After 25 years of apparently successful eutrophication management, a record‐breaking mixed bloom of Ceratium furcoides and Aphanizomenon flos‐aquae developed unexpectedly in 2019 in the large, polymictic Lake Balaton. The peak concentration of chlorophyll exceeded 300 mg/m³, 1.5 times higher than the pre‐management maxima. The external load was insufficient to provide the phosphorus (P) required to support this bloom, and its taxonomic composition was radically different from blooms in the 1980s. We hypothesised: (1) that unusually long periods of intermittent stratification led to anoxic P release from the sediments, providing the required P; and (2) that the surprising bloom composition indicated a potential regime shift. To test hypothesis 1, we analysed decade‐long high‐frequency data on drivers of lake metabolism recorded at a shallow near‐shore observatory. To explore likely dissolved oxygen (DO) conditions in deep water in 2019, we performed a sensitivity analysis of a one‐dimensional DO model driven by the General Ocean Turbulence Model, the latter being calibrated against high‐frequency hydrometeorological data measured in the open water. Hypothesis 2 was examined by a sequential t‐test analysis applied to multidecadal (1976–2020) data on phytoplankton biomass and composition, nutrient loads and summer mean air temperature. We found that DO depletion (<1 g/m³) was the immediate precursor of the large 2019 bloom. The synergistic impact of a climatic regime shift and multiple coincident management actions provoked a shift in phytoplankton composition in 2013 and forced the ecosystem into the state of internal eutrophication. In the present ecosystem state, similarly large blooms could develop in any summer depending on the sequence of weather events. Within the present, warmer climate regime identified here, the effectiveness of external nutrient control in eutrophication management may be limited by changes in the thermal structure. Therefore, it is vital to supplement external nutrient control with internal nutrient control strategies, such as water level regulation, to manage eutrophication and prevent future blooms. Large, continuous polymictic lakes are susceptible to small changes in water level, because the likelihood of stratification (and thus the likeliness of a eutrophication event) increases steeply and non‐linearly with increasing depth. With a climate that continues to warm, and the associated changes expected in internal lake processes, it is possible that we will see regime shifts (such as that identified in Lake Balaton) across more lakes globally. Managing these conditions will require case‐specific modifications of lake management plans.

Phytoplankton biomass and production regulates key aspects of freshwater ecosystems yet its variability and subsequent predictability is poorly understood. We estimated within‐lake variation in biomass using high‐frequency chlorophyll fluorescence data from 18 globally distributed lakes. We tested how variation in fluorescence at monthly, daily, and hourly scales was related to high‐frequency variability of wind, water temperature, and radiation within lakes as well as productivity and physical attributes among lakes. Within lakes, monthly variation dominated, but combined daily and hourly variation were equivalent to that expressed monthly. Among lakes, biomass variability increased with trophic status while, within‐lake biomass variation increased with increasing variability in wind speed. Our results highlight the benefits of high‐frequency chlorophyll monitoring and suggest that predicted changes associated with climate, as well as ongoing cultural eutrophication, are likely to substantially increase the temporal variability of algal biomass and thus the predictability of the services it provides.

Many pharmaceuticals inevitably end up in surface waters, exerting unwanted biological activity in nontarget organisms. This effect is confined by the compound's environmental persistence. Regulatory laboratory simulation tests are used in persistence assessment and exposure modeling. While doubt has been expressed about the usefulness of laboratory‐derived persistence indicators under field conditions, these remain the only inputs for chemical fate models due to difficulties of measuring persistence in situ, especially at large scales. To improve understanding about relationships between laboratory experiments and the environmental fate in streams, we developed a mathematical model of biodegradation in stream networks and combined it with in‐stream monitoring data to (i) test if persistence could be evaluated from field data, (ii) check if persistence extracted from laboratory tests applied in the field, and (iii) locate hot spots of biodegradation in a large river basin. The model describes partitioning, and particle settling and resuspension, and is structurally compatible with those applied for evaluating laboratory simulation tests. Application to the Rhine river basin suggests that biotransformation rate constants extracted from laboratory tests underestimate those in the field, yet the percentage of biotransformation in the Rhine basin is less than in the laboratory tests due effective biotransformation being limited to small‐ and medium‐sized streams. In conclusion, our data show that biotransformation rates can accurately be predicted if (i) monitoring is performed across a wide range in stream order and (ii) precise estimates for consumption and removal rates at wastewater treatment plants are known.

Studying aquatic biotransformation of chemicals in laboratory experiments, i.e., OECD 308 and OECD 309 studies, is required by international regulatory frameworks to prevent the release of persistent chemicals into natural water bodies. Here, we aimed to address several previously described shortcomings of OECD 308/309 studies regarding their variable outcomes and questionable environmental relevance by broadly testing and characterizing a modified biotransformation test system in which an aerated water column covers a thin sediment layer. Compared to standard OECD 308/309 studies, the modified system showed little inter-replicate variability, improved observability of biotransformation, and consistency with first-order biotransformation kinetics for the majority of 43 test compounds, including pharmaceuticals, pesticides, and artificial sweeteners. To elucidate the factors underlying the decreased inter-replicate variability compared to OECD 309 outcomes, we used multidimensional flow cytometry data and a machine learning-based cell type assignment pipeline to study cell densities and cell type diversities in the sediment and water compartments. Our here presented data on cell type composition in both water and sediment allows, for the first time, to study the behavior of microbial test communities throughout different biotransformation simulation studies. We found that sediment-associated microbial communities were generally more stable throughout the experiments and exhibited higher cell type diversity than the water column-associated communities. Consistently, our data indicate that aquatic biotransformation of chemicals can be most robustly studied in test systems providing a sufficient amount of sediment-borne biomass. While these findings favor OECD 308-type systems over OECD 309-type systems to study biotransformation at the water–sediment interface, our results suggest that the former should be modified toward lower sediment–water ratios to improve observability and interpretability of biotransformation.

Identifying a chemical's potential for biotransformation in the aquatic environment is crucial to predict its fate and manage its potential hazards. Due to the complexity of natural water bodies, especially river networks, biotransformation is often studied in laboratory experiments, assuming that study outcomes can be extrapolated to compound behavior in the field. Here, we investigated to what extent outcomes of laboratory simulation studies indeed reflect biotransformation kinetics observed in riverine systems. To determine in-field biotransformation, we measured loads of 27 wastewater treatment plant effluent-borne compounds along the Rhine and its major tributaries during two seasons. Up to 21 compounds were detected at each sampling location. Measured compound loads were used in an inverse model framework of the Rhine river basin to derive k'_bio,field values - a compound-specific parameter describing the compounds' average biotransformation potential during the field studies. To support model calibration, we performed phototransformation and sorption experiments with all the study compounds, identifying 5 compounds that are susceptible towards direct phototransformation and determining K_oc values covering four orders of magnitude. On the laboratory side, we used a similar inverse model framework to derive k'_bio,lab values from water-sediment experiments run according to a modified OECD 308-type protocol. The comparison of k'_bio,lab and k'_bio,field revealed that their absolute values differed, pointing towards faster transformation in the Rhine river basin. Yet, we could demonstrate that relative rankings of biotransformation potential and groups of compounds with low, moderate and high persistence agree reasonably well between laboratory and field outcomes. Overall, our results provide evidence that laboratory-based biotransformation studies using the modified OECD 308 protocol and k'_bio values derived thereof bear considerable potential to reflect biotransformation of micropollutants in one of the largest European river basins.

In densely populated areas, surface waters are affected by many sources of pollution. Besides classical pollutants like nutrients and organic matter that lead to eutrophication, micropollutants from various point- and non-point sources are getting more attention by water quality managers. For cost-effective management an integrated assessment is needed that takes into account all relevant pollutants and all sources of pollution within a catchment. Due to the difficulty of identifying and quantifying sources of pollution and the need for considering long-term changes in boundary conditions, typically substantial uncertainty exists about the consequences of potential management alternatives to improve surface water quality. We therefore need integrated assessment methods that are able to deal with multiple objectives and account for various sources of uncertainty. This paper aims to contribute to integrated, prospective water management by combining a) multi-criteria decision support methods to structure the decision process and quantify preferences, b) integrated water quality modelling to predict consequences of management alternatives accounting for uncertainty, and c) scenario planning to consider uncertainty from potential future climate and socio-economic developments, to evaluate the future cost-effectiveness of water quality management alternatives at the catchment scale. It aims to demonstrate the usefulness of multi-attribute value functions for water quality assessment to i) propagate uncertainties throughout the entire assessment procedure, ii) facilitate the aggregation of multiple objectives while avoiding discretization errors when using categories for sub-objectives, iii) transparently communicate the results. We show how to use such multi-attribute value functions for model-based decision support in water quality management. We showcase the procedure for the Mönchaltorfer Aa catchment on the Swiss Plateau. We evaluate ten different water quality management alternatives, including current practice, that tackle macro- and micropollutants from a wide spectrum of agricultural and urban sources. We evaluate costs and water quality effects of the alternatives under four different socio-economic scenarios for the horizon 2050 under present and future climate projections and visualize their uncertainty. While the performance of alternatives is catchment specific, the methods can be transferred to other places and other management situations. Results confirm the need for cross-sectoral coordination of different management actions and interdisciplinary collaboration to support the development of prospective strategies to improve water quality.