Modeling Catchbasins and Inlets in SWMM

Numerical and computational modelling of flow and pollutant dynamics in urban drainage systems is becoming more and more integral to planning and design. The main aim of integrated flow and pollutant models is to quantify the efficiency of different measures at reducing the amount of pollutants discharged into receiving water bodies and minimise the consequent negative water quality impact. The open source toolbox CITY DRAIN developed in the Matlab/Simulink environment, which was designed for integrated modelling of urban drainage systems, is used in this work. The goal in this study was to implement and test computational routines for representing sediment and pollutant loads in order to evaluate catchment surface pollution. Tested models estimate the accumulation, erosion and transport of pollutants--aggregately--on urban surfaces and in sewers. The toolbox now includes mathematical formulations for accumulation of pollutants during dry weather period and their wash-off during rainfall events. The experimental data acquired in a previous research project carried out by the Environmental Engineering Research Centre (CIIA) at the Universidad de los Andes in Bogotá (Colombia) was used for the calibration of the models. Different numerical approaches were tested for their ability to calibrate to the sediment transport conditions. Initial results indicate, when there is more than one peak during the rainfall event duration, wash-off processes probably can be better represented using a model based on the flow instead of the rainfall intensity. Additionally, it was observed that using more detailed models (compared with an instantaneous approach) for representing pollutant accumulation do not necessarily lead to better results.

Isotope-based source assessment of water flowing from storm sewer systems to a receiving river during dry weather periods

Guest Editorial: Special issue on flexible and resilient urban energy systems

Life, services and activities within cities have commonly been studied by separate disciplines, each one independent from the others. One such approach is the computer simulation, which enables in-depth modelling and cost-effective evaluation of city phenomena. However, the adoption of integrated city simulations faces several barriers, such as managerial, social, and technical, despite its potential to support city planning and policymaking.This paper introduces the City Physiology: a new conceptual framework to facilitate the integration of city layers when designing holistic simulators. The physiology is introduced and applied through a process of three steps. Firstly, a literature review is offered in order to study the terminology and the progress already made towards integrated modelling of different urban systems. Secondly, interactions between urban systems are extracted from the approaches studied before. Finally, the pipeline to carry out the integration strategy is described.In addition to providing a conceptual tool for holistic simulations, the framework enables the discovery of new research lines generated by previously unseen connections between city layers. Being an open framework, available to all researchers to use and broaden, the authors of this paper envisage that it will be a valuable resource in establishing an exact science of cities.

Uncertainty-based urban drainage systems in Winter Olympic host cities face unique challenges from extreme weather and high water demands. This study presents an uncertainty-based urban drainage system (UUDS) model integrating multi-objective optimization and stochastic simulation to address these issues. Using rainfall data, urban block parameters, and hydraulic models, the UUDS employs NSGA-II and SWMM for designing rainwater and sewage systems. Results show optimized rainwater systems reduced lifecycle costs and enhanced adaptability to variable rainfall, while sewage systems achieved cost savings, pollution reductions, and improved reclaimed water utilization. These advancements mitigate urbanization and climate pressures, ensuring resilient infrastructure for Winter Olympics and sustainable urban development. The UUDS model exemplifies robust, adaptive planning for large-scale events under uncertainty, setting a benchmark for urban water management.

Geographic Information Systems (GIS) have been recognised as a powerful means to integrate and analyse data from various sources in the context of comprehensive floodplain management. As part of this comprehensive approach to floodplain management, it is very important to be able to predict the consequences of different scenarios in terms of flooded areas and associated risk. Hydrologic and hydraulic modelling plays a crucial role and there is much to gain in incorporating these modelling capabilities in GIS. This is still a rather complex task and research is being done on the full integration of these models. Interfacing between these models and GIS may be a very efficient way of overcoming the difficulties and getting very good results in terms of engineering practice. This paper presents results based on the use of Intergraph GIS coupled with Idrisi GIS. Using these two systems substantially increased the flexibility of using GIS as a tool for flood studies. A lumped (XSRAIN) and a distributed (OMEGA) hydrologic models were used to simulate flood hydrographs. The well known HEC-2 Hydraulic model was used to compute flooded areas. These models were applied in the Livramento catchment with very good results. The computation of flooded areas for different flood scenarios, and its representation in GIS, can be used in the assessment of affected property and associated damages. This is a very useful GIS-based approach to floodplain management.

Relevance of urban glyphosate use for surface water quality

The resolution of digital elevation model (DEM) used for watershed delineation and data extracted from the delineated watershed relevant to hydrography plays an important role in the calibration of hydrological models. The effect of DEM resolution on the drainage length, watershed area and average slope are studied in many earlier works. DEM resolution can have impact on hydrologic modeling parameters, water quality simulation results. The impacts of DEM resolution on results of the watershed response and water quality modeling are studied using HSPF (Hydrologic Simulation Program Fortran) model applied to a watershed in Kentucky in this study. Three major streams in this watershed are impaired due to pathogen pollution according to the current state of Kentucky water quality standard set for recreation. The effects of DEM resolution on water quality modeling and calibration for fecal coliform bacteria are studied using a HSPF model. Results from this study indicate that the DEM resolution affects hydrography parameters of the delineated watershed, influence the calibration process, and the non-point and point source loadings to the streams in the watershed.

Conventional UDS models are mechanistic which though appropriate for design purposes are less well suited to real-time control because they are slow running, difficult to calibrate, difficult to re-calibrate in real time and have trouble handling noisy data. At Salford University a novel hybrid of dynamic and empirical modelling has been developed, to combine the speed of the empirical model with the ability to simulate complex and non-linear systems of the mechanistic/dynamic models. This paper details the ‘knowledge acquisition module’ software and how it has been applied to construct a model of a large urban drainage system. The paper goes on to detail how the model has been linked with real-time radar data inputs from the MARS c-band radar.

Storm Tracking based on Rain Gauges for Flooding Control in Urban Areas

Investigation and numerical modelling of roof drainage systems under extreme events

Whilst the importance of integrated modelling of urban wastewater systems is ever increasing, there is still no concise procedure regarding how to carry out such modelling studies. After briefly discussing some earlier approaches, the guideline for integrated modelling developed by the Central European Simulation Research Group (HSG - Hochschulgruppe) is presented. This contribution suggests a six-step standardised procedure to integrated modelling. This commences with an analysis of the system and definition of objectives and criteria, covers selection of modelling approaches, analysis of data availability, calibration and validation and also includes the steps of scenario analysis and reporting. Recent research findings as well as experience gained from several application projects from Central Europe have been integrated in this guideline.

In order to cope with urban flooding, water scarcity and rainfall-runoff pollution comprehensively, a conceptual tank model of urban storm water system is proposed. Tank model is a multi-layer, multi-objective model, so design of urban storm water system is more complex than that of urban storm sewer system. Some principles of design of urban storm water system are discussed, such as Low Impact Development, Smart storm water management, and so on.

In an increasingly urbanized world, there are pressures being placed on our cities to which planners, decision-makers and communities need to respond. Leading thinkers suggest a need for enhanced expertise in urban systems and call for additional and improved case studies as well as models for planning, management, policy development and decision-making. Planning support systems (PSS) offer demonstrated strengths in modelling and analysis of urban systems, communication of information and public engagement. However, in spite of the demonstrated need, a dominant thread in the planning support literature is the under-utilization and lack of implementation of systems and instruments. This chapter investigates the relevance of PSS to address urban problems in the twenty-first century by reviewing project-based use cases and the social context in which they take place. The review confirms the utility of PSS but finds that planning and governance are strongly dictated by a number of societal trends that are antithetical to effective and impactful PSS implementation.

BackgroundAchieving climate neutrality in cities is a major challenge, especially in light of rapid urbanization and the urgent need to combat climate change. This paper explores the role of advanced computational methods in the transition of cities to climate neutrality, with a focus on energy supply and transportation systems. Central to this are recent advances in artificial intelligence, particularly machine learning, which offer enhanced capabilities for analyzing and processing large, heterogeneous urban data. By integrating these computational tools, cities can develop and optimize complex models that enable real-time, data-driven decisions. Such strategies offer the potential to significantly reduce greenhouse gas emissions, improve energy efficiency in key infrastructures and strengthen the sustainability and resilience of cities. In addition, these approaches support predictive modeling and dynamic management of urban systems, enabling cities to address the multi-faceted challenges of climate change in a scalable and proactive way.Main textThe methods, which go beyond traditional data processing, use state-of-the-art technologies such as deep learning and ensemble models to tackle the complexity of environmental parameters and resource management in urban systems. For example, recurrent neural networks have been trained to predict gas consumption in Ljubljana, enabling efficient allocation of energy resources up to 60 h in advance. Similarly, traffic flow predictions were made based on historical and weather-related data, providing insights for improved urban mobility. In the context of logistics and public transportation, computational optimization techniques have demonstrated their potential to reduce congestion, emissions and operating costs, underlining their central role in creating more sustainable and efficient urban environments.ConclusionsThe integration of cutting-edge technologies, advanced data analytics and real-time decision-making processes represents a transformative pathway to developing sustainable, climate-resilient urban environments. These advanced computational methods enable cities to optimize resource management, improve energy efficiency and significantly reduce greenhouse gas emissions, thus actively contributing to global climate and environmental protection.