Storm Water Management Model Research Articles

Urban Flood Simulation in Erbil City by Using Storm Water Management Model (SWMM)

Urban Flood Simulation in Erbil City by Using Storm Water Management Model (SWMM)

Development of a cellular automata-based distributed hydrological model for simulating urban surface runoff

The present study developed a distributed urban surface runoff model based on the cellular automata framework (CA-DUSRM) to simulate two-dimensional urban surface runoff processes. CA-DUSRM uses an improved nonlinear reservoir algorithm to simulate the generation of surface runoff outflow at grid cell scale and flow interactions among cells. The CA-DUSRM simulation results (total surface runoff and peak discharge) for an asphalt road sub-catchment during different rainfall events were in good agreement with actual measurements of both surface runoff and rainfall processes. The performance of CA-DUSRM was comprehensively and systematically analyzed under different rainfall–terrain scenarios. The results showed that increasing the temporal grain resulted in uncertain simulation results in relation to the rainfall intensity, slope, and terrain undulation employed; and significant spatial grain effects were mainly caused by large terrain undulation changes. Compared with the storm water management model (SWMM), CA-DUSRM performed better overall and was more sensitive to variation in terrain undulation and fluctuations in rainfall. Based on model structure and simulation mechanism perspectives, CA-DUSRM can present the influence of spatially heterogeneous underlying surface characteristics on the runoff processes and describe the lateral water exchanges, which makes the simulated processes closer to the actual processes when compared with SWMM. CA-DUSRM is potentially suitable for application in urban areas that have complex underlying surfaces.

A new approach for sensitivity analysis of the StormWater Management Model applied in an airport.

Sensitivity analysis of urban flood model parameters is important for efficient and accurate flood simulation. In order to explore the problems of large sampling parameters and nonlinear correlation between input and output variables, this paper proposed a new correlation analysis approach. The type, strength, and the order of sensitive parameters to the four outputs are analyzed using the proposed approach. The results show that the R values of Manning-N are biggest, its distribution is linear in heat maps, and the Manning-N has a strong linear correlation with Average Depth, Hour of Maximum Flooding, and Time to Peak. For Average Depth, the second sensitive parameter is Conductivity. For Hour of Maximum Flooding, the second and third more sensitive parameters are Conductivity and N-perv; however, there are certain nonlinear correlations from heat maps. For Total Inflow, the R values of each parameter are between 0.021 and 0.534. Most sensitive parameters are none; however, the more sensitive parameters are Conductivity, N-perv, and initial deficit. For Time to Peak, the second and third more sensitive parameters are N-perv and N-Imperv; however, there are certain non-linear correlations from heat maps. The results can provide theoretical guidance for application and parameter calibration of SWMM in airport.

Evaluation of total infiltration and storage capacities for different soil types in Sarawak using SWMM

Climate change and irresponsible urbanization practices are anticipated to further exacerbate flood risk. The different soil types’ physical, chemical, and biological characteristics significantly impact surface water movement, porosity, permeability, and infiltration potential. Therefore, soil infiltration is perhaps the most challenging and crucial process to characterize on a field scale. Various methods have been developed to measure the infiltration rate empirically and theoretically. However, the relationship between different soil types and infiltration rates in Sarawak remains unknown as no previous study has been conducted. This study uses the Storm Water Management Model (SWMM) to evaluate the infiltration rates for five different soil types: clay, clay loam, loam, sandy loam, and sand. 30 samples of various types and soil depths were examined at intervals of 0.5 m, 1.0 m, 1.5 m, 2.0 m, 2.5 m, and 3 m. The study was carried out using a standardized slope of 0.7% and an impervious land of 25% with a catchment size of 2 acres. Extreme rainfall data on the 5th and 6th of December 2021 was input into the infiltration model. Results showed that the difference between initial and final water storage of all the investigated soil depths for clay, clay loam, loam, sandy loam, and sand was found to be 48.42 mm, 51.20 mm, 58.01 mm, 66.96 mm, and 115.54 mm, respectively. The findings demonstrated that clay has the lowest water storage capability, followed by clay loam, loam, and sandy loam. Sand could store a comparatively large amount of rainwater. In contrast, sand has the highest infiltration rate with 2.541 mm/h, followed by sandy loam with 1.835 mm/h, loam with 1.432 mm/h, clay loam with 1.039 mm/h. Clay has the lowest infiltration rate, with 0.852 mm/h. This research concluded that sandy soil could significantly reduce surface runoff and help reduce flood risk in urban regions.

Improving water quality modelling for green roof runoff using Storm Water Management Model

Modelling of green roof is important for assessing its effectiveness as a low impact development (LID) practice. This study evaluated the performance of Storm Water Management Model (SWMM)’s LID Controls for modelling runoff water quality load from a green roof in Portland, Oregon, USA, for possible improvement. Such an attempt has been made rarely, although plenty of studies have evaluated SWMM's LID Controls for predicting green roof runoff. From the evaluation, the applicability of the SWMM for evaluating water quality such as total phosphorous (TP) and total nitrogen (TN) was “not satisfactory”. Nash-Sutcliffe Efficiency (NSE) value was 0.27 for TN and −1.45 for TP when using “rain concentration”, the only parameter available for water quality simulation of green roof runoff. The applicability was improved to “very good” level for TP with an NSE value of 0.66 when buildup and washoff were made possible by creating an imaginary twin subcatchment of the same size as the original one. However, NSE value for TN was not improved. The value of the work is that it could lead to a more accurate green roof design by minimizing pollutant release with improved prediction technology for TP.

Automatic SWMM Parameter Calibration Method Based on the Differential Evolution and Bayesian Optimization Algorithm

In response to the low accuracy exhibited by the Storm Water Management Model (SWMM), we propose an enhanced Differential Evolution and Bayesian Optimization Algorithm (DE-BOA). This algorithm integrates the global search capability of the differential evolution algorithm with the local search capability of the Bayesian optimization algorithm, which enables a more comprehensive exploration of the vector solution space. A comparative analysis of various types of rainfall events is conducted. For model calibration and validation, a drainage subzone in Jinshazhou, Guangzhou City, is selected as the research subject. In total, 20 specific rainfall events are selected, and the DE-BOA algorithm outperforms the manual calibration, the differential evolution algorithm, and the Bayesian optimization algorithm regarding model calibration accuracy. Furthermore, the DE-BOA algorithm exhibits robust adaptability to rainfall events characterized by multiple peaks and higher precipitation levels, with the Nash–Sutcliffe efficiency coefficient values surpassing 0.90. This study’s findings could hold significant reference value for dynamically updating model parameters, thereby enhancing the model simulation performance and improving the accuracy of the urban intelligent water management platform.

PRIMJENA APLIKACIJSKOG SOFTVERA SWMM ZA UPRAVLJANJE URBANIM OBORINSKIM VODAMA

The paper presents a hydrological-hydraulic analysis of the impact of LID elements (LOW IMPACT DEVELOPMENT) for urban stormwater management using the SWMM (Storm Water Management Model) application software created only for this type of research. A hydrological model was created with the application of LID elements in the form of green roofs. at the Faculty of Architecture and Civil Engineering and related facilities (Institutes) in Sarajevo, Sarajevo Canton. For which the results of the calculation and estimation of the amount of water in the analyzed area are given. A concrete example showed the positive impact of the application of LID elements, i.e. green elements in urban areas in the function of flood protection and environmental protection.

Modeling the Hydraulic Performance of Pilot Green Roofs Using the Storm Water Management Model: How Important Is Calibration?

Green roofs are low-impact development (LID) that assist in regulating stormwater runoff by reducing the peak flow rate and total runoff volume, among other benefits. In this study, the hydraulic performance of green roofs was modeled using the SWMM 5.2 software, taking field data into account for calibration purposes. A Storm Water Management Model (SWMM) was built using field data from pilot green roofs installed at the Instituto Superior Técnico Campus, University of Lisbon, Portugal. The simulated results with and without calibration were compared, as well as the results obtained in the field studies. The results from the uncalibrated model were unsatisfactory. After calibration, the average Nash–Sutcliffe model efficiency (NSE) was 0.72, and the volume error was 5.9%, with most of the results classified as very good and good. This study shows relevant insights on the use of the SWMM to model green roofs, demonstrating the crucial importance of the calibration process for the correct prediction of hydraulic performance and indicating the porosity parameter as one of the most sensitive to the results. In addition, it provides estimates of LID parameters that can help in the development of projects carried out in the Mediterranean climate.

An alternative for predicting real-time water levels of urban drainage systems

Storm Water Management Model (SWMM) developed by the United States Environmental Protection Agency (EPA) has been widely applied throughout the world for analysis associated with stormwater runoff, combined sewers, and other drainage facilities. To appropriately manage the runoff in urban areas, an integrated system including the simulations of sewer flow, street flow, and regional channel flow, called the 1D/1D SWMM model, was advocated to be performed. Nevertheless, the execution efficiency of this integrated system still needs to be promoted to meet the demand for real-time forecasting of urban floods. The objective of this study is to seek an alternative for predicting water levels both in the sewer system and on the streets within an urban district during rainstorms by utilizing a dynamic neuron network model. To strengthen the physical structure of the artificial intelligence (AI) model and simultaneously make up for the lack of measured data, simulation results of the 1D/1D SWMM model are provided as labels for the training of the proposed model. The novelty of this research is to propose a new methodology to effectively train the AI model for predicting the spatial distributions of depths based on the hydrologic conditions, geomorphologic properties, as well as the network relation of the drainage system. A two-stage training procedure is proposed in this study to consider more possible inundation conditions in both sewer and street (open channel) drainage networks. The research findings show that the proposed methodology is capable of reaching satisfactory accuracy and assisting the numerical-based SWMM model for real-time warning of drainage systems in the urban district.

ASSESSMENT OF THE RISK OF FLOODING IN AN URBANIZED AREA IN RESPONSE TO HEAVY RAINS: CASE STUDY OF THE WARSAW CHOPIN AIRPORT

The paper presents the adaptation of the hydrodynamic Storm Water Management Model(SWMM) for the subcatchment area of the Służewiecki Stream in Warsaw, the major part of whichis the area of the Chopin Airport. The SWMM model was used for calculating the water outflowfrom the studied urbanized area in response to rainfall with the probabilities of 20 and 10%, andthen to assess the possibility of flooding as a result of rainwater spillage from the Airport drainagesystem. The scope of the work also comprised assessing the impact of the existing retention tanksat the Airport on the reduction of maximum flows in the Służewiecki Stream channel. Simulationsin response to the rainfall event with a probability of 10% have shown that there are locations(sewer manholes) within the airport area where short-term rainwater overflows may potentiallyoccur. Retention of rainwater in tanks and flow control through gate valves have a significantimpact on a reduction of flows in the Słuzewiecki Stream, thus reducing the risk of flooding in thestudied catchment area.

Impact of Storm Characteristics on Infiltration Dynamics in Sponge Cities Using SWMM

Effective stormwater management in urban areas requires enhancing the permeability of underlying surfaces. However, the impact of storm characteristics on infiltration processes in sponge cities remains insufficiently explored. This study uses the Horton method within the storm water management model to investigate how uniform and Chicago storm parameters affect infiltration rates. Our findings provide valuable insights: (1) Increasing porous pavement area proportionally reduces subarea sizes within subcatchments, and infiltration rates of porous pavements are supply-controlled. (2) Uniform storms result in consistent initial infiltration rates across pervious areas, subcatchments, and the entire catchment. The duration of this stable state decreases with higher return periods. Catchment infiltration volumes exhibit linear growth with greater storm intensities (R-squared = 0.999). (3) Peak infiltration rates and moments for pervious areas, subcatchments, and the overall catchment exhibit correlations with both the return period and the time-to-peak coefficient, with correlation coefficients ranging from −0.9914 to 0.9986 and p-values ranging from 0.0334 to 0.6923. This study quantifies the influence of design storm parameters on infiltration, providing valuable insights for stormwater infrastructure design and urban stormwater control.

Simulation and optimization framework for evaluating the robustness of low-impact development placement solutions under climate change in a small urban catchment

ABSTRACT The lack of acceptable temporal resolution of climate projections hinders proper assessment of the future performance of low-impact development (LID) systems in small catchments when continuous simulations are required (e.g. to evaluate infiltration). This study applied a simulation optimization approach to maximize infiltration with LIDs at minimum costs in a small urban catchment (0.67 km2). We coupled the Storm Water Management Model (SWMM) with the Nondominated Sorting Genetic Algorithm II (NSGA-II) to determine near-optimal locations of bioretentions, green roofs, and permeable pavements. The temporal resolution of rainfall projections was disaggregated from 24 hours to 15 minutes using the Bartlett-Lewis rectangular method to evaluate the performance and robustness of the optimized solutions under different budgets and climate scenarios. Results suggest that LIDs can mitigate climate change impacts with relatively inexpensive solutions. However, the robustness analysis showed that climate change could compromise the expected performance of LIDs sized with historical rainfall.

Optimized Design of Sponge-Type Comprehensive Pipe Corridor Rainwater Chamber Based on NSGA-III Algorithm

Currently, most of the studies using optimization algorithms to mitigate the urban flooding problem have no more than three optimization objectives, and few of them take the operation status of the traditional drainage system as one of the optimization objectives, which is not conducive to the overall design of the city. This study proposes to mitigate the urban flooding problem by using a sponge-type comprehensive pipe corridor rainwater chamber. A four-objective optimization model is established by coupling the Storm Water Management Model (SWMM) and the Non-dominated Sorting Genetic Algorithm-III (NSGA-III), and two traditional drainage system state indicators (pipe overload time, node overflow volume), surface runoff coefficient, and total investment cost are selected as the optimization objectives for solving the problem. The results show that (1) the reduction rates of surface runoff coefficient, pipe overload time, and node overflow volume rate by the optimization model are 37.015–56.917%, 81.538–91.435%, and 51.578–84.963%, respectively; and the total investment cost is RMB 4.311–4.501 billion. (2) The effectiveness of combining SWMM and NSGA-III for an optimization solution is verified, and the relationship between the four objectives is explored. The study may provide useful information for urban flood control.

An advanced tool integrating failure and sensitivity analysis into novel modeling of the stormwater flood volume

Abstract. An innovative tool for modeling the specific flood volume was presented that can be applied to assess the need for stormwater network modernization as well as for advanced flood risk assessment. Field measurements for a catchment area in Kielce, Poland, were used to apply the model and demonstrate its usefulness. This model extends the capability of recently developed statistical and machine learning hydrodynamic models developed from multiple runs of the US Environmental Protection Agency (EPA) Storm Water Management Model (SWMM). The extensions enable the inclusion of (1) the characteristics of the catchment and its stormwater network, calibrated model parameters expressing catchment retention, and the capacity of the sewer system; (2) extended sensitivity analysis; and (3) risk analysis. Sensitivity coefficients of calibrated model parameters include correction coefficients for percentage area, flow path, depth of storage, and impervious area; Manning roughness coefficients for impervious areas; and Manning roughness coefficients for sewer channels. Sensitivity coefficients were determined with respect to rainfall intensity and characteristics of the catchment and stormwater network. Extended sensitivity analysis enabled an evaluation of the variability in the specific flood volume and sensitivity coefficients within a catchment, in order to identify the most vulnerable areas threatened by flooding. Thus, the model can be used to identify areas particularly susceptible to stormwater network failure and the sections of the network where corrective action should be taken to reduce the probability of system failure. The simulator developed to determine the specific flood volume represents an alternative approach to the SWMM that, unlike current approaches, can be calibrated with limited topological data availability; therefore, the aforementioned simulator incurs a lower cost due to the lower number and lower specificity of data required.

Impact and analysis of urban water system connectivity project on regional water environment based on Storm Water Management Model (SWMM)

SWMM stands as a dynamic rainfall-runoff urban hydrological model, crafted by the U.S. Environmental Protection Agency. It adeptly replicates urban rainfall production and ground runoff, and the Pressure-State-Response (PSR) theory artfully reflects human-environment interactions. Urban Water System Connectivity (UWSC) fortifies connections among regional rivers, transforming them into an intricate, dense network. To study the improvement of the regional water environment by the UWSC project, this study takes the UWSC project in Runan County, Zhumadian, China as the research object, constructs a hydrodynamic and water quality model based on SWMM, and simulates it under different rainfall situations. At the same time, the ecological service value theory and PSR theory are used to establish an ecological effect index system to analyze the ecological effect before and after the UWSC project. This paper evaluates the urban water system connectivity project from three aspects: hydrodynamic, water quality, and ecological effects. Results underscore that the post-UWSC era witnesses decreased river flood flows in varied design rainfalls, accompanied by delayed flood peaks. Under P = 10a and P = 20a rainfalls, peak flow reductions of 6.6% and 4.96% respectively, manifest with peak time lags of 0.17h. Flow rate adjustments align with flow behavior, exhibiting decreased peak flow rates accompanied by temporal lags. Initial river flow increases by about 30%, amplifying river water body exchange capacity. Following the UWSC implementation, the study area's five primary rivers display TSS reductions ranging from 46% to 86.94%, COD from 36.98% to 92.43%, TN from 45.9% to 92.96%, TP from 35.71% to 84.78%, and NH3–N from 38.52% to 92.19%. Simulated point source pollution, under comparable initial conditions, indicates pollutant decay rates escalating from 3.2%–20%–35.71%–92.43%. Ecological impact ascends from 6.33 × 108 RMB to 9.65 × 108 RMB, reflecting a 52.5% escalation. The UWSC initiative enriches urban river flood resilience and water quality, emblematic of urban water ecological civilization progress.

Comprehensive effect evaluation of LID facilities implemented in sponge campuses: A case study

A sponge campus is a campus implemented with Low Impact Development (LID) facilities aimed to improve its urban resilience and stability against the effects of climate change by enhancing storm water management. In this study, a secondary school campus was designed and transformed into sponge campus by installing LID facilities, and we found it of high runoff control effect and energy conservation potential. Using the Storm Water Management Model (SWMM), this study assessed the performance of the LID facilities under different rainfall intensities with recurrence periods of 1, 5, 10, and 20 years. The SWMM modelling revealed that the peak flow was reduced 59.1%, 54.5%, 47.9% and 46.8%, respectively, while the total inflow was reduced 33.9%, 28.0%, 25.4% and 24.6%, respectively. The sponge campus achieved a total suspended solids (TSS) removal of 75%. Specifically, this sponge campus accounted for 9.2% of the plot area while contributed 15.1% water storage capacity to the neighbouring region (recurrence period 1a). Furthermore, the implementation of LID facilities on the school campus could help conserve 17,524 kW·h of electricity annually. Overall, this study provides useful insights and guidance for the construction of sponge campuses.

Developing nitrogen removal models for stormwater bioretention systems.

Bioretention systems have the potential of simultaneous runoff volume reduction and nitrogen removal. Internal water storage (IWS) layers and real-time control (RTC) strategies may further improve performance of bioretention systems. However, optimizing the design of these systems is limited by the lack of effective models to simulate nitrogen transformations under the influences of IWS design and environment conditions including soil moisture and temperature. In this study, nitrogen removal models (NRMs) are developed with two complexity levels of nitrogen cycling: the Single Nitrogen Pool (SP) models and the more complex 3 Nitrogen Pool (3P) models. The 0-order kinetics, 1st order kinetics, and the Michaelis-Menten equations are applied to both SP and 3P models, creating six different NRMs. The Storm Water Management Model (SWMM), in combination with each NRM, is calibrated and validated with a lab dataset. Results show that 0-order kinetics are not suitable in simulating nitrogen removal or transformations in bioretention systems, while 1st order kinetics and Michaelis-Menten equation models have similar performances. The best performing NRM (referred to as 3P-m) can accurately predict nitrogen event mean concentrations in bioretention effluent for 20% more events when compared to SWMM. When only calibrated with soil moisture conditions in bioretention systems without internal storage layers, 3P-m was sufficiently adaptable to predict cumulative nitrogen mass removal rates from systems with IWS or RTC rules with less than ±7% absolute error, while the absolute error from SWMM prediction can reach -23%. In general, 3P models provide higher prediction accuracy and improved time series of biochemical reaction rates, while SP models improve prediction accuracy with less required user input for initial conditions.

Catchment-scale life cycle impacts of green infrastructures and sensitivity to runoff coefficient with stormwater modelling

Urban green infrastructure (GI) has been widely used in sponge city construction to manage hydrological processes. While studies on environmental benefits of GI from the perspective of whole life cycle assessment (LCA) have been reported in recent years, few have explored and compared the environmental performance of different GIs within a single catchment, which is directly linked to catchment-scale hydrological control. This study focuses on a Sponge City pilot project in Shenzhen, China, including three typical types of GI: permeable pavement, green roof, and sunken green space. By collecting hydrological data, land use, and life cycle inventory of GI and employing SWMM (Storm Water Management Model)-based stormwater modelling, we have revealed the environmental impacts at different stages of the life cycle of the GI scenario and three GIs through comparative and sensitivity analyses. Notably, we have disclosed, for the first time, the effect of the runoff coefficient in LCA. Our findings indicate that over the 30-year life cycle, the total environmental impact of the GI scenario is 24 % smaller than that of the hypothetical grey scenario. Permeable pavement exhibits the largest environmental impact per unit area, being 1.8 times and 7.6 times greater than that of the green roof and sunken green space, respectively. The operation stage of the three GIs significantly mitigates eutrophication and climate change. Furthermore, sensitivity analysis demonstrates that an increase in surface runoff undermines the environmental benefits of GIs. These results highlight the importance of embedding stormwater modelling into LCA, enabling catchment-scale integrated evaluation and equivalent assessment of different GIs within a single catchment whereby the influence of external factors such as climate change can be described, which aids in understanding the dynamic environmental performance of GIs. The proposed research framework and results are anticipated to provide valuable guidance for future GI construction and carbon-neutral policies.

Development of a Simple Design Method for an Infiltration Facility using Curve Number

As climate change accelerates, the scale and intensity of natural disasters are increasing, and the frequency of localized heavy rains is increasing rapidly. Urban watersheds have a high ratio of impervious areas. So there is a high possibility of flooding in the event of torrential rain, and large-scale damage can occur because the population and material resources are highly concentrated. River improvements, sewerage maintenance, and rainwater storage installations are frequently used to reduce flood damage. More recently, low-impact development (LID) techniques have been proposed; however, LID facilities are installed on a small scale and in various locations. In addition, they require many parameters for a rainfall-runoff analysis; consequently, the design of these facilities require too much manpower and time. In this study, we analyze the rainfall-runoff reduction effect based on the size of a facility to present a simpler, more efficient design method. Next, the curve number (CN) corresponding to the capacity of each facility is estimated and the correlation between the facility’s area ratio and the CN is analyzed. As a result, a relational expression that can calculate the new CN with the same rainfall-runoff reduction effect as the installed LID facility was derived by using the CN before the installation of the LID facility and the facility area ratio. Thus, we present a simple design method using the recalculated CN without directly entering LID facilities into a storm water management model (SWMM).

Automatic calibration toolbox for SWMM5

The objective of this paper is to introduce an enhanced automatic calibration toolbox for the Storm Water Management Model (SWMM5), one of the most widely used softwares for simulating urban stormwater systems. Within the process of accurately modelling urban runoff and drainage, proper calibration is the most time-intensive sub-task. Thus, a toolbox is developed that eliminates the need to manually re-read input files for each parameter change made during calibration. The Excel-based toolbox written in Visual Basic for Applications (VBA) makes use of a Dynamic-link library (DLL), which has been extended with several new functions. The functionality of the automatic calibration toolbox has been increased by adding new groundwater-related parameters to the DLL. In this article the authors successfully tested automatic calibration toolbox performance test cases, based on example input files provided by SWMM5 developers. The first test case was chosen to demonstrate the program's ability to calibrate using measurements from the conduits, and the second one to demonstrate the performance of using measurements from nodes.