Assessing the effects of four SUDS scenarios on combined sewer overflows in Oslo, Norway: evaluating the low-impact development module of the Mike Urban model
Abstract:AbstractPaved 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… Show more
“…Simulated outflows from the two models were used to derive FDCs. The derivation of FDCs was similar to the study of Hernes et al (2020). Firstly, outflow values were ranked from highest to lowest.…”
Section: Derivation Of Fdcsmentioning
confidence: 79%
“…They applied FDCs to evaluate different scenarios of SUD implementation in a catchment. Similarly, Hernes et al (2020) applied FDCs to investigate the performance of different SUD scenarios, and they concluded the suitability of FDC as a practical measure to evaluate the hydrological performance of SUDs. Using the FDC to evaluate the performance of PP was found to be limited to few studies in the literature (Fassman & Blackbourn 2010;Brown et al 2012;Winston et al 2018), and no study was found to apply the FDC for LPP systems.…”
Lined permeable pavements (LPPs) are types of sustainable urban stormwater systems (SUDs) that are suitable for locations with low infiltration capacity or shallow groundwater levels. This study evaluated the hydrological performance of an LPP system in Norway using common detention indicators and flow duration curves (FDCs). Two hydrological models, the Storm Water Management Model (SWMM)-LID module and a reservoir model, were applied to simulate continuous outflows from the LPP system to plot the FDCs. The sensitivity of the parameters of the SWMM-LID module was assessed using the generalized likelihood uncertainty estimation methodology. The LPP system was found to detain the flow effectively based on the median values of the detention indicators (peak reduction = 89%, peak delay = 40 min, centroid delay = 45 min, T50-delay = 86 min). However, these indicators are found to be sensitive to the amount of precipitation and initial conditions. The reservoir model developed in this study was found to yield more accurate simulations (higher NSE) than the SWMM-LID module, and it can be considered a suitable design tool for LPP systems. The FDC offers an informative method to demonstrate the hydrological performance of LPP systems for stormwater engineers and decision-makers.
“…Simulated outflows from the two models were used to derive FDCs. The derivation of FDCs was similar to the study of Hernes et al (2020). Firstly, outflow values were ranked from highest to lowest.…”
Section: Derivation Of Fdcsmentioning
confidence: 79%
“…They applied FDCs to evaluate different scenarios of SUD implementation in a catchment. Similarly, Hernes et al (2020) applied FDCs to investigate the performance of different SUD scenarios, and they concluded the suitability of FDC as a practical measure to evaluate the hydrological performance of SUDs. Using the FDC to evaluate the performance of PP was found to be limited to few studies in the literature (Fassman & Blackbourn 2010;Brown et al 2012;Winston et al 2018), and no study was found to apply the FDC for LPP systems.…”
Lined permeable pavements (LPPs) are types of sustainable urban stormwater systems (SUDs) that are suitable for locations with low infiltration capacity or shallow groundwater levels. This study evaluated the hydrological performance of an LPP system in Norway using common detention indicators and flow duration curves (FDCs). Two hydrological models, the Storm Water Management Model (SWMM)-LID module and a reservoir model, were applied to simulate continuous outflows from the LPP system to plot the FDCs. The sensitivity of the parameters of the SWMM-LID module was assessed using the generalized likelihood uncertainty estimation methodology. The LPP system was found to detain the flow effectively based on the median values of the detention indicators (peak reduction = 89%, peak delay = 40 min, centroid delay = 45 min, T50-delay = 86 min). However, these indicators are found to be sensitive to the amount of precipitation and initial conditions. The reservoir model developed in this study was found to yield more accurate simulations (higher NSE) than the SWMM-LID module, and it can be considered a suitable design tool for LPP systems. The FDC offers an informative method to demonstrate the hydrological performance of LPP systems for stormwater engineers and decision-makers.
“…For instance, in Norway, sewers comprising of CSSs and stormwater drains account for 35,900 km and 15,700 km, respectively, of the entire sewer system [2]. Rapid urbanization combined with increased precipitation due to climate change and low investments in the rehabilitation of aging CSSs have resulted in the increased incidence of combined sewer overflows (CSOs) in Norway [3][4][5]. A study by Nilsen et al [5] revealed a significant relationship between the volume of CSOs in the sewer network in Oslo and flood events.…”
Predicting discharges in sewage systems play an essential role in reducing sewer overflows and impacts on the environment and public health. Choosing a suitable model to predict discharges in these systems is essential to realizing these aforementioned goals. Long Short-Term Memory (LSTM) has been proposed as a robust technique for predicting discharges in wastewater networks. This study explored the potential application of an LSTM model to predict discharges using 3-month data set in a sewer network in Ålesund city, Norway. Different sequence-to-sequence LSTMs were investigated using various input and output datasets. The impact of data aggregation (10-min and 30-min intervals) was examined and compared to original sensor data (5-min intervals) to evaluate the performance of the LSTM model. The results show that 50-neuron LSTM architecture performed better (MAPE = 0.09, RMSE = 0.0008, R2 = 0.8) in predicting discharges for the study area. The study indicates that using the same sequence length for the prior and the forecast can improve the effectiveness of the LSTM model. Based on the results, using a 10-min aggregated discharge dataset reduces energy consumption, transmission bandwidth, and storage capacity. Additionally, it improves prediction performance compared to an original 5-min interval data in Ålesund city.
“…Efficient sewage network management depends not only on accurate information on the momentary sewer flows but also the future expected flows, which enables to predict and avert emergencies [9,10]. In addition, the possibility of predicting the hydraulic load of WWTP during extreme rainfalls enables to devise the algorithms for optimization of the treatment process [11].…”
Sanitary sewage network is relatively rarely considered as the cause of urban floods. Its hydraulic overload can result not only in flooding, but also sanitary contamination of subcatchments. Stormwater is the main reason for this overload. In contrast to the stormwater or combined sewer system, these waters infiltrate into the network in an uncontrolled way, through ventilation holes of covers or structural faults and lack of tightness of manholes. Part of stormwater infiltrates into the soil, where it leaks into pipelines. This greatly hinders assessing the quantity of stormwater influent into the sanitary sewer system. Standard methods of finding correlation between rainfall and the intensity of stormwater flow are ineffective. This is confirmed, i.a. by the studies performed in an existing network, presented in this paper. Only when residuals analysis was performed using the ARIMA and ARIMAX methods, the authors were able to develop a mathematical model enabling to assess the influence of rainfall depth on the stormwater effluent from the sewage network. Owing to the possibility of using the rainfall depth forecasts, the developed mathematical model enables to prepare the local water and sewerage companies for the occurrence of urban floods as well as hydraulic overload of wastewater treatment plants.
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