Search Results (30)

This manuscript investigates the applicability and behavior of the Generalized Extreme Value (GEV) distribution in flood frequency analysis, comparing it with the Pearson III and Wakeby distributions. Traditional approaches often rely on a limited set of statistical distributions and estimation techniques, which may not adequately capture the behavior of extreme events. The study focuses on four hydrometric stations in Romania, analyzing maximum discharges associated with rare and very rare events. The research employs seven parameter estimation methods: the method of ordinary moments (MOM), the maximum likelihood estimation (MLE), the L-moments, the LH-moments, the probability-weighted moments (PWMs), the least squares method (LSM), and the weighted least squares method (WLSM). Results indicate that the GEV distribution, particularly when using L-moments, consistently provides more reliable predictions for extreme events, reducing biases compared to MOM. Compared to the Wakeby distribution for an extreme event (T = 10,000 years), the GEV distribution produced smaller deviations than the Pearson III distribution, namely +7.7% (for the Danube River, Giurgiu station), +4.9% (for the Danube River, Drobeta station), and +35.3% (for the Ialomita River). In the case of the Siret River, the Pearson III distribution generated values closer to those obtained by the Wakeby distribution, being 36.7% lower than those produced by the GEV distribution. These results support the use of L-moments in national hydrological guidelines for critical infrastructure design and highlight the need for further investigation into non-stationary models and regionalization techniques. Full article

This study evaluates the water quality of the Tamiš River, a tributary of the Danube, at the Jaša Tomić and Pančevo hydrological stations from 2011 to 2016 and from 2018 to 2022, using the Water Pollution Index (WPI) and seasonal analysis. The analysis revealed elevated nitrite and orthophosphate concentrations at both stations, with Pančevo exhibiting extreme microbial contamination in 2015, attributed to urban runoff, agricultural activities, and inadequate wastewater treatment. Seasonal results indicate that while spring conditions align with Class I water standards, summer presents critical risks, especially at Pančevo, where the highest WPI value (26.47 in 2015) was recorded. Autumn shows stabilization, though sporadic WPI peaks reflect the impact of nutrient runoff. Winter conditions are marked by stability, with favorable dissolved oxygen levels but occasional exceedances in heavy metals, particularly at Jaša Tomić. Increased concentrations of suspended solids and heavy metals at Jaša Tomić emphasize diverse pollution sources, including industrial discharges and soil erosion. These findings underscore the necessity of integrated water management strategies, such as wastewater treatment upgrades and sustainable agricultural practices, to mitigate pollution. Protecting the Tamiš River is crucial for supporting biodiversity, safeguarding public health, and ensuring sustainable use of this vital Danube tributary. Full article

This study assesses the spatial distribution and contamination levels of some heavy metals (HMs), i.e., cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), and lead (Pb), in seawater and surface sediments along the Romanian Black Sea coast (RBSC). Sampling was conducted at 40 stations across 12 transects during May–June 2021, and the measured levels of HM concentrations were compared with Environmental Quality Standards (EQS), i.e., maximum allowable concentration (MAC) values, for seawater and effects range-low (ERL) thresholds for sediments. HM concentrations were measured using high-resolution continuum source atomic absorption spectrometry (HR-CS AAS). In seawater, the levels of Cd, Cu, and Pb concentrations exceeded the MAC values at three stations located in areas influenced by the Danube River or anthropogenic activities. In sediments, exceedances of ERL thresholds were found for Ni at 11 stations, for Cu at three stations, and for Pb at one station. HM contamination of sediment samples collected from these stations can be caused by both natural and anthropogenic sources, e.g., the Danube River, rock/soil weathering and erosion, agricultural runoff, port and construction activities, maritime and road transport, coastal tourism, petrochemical industry, wastewater discharges, offshore oil and gas extraction. Principal Component Analysis (PCA) provided valuable information about the relationships between relevant variables, including water depth and HM concentrations in seawater and sediments, and potential sources of contamination. The results highlight the influence of fluvial inputs and localized human activities on HM contamination. While the overall chemical status of Romanian Black Sea waters and sediments remains favorable, targeted management strategies are needed to address localized pollution hotspots and mitigate potential ecological risks. These findings provide valuable insights for environmental monitoring and sustainable coastal management. Full article

The objective of this paper is to propose an artificial neural network (ANN) model to forecast the Danube River temperature at Chiciu–Călărași, Romania, bordered by Romanian and Bulgarian ecological sites, and situated upstream of the Cernavoda nuclear power plant. Given the temperature increase trend, the potential of thermal pollution is rising, impacting aquatic and terrestrial ecosystems. The available data covered a period of eight years, between 2008 and 2015. Using as input data actual air and water temperatures, and discharge, as well as air temperature data provided by weather forecasts, the ANN model predicts the Danube water temperature one week in advance with a root mean square deviation (RMSE) of 0.954 °C for training and 0.803 °C for testing. The ANN uses the Levenberg–Marquardt feedforward backpropagation algorithm. This feature is useful for the irrigation systems and for the power plants in the area that use river water for different purposes. The results are encouraging for developing similar studies in other locations and extending the ANN model to include more parameters that can have a significant influence on water temperature. Full article

In this study, we employed a novel machine learning (ML) methodology to predict water levels (WLs) from their constituent components at both entrances of a sea strait, namely the Bosphorus. The principal components of WLs in the strait are mean sea level pressure (MSLP), wind speeds (W, U, V), discharges from the Danube River (Q), and tidal conditions (T). Following the application of the t-test, SFS, PCA, and VIF analyses, and the consideration of a range of ML techniques (including Linear Regression (LR), Regression Trees (RT), Support Vector Machine Regression (SVMR), Gaussian Process Regression (GPR), and Artificial Neural Networks (ANNs)), the number of predictors was reduced in order to obtain the most flexible and accurate regression model. As a consequence of this process, MSLP, W, and Q were retained, while the remaining variables (tide) were excluded. Furthermore, the order of importance for the optimal regression model was identified as Q_lagged, MSLP, V_lagged, and U at the north entrance model, while at the south entrance model, the order was MSLP, Q_lagged, U, and V. The models were trained using 80%, 50%, and 33% of the data, respectively. The model trained on 80% of the data yielded the most accurate predictions, with a correlation coefficient of R ≅ 0.95 and a root mean square error (RMSE) of 0.02 m. The model demonstrated a markedly superior predictive capacity compared to previous studies in the region, which is attributed to two factors that are regarded as the novelty of the study. The first factor was the random selection of training data from each month of the year, which allowed for the representation of the general pattern of water level (WL) behaviours. The second factor was the selection of the physically most meaningful inputs, which were selected according to the results of the significance and multicollinearity check. Furthermore, the predicted and measured WLs were employed as boundary conditions in a hydrodynamic model to evaluate the predictive capacity of the predicted WLs on the current results in the strait in comparison to the use of observed WLs. The 80% data-trained model exhibited similar current velocities to the observed WL model used, whereas the 50% and 30% data-trained models yielded slightly different results. Full article

Large watercourses are characterised by significant spatial and temporal changes in water quality due to both natural and anthropogenic impacts. The paper analyses changes in the Danube’s spatial and seasonal water quality in its middle part at five monitoring stations for the period 2018–2022. Examined water quality parameters include oxygen saturation (Os), ammonium (N-NH₄), pH, 5-day biochemical oxygen demand (BOD), nitrate nitrogen (N-NO₃), orthophosphates (P-PO₄), suspended solids (SS), electrical conductivity (EC), and water temperature (WT). Furthermore, the analysis took into account the influence of two additional factors: air temperature (AT) and discharge (D). Throughout the entire period under study, all parameters were characterised by low concentration values, which met the environmental objective of good ecological status. The use of multivariate statistical methods allowed for the identification of EC, N-NH₄, WT, Os, SS, and N-NO₃ as determining the greatest spatio-seasonal variability of water quality in a selected section of the Danube. Regression models determined WT, EC and nitrogen nitrate changes as depending on AT, flow, and exposure time. Knowing models not only gives a better understanding of the dynamics of changes in water quality in the stretch of the Danube under study but potentially allows the prediction of these parameters based on easily measurable environmental variables. Full article

Eutrophication, driven by excessive nutrient enrichment from sources like agricultural runoff, industrial discharge, and urbanisation, has severely impacted the Black Sea since the 1980s. This study aimed to assess eutrophication dynamics in the Romanian Exclusive Economic Zone from 2020 to 2022 using the Black Sea Eutrophication Assessment Tool (BEAST), an integrated approach to the causes and effects of eutrophication. Data were collected from 68 stations during five oceanographic expeditions, analysing 617 water samples for nutrients, chlorophyll a, zooplankton species Noctiluca scintillans, and dissolved oxygen. Additionally, 179 zoobenthic and 251 phytobenthic community samples were collected. The results indicate that coastal waters exhibit higher nutrient levels and algal blooms compared to offshore waters, necessitating significant reductions in nutrient concentrations to achieve good environmental status. In transitional waters, within the Danube Delta Biosphere Reserve, a 55% reduction in inorganic phosphorus and a 43% reduction in inorganic nitrogen concentrations are required, while coastal waters need reductions of 38% and 37%, respectively. The study highlights the need for improved wastewater treatment, stricter agricultural runoff controls, and continuous monitoring. Effective ecosystem-based management strategies, integrated coastal zone management, and international cooperation are essential to mitigate eutrophication and promote the long-term health of the Black Sea ecosystem. Full article

In this study, the concentrations of metals and trace elements (As, Cd, Co, Cr, Cu, Hg, Ni, Pb and Zn) were determined in the muscle tissue of adult roach and white bream at two different sites in the Belgrade section of the Danube. Twenty-six fatty acids, consisting of nine saturated FA (SFAs), seven monosaturated FA (MUFAs) and ten polysaturated FA (PUFAs), were identified. The analysis of the concentration of metals and trace elements of the roach and white bream showed species-specific differences in their bioaccumulation. Four of all elements analyzed (As, Hg, Ni and Pb) correlated significantly with the changes in FA profiles in fish from both sampling sites, with the exception of Cu, which correlated with the FA profile at the site before, and Zn, whose concentration influenced the FA profile at the site after wastewater discharges. The lower PUFA content in the fish from a site under higher environment pressure could indicate that the fish are stressed. The results suggest that changes in lipid composition may be one of the protective mechanisms of cells to cope with anthropogenic stressors. Full article

The purpose of this study was to analyze the influence of solar activity described by the sunspot number (SSN) on certain terrestrial variables that might impact the Southeastern European climate at different spatio-temporal scales (the North Atlantic Oscillation Index, NAOI, and the Greenland–Balkan Oscillation Index, GBOI—on a large scale; the Palmer Hydrological Drought Index, PHDI—on a regional scale; the Danube discharge at the Orsova (lower basin), Q, representative of the Southeastern European climate—on a local scale). The investigations were carried out for the 20th century using the annual and seasonal averages. To find the connections between terrestrial (atmospheric and hydrological) parameters and SSN, the wavelet coherence were used both globally and in the time–frequency domain. The analyses were carried out for the time series and considered simultaneously (in the same year or season), as well as with lags from 1 to 5 years between the analyzed variables. For the annual values, the type of correlation (linear/non-linear) was also tested using elements from information theory. The results clearly revealed non-linear links between the SSN and the terrestrial variables, even for the annual average values. By applying the wavelet transform to test the solar influence on the terrestrial variables, it was shown that the connections depend on both the terrestrial variable, as well as on the considered lags. Since, in the present study, they were analyzed using wavelet coherence, but only the cases in which the coherence was significant for almost the entire analyzed time interval (1901–2000) and the terrestrial variables were in phase or antiphase with the SSN were considered. Relatively few results had a high level of significance. The analysis of seasonal averages revealed significant information, in addition to the analysis of annual averages. Thus, for the climatic indices, the GBOI and NAOI, a significant coherence (>95%) with the solar activity, associated with the 22-year (Hale) solar cycle, was found for the autumn season for lag = 0 and 1 year. The Hale solar cycle, in the case of the PHDI, was present in the annual and summer season averages, more clearly at lag = 0. For the Danube discharge at Orsova, the most significant SSN signature (~95%) was observed at periods of 33 years (Brüuckner cycle) in the autumn season for lags from 0 to 3 years. An analysis of the redundancy–synergy index was also carried out on the combination of the terrestrial variables with the solar variable in order to find the best synergistic combination for estimating the Danube discharge in the lower basin. The results differed depending on the timescale and the solar activity. For the average annual values, the most significant synergistic index was obtained for the combination of the GBOI, PHDI, and SSN, considered 3 years before Q. Full article

The subject of this study is an approximately 300 km long Middle Danube River reach that spans from river kilometer (rkm) 1581 in Hungary to (rkm) 1255 in Serbia. The observed drying of floodplains in Hungary some thirty years ago initiated the hydrological studies. However, problems related to the navigation route maintenance of the Danube River and those in the water supply of irrigation and drainage canal networks are now present in the whole free-flowing middle Danube region. The study aims at investigating the correlation between the observed water level decrease and recorded incision of the river bed at gauging stations and the indirect estimation of the long-term sediment transport along the sand-bed reach based on the surveillance cross-sections’ data collected during regular monitoring surveys on the navigable Danube. It starts with hydrological analyses of the 70-year-long time series of water level and discharge yearly data and continues with morphological and correlation studies. It ends with the estimation of sediment transport. The decreasing trend in water levels and the increasing trend in cross-sectional areas are persistent. There is a linear correlation between the two. Longitudinal changes in sediment transport indicate the existence of both degrading and aggrading riverbed reaches. Full article

This paper presents the extent to which the combination of extra-atmospheric and hydroclimatic factors can be deciphered to record their contribution to the evolution and forecasting of the Danube discharge (Q) in the lower basin. A combination of methods such as wavelet filtering and deep learning (DL) constitutes the basic method for discriminating the external factors (solar activity through Wolf numbers) that significantly contribute to the evolution and prediction of the lower Danube discharge. An ensemble of some of the most important factors, namely, those representing the atmospheric components, i.e., the Greenland-Balkan Oscillation Index (GBOI) and the North Atlantic Oscillation Index (NAOI); the hydroclimatic indicator, the Palmer Hydrological Drought Index (PHDI); and the extra-atmospheric factor, constitutes the set of predictors by means of which the predictand, Q, in the summer season, is estimated. The external factor has to be discriminated in the Schwabe and Hale spectra to make its convolutional contribution to the Q estimation in the lower Danube basin. An interesting finding is that adding two solar predictors (associated with the Schwabe and Hale cycles) to the terrestrial ones give a better estimation of the Danube discharge in summer, compared to using only terrestrial predictors. Based on the Nash–Sutcliffe (NS) index, a measure of performance given by the extreme learning machine (ELM), it is shown that, in association with certain terrestrial predictors, the contribution of the Hale cycle is more significant than the contribution of the Schwabe cycle to the estimation of the Danube discharge in the lower basin. Full article

Sediment balance is essential for understanding changes in river morphology and ecosystems and related services depending on them. However, the currently used methods to quantify riverine sediment processes are not adequate enough. We have examined the sediment regime of the Danube River, particularly the suspended sediment yield. This parameter can be calculated based on stage or discharge using a suspended sediment yield rating curve; however, the uncertainty of this method can reach even 150%. The suspended sediment yield of a section does not only depend on processes that take place in the riverbed; thus, it cannot be described by only one easily measurable parameter. An integrated surrogate method based on turbidity registration is tested in order to determine suspended sediment yield on the lower Hungarian (sand-bed alluvial) reach of the Danube River. The near-bank turbidity is converted into suspended sediment concentration and then into suspended sediment yield. The turbidity is measured with a built-in turbidity probe, while the suspended sediment yield is determined with traditional methods (discharge measurement, suspended sediment sampling, laboratory processing, and calculation). The traditional and integrated surrogate methods are compared based on the results of the measurements, and different aspect correlations are established between flow parameters, turbidity, and suspended load. The results achieved with the integrated method are promising, but more measurements are required in order to refine the relationships in a broader interval. Full article

The objective of the present work was to perform a 30-year analysis of some significant meteorological and hydrological processes along the Lower Danube. This was motivated by the fact that, due to the effects of climate change, the global configuration of the environmental matrix has suffered visible transformations in many places. Another important factor considered is related to the constant development noticed in the last few decades of European inland navigation, in general, and in the Lower Danube sector, in particular. From this perspective, the processes analysed were the wind speed at a 10 m height, the air temperature at a 2 m height, precipitation, and river discharge. The 30-year period of 1991–2020 was considered for analysis. The ERA5 reanalysis data were processed and analysed in the case of the first three processes, while for the river discharge, the data provided by the European Flood Awareness System were used. The emphasis was placed on the evolution of the extreme values and on the identification of the geographical locations with a higher probability of occurrence. The average values and the seasonal variations of the four processes were also considered. The results indicated that the maximum wind speed and air temperature values along the Lower Danube did not suffer significant changes in the last few decades. However, the values of the minimum air temperatures increased with an average value of about 0.8 °C per decade, and the same tendency was noticed also for the average temperatures. Regarding the precipitation, the trend indicated a tendency to decrease by about 0.5 mm per decade, while for the river discharge, a clear increase of more than 1200 m³/s corresponded to each ten-year period. Finally, it can be concluded that the present analysis provided a global and more comprehensive perspective of the recent environmental dynamics along the Lower Danube, delivering useful information for inland navigation, as well as for other human activities. Full article

Managed aquifer recharge (MAR) is an increasingly popular technique; however, the significance of groundwater flow dynamics is rarely examined in detail regarding MAR systems. In general, a high hydraulic gradient is not favoured for MAR implementation, as it causes higher water loss and mixing of recharge water with native groundwater. However, during groundwater-dependent ecosystem (GDE) rehabilitation, these hydraulic gradient-driven flow processes can be taken advantage of. The aim of this research is to test this hypothesis by evaluating the effect of groundwater table inclination, topography, and other local characteristics on MAR efficiency from the perspective of GDE restoration. MAR efficiency was examined from recharge to discharge area in a simple half-basin based on theoretical flow simulations, using GeoStudio SEEP/W software. Different scenarios were compared to analyse the groundwater level increase and the infiltrated water volumes and to assess the efficiency of MAR based on these parameters in each scenario. The theoretical results were applied to a close-to-real situation of Lake Kondor, a GDE of the Danube-Tisza Interfluve (Hungary), which dried up in the past decades due to groundwater decline in the area. Based on the results, initial hydraulic head difference, model length, and hydraulic conductivity are the most critical parameters regarding water level increase at the discharge area. The water amount needed for increasing the water table is mainly influenced by the thickness of the unsaturated zone and the material properties of the aquifer. The findings can help better understand MAR efficiency in light of local groundwater flow processes and contribute to optimising MAR systems. The results of the study suggest that, if water is infiltrated at the local recharge area, the water table will also increase at the corresponding discharge area, which positively effects the connected GDEs. This approach can serve as a nature-based solution (NBS) to sustain sensitive ecosystems in changing climatic conditions. Full article

Climate change affects the hydrological cycle of river basins and strongly impacts water resource availability. The mechanistic hydrological model PROMET was driven with an ensemble of EURO-CORDEX regional climate model projections under the emission scenarios RCP2.6 and RCP8.5 to analyze changes in temperature, precipitation, soil water content, plant water stress, snow water equivalent (SWE) and runoff dynamics in the Danube River Basin (DRB) in the near (2031–2060) and far future (2071–2100) compared to the historical reference (1971–2000). Climate change impacts remain moderate for RCP2.6 and become severe for RCP8.5, exhibiting strong year-round warming trends in the far future with wetter winters in the Upper Danube and drier summers in the Lower Danube, leading to decreasing summer soil water contents, increasing plant water stress and decreasing SWE. Discharge seasonality of the Danube River shifts toward increasing winter runoff and decreasing summer runoff, while the risk of high flows increases along the entire Danube mainstream and the risk of low flows increases along the Lower Danube River. Our results reveal increasing climate change-induced discrepancies between water surplus and demand in space and time, likely leading to intensified upstream–downstream and inter-sectoral water competition in the DRB under climate change. Full article