Ecological Status Of Water Bodies Research Articles

Review Paper: A Quarter of a Century of the European Water Framework Directive — The Slow Path Towards Sustainable Water Management

The European Water Framework Directive (WFD) is one of the most analyzed environmental legislations in the scientific literature, influencing the water management in some non-European countries. The WFD has the strong ambition of achieving a good ecological status of water bodies across all river basins in Europe. However, the advances towards sustainable management are falling far behind the planned schedule. The emphasis of the Directive is focused on water quality rather than on water quantity. The advances during the last quarter of the century since its inception have been strong on urban and industrial point pollution, but not on agricultural non-point pollution that remains high and even increases in major basins. Water quantity aspects have been mostly left aside in the Directive, despite the fact that water scarcity is a serious problem in Southern European countries, and will become more critical with climate change in most basins across Europe. Some policy measures of the WFD need to be reformed, in particular measures for abating agricultural pollution, and new measures for addressing water scarcity. The narrow focus of the WFD on water pricing to solve at the same time issues of financing, water allocation and efficiency, environment, opportunity costs and pollution abatement, should be broadened. The challenge is giving more emphasis to command & control and collective action instruments, and designing combinations of instruments adapted to sectoral and spatial locations in basins. This overhaul of the water policy instruments by the European Commission will be needed to advance in the sustainable management of river basins in Europe.

Target Screening of Chemicals of Emerging Concern (CECs) in Surface Waters of the Swedish West Coast

The aquatic environment faces increasing threats from a variety of unregulated organic chemicals originating from human activities, collectively known as chemicals of emerging concern (CECs). These include pharmaceuticals, personal-care products, pesticides, surfactants, industrial chemicals, and their transformation products. CECs enter aquatic environments through various sources, including effluents from wastewater treatment plants, industrial facilities, runoff from agricultural and residential areas, as well as accidental spills. Data on the occurrence of CECs in the marine environment are scarce, and more information is needed to assess the chemical and ecological status of water bodies, and to prioritize toxic chemicals for further studies or risk assessment. In this study, we describe a monitoring campaign targeting CECs in surface waters at the Swedish west coast using, for the first time, an on-site large volume solid phase extraction (LVSPE) device. We detected up to 80 and 227 CECs in marine sites and the wastewater treatment plant (WWTP) effluent, respectively. The dataset will contribute to defining pollution fingerprints and assessing the chemical status of marine and freshwater systems affected by industrial hubs, agricultural areas, and the discharge of urban wastewater.

From national monitoring to transnational indicators: reporting and processing of aquatic biology data under the European Environment Agency’s State of the Environment data flow

Biological monitoring data from aquatic ecosystems are collected from European countries on a yearly basis by the European Environment Agency (EEA) through the Water Information System for Europe (WISE). The WISE-SoE (State of Environment) data flows provide indicators of pressures, states and impacts of surface waters and groundwaters on a pan-European scale. The WISE-2 Biology was established to obtain a harmonised flow of biology data reported annually as Ecological Quality Ratios (EQRs) from European surface waters, as a supplement to the mandatory 6-yearly reporting of ecological status of water bodies for the Water Framework Directive. The purposes of this paper are 1) to describe the compilation of national aquatic biology monitoring data indicators and to inform about the public availability of these data, 2) to give an overview of the reported data and indicate the potential for assessments based on these data, and 3) to illustrate the potential for further use of the underlying species abundance data in biodiversity research and assessment. WISE-2 data are reported for the following biological quality elements: phytoplankton, phytobenthos, macrophytes, macroalgae, angiosperms, benthic invertebrates and fish in rivers, lakes, transitional and/or coastal waters. The EQR values represent the deviation from reference conditions. The final processed and quality-checked data are published in EEA’s database Waterbase - Biology, which currently holds data from more than 13,000 waterbodies in 26 countries from the reporting years 2011–2021. Examples of time series aggregated by geographic regions give an indication of the type of trends that can be obtained from the reported data at the nEQR scale. However, the current results are representative only for certain geographic regions with high coverage of water bodies. Within the European research project EuropaBON (Europa Biodiversity Observation Network), the use of WISE-2 data can be leveraged to support biodiversity policy and conservation planning. EuropaBON’s online database provides an overview of how biodiversity monitoring schemes across Europe flows through different integration nodes, to produce Essential Biodiversity Variables and other policy-relevant indicators. Here, we use the EuropaBON visualisation tool to illustrate the WISE-2 as a European integration node for 157 biology datasets via the national integration nodes.

Evaluating the riparian forest quality index (QBR) in the Luchena River by integrating remote sensing, machine learning and GIS techniques

The Water Framework Directive (WFD 2000/60/EU) is a mandatory standard that aims to improve and protect water quality in Europe. It covers, among other issues, the need to establish particular reference conditions for assessing river ecosystems and defines the ecological status of water bodies and conserve the hydromorphological characteristics of rivers. The quality of riparian vegetation is an important component of stream status and contributes directly to a river's ecological stability. QBR index (“Qualitat del Bosc de Ribera”) is one of the most widely used methods of evaluating riparian quality. This paper presents a new methodological version of the QBR index (QBR-GIS) to assess the ecological status of riparian forests. For this purpose, we have considered the four major conceptual blocks of the QBR index (total vegetation cover, cover structure, cover quality and channel alteration) using geographically referenced information, remote sensing and machine learning techniques. To obtain the cover quality indicator, several vegetation indices were calculated and a sensitivity analysis was performed. The QBR-GIS was validated from the results obtained from the QBR index. QBR-GIS provides greater reliability and objectivity in the results. Furthermore, it reduces the time spent on field visits and increases accuracy in obtaining the status of riparian quality. Furthermore, it is a useful tool for landscape planning and management, improved ability to apply the QBR Index to larger areas of the river catchment, resulting in more information on riparian quality.

BIOTESTING OF THE ANTHROPOGENIC IMPACT ON AREAS OF THE R. ROS AND R. PROTOKA ON THE VIABILITY OF CARP FISH EGGS AND LARVAE

BIOTESTING OF THE ANTHROPOGENIC IMPACT ON AREAS OF THE R. ROS AND R. PROTOKA ON THE VIABILITY OF CARP FISH EGGS AND LARVAE

Nature-based solutions for nutrient pollution control in European agricultural regions: A literature review

There is an increasing attention on Nature-based Solutions (NBS) to address social and environmental challenges in Europe, including the control of excess nutrients conveyed by agricultural regions. Indeed, the excess of nutrients has been recognized as one of the main reasons of failure to achieve a good ecological status of water bodies, according to the European Water Framework Directive 2000/60/EC. In this study we consider NBS to control two main sources of nutrient pollution in agricultural regions: excess manure from intensive livestock breeding, and diffuse pollution due to fertilizers applied on fields. The NBS typically adopted to address these two sources of pollution are wetlands in addition, for diffuse pollution, to vegetated drainage ditches and buffer strips. From a review of 767 peer review articles, we built a dedicated dataset including a total number of 444 NBS cases. These were analysed to obtain an overview of: (i) the range of climate, landscape and design variables under which the above typologies of NBS have been implemented; (ii) their performances for nutrient pollution control, in terms of removal efficiency on concentrations and mass load removal per unit of surface; (iii) the relationships between landscape, climate, and design variables and treatment performance. The results are presented in order to guide future planning of NBS for pollution control in European agricultural regions.

Recent regional changes in nutrient fluxes of European surface waters

We have quantified inputs and fate of nutrients in European fresh and marine waters from 1990 to 2018. We have used the conceptual model GREEN to assess the impact of efforts on curbing nutrient pollution in European regions. In the first two decades, i.e. in the 1990s and through the start of the new millennium, nutrient inputs to waters decreased significantly. Nutrient pollution in freshwaters and to the sea largely reduced in all regions, although at different pace. However, around 2008–2010 trends in nutrient inputs changed, marking an increase in the last decade, particularly from agricultural diffuse sources. In some regions, current nutrient inputs to waters are close to those estimated at the beginning of the 1990s. At the end of the study period, nutrient concentrations in freshwaters remain above thresholds congruent with good ecological status of water bodies in most downstream reaches. European policies tackling point sources are close to reach their maximum impact. In the face of this approaching ceiling, sustainable nutrient management on agricultural land becomes pivotal for effective nutrient control in river basins. The regional approach highlighted differences across Europe that may provide tailored opportunities to plan effective strategies for achieving environmental targets.

Prediction of ecological status of surface water bodies with supervised machine learning classifiers

Ensuring a good ecological status of water bodies is one of the key challenges of communities and one of the objectives of the European Water Framework Directive. Although recent works identified the most significant stressors affecting the ecological quality of rivers, the ability to predict the overall ecological status of rivers based on a limited amount of easily accessible geospatial data has not been investigated so far. Most of the analyses focus on detailed local modelling and measurements which cannot be systematically applied at regional scales for the purposes of water resources management. The aim of this work is to understand the capabilities of five supervised machine learning classifiers of predicting the ecological status of rivers based on land use, climate, morphology, and water management parameters extracted over the river catchments corresponding to the ecological monitoring stations. Moreover, the performances of machine learning classifiers are compared to the results of the canonical correlation analysis. The method is applied to 360 catchments in Tuscany (central Italy) with a median size of 33.6 km2 and a Mediterranean climate. The results show (i) a significant correlation of ecological status with summer climate (i.e., maximum temperatures and minimum precipitation), land use and water exploitation, (ii) an 80 % precision of Random Forest algorithm to predict ecological status and (iii) higher capability of all classifiers to predict at least good ecological status. In perspective, such predictive capabilities can support decision making in the land and water resources management and highlight strategies for river eco-hydrological conservation.

Computer-Assisted Bioidentification Using Freshwater Macroinvertebrates: A Scoping Review

Background: Evaluation and prediction of the freshwater status based on freshwater macroinvertebrates (FwM) has become valuable in bioindication because they provide a more general and accurate picture of the ecological status of water bodies over time. Recent research on bioindication through FwM has increased the use of computational technologies, mainly in the classification and data analysis stages of water quality assessment and prediction. Objective: This scoping review aims to provide an overview of different approaches in computer-assisted bioindication with FwM. Particularly, the objective is to identify the techniques and strategies employed for FwM automatic classification or data treatment, characterize their use in recent years, and discuss gaps and challenges to broaden the scope of bioindication as a tool for understanding real conditions in a water body. Design: The scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) extension for scoping reviews (ScR). Scopus and Web of Science databases were used to identify articles published between 1999 and 2022. We selected 81 publications that used computational technology for automatic FwM classification or data analysis to predict water quality using biological indices. Results and conclusions: We identified two areas of applying computational technologies in bioindication studies with FwM. Firstly, computer-assisted technologies are used to evaluate water quality through samples already classified by human experts which correspond to 57% of the documents analyzed. The second application area is the automatic classification of FwM. In addition, we determined the main critical factors affecting strategy selection in each of the studies, such as taxonomic resolution, sample size and quality, image quality, data size, and complexity. Finally, we established the relationship between the strategies and algorithms employed in a timeline for automatic classification according to available FwM image databases. The research will allow taxonomic and related experts to better understand the role of computational technologies in FwM studies and thus increase confidence in these techniques to extend their use in bioassessment tasks.

Negative influence of a threatened species on ecological status classification: A case study of the influence of European eel within the Swedish fish index VIX

Biological indicators are important quality elements for classification of ecological status of water bodies according to the European Water Framework Directive. Multimetric indices are commonly regarded as robust and reliable indicators of human impact and are often used as quality elements. In fish-based indices, species are often grouped into guilds based on general tolerance to common anthropogenic pressures, with higher proportion of tolerant species being indicative of degraded systems. Within the Swedish electrofishing index VIX, the critically endangered European eel Anguilla anguilla (L.) is classified as a tolerant indicator species, and it therefore has a negative effect on classified ecological status. The scientific literature, however, suggests that eels are not generally tolerant and they benefit from similar environmental conditions as many insensitive species. VIX has been criticized for being too sensitive to the presence of eel in catch data, leading to low status classifications when eels are caught in the monitoring surveys. In a case study using manipulations of historical electrofishing data, we assessed the influence of eel presence and abundance on the ecological status classification as determined by VIX. We demonstrate that reduction of eels in survey data have positive effects on the classified status, in many cases substantial effects. An increase of eels in the data had the reverse effect. Mere presence of eel had a strong negative effect, which is problematic if the aim is to increase the endangered eel population. Given the Swedish classification system where the quality element indicating the worst status is decisive, the classified ecological status of Swedish rivers can theoretically be improved by management actions disfavouring eel, unless the results from VIX are carefully evaluated by experts. Along the same lines, measures implemented with an aim to increase the endangered eel population will lead to a decrease in assessed ecological status of Swedish rivers. Our conclusion is that the usage of VIX within Swedish water management is problematic and needs revision. From a broader perspective, the classification of species as generally tolerant need to be approached with great caution when developing new indices for assessing ecological status and integrity.

Low-flow estimation beyond the mean – expectile loss and extreme gradient boosting for spatiotemporal low-flow prediction in Austria

Abstract. Accurate predictions of seasonal low flows are critical for a number of water management tasks that require inferences about water quality and the ecological status of water bodies. This paper proposes an extreme gradient tree boosting model (XGBoost) for predicting monthly low flow in ungauged catchments. Particular emphasis is placed on the lowest values (in the magnitude of annual low flows and below) by implementing the expectile loss function to the XGBoost model. For this purpose, we test expectile loss functions based on decreasing expectiles (from τ=0.5 to 0.01) that give increasing weight to lower values. These are compared to common loss functions such as mean and median absolute loss. Model optimization and evaluation are conducted using a nested cross-validation (CV) approach that includes recursive feature elimination (RFE) to promote parsimonious models. The methods are tested on a comprehensive dataset of 260 stream gauges in Austria, covering a wide range of low-flow regimes. Our results demonstrate that the expectile loss function can yield high prediction accuracy, but the performance drops sharply for low expectile models. With a median R2 of 0.67, the 0.5 expectile yields the best-performing model. The 0.3 and 0.2 perform slightly worse, but still outperform the common median and mean absolute loss functions. All expectile models include some stations with moderate and poor performance that can be attributed to some systematic error, while the seasonal and annual variability is well covered by the models. Results for the prediction of low extremes show an increasing performance in terms of R2 for smaller expectiles (0.01, 0.025, 0.05), though leading to the disadvantage of classifying too many extremes for each station. We found that the application of different expectiles leads to a trade-off between overall performance, prediction performance for extremes, and misclassification of extreme low-flow events. Our results show that the 0.1 or 0.2 expectiles perform best with respect to all three criteria. The resulting extreme gradient tree boosting model covers seasonal and annual variability nicely and provides a viable approach for spatiotemporal modeling of a range of hydrological variables representing average conditions and extreme events.

Using an unmanned aerial vehicle (UAV) for lake management: ecological status, lake regime shift and stratification processes in a small Mediterranean karstic lake

High-resolution remote sensing imagery by unmanned aerial vehicles (UAVs) has been used as a tool for the environmental management of natural resources. Monitoring programmes that evaluate the ecological status of water bodies according to the Water Framework Directive (WFD) involve significant costs and sampling efforts that can be reduced by using UAVs. UAV imagery was used to measure some metrics of the “macrophytes and phytobenthos” biological quality element (BQE), which is required to assess the ecological status of European lakes; e.g. the percentage cover of hydrophytes and helophytes. Eight UAV flights took place during an annual cycle (July 2016 to July 2017) in a small karstic lake located in southeast Spain. Limnolog¬ical surveys of physicochemical (temperature, conductivity, dissolved oxygen, pH) and biological (pigments) parameters were simultaneously performed to correctly interpret the UAV images. For each flight performance, an orthomosaic of georeferenced RGB images was obtained, and the different features of interest were monitored and quantified by an automated identification and classification system (the LAIC software). The UAV images allowed us to not only evaluate the lake’s ecological status by measuring macrophyte metrics, but to also detect relevant ecological events for environmental management. A gradual burial process of charophyte meadows by the proliferation of periphytic cyanobacterial was detected in an early state by UAV images. Stratification processes, such as hypolimnetic sulphur bacteria blooms or metalimnetic white colloidal layers, were also ob¬served by UAV imaging. We conclude that UAV imagery is a useful tool for environmental lake management.

Machine Learning and Multiple Imputation Approach to Predict Chlorophyll-a Concentration in the Coastal Zone of Korea

The concentration of chlorophyll-a (Chl-a) is an integrative bio-indicator of aquatic ecosystems and a direct indicator that evaluates the ecological status of water bodies. In this study, we focused on predicting the Chl-a concentration in seawater using machine learning (after replacing missing values). To replace the missing values among marine environment observation data, a comparison experiment was performed using multiple built-in imputation methods (i.e., pmm, cart, rf, norm, norm.nob, norm.boot, and norm.predict) of the mice package in R. The cart method was selected as the most suitable. We generated each regression model using six machine learning algorithms (regression tree, support vector regression (SVR), bagging, random forest, gradient boosting machine (GBM), and extreme gradient boosting (XGBoost)) to predict the Chl-a concentration based on the complete imputed dataset. The prediction performance of the models was evaluated by four evaluation criteria using 10-fold cross-validation tests. XGBoost, an ensemble learning approach, outperformed other models in predicting the Chl-a concentration; SVR, a single model, also showed a good performance. The most important environmental factor in predicting the Chl-a concentration was an organic carbon particulate; however, dissolved oxygen also showed potential. This study was conducted with field observations in the spring and summer in the coastal zone of Korea. There exists a limit in machine learning applications, which excludes temporal and spatial factors. However, extensions to time series forecasting for deep learning or machine learning can lead to meaningful regional and seasonal analysis. It can also improve prediction performance as a result of the long-term data accumulation of field observations of more varied features (such as meteorological and hydrodynamic) besides water quality.

Water facilities of Ternopil: hydrography, ecological condition and water supply

The most sensitive to anthropogenic pressure in the city are water resources: surface and groundwater, water sources, etc. Therefore, the analysis of hydrographic features and ecological status of water bodies of the city of Ternopil is an urgent and important scientific and practical task. Purpose. Analysis of hydrographic features and ecological condition of water bodies of the Ternopil city, coverage of problems of pollution of the reservoir, quality of water in the Seret River and the main water intakes of the city Methods. Generalization and systematization, descriptive, SWOT-analysis, cartographic, geoinformation, geochemical assessment of the ecological status of water bodies of the city. Results. Generalization and systematization of data on the hydrographic network of the Ternopil city, allows us to state the fact that the main water bodies of the city is the Seret River – 242 km long (within the city 1.8 km) and the basin area 3900 km2; reservoir – an area of 300 hectares, nameless right tributary and canalized left tributary of the Seret River – the Rudka. The ecological condition of the Seret River within the city of Ternopil can be assessed as unsatisfactory. The main geoecological problems of the river are construction within the the coastal protection zone, water pollution by solid waste, exceeding the maximum allowable concentrations of suspended solids, sulfates, chlorides, nitrates and petroleum products. Environmental problems of the Ternopil reservoir are water pollution by chemical compounds of ammonium nitrogen, petroleum products, suspended solids and total iron; high siltation and pollution of the bottom sediments of the reservoir with heavy metals and mobile forms of biogenic elements. Water supply in the Ternopi cityl is carried out from two water intakes - Biletsky and Verkhnyo-Ivachivsky. Verkhno-Ivachivsky water intake is located at a distance of 3 km from the city landfill in the village of Malashivtsi. In the last few years, the process of drilling and using private artesian wells with a depth of more than 50 m has become widespread in the city. According to the results of the analysis, installed compliance with sanitary requirements of water samples in these wells, pH in all samples within normal limits (6,5-8,5). Conclusions. Current trends in climate change and the growth of anthropogenic pressure on water bodies, increases the risks of degradation and changes in some components of the hydrographic network of the city. In Ternopil, it is necessary to modernize the centralized water supply system, reconstruct rainwater collectors and strengthen control over unauthorized discharges into surface water bodies. It is also worth designing another water intake for the city in environmentally secure conditions.

The Macrophyte Indices for Rivers to Assess the Ecological Conditions in the Klina River in the Republic of Kosovo.

Macrophytes are important elements of aquatic ecosystems that grow in or near water. Their taxonomic composition, species diversity, depth, and density are indicators of environmental health; as such, Macrophytes are used to assess the ecological status of water bodies. Under the aim of assessing the ecological status of the Klina River in Kosovo, a survey was conducted at eight sampling sites along the river course to analyze macrophyte composition, diversity, density, and cover. Three samples were collected at each sampling site from early June to late September. The following macrophyte indices were used to assess the ecological status of the river: Macrophyte Index for Rivers (MIR), River Macrophyte Nutrient Index (RMNI), and River Macrophyte Hydraulic Index (RMHI). Our sampling area included the upper reaches of the river where no organic pollution was detected (oligotrophic), the middle reaches where polluted water from farms is discharged into the river, and the lower reaches characterized by heavy organic pollution from settlements and various industrial activities. There is a positive correlation (p < 0.05) between water temperature, turbidity, electrical conductivity (EC), total dissolved solids (TDS), orthophosphates (PO43−), ammonia (NH4+), nitrites (NO2−), calcium (Ca2+), and potassium (K+) with plant density, RMNI, RMHI, EQR-RMNI, EQR-RMHI, and MIR. Sodium (Na+) has stronger positive correlation (p < 0.01) with RMNI and RMHI indices and negative correlation with EQR-RMNI and EQR-RMHI. Our results show that ecological status along the river varies from high and good upstream to poor, bad, and moderate running downstream.

Analysis of a probabilistic approach for modelling and assessment of the water quality of rivers

Abstract Monitoring the ecological status of water bodies is crucial to guarantee human health and economic development. However, monitoring is often deficient in developing regions due to high installation and maintenance costs, thus it is frequently supported by water quality models, whose results are themselves affected by the lack of detailed input data. A possible solution is to use probabilistic models that consider the inherent uncertainty of the different inputs. In this research, we extended a simple water quality model (QUAL-UFMG, based on Qual2E) through Monte Carlo simulations to generate probabilistic results and applied it to a representative case study in Brazil. Results showed that, depending on the distribution of probabilities and variability of parameters adopted, the outcome of a non-deterministic modelling approach may differ significantly from a deterministic one regarding compliance with water quality standards. Moreover, the probabilistic strategy is more scientifically transparent and robust, as it explicitly communicates the uncertainty in both the measured data and modelling results. We conclude that a probabilistic approach is particularly useful in regions with a low data availability such as developing countries, as uncertainties are high due to insufficient monitoring, and the risk to human health is elevated due to a low prevalence of sanitation. HIGHLIGHTS A deterministic water quality model is expanded through Monte Carlo simulations. Outcomes of deterministic and probabilistic approaches to water quality modelling are compared for a case study in Brazil. There may be significant differences in modelling results regarding compliance to water quality standards depending on the approach taken; this is key in data scarce regions with deficient monitoring networks.

The influence of land use in the catchment area of small waterbodies on the quality of water and plant species composition

Land use significantly affects the function of waterbodies in landscape. Although there have been numerous studies on the influence of the catchment area on the trophic and ecological status of waterbodies, still is not reached an agreement on the width of the buffer zone that is necessary for effective protection of waterbodies. The aim of the study was to show whether small waterbodies are predominantly influenced by land use in the entire catchment area or only in the zone extending 100 or 200 m away from the shoreline. For two years the waters in six small waterbodies located in the Wielkopolska region (Poland) were analysed. The canonical correspondence analysis (CCA) showed that the influence of land use, especially agricultural land, was much greater within the zone extending 100 m from the shore line of the waterbodies than in the total catchment area. Therefore, it would be advisable to move the border of intensive farming areas beyond the entire 100-m-wide buffer zone, or at least to reduce the intensity of agriculture and to introduce diversified and perennial vegetation creating effective biogeochemical barriers.

Alien species and the Water Framework Directive: Recommendations for assessing ecological status in fresh waters in Norway

Abstract Invasive alien species are recognized as a significant anthropogenic threat to freshwater ecosystems, because they may bring about the local extinction of native species and the collapse of habitat types of special conservation concern. The Water Framework Directive (WFD) is an important legislative tool for the protection of water bodies in the European Union. It requires the assessment of the ecological status of water bodies and the enforcement of measures for water bodies that fail to achieve at least ‘good’ ecological status. However, the WFD does not explicitly mention alien species. This article presents a three‐tiered approach for the incorporation of data on alien species into WFD assessments of freshwater bodies in Norway. If relevant data on WFD parameters are available for the water body under consideration, its ecological status should be inferred from those data. Otherwise, ecological status should be assessed using evidence of the effects that the alien species have on biological quality elements in comparable water bodies. If neither of these options is feasible, the ecological status should be classified according to the ecological effect score of the alien species present, as obtained from the Generic Ecological Impact Assessment of Alien Species (GEIAA). It is further recommended that a water body cannot be assigned ‘high ecological status’ if at least one alien species is present. If more than one alien species is present, the status is determined by the species with the highest impact. Ecological, geographical, historical, and taxonomic delimitation criteria for this assessment method are presented, together with a list of the alien species that at present fulfil these delimitations in Norway. The management implications of the recommendations are that more Norwegian freshwater bodies are likely to require measures, and that further data must be collected. Most of the recommendations are transferable to other countries, although they may require adjustment.

WATER QUALITY FORECASTING UNDER THE INFLUENCE OF RUNOFF BASED ON A MATHEMATICAL MODEL

Runoff is one of the main sources of diffuse pollution of surface water. Suspended solids are the most typical contaminants of runoff. Suspended solids have a great influence on water quality and ecological status of water bodies. The content of suspended solids in water bodies is determined not only by their receipt from external sources, but also by the ability to transport sediments by flow. There is a permanent exchange of suspended solids between water bodies and bottom deposits. This fact stimulates specific requirements for modeling the transfer of suspended solids. For the most cases, models which describe the transfer of suspensions in a turbulent flow are based on the three-dimensional equation of turbulent diffusion or its two-dimensional simplification, which allows to take into account the spatial distribution of substances or it’s distribution to the width of stream. The use of such models requires a significant amount of initial data to determine the parameters of the models and is associated with a significant amount of calculations. At the same time, one-dimensional interpretation of processes is common and practically sufficient for shallow watercourses. It is more important to take into account the dynamics of the exchange of suspended solids between water mass and bottom deposits. The article is devoted to the development of a mathematical model for estimating the influence of non - point sources of pollution on the content of suspended solids in narrow watercourses. The model is based on the principle of mass balance of substances entering the flow section and takes into account the processes of sedimentation and resedimentation of suspended solids. A mathematical model in the form of a differential equation for the case of normal and overloaded flow is developed. Analytical solutions of equations for both cases are obtained. The influence of hydraulic size of the suspension on the content of suspended solids in watercourses and its distribution along the flow length is analyzed. The developed model was used for estimation of the impact of runoff from the urban area of Kharkiv city (Ukraine) on the river Lopan. The model demonstrated satisfactory compliance with field data.

Catchments of German surface water bodies

AbstractThe European Water Framework Directive demands to assess and report the chemical and ecological status of water bodies (WB). Linking their status to drivers and pressures and deriving suitable mitigation measures require knowledge of the shape and area of WB catchments. We derived a network of 26 570 WB catchments in Germany using the hydrologically‐defined drainage basins of the German federal states. We established a network of 338 149 drainage basins. This network underwent plausibility checks and a validation with the catchment areas of 348 monitoring stations across Germany. To this network, we assigned the longest intersecting or the next downstream WB code. To account for geometric inaccuracies we revised spurious intersections resulting in splittings and cycles in the WB network. As WB may be ecologically but not hydrologically well defined, we split them at confluences and intersections. The network of drainage basins matched the monitoring stations with a Nash‐Sutcliffe efficiency of 1.00. The final WB network contained 11 005 out of the 11 586 original WBs longer than 1 m. The corresponding local catchment areas range from &lt;&lt;0.0001 to 446 km2, with a median of 10 km2. The dataset combines the requirements of hydrological and ecological modelling applications at basin or national scales with the needs of the EU reporting which can foster their acceptance by state authorities and river‐basin management.