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Article

Green Stormwater Infrastructure (GSI) Performance Assessment for Climate Change Resilience in Storm Sewer Network

by
Teressa Negassa Muleta
1,2,* and
Marcell Knolmar
1
1
Department of Sanitary and Environmental Engineering, Faculty of Civil Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
2
Department of Hydraulic and Water Resources Engineering, School of Civil and Environmental Engineering, Hachalu Hundessa Campus, Ambo University, Ambo P.O. Box 19, Ethiopia
*
Author to whom correspondence should be addressed.
Water 2025, 17(17), 2510; https://doi.org/10.3390/w17172510
Submission received: 20 July 2025 / Revised: 17 August 2025 / Accepted: 18 August 2025 / Published: 22 August 2025
(This article belongs to the Special Issue Sustainable Urban Water Management: The Role of Nature-Based Solutions)
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Abstract

Urban flooding and the management of stormwater present significant challenges that necessitate innovative and sustainable solutions. This research examines the effectiveness of green stormwater infrastructure (GSI) for resilient storm sewer systems using the Storm Water Management Model (SWMM), based on customized local climate scenarios. Daily climate data downscaled by four CMIP6 models—CESM2, GFDL-CM4, GFDL-ESM4, and NorESM2-MM—was used. The daily data was disaggregated into 15 min temporal resolution using the HyetosMinute R-package. Two GSI types—bio-retention and rain gardens—were evaluated with a maximum coverage of 30%. The analysis focuses on two future climate scenarios, SSP2-4.5 and SSP5-8.5, predicted under the Shared Socioeconomic Pathways (SSPs) framework. The performance of the stormwater network was assessed for mid-century (2041–2060) and late century (2081–2100), both before and after integration of GSI. Three performance metrics were applied: node flooding volume, number of nodes flooded, and pipe surcharging duration. The simulation results showed an average reduction in flooding volumes ranging between 86 and 98% over the area after integration of GSI. Similarly, reductions ranging between 78 and 89% and between 75 and 90% were observed in pipe surcharging duration and number of nodes vulnerable to flooding, respectively, following GSI. These findings underscore the potential of GSI in fostering sustainable urban water management and enhancement of sustainable development goals (SDGs).

1. Introduction

Urban areas are increasingly confronted with the challenges posed by climate change, including an increase in the frequency and intensity of storm events, which aggravate stormwater management issues [1,2]. Conventional gray infrastructures, including pipes and culverts, are often unable to convey these evolving conditions, resulting in urban flooding, deterioration of water quality, and ecological disruptions. In response to these issues, Green Stormwater Infrastructure (GSI) is emerging as a viable sustainable alternative or complement to traditional gray systems. Multiple studies have demonstrated the effectiveness of GSI practices for stormwater management [3,4,5,6,7,8,9].
Upon reviewing the literature in this area, several significant limitations have been observed. Many of these studies are confined to national or regional levels, often lacking the necessary finer spatial and temporal resolutions to effectively support local adaptation planning [10,11]. This may overlook the unique operational characteristics and vulnerabilities of sewer networks at the urban or sub-catchment scale. Additionally, most of the existing research tends to center on developed regions. There is a notable deficit of localized studies in developing countries or secondary cities, where sewer systems are frequently underdeveloped and data availability is limited. Ref. [12] pointed out the lack of a comprehensive approach in the present literature for GI performance assessment particularly in terms of the socio-economic aspect. The importance of climate change studies at the local level and the integration of climate resilience strategies like GSI was emphasized by different studies for sustainable development [13,14,15].
Moreover, a variety of studies, including [5,8,16,17], have assessed the effectiveness of GSI in controlling flood and city resilience using emission scenarios like RCPs. However, their reliance on RCP scenarios, which omit socio-economic insights and policy dynamics, may restrict the effective assessment of GSI performance regarding urban resilience in the context of the Sustainable Development Goals (SDGs). Furthermore, the lack of updated models, such as ensembles from CMIP6, may result in overly simplistic assumptions that could exaggerate the effectiveness of GI under severe future scenarios, thereby constraining the relevance of their conclusions for current planning and design initiatives.
Additionally, studies by [18,19,20,21,22] have concentrated mainly on event-based simulations to forecast design storms and evaluate infrastructure capacity. These studies have not engaged in continuous long-term simulations that would encompass the complete variability and uncertainty of future climate conditions, which are necessary for analyzing seasonal climate trends and fostering sustainable resilience to climate change. Of course, event-based analysis is more suitable for the design parameters of grey infrastructures. However, this approach limits the comprehension of GSI performance under ongoing, cumulative climate pressures. Furthermore, some studies, such as [23,24], were based solely on historical and present meteorological data and did not integrate projected climate scenarios in their studies. A tabular summary of limitations observed in the literature and modified in the current study is given below (Table 1).
In view of these deficiencies, there is a requirement for localized and continuous simulation-based research that integrates climate uncertainty and assesses the long-term efficacy of GSI strategies anticipated under climate change conditions. This study seeks to fill these gaps by conducting a localized assessment of sewer systems’ performance under tailored, downscaled, and bias-corrected climate change scenarios, leveraging the latest climate projections from the CMIP6 model ensemble. This study applies the SWMM model to a specific urban area, facilitating detailed simulations of the sewer network’s response to anticipated rainfall extremes. It develops custom local climate scenarios through bias correction and disaggregation techniques to address the scarcity of future climate data at the necessary sub-hourly resolution in developing countries, particularly in Ethiopia. The novelty of this study lies in its holistic approach to filling the above-mentioned gaps through an emphasis on local infrastructure resilience, the creation of customized local projected climate scenarios, and the utilization of updated climate data and modeling frameworks. Such insights that are specific to the context are essential for informing tailored adaptation strategies and shaping policies that align with different sustainable development objectives, particularly SDG 13 (Climate Action) and SDG 11 (Sustainable Cities and Communities).

2. Methodology

2.1. Case Study Overview

The research area is situated in Addis Ababa, the capital city of Ethiopia. From an administrative perspective, Addis Ababa is divided into 11 sub-cities. The elevation of the city varies from 2051 m in the lower Akaki plain to 3041 m above sea level at the peak of Mount Entoto. As reported by [25], the city has experienced an average daily maximum temperature of 22.9 °C and an average daily minimum temperature of 10.2 °C over the past six decades, along with an average annual rainfall of 1184 mm. The city’s terrain generally features a nearly flat slope in the southeast, transitioning to steeper inclines in the northwest, where vertisols and nitisols are the predominant soil types [26]. The natural drainage of the city is facilitated by three primary rivers: the Kebena, Little Akaki, and Big Akaki.
The current case study is located in the eastern part of Lemi Kura sub-city, specifically known as the Bole-Arabsa site. It covers an area of about 1.5 km2 with the predominant land use being low-density residential, with Pellic vertisols the main soil type in the area. The Bole-Arabsa stormwater system serves as an important component of Addis Ababa’s urban water management strategy, particularly as the city faces increasing vulnerability to flooding caused by alterations in precipitation patterns and intensity. According to the city’s 10th master plan, there is a commitment to enhance green space and public recreational areas to encompass 30% of the urban landscape [27]. According to a report from the Addis Ababa Environmental Protection and Green Development Commission, as cited in [28], the decrease in green areas is estimated to contribute to 40% of the flooding and landslides in Addis Ababa.

2.2. SWMM Model Configuration

The Storm Water Management Model (SWMM version 2) is an advanced tool created by the U.S. Environmental Protection Agency (EPA) to simulate rainfall–runoff processes in urban areas [29]. This improved version can also be utilized for modeling GSI, particularly in efforts to address climate change impacts like heightened flooding, stormwater runoff, and the urban heat island phenomenon. The SWMM was set up using hydrometeorological data and spatial information gathered from the research area. In the current study, the model was configured with 761 sub-basins and 17 outlets to represent the drainage systems draining an area of 1.5 km2 that ultimately drains into the Big Akaki natural drainage systems (see Figure 1).

Model Calibration and Validation

Historical data on rainfall and runoff was used both for calibration and validation of the SWMM. Calibration was performed using daily rainfall data obtained from the Bole Meteorological Station and daily instantaneous flow data transferred from the Akaki hydrological station covering 10 years (1990–1999), while validation relied on 5 years of observed data (2000–2004) to assess the model’s predictive capabilities. The flow data from the Akaki station was transferred to the outfall location of the study area using the area ratio method. Key parameters for calibration included depression storage, infiltration parameters (suction head, conductivity, and initial deficit), percentage of impervious surfaces, and pervious surface roughness coefficient.
The calibration process entailed a comparison between the model’s runoff outputs and the actual flow data from the drainage systems for the years 1990 to 1999. Improvements in calibration efficiency were achieved through the thorough adjustment of parameters such as the roughness coefficient, infiltration rate, and depression storage coefficient. The efficiency of the model was assessed using multiple metrics, including Nash–Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Percent Bias (PB).

2.3. Data Processing

2.3.1. Data Collection

Daily meteorological records from 1990 to 2022 were collected from the National Meteorological Agency of Ethiopia for the Bole Meteorological Station, which is about 8 km from the study area. Additionally, 15 min rainfall data covering an 11-year period (2011–2022) was obtained from the same gauge station to facilitate the disaggregation of daily climatic information for use in the Storm Water Management Model. Daily instantaneous flow data for the Akaki hydrological station was collected from the Ministry of Water and Energy in Ethiopia, covering the years 1980 to 2004. The station is located about 13 km downstream of the study site and the area ratio method was applied to transfer flow data to outfall location. Corresponding spatial data, including Digital Elevation Models (DEMs) of 30 m by 30 m resolution, land use, soil types, and river basins in Ethiopia, were also gathered from this ministry. Information on urban planning, land use in Addis Ababa, and details pertaining to the city and its sub-cities were obtained from the Addis Ababa City Planning and Development office. Moreover, the design and planning information for the storm sewer network were collected from the Addis Ababa City Road Authority.
Climate change forecasts were acquired from the NASA Earth Exchange (NEX) Global Daily Downscaled Projections (GDDPs) dataset with a spatial resolution of 0.25° and daily temporal resolution. Four CMIP6 models—CESM2, GFDL-CM4, GFDL-ESM4, and NorESM2-MM—were selected based on their effectiveness in reflecting observed data from previous studies conducted in the country [30]. These models offered a broad spectrum of potential future climate scenarios, characterized by high-resolution, bias-corrected data and enhanced simulations of climatic extremes [31,32]. The dataset includes historical climate data from 1950 to 2014 and projected data from 2015 to 2100, which were obtained from the NASA NEX GDDPs for the four selected CMIP6 models. From this dataset, the climatic data relevant to the study area was extracted using the ONCF-V1.1 application of AgriMetSoft (ONC v1.1 of AgriMetSoft and HyetosMinute v2.2) from the downscaled Network Common Data Form (NetCDF) files. Finally, the data was categorized into baseline data (2001–2020), mid-century projections (2041–2060), and late-century projections (2081–2100), aligned with two Shared Socioeconomic Pathways (SSPs), SSP2-4.5 and SSP5-8.5.

2.3.2. Bias Correction

To achieve precise climate projections, bias correction was applied to the CMIP6 model outputs. This process involved comparing historical model outputs with observed data spanning 31 years (1991–2021) and adjusting future projections to eliminate systematic biases. The models exhibited temporal inconsistencies, demonstrating substantial variations in their capability to replicate observed data across different seasons. Therefore, prior to the bias correction, the performance of the four models was reassessed for each of the 12 months using the Root Mean Square Error (RMSE) method. Subsequently, the output data from the model that performed best was used for bias correction and projection data for each corresponding month.
Studies conducted earlier in the same basin by [33,34] demonstrated the effectiveness of distribution mapping (DM) techniques in aligning the statistical characteristics of model outputs with actual observations. Several researchers, including [35,36,37,38,39], have utilized the Quantile Mapping (QM) method for bias correction, achieving reliable outcomes, particularly in aligning distributions with observed data. Consequently, this study implemented the robust empirical quantile mapping (REQM) package within R programming to correct biases in the CMIP6 downscaled dataset from the selected models prior to its application in SWMM simulations.

2.3.3. Disaggregation

Climate model data typically has a daily temporal resolution; however, a finer temporal resolution, such as sub-hourly intervals, is required for urban drainage system simulations. In this investigation, the HyetosMinute software v2.2 was integrated with R programming to disaggregate daily rainfall data into 15 min intervals. Various temporal disaggregation methods exist to convert daily or monthly projections into hourly or sub-hourly rainfall data while preserving the original data’s statistical properties and variability. Numerous studies [40,41,42,43] have indicated that the disaggregation utilizing the Bartlett–Lewis (BL) process can effectively replicate the statistical behavior of the variable.
As a result, this study employed the BL process from the HyetosMinute package in R programming to disaggregate the downscaled daily rainfall data into 15 min intervals suitable for the SWMM’s effective simulation of stormwater drainage systems. For this purpose, a series of 15 min observed rainfall data covering 11 years (2011–2022) alongside the corresponding daily observed data was used to establish the BL parameters. To mitigate the influence of seasonal variations, the BL parameters were developed monthly.

2.3.4. Scenario Development

To tackle the adaptation and mitigation issues posed by climate change in localized contexts [44,45,46,47], this study incorporated the recently established climate change scenarios reflecting predicted global socioeconomic shifts, known as “Shared Socioeconomic Pathways (SSPs)”. Two of the five SSP scenarios, the middle-of-the-road scenario (SSP2-4.5) and the fossil-fueled scenario (SSP5-8.5), were selected. The focus was to assess the impact of climate change on storm sewer infrastructure under SSP2-4.5, which aligns with the country’s ongoing efforts in alternative energy, land use planning, and climate change mitigation and adaptation strategies [48]. The fossil-fueled scenario (SSP5-8.5) was selected to examine the potential maximum impact of climate change on storm drainage infrastructure. Furthermore, ref. [49] noted in their comparative analysis that future climate scenarios from CMIPs reflecting high GHG emissions closely align with realistic expectations.
The bias-corrected and disaggregated climate forecasts derived from four chosen CMIP6 models (CESM2, GFDL-CM4, GFDL-ESM4, and NorESM2-MM) were integrated into the SWMM model. Simulations were conducted for both mid-century (2041–2060) and late-century (2081–2100) projections under the SSP2-4.5 and SSP5-8.5 climate scenarios. For each scenario, the performance of the storm sewer network was assessed based on node flooding volume, number of flooded nodes, and pipe surcharge durations. These results were compared to the baseline period (2001–2020) to measure the impact of climate change on the functionality of storm sewer systems.

2.4. Green Stormwater Infrastructure (GSI) Integration

The SWMM incorporates inbuilt LID controls to model GSI components. For the two specific GSI practices examined in this study—bio-retention and rain gardens—key data requirements include surface, soil, storage, and drain layer characteristics for the GSI setup. Information pertaining to surface features (such as roughness and slope), soil properties (including porosity, conductivity, and suction head), and storage parameters (like void ratio and seepage rate) were derived from the spatial data of the study area corresponding to the recommended engineering soil types. Additional parameters (refer to Table 2), such as berm height, soil and storage thickness, and flow coefficients or exponents, were adapted from guidelines provided in various user manuals and academic references [50,51].
To implement GSI in the SWMM, different controls were integrated into the model. We selected two types of GSI controls: bio-retention cells and rain gardens. The essential parameters for the surface, soil, storage, and drain layers were established for each control type, depending upon the spatial data. These GSI controls were strategically placed within each sub-basin, ensuring that runoff from roadways and parking lots was primarily managed by bio-retention cells, while runoff from residential impermeable surfaces was mainly directed to rain gardens. The maximum coverage for both bio-retention and rain garden GSIs was kept at 30%, in accordance with the city’s 10th master plan [27]. From the 30% GSI area, about 60% was allotted for bio-retention and the remaining 40% for rain gardens.
This study followed concise procedures with key considerations for the design and strategic placement of GI tailored to ensuring effective implementation and long-term sustainability. To achieve this, site suitability was conducted using Geographic Information Systems (GISs), focusing on factors such as land use, urban planning, and drainage system data. The optimization of GI design and dimensions was supported by a simulation-based framework established in this research. This framework facilitates the strategic and optimum placement of GI by identifying flood-prone areas and assessing the efficiency of various sizing scenarios. The impact of GSI on minimizing node flooding and pipe surcharge was evaluated by comparing the performance metrics of the stormwater systems before and after GSI implementation. The comparisons were conducted under two climate change scenarios (SSP2-4.5 and SSP5-8.5) to explore the potential influence of GSI on climate change resilience.

3. Results and Discussion

3.1. Preliminary Data Processing Results

3.1.1. Rainfall Characteristics

As expected, the intensity of rainfall is anticipated to rise due to climate change scenarios, showing a potential increase both in the frequency and severity of stormwater flooding incidents. The analysis of rainfall data for this study indicates a general trend in which the intensity of rainfall increases from moderate scenarios to those reliant on fossil fuels, as well as from mid-century predictions to late-century ones (refer to Figure 2).

3.1.2. SWMM Calibration and Validation Results

The model performance assessed using the above-mentioned performance indices showed NSE = 0.75 and 0.58, RMSE = 0.05 and 0.2, and Percent Bias = −0.1 and 0.13, for calibration and validation, respectively, indicating a good performance of the model. Hence, the SWMM was applied to evaluate the potential of GSI to enhance the resilience of stormwater systems and the simulation results are briefly presented in tabular and figure forms.

3.1.3. Baseline Scenario

The simulation result for the baseline (2001–2020) climate conditions exhibited adequate functionality, with no considerable instances of surcharging or flooding. This is attributed to the recent modification of the storm sewer systems following repeated flooding incidents over the area. However, the modification was carried out based on past hydrometeorological data and investigation of its level of susceptibility under future climate scenarios and integration of GSI is crucial for its sustainability. However, attention needs to be given to the too-low velocities observed under the baseline scenario in certain pipes such as C681, C280, C231, C183, and C293, as this could lead to sediment accumulation and restrictions in flow, ultimately contributing to surcharging in conduits and potential flooding events.

3.2. Mid-Century (2041–2060) Projection Scenario

3.2.1. Pre-GSI-Integration Scenario for Mid-Century

The introduction of bio-retention cells and rain gardens significantly reduced the flooding volume at nodes within the Bole-Arabsa storm sewer systems. In the SSP2-4.5 scenario, flooding was initiated at 12 nodes out of a total of 761, resulting in a cumulative flooding volume of 8.1 × 103 m3 over the study area for a period of 1.3 h, with the highest flooding rate of 2.7 m3/s recorded at node J773. The simulation indicated an increased frequency of flooded junctions, overloaded pipes, and greater flooding volume under the SSP5-8.5 scenario compared to the middle-of-the-road scenario. The number of flooded nodes increased to 19, with the peak flooding rate at node J773 rising to 3.6 m3/s. Furthermore, a total flooding volume of 13.3 × 103 m3 was observed, lasting for 2.8 h across the area.
In the SSP2-4.5 scenario, 12 pipes were recorded as experiencing surcharge conditions, with an extended duration of above-normal flow lasting 1.3 h at conduit C769, highlighting limited capacity issues. In the more severe SSP5-8.5 scenario, 19 pipes were noted to be in a surcharge condition for a maximum duration of 2.8 h, again recorded at conduit C769.

3.2.2. Post-GSI-Integration Scenario for Mid-Century

Following the implementation of GSI, the number of flooding nodes was reduced to two, with a peak flooding rate of merely 0.4 m3/s recorded at node J773 during the SSP2-4.5 scenario simulation. The total flood volume within the study area was constrained to 2.0 × 102 m3, owing to GSI’s role in managing flood control. In a similar manner, the SSP5-8.5 scenario indicated a decrease in total flooding volume across the area to 9.0 × 102 m3, with a maximum flooding rate of only 1.0 m3/s at node J773 and just two nodes experiencing flood events. Additionally, the number of conduits under surcharging conditions was limited to two pipes for both emission scenarios following GSI integration. The durations of maximum surcharge above normal flow were recorded to be 0.2 h and 0.3 h for the SSP2-4.5 and SSP5-8.5 scenarios, respectively. A summary of GSI performance indicators is provided in Table 3, illustrating the conditions before and after GSI implementation.
Generally, areas that experienced flooding problems before the implementation of GSI showed a significant decline in overall flooding volume, ranging from 93% to 98% across the SSP2-4.5 and SSP5-8.5 scenarios after GSI was put in place. This reduction is credited to the capacity of bioretention cells and rain gardens to absorb and hold water, which contributed to lessening peak runoff and enhancing processes like evapotranspiration. Consequently, this alleviated the burden on stormwater systems. The findings suggest that adopting GSI strategies would result in a notable decrease in flooding at nodes and conditions of pipe surcharging in the future.

3.3. Late-Century (2081–2100) Projection Scenario

3.3.1. Pre-GSI-Integration Scenario for Late-Century

Under the SSP2-4.5 scenario, the number of flooded nodes rose to 19, with a peak flooding rate reaching 3.6 m3/s at node J773, resulting in a total flooding volume of 16.1 × 103 m3 in the study area. In the SSP5-8.5 scenario, a notably larger number of nodes began to overflow, totaling 24 flooded nodes, with the maximum flooding rate at node J773 increasing to 3.8 m3/s. This led to a cumulative flooding volume of 19.7 × 103 m3 across the catchment area. The analysis revealed that 19 pipes experienced surcharge under the SSP2-4.5 projection, with the maximum flooding duration recorded at 3.67 h for conduit C769. In the same manner, the SSP5-8.5 projection indicated an increase in the number of pipes under surcharge to 24, with the duration extending to 6.4 h for conduit C769.

3.3.2. Post-GSI-Integration Scenario for Late-Century

In the context of the SSP2-4.5 scenario, the number of flooded nodes was reduced to 2, with node J773 recording a flooding rate of 0.83 m3/s, resulting in a cumulative flooding volume of 8.0 × 102 m3. In a similar fashion, the SSP5-8.5 scenario indicated a decrease in flooded nodes to 6, leading to a total flooding volume of 2.7 × 103 m3 following the introduction of GSI. The highest flooding rate in this scenario was 1.646 m3/s at node J773.
Under the SSP2-4.5 scenario, the number of pipes experiencing surcharge conditions also dropped to two due to GSI interventions, considerably reducing the flooding duration to 0.8 h. In the SSP5-8.5 scenario, the number of surcharged pipes decreased to six, with the surcharge duration shortened to 0.92 h in the post-GSI situation. The simulation outcomes for the late century are compiled in Table 4.
In general, most regions that experienced flooding in the pre-GSI scenario saw a significant decrease in flooding issues within the study area. The reductions in flooding volumes were observed to be between 86% and 95% over the late century across the analyzed scenarios, as shown in Table 3. This improvement is largely due to the enhanced infiltration and retention capabilities of bio-retention cells and rain gardens. These features contribute to delaying peak runoff events and enhancing processes like evapotranspiration, which collectively alleviate the strain on stormwater management systems. Figure 3 summarizes the number of nodes affected by flooding and the corresponding flooding volumes before and after the integration of GSI across all scenarios.
The implementation of GSI measures would lead to an average reduction of about 86% in the number of locations at risk of flooding during simulated peak rainfall series. Sewer systems that were previously under inadequate capacity were able to operate under normal conditions free of surcharge after the integration of GSI. This result underscores the significance of decentralized stormwater management approaches in reducing system-wide vulnerabilities. Overall, the integration of bio-retention cells and rain gardens was proven effective in bolstering urban sewer systems’ resilience against climate change. As shown in Figure 4 and Figure 5, for comparison, the spatial distribution of flooding and surcharging critical points was effectively minimized after the implementation of GSI. The results indicate that even under the worst-case scenario in the late-century projection period (2081–2100), GSI can provide a viable and sustainable solution for managing urban flooding and protecting essential infrastructure.
However, the percentage of coverage of GSI is an important factor affecting the efficacy of GSI. As a matter of fact, it is hard to achieve urban planning that provides sufficient space for GSI development, particularly in the old cities of developing countries. Therefore, integrating different types of GSI, like green roofs, permeable pavements, and other locally suitable source control strategies with the current bio-retention cells and rain gardens will improve the overall efficiency of flood mitigation. Supportive policies that encourage integrated water resources management and GSI integration into urban planning, and prioritize climate adaptation strategies at both national and local scales, are essential to improve the efficiency and sustainability of GSI. Collaboration among different sectors, including environmental agencies, urban planners, and community stakeholders will also help boost and maintain the effectiveness of GSI.

3.4. Identification of Critical Spots

Identifying nodes and conduits that are vulnerable to flooding incidents is important information for focusing during the integration of GSI. Moreover, the information is valuable to urban planners, decision-makers, and the community as well, to prioritize planning for climate change adaptation and mitigation strategies for the study area. There are also nodes identified where it is impossible to manage the projected flooding, even with the 30% GSI integration, that need additional measures to ensure their sustainability. The most vulnerable locations identified include nodes J279, J773, J665, J62, and J402; these sites experienced significant flooding larger than 1.0 m3/s, with a peak rate of 3.8 m3/s recorded at node J279. Likewise, pipes C769, C279, C661, and C539 leading flow to outfalls demonstrated considerable capacity deficiencies and endured flooding durations exceeding one hour, peaking at 6.4 h at pipe C679 (see Figure 6).
The simulation results presented so far showed repeated flooding incidences at specific nodes like J773, J279, and J402, and specific pipes such as C769, C279, and C661, across all scenarios. This is because the locations of all the catch basins (nodes) in the study area are of the “on grade” type, which allows the runoff along the road sideways to partly bypass the inlets. This runoff finally collects into the far downstream end located nodes, causing over-flooding. As a result, the nodes and pipes that are located next to or closer to the outfalls frequently experience flooding or surcharging, as reported in this study, as illustrated by Figure 6.
The notable decrease in flooding volumes and pipe surcharging duration observed in this research emphasizes the role of GSI as a decentralized approach to stormwater sustainable development goals. Bio-retention cells and rain gardens facilitate increased infiltration and reduction of runoff peaks, thus alleviating pressure on traditional stormwater systems. On average, there would be a reduction in flooding volume of between 86% and 95% for the Bole-Arabsa stormwater management systems across various flood-prone locations through the integration of bio-retention and rain garden systems.
Generally, the locally customized and finer resolution climate data facilitated detailed simulations of the sewer network’s response to anticipated rainfall extremes. Furthermore, the continuous simulation enabled better capturing of climatic variabilities and the cumulative effects of overlapping or consecutive storm events, which were usually overlooked in event-based simulations in previous similar studies. Hence, the obtained results can be conceived as a reliable simulation outcome as they are based on the latest generation of climate models (CMIP6) and SSP-based scenarios that provide a more thorough and policy-relevant framework. Such context-specific insights are vital for guiding targeted adaptation strategies and policy formulation in alignment with various sustainable development goals, including SDG 13 (Climate Action) and SDG 11 (Sustainable Cities and Communities).
The findings in this study are consistent with various previous studies. For example, the study conducted in South Korea (Seogok Park) by [52] evaluated the efficiency of bio-retention cells, and after three years of monitoring, they found that bio-retention has the ability to reduce runoff by more than 85%. The result agrees well with the current study’s finding of flooding volume reduction by GSI. Irrespective of the differences in the methods used, the similarity of results with this rigorous experimental assessment can imply the reliability of the model parameters used in the current study. A similar experimental investigation by [53], after over 2.5 years of rigorous assessment, showed that bio-retention can reduce runoff by 48% to 74%, which is still closer to our finding. The current study’s finding agrees well with the research findings conducted in Tehran (Iran) [54]. They applied the same model (SWMM) and evaluated the effectiveness of bio-retention, with similar model parameters, to reduce runoff and found that bio-retention can reduce runoff volume by 75.6% to 60.7% during rainfall events with recurrence intervals ranging from two to one hundred years. The result is closer to our findings. The slight variation might have arisen from differences in a few model parameters (for example, they used 150 mm and 390 mm for berm height and storage layer thickness, respectively, which are slightly lower than those used in our study). Variation in local conditions might have also contributed to the differences. Irrespective of discrepancies in the obtained efficiency of GSI, the result agrees with the study conducted by [55] in that both findings showed that the impact of climate change on stormwater systems cannot be completely removed by GSI alone.
On the contrary, an investigation into urban storm runoff in Japan found a lower effectiveness of rain gardens that ranges from 17.38% to 12.11% for rainfall events with return periods of 3 to 100 years, compared to the current study [56]. They applied a similar model (SWMM), and the lower value in the runoff volume reduction is most likely caused by the lack of storage and drainage layers for the rain gardens compared to the bio-retention cells we applied. Ref. [24], using a similar method, has also found relatively lower values of 6.36% and 7.23% for peak and volume of runoff reduction using a 5% GSI coverage. The relatively lower values observed in that study, in comparison to the current study, are likely due to the lower GSI coverage applied and differences in local conditions.

3.5. Limitations and Future Research Directions

Despite the effective reduction in node flooding and pipe surcharging, the results indicated that flooding cannot be completely mitigated at some critical locations, especially under the fossil-fueled scenario. This suggests the need to complement GSI with different source control strategies to adequately tackle stormwater management challenges. Furthermore, the successful implementation of GSI necessitates supportive policies, collaboration across various disciplines, and engagement from stakeholders. This underscores the necessity of incorporating GSI solutions into a holistic stormwater management strategy that encompasses hydraulic modeling and community involvement. While this study offers important insights, it did not incorporate land use change scenarios and the integration of social aspects due to time constraints and insufficient data. Future research should focus on the long-term effectiveness of GSI across diverse climate and urbanization scenarios, considering policy and socio-economic dimensions of GSI.

4. Conclusions

This research evaluated the efficacy of two GSI methods—bioretention and rain gardens—for enhancing the resilience of the Bole-Arabsa storm sewer systems in light of anticipated climate change effects, assuming a 30% coverage of GSI. The simulation was performed using the SWMM under two SSP scenarios—SSP2-4.5 and SSP5-8.5—for mid- and late-century future time spans. The simulation showed that the integration of GSI could reduce flooding volume and node flooding by 86% and 75%, respectively, even under the SSP5-8.5 scenario, highlighting the potential of bioretention cells and rain gardens in enhancing the resilience of urban storm sewer systems to climate change impacts. These results emphasize the promise of locally customized nature-based approaches in fostering sustainable development goals.
While this research provides valuable insights, it does not account for land use change scenarios and is missing an evaluation of receiving water quality improvement, primarily due to time limitations and a lack of adequate data. Future studies could concentrate on evaluating the long-term effectiveness of GSI across various climate and urbanization scenarios while accounting for water quality contexts and integrating a socio-economic dimension to improve GSI efficiency.

Author Contributions

T.N.M.: Conceptualization; Data curation; Methodology; Software; Visualization; and Writing—original drafting and reviewing. M.K.: Conceptualization; Resources; Supervision; Validation; and Writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data will be available on request.

Acknowledgments

We would like to sincerely thank Kelbessa Bitima Mijena from the Addis Ababa Road Authority for his invaluable assistance in collecting essential data concerning the Bole-Arabsa storm sewer network, which was utilized in this research.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Location map of the study area (AA = Addis Ababa; ETH = Ethiopia).
Figure 1. Location map of the study area (AA = Addis Ababa; ETH = Ethiopia).
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Figure 2. Variation in rainfall intensity under different projection scenarios.
Figure 2. Variation in rainfall intensity under different projection scenarios.
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Figure 3. Summary of number of nodes under flooding and flooding volumes across all scenarios.
Figure 3. Summary of number of nodes under flooding and flooding volumes across all scenarios.
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Figure 4. Color map of node-flooding spatial distribution under SSP5-8.5 scenario for 2081–2100 before GI.
Figure 4. Color map of node-flooding spatial distribution under SSP5-8.5 scenario for 2081–2100 before GI.
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Figure 5. Color map of node-flooding spatial distribution under SSP5-8.5 scenario for 2081–2100 after GI.
Figure 5. Color map of node-flooding spatial distribution under SSP5-8.5 scenario for 2081–2100 after GI.
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Figure 6. SWMM output map showing critical nodes and piped spatial locations under SSP2-4.5 scenario for 2081–2100.
Figure 6. SWMM output map showing critical nodes and piped spatial locations under SSP2-4.5 scenario for 2081–2100.
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Table 1. Tabular summary of observed previous studies’ limitations improved in the current study.
Table 1. Tabular summary of observed previous studies’ limitations improved in the current study.
Category of Previous LiteratureMajor Limitation of Previous Studies ObservedModification by Current StudyRemark
1. e.g.: [1,9,10]Lack of necessary finer resolutionBased on 15 min temporal resolutionEffective stormwater system simulation needs sub-hourly resolution
2. e.g.: [5,8,16,17]Based on SRES or RCP, which do not explicitly include socio-economic development pathwaysBased on combined SSP and radiative forcing scenariosSSP–radiative forcing complement better represents realistic future scenarios
3. e.g.: [18,19,20,21]Applied event-based simulation, less capture of climate variabilityApplied continuous simulationsContinuous simulation is more effective for sustainable GSI performance analysis
4. e.g.: [23,24]Based solely on historical dataProjected future climate scenariosSystems based on historical data are vulnerable under future climate conditions
Table 2. Model parameters for the bio-retention cell (BRC) and rain garden (RG) used in this study.
Table 2. Model parameters for the bio-retention cell (BRC) and rain garden (RG) used in this study.
LayerParameterUnitValue (BRC)Value (RG)
SurfaceBerm Heightmm400300
Vegetation Volume Fraction(-)0.20.2
Surface Roughness(-)0.40.4
Surface Slope%0.10.1
SoilThicknessmm600600
Porosity(-)0.50.5
Field Capacity(-)0.20.2
Wilting Point(-)0.10.1
Conductivitymm/h1111
Conductivity Slope(-)4040
Suction Headmm110110
StorageThicknessmm400(-)
Void ratio(-)0.65(-)
Seepage Ratemm/h0.50.5
Clogging Factor(-)0(-)
DrainFlow Coefficient(-)2(-)
Flow Exponentmm/h0.5(-)
Offsetmm300(-)
Open Levelmm0(-)
Closed Levelmm0(-)
Control Curve(-)(-)(-)
Table 3. Summary of GSI performance indicators in mid-century projection.
Table 3. Summary of GSI performance indicators in mid-century projection.
IndexSSP2-4.5SSP5-8.5
Pre-GSIPost-GSI%ChangePre-GSIPost-GSI%Change
Number of nodes flooded12283.319289.5
Total flooding volume (×103 m3)8.10.297.513.30.993.2
Max. node flooding rate (m3/s)2.70.485.23.61.072.2
Flooding duration (h)1.30.284.62.80.389.3
Number of pipes surcharged12283.319289.5
Max. surcharging duration (h)1.30.284.62.80.389.3
Table 4. Summary of GSI performance indicators in late-century projection.
Table 4. Summary of GSI performance indicators in late-century projection.
IndexSSP2-4.5SSP5-8.5
Pre-GSIPost-GSI%ChangePre-GSIPost-GSI%Change
Number of nodes flooded19289.524675.0
Total flooding volume (×103 m3)16.10.895.019.72.786.3
Max. node flooding rate (m3/s)3.60.877.83.81.657.9
Flooding duration (h)3.70.878.46.40.985.9
Number of pipes surcharged19289.524675.0
Max. surcharging duration (h)3.70.878.46.40.985.9
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Muleta, T.N.; Knolmar, M. Green Stormwater Infrastructure (GSI) Performance Assessment for Climate Change Resilience in Storm Sewer Network. Water 2025, 17, 2510. https://doi.org/10.3390/w17172510

AMA Style

Muleta TN, Knolmar M. Green Stormwater Infrastructure (GSI) Performance Assessment for Climate Change Resilience in Storm Sewer Network. Water. 2025; 17(17):2510. https://doi.org/10.3390/w17172510

Chicago/Turabian Style

Muleta, Teressa Negassa, and Marcell Knolmar. 2025. "Green Stormwater Infrastructure (GSI) Performance Assessment for Climate Change Resilience in Storm Sewer Network" Water 17, no. 17: 2510. https://doi.org/10.3390/w17172510

APA Style

Muleta, T. N., & Knolmar, M. (2025). Green Stormwater Infrastructure (GSI) Performance Assessment for Climate Change Resilience in Storm Sewer Network. Water, 17(17), 2510. https://doi.org/10.3390/w17172510

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