An improved global resilience assessment method for urban drainage systems: A case study of Haidian Island, south China.

Resilience assessment for urban drainage systems is a fundamental aspect of building resilient cities. Recently, some scholars have proposed the Global Resilience Analysis (GRA) method, which assesses resilience based on the functional performance of different system failure scenarios. Compared to traditional system dynamics methods, the GRA method considers the impact of internal structural failure on resilience but requires a large amount of computation. This research proposed an improved GRA method to enhance computational efficiency and practicality by reducing the number of system scenario simulations. Firstly, a hydrodynamic model of the drainage network of Haidian Island has been constructed using the Storm Water Management Model (SWMM) and Python. Secondly, the GRA method was improved using cluster analysis and convergence analysis to reduce the simulation scenarios. Thirdly, a resilience assessment index was established through system function functions, and two types of resilience enhancement measures, centralized and distributed, were proposed. The results show: (i) resilience assessment increases the computational efficiency by 25% compared to the traditional GRA method; (ii) the resilience index of the existing drainage network within Haidian Island is less than the design value (0.7) in all failure scenarios, indicating a lower level of recovery capability; (iii) compared to the centralized strategy, which is only effective when the system failure level is less than 9%, the distributed strategy enhances the resilience of the urban drainage system at a higher failure level (77%).

Tool for fast assessment of stormwater flood volumes for urban catchment: A machine learning approach.

Specific flood volume is an important criterion for evaluating the performance of sewer networks. Currently, mechanistic models - MCMs (e.g., SWMM) are usually used for its prediction, but they require the collection of detailed information about the characteristics of the catchment and sewer network, which can be difficult to obtain, and the process of model calibration is a complex task. This paper presents a methodology for developing simulators to predict specific flood volume using machine learning methods (DNN - Deep Neural Network, GAM - Generalized Additive Model). The results of Sobol index calculations using the GSA method were used to select the ML model as an alternative to the MCM model. It was shown that the DNN model can be used for flood prediction, for which high agreement was obtained between the results of GSA calculations for rainfall data, catchment and sewer network characteristics, and calibrated SWMM parameters describing land use and sewer retention. Regression relationships (polynomials and exponential functions) were determined between Sobol indices (retention depth of impervious area, correction factor of impervious area, Manning's roughness coefficient of sewers) and sewer network characteristics (unit density of sewers, retention factor - the downstream and upstream of retention ratio) obtaining R2 = 0. 55-0.78. The feasibility of predicting sewer network flooding and modernization with the DNN model using a limited range of input data compared to the SWMM was shown. The developed model can be applied to the management of urban catchments with limited access to data and at the stage of urban planning.

Deep learning, geometric characterization and hydrodynamic modeling for assessing sewer defect impacts on urban flooding: A case study in Guangzhou, China.

Deep learning techniques have offered innovative and efficient tools for accurate and automated detection of sewer defects by leveraging large-scale sewer data and advanced feature learning algorithms. However, there has been a lack of thorough characterization of the geometric properties of segmented defects, let alone systematically calculate the severity level of sewer defects and quantitatively evaluate their impacts on flood conditions in hydrodynamic models. This study proposed a comprehensive framework and related metrics to accurately and automatically detect, segment, characterize, and evaluate the impacts of sewer defects on flooded nodes and volumes by integrating a DeepLabv3+-based segmentation technique, an automated geometric characterization and severity quantification module, and a GIS and SWMM-based hydrodynamic modeling. The results clearly showed in details where and how much the urban flooding was affected by the different defect types. The segmentation model achieved satisfactory detection performance, with mean pixel accuracy (MPA), mean intersection over union (MIoU), and frequency weighted intersection over union (FWIoU) of 0.99, 0.74 and 0.95, respectively. In terms of severity level quantification, there were 98%, 90%, 90% and 83% of predictions consistent with real conditions for falling off, obstacle, disjoint and leakage. It was shown that the number of surcharging manholes and total flood volume (TFV) were greatly affected by sewer defects, with over 16% increase in TFVs under all investigated rainfall events. The results addressed the impacts of sewer defects on urban flooding and demonstrated the powerful tools provided by the proposed framework for decision-making on sewer defect detection and management.

Connecting blue-green infrastructure elements to reduce combined sewer overflows.

By infiltrating and retaining stormwater, Blue-Green Infrastructure (BGI) can help to reduce Combined Sewer Overflows (CSOs), one of the main causes of urban water pollution. Several studies have evaluated the ability of individual BGI types to reduce CSOs; however, the effect of combining these elements, likely to occur in reality, has not yet been thoroughly evaluated. Moreover, the CSO volume reduction potential of relevant components of the urban drainage system, such as detention ponds, has not been quantified using hydrological models. This study presents a systematic way to assess the potential of BGI combinations to mitigate CSO discharge in a catchment near Zurich (Switzerland). Sixty BGI combinations, including four BGI elements (bioretention cells, permeable pavement, green roofs, and detention ponds) and four different implementation rates (25%, 50%, 75%, and 100% of the available sewer catchment area) are evaluated for four runoff routing schemes. Results reveal that BGI combinations can provide substantial CSO volume reductions; however, combinations including detention ponds can potentially increase CSO frequency, due to runoff prolongation. When runoff from upstream areas is routed to the BGI, the CSO discharge reductions from combinations of BGI elements differ from the cumulative CSO discharge reductions achieved by individual BGI types, indicating that the sum of effects from individual BGI types cannot accurately predict CSO discharge in combined BGI scenarios. Moreover, larger BGI implementation areas are not consistently more cost-effective than small implementation areas, since the additional CSO volume reduction does not outweigh the additional costs. The best-performing BGI combination depends on the desired objective, being CSO volume reduction, CSO frequency reduction or cost-effectiveness. This study emphasizes the importance of BGI combinations and detention ponds in CSO mitigation plans, highlighting their critical factors-BGI types, implementation area, and runoff routing- and offering a novel and systematic approach to develop tailored BGI strategies for urban catchments facing CSO challenges.

Efficiency evaluation of low impact development practices on urban flood risk.

Urban flood risk assessment delivers invaluable information regarding flood management as well as preventing the associated risks in urban areas. The present study prepares a flood risk map and evaluate the practices of low-impact development (LID) intended to decrease the flood risk in Shiraz Municipal District 4, Fars province, Iran. So, this study investigate flood vulnerability using MCDM models and some indices, including population density, building age, socio-economic conditions, floor area ratio, literacy, the elderly population, and the number of building floors to. Then, the map of thematic layers affecting the urban flood hazard, including annual mean rainfall, land use, elevation, slope percentage, curve number, distance from channel, depth of groundwater, and channel density, was prepared in GIS. After conducting a multicollinearity test, data mining models were used to create the urban flood hazard map, and the urban flood risk map was produced using ArcGIS 10.8. The evaluation of vulnerability models was shown through the use of Boolean logic that TOPSIS and VIKOR models were effective in identifying urban flooding vulnerable areas. Data mining models were also evaluated using ROC and precision-recall curves, indicating the accuracy of the RF model. The importance of input variables was measured using Shapley value, which showed that curve number, land use, and elevation were more important in flood hazard modeling. According to the results, 37.8 percent of the area falls into high and very high categories in terms of flooding risk. The study used a stormwater management model (SWMM) to simulate node flooding and provide management scenarios for rainfall events with a return period ranging from 2 to 50 years and five rainstorm events. The use of LID practices in flood management was found to be effective for rainfall events with a return period of less than 10 years, particularly for two-year events. However, the effectiveness of LID practices decreases with an increase in the return period. By applying a combined approach to a region covering approximately 10 percent of the total area of Shiraz Municipal District 4, a reduction of 2-22.8 percent in node flooding was achieved. The analysis of data mining and MCDM models with a physical model revealed that more than 60% of flooded nodes were classified as "high" and "very high" risk categories in the RF-VIKOR and RF-TOPSIS risk models.

A framework for real-time operation of urban detention reservoirs: Application of the cellular automata and rainfall nowcasting.

Detention reservoirs are employed in urban drainage systems to reduce peak flows downstream of reservoirs. In addition to the volume of detention reservoirs, their operational policies could significantly affect their performance. This paper presents a framework for the real-time coordinated operation of detention reservoirs using deep-learning-based rainfall nowcasting data. Considering the short concentration time of urban basins, the real-time operating policies of urban detention reservoirs should be developed quickly. In the proposed framework, a cellular automata (CA)-based optimization algorithm is linked with the storm water management model (SWMM) to optimize real-time operating policies of gates at the inlets and outlets of detention reservoirs. As CA-based optimization models are not population-based, their computational costs are much less than population-based metaheuristic optimization techniques such as genetic algorithms. To evaluate the applicability and efficiency of the framework, it is applied to the east drainage catchment (EDC) of Tehran metropolitan area in Iran. The results illustrate that the proposed framework could reduce the overflow volume by up to 60%. For complete flood control in the study area, in addition to the real-time operation of detention reservoirs, constructing five tunnels with a total length of 13200 m is recommended. To evaluate the performance of the CA-based optimization model, its results are compared with those obtained from the non-dominated sorting genetic algorithm III (NSGA-III). It is shown that the CA-based model provides similar results with only 5% of the run-time of NSGA-III. A sensitivity analysis is also performed to evaluate the effects of optimization models' parameters on their performance.

A model-based analysis for trapping suspended sediment in stormwater inlets of urban drainage network.

Raised awareness of environmental constraints in recent decades has led stormwater management to incorporate quality components and focus on the treatment of urban runoff water at pollutant source areas. This study evaluated the impact of a developed type of sediment trap, installed into stormwater inlets, on the total suspended solids (TSS) load in an urban city center catchment in Finland. The objective was to outline a modelling approach to assess efficiency of the traps to treat TSS originating from different land uses (green areas, pavement, parking, roof, street, and other areas not belonging to the main land uses). A Storm Water Management Model (SWMM) parametrization of a 5.87 ha catchment in the Lahti city center, Finland was utilized as the computation engine. The model had separate subcatchments for each land use, allowing the use of literature-based Event Mean Concentrations (EMC) to estimate the TSS pollutant washoff for the land uses. A method to assess the individual stormwater inlet pollutant loads and potential removal effect of the sediment traps was introduced. The hydrological and TSS load simulations covered a period of 6 months. The stormwater network inlets installed with sediment traps were ranked according to their potential removal of TSS. One out of five EMC sets was selected to be representative of the urban land uses in the study site (green areas 75 mg/l, pavement 46 mg/l, parking 44 mg/l, roof 20 mg/l, street 64 mg/l, other 46 mg/l). The simulation results showed the influence of land uses on the pollutant load and revealed the optimal set of locations for the sediment traps. Additionally, the effect of regular maintenance intervals on the pollutant load, given a maximum storage capacity of the traps, was explored. The results showed a large variation in TSS removal depending on the inlets chosen for the sediment traps, with removal rates ranging from about 0 % to 10 % of catchment TSS load. The maximum TSS removal was 63 %, which was the reported efficiency of the traps. These results highlighted the need for an informed decision when selecting trap locations. Streets and parking lots were the largest TSS contributors, with stormwater inlets on streets being the desired sediment trap locations. While the absolute level of simulated TSS load was found to be dependent on the EMCs, the ranking of sediment trap locations was similar for the simulations with different EMC data sets.

Parameterization of nutrients and sediment build-up/wash-off processes for simulating stormwater quality from specific land uses.

Urbanization changes land cover through the expansion of impermeable surfaces, leading to a significant rise in runoff, sediment, and nutrient loading. The quality of stormwater is related to land use and is highly variable. Currently, stormwater is predominantly described through watershed models that rely minimally, if at all, on field monitoring data. The simple event mean concentration (EMC) wash-off approach by land use is a common method for estimating urban runoff loads. However, a major drawback of the EMC approach is it assumes concentration remains constant across events for a specific land use. Build-up/wash-off equations have been formulated to consider variations in concentration between events. However, several equation parameters are challenging to estimate, making them difficult to use. We conducted a monitoring and modeling study and investigated the impact of land use on stormwater quantity and quality and optimized and investigated the build-up/wash-off parameters for three homogenous urban land uses to estimate nutrients (nitrogen and phosphorus) and sediment loads. Stormwater from commercial, medium-density residential, and transportation land uses was sampled using automatic samplers during storm events, and water quality was characterized for a variety of them for 14 months. Analysis of stormwater samples included assessments for total nitrogen, total phosphorus, and total suspended solids. Results showed that medium-density residential land use had the highest median total nitrogen and total phosphorus event mean concentrations and commercial had the highest median total suspended solids EMCs. Water quality parameters (or build-up/wash-off parameters) exhibited significant variation between land uses, confirming that land use is a key determinant of stormwater quality. The median particle size for each land use was less than 150 µm, indicating that the most common particle size in stormwater was a very fine sand or smaller. This small size should be considered by stakeholders in the design of stormwater treatment systems.

Study on the response analysis of LID hydrological process to rainfall pattern based on framework for dynamic simulation of urban floods.

An in-depth analysis of the urban flood disaster level in response to different rainfall characteristics and Low Impact Development (LID) measures is of significant importance for addressing unfavorable management conditions and implementing effective flood control measures. This study proposes a dynamic urban flood simulation framework based on the Storm Water Management Model (SWMM) and Geographic Information System (GIS) spatial analysis, incorporating an active inundation seed search algorithm. The framework is calibrated and validated using nine historical urban flood events. Subsequently, the impact of rainfall patterns on urban inundation under LID measures is analyzed based on the dynamic urban flood simulation framework. The results show that the urban flood simulation framework exhibits good applicability, with Nash-Sutcliffe Efficiency (NSE) values of 0.825 and 0.763 during the calibration and validation periods, respectively. The extent of inundation shows little variation for rainfall events with a return period greater than 20 years, and the location of flooding is minimally affected by rainfall patterns. LID measures have a decreasing effect on urban inundation control as the return period of rainfall increases, and there are variations in hydrological responses to different rainfall patterns under the same return period. For single-peak rainfall events with the same return period, the control rates of inundation volume, flow, and infiltration decrease as the rainfall peak coefficient increases, indicating a weakening effect of LID measures on flood control with increasing rainfall peak coefficient. Under the same return period conditions, LID measures exhibit the best runoff control effect for uniform rainfall, while their effectiveness is lower for double-peak rainfall events and single-peak rainfall events with an r = 0.75 coefficient. The findings of this study provide a theoretical basis for urban flood warning and management of Low Impact Development measures.

New optimization strategies for SWMM modeling of stormwater quality applications in urban area.

Build-up/wash-off models were originally developed for small-scale laboratory facilities with uniform properties. The effective translation of these models to catchment scale necessitates the meticulous calibration of model parameters. The present study combines the Mat-SWMM tool with a genetic algorithm (GA) to improve the calibration of build-up and wash-off parameters. For this purpose, Mat-SWMM was modified to equip it with the capacity to provide comprehensive water quality analysis outcomes. Additionally, this research also conducts a comparative examination of two distinct types of objective functions in the optimization. Rather than depending on previous literature, this study undertook a numerical campaign to ascertain an appropriate range for the relevant parameters within the case study, thereby ensuring the optimization algorithm's efficient functionality. This research also implements an integrated event calibration approach, i.e., a novel method that calibrates all rainfall events collectively, thus improving systemic interaction representation and model robustness. The findings indicate that employing this methodology significantly enhances the reliability of the outcomes, thereby establishing a more robust procedure. The first objective function (TSS instantaneous less squared difference function, OF 1), which is widely employed in the literature, was designed to minimize the difference between observed and predicted instantaneous Total Suspended Solids (TSS) concentrations. In contrast, the second function (mass and mass peak consistency function, OF 2) considers integral model outputs, i.e., the overall mass balance, the time of the peak mass flow rate, and its intensity. The analysis of the outputs revealed that both objective functions demonstrated sufficient performance. OF 1 provided slightly better performance in predicting the TSS concentrations, whereas OF 2 demonstrated superior ability in capturing global event characteristics. Notably, the optimal parameter set identified through OF 2 aligned with the physically plausible ranges traditionally recommended in technical manuals for urban catchments. In contrast, OF 1's optimal set necessitated an expansion in the acceptable parameter ranges. Finally, from a computational burden viewpoint, OF 1 demanded a significantly higher number of function evaluations, thus implying an escalating computational cost as the range expands. Conversely, OF 2 necessitated fewer evaluations to converge toward the optimal solution.

Determining the most appropriate stormwater management strategies within the development of urban landscape infrastructure using the TOPSIS method: applications to Rize city.

In this study, the application of multi-criteria decision-making (MCDM) methods in determining the most appropriate stormwater management strategy is examined using different areas in Rize. The determination of the most appropriate stormwater management practices for the Rize coastal park and Güneysu-Rize connection highway with TOPSIS is presented in detail within this study. In this context, commonly used applications suitable for urban areas are discussed. The criteria and their weights used for the evaluation of the selected applications were determined by consulting expert opinions from leading researchers. The most suitable applications in different scenarios such as changes in the cost or the amount of precipitation for Rize coastal park and Güneysu-Rize connection road were determined by the TOPSIS method. The TOPSIS analyses' ranking of the ideal solutions matches the results of the SWMM simulations one to one. SWMM results confirm that the outcomes of TOPSIS are the alternatives that provide maximum decrease in surface runoff.

Characterizing Spatial Information Loss for Wastewater Surveillance Using crAssphage: Effect of Decay, Temperature, and Population Mobility.

Populations contribute information about their health status to wastewater. Characterizing how that information degrades in transit to wastewater sampling locations (e.g., wastewater treatment plants and pumping stations) is critical to interpret wastewater responses. In this work, we statistically estimate the loss of information about fecal contributions to wastewater from spatially distributed populations at the census block group resolution. This was accomplished with a hydrologically and hydraulically influenced spatial statistical approach applied to crAssphage (Carjivirus communis) load measured from the influent of four wastewater treatment plants in Hamilton County, Ohio. We find that we would expect to observe a 90% loss of information about fecal contributions from a given census block group over a travel time of 10.3 h. This work demonstrates that a challenge to interpreting wastewater responses (e.g., during wastewater surveillance) is distinguishing between a distal but large cluster of contributions and a near but small contribution. This work demonstrates new modeling approaches to improve measurement interpretation depending on sewer network and wastewater characteristics (e.g., geospatial layout, temperature variability, population distribution, and mobility). This modeling can be integrated into standard wastewater surveillance methods and help to optimize sewer sampling locations to ensure that different populations (e.g., vulnerable and susceptible) are appropriately represented.

Investigating relationships between landscape patterns and surface runoff from a spatial distribution and intensity perspective.

Rapid urbanization changes landscape patterns and results in frequent urban waterlogging issues, which affect citizens' daily lives and cause economic loss. Understanding the spatial patterns and impact factors associated with urban waterlogging under different rainfall intensities has significant implications for mitigating this hazard. In this study, the runoff depth calculated according to the Storm Water Management Model (SWMM) simulation results was used to investigate the spatial characteristics of urban waterlogging. Multiple scenario-based designs, a correlation analysis, and a stepwise regression model were employed to detect the relationship between surface runoff depth and landscape patterns under different rainfall intensities. The results show that when the rainfall intensity reached 12.5 mm/12 h, the conversion rate of rainfall to runoff increased significantly, indicating an increased waterlogging risk. Areas with impervious surface proportions of 25-50% and 75-100% were shown to require more attention due to the strong sensitivity of the surface runoff depth to an increase in the impervious surface. It is most cost-effective to maintain the original high-density vegetation or increase the vegetation density from 0-25% to 25-50% for urban green space. Additionally, the landscape configuration also affects the surface runoff depth. The fragmented, scattered, or regular shape of impervious surface patches can reduce surface runoff effectively; larger and less fragmented green space was also shown to have a surface runoff controlling. The adjusted R2 values were greater than 0.6 for all stepwise regression models, indicating that the landscape variables selected in the study can effectively predict the surface runoff depth. These models also showed that the landscape composition had a more profound contribution than the landscape configuration on runoff depth. These findings provide meaningful insights and perspectives for urban waterlogging hazard mitigation, quantitative landscape planning, and risk management. The method proposed by this study provides a referable framework for future studies on urban waterlogging and its response to the landscape in the context of global climate change.

Optimal designs of LID based on LID experiments and SWMM for a small-scale community in Tianjin, north China.

Urban flooding and waterlogging are becoming increasingly serious due to rapid urbanization and climate change. The stormwater management philosophy of low-impact development (LID) has been applied in urban construction to alleviate these problems. The selection and placement of LID designs are the most important tasks. In this study, LID experiments were performed to calibrate the Storm Water Management Model (SWMM). Then, a multi-objective optimization model, which adopted the minimum surface runoff coefficient, surcharge time, and investment cost as objectives, was established by coupling the SWMM and non-dominated sorting genetic algorithm-II (NSGA-II). Hydrological simulations were performed with the SWMM, and optimal calculations were conducted with NSGA-II. Real-coded optimal variables containing detailed size and location information of multiple LID measures were generated, and a decision space for LID design selection was obtained. The optimization designs reduced the surface runoff coefficient from 0.7 to approximately 0.5, the conduit surcharge duration was reduced from 1.62 h to 0.04-0.47 h, and the total investment cost only ranged from 395,000-872,000 ¥. Thus, the optimization model could achieve synchronous optimization of all objectives. This study could provide valuable information for LID design with the aim of urban flooding and waterlogging control.

The simulation, regulation capacity assessment and coping strategy of rainstorm runoff waterlogging in Zhu pai-chong Basin of Nanning, China.

Currently, China is experiencing a phase of rapid urbanization. With the frequent occurrence of extreme rainfall events within the context of climate change, the problem of heavy rainfall and waterlogging in many cities is very prominent. In November 2020, China issued a proposal for the construction of sponge cities across the entire region to significantly enhance the rainfall flood prevention and drainage capacity of cities and effectively improve the resilience of sponge city systems for flooding management. Therefore, this paper selected the Zhu pai-chong watershed in Nanning with frequent waterlogging disasters as an example. Based on underlying surface information, We used a coupled SWMM-LISFOOD model to simulate runoff and waterlogging processes and analyze the spatial and temporal evolution characteristics of the basin under 10 designed rainstorm return periods (0.25a-50a). The results confirm the substantial spatial and temporal variabilities of the runoff coefficient in the study area; impermeability was the main factor contributing to high runoff coefficient values. The spatial distribution characteristics of inundation area was general dispersion and local linear aggregation. Furthermore, this study assessed the effect of the control rate of blue‒green‒gray facilities on the actual storms, and the value ranged from only 48.6% (0.25a)-24.05% (50a). This study quantified the two-dimensional distribution of rainfall storage volume thresholds with or without considering the discharged from the pipe network. Quantitative mapping between the elements of "rainfall-storage volume of blue‒green‒gray facilities-runoff-drainage capacity of the pipe network-waterlogging level" was conducted within the study area as an example. Finally, an overall technical process scheme for rainfall and waterlogging management was proposed. The scheme covered the hydrological‒hydraulic mechanism, storage function of sponge facilities, engineering control response, nonengineering measures and intelligent management of rainfall and waterlogging during sponge city construction, which could provide critical scientific support for effective promotion of the construction of sponge cities in China.

BK-SWMM flood simulation framework is being proposed for urban storm flood modeling based on uncertainty parameter crowdsourcing data from a single functional region.

In recent years, urban flood disasters caused by sudden heavy rains have become increasingly severe, posing a serious threat to urban public infrastructure and the life and property safety of residents. Rapid simulation and prediction of urban rain-flood events can provide timely decision-making reference for urban flood control and disaster reduction. The complex and arduous calibration process of urban rain-flood models has been identified as a major obstacle affecting the efficiency and accuracy of simulation and prediction. This study proposes a multi-scale urban rain-flood model rapid construction method framework, BK-SWMM, focusing on urban rain-flood model parameters and based on the basic architecture of Storm Water Management Model (SWMM). The framework comprises two main components: 1) constructing a SWMM uncertainty parameter sample crowdsourcing dataset and coupling Bayesian Information Criterion (BIC) and K-means clustering machine learning algorithm to discover clustering patterns of SWMM model uncertainty parameters in urban functional areas; 2) coupling BIC and K-means with SWMM model to form BK-SWMM flood simulation framework. The applicability of the proposed framework is validated by modeling three different spatial scales in the study regions based on observed rainfall-runoff data. The research findings indicate that the distribution pattern of uncertainty parameters, such as depression storage, surface Manning coefficient, infiltration rate, and attenuation coefficient. The distribution patterns of these seven parameters in urban functional zones indicate that the values are highest in the Industrial and Commercial Areas (ICA), followed by Residential Areas (RA), and lowest in Public Areas (PA). All three spatial scales' REQ, NSEQ, and RD2 indices were superior to the SWMM and less than 10%, greater than 0.80, and greater than 0.85, respectively. However, when the study area's geographical scale expands, the simulation's accuracy will decline. Further research is required on the scale dependency of urban storm flood models.

Assessing runoff control of low impact development in Hong Kong's dense community with reliable SWMM setup and calibration.

Low impact development (LID) is a sustainable practice to managing urban runoff. However, its effectiveness in densely populated areas with intense rainfall, such as Hong Kong, remains unclear due to limited studies with similar climate conditions and urban patterns. The highly mixed land use and complicated drainage network present challenges for preparing a Storm Water Management Model (SWMM). This study proposed a reliable framework for setting up and calibrating SWMM by integrating multiple automated tools to address these issues. With a validated SWMM, we examined LID's effects on runoff control in a densely built catchment of Hong Kong. A designed full-scale LID implementation can reduce total and peak runoffs by around 35-45% for 2, 10 and 50-year return rainfalls. However, LID alone may not be adequate to handle the runoff in densely built areas of Hong Kong. As the rainfall return period increases, total runoff reduction increases, but peak runoff reduction remains close. Percentages of reduction in total and peak runoffs decline. The marginal control diminishes for total runoff while remaining constant for peak runoff when increasing the extent of LID implementation. In addition, the study identifies the crucial design parameters of LID facilities using global sensitivity analysis. Overall, our study contributes to accelerating the reliable application of SWMM and deepening the understanding of the effectiveness of LID in ensuring water security in densely built urban communities located near the humid-tropical climate zone, such as Hong Kong.

Quantifying and improving flood resilience of urban drainage systems based on socio-ecological criteria.

In this paper, a new framework is developed for evaluating the resilience of urban drainage systems (UDSs) under floods by proposing and quantifying some technical and socio-ecological (SE) criteria. The proposed criteria are used to quantify the seven principles of building resilience in socio-ecological systems. The criteria mainly focus on preserving diversity and multiplicity in a UDS, managing variables that gradually change over time (slow variables), improving structural and functional connectivity, maintaining system adaptability, encouraging learning, broadening participation, and promoting polycentric governance systems. For evaluating the efficiency of the proposed framework, it is applied to a real-world case study of improving resilience of the UDS in the eastern part of Tehran metropolitan area. Three scenarios for flood management are proposed based on the Low Impact Development (LID) practices which are simulated using the Storm Water Management Model (SWMM). The Entropy method is used to consider the uncertainty in the relative importance of different criteria in estimating the flood resilience. The estimated values for the proposed criteria regarding the current drainage system in the study area show its undesirable condition in many sub-catchments. The results also show that using around 2.3 km2 of LID practices in this urban watershed can significantly improve the resilience in many sub-catchments (nearly, 30%) and reduce the total volume of the overflow (about 50%). The results also show that using the flood management scenarios, improving connectivity is the most influential factor that enhances the general resilience of the system.

Simulation and investigation into the pluvial effect on peri-urban unsewered drainage basin of Kolkata due to rapid urbanization.

A pluvial effect is a geologic event caused by the action of water during excessive precipitation in a particular region, resulting in water logging, which affects the drainage system of that area, or it may be caused by the spill of a large amount of water beyond the normal limit from the water bodies. The pluvial effect is also referred to as a flood having highly devastating consequences when it affects a region's urban or peri-urban areas. It affects the day-to-day activities of people staying in those areas and causes various social and economic losses. This effect would even grow further if proper planning and management of land were not done in a given time. Therefore, through this paper, the authors try to address the issue of urban flooding along with its consequences and impacts. For this study, the peri-urban region of the Tollygunge-Panchannagram Basin in Kolkata, India, is considered. The zero inertia model, Triangular Irregular Network Flood (TINFLOOD), is employed for surface flow simulation, whereas the storm water management model (SWMM) is used to determine the lateral flow. The output of this study provides various hyetographic presentations, considering infiltration during advanced, intermediate, and delayed rainfall conditions. Here, the time of concentration is also examined for different rainfall intensities to observe the time for peak flow. The simulated data obtained from this model has been validated with the real-time data of a pumping station situated at Chowbhaga. Nevertheless, this study helps assess flood risk management upstream of a region's basin and peri-urban areas.

A novel spatial optimization approach for the cost-effectiveness improvement of LID practices based on SWMM-FTC.

Due to the increasingly frequent occurrence of urban waterlogging, the spatial optimization of low impact development (LID) practices has been commonly used to detain and reduce storm water runoff in the most cost-effective way. In this study, the flow transmission chain (FTC) was proposed to replace the routing portion of the Storm Water Management Model (SWMM) and was combined with the runoff component of the SWMM to simulate LID practices (SWMM-FTC). In the SWMM-FTC, the third Evolution Step of Generalized Differential Evolution (GDE3) was employed to optimize the LID layout design. The results showed that the relative error between the modified SWMM-FTC and the calibrated SWMM was less than 0.25% under various LID scenarios, and the computational efficiency of the SWMM-FTC was improved by 19.3 times. Moreover, the GDE3 outperformed the commonly used non-dominated sorting genetic algorithm (NSGA-II), the strength Pareto evolutionary algorithm (SPEA2), and the multi-objective shuffled frog leaping algorithm (MOSFLA) due to its ability to find the most cost-effective solution. The LID layout obtained from the SWMM-FTC with the GDE3 saved $210-1067 to achieve a 1% reduction in storm water runoff. This result demonstrates that the SWMM-FTC with the GDE3 can achieve higher environmental benefits than comparable models, providing better guidance for managers and stakeholders.