2020 were the worst drought years, with 66% and 71% of the region affected. Mild and moderate droughts were consistently identified across almost the entire period, while normal wet conditions were indicated in 2004–2006. The spatial analysis and the drought frequency maps revealed that the central, southern, and northwestern areas were particularly vulnerable, underlining the need for targeted drought mitigation measures. The trend analysis results indicated a nonuniform spatial feature of the negative (drying)/positive (wetting) trends at the regional level, with statistically significant trends identified only over small areas. Further results showed a robust relationship among the VHI and SPEI, particularly on 1-month and seasonal timescales. The extended correlation analysis results showed very strong positive relationships among all the vegetation indices, positive relations with rainfall, and strong negative ties with land surface temperature. Moreover, the seasonal VHI proved to be effective for drought monitoring across areas with diverse crop types. The results we obtained are consistent with previous studies on the incidence of drought in the area and hold practical significance for decision-makers responsible for drought management planning within Dobrogea, including setting up an early warning system using the VHI.

This paper discusses a study that examined the severity of droughts and their changes in the Dobrogea region in southeastern Romania between 2001 and 2021 and develops a high-resolution (1 km) Composite Drought Index (CDI) dataset. To explore the effectiveness of the index, we carried out a correlation analysis between the CDI, the Standardized Precipitation Index (SPI), and the Standardized Precipitation Evapotranspiration Index (SPEI), which shows a strong positive relationship among these indices. Analysis of the CDI time series reveals an increase in drought frequency for the study period, due to high temperature and below-normal rainfall. Most parts of the region were affected by moderate, severe, or extreme droughts, except for the years 2002–2005 and 2013. The worst drought events were in 2011, 2012, and 2020, when the region was under severe land surface temperature stress, with values up to 39.13 °C. The central and northern areas of the region had the longest period of drought, at 22 months, which started in 2018 and culminated in 2020 when extreme drought covered over 70% of the region. Another major event was in 2015 when 95% of the region experienced severe drought. These results show the potential of the CDI as one of the significant indices in the assessment of drought and provide useful insights into drought monitoring in the future. More than that, we consider that the GPM IMERG satellite product can be used in the implementation of Drought Management Plans in Dobrogea in order to calculate drought indices derived from remote sensing data.

Droughts exert a profound influence on water availability, agriculture, and ecosystems worldwide, and Dobrogea, one of Romania’s driest regions, has been repeatedly affected by severe droughts over recent decades. Given the increasing frequency and intensity of such events under climate change, accurate drought prediction is vital for managing agriculture and water resources in this vulnerable region. This study evaluates the performance of statistical (SARIMA) and machine learning models (Rthe seasonal Mann-Kendallandom Forest, Gradient Boosting, Support Vector Regression) in forecasting the Standardized Precipitation Evapotranspiration Index (SPEI) at multiple time scales (1, 3, 6, 12, and 24 months). The models were developed using in situ data from six meteorological stations and satellite-based datasets covering 1967–2021, providing both local and regional perspectives on drought variability. Lagged predictors (1–12 months) were incorporated to capture temporal memory effects and enhance forecast accuracy. Residual diagnostics confirmed the statistical adequacy and proper specification of the selected models. Results reveal that Dobrogea has experienced widespread, recurrent moderate to severe drought conditions, with extreme events becoming more frequent since 2001. In terms of SPEI prediction, model performance improved with increasing timescale. Among the predictive approaches, SVR and SARIMA achieved the best overall results, showing higher NSE (~ 0.95) and lower RMSE and MAE values compared to the other models. Overall, the findings highlight Dobrogea’s persistent drought exposure and demonstrate the potential of machine learning —particularly SVR—to enhance drought forecasting precision, supporting sustainable agricultural practices and adaptive management under a changing climate.

At each port of destination, some containers are unloaded from a vessel and stored in the terminal to be delivered to their customers. One of the strategies used to arrange the containers in a terminal is residence time strategy: based on their delivery deadlines, each incoming container being assigned to a priority class. The aim of this study is to determine a valid arrangement of incoming containers in a block (part) of the terminal, in the shortest amount of time, with higher priority containers located above lower priority ones. In this way, some of the main objectives of a container terminal may be achieved: avoiding further reshuffles (number of relocations) and reducing the vessel berthing time. We developed a genetic algorithm and its performance is evaluated against a random stacking strategy used as benchmark for the experiments, and through several sets of tests on control parameters. All the tests showed that, if a reliable estimation of the delivery time can be assigned to every incoming container, the proposed method may be a useful tool for container terminal operators.