Storm Water Management Model Research Articles

A Study on the Water Management Knowledge of Traditional Villages from the Perspective of Stormwater Resilience—A Case Study of Changqi Ancient Village in Guangdong, China

With the advancement of resilience concepts, enhancing resilience capacity has become an effective approach to addressing rainwater and flooding issues. Most rural planning and construction efforts adopt urban planning models from economically developed regions, often leading to surface hardening, which subsequently causes drainage difficulties and severe surface water accumulation during the rainy season. In contrast, traditional Lingnan villages, exemplified by Guangdong’s Changqi Ancient Village, continue to function normally in flood-prone areas, suggesting that their water management knowledge merits investigation. Previous research on rainwater management in traditional Chinese villages has predominantly been qualitative, lacking scientific data support. This study employs an eco-social resilience perspective, combining field surveys and interviews with villagers, and utilizes the SWMM (Storm Water Management Model) software to conduct both qualitative and quantitative analyses of Changqi Ancient Village. The findings reveal the following: (1) The SWMM effectively quantifies rainwater and flood management in traditional villages. (2) From an ecological resilience perspective, the village’s geographical location is crucial. The topography, along with a rainwater regulation system comprising rivers, ponds, ditches, and permeable pavements, significantly influences the village’s drainage performance. (3) From a social resilience perspective, community participation is vital to the long-term stable development of traditional villages. This includes post-disaster collective fundraising by villagers for the restoration of rainwater and flood management facilities, the formulation of village regulations, and the construction and restoration of spiritual sites. (4) From an eco-social resilience perspective, the eco-social resilience system exhibits adaptive cyclical characteristics, where the geographical environment and the local economy significantly shape the ecological spatial patterns of Changqi, while positive interaction between nature and human society ensures the system’s dynamic equilibrium.

Effects of low impact development on runoff pollution and water quality resilience in an urbanized estuary area

In urbanized estuary areas, water quality often deteriorates due to the impact of surface runoff or overflow during heavy rainfall events. Low impact development (LID) can mitigate this impact and improve water quality resilience. However, there were few studies on quantifying this effect of LID, particularly in estuary areas. In this study, water quality resilience is expressed as the abnormal rate of water quality during rainfall events and the time required for water quality to recover from abnormality to normal. In this study, a coupled Storm Water Management Model (SWMM) and Environmental Fluid Dynamic Code (EFDC) model is used to examine the effects of LID on runoff pollution and water quality resilience in the Shenzhen River estuary (including Shenzhen River and Deep Bay) in China. The results indicate that (1) The inflow volume into the estuary is not only affected by the drainage area but also by the service area and capacity of the sewer interception system; LID can reduce not only the stormwater runoff but also the overflow from the interception systems along rivers. (2) The marginal benefit of the LID proportion increase in pollution load reduction diminishes as the proportion of LID implementation increases. (3) LID can reduce the high value of pollution concentration caused by rainfall runoff; and the reduction effect on the rivers is greater than that on the bay. (4) The rivers and the estuary have a much higher abnormal rate than the inner bay and the outer bay; however, the estuary and the inner bay have longer recovery time than the rivers and the outer bay. The maximum abnormal rate, average abnormal rate, and recovery time of different indicators in the entire estuary area are decreased by 10%-189%, 6%-71%, and 0.8–1.1 cycles when the runoff from 80% of built-up area is treated by LID facilities; LID can significantly reduce abnormal rate of the rivers and recovery time of the inner bay. Therefore, the proposed metric and model can fully reflect the spatial variations in water quality resilience, and LID can enhance the water quality resilience of the urbanized estuary areas under rainfall.

Analysis and Validation of Sponge City Construction in Mountainous Cities based on SWMM - Taking Nanan District of Chongqing as an Example

Sponge city construction is one of the key topics in today’s research. China’s sponge city construction has gradually entered the stage of optimization and effect evaluation. However, there are still some gaps in issues such as the verification of the effect of sponge cities. Therefore, in this paper, Storm Water Management Model (SWMM) is carried out by collecting data on surface runoff, waterlogging, peak flow, control rate, etc. in Nanan District of Chongqing City. At the same time, reasonable Low Impact Developments (LID) facilities such as rain gardens, permeable paving, bioretention tanks, rain barrels, rooftop rainwater harvesting and rooftop gardens are incorporated into the sponge city design and verified by using a typical rainfall process with a return period of 5a and a duration of 3 hours. The results of the study showed that the surface runoff drainage time in one catchment was 11.1% faster before and after the LID facilities were installed in contrast, the peak flow reduction rate of the main pipe was 71.55%, and the average runoff control rate in each catchment was around 40.57%. In conclusion, the LID facilities are not effective enough to reduce the rainfall runoff above the medium rainfall, while they are more effective below the medium rainfall.

Comparative Analysis of Sponge City Drainage System Schemes Based on SWMM

In this paper, the drainage system schemes of sponge city are compared and analyzed based on Storm Water Management Model (SWMM). First, this paper builds a model in SWMM to explore the effect of green roof, rain garden and reservoir on solving the excessive pressure of urban drainage system during rainstorm, and analyzes the results of the model. Through comparative analysis, the best scheme is obtained. The results show that the green roof has the best effect among the three schemes, which can reduce the flow of the pipeline, increase the drainage cycle and thus reduce the pressure of the drainage system. Second, limited by the size of the area, it can only reduce the flow of the drainage system. The reservoir has the worst effect, mainly because the intensity is not large enough, and if it encounters greater rainfall, the reservoir can play a key role in regulating and storing. Through the analysis of the model results, an effective scheme can be obtained to relieve the pressure of urban drainage system in rainstorm, so as to reduce the drainage pipe flow and reduce the risk of urban waterlogging. The purpose of this paper is to provide effective reference for the construction of sponge city, relieve the pressure of drainage system and reduce the hidden danger of urban waterlogging.

Optimizing urban flood management: enhancing urban drainage system efficiency under extreme rainfall events

ABSTRACT Extreme rainfall events, particularly those induced by tropical cyclones, pose a heightened risk to the urban drainage system (UDS). Existing UDSs, having been established long ago, often fail to account for the extreme rainfall caused by cyclones. To address this issue, this study designs a multi-objective intelligent scheduling model within a simulation -optimization framework, aiming to optimize the operation of urban drainage infrastructure and hydraulic structures. This is achieved by integrating the Storm Water Management Model (SWMM) with the multi-objective particle swarm optimization algorithm (MOPSO) and distinctly evaluating typhoons and torrential rains for their impact on extreme rainfall. The study results show that the multi-objective intelligent scheduling model can effectively devise operation strategies for pumping stations and weirs in the study area, thereby optimizing their use for urban drainage. The model was successful in reducing the total flood volume (TFV) and the water level fluctuation (WLF) by 3.11%–57.77% and 26.32%–65.48%, respectively. This not only mitigates urban flooding but also enhances the infrastructure stability of the UDS. The model outperformed the local adaptation strategy in most scenarios for the two selected objectives, suggesting that the efficiency can be significantly improved by optimizing UDSs without expansion of existing infrastructure or additional costs.

Modeling the effect of land-use change on runoff water quality in the urban watershed: a case study of Settat city, Morocco

ABSTRACT The purpose of this study is to create a model of the quantity and quality of runoff from the urban watershed. The modeling was performed using a personal computer stormwater management model (PCSWMM) tool and was based on washoff and buildup for total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP) measured in the field. In addition, low impact development (LID) was used under four different scenarios to improve runoff control of the urban hydrologic cycle and reduce pollutant concentrations. Model performance was analyzed using the R2 and NSE goodness-of-fit indices. Thus, three typical rainfall events, as well as field measurement parameters (TSS, TN, and TP), were used for the model's calibration and validation. The base scenario quantified the runoff volume and pollutant load without any intervention in the runoff process control. Scenarios S2 (BR: bioretention) and S3 (PP: permeable pavement) contributed, separately, in reducing runoff and pollutant load but with relatively small percentages. In addition, the results show that scenario S4 (BR and PP) has the most significant impact on flow reduction, by about 10% compared with the base scenario, as well as on the pollutant load reduction, by about 31, 29, and 40% for the parameters TSS, TN, and TP, respectively.

Comparative assessment of pollution control measures for urban water bodies in urban small catchment by SWMM

Under the current increasingly serious water pollution situation in urban, the comprehensive treatment and protecting measures in urban small catchment are the necessary step to the water pollution treatment, not only focus on the water bodies themself. In this paper, the Luojiagang river channel and its catchment in Wuhan, which has been heavily impacted by water pollution, are taken as an example for the comparative assessment of pollution control measures in order to explore the effective and sustainable schemes including the LID (Low Impact Development---LID) by using SWMM model (Storm Water Management Model---SWMM). Through the comparison of pollution treatment measures based on scenarios simulation with the baseline scenarios, it was found that the reduction effects of pollutant peak concentrations at the outlet node of Luojiagang River had the decreasing order of: the combined measures (COS) > LID control > the water diversion from Donghu Lake (WAD) > the sewage treatment (SET) > the sediment dredging (SED), and the reduction effects of total pollutant load had the decreasing order of: LID > COS > SET > SED > WAD. The results show that the control of non-point source pollution is the key for the improvement of water quality in Luojiagang River, and LID plays important role in the reduction of both pollutant peak concentrations and load. This study has the implication for the decision making of the water pollution controlling schemes in urban small catchment.

Comparing the hydrological performance of blue green infrastructure design strategies in urban/semi-urban catchments for stormwater management

ABSTRACT Blue green infrastructure (BGI), in recent decades, have been increasingly recognized as robust stormwater control measures to reduce urban flooding, promote infiltration, and restore a catchment's flow to its pre-development stage. However, studies comparing the hydrological benefits of BGI alternatives at catchment scale are often limited to single catchment or single/few BGI options scaled over a catchment. This study designed a set of BGI alternatives as a combination of different BGI facilities in terms of the following: (a) spatial distribution scale (end-of-pipe vs. decentralized) and (b) naturalness scale (less engineered vs. more engineered), in three different urban catchments representing an inner city, a residential suburb, and a new urban housing. In addition, their hydrological performances were compared. A 10-year return period design rain and a continuous rain series of 11 years were modelled for each BGI alternative using the computer model (stormwater management model). It was observed that in most catchments, decentralized alternatives (both engineered and natural) showed better potential to reduce the magnitude and frequency of flooding than centralized measures. Similarly, the tested decentralized natural, less engineered alternatives showed higher potential to increase infiltration than the decentralized engineered alternatives in all three catchments. Meanwhile, infiltration-based BGI alternatives showed similar potential to mimic pre-development flow as other decentralized BGI alternatives.

Water quality benefits of implementing Green and Blue Infrastructure in a peri-urban catchment – Case study of a Brazilian metropolis

This paper investigates the effectiveness of Green and Blue Infrastructure (GBI) in managing non-point source pollution in the 120 km2 catchment of the Vargem das Flores reservoir, which supplies water to 600,000 inhabitants in the Belo Horizonte metropolitan area, Brazil's third-largest metropolis. A calibrated water quality model within the Storm Water Management Model (SWMM) was used to simulate total suspended solids (TSS) and nutrients loads under different future land use scenarios. The first scenario reflects maximum urban development according to current legislation. An alternative scenario was proposed considering the protection of forest remnants and the adoption of the most cost-effective GBI techniques in newly developed areas. Results indicated a substantial contribution of diffuse pollution to the reservoir's water quality degradation, mainly due to TSS loads transported from the most urbanized subcatchments. The application of bioretention cells to treat 12.6 km2 new impervious areas effectively mitigated diffuse pollution, removing 52.1% of TSS, 19.5% of total phosphorus (TP), and 17.1% of chemical oxygen demand (COD) loads flowing into the reservoir. The GBI implementation would maintain pollutant loads similar to current levels despite the increased urbanization.

Modeling transient mixed flows in sewer systems with data fusion via physics-informed machine learning

Transitions between free-surface and pressurized flows, known as transient mixed flows, have posed significant challenges in urban drainage systems (UDS), e.g., pipe bursts, road collapses, and geysers. However, traditional mechanistic modeling for mixed flows is challenged by the difficult integration of multi-source data, complex equation forms for the discovery of dynamic processes, and high computational demands. In response, we proposed a data-driven model, TMF-PINN, which utilizes a Physics-Informed Neural Network (PINN) to simulate and invert Transient Mixed Flow (TMF) in sewer networks. This model integrates experimental data, simulation results and Partial Differential Equations (PDEs) into its loss function, leveraging the extensive data available in smart urban water systems. A status factor (α) has been introduced to seamlessly link open channel and pressurized flow dynamics, facilitating rapid adjustments in wave speed. On this basis, Fourier feature extraction and quadratic neural networks have been employed to capture complex dynamic processes featuring high-frequency. Validation through three classical cases using the Storm Water Management Model (SWMM) and comparisons with finite volume Harten-Lax-van Leer (HLL) solver reveal that the proposed model circumvents the constraints of spatiotemporal resolution, yielding accurate flow field predictions.

Integrated urban and riverine flood risk management in the Fujiang River Basin-Mianyang city

China’s monsoon climate, characterized by hot summers and substantial rainfall, leads to significant seasonal water accumulation in river basins. This often results in short-term heavy rainfall events, causing rapid water flow into river basins and subsequent flooding. River embankments, which narrow channels, exacerbate downstream flooding and pose severe safety threats. This study explores integrated urban and riverine flood risk management strategies within the Fujiang River Basin, focusing on Mianyang City. The research addresses the growing challenges of global climate change and rapid urbanization, which have intensified flood risks. Utilizing the one-dimensional Storm Water Management Model (SWMM) and two-dimensional hydrodynamic Rainfall-Runoff-Inundation (RRI) model, the study simulates flood events and urban flooding in Mianyang City and the Fujiang River Basin. These models were validated with data from hydrological stations and satellite imagery. The study evaluates extreme flood events in Mianyang City between 2015 and 2021. The findings highlight the significant role of sponge city initiatives and low-impact development (LID) facilities in mitigating flood impacts. Despite their benefits, these measures have limitations under extreme flood conditions. This study contributes to understanding the interactions between urban and riverine systems and provides a basis for improving flood risk management and resilience in rapidly urbanizing river basins. It supports ongoing efforts to implement sponge city concepts in China, offering crucial insights into effective strategies for managing flood risks in similar geographic and climatic contexts.

Wastewater flooding risk assessment for coastal communities: Compound impacts of climate change and population growth

The study introduces a wastewater modeling framework that evaluates the compound impacts of intense rainfall, groundwater infiltration, sewer aging based roughness and population growth on wastewater systems. It integrates property and city-level flooding risk assessment and wastewater treatment plant (WWTP) capacity analysis into a single methodological approach. The framework applied to the coastal city of Charlottetown, whose population increased from 30,887 in 1981 to 42,440 in 2023, but the wastewater system did not expand accordingly, resulting in frequent sewer backups, street and basement flooding, as witnessed during the extreme wet weather event of September 2, 2021. Using PCSWMM (Personal Computer Storm Water Management Model), the study assessed that the city-wide wastewater flooding risk in Charlottetown, based on 2023 population data and historical IDF curves, affects 13.31% of the network during a 2-year storm and 18.38% during a 100-year storm. These risks increase to 14.5% and 22.6% under future IDF scenarios, reaching 17.89% and 26.4% by 2060 with projected population growth. The WWTP capacity is exceeded by 27.8% during peak wet weather flows from a 2-year storm and by 86.3% during a 100-year storm, based on 2023 population and historic IDFs. Under future IDF scenarios for 2060 population, exceedances rise to 103.6% and 169.1% respectively, for a 2-year and 100-year storm. Basement flooding risk affects 13.35% of basements during a 2-year storm and 18.31% during a 100-year storm, for 2023 population and historic IDFs. Future IDF scenarios indicate risk increasing to 17.77% and 25.80% by 2060 for a 2-year and 100-year storm respectively. The hydraulic modeling results indicate that GWI is not currently impacting the study area, nor is it expected to in near future, because the groundwater table is over 10 m deep, while wastewater pipes are no deeper than 6 m. The framework and study have significant social implications and benefits, including protecting public health, enhancing the resilience of urban infrastructure, and safeguarding the environment, ultimately improving the quality of life for residents in coastal communities like Charlottetown.

Impacts of land use/cover change on urban rainfall-flooding processes based on numerical simulations

The evolution of surface conditions caused by urbanization has a great impact on urban flooding, whose increasing frequency of occurrence has severely affected urban safety. Most previous studies have focused on land use/cover change (LUCC) but haven’t specifically explored the effects of LUCC on the spatial distribution of flood points and the ponded depth. We combined the storm water management model (SWMM) with multi-year remote sensing data to simulate the flood points and ponded depth in different rainstorm types. Results showed that the ratios of flood points with ponded depths > 0.7 m increased from 2.58% to 17.12%, with the evolution of the urban surface conditions in 1988-2021. And the flood points with ponded depths > 0.7 m were mainly located in the southern and northwestern parts of Bantian Street. These results can provide better-informed decision making for storm water management and early warning of flooding disasters in Shenzhen.

Applying Low-Impact Development Techniques for Improved Water Management in Urban Areas

Worldwide, the increase in impervious surfaces due to urbanization has led to significant water cycle issues such as groundwater depletion, urban heat islands, and flooding. To address these challenges, Low-Impact Development (LID) techniques are increasingly being applied in stormwater management. This study focuses on Ulsan, designated as a water cycle model city in South Korea, with a particular emphasis on the highly urbanized Okgyo drainage watershed. Using the Stormwater Management Model (SWMM) version 5.1, long-term runoff simulations were conducted to evaluate the effects of LID implementation on water cycle change rates and recovery rates. The model incorporates detailed hydrological and hydraulic parameters, including inflow, runoff, infiltration, and evapotranspiration for six subcatchments within the watershed. The SWMM was calibrated and validated using 30 years of historical rainfall data (1987–2016) from the Ulsan weather station. Calibration and validation processes used the NRCS-CN (Curve Number) method to ensure accuracy in simulating runoff patterns and water balance. The study specifically evaluated the effectiveness of two LID techniques: bioretention and permeable pavements. Three scenarios were modeled: bioretention applied to 5% of the area, permeable pavements applied to 5% of the area, and a combined application of both techniques. The results showed that the combined scenario provided the best outcome, with a 7.80% reduction in surface runoff and a 14.56% improvement in water cycle health. The LID application scenario confirmed the potential to achieve the water cycle management target of handling 25.5 mm of rainfall. These findings demonstrate that the introduction of LID techniques in public spaces can significantly enhance water management. This research provides insights into effective water cycle management methods tailored to specific urban land uses, laying a foundation for future urban planning and sustainable development.

Predicting the urban stormwater drainage system state using the Graph-WaveNet

Graph Neural Networks (GNNs) have been applied to network data such as traffic flow and water distribution systems, yet their use in predicting the state of urban stormwater drainage systems remains rare. This study investigates the application of Graph-WaveNet (GWN), a type of GNN, in forecasting the states of stormwater systems in Kowloon, Hong Kong. Data was sourced from the Storm Water Management Model (SWMM) spanning 43 rainfall events from 2020 to 2023. Based on the preceding 30 to 60 min of network states and rainfall data, GWN predicted junction inflows, pipe flow rates, and relative water depths (fraction of full area filled by flow) for lead times up to 20, 20, and 30 min, with an R2 greater than 0.6, respectively. Prediction accuracy declines with longer forecast horizons. GWN predicts more time steps ahead for pipes’ flow rates and junctions’ inflows, but fewer for relative water depths during peak versus non-peak periods. It is also more effective at predicting states of large pipes and connected junctions downstream, compared to smaller upstream components. GWN's accuracy improves significantly with precise rainfall nowcasting inputs. This study establishes a significant baseline for GWN's performance in predicting urban stormwater systems during rainfall events.

Machine learning-based rainfall forecasting in real-time optimal operation of urban drainage systems

This paper presents a rainfall forecast-based real-time optimal operation model of urban drainage systems (UDSs) investigating the role of rainfall forecasts in predictive real-time operation of UDSs. Two rainfall forecast models, namely long short-term memory (LSTM) and optimized-by-PSO (particle swarm optimization) support vector regression, are linked to an integrated simulation–optimization model. For any given operation policies, the rainfall-runoff stormwater management model (SWMM) simulates the urban system’s operation under flooding conditions in which the operation policies are optimized by the metaheuristic harmony search (HS) optimizer. The objective function of the HS algorithm is minimizing flood volumes at control points. The presented approach is therefore a dynamic predictive real-time operation (PRTOP) model that continuously updates rainfall forecasts and operational decisions during flood events. The performance of the developed forecast-based integrated SWMM-HS simulation–optimization modeling framework is tested through its application in a real urban system case study located in Tehran, Iran. Results demonstrate that a notable reduction in flood volume (11.8%) is achieved by using the LSTM-driven rainfall forecasts in the proposed PRTOP model compared to a reactive real-time operation (RTOP) model not utilizing rainfall forecasts.

Using a multiphysics coupling-oriented flood modelling approach to assess urban flooding under various regulation scenarios combined with rainstorms and tidal effects

Climate change and rapid urbanisation have intensified urban flooding. Flood models constructed using hydrological and hydrodynamic approaches are crucial tools for simulating and regulating urban flooding processes. Urban flooding arises from the interplay of various physical processes in urban water systems, influenced by heterogeneous urban surfaces, underground structures, and intricate river networks influencing surface flow, which must be considered in flood models. For coastal cities with intricate drainage systems, the disasters of combined rainstorms and tidal flooding can be mitigated through river scheduling and real-time control of hydraulic structures. However, a lack of research on the effectiveness of flood regulations limits the application of flood models. This study proposes a flood modelling approach oriented towards the coupling of multiphysics processes, achieving a comprehensive simulation and regulation of the flood process. A surface flood control model was created by coupling hydrological and hydrodynamic processes using the Storm Water Management Model and the orthogonal storage cell model, enabled by data exchange synchronisation technology. Subsequently, the model’s accuracy was validated using observed rainstorm events, identifying drainage system overflows and land surface inundation. Finally, the developed model was applied to analyse the enhancing effect of tides on rainstorm-induced flooding and the impact of various control scenarios. This study offers theoretical direction for large-scale urban flood modelling and model development, offering significant references for revisiting urban planning and formulating flood prevention strategies under the joint impact of rainstorms and tides.

Intersecting social welfare with resilience to streamline urban flood management

Urban policymakers have long searched for stormwater management plans that incentivize stakeholders to adopt Green Infrastructure (GI) while effectively reducing the vulnerability of drainage systems. In this regard, our research introduces a novel framework to develop GI strategies that provide both hydrological resiliency and social acceptance. To achieve this, first, using a coupled Stormwater Management Model (SWMM) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II), optimal alternatives for GI planning were generated. In the optimization process, we used a novel Simple Urban Flood Resilience Index (SUFRI) to consider the internal performance of the system in identifying resilient plans. Derived management strategies warrant runoff volume reduction and resilience improvement up to 31.3% and 55.1%, respectively. In the next step, Utilitarian-based Social Welfare (USW) was employed to clarify the socio-economic behavior of management strategies. Results indicate that while financial incentives significantly motivate developers to implement GI, they cannot guarantee high social welfare, and achieving a sustainable solution requires evaluating both SUFRI and USW layers under different subsidy levels. Visualizing the SUFRI layer revealed a critical failure in the resiliency trend of solutions that cannot be detected by evaluating simpler metrics, such as runoff volume reduction. This highlights the importance of the SUFRI method in conducting deeper evaluations and preventing financial waste. Finally, we navigated the intersection of USW and SUFRI measures to reach an ideal management plan with optimal supporting level. Our findings showed that the selected solution with the highest social acceptability can improve the resiliency of the system by 29 %. This study is a novel combination of the hydrological and social aspects of stormwater management, enabling decision-makers to take significant steps towards sustainable urban development.

A Low-Impact Development-Based Modeling Framework for Flood Mitigation in a Coastal Community

Urbanization is known to increase the volume of stormwater runoff and peak flow rates, which leads to changes in the natural flow regime and increases the likelihood of flooding. Low-impact development (LID) practices seek to reduce runoff volume and peak flow and are generally considered to be a more sustainable solution for urban stormwater management. In this study, we present a systematic approach to address nuisance flooding issues in small cities and communities. As an application, the effectiveness of two LID practices, rain barrels and permeable pavements, were explored in mitigating the urban flooding problem of a highly urbanized small coastal watershed in Alabama, USA. The EPA Stormwater Management Model (SWMM) was first calibrated for water depth using data collected at multiple sites within the watershed during the 2014–2015 period. The calibrated model was then used to first identify the areas prone to flooding using design storms with 1, 2, 5-, 10-, 50-, and 100-year return periods. Floodplain maps were generated for those design storms with HEC-RAS. Next, LID options upstream of those flood-prone areas were assessed to potentially minimize the flooding risks. The results indicate that LID controls can have considerable benefits for stormwater management by reducing runoff volume (1–24%), peak flow rates (18–25%), and water depth (5–15%), potentially returning watersheds to their natural flow regimes, thereby minimizing the flooding risk in urbanized areas. However, the effectiveness of LIDs, especially for the runoff volume, quickly diminishes as the return periods of the storms increase. Rain barrels were identified as the most economical and effective LID within the drainage system.

Enhancing water management and urban flood resilience using Hazard Capacity Factor Design (HCFD) model: Case study of Eco-Delta city, Busan

This study evaluated future climate scenarios and the changes in urban flood resistance capacity in Busan Eco-Delta City using the hazard capacity factor design (HCFD) model. It analyzed the flood reduction effects of both gray and green infrastructure. Despite existing flood safety systems, there is a growing need to enhance urban flood resilience due to increasing heavy rainfall, unpredictable precipitation, and frequent typhoons driven by climate change. The HCFD model predicted urban flood volumes of Busan Eco-Delta City and analyzed the effectiveness of gray and green infrastructure in flood control. A stormwater management model (SWMM) simulated urban flood resistance capacity under the SSP1–2.6 climate change scenario. Results indicate that for an anticipated 500 mm rainfall over 3 h, green infrastructure can mitigate floods by 9 % to 17.6 %, while gray infrastructure can reduce flooding by 24 % to 32.1 %. The integration of gray and green infrastructure leads to an overall flood mitigation ranging from 47.1 % to 63.3 %. A notable contribution of this research is its predictive analysis of future flood scenarios using model-based scenario analysis and decision support algorithms, offering valuable insights into changes in urban flood resistance capacity and strategies for effective flood control decision-making.