Transit time distributions (TTDs) are crucial descriptors of flow and transport processes in catchments, which can be determined from stable water isotope data. Recently, the young water fraction (F yw ) has been introduced as an additional metric derivable from seasonal isotope cycles. In this study, we calculated F yw and TTDs using monthly isotope data from 24 contrasting subcatchments in a mesoscale catchment (3,300 km 2 ) in Germany. F yw ranged from 0.01 to 0.27 (mean = 0.11) and was smallest in mountainous catchments. Assuming gamma-shaped TTDs, we determined stationary TTDs with the convolution integral method for each subcatchment. The convolution integral was first calibrated against the isotope data only (i.e., traditional calibration) and, second, using a multiobjective calibration with the F yw estimates as an additional constraint. This yielded largely differing TTD parameters even for neighboring catchments, with F yw values below 0.1 generally involving a delayed peak in TTDs (i.e., gamma-distribution shape parameter > 1). While the traditional calibration resulted in large uncertainties in TTD parameters, these uncertainties were reduced with the multiobjective calibration, thereby improving the assessment of mean transit times (2 years on average, ranging between 9.6 months and 5.6 years). This highlights the need for uncertainty assessment when using simple isotope models and shows that the traditional calibration might not yield an optimum solution in that it may give a TTD nonconsistent with F yw . Given the robustness of F yw estimates, isotope models should thus aim at accurately describing both F yw and measured isotope data in order to improve the description of flow and transport in catchments.Plain Language Summary Information on the age of river water is crucial for assessing the vulnerability of rivers to weather extremes and pollution. The age of river water is defined as the time that water has spent underground after rainfall infiltration and before ending up in the river. The probability distribution of river water age can be determined using environmental tracers, which are tracers that naturally occur in the system such as stable water isotopes. In this study, we used isotope models to analyze time series of stable water isotopes in rainfall and streamwater measured in 24 subcatchments of the Bode catchment in central Germany. We found that the mean age of river water ranges between 9.6 months and 5.6 years depending on catchment characteristics such as climate and soil type. Moreover, river water with an age of below 2 to 3 months accounts for between 1% and 27% of the entire age distribution. We demonstrate how to use this information on young river water to constrain other metrics such as the mean water age. We suggest that this method is valuable for future studies using environmental tracers and models to determine water age in catchments.

The present monitoring and assessment of the chemical status of water bodies fail to characterize the likelihood that complex mixtures of chemicals affect water quality. The European Collaborative Project SOLUTIONS suggests that this likelihood can be estimated with effect-based methods (EBMs) complemented by chemical screening and/or impact modeling. These methods should be used to identify the causes of impacted water quality and to develop programs of measures to improve water quality. Along this line of reasoning, effect-based methods are recommended for Water Framework Directive (WFD) monitoring to cover the major modes of action in the universe of environmentally relevant chemicals so as to evaluate improvements of water quality upon implementing the measures. To this end, a minimum battery of bioassays has been recommended including short-term toxicity to algae, Daphnia and fish embryos complemented with in vitro and short-term in vivo tests on mode-of-action specific effects as proxies for long-term toxicity. The likelihood of adverse impacts can be established with effect-based trigger values, which differentiate good from poor water quality in close alignment with Environmental Quality Standards for individual chemicals, while taking into account mixture toxicity. The use of EBMs is suggested in the WFD as one avenue to establish the likelihood of adverse effects due to chemical pollution in European water systems. The present paper has been written as one component of a series of policy briefs to support decisions on water quality monitoring and management under the WFD.

Currently, chemical monitoring based on priority substances fails to consider the majority of known environmental micropollutants not to mention the unexpected and unknown chemicals that may contribute to the toxic risk of complex mixtures present in the environment. Complementing component-and effect-based monitoring with widescope target, suspect, and non-target screening (NTS) based on high-resolution mass spectrometry (HRMS) data is recommended to support environmental impact and risk assessment. This will allow for detection of newly emerging compounds and transformation products, retrospective monitoring efforts, and the identification of possible drivers of toxicity by correlation with effects or modelling of expected effects for future and abatement scenarios. HRMS is becoming increasingly available in many laboratories. Thus, the time is right to establish and harmonize screening methods, train staff, and record HRMS data for samples from regular monitoring events and surveys. This will strongly enhance the value of chemical monitoring data for evaluating complex chemical pollution problems, at limited additional costs. Collaboration and data exchange on a European-to-global scale is essential to maximize the benefit of chemical screening. Freely accessible data platforms, inter-laboratory trials, and the involvement of international partners and networks are recommended.

The present monitoring and assessment of water quality problems fails to characterize the likelihood that complex mixtures of chemicals affect water quality. The European collaborative project SOLUTIONS suggests that this likelihood can be estimated, amongst other methods, with improved component-based methods (CBMs). The use of CBMs is a well-established practice in the WFD, as one of the lines of evidence to evaluate chemical pollution on a per-chemical basis. However, this is currently limited to a pre-selection of 45 and approximately 300 monitored substances (priority substances and river basin-specific pollutants, respectively), of which only a few actually co-occur in relevant concentrations in real-world mixtures. Advanced CBM practices are therefore needed that consider a broader, realistic spectrum of chemicals and thereby improve the assessment of mixture impacts, diagnose the causes of observed impacts and provide more useful water management information. Various CBMs are described and illustrated, often representing improvements of well-established methods. Given the goals of the WFD and expanding on current guidance for risk assessment, these improved CBMs can be applied to predicted or monitored concentrations of chemical pollutants to provide information for management planning. As shown in various examples, the outcomes of the improved CBMs allow for the evaluation of the current likelihood of impacts, of alternative abatement scenarios as well as the expected consequences of future pollution scenarios. The outputs of the improved CBMs are useful to underpin programmes of measures to protect and improve water quality. The combination of CBMs with effect-based methods (EBMs) might be especially powerful to identify as yet underinvestigated emerging pollutants and their importance in a mixture toxicity context. The present paper has been designed as one in a series of policy briefs to support decisions on water quality protection, monitoring, assessment and management under the European Water Framework Directive (WFD).

The European Union Water Framework Directives aims at achieving good ecological status in member states' water bodies. Insufficient ecological status could be the result of different interacting stressors, among them the presence of many thousands of chemicals. The diagnosis of the likelihood that these chemicals negatively affect the ecological status of surface waters or human health, and the subsequent development of abatement measures usually relies on water quality monitoring. This gives an incomplete picture of chemicals' contamination, due to the limited number of monitoring stations, samples and substances. Information gaps thus limit the possibilities to protect against and effectively manage chemicals in aquatic ecosystems. The EU FP7 SOLUTIONS project has developed and validated a collection of integrated models ("Model Train") to increase our understanding of issues related to emerging chemicals in Europe's river basins and to complement information and knowledge derived from field data. Unlike pre-existing models, the Model Train is suitable to model mixtures of thousands of chemicals, to better approach a "real-life" mixture exposure situation. It can also be used to model new chemicals at a stage where not much is known about them. The application of these models on a European scale provides temporally and spatially variable concentration data to fill gaps in the space, time and substance domains left open by water quality monitoring, and it provides homogeneous data across Europe where water quality data from monitoring are missing. Thus, it helps to avoid overlooking candidate chemicals and possible hot spots for management intervention. The application of the SOLUTIONS Model Train on a European scale presents a relevant line of evidence for water system level prognostic and diagnostic impact assessment related to chemical pollution. The application supports the design of cost-effective programmes of measures by helping to identify the most affected sites and the responsible substances, by evaluating alternative abatement options and by exploring the consequences of future trends. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.