Search Results (28)

Accelerating urbanization and the intensifying pace of climate change have heightened the occurrence of urban pluvial flooding, threatening urban sustainability. As the preferred approach to urban stormwater management, coupled gray and green infrastructure (GI–GREI) integrates GREI’s rapid runoff conveyance with GI’s infiltration and storage capacities, and their siting and scale can affect life-cycle cost (LCC) and urban drainage system (UDS) resilience. Focusing on Fengxi New City, China, this study develops a multi-objective optimization framework for the GI–GREI system that integrates GI suitability and pipe-network importance assessments and evaluates the Pareto set through entropy-weighted TOPSIS. Across multiple rainfall return periods, the study explores optimal trade-offs between UDS resilience and LCC. Compared with the scenario where all suitable areas are implemented with GI (maximum), the TOPSIS-optimal schemes reduce total life-cycle cost (LCC) by CNY 3.762–4.298 billion (53.36% on average), rebalance cost shares between GI (42.8–47.2%) and GREI (52.8–57.2%), and enhance UDS resilience during periods of higher rainfall return (P = 20 and 50). This study provides an integrated optimization framework and practical guidance for designing cost-effective and resilient GI–GREI systems, supporting infrastructure investment decisions and climate-adaptive urban development. Full article

The compound effects of extreme climate change and intensive urban development have led to more frequent urban inundation, highlighting the urgent need for the fine-scale evaluation of stormwater drainage system performance in high-density urban built-up areas. A typical basin, located in Shenzhen, was selected, and a pipe–river coupled SWMM was developed and calibrated via a genetic algorithm to simulate the storm drainage system. Design storm scenario analyses revealed that regional inundation occurred in the central area of the basin and the enclosed culvert sections of the midstream river, even under a 0.5-year recurrence period, while the downstream open river channels maintained a substantial drainage capacity under a 200-year rainfall event. To systematically identify bottleneck zones, two novel metrics, namely, the node cumulative inundation volume and the conduit cumulative inundation length, were proposed to quantify the local inundation severity and spatial interactions across the drainage network. Two critical bottleneck zones were selected, and strategic improvement via the cross-sectional expansion of pipes and river culverts significantly enhanced the drainage efficiency. This study provides a practical case study and transferable technical framework for integrating hydraulic modeling, spatial analytics, and targeted infrastructure upgrades to enhance the resilience of drainage systems in high-density urban environments, offering an actionable framework for sustainable urban stormwater drainage system management. Full article

Under the dual pressures of global climate change and rapid urbanization, urban drainage systems (UDS) face severe challenges caused by extreme precipitation events and altered surface hydrological processes. The drainage paradigm is shifting toward resilient systems integrating grey and green infrastructure, necessitating a comprehensive review of the design and operation of grey infrastructure. This study systematically summarizes advances in urban stormwater process-wide regulation, focusing on drainage network design optimization, siting and control strategies for flow control devices (FCDs), and coordinated management of Quasi-Detention Basins (QDBs). Through graph theory-driven topological design, real-time control (RTC) technologies, and multi-objective optimization algorithms (e.g., genetic algorithms, particle swarm optimization), the research demonstrates that decentralized network layouts, dynamic gate regulation, and stormwater resource utilization significantly enhance system resilience and storage redundancy. Additionally, deep learning applications in flow prediction, flood assessment, and intelligent control exhibit potential to overcome limitations of traditional models. Future research should prioritize improving computational efficiency, optimizing hybrid infrastructure synergies, and integrating deep learning with RTC to establish more resilient and adaptive urban stormwater management frameworks. Full article

This study addresses the growing inadequacies of traditional architectural concepts and techniques in stormwater management amid the increasing frequency of extreme weather events, particularly in densely built urban micro-spaces. To tackle these challenges, we propose an integrated theoretical and practical framework applied to a case study of a small-scale urban public space in Chang’an District, Shijiazhuang City, Hebei Province, covering an area of about 2.15 hectares in North China. The framework combines Best Management Practices Planning (BMP-P) with Low Impact Development Design (LID-D). The framework optimizes sub-catchment delineation, strategically locates drainage outlets, and configures network layouts to reduce runoff path lengths, thereby reducing total runoff volume, enhancing drainage capacity, and alleviating surface water accumulation, which, in turn, informs the parametric design of LID facilities. In the BMP-P phase, four source-control measures were developed based on runoff control and stormwater retention: adjusting terrain slopes, adding or removing curbs and facilities, redistributing infiltration areas, and adjusting drainage outlet and piping layouts. By shortening runoff paths and reducing potential waterlogging areas, these measures effectively reduced total runoff volume (Trv) by 31.5% to 35.7% and peak runoff volume (Prv) by 19.4% to 32.4%. Moreover, by remodeling the stormwater network with a different layout, larger pipe diameters, and substantially increased network capacity, the total discharge (Tdv) increased by 1.8% to 50.2%, and the peak discharge rate (Pdr) increased by 100% to 550%, thus minimizing surface flooding. In the LID-D phase, we developed a Grasshopper-based parametric design program for the layout and design of LID facilities. This approach significantly reduces interdisciplinary communication costs and enhances urban planning efficiency. By integrating BMP and LID strategies, the proposed framework offers a flexible, rapid, and efficient solution for achieving resilient stormwater management in the context of urban micro-renewal. Full article

Topographical data are essential for hydrological analysis and can be gathered through on-site surveys, UAVs, or remote sensing methods such as Digital Elevation Models (DEMs). These tools are crucial in hydrological studies for accurately modeling basin morphology and surface stream network patterns. Two different DEMs with resolutions of 0.13 m and 5 m were used, as well as tools which carry out urban basin delineation by analyzing their morphometric parameters to process the hydrography of the study area, using three Geographic Information Systems (GIS): ArcGIS, GlobalMapper, and SAGA GIS. Each piece of software uses different algorithms for the pre-processing of DEMs in the calculation of morphometric parameters of the study area. The results showed variations in the quantity of delineated stream networks between the different GIS tools used, even when using the same DEM. Similarly, the morphometric parameters varied between GIS tools and DEMs, which tells us that the tools and topographic data used are important. The stream network generated using ArcGIS and the DEM obtained with UAV offered a more precise description of surface flow behavior in the study area. Concerning ArcGIS, it can be observed that between the resolutions of the INEGI DEM and the UAV DEM, the delimited area of micro-basin 1 presented a minimum difference of 0.03 km². In contrast, micro-basin 2 had a more significant difference of 0.16 km². These discrepancies in results are attributed to the different algorithms used by each piece of software and the resolution of each DEM. Although some studies claim to have obtained the same results using different software and algorithms, in this research, different results were obtained, and emphasize the importance of establishing procedural standards, as they can significantly impact the design of stormwater drainage systems. These comparisons will allow decision-makers to consider these aspects to standardize the tools and topographic data used in urban hydrological analyses. Full article

The rational method (RM) adopting steady uniform flow assumption is a simple and mainstream approach for the design of urban stormwater-drainage systems (USDSs). However, when designing large-scale USDSs, the RM significantly deviates from the actual flow regime due to inappropriate assumptions. To improve the accuracy and reliability of the design method, a dynamic-wave-simulation-based method (DWSBM) is proposed. Firstly, a numerical model which is equivalent to the surface runoff yield process of RM is established and validated. Then the dynamic wave module is adopted for pipe flow calculations. This method integrates hydraulic models for the whole design process. Both DWSBM and RM are used for USDS design of two areas, and design comparison demonstrates that the design flow rates computed using DWSBM are greater than obtained by RM. With the increase in pipeline length, the design flow differences between the two methods in the two areas increased from 3.09% to 28.97% and from 16.01% to 45.40% respectively. Adopting the DWSBM for design flow rate calculation can effectively improve the design reliability and drainage capacity of USDSs. Full article

Climate change, through more frequent extreme weather events, and urban sprawl, by increasing runoff, are two critical threats to drainage networks, impacting both public health and property. Augmenting drainage networks to withstand additional stress by enlarging conduits or constructing new detention facilities requires a significant financial investment. The goal of this study is to enhance urban resilience by optimizing real-time control (RTC) systems for drainage networks that optimize the flow control devices (FCDs), which could mitigate the need to invest in major construction costs. RTC is an approach that can help mitigate flooding in urban areas. This study is the first to optimize feedback controllers in SWMM, as well as the first to simultaneously optimize the number, location, and proportional–integral–derivative (PID) controllers for FCDs through two nested genetic algorithms (GAs), and especially within a unified environment (i.e., Python), which led to more efficient management of the process, thereby enhancing the efficiency of urban drainage network optimization. This study examined the impact of optimized RTC on the urban drainage network (UDN) in a part of New Orleans, LA, USA, under 1-, 2-, 5-, and 10-year storm events. The optimized RTC resulted in an improvement of up to 50% in network performance during a design storm. The results demonstrate the applicability in an urban environment where storms, flooding, and financial investments are critical to the management of stormwater drainage. Full article

Urbanization and climate change increasingly challenge urban water management. In this context, the design of stormwater drainage systems, which traditionally relies on historical rainfall records, is being questioned. Although significant efforts have been dedicated to optimizing drainage networks, the upgrading of existing systems remains understudied. This research devised a set of viable stormwater drainage networks, referencing the road network of the Sino-Singapore Tianjin Eco-City (data from Google Maps). On this basis, utilizing design rainfall data (sourced from the local meteorological center), an extensive array of scenario analyses was conducted. The investigation assessed the performance of implementing two redundancy-based interventions—introducing loops and enlarging pipe diameters—as well as the patterns of flood risk response, and by integrating a multi-objective optimization algorithm, this study proposes a framework for the optimization of grey infrastructure upgrades based on component replacement. The findings suggest that a precise deployment strategy for grey infrastructure is essential. The former improves the effective flow distribution of the drainage system, while the latter enhances its flow capacity, making each intervention suitable for drainage systems with a different degree of centralization. Further research shows that an integrated hybrid scheme brings significant flood risk improvement with strong applicability for most urban drainage systems. The upgrade model proposed in this study could be a valuable initiative, offering theoretical insights for the construction and development of resilient cities. Full article

The first flush (FF) phenomenon is commonly associated with a relevant load of pollutants, raising concerns about water quality and environmental management in agro-urban areas. An FF event can potentially transport contaminated water into a receiving water body by activating combined sewer overflow (CSO) systems present in the drainage urban network. Therefore, accurately characterizing FF events is crucial for the effective management of sewer systems and for limiting environmental degradation. Given the ongoing controversy in the literature regarding the delineation of FF event occurrences, there is an unavoidable necessity for further investigations, especially experimental-based ones. This study presents the outcomes of an almost two-year field campaign focused on assessing the water quantity and quality of two combined sewer systems in Northern Italy. For this purpose, various hydro-meteorological variables, including precipitation, flow rate, temperature, and solar radiation, in addition to water quality analytics, were measured continuously to capture stormwater events. Throughout the monitoring period, sixteen stormwater events were identified and analyzed using five indices usually adopted in the literature to identify FF occurrences. The results indicate that there is a strong positive correlation between the mass first flush ratios calculated for nutrients and three factors, including maximum rainfall intensity, maximum flow rate, and antecedent dry weather period. Furthermore, rainfall duration was found to possess a strong negative correlation with the mass first flush ratios calculated for nutrients. However, for the same rainfall event, the occurrence of FF has never been unanimously confirmed by the indices examined in this study. Moreover, different macro-groups of pollutants can behave differently. Thus, it becomes apparent that relying solely on a priori analyses, without the support of data from experimental monitoring campaigns, poses a risk when designing actions for the mitigation of FF occurrences. Full article

Urbanization and climate change have increased the need for stormwater management and nature-based solutions. Decisions made at the project level impact the emergence of the systemic traits of the stormwater network and the functionality of the catchment areas in urban planning. To that end, it is vital to introduce the decision-making tools for analysing both the utilities and amenities of nature-based solutions (NBS) to increase their adoption to reduce the peak loads in the stormwater system and, to that end, mitigate the impacts of climate change. There is a deficiency in employing a software-based approach to analyse the qualitative and quantitative aspects of NBSs to back up design decisions. This paper demonstrates a workflow using drone-based photogrammetry, 3D modelling, and simulation software to generate visual and functional models assisting in informed decision-making in the design of stormwater systems as functional landscape architecture. Using aerial data from drones and modelled design solutions, the proposed workflow simulates rain events, infiltration, evaporation, water flow, and the accumulation of stormwater in a way that allows the visual and quantified analysis of detailed landscape architecture designs. The paper provides an example of a rooftop site simulation demonstrating the infiltration and flow of water to the drainage. The visual decision-making method provided can aid in investment decisions for functional landscape design in support of stormwater management. Full article

Urban stormwater drainage systems, which include many personholes to collect and discharge precipitation within a city, are extensively constructed to prevent streets and buildings from flooding. This research intends to build a machine learning model to predict whether a personhole will overflow soon, which is crucial to alleviate the damage caused by floods. To address the challenges posed by many diverse personholes, we proposed segmenting the personholes into several groups and have designed two methods employing different personhole features. The first, the geography-based method, uses the geographical locations of the personholes for the grouping. The second, the hydrology-based method, uses the characteristics that are directly related to the overflowing situation, such as the depth of the personhole, and the average and the maximum water level of the personholes. We also investigated several machine learning techniques, such as the multilayer perceptron (MLP) model and a fine-tuning architecture. The study area was located in the new Taipei city and the experimental results have shown the impressive predictive ability of the proposed approaches. Particularly, by applying the hydrology-based grouping method, and using a hybrid model combining the machine learning model prediction results with heuristic rules, we can obtain the best prediction result, and the accuracy is over 99%. We have also noticed the influence of the activation function used in the neural network and the number of frozen layers in the fine-tuning architecture. Particularly, using the tanh function with one frozen layer is good in some cases. However, since it is not general enough, we suggest the readers perform empirical studies before choosing the best setting in their own environment. Full article

Climate change is contributing to extreme weather conditions, which transform the scale and degree of flood events. Therefore, it is important for relevant government agencies to effectively respond to both extreme climate conditions and their impacts by providing more efficient asset management strategies. Although international research projects on water-sensitive urban design and rural drainage design have provided partial solutions to this problem, road networks commonly serve unique combinations of urban-rural residential and undeveloped areas; these areas often have diverse hydrology, geology, and climates. Resultantly, applying a one-size-fits-all solution to asset management is ineffective. This paper focuses on data-driven flood modelling that can be used to mitigate or prevent floodwater-related damage in Western Australia. In particular, a holistic and coherent view of data-driven asset management is presented and multi-criteria analysis (MCA) is used to define the high-risk hotspots for asset damage in Western Australia. These state-wide hotspots are validated using road closure data obtained from the relevant government agency. The proposed approach offers important insights with regard to factors influencing the risk of damage in the stormwater management system. Full article

The frequency of urban storms has increased, influenced by the climate changing and urbanization, and the process of urban rainfall runoff has also changed, leading to severe urban waterlogging problems. Against this background, the risk of urban waterlogging was analyzed and assessed accurately, using an urban stormwater model as necessary. Most studies have used urban hydrological models to assess flood risk; however, due to limited flow pipeline data, the calibration and the validation of the models are difficult. This study applied the MIKE URBAN model to build a drainage system model in the Beijing Future Science City of China, where the discharge of pipelines was absent. Three methods, of empirical calibration, formula validation, and validation based on field investigation, were used to calibrate and validate the parameters of the model. After the empirical calibration, the relative error range between the simulated value and the measured value was verified by the formula as within 25%. The simulated runoff depth was consistent with a field survey verified by the method of validation based on field investigation, showing the model has good applicability in the study area. Then, the rainfall scenarios of different return periods were designed and simulated. Simulation results showed that, for the 10-year return period, there are overflow pipe sections in northern and southern regions, and the number of overflow pipe sections in the northern region is more than that in the southern region. For the 20-year return period and 50-year return period, the number of overflow pipe sections and nodes in the northern region increased, while for the 100-year return period, the number of overflow nodes both increased. With the increase in the rainfall return period, the pipe network load increased, the points and sections prone to accumulation and waterlogging increased, and the regional waterlogging risk increased. The southern region is prone to waterlogging because the pipeline network density is higher than that in the northern region and the terrain is low-lying. This study provides a reference for the establishment of rainwater drainage models in regions with similar database limitations and provides a technical reference for the calibration and validation of stormwater models that lack rainfall runoff data. Full article

Climate change is a significant problem that many countries are currently facing, and green roofs (GRs) are one of the suitable choices to confront it and decrease its impacts. The advantages of GRs are numerous, such as stormwater management, thermal need reduction, runoff quality, and life quality improvement. However, there are some limitations, including the weight, limits in water retention, irrigation in the drought period, suitability of harvested water for building usages, installation on sloped roofs, and high cost. Therefore, developing a novel system and design for GRs with higher efficiency and fewer negative points seems necessary and is the main scope of this research. In this regard, a novel multipurpose self-irrigated green roof with an innovative drainage layer combined with specific multilayer filters has been developed. The application of the proposed system in terms of water retention capacity, water storage volume, runoff treatment performance, irrigation system, drainage layer, application of the harvested water for domestic purposes, and some other aspects has been analyzed and compared with the conventional systems with a focus on extensive green roofs. The results demonstrate that this novel green roof would have many advantages including less weight due to the replacement of the gravel drainage layer with a pipeline network for water storage, higher water retention capacity due to the specific design, higher impacts on runoff treatment due to the existence of multilayer filters that can be changed periodically, easier installation on flat and sloped roofs, the possibility of using the collected rainfall for domestic use, and fewer irrigation water demands due to the sub-surface self-irrigation system. Full article

This paper describes an innovative Decentralized Technical Sustainable Drainage System (SuDS) concept, which is based on technical devices, such as sieves, sedimentation barriers, floating barriers and a magnetic module, which addresses, mainly, the fine matter. The SuDS is designed as a retrofit system so that no costly and time-consuming conversion measures are necessary. Due to the possibility of free configurability of individual modules in the three levels, road, gully and drain, a novel solution approach is presented, which is not available on the market, for a reduction in solids in general and microplastics in particular. The retention performance of selected modules and their combinations is demonstrated by means of bench tests according to the test procedure of the German Institute for Construction Engineering (DIBt) for the evaluation of decentralized treatment systems. Four different rain intensities, from light to medium up to heavy rain, are charged to the filter modules. Collected and fractionated road-deposited sediment (RDS) was selected as the test substance (10 kg). Additional tests with tyre powder, PE pellets, cigarette butts and candy wrappers helped to make clear the filter process of the particulate matter. The retention performance was determined by the mass balance between the defined dosage and at the outlet. For this purpose, the total volume flow of the effluent was passed over a stainless-steel sieve with a diameter of 600 mm and a mesh size of 20 µm. For the test substance, RDS retention rates up to 97% were measured. Very fine matter, particularly, was technically challenging to obtain; <63 µm up to 66% could be retained by the filter modules. Modules in the road space, such as porous asphalt or additional retention spaces, in the area of the curb as well as direct infiltration in the road drainage shaft are theoretically described and discussed. The outlook also addresses the potential of an intelligent network to reduce the input of pollution from urban stormwater runoff. Full article