SWMM Model Research Articles

PREDIKSI DAMPAK PERUBAHAN TATAGUNA LAHAN TERHADAP DEBIT LIMPASAN, STUDI KASUS: GEREJA RANCASARI

Flooding is one of the most frequent natural disasters in Indonesia. Based on data, as of 2024, there have been 137 cases of flooding incidents. One of the causes of flooding, particularly in urban areas, is due to land use change. This study examines the impact of land use changes on runoff discharge in a Church area in the Bandung City. Additionally, this study investigates the effect of the development of this area on flood water levels in the Riung channel, which serves as an outlet channel. In this study, modeling is conducted using the SWMM model. The study utilizes data from the BMKG Bandung and GPM satellite rainfall data from 2001 to 2022 for rainfall analysis. The results indicate that in the study area, the duration of extreme rainfall producing maximum discharge is rainfall lasting for 7 hours. As a result of the church development, runoff discharge increases by 26.3 – 34.4% across various return periods. For time to peak, there is no significant change in conditions before and after the area is developed. This is due to area is relatively small, resulting in a relatively shorter concentration time. Based on the hydraulic analysis, the Riung channel is still able to accommodate additional runoff generated due to development, leaving a freeboard height of 4.5 cm for a 10-year return period. However, based on observations, special attention is needed regarding the issue of sedimentation and trash, which often causes backflow in several segments of the trenches.Keywords:runoff, land use change, urban drainage, SWMM, Bandung

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.

SWMMLIDopt: a tool for optimization of low-impact development (LID) measures using the SWMM model

SWMMLIDopt: a tool for optimization of low-impact development (LID) measures using the SWMM model

Comparative Evaluation of Water Level Forecasting Using IoT Sensor Data: Hydrodynamic Model SWMM vs. Machine Learning Models Based on NARX Framework

This study evaluates the accuracy of water level forecasting using two approaches: the hydrodynamic model SWMM and machine learning (ML) models based on the Nonlinear Autoregressive with Exogenous Inputs (NARX) framework. SWMM offers a physically based modeling approach, while NARX is a data-driven method. Both models use real-time precipitation data, with their predictions compared against measurements from a network of IoT sensors in a stormwater management system. The results demonstrate that while both models provide effective forecasts, NARX models exhibit higher accuracy, with improved Nash–Sutcliffe Efficiency (NSE) coefficients and 33–37% lower mean absolute error (MAE) compared to SWMM. Despite these advantages, NARX models may struggle with limited data on extreme flooding events, where they could face accuracy challenges. Enhancements in SWMM modeling and calibration could reduce the performance gap, but the development of SWMM models requires substantial expertise and resources. In contrast, NARX models are generally more resource-efficient. Future research should focus on integrating both approaches by leveraging SWMM simulations to generate synthetic data, particularly for extreme weather events, to enhance the robustness of NARX and other ML models in real-world flood prediction scenarios.

The impact of blue-green infrastructure on trace contaminants: A catchment-wide assessment

Blue-green infrastructure (BGI) reduce urban combined sewer overflows (CSOs) and stormwater outlets (SWOs). However, most conventional BGI are not designed to remove trace organic contaminants. Little is known about the potential of conventional BGI to improve surface water quality by reducing the discharge of trace organic contaminants. We derived wash-off loads for street runoff (6PPD-q, DPG, and HMMM), construction materials (diuron), and wastewater-derived contaminants (diclofenac) based on measurements in the combined sewer system. Subsequently, the performance of four BGI types (bioretention cells, green roofs, porous pavements, and urban wetlands) to reduce the discharge of trace organic contaminants via SWOs and CSOs was quantified with a hydrodynamic SWMM model. Moreover, the catchment-wide impact of SWOs and CSOs on surface water was assessed using risk quotients. We found that the annually discharged load can be considerably reduced by implementing BGI. Among the studied BGI types, bioretention cells are the most effective, with a load reduction of up to 80% to surface waters, mainly due to a larger suitable implementation area and a substantial stormwater infiltration. BGI implemented in the separate sewer system are more effective in reducing stormwater contaminant loads than BGI in the combined system. The assessment of the risk quotient in the surface water showed that the concentrations during SWO and CSO discharges exceed the acute environmental threshold in the surface water for 6PPD-q, DPG, diuron, and diclofenac during several events. The implementation of BGI reduced the hours of exceeded risk quotient in the surface water by 93% for bioretention cells. These findings underscore the need for a catchment-wide assessment of future BGI implementations to quantify, manage, and mitigate the impacts of urban pollution.

Clarifying urban flood response characteristics and improving interpretable flood prediction with sparse data considering the coupling effect of rainfall and drainage pipeline siltation

With climate warming and accelerated urbanisation, severe urban flooding has become a common problem worldwide. Frequent extreme rainfall events and the siltation of drainage pipes further increase the burden on urban drainage networks. However, existing studies have not fully considered the effects of rainfall and pipeline siltation on the response characteristics of flooding when constructing numerical models of urban flooding simulations. To solve this problem, a surface-subsurface coupling model was constructed by combining the Saint-Venant equation, Manning equation, a one-dimensional pipeline model (SWMM), and a two-dimensional surface overflow model (LISFLOOD-FP). Then, the SWMM model considering pipeline siltation and the two-dimensional surface overflow model (LISFLOOD-FP) were coupled with the flow exchange governing equation, and the urban flooding response characteristics considering the coupling effect of “rainfall and drainage pipeline siltation” were analysed. To enhance the solvability of waterlogging prediction, an intelligent prediction model of urban flooding based on Bayes-CNN-BLSTM was established by combining a convolutional neural network (CNN), bidirectional long short-term memory neural network (BLSTM), Bayesian optimisation (Bayes), and an interpretable loss function error correction method. The actual rainfall events and flooding processes recorded by the monitoring equipment at Huizhou University were used to calibrate and verify the model. The results show that in the Rainfall 1 and Rainfall 2 scenarios, the overload rates of the pipelines in the current siltation scenario were 60.06 % and 68.37 %, respectively, and the proportions of overflow nodes were 24.87 % and 25.89 %, respectively. When the drainage network was initially put into operation, the overload rates of the pipeline were 36.67 % and 41.16 %, and the overflow nodes accounted for 3.05 % and 4.06 %, respectively. The inundated area and volume of urban flooding increased when the combined siltation coefficient (CSC) was 0.2; therefore, two desilting schemes were determined. Under Rainfall 1, Rainfall 2, and the four rainfall recurrence periods, the Bayes-CNN-BLSTM model had clear advantages in terms of accuracy, reliability, and robustness.

The role of Nature-Based Solutions for the water flow management in a Mediterranean urban area

Due to climate change and rapid urban sprawl, central Mediterranean regions are subjected to short but intensive storm events resulting in a significant increase of flow rates and runoff volumes in low-lying coastal urban areas. Within the framework of green urban infrastructures (GUIs), a suitable combination of Nature-Based Solutions (NBS) with traditional grey infrastructures should be adopted to mitigate flood risk effects in urban and sub-urban areas contributing at the same time to achieve multiple benefits for both the environment and the community. The aim of this study is (i) to evaluate flood hazard maps for identifying the areas within a Sicilian hydrological river basin (Toscano catchment) with a high risk of flooding by implementing the HEC-RAS model at catchment scale; (ii) to quantify the peak flow and floodplain area reduction; (iii) to assess the effectiveness of small-scale NBS (green roofs, porous pavements, rain gardens and rain barrel), in terms of peak flow and runoff volume reductions, at urban block scale by using the EPA SWMM model Model simulations are performed integrating the hydraulic (HEC-RAS) and hydrological (EPA SWMM) models for return periods of 10, 50 and 200 years. The results showed that the estimated peak flow (m3 s−1) obtained from the simulations performed at catchment scale for each T in the current scenario (without NBS) using HEC-RAS and EPA SWMM models are very close to each other (R2 = 0.99). In addition, the utopian scenario simulation showed the HEC-RAS model sensitivity to CN calibration achieving a peak flow reduction up to about 60% with the floodplain area decreasing up to about 17%. Finally, the model EPA SWMM shows its sensitivity to NBS implementation at urban block scale with a peak flow reduction up to about 16% and a runoff volume (mm) reduction up to about 24%. These reductions decrease as T increases with a higher NBS mitigation effects for the lowest T. The results highlighted that the integration of NBS in urban areas could have hydrological and hydraulic positive effects, particularly in terms of peak flow and runoff volume reduction. Furthermore, the results suggest that small-scale NBS have a potential to be effective to smaller rainfall events, but a combination with large-scale NBS is necessary to cope with extreme events.

Runoff Control Performance of Three Typical Low-Impact Development Facilities: A Case Study of a Community in Beijing

The development of sponge cities advocates for sustainable urban rainwater management, effectively alleviating urban flood disasters, reducing non-point-source pollution, and promoting the recycling of rainwater resources. Low-Impact Development (LID) serves as a key strategy in this context, providing essential support for urban rainwater control and pollution reduction. To investigate the runoff control effects of LID measures and to reveal the relationship between facility runoff control performance and installation scale, this study focuses on a sponge community in Beijing. A SWMM model was constructed to analyze the rainwater flood control and pollutant load reduction effects of different LID facilities, including bio-retention cells, green roofs, and permeable pavements. Using evaluation indicators such as surface runoff, node overflow, and pollutant control rates, this study examined how facility performance varies with installation scale under different rainfall conditions. The combination scheme of LID equipment optimal configuration is designed by using multiple criteria decision analysis (MCDA) and cost–benefit theory. The results indicate significant differences in performance among the various LID facilities across different rainfall scenarios. Specifically, the optimal installation proportion for runoff and overflow control of permeable pavements were found to be between 30% and 70%. Green roofs demonstrate superior performance in handling extreme rainfall events, while bio-retention cells exhibit significant effectiveness in controlling Total Suspended Solids (TSSs). Through comprehensive performance evaluation, this study identified the optimal combination scale under a 3-year rainfall recurrence interval as 30% permeable pavements, 20% green roof, and 60% bio-retention cells. This combination effectively leverages the strengths of each facility, ensuring system stability and efficiency while also demonstrating optimal management efficiency in cost–benefit analyses. The findings of this research provide valuable insights for future urban water management and infrastructure development.

Assessment of urban inundation and prediction of combined flood disaster in the middle reaches of Yellow river basin under extreme precipitation

Watershed Cities often face the dual flood risks of upstream floods and urban waterlogging. This compound disaster is caused by the interaction of multiple factors (topography, storms, upstream water and urban drainage, etc.), and is particularly affected by extreme rainfall events and the large and small drainage carrying capacity. To explore the combined effects of extreme rainfall and drainage systems, this study applied the Chicago rainfall hyetograph method and constructed design under different return periods. A comprehensive model combining the verified SWMM and LISFLOOD hydraulic models was utilised to build one-dimensional and two-dimensional coupled watershed urban flood models, simulating inundation depth and duration. By considering urban rainfall, runoff, topography, and drainage system characteristics, the flood processes under different extreme rainfall scenarios were simulated, and the node overflow and inundation depth were obtained. A flood risk assessment of watershed cities was conducted by combining methods such as the HR threshold method, evaluation level, and threshold division. In this study, Huyi District of Xi’an City, China, was used as the research area. The main research findings show: (1) the precipitation design values for different return periods were calculated based on the proposed method, with amount for a 100-year event being 87.196 mm, suitable for composite flood simulation; (2) applying the SWMM-LISFLOOD coupled model constructed for midstream urban areas ensured the accurate simulation of composite flood processes. Under different extreme rainfall return period scenarios, the percentage of flood inundation depth exceeding 1 m caused by multiple disaster factors increased from 0.73 % to 5.19 %; (3) extreme rainfall leads to different levels of internal flooding risk. When the return period of extreme rainfall is 5 years, the area inundated by composite floods in the research area is 67.21 km2. In comparison, when the return period of extreme rainfall is 50 years, the area inundated by composite floods in the research area significantly increases to a total inundation area of 286.85 km2. This study is expected to provide scientific references for flood prevention planning, composite disaster risk management, and urban resilience building in watershed urban areas while significantly impacting modern economic growth, social stability, and the safety of watershed cities.

Assessment of hydrological parameter uncertainty versus climate projection spread on urban streamflow and floods

Assessing the uncertainty associated with projections of climate change impacts on hydrological processes can be challenging due to multiple sources of uncertainties within and between climate and hydrological models. Here we compare the effects of parameter uncertainty in a hydrological model to inter-model spread from climate projections on hydrological projections of urban streamflow in response to climate change. Four hourly climate model outputs from the RCP8.5 scenario were used as inputs to a distributed hydrologic model (SWMM) calibrated using a Bayesian approach to summarize uncertainty intervals for both model parameters and streamflow predictions. Continuous simulation of 100 years of streamflow generated 90 % prediction intervals for selected exceedance probabilities and flood frequencies prediction intervals from single climate models were compared to the inter climate model spread resulting from a single calibration of the SWMM model. There will be an increase in future flows with exceedance probabilities of 0.5 %-50 % and 2-year floods for all climate projections and all 21st century periods, for the modeled Ohio (USA) watershed. Floods with return periods of ≥ 5 years increase relative to the historical from mid-century (2046–2070) for most climate projections and parameter sets. Across the four climate models, the 90th percentile increase in flows and floods ranges from 17-108 % and 11–63 % respectively. Using multiple calibration parameter sets and climate projections helped capture the most likely hydrologic outcomes, as well as upper and lower bounds of future predictions. For this watershed, hydrological model parameter uncertainty was large relative to inter climate model spread, for near term moderate to high flows and for many flood frequencies. The uncertainty quantification and comparison approach developed here may be helpful in decision-making and design of engineering infrastructure in urban watersheds.

A web-based urban hydrology model for municipal scale applications

Extensive data and computational requirements limit the application of existing urban hydrology models at municipal scales. Community-enabled Lifecycle Analysis of Stormwater Infrastructure Costs (CLASIC) is a web-based deployment of the SWMM model with decoupled hydrologic and hydraulic components to enable hydrologic assessment at the municipal and larger scales. This study comprehensively evaluates the performance validity of CLASIC for characterization of hydrologic responses against SWMM and observed data. Furthermore, global sensitivity analysis is used to explore the significance of hydrologic and hydraulic model parameters across spatial and temporal scales. CLASIC reliably represents the urban hydrological processes and accurately quantifies stream discharge at the municipal scale and temporal scales greater than the catchment's time of concentration. Notably, the computational requirements of CLASIC are substantially lower than those of SWMM as the catchment drainage area increases. The application of CLASIC for flood assessment may be conducted with careful examination of the estimated peak discharge at sub-daily timescales.

Automatic calibration of SWMM parameters based on multi-objective optimisation model

Abstract To address the issue of low accuracy and inefficiency in the traditional parameter calibration methods for the SWMM model, this paper constructs an automatic parameter calibration model based on multi-objective optimisation algorithms. Firstly, the Sobol method and GLUE method are utilised to determine sensitive parameters and their ranges, aiming to narrow down the solution space and expedite the model-solving speed. Secondly, the NSGA-3 multi-objective optimisation algorithm based on the Pareto theory is applied for the optimisation and calibration of sensitive parameter sets. The model is validated in the rainwater drainage system with independent runoff in a residential area in a northwestern city in China. The results show that parameters such as N-Imperv and KSlope are highly sensitive to the model output under the land-use conditions of the study area. The simulation accuracy of the multi-objective continuous optimisation algorithm is significantly better than that of the single-objective genetic algorithm. The simulation results of the SWMM model under multi-objective optimisation demonstrate a certain level of reliability and stability. The research findings can provide technical support for the automatic calibration of SWMM model parameters, accurate model simulation, and application.

Research on Urban Storm Flood Simulation by Coupling K-means Machine Learning Algorithm and GIS Spatial Analysis Technology into SWMM Model

Research on Urban Storm Flood Simulation by Coupling K-means Machine Learning Algorithm and GIS Spatial Analysis Technology into SWMM Model

Calibration and verification of a hydrological SWMM model for the ungauged Kinyerezi River catchment in Dar es Salaam, Tanzania

Calibration and verification of a hydrological SWMM model for the ungauged Kinyerezi River catchment in Dar es Salaam, Tanzania

A fuzzy TOPSIS-based approach for prioritizing low-impact development methods in high-density residential areas.

The study successfully implemented six low-impact development (LID) methods to manage surface runoff in urban areas: green roof, infiltration trench, bio retention cell, rain barrel, green roof combined with infiltration trench, and rain barrel combined with bio-retention cell. Each method has unique benefits in mitigating surface runoff effects in urban environments. The following four indicators were used to evaluate the methods: runoff volume reduction, peak runoff flow rate reduction, economic sustainability, and social sustainability. The study, which lasted approximately 4 months, was conducted in an eastern Tehran metropolis residential area with a mix of old and new buildings. The SWMM model determined runoff volume and peak flow values, and a price analysis list determined the economic index. Local experts completed 25 questionnaires to evaluate the social index. Fuzzy TOPSIS multi-indicator decision criteria were used to prioritize LID methods, and the Rain barrel + Bio retention cell combined scenario emerged as the best option based on all four criteria. The method reduced peak runoff flow by 23.1-66.1% under rainfall with 10-year return periods. The green roof + infiltration trench method had the highest percentage reduction of 2,737 m3, while the infiltration trench had the lowest reduction of 273 m3.

Simulation Analysis of Urban Drainage System Utilizing StormDesk Software

Employing the data from comprehensive drainage system surveys and pipeline defect diagnostics, the StormDesk software was utilized to construct a SWMM model. This model facilitates the simulation of operational scenarios within the pipe network, both with the existing network defects and following their rectification, and provides crucial insight for the repair and renovation of the drainage infrastructure. The results indicate that the current drainage system in the study area is inadequate, with a high incidence of pipeline defects. This poses a significant flood risk during intense rainfall events. Given the projected increase in rainfall intensity, it is imperative to substantially enhance the drainage capacity of the existing system.

Multi-Objective Performance of Detention Basins and Rainwater Harvesting Systems Using Real-Time Controls with Rainfall Forecasts

Climate change and an increase in urbanization are severely testing urban drainage systems; at the same time, population growth is leading to an increase in demand for water resources, while climate change is more likely to reduce the amount of water that is available to meet this demand. The present study finds a solution to both problems by assuming a hybrid use of detention basins, i.e., providing a real-time control system (RTC) for the outfall discharge managed according to the rainfall forecast and the water level in the tank, to reuse rainwater for non-potable use and, at the same time, to guarantee the hydraulic protection of the downstream system. Twenty-seven scenarios were simulated using the numerical model SWMM 5.1, assuming different types of controls on the discharge. The simulations show a non-potable water-saving efficiency from a minimum of 32% to a maximum of 90%, and the reduction in volume discharged is between 11% and 31%, while the peak flow rate varies more significantly depending on the type of control used. These results highlight the detention basins’ potential deriving from the hybrid use of this system with rainwater harvesting systems.

Block-scale use of bioretention cells to restore the urban water balance: A case study in Tehran metropolis

Study regionA densely populated urban area located in the 13th municipality of Tehran metropolis, Iran. Study focusBioretention cell is one of the low-impact development methods that aims to restore the hydrological cycle in city areas before urban development. However, the bioretention cell's hydrological performance can vary in urban environments. As a result, this research investigated the effectiveness of a bioretention cell in reducing runoff and recharging groundwater in a densely populated metropolitan area located in eastern Tehran, Iran. Groundwater and surface water modeling were conducted separately. The SWMM model was used for surface water modeling, while a novel approach that utilized the SWMM groundwater module was implemented to assess the bioretention cell's impact on groundwater recharge quantitatively. New hydrological insights for the regionThe study found that implementing bioretention cells can significantly reduce total runoff volume, ranging from 75.6% to 60.7% for rainfall with a return period of 2–100 years. This reduction is due to increased infiltration from the bioretention cells, which can lead to a maximum monthly increase of 12.2–44.0 millimeters of groundwater table for the same rainfall events. The study highlights the effectiveness of retaining runoff through bioretention cells in mitigating flooding, restoring the hydrological cycle, and reviving aquifers in urban areas.

Flood analysis for agriculture area using SWMM model: case study on Sindae drainage basin

Flood analysis for agriculture area using SWMM model: case study on Sindae drainage basin

Short-term Prediction of Urban Inundation based on Radar-predicted Rainfall through Real-time SWMM Parameter Calibration

In recent times, the rapid pace of climate change has been causing urban inundation in areas where drainage of inland water is difficult. In response to this concern, studies that use SWMM are being actively conducted to predict urban inundation. However, the parameters applied to SWMM use historical values, resulting in suboptimal accuracy in predictions. Therefore, this study employed the genetic algorithm, among various optimization techniques, to bolster the accuracy of rainfall-runoff analysis of the SWMM model. This was achieved by linking ASOS data and real-time ground-observation rainfall to real-time water gauge data. Consequently, the water level could now be predicted by applying radar predicted rainfall at 10-minute intervals to the completed SWMM module. The required time for prediction of urban inundation was about 2 minutes. This timeframe proved highly practical when the radar rainfall data was entered into the SWMM module at 10-minute intervals. The water level predictions, when applied to the four rainfall scenarios, also aligned within a reasonable range.