Optimizing Green Infrastructure for Urban Flood Mitigation Using the SWMM: A Case Study in Kozhikode, India

Multiobjective optimization of low impact development stormwater controls

Green infrastructure (GI) has been implemented globally to mitigate the negative effects of urbanization. GI also regulates the urban runoff process and reduces non-point source pollution by intercepting initial runoff pollution and stormwater storage. In this paper, the impacts on GI were quantified and analyzed, considering eight designed storms with a 24 h duration and eight others with a 2 h duration with the combination of two characteristics (return period and peak time). The runoff process and reduction effect of pollutants were simulated for GI combinations (green roofs, vegetative swale, bio-retention units, and permeable pavement) using the Storm Water Management Model, taking the Dongshan campus of Shanxi University as an example case study. The results show that the GI combination can reduce runoff, suspended solids (SS), and chemical oxygen demand (COD). For short- and long-duration rainstorms, the average reduction rates of runoff, SS, and COD were 39.7%, 38.8%, and 39.6%, and 36.5%, 31.7%, and 32%, respectively, indicating its better effectiveness for short-duration storms. The GI’s effect was more sensitive during the short-duration storms owing to the greater absolute value of the 2 h elastic coefficients versus that of the 24 h, and the best reduction effect was observed with a rainfall peak coefficient of 0.1. These results provide a scientific reference for GI planning and implementation under a changing climate in the future.

Integrated assessments of green infrastructure for flood mitigation to support robust decision-making for sponge city construction in an urbanized watershed

Intense urbanization results in greater soil sealing and a consequent increase in surface runoff. When high soil sealing rates and high slopes are encountered in small catchments, high volume and short duration precipitation events increase the probability of flash floods. This study evaluates the hydrological response of an urban catchment with regard to rainfall-runoff events in the following scenarios: current land use, without a detention basin which has been part of the system since the 1970s, with the maximum soil sealing allowed by legislation, and with green infrastructure implantation in 100%, 50% and 10% of the suitable area. Hydrological modelling was performed using the Storm Water Management Model (SWMM). Six level gauges installed along the length of the stream provided the data used in model calibration and validation. The model calibration process provided adequate results: the average Nash-Sutcliffe coefficient was 0.72, the mean error of peak flow was 11% and the mean error of the runoff volume was 12%. Rainfall based on 2-, 10-, 50- and 100-year events were simulated for each scenario, as well as two observed events. In the scenario without the detention basin, an average elevation of 10% in the peak flow was observed in the catchment outlet. In the scenario with maximum soil sealing, the catchment outlet peak flow increased by 30% on average. On the other hand, in the scenario with green infrastructure implementation in 100%, 50% and 10% of the available areas, the outlet peak flow was reduced by 60%, 30% and 5%, respectively. Results indicated the efficiency of the detention basin to reduce flooding, the importance of green area preservation to reduce peak flows, as well as the catchment potential of green infrastructure implementation and the hydrological benefits that they can provide, increasing infiltration and reducing runoff volume and peak flow.

In this study, we evaluated the cumulative hydrologic performance of green infrastructure in a residential area of the city of Parma, Ohio, draining to a tributary of the Cuyahoga River. Green infrastructure included the following spatially distributed devices: 16 street‐side bioretention cells, 7 rain gardens, and 37 rain barrels. Data consisted of rainfall and outfall flow records for a wide range of storm events, including pretreatment and treatment periods. The Stormwater Management Model was calibrated and validated to predict the hydrologic response of green infrastructure. The calibrated model was used to quantify annual water budget alterations and discharge frequency over a 6 year simulation period. For the study catchment, we observed a treatment effect with increases of 1.4% in evaporation, 7.6% in infiltration, and a 9.0% reduction in surface runoff. The hydrologic performance of green infrastructure was evaluated by comparing the flow duration curve for pretreatment and treatment outfall flow scenarios. The flow duration curve shifted downward for the green infrastructure scenario. Discharges with a 0.5, 1, 2, and 5 year return period were reduced by an average of 29%. Parameter and predictive uncertainties were inspected by implementing a Bayesian statistical approach.

Pokhara City, in Nepal, experiences heavy rainfall during four months of monsoon, causing stress on drainage infrastructures. This situation is exacerbated by poor drainage systems, ineffective urban planning, and rapid urbanization. This study aims to develop mitigation strategies for urban roadside flooding in the Barahi Chowk region in Pokhara City. The focus is to identify the causes and potential solutions for the recurring issue of urban roadside flooding. For this purpose, the study utilizes Storm Water Management Model (SWMM) computer modeling to analyze the hydraulic capacity of existing drainage systems during peak flows. The SWMM modeling reveals that the current drainage usage is between 80% and 100% during peak times, leading to flooding. Consequently, resizing or expanding various drainage sections using SWMM modeling is suggested. The study introduces Low Impact Development (LID) controls, such as rain gardens and permeable pavements, to manage surface runoff. Implementing rain gardens on just 3% of the impervious area in each sub-catchment showed an average 21.5% reduction in peak runoff and an average 6.68% reduction in the total runoff while implementing 5% in each sub-catchment, permeable pavements reduced peak runoff by 22-26% and total runoff by 8-11%. The research explores the impact of land use and land cover change and unplanned urban growth on roadside flooding, resulting in impermeable urban surfaces that disturb the natural drainage system and infiltration pattern. Thus, the outcomes of this study should be carefully considered by policymakers and stakeholders to address issues through sustainable urban planning and infrastructure development. Also, implementing measures like resizing drains and LID controls appears to be effective in controlling flooding during the study period and should be considered for future studies as well.

A comprehensive hydrological model, like the storm water management model (SWMM), has been widely used for rainfall-runoff simulation. In recent years, simple and effective modern modeling techniques have also brought great attention to the prediction of runoff with rainfall input. A comparative case study between SWMM and a presently developed fuzzy logic model for the predictions of total runoff within the watershed of Cascina Scala, Pavia in Italy is presented. A data set of 23 events from 2000 to 2003 including with the total rainfall and total runoff are adopted to train fuzzy logic parameters. Other data (1990–1995) with detailed time variations of rainfall and runoff are available for the setup and calibration of SWMM for runoff modeling. Among the 1990–1995 data, 35 independent rainfall events are selected to test the prediction performance of the SWMM and fuzzy logic models by comparing the predicted total runoffs with measured data. Comparisons and performance analyses in terms of the root-mean-squared error and coefficient of efficiency are made between the SWMM and the fuzzy logic model. The predicted total runoffs from either the SWMM or the fuzzy logic model are found to agree reasonably well with the measured data. For large rainfall events, the fuzzy logic model generally outperforms the SWMM unless the modification of the impervious ratio is applied to improve the SWMM results. However, the SWMM can produce the time varying hydrograph whereas fuzzy logic is subject to limitation of the methodology and is unable to generate such an output.

Low impact development (LID) methods have been shown to be efficient in reducing the peak flow and total volume of urban stormwater, which is a top priority for effective urban stormwater management in many municipalities. However, decision-makers need information on the effects of LIDs and their associated costs before allocating limited resources. In this study, the Storm Water Management Model (SWMM) was used to investigate the effects of five different LID scenarios on urban flooding in a district in Tehran, Iran. The LID scenarios included rain barrel (RB) at two sizes, bio-retention cell (BRC), and combinations of the two structures. The results showed that significant node flooding and overflow volume would occur in the study area under the existing conditions, especially for rainfall events with longer return periods. BRC and combinations of BRC and RBs were the most effective options in reducing flooding, while the smaller-size RB was the cheapest alternative. However, normalized cost, obtained through dividing the total cost by the percent reduction in node flooding and/or overflow volume, was smallest for BRC. The results of this study demonstrate how hydraulic modeling can be combined with economic analysis to identify the most efficient and affordable LID practices for urban areas.

In arid regions, rainfall is scarce, summer-concentrated, and prone to extreme events, while evaporation exceeds precipitation, creating fragile ecosystems that need scientific stormwater management for flood resilience. Sponge cities, through the implementation of green infrastructure, can alleviate urban flooding, improve rainwater utilization, and enhance the urban ecological environment. Under the “dual carbon” target, sponge city construction has gained new developmental significance. It must not only ensure core functions and minimize construction costs but also fully leverage its carbon reduction potential, thereby serving as a crucial pathway for promoting urban green and low-carbon development. Therefore, this study focused on Xining, a typical arid city in Northwest China, and couples the Non-dominated Sorting Genetic Algorithm-III (NSGA-III) with the Storm Water Management Model (SWMM) to construct a multi-objective optimization model for Low Impact Development (LID) facilities. The layout optimization design of LID facilities is conducted from three dimensions: life cycle cost (LCC), rainwater utilization rate (K), and carbon emission intensity (CI). Hydrological simulations and scheme optimizations were performed under different design rainfall events. Subsequently, the entropy-weighted TOPSIS method was utilized to evaluate and compare these optimized schemes. It is shown by the results that: (1) The optimized LID schemes achieved a K of 76.2–80.43%, an LCC of 2.413–3.019 billion yuan, and a CI of −2.8 to 0.19 kg/m2; (2) Compared with the no-LID scenario, the optimized scheme significantly enhanced hydrological regulation, flood mitigation, and pollutant removal. Under different rainfall return periods, the annual runoff control rate increased from 64.97% to 80.66–82.23%, with total runoff reduction rates reaching 46.41–49.26% and peak flow reductions of 45–47.62%. Under the rainfall event with a 10-year return period, the total number of waterlogging nodes decreased from 108 to 82, and the number of nodes with a ponding duration exceeding 1 h was reduced by 62.5%. The removal efficiency of total suspended solids (TSS) under the optimized scheme remained stable above 60%. The optimized scheme is highly adaptable to the rainwater management needs of arid areas by prioritizing “infiltration and retention”. Vegetative swales emerge as the primary facility due to their low cost and high carbon sink capacity. This study provides a feasible pathway and decision-making support for the low-carbon layout of LID facilities in arid regions.

LID (Low impact Development) has been widely promoted as a measure against urban flooding which has been recently increasing due to urbanization and climate change. However, studies that consider the combined effects of spatially distributing multiple LIDs simultaneously in an urban area of specific size as urban planning scenarios are few. In this study, two cases of past rainfall data (short duration and long duration heavy rainfall), are used to simulate the effects of 3 different urban planning scenarios of distributed LID (i.e. combined LIDs implementable at the 1) building-level, 2) in open spaces, and 3) combined implementation of both 1&2), in addition to simulating for separate implementation of each LID type using the Storm Water Management Model (SWMM). The results show that in the case of short duration heavy rainfall, all 3 LID scenarios provide a greater decrease in the rate of flood volume with comparison to the case of long duration heavy rainfall. This points to the fact that LIDs have a high potential to mitigate flood volumes resulting from short duration heavy rainfall - the type of rainfall which has become more frequent recently. Furthermore, the paper also organizes the LID types according to their implementability under different urban planning scenarios, and shows that such combined use of LIDs results in higher flood mitigation efficacy.

High urbanization rate and climate change are the main drivers of urban floods in developing countries. The increase in urban flooding incidents has become a significant threat to cities, which also result into considerable losses of life and the economy. Adapting to the risks of a changing climate and ill-effects of urbanization is imperative for national and local governments. This calls for a functionally and structurally resilient urban drainage infrastructure. Functional resilience is the coping capacity of system against external threats such as urbanization and climate change, whereas structural resilience is the resilience against internal failures such as blockage of inlets or sewers, structural damage of a pipe, bed load sediment deposition, asset aging/decay, and sewer collapse. This work aims to understand the impact of nature-based solutions on urban drainage resilience. Various researchers have identified Low Impact development (LID) practices as a potential solution to enhance drainage systems' resilience. LID can be defined as a land development and retrofit strategy that emphasizes the protection and use of distributed interventions to reduce the volume and rate of stormwater runoff from a developed landscape. In the present study, the green roofs and rain gardens are simulated in a part of Gurugram city of India using the Storm Water Management Model (SWMM) 5.2. Sensitivity analysis is conducted to overcome the problem of a lack of in-depth data to perform model calibration and validation. The simulations were carried out by developing various scenarios for functional and structural resilience assessment. The results indicate that if 25% of potential subcatchments are deployed with LIDs, functional resiliency of the system enhances by 25%, and structural resilience of vulnerable nodes decreases by 17%.  The study reveals that introduction of LIDs aids into enhancing the functional resilience of the system rather than structural resilience. This research provides evidence of LIDs' positive influence on the resilience performance of drainage systems. Overall, the study can help urban planners and drainage management engineers to develop understanding on LIDs role vis-à-vis city's resilience to urban flood problems.

Optimization of low impact development layout designs for megacity flood mitigation

A tight coupling model for urban flood simulation based on SWMM and TELEMAC-2D and the uncertainty analysis

SWMM (Stormwater Management Model) is one of the most widely used computation tools in urban water resources management. Traditionally, the choice of rainfall data for calibrating the SWMM model has been arbitrary, lacking clarity on the most suitable rainfall types. In addition, the simplification in the SWMM hydrological module of the rainfall–runoff process, coupled with measurement errors, introduces a high level of uncertainty in the calibration. This study investigates the influences of rainfall types on the highly uncertain SWMM model calibration by implementing the Bayesian inference theory. A Bayesian SWMM calibration framework was established, in which an advanced DREAM(zs) (Differential Evolution Adaptive Metropolis, Version ZS) sampling method was used. The investigation focused on eight key hydrological parameters of SWMM. The impact of rainfall types was analyzed using nine rainfall intensities and three rainfall patterns. Results show that rainfall events equivalent to a one-year return period (R5, 42.70 mm total depth) or higher generally yield the most accurate parameters, with posterior distribution standard deviations reduced by 40–60% compared to low-intensity rainfalls. Notably, three parameters (impervious area percentage [Imperv], storage depth of impervious area [S-imperv], and Manning’s coefficient of impervious area [N-imperv]) demonstrated consistent accuracy irrespective of rainfall intensity, with a coefficient of variation below 0.05 for Imperv and S-imperv across all rainfall intensities. Furthermore, it was found that rainfall events with double peaks resulted in more satisfactory calibration compared to single or triple peaks, reducing the standard deviation of the Width parameter from 168.647 (single-peak) to 110.789 (double-peak). The findings from this study could offer valuable insights for selecting appropriate rainfall events before SWMM model calibration for more accurate predictions when it comes to urban non-point pollution control strategies and watershed management.

The storm water management model (SWMM) is a widely used tool for urban drainage design and planning. Hundreds of peer-reviewed articles and conference proceedings have been written describing applications of SWMM. This review focuses on collecting information on model performance with respect to calibration and validation in the peer-reviewed literature. The major developmental history and applications of the model are also presented. The results provide utility to others looking for a quick reference to gauge the integrity of their own unique SWMM application. A gap analysis assesses the model's ability to perform water-quality simulations considering green infrastructure (GI)/low impact development (LID) designs and effectiveness. It is concluded that the level of detail underlying the conceptual model of SWMM versus its overall computational parsimony is well balanced-making it an adequate model for large and medium-scale hydrologic applications. However, embedding a new mechanistic algorithm or providing user guidance for coupling with other models will be necessary to realistically simulate diffuse pollutant sources, their fate and transport, and the effectiveness of GI/LID implementation scenarios.