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Paved surfaces, increased precipitation intensities in addition to limited capacity in the sewer systems, cause a higher risk of combined sewer overflows (CSOs). Sustainable drainage systems (SUDS) offer an alternative approach to mitigate CSO by managing the stormwater locally. Seven SUDS scenarios, developed based on the concept of effective impervious area reduction, have been implemented in the Grefsen catchment using the Mike Urban model. This study evaluated the hydrological performance of two SUDS controls (i.e. green roof (GR) and rain garden (RG)) modules of the model and the effect of the SUDS scenarios on the CSOs using event-based and continuous simulations. The Nash-Sutcliffe efficiency (NSE) along with flow duration curves (FDCs) has been used for evaluating the model performance. Event-based evaluations revealed the superior performance of the RG in reducing CSOs for larger precipitation events, while GRs were proven to have beneficial outcomes during smaller events. The study illustrated another way of assessing the continuous simulations by employing the FDCs. The FDCs were assessed against a discharge threshold at the outlet (which authorities can set as design criteria) of the catchment in terms of the extent, each scenario reduced occurrence and duration of outflow that invokes flow in the overflow pipe. [ABSTRACT FROM AUTHOR]

Floods are one of the major climate-related hazards and cause casualties and substantial damage. Accurate and timely flood forecasting and design flood estimation are important to protect lives and property. The Distance Distribution Dynamic (DDD) is a parsimonious rainfall-runoff model which is being used for flood forecasting at the Norwegian flood forecasting service. The model, like many other models, underestimates floods in many cases. To improve the flood peak prediction, we propose a dynamic river network method into the model. The method is applied for 15 catchments in Norway and tested on 91 flood peaks. The performance of DDD in terms of KGE and BIAS is identical with and without dynamic river network, but the relative error (RE) and mean absolute relative error (MARE) of the simulated flood peaks are improved significantly with the method. The 0.75 and 0.25 quantiles of the RE are reduced from 41% to 23% and from 22% to 1%, respectively. The MARE is reduced from 32.9% to 15.7%. The study results also show that the critical support area is smaller in steep and bare mountain catchments than flat and forested catchments. [ABSTRACT FROM AUTHOR]

Three bioretention cells in Norway were monitored for 23 to 36 months to evaluate the hydrological performance of bioretention cells operated in regions with cold climates and to test if cell size equations can be used to predict hydrological performance. Values of saturated hydraulic conductivity (Ksat) were determined for separate events by analyzing the observed infiltration rates and via infiltration tests. The two cells with the highest Ksat values (15.9 and 45.0 cm/h) performed excellently during the study period infiltrating nearly all of the incoming runoff. In contrast, the cell with low Ksat value (1.3 cm/h) infiltrated barely half of the incoming runoff. The latter cell had a clear seasonal variation in hydrological performance relating to changes in the Ksat values over the year. The size equation that gave the best predictions of the observed hydrological performance accounts for both surface storage and infiltration. By using this equation to evaluate various bioretention cell designs, it was found that the most effective way to increase the hydrologic performance is to have a Ksat value above 10 cm/h. [ABSTRACT FROM AUTHOR]

In this study, we show that pollutants of emerging concern are, by nature, prone to the emergence of epistemic uncertainty. We also show that the current uncertainty quantification methods used for pollutant modelling rely almost exclusively on parameter uncertainty, which is not adequate to tackle epistemic uncertainty affecting the model structure. We, therefore, suggest a paradigm shift in the current pollutant modelling approaches by adding a term explicitly accounting for epistemic uncertainties. In a proof-of-concept, we use this approach to investigate the impact of epistemic uncertainty in the fluctuation of pollutants during wet-weather discharge (input information) on the distribution of mass of pollutants (output distributions). We found that the range of variability negatively impacts the tail of output distributions. The fluctuation time, associated with high covariance between discharge and concentration, is a major driver for the output distributions. Adapting to different levels of epistemic uncertainty, our approach helps to identify critical unknown information in the fluctuation of pollutant concentration. Such information can be used in a risk management context and to design smart monitoring campaigns., Competing Interests: The authors declare no conflicts of interest., (© 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).)