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Article

Capacity Assessment of a Combined Sewer Network under Different Weather Conditions: Using Nature-Based Solutions to Increase Resilience

by
Panagiota Galiatsatou
1,2,*,
Antigoni Zafeirakou
1,
Iraklis Nikoletos
1,3,*,
Argyro Gkatzioura
1,
Maria Kapouniari
1,
Anastasia Katsoulea
1,
Dimitrios Malamataris
1 and
Ioannis Kavouras
2
1
Department of Civil Engineering, School of Engineering, Aristotle University of Thessaloniki (A.U.Th), 54124 Thessaloniki, Greece
2
Executive Division of Strategic Planning, Hydraulic Works & Development, Thessaloniki Water Supply and Sewerage Company S.A. (EYATH S.A.), 54635 Thessaloniki, Greece
3
Lithos Group Inc., 150 Bermondsey Road, Toronto, ON M4A 1Y1, Canada
*
Authors to whom correspondence should be addressed.
Water 2024, 16(19), 2862; https://doi.org/10.3390/w16192862
Submission received: 14 August 2024 / Revised: 20 September 2024 / Accepted: 30 September 2024 / Published: 9 October 2024
(This article belongs to the Section Hydrology)
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Abstract

:
Severe weather conditions and urban intensification are key factors affecting the response of combined sewer systems, especially during storm events. In this regard, the capacity assessment of combined sewer networks under the impact of rainfall storm events of different return periods was the focus of this work. The selected case study area was a mixed-use catchment in the city centre of Thessaloniki, Greece. The hydraulic performance of the examined sewer network was assessed using an InfoWorks ICM model. The results indicated that mitigation strategies, such as the application of nature-based solutions (NBSs) or low-impact developments (LIDs) are considered essential for controlling combined sewer overflows. A multicriteria analysis was conducted to select the most appropriate NBSs/LIDs to be located in the study area to enhance the system’s capacity. The results of this multicriteria analysis were used to propose a combined sewer overflow mitigation scenario, based on the installation of green roofs, as the most highly ranked solution in the analysis performed. Incorporating the proposed NBS/LID in the hydrologic-hydraulic model significantly increased the performance of the studied combined sewer network.

1. Introduction

Urban floods are nowadays considered as one of the most destructive and, sometimes, even deadly natural disasters worldwide, leading to severe health, economic, social, and environmental impacts. Climate change and urbanization are expected to contribute to the increasing frequency and severity of urban flood events [1]. Climate change, associated with more frequent and intense precipitation events and changes in precipitation patterns, including more prolonged and intense storm events, and urbanization, typically increasing impervious surfaces and encroaching possible floodplains or natural flood buffers, lead to higher volumes of stormwater runoff, overwhelming urban drainage systems and causing overflows. The impacts of climate change and rapid urbanization, combined with ageing infrastructures, exert significant stress on urban drainage or combined sewer networks [2,3]. As a result of the aforementioned factors, urban flooding occurs more frequently due to the exceedance of sewer capacity [4].
Langeveld et al. [5] studied the effects of climate change on urban wastewater infrastructure, revealing weak parts of the system under future conditions and limited knowledge on sewer processes. Willems [6] revised the urban drainage design rules at Uccle in Belgium taking into account precipitation extreme trends until the end of the century, concluding that an increase in storage capacity is necessary in order to keep the overflow frequency to the current level. Kourtis and Tsihrintzis [7] presented a review of the main challenges associated with adaptation of urban drainage networks to climate change, emphasizing the spatial and temporal scales of climate projections needed for urban drainage studies, climate change impacts on intensity–duration–frequency (IDF) curves and urban flooding, as well as the necessity of urban drainage adaptation to future conditions. Galiatsatou and Iliadis [4] introduced a methodological approach to design sustainable urban drainage networks in a changing climate based on the concepts of nonstationary extreme value analysis, the multifractal behavior of rainfall, and hydrologic and hydraulic modeling using the StormWater Management Model (SWMM). Gogien et al. [3] used rainfall simulation results from five regional climate models (RCMs) produced within the Euro-Cordex Program, spatially downscaled and temporally disaggregated, as input to a hydrological model to assess future evolutions of combined sewer overflow (CSO) volumes and frequencies, identifying an increase that is expected to not comply with the current regulations.
Semadeni-Davies et al. [8] examined the combined effects of climate change and increasing urbanization on the CSOs of Helsingborg in South Sweden. Existing urban drainage problems were estimated to deteriorate under the impacts of city growth and increased storminess, both individually and combined. Zhou [9] reviewed the key concepts and standards of sustainable urban drainage design considering challenges introduced by climate change and urbanization. Yazdanfar et al. [10] reviewed urban drainage network efficiency under the effects of climate change and urbanization, providing information on adaptation alternatives to improve the operation of these networks. Chen et al. [11] explored the impacts of urban expansion and the intensification of surface runoff in the U.S., noticing that population growth cannot on its own represent urbanization. Salerno et al. [12] investigated the effects of climate change and urbanization on CSOs and the quality of the receiving water body in northern Italy, identifying a significant deterioration of river water quality, and they introduced appropriate adaptation strategies. Zhou et al. [13] investigated urbanization impacts on hydrological runoff and urban flooding in northern China and compared them with climate change effects. They found out that urbanization has significant impacts on annual flood volumes in the area, also highlighting the importance of urban drainage system design in mitigating urban floods. Hassan et al. [14] examined the effects of rapid urbanization on urban drainage networks and urban flooding and compared them with climate change effects on runoff.
Achieving resilience is the best technique for dealing with urban floods [15,16]. Given the future conditions associated with extreme weather events driven by climate change, and the consequences of increasingly rapid urbanization, urban resilience emerges as a novel and effective strategy for flood hazard mitigation and management [17]. The adaptability and multifunctionality of urban flood-risk adaptation strategies are, therefore, needed for them to be properly incorporated in the urban fabric, considering that the impacts of urban floods will not only differ due to geographical location and climate conditions in certain areas, but also due to local spatial variations and microclimate [18]. NBSs encompass a diverse array of strategies designed to enhance urban resilience in the context of flood risk management and climate adaptation, including Water-Sensitive Urban Design (WSUD), Sustainable Urban Drainage Systems (SUDSs), Integrated Urban Water Management (IUWM), Best Management Practices (BMPs), and low impact-development (LID) techniques [19]. These methodologies offer a holistic approach to addressing a wide range of environmental challenges by harnessing natural processes and ecosystems. Collectively, these strategies can be grouped under the broader framework of NBSs [20], which serves as an umbrella term for these older concepts [21,22]. Green roofs, rain gardens, permeable pavements, and bioretention areas have been widely recognized as efficient NBSs towards mitigating urban flooding [23,24,25,26,27]. Nature-based solutions (NBSs) or low-impact developments (LIDs) offer a holistic approach to addressing the aforementioned required properties. These solutions refer to strategies or interventions in the urban fabric that are inspired and supported by nature and provide environmental, social, and economic benefits, therefore promoting sustainability and resilience [28]. NBSs/LIDs promote the sustainable management of natural resource maintaining ecosystem services, reduce vulnerability to climate impacts and, therefore, enhance the climate resilience of urban areas, support habitats for various species, reduce urban heat, and improve air quality. They also constitute cost-effective solutions, with their effectiveness critically depending on involving a wide range of stakeholders, ensuring solutions are locally relevant and supportive [29,30]. Even if NBSs/LIDs are less effective at coping with pluvial flooding caused by short-duration extreme precipitation events, they could effectively mitigate urban flooding caused by high-frequency precipitation events.
Majidi et al. [31] presented a framework to maximize the efficiency of NBSs/LIDs, based on hazard assessment, combining modeling techniques and field measurements. The presented methodology achieves an effective combination of measures and their locations for flood risk reduction and thermal comfort enhancement. Oral et al. [32] reviewed NBSs for urban water management, emphasizing that they also provide additional benefits, i.e., water quality improvement, biodiversity enhancement, social co-benefits, urban microclimate improvement, and the reduction of energy consumption. Singh et al. [33] presented a non-linear programming optimization technique by combining three ecological management practices to reduce flood risks in Costa Rica. Huang et al. [34] conducted a systematic survey of various NBSs adopted in different regions of the world, elaborating on their benefits and limitations. Quaranta et al. [35] used NBSs to manage CSOs and assess the potential reduction of CSO volumes and duration, also estimating costs and benefits. Ramísio et al. [36] identified and presented synergies between NBSs and urban drainage systems, highlighting the positive effects of NBSs for both wastewater and stormwater management. Hamers et al. [37] presented an approach to identify the most suitable locations for portfolios of NBSs, reducing the impacts of urban flooding at the regional scale.
This study combines IDF curves specially constructed for the city center of Thessaloniki using modern statistical methods and techniques [1], with an advanced hydrologic-hydraulic model to assess the capacity of the combined sewer network of the city under different rainfall storm events, and decision-making tools, such as the multicriteria analysis, to select among different NBS/LID solutions to mitigate urban flooding risk. The main innovations of this work can be summarized as follows: (i) it creates a simple and easy-to-apply methodological framework for the capacity assessment of existing combined sewer networks, incorporating different NBSs/LIDs compared by means of multicriteria analysis, which can be utilized by scientists, practitioners, and engineers in a comprehensible and comprehensive manner. (ii) It introduces two quite simple failure criteria of combined sewer networks in large urban centers, basement flooding, and surface flow, associated with medium and high rainfall return periods, respectively, which assisted in locating NBSs/LIDs in the study area and showcase their efficiency in reducing urban flood risk. (iii) It proves that the application of the selected NBS, green roofs in this work, in a large part (~35–40%) of a highly impervious area can significantly improve the operability of the combined sewer network and increase its resilience. This study aimed to present a detailed framework for the capacity assessment of combined sewer networks under different storm events of varying frequency of occurrence, incorporating NBSs/LIDs and promoting sustainability and resilience. Its results can provide the basis for a more detailed hydraulic analysis of the city’s combined sewer network, with a potential to significantly assist in the design and planning of resilient solutions against urban floods, contributing to urban flood risk management and reduction. The proposed framework can be used by local authorities, water and sewerage utilities, and engineers to adjust their flood adaptation strategies and measures in the general framework of adaptation to climate change impacts.

2. Materials and Methods

2.1. Hydrologic-Hydraulic Simulation of a Combined Sewer Network

Urban drainage systems usually include a hydrological module for modeling the rainfall–runoff processes, a hydraulic module for routing the flows through the combined sewer or stormwater system and its elements, and a water quality module for modelling the dynamics of quality parameters in the system [38]. Salvadore et al. [39] examined physical processes by configuring hydrology at the urban catchment scale focusing on the spatial and temporal scales of these processes, and they reviewed current practices for urban hydrological modeling. Therefore, regarding the hydrological module of urban drainage systems, lumped, semi-distributed, and distributed models are used to simulate the hydrological processes. Lumped (homogeneous) hydrological models average spatial characteristics of the urban catchment, clearly sacrificing space for simplicity and flexibility. Semi-distributed and distributed (heterogeneous) physical-based models are more detailed, accounting for spatial variability using spatial datasets of soils, vegetation, and land-use [40]. Distributed models are expected to obtain better and more precise results than those of lumped models [41,42]; however, some studies have shown that lumped models can offer equivalent results [43,44]. Regarding the hydraulic module, there exist many software tools solving the Saint–Venant equations for the gradually varied unsteady flow [38].
In this work, the InfoWorks ICM (Integrated Catchment Modelling) model was selected to assist in the evaluation and assessment of the drainage system of the study area. InfoWorks ICM [45], developed by Innovyze, is a sophisticated software used for the management of combined sewer systems, as well as stormwater systems, which enables the detailed hydrologic and hydraulic modeling and analysis of sewer networks and can perform the capacity assessment of existing sewer infrastructures or can be used in planning network upgrades. InfoWorks ICM has been widely used in urban catchment modeling, as well as in urban flood hazard and flood risk studies [46,47,48,49,50,51]. It is used to perform integrated 1D and 2D hydrological and hydraulic modeling. The nonlinear shallow water equations were implemented to represent the surface flow. The continuity and momentum equations were solved by the finite volume scheme using a Riemann solver [49].
To set up the model in this case study, land use data were collected from Copernicus database (https://land.copernicus.eu/en, accessed on 24 June 2024) for the area of interest. Detailed and comprehensive data of the sewer system in the area of interest were also collected from the Geographic Information System (GIS) database of Thessaloniki’s Water Supply and Sewerage Company S.A. (EYATH S.A). The collected analytical data included information on the system links (conduits), such as lengths (m), diameters (mm), and materials, and also data regarding system nodes (inlets, manholes, etc.), such as ground and invert elevations (m). Shapefiles with all network elements were imported to InfoWorks ICM by means of InfoWorks open data import center. IDF curves constructed in Iliadis et al. [1], referring to the specific area of Thessaloniki’s city center, were used in this work. Rainfall amounts corresponding to return periods of 2, 10, 50, and 100 years and a duration of 1 h were first assessed. The total rainfall amounts for the selected return periods were then distributed to the duration of the rainfall using the alternating block method.
The study area was divided into subcatchments, each draining to a specific discharge point/node of the network. Subcatchments in this work correspond to city blocks of the center of Thessaloniki city. In this work, the SWMM engine was utilized for simulating the rainfall-runoff process (subcatchment routing model). The SWMM runoff model consists of three components: the initial losses (depression storage), the runoff volume, and the runoff routing model. When not accounting for snow melting, the SWMM runoff model divides each subcatchment in two surfaces, namely, impervious with depression storage, and pervious with depression storage and Horton or Green-Ampt infiltration [52]. Initial losses for impervious and pervious areas can be considered constant or slope-related. Considering the SWMM routing model, flow is routed using a single non-linear reservoir, with a routing coefficient depending on surface roughness, surface area, ground slope, and catchment width [52]. The kinematic wave equation is utilized to route subcatchment runoff to the sewer system manholes. Therefore, a rainfall-runoff analysis requires a wide range of subcatchment parameters including area (m2), imperviousness (%), depression storage for impervious and pervious areas (mm), infiltration (mm), slope (%), catchment width (m), and Manning’s roughness coefficient n for impervious and pervious areas.
InfoWorks ICM uses the RTK method to generate a hydrograph used to determine RDII (rain-dependent inflow and infiltration) from a subcatchment. The RTK method forms the hydrograph by combining triangular hydrographs from rapid inflow (short-term response), moderate infiltration (medium-term response), and slow infiltration (long-term response), and it is defined by parameters R, the fraction of precipitation that enters the collection system for a particular flow component (direct inflow); T, the time from the precipitation onset to the peak of a particular component of the hydrograph; and K, the ratio of time to recession to time to peak. The parameters for the design flow rates are, therefore, defined as follows [53]:
1 2 T i + T i K i Q p i = R i P A 3600     f o r   i = 1 ,   2 ,   3
where Ti represents the time before the hydrograph peak (h), Ki the ratio between time to end to time to hydrograph peak, Ri the runoff volumetric coefficient, Qpi the peak flow rate (m3/s), P the precipitation amount (m), and A the subcatchment area (m2). RDII is defined by a combination of the three triangular hydrographs (short-term, medium-term, and long-term responses) using the following formulas [53]:
Q = Q p i t T i   f o r   t < T i
Q = Q p i 1 − t − T i T i K i   f o r   T i ≤ t ≤ T i + T i K i
The calibration of InfoWorks ICM is usually performed for dry and wet weather conditions, and it requires the adjustment of model parameters, summarized in the RTK unit hydrograph parameters, direct runoff surface, roughness, and depression storage, based on observed flow data in the system. As mentioned above, the current analysis employs a combination of the fixed runoff coefficient approach and the RTK method to estimate stormwater flow entering the combined sewer network. In the absence of flow monitoring data, a qualitative calibration was performed in this work. Considering dry weather conditions, the model parameters to be calibrated include the baseflow, diurnal pattern factor, per capita flow rate, and population for each subcatchment. For wet weather conditions, model parameters to be calibrated include initial losses (depression storage), runoff coefficients, and runoff routing values for both impervious and pervious areas of all subcatchments.

2.2. Nature-Based Solutions Located in the Urban Fabric

The rapid expansion of urban areas has exacerbated environmental problems such as stormwater runoff, flooding, air pollution, climate change, urban heat islands, habitat destruction, etc. Traditional gray infrastructure often falls short in addressing these issues sustainably [54,55]. In contrast, NBSs/LIDs leverage natural processes to provide ecosystem services, enhancing urban resilience and sustainability. They offer a harmonious approach to environmental, social, and economic issues [56]. This study focused on green roofs, rain gardens, permeable pavements, and bioretention areas’ implementation and benefits. NBSs, such as the four abovementioned options, offer multifaceted benefits that address urban environmental challenges sustainably. These solutions not only manage stormwater and improve water quality, but also provide recreational spaces. Integrating NBSs/LIDs into urban planning and development is integral to building resilience and sustainable cities.
Green roofs are vegetated surfaces installed on building rooftops. They provide several benefits, including stormwater management, energy efficiency, and biodiversity enhancement. Green roofs absorb rainfall, reducing runoff and mitigating urban flooding [57]. A study by Berardi et al. [58] demonstrated that green roofs can reduce stormwater runoff by up to 65%, highlighting their potential for effective urban water management. Another research by Vijayaraghavan [59] reviewed the energy savings associated with green roofs, noting that they can reduce energy use for air conditioning by up to 25%. Rain gardens are shallow depressions planted with native vegetation that collect and absorb runoff from impervious surfaces. By capturing and infiltrating runoff, rain gardens reduce peak flow and volume. They play a crucial role in mitigating the impact of urbanization and climate change on water quality. Rain gardens are intended for collecting runoff from rooftops. Daniels and Yeakley [60] modeled rain garden implementation in a 3.1 km2 suburban catchment in Columbia and uncovered that treating 100% of residential rooftops reduced peak flows by 14.3% and runoff volumes by 11.4%, and increased lag times by 3.2%.
Permeable pavements are an essential component of sustainable stormwater management that can reduce runoff caused by heavy precipitation events [61]. These pavements significantly decrease surface runoff, as well as facilitate the natural infiltration of rainwater, replenishing groundwater reserves [54]. Research by Lee et al. [62] showed that permeable pavements can effectively reduce the peak discharge to 60~75% of the original. Bioretention areas are still a type of NBSs used in urban areas to mitigate the effects of stormwater runoff. Their primary objective is to attenuate peak runoff [63]. By slowing down the flow of stormwater, they reduce the intensity of flooding during heavy rainfall events. Additionally, a study by Brown and Hunt [64] demonstrated that bioretention areas could reduce peak flow rates by 90%, making them highly effective for flood control.
A multicriteria analysis was conducted in this work to assess the suitability of different NBSs/LIDs. Four NBSs/LIDs were evaluated: (a) green roofs, (b) rain gardens, (c) permeable pavements, and (d) bioretention areas based on these five criteria: (i) flood mitigation potential, (ii) environmental benefits, (iii) social acceptance, (iv) applicability in central urban areas, and (v) improvement of energy efficiency. The Analytic Hierarchy Process (AHP) method [65] was applied to choose weights for each one of the five criteria. The AHP method is based on pairwise comparisons between criteria and the construction of a matrix of comparisons of size n × n, where n is the number of criteria. Each cell of the matrix contains a value representing which criterion of the pair is more important than the other. A scale from 1 (equal importance) to 9 (extreme importance) is proposed. If a value of 3 is entered in the cell f(i, j), it means that criterion i is moderately more important over j and the value 1/3 is entered in the cell f(j, i). Following this procedure, the whole comparison matrix is constructed. Then, the Simple Additive Weighting (SAW) method [66,67] is applied for the ranking of different NBS/LID solutions. In the SAW method, the ranking is based on the weighted sum of normalized values, which is calculated for each alternative solution. A value is assigned to each alternative to represent its performance for a certain criterion: from 1 (poor performance) to 5 (excellent performance). The judgment for assigning these values is based on a literature review [68,69,70,71] and professional judgment.

2.3. Study Area and Capacity Assessment Process

The study area of this work is the historic center of Thessaloniki city, located in northern Greece. Thessaloniki is the second-largest city in Greece with over one million residents, while its population density is very high especially in the city center, calculated at 200 residents per hectare. The annual average rainfall in the city of Thessaloniki was assessed to be equal to 445.7 mm for the interval 1960–2020 (monthly rainfall data provided by the Hellenic National Meteorological Service (HNMS)). The average monthly rainfall ranges from 21.1 mm (August) to 54.5 mm (December) for the same time interval. The study area of this work was the catchment bounded in its western and eastern parts by the streets of Aristotelous and Aggelaki, respectively. Its northern boundary is located in Egnatia and Svolou streets, while its southern boundary is located on the coastal front of the city on Leoforos Nikis street. The selected study area as well as the layout of Thessaloniki’s examined sewer network are shown in Figure 1. The study area was assessed to be equal to about 40 ha.
The vast majority of the sewer pipes are combined sewer pipes, while there also exist few sanitary pipes located mostly upstream of the combined ones. The existing population density at the study area is assumed at 200 persons/ha. The impervious area of the city center is assumed to be equal to 95%. The catchment area under study is divided into forty (40) subcatchments, corresponding to forty city blocks. The sewer network of the study area includes 250 manholes, represented as nodes in the InfoWorks ICM model, and 281 conduits, represented as links. The analyzed network is composed of circular and egg-shaped conduits with diameters ranging from 300 mm to 1500 mm and from 600 × 1000 mm to 1500 × 2600 mm, respectively. There exist eight (8) diversion points in the study area, while on the coastal front, six (6) combined sewer overflow (CSO) points ensure the spilling of water in Thermaikos Gulf, reducing the risk of sewage backing up during heavy rainfall and of flooding homes and businesses. Two (2) nodes in the network receive external flows from subcatchments of adjacent drainage areas. The outlet of the sewer network is located in the southeast corner, where the flow is ultimately discharged into a pumping station. Table 1 summarizes the characteristics of the combined sewer pipes within the study area.
A principal aim of this study was to examine how high-frequency to rare and intense storm events could affect urban combined sewer networks. Therefore, a detailed estimation of the network performance under different rainfall storm events is considered essential in order to assess whether the sewer system has adequate capacity or whether mitigation measures are required to support the flows from the existing urban developments and continuously intensifying storm events [72,73,74,75]. The methodological framework of the capacity assessment process implemented at the combined sewer network of Thessaloniki city is presented in Figure 2. The first step of the process involves the model setup based on GIS layers, sewer assets, and the available geodatabase from EYATH S.A.’s GIS database. The hydrologic and hydraulic simulation of the combined sewer network is then performed based on four scenarios of weather conditions: (i) scenario 1 corresponding to a 2-year storm event, (ii) scenario 2 corresponding to a 10-year storm event, (iii) scenario 3 accounting for a 50-year storm event, and (iv) scenario 4 accounting for a 100-year storm event. A simplified approach is followed in this research to determine the criteria of adequate capacity of the combined sewer system: for high- to medium-frequency storm events, such as 2-year (scenario 1) and 10-year (scenario 2) storm events, adequate system capacity is secured if there is no basement flooding in the study area, interpreted as a minimum available freeboard of 2 m or a hydraulic grade line (HGL) at least 2 m below ground surface (criterion 1). For storm events corresponding to return periods of 50 (scenario 3) and 100 (scenario 4) years, an adequate system capacity is reserved if there is no surface flow in the study area (criterion 2). If criterions 1 and 2 are not fulfilled, mitigation measures based on NBSs/LIDs are proposed and located in the study area, following the multicriteria analysis presented in Section 2.2. If there is no way to achieve fulfillment of criteria 1 and 2 using NBSs/LIDs, mitigation measures that minimize overflow and surface flow risk in the study area should be selected.

3. Results

The rainfall storm events used in this work were obtained from IDF curves constructed in Iliadis et al. [1], combining modern statistical methods and techniques. These IDF curves were specially designed for the area of Thessaloniki’s city center, also studied in this work, incorporating the most recent daily storm events. Total rainfall amounts corresponding to return periods of 2, 10, 50, and 100 years and a duration of 1 h were assessed at 19.3 mm, 27.3 mm, 38.6 mm, and 44.8 mm, respectively. To construct rainfall storm events used as input in InfoWorks ICM, the total rainfall amounts for the selected return periods were distributed in the 1 h duration of the event using the alternating block method (Figure 3).
In the absence of flow monitoring data at the center of Thessaloniki city, a calibration of InfoWorks ICM was performed qualitatively in this work. More specifically, during dry weather conditions and observed storm events with a return period of less than 2 years (scenario 1), no surcharging of the studied conduits occurred. During extreme wet weather flow conditions, and especially for storm events corresponding to high return periods, the calibration of the model parameters critically depended on problems (i.e., overland flow, flooding, etc.) observed in the past and marked on the GIS system of EYATh S.A. The model was calibrated in a stepwise manner, with dry weather flow calibrated first and then followed by wet weather calibration. Since subcatchments of this work correspond to city blocks, population within each subcatcment was assessed based on empirical methods, considering the existing population density. Per capita flow rate was assessed using a weighted average of land uses in the study area, yielding a value of about 300 l/c/d. Therefore, only the parameters of baseflow (l/s) and diurnal pattern factor (-) in each subcatchment were adjusted during the calibration procedure. For wet weather calibration, significant historic rainfall events, known to cause flooding, represent good data for calibrating the model against low-frequency return events. This is a crucial step in the calibration process, as it helps confirm the model’s accuracy in predicting flooding against locations with known flooding. Three historic events were selected: (a) 11 May 2018, (b) 17 June 2023, and (c) 9 May 2024 were used to assess the parameters of the RTK hydrograph, infiltration and depression storage/initial loss (m), direct runoff coefficients (-), and runoff routing values (-) for impervious and pervious surfaces. Parameter adjustment for wet weather conditions was performed based on observations of surface flow conditions at various locations (manholes) during the aforementioned historic rainfall events in the study area. Table 2 and Table 3 present model input parameters resulting during the calibration process for dry and wet weather conditions, respectively.
After calibrating the model, the capacity assessment of the combined sewer network of Thessaloniki’s city center was performed based on the four scenarios presented in Figure 2. Figure 4 presents results of the hydraulic analysis for scenario 1 in the study area. Flooding correlation was estimated by analyzing the surcharge condition of the conduits and the HGL at the nodes in relation to a theoretical pre-defined basement elevation of 2 m below the ground. The slope of the HGL in each sewer pipe can help determine whether the surcharge is due to the sewer’s overcapacity (bottleneck) or if it results from backwater from a downstream sewer (backwater surcharge). The nodes represent the maximum water level in the combined sewer system. To simplify the illustration of the results, the color-coded figures depict only the HGL. Black dots correspond to manholes, and dark blue lines at the coastal front represent CSO points of the network. Sewer segments are colored green if the HGL is more than 2 m from the surface, purple if basement flooding occurs (HGL is less than 2 m from the surface, but below the ground elevation), and red if surface flow is detected. In Figure 3, it is evident that for scenario 1, corresponding to high-frequency storm events (return period equal to 2 years), all 281 pipes of the studied network are assessed with free flow conditions. Therefore, for frequent storm events, the studied network does not experience critical problems related to basement flooding, surface flow, or flooding conditions.
Figure 5 presents related results for scenario 2 in the study area. It can be observed that even for the quite frequent storm events of scenario 2 (return period equal to 10 years), there exist some pipes presenting freeboard heights less than 2 m, resulting in basement flooding of the adjoining buildings and a limited number of sewer segments experiencing surface flow. More specifically, 98 sewer segments/pipes are assessed with freeboard heights lower than 2 m, and three (3) sewer segments present surface flow conditions. The former segments/pipes are dispersed in the studied network, while six (6) of them are located on the coastal front. Basement flooding issues are also detected in other critical segments of the network close to densely populated streets with really old buildings with basements housing shops and their repositories.
Figure 6 and Figure 7 present results of the hydraulic analysis for scenarios 3 and 4, respectively, in the study area. For storm events of scenario 3 (return period equal to 50 years), all sewer segments in the study area experience problems. More specifically, 229 sewer segments present freeboard heights less than 2 m, resulting in basement flooding of the adjoining buildings, while 52 segments present surface flow. Surface flow is detected in nine (9) sewer segments of the coastal front and some pipes of the confluent streets. Large parts of vertical axes of the study area also present similar capacity problems. Sewer segment capacity problems worsen for even higher return periods, i.e., return periods of 100 years (scenario 4). Surface flow is detected in 87 sewer pipes of the network for this scenario, while all segments on the coastal front also present surface flow conditions. For scenario 4 and for the rest 194 sewer segments/pipes of the network, basement flooding problems of the adjoining buildings appear.
From Figure 4, Figure 5 and Figure 6, it can be observed that criterion 1 for scenario 2 and criterion 2 for scenarios 3 and 4 (see Figure 2) cannot be fulfilled. To mitigate or reduce the impacts of different weather conditions (in particular rainfall storm events with return periods equal to or higher than 10 years) on the existing infrastructure, NBSs/LIDs can be implemented. A multicriteria analysis (see Section 2.2) was conducted in this work to select the most profitable NBS to enhance the system’s capacity. As mentioned earlier in Section 2.2, four NBSs/LIDs were evaluated: (a) green roofs, (b) rain gardens, (c) permeable pavements, and (d) bioretention areas. Five criteria were included in the analysis conducted: (a) flood mitigation potential, (b) environmental benefits, (c) social acceptance, (d) applicability in central urban areas, and (e) improvement of energy efficiency. Table 4 presents weights of the five criteria assessed using the AHP method. The consistency ratio (CR), defined by the ratio of consistency index (CI) and random index (RI) for the five criteria, is equal to 0.068, which is low (CR < 0.1), and thus acceptable. Table 5 presents the final score of each one of the four NBS/LID solutions using the SAW method. Based on this score, the final ranking of the solutions is as follows: (1) green roofs, (2) rain gardens, (3) permeable pavements, and (4) bioretention areas.
It should be mentioned that this is just a preliminary analysis bounded by the subjectivity of AHP weights of the five criteria and SAW values assigned to all NBS/LID alternatives. For the scope of this study, the results of this multicriteria analysis are used to propose a CSOs mitigation scenario. Green roofs are assessed with the highest SAW final score for this specific case study, designated as the most appropriate NBS/LID to increase the resilience of the combined sewer network in the study area. A combination of green roofs and rain gardens, as the two most highly ranked solutions, could also be proposed. In this work, the NBS of green roofs is proposed to be applied in the study area, covering almost 40% of the total drainage area. In recent years, numerous studies have been carried out on the runoff reduction capacity of green roofs [76,77]. Given the prevailing climate in the region of Thessaloniki, a conservative runoff reduction rate of 50–60% for green roofs is utilized. Green roofs are, therefore, located in the city blocks of the study area, upstream of sewer segments experiencing critical problems of surface flow for scenarios corresponding to the highest return periods of storm events considered (see Figure 6). A critical goal of the proposed NBS location is to eliminate all sewer segments experiencing surface flow for a storm event corresponding to a return period of 100 years (scenario 4). It should be noted that this is just an initial approach to ensuring the adequate capacity of the studied combined sewer network. A more detailed and in-depth approach should be developed to optimally locate and even combine NBSs/LIDs to mitigate the adverse consequences of rainfall storm events in the study area. Table 6 presents the hydrologic-hydraulic model input parameters used for the areas where NBSs (green roofs) are located. It should be noted that the proposed green roofs are of the extensive type, enabling greater water retention and potential for reuse.
Figure 8 presents results of the hydraulic analysis for scenario 2 in the study area, including surfaces with green roofs in the hydrologic-hydraulic analysis. Black dots correspond to manholes, and dark blue lines at the coastal front represent CSO points of the network. Sewer segments are colored green if they are characterized by free flow conditions, purple if basement flooding with freeboard exists, and red if surface flow is detected. Hatched green areas represent the NBS feature used in this case study. From Figure 7, it is observed that for scenario 2, corresponding to storm events with a return period of 10 years, only two (2) sewer segments/pipes experience problems of basement flooding of the connected buildings, while the rest 279 pipes of the studied network are assessed with free flow conditions. Therefore, comparing the existing (Figure 4) and proposed (Figure 7) capacity conditions of the studied sewer network, a reduction of almost 98% of the sewer segments presenting basement flooding problems of the adjoining buildings is achieved using the proposed NBS feature.
Figure 9 and Figure 10 present results of the hydraulic analysis for scenarios 3 and 4, respectively, including surfaces with green roofs in the hydrologic-hydraulic analysis of the studied combined sewer network. From Figure 8, it is observed that for scenario 3, corresponding to storm events with a return period of 50 years, only 23 sewer segments/pipes experience problems of basement flooding of the connected buildings, while the rest 258 pipes of the studied network are assessed with free flow conditions. Comparing existing (Figure 5) and proposed (Figure 8) capacity conditions of the studied sewer network, a reduction of almost 90% of the sewer segments/pipes presenting basement flooding problems of the adjoining buildings is achieved using the proposed NBS feature. This reduction exceeds 77% for scenario 4 (storm event with a return period of 100 years), where basement flooding problems are observed in only 44 sewer segments, when the proposed NBS feature is included in the hydrologic-hydraulic model of the studied sewer network. Seven (7) of these sewer segments are detected on the coastal front of the network. Sewer segments/pipes experiencing surface flow problems are eliminated for all weather conditions when the proposed NBS is included in the analysis.
Table 7 summarizes the number of sewer pipes experiencing basement flooding and surface flow problems for the existing and proposed (including the proposed NBS in the analysis) conditions of the study area, for storm events corresponding to different return periods (scenarios 1–4). The number of sewer segments/pipes presenting free flow conditions (no basement flooding) in the study area is also included in Table 7 for all cases considered.

4. Conclusions

In this study, the impact of different weather conditions on combined sewer networks was investigated. In this regard, the capacity assessment of combined sewer networks under the impact of rainfall storm events of different return periods was conducted. The selected study area is a mixed-use catchment at the city center of Thessaloniki city in Greece. The hydraulic performance of the examined sewer network was assessed using an InfoWorks ICM model. In the absence of flow monitoring data in the area, a qualitative calibration of the model was performed, mainly based on surface flow observations for historic rainfall storm events of high return periods.
The modeling results showed that under rainfall events of medium return periods (return periods equal to 10 years), basement flooding of buildings connected to the studied combined sewer network (available freeboard height less than 2 m) appeared in a large number of sewer segments/pipes. For higher return periods of rainfall storm events (return periods of 50 and 100 years), surface flow also appeared in many sewer segments/pipes (52 and 87 for return periods of 50 and 100 years, respectively) of the combined sewer system. For such weather scenarios, all sewer segments of the network experienced capacity problems, with none of them presenting free flow conditions. Considering the abovementioned results, the criteria for adequate sewer network capacity, defined in the capacity assessment framework of this work, were not fulfilled. This necessitates the use of modern mitigation measures able to reduce the impacts of rainfall storm events on the urban fabric and increase the resilience of the local communities.
To mitigate or reduce the impacts of medium to extreme rainfall events (rainfall storm events with return periods equal to or higher than 10 years) on the existing infrastructure, NBS/LID methods can be implemented. A multicriteria analysis was conducted in this work to select the most profitable NBS to enhance the system’s capacity. The NBS of green roofs was assessed to be the most highly ranked solution and was applied in the study area covering almost 40% of the total drainage area. Including the proposed NBS in the hydrologic-hydraulic analysis of this work, sewer segments/pipes experiencing surface flow problems were eliminated for all weather conditions. Sewer segments/pipes presenting basement flooding problems of the adjoining buildings were significantly reduced up to 98%, 90%, and more than 77% for rainfall storm events of return periods 10, 50, and 100 years, respectively.
It should be noted that this is just an initial study on analyzing and mitigating problems of the combined sewer network of the city center of Thessaloniki city. Limitations of this study are mainly focused on the following: (i) calibrating and validating the hydrologic-hydraulic model of the study area using detailed and extended measurements of the hydraulic characteristics of the network; (ii) using more sophisticated and data-driven techniques to select the best NBSs/LIDs for the study area; and (iii) applying advanced methods and techniques to optimally locate the selected NBSs/LIDs in the urban fabric.

Author Contributions

Conceptualization, P.G., I.N., and A.Z.; methodology, P.G., I.N., and A.Z.; software, P.G., I.N., M.K., and A.G.; validation, P.G., I.N., A.G., and A.Z.; formal analysis, P.G., and I.N.; investigation, P.G., I.N., M.K., A.G., A.K., and D.M.; resources, P.G., I.N., A.K., and I.K.; data curation, I.N., M.K., A.G., A.K., D.M., and I.K.; writing—original draft preparation, P.G., I.N., M.K., and A.G.; writing—review and editing, P.G., A.Z., I.N., A.G., M.K., A.K., D.M., and I.K.; visualization, P.G., I.N., M.K., and D.M.; supervision, P.G., I.N., and A.Z.; project administration, A.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data is contained within the article.

Acknowledgments

The authors wish to thank EYATH S.A. for providing the necessary data for this research and Lithos Group Inc. for granting the InfoWorks ICM 2023.2.6 software license used in conducting the hydraulic simulations.

Conflicts of Interest

Author Panagiota Galiatsatou and Ioannis Kavouras were employed by the company EYATH S.A., while Iraklis Nikoletos was employed by the company Lithos Group Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Water 16 02862 g001
Figure 1. Combined sewer network drainage area of Thessaloniki’s city center.
Figure 1. Combined sewer network drainage area of Thessaloniki’s city center.
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Water 16 02862 g002
Figure 2. Methodological framework for capacity assessment of the combined sewer network at the city center of Thessaloniki.
Figure 2. Methodological framework for capacity assessment of the combined sewer network at the city center of Thessaloniki.
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Water 16 02862 g003
Figure 3. Rainfall storm events for return periods (a) 2, (b) 10, (c) 50, and (d) 100 years.
Figure 3. Rainfall storm events for return periods (a) 2, (b) 10, (c) 50, and (d) 100 years.
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Water 16 02862 g004
Figure 4. Sewer segments/pipes and associated capacity assessment in the studied combined sewer network of Thessaloniki’s city center assessed for scenario 1.
Figure 4. Sewer segments/pipes and associated capacity assessment in the studied combined sewer network of Thessaloniki’s city center assessed for scenario 1.
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Water 16 02862 g005
Figure 5. Sewer segments/pipes and associated capacity assessment in the studied combined sewer network of Thessaloniki’s city center assessed for scenario 2.
Figure 5. Sewer segments/pipes and associated capacity assessment in the studied combined sewer network of Thessaloniki’s city center assessed for scenario 2.
Water 16 02862 g005
Water 16 02862 g006
Figure 6. Sewer segments/pipes and associated capacity assessment in the studied combined sewer network of Thessaloniki’s city center assessed for scenario 3.
Figure 6. Sewer segments/pipes and associated capacity assessment in the studied combined sewer network of Thessaloniki’s city center assessed for scenario 3.
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Water 16 02862 g007
Figure 7. Sewer segments/pipes and associated capacity assessment in the studied combined sewer network of Thessaloniki’s city center assessed for scenario 4.
Figure 7. Sewer segments/pipes and associated capacity assessment in the studied combined sewer network of Thessaloniki’s city center assessed for scenario 4.
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Water 16 02862 g008
Figure 8. Sewer segments/pipes and associated capacity assessment in the studied combined sewer network of Thessaloniki’s city center assessed for scenario 2, after including an NBS feature in the hydrologic-hydraulic model of the network.
Figure 8. Sewer segments/pipes and associated capacity assessment in the studied combined sewer network of Thessaloniki’s city center assessed for scenario 2, after including an NBS feature in the hydrologic-hydraulic model of the network.
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Water 16 02862 g009
Figure 9. Sewer segments/pipes and associated capacity assessment in the studied combined sewer network of Thessaloniki’s city center assessed for scenario 3 after including an NBS feature in the hydrologic-hydraulic model of the network.
Figure 9. Sewer segments/pipes and associated capacity assessment in the studied combined sewer network of Thessaloniki’s city center assessed for scenario 3 after including an NBS feature in the hydrologic-hydraulic model of the network.
Water 16 02862 g009
Water 16 02862 g010
Figure 10. Sewer segments/pipes and associated capacity assessment in the studied combined sewer network of Thessaloniki’s city center assessed for scenario 4 after including an NBS feature in the hydrologic-hydraulic model of the network.
Figure 10. Sewer segments/pipes and associated capacity assessment in the studied combined sewer network of Thessaloniki’s city center assessed for scenario 4 after including an NBS feature in the hydrologic-hydraulic model of the network.
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Table 1. Combined pipes characteristics.
Table 1. Combined pipes characteristics.
CharacteristicDescription
(Range)
ShapeEgg-shaped and Circular
DiameterEgg-shaped: 600 × 1000 mm–1500 × 2600 mm, Circular: 300 mm–1500 mm
Slope0.1–4% (flat sewers and sewers with reverse slope are not included)
Length4 m–120 m
Age10–100 years
Table 2. Model input parameters for dry weather conditions.
Table 2. Model input parameters for dry weather conditions.
Baseflow (l/s)Diurnal Pattern FactorPer Capita Flow Rate (l/c/d)Population within a Subcatchment
0.00–3.50.22–1.633000–723
Table 3. Model input parameters for wet weather conditions.
Table 3. Model input parameters for wet weather conditions.
Surface TypeParameters
Initial Loss (m)Runoff CoefficientRunoff Routing Value
Impervious0.00–0.0070.1–1.000.013–1.00
Pervious0.005–0.0120.10–0.500.41
Table 4. Estimation of weights of the five criteria to evaluate NBSs/LIDs using the AHP method.
Table 4. Estimation of weights of the five criteria to evaluate NBSs/LIDs using the AHP method.
CriterionFlood
Mitigation		Environmental BenefitsSocial
Acceptance		ApplicabilityEnergy EfficiencyWeights
Flood mitigation1351/350.258
Environmental benefits1/3131/530.127
Social acceptance1/51/311/71/30.046
Applicability357160.491
Energy efficiency1/51/331/610.078
Table 5. Score of NBSs/LIDs based on the SAW method’s results.
Table 5. Score of NBSs/LIDs based on the SAW method’s results.
CriterionWeights AHP MethodGreen RoofsRain GardensPermeable
Pavements		Bioretention
Areas
Flood mitigation0.2584335
Environmental benefits0.1273434
Social acceptance0.0464342
Applicability0.4915431
Energy efficiency0.0784111
Final score 0.9170.7290.6100.526
Table 6. Model input parameters for areas where NBSs are located.
Table 6. Model input parameters for areas where NBSs are located.
Type of NBSParameters
Surface Soil Drainage Mat
Storage Depth: 15 mmPorosity: 0.40Thickness: 50 mm
Green RoofsVeg. Vol. Frac.: 0.9Conductivity: 5 mm/hrVoid Fraction: 0.80
Surface Slope: 1%Field Capacity: 0.30Flow Coef.: 0.40
Table 7. Number of sewer pipes associated with building basement flooding and surface flow problems for existing conditions and including a proposed NBS in the study area for different weather conditions.
Table 7. Number of sewer pipes associated with building basement flooding and surface flow problems for existing conditions and including a proposed NBS in the study area for different weather conditions.
Return Period of Storm EventsConditionsNo Basement Flooding (No. of Pipes)Basement Flooding (No. of Pipes)Surface Flow (No. of Pipes)
2 years (scenario 1)Existing281--
Proposed281--
10 years (scenario 2)Existing180983
Proposed2792-
50 years (scenario 3)Existing-22952
Proposed25823-
100 years (scenario 4)Existing-19487
Proposed23744-
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Galiatsatou, P.; Zafeirakou, A.; Nikoletos, I.; Gkatzioura, A.; Kapouniari, M.; Katsoulea, A.; Malamataris, D.; Kavouras, I. Capacity Assessment of a Combined Sewer Network under Different Weather Conditions: Using Nature-Based Solutions to Increase Resilience. Water 2024, 16, 2862. https://doi.org/10.3390/w16192862

AMA Style

Galiatsatou P, Zafeirakou A, Nikoletos I, Gkatzioura A, Kapouniari M, Katsoulea A, Malamataris D, Kavouras I. Capacity Assessment of a Combined Sewer Network under Different Weather Conditions: Using Nature-Based Solutions to Increase Resilience. Water. 2024; 16(19):2862. https://doi.org/10.3390/w16192862

Chicago/Turabian Style

Galiatsatou, Panagiota, Antigoni Zafeirakou, Iraklis Nikoletos, Argyro Gkatzioura, Maria Kapouniari, Anastasia Katsoulea, Dimitrios Malamataris, and Ioannis Kavouras. 2024. "Capacity Assessment of a Combined Sewer Network under Different Weather Conditions: Using Nature-Based Solutions to Increase Resilience" Water 16, no. 19: 2862. https://doi.org/10.3390/w16192862

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