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Abstract. Impairment of water quality by organic micropollutants such as pesticides, pharmaceuticals or household chemicals is a problem in many catchments worldwide. These chemicals originate from different urban and agricultural usages and are transferred to surface waters from point or diffuse sources by a number of transport pathways. The quantification of this form of pollution in streams is challenging and especially demanding for diffuse pollution due to the high spatio-temporal concentration dynamics, which require large sampling and analytical efforts to obtain representative data on the actual water quality.Models can also be used to predict to what degree streams are affected by these pollutants. However, spatially distributed modelling of water quality is challenging for a number of reasons. Key issues are the lack of such models that incorporate both urban and agricultural sources of organic micropollutants, the large number of parameters to be estimated for many available water quality models, and the difficulty to transfer parameter estimates from calibration sites to areas where predictions are needed.To overcome these difficulties, we used the parsimonious iWaQa model that simulates herbicide transport from agricultural fields and diffuse biocide losses from urban areas (mainly façades and roof materials) and tested its predictive capabilities in the Rhine River basin. The model only requires between one and eight global model parameters per compound that need to be calibrated. Most of the data requirements relate to spatially distributed land use and comprehensive time series of precipitation, air temperature and spatial data on discharge. For larger catchments, routing was explicitly considered by coupling the iWaQa to the AQUASIM model.The model was calibrated with datasets from three different small catchments (0.5-24.6 km 2 ) for three agricultural herbicides (isoproturon, S-metolachlor, terbuthylazine) and two urban biocides (carbendazim, diuron). Subsequently, it was validated for herbicides and biocides in Switzerland for different years on 12 catchments of much larger size (31-35 899 km 2 ) and for herbicides for the entire Rhine basin upstream of the Dutch-German border (160 000 km 2 ) without any modification. For most compound-catchment combinations, the model predictions revealed a satisfactory correlation (median r 2 : 0.5) with the observations. The peak concentrations were mostly predicted within a factor of 2 to 4 (median: 2.1 fold difference for herbicides and 3.2 for biocides respectively). The seasonality of the peak concentration was also well simulated; the predictions of the actual timing of peak concentrations, however, was generally poor.Limited spatio-temporal data, first on the use of the selected pesticides and second on their concentrations in the river network, restrict the possibilities to scrutinize model performance. Nevertheless, the results strongly suggest that input data and model structure are major sources of predictive uncertainty. The latter is for exampl...

Abstract. Impairment of water quality by organic micropollutants such as pesticides, pharmaceuticals or household 10 chemicals is a problem in many catchments worldwide. These chemicals originate from different urban and agricultural usages and are transferred to surface waters from point or diffuse sources by a number of transport pathways. The quantification of this form of pollution in streams is challenging and especially demanding for diffuse pollution due to the high spatio-temporal concentration dynamics, which requires large sampling and analytical efforts to obtain representative data on the actual water quality. 15 Models can also be used to predict information to which degree streams are affected by these pollutants. However, spatially distributed modeling of water quality is challenging for a number of reasons. Key issues are the lack of such models that incorporate both urban and agricultural sources of organic micropollutants, the large number of parameters to be estimated for many available water quality models, and the difficulty to transfer parameter estimates from calibration sites to areas 20 where predictions are needed.To overcome these difficulties, we used the parsimonious iWaQa model that simulates herbicide transport from agricultural fields and diffuse biocide losses from urban areas (mainly façades and roof materials) and tested its predictive capabilities in the Rhine River basin. The model only requires between one and eight global model parameters per compound that need to 25 be calibrated. Most of the data requirements relate to spatially distributed land use and comprehensive time series of precipitation, air temperature and spatial data on discharge.The model was calibrated with data sets from three different small catchments (0.5 -24.6 km 2 ) for three agricultural herbicides (isoproturon, S-metolachlor, terbuthylazine) and two urban biocides (carbendazim, diuron). Subsequently, it was 30 validated for different years on 12 catchments of much larger size (31 -160'000 km 2 ) without any modification. For most compound-catchment combinations, the model predictions revealed a satisfactory correlation (median r 2 : 0.5) with the observations and the peak concentrations mostly predicted within a factor of two to four (median: 2.1 fold difference for herbicides and 3.2 for biocides respectively). The seasonality of the peak concentration was also well simulated, the predictions of the actual timing of peak concentrations however, was generally poor. 35Limited spatio-temporal data, first on the use of the selected pesticides and second on their concentrations in the river network, restrict the possibilities to scrutinise model performance. Nevertheless, the results strongly suggest that input data and model structure are major sources of predictive uncertainty. The latter is for example seen in background concentrations that are systematically overestimated in certain regions, which is most probably linked to the modelled coupling of 40 background concentrations to land use intensity.H...

Accelerating energy transitions that are both sustainable and just remains an important challenge, and social innovation can have a key role in this transition. Here, we examine the diversity and potential of social innovation in energy systems transformation, synthesizing original mixed methods data from expert interviews, document analysis, social innovation experiments, a representative survey, and an expert survey. Based on a thematic analysis of these data, we advance four key findings: (1) the diversity of social innovation in energy is best understood when recognizing core social practices (thinking, doing, and organizing) and accounting for changes in social relations (cooperation, exchange, competition, and conflict); (2) governance, policy networks, and national context strongly shape social innovation dynamics; (3) processes of social innovation are implicated by multidimensional power relations that can result in transformative changes; and (4) social innovation in energy generally has strong social acceptance among citizens, benefits local communities and is legitimized in key community and city organizations. We discuss an agenda for 9 future research directions on social innovation in energy, and conclude with insights related to national context, governance, and acceleration.