Modelling transitions in urban water systems

Exploring transitions of sewer wastewater infrastructure towards decentralisation using the modular model TURN-Sewers

We present a new modular model called TURN-Sewers for exploring different adaptations of centralised wastewater infrastructure towards more decentralised wastewater systems under different urban development scenarios. The modular model is flexible and computationally efficient in exploring transitions at the city scale, allowing for the comparison of different policies and management strategies for sanitary wastewater infrastructure. TURN-Sewers includes independent modules that simulate the generation, dimensioning, deterioration, management, and calculation of performance indicators for different wastewater systems. This model can use readily available spatial information to support infrastructure planners and other stakeholders in exploring different transition pathways from centralised to decentralised wastewater infrastructure. An illustrative example demonstrates how TURN-Sewers can generate multiple future alternatives, define different infrastructure management strategies regarding system expansion, rehabilitation and transition, and assess the economic, hydraulic and structural impacts.

Machine learning parallel system for integrated process-model calibration and accuracy enhancement in sewer-river system

The process-based water system models have been transitioning from single-functional to integrated multi-objective and multi-functional since the worldwide digital upgrade of urban water system management. The proliferation of model complexity results in more significant uncertainty and computational requirements. However, conventional model calibration methods are insufficient in dealing with extensive computational time and limited monitoring samples. Here we introduce a novel machine learning system designed to expedite parameter optimization with limited data and boost efficiency in parameter search. MLPS, termed the machine learning parallel system for fast parameter search of integrated process-based models, aims to enhance both the performance and efficiency of the integrated model by ensuring its comprehensiveness, accuracy, and stability. MLPS was constructed upon the concept of model surrogation + algorithm optimization using Ant Colony Optimization (ACO) coupled with Long Short-Term Memory (LSTM). The optimization results of the Integrated sewer network and urban river model demonstrate that the average relative percentage difference of the predicted river pollutant concentrations increases from 1.1 to 6.0, and the average absolute percent bias decreases from 124.3% to 8.8%. The model outputs closely align with the monitoring data, and parameter calibration time is reduced by 89.94%. MLPS enables the efficient optimization of integrated process-based models, facilitating the application of highly precise complex models in environmental management. The design of MLPS also presents valuable insights for optimizing complex models in other fields.

Monitoring and control of smart urban drainage systems using NB-IoT cellular sensor networks

Narrow-Band-Internet of Things (NB-IoT) is a promising wireless communication technology for harsh signal propagation conditions, including underground connectivity. The purpose of this study is to evaluate the real-life signal quality of an NB-IoT cellular sensor network for the monitoring and control of urban drainage systems. The implementation of the control systems toward smart infrastructures increase the need for low-cost and low-energy-consumption underground sensing devices. A measurement campaign was conducted in Rakvere, Estonia, to analyze the NB-IoT network's coverage and underground signal quality. The results were used to analyze the flood risk reduction potential in the pilot catchment, considering the quality of NB-IoT cellular network coverage. The signal measurements showed that in the case of plastic manholes, the best cellular transmitter placement is 0.5 m below the manhole cover, and at the ground level NB-RSRP reference signal strength of -104 dBm or better must be ensured to have a link budget with optimal energy consumption for communication. The results of flood risk reduction showed that excluding control locations with insufficient signal strength increased the number of major flooding nodes by 3.9% and mean flooding duration by 4.2% which is a relatively small change and characterizes the flexibility of the developed control system.

Green Infrastructure and Urban-Renewal Simulation for Street Tree Design Decision-Making: Moderating Demands of Stormwater Management, Sunlight and Visual Aesthetics

The design of green infrastructure in urban renewal sites is complex, requiring engagement with existing communities and future sustainable development goals, consideration of existing and future urban forms, changing climatic conditions, and the sites often being in low-lying and flood-prone areas. Traditional street tree decision-making approaches are inadequate for addressing the scale, environmental complexity, and mutability of decisions involved in urban renewal projects—new tree selection approaches that consider complex competing criteria for tree selections addressing stormwater management systems, visual assessment and solar amenity are needed. This paper describes a new method of multi-criteria street design decision modelling that combines outputs from hydrology modelling, digital procedural tree modelling and urban form analysis, with animation and gaming technologies. We evaluate our approach through application to the design of a large-scale, urban renewal project underway in Melbourne, Australia. The results of the study demonstrate the functionality of our model, which allowed the simultaneous output of streetscape visualisation, with tree selection responding to integrated stormwater management infrastructure and flooding, along with the likely overshadowing conditions of urban renewal built-form. Our multi-criteria approach makes a significant contribution to the tools available to urban designers, planners and landscape architects in their pursuit of smarter streetscape design decisions that respond to complex spatial, cultural and climatic urban challenges.

Water neutrality framework for systemic design of new urban developments

The climate emergency and population growth threaten urban water security in cities worldwide. Growth, urbanisation, and changes to way of life have increased housing demand, requiring cities such as London to increase their housing stock by more than 15% over the next 10 years. These new urban developments will increase water demand, urban flood risk, and river water pollution levels; therefore, an integrated systems-based approach to development and water management is needed. Water Neutrality (WN) has emerged as a concept to frame the concerns about escalating water stresses in cities. We frame WN as a planning process for new urban developments that aims to minimise impacts on urban water security and offset any remaining stresses by retrofitting existing housing stock. In this work, we present a novel systemic design framework for future urban planning called CityPlan-Water, which guides how WN might be achieved to tackle current and future water pressures at a city scale. CityPlan-Water integrates spatial data with an integrated urban water management model, enabling urban design at a systems level and systematic assessment of future scenarios. We define a Water Neutrality Index that captures how successful a given urban planning scenario is in achieving WN and how multiple interventions could be combined at a city scale to improve WN. Results from CityPlan-Water suggest that it will be necessary to retrofit almost the same number of existing homes with WN design options to completely offset the impact imposed by proposed new developments. Combining options such as water efficient appliances, water reuse systems, and social awareness campaigns can offset the impact of new development on water demand by 70%, while to neutralise potential flood risk and water pollution at a city scale, interventions such as rainwater harvesting and Blue Green Infrastructure need to be added both in new urban developments and 432,000 existing London households. We see CityPlan-Water as a tool that can support the transition of urban planning towards using data-driven analysis to effectively design water neutral housing and drive sustainable development.

A Simplified Sanitary Sewer System Generator for Exploratory Modelling at City-Scale

Future climatic, demographic, technological, urban and socio-economic challenges call for more flexible and sustainable wastewater infrastructure systems. Exploratory modelling can help to investigate the consequences of these developments on the infrastructure. In order to explore large numbers of adaptation strategies, we need to re-balance the degree of realism of sewer network and ability to reflect key performance characteristics against the model's parsimony and computational efficiency. We present a spatially explicit algorithm for creating sanitary sewer networks that realistically represent key characteristics of a real system. Basic topographic, demographic and urban characteristics are abstracted into a squared grid of 'Blocks' which are the foundation for the sewer network's topology delineation. We compare three different pipe dimensioning approaches and found a good balance between detail and computational efficiency. With a basic hydraulic performance assessment, we demonstrate that we attain a computationally efficient and high-fidelity wastewater sewer network with adequate hydraulic performance. A spatial resolution of 250 m Block size in combination with a sequential Pipe-by-Pipe (PBP) design algorithm provides a sound trade-off between computational time and fidelity of relevant structural and hydraulic properties for exploratory modelling. We can generate a simplified sewer network (both topology and hydraulic design) in 18 s using PBP, versus 36 min using a highly detailed model or 1 s using a highly abstract model. Moreover, this simplification can cut up to 1/10th to 1/50th the computational time for the hydraulic simulations depending on the routing method implemented. We anticipate our model to be a starting point for sophisticated exploratory modelling into possible infrastructure adaptation measures of topological and loading changes of sewer systems for long-term planning.

Prioritizing sponge city sites in rapidly urbanizing watersheds using multi-criteria decision model

Spatial planning is crucial for sponge city (SC) construction; however, prioritizing SC sites at the watershed scale has not been fully explored. In this study, a multi-criteria decision model, considering demand and suitability of SC construction, was established by monitoring, model simulation, and index calculation. This new model was then tested in a rapidly urbanizing watershed, Beijing, China, and the priority of SC construction at both grid scale (1km×1km) and subwatershed scale was ranked. The results showed that the highest priority was found in emerging regions where urbanization is ongoing and followed by urban core areas. In addition, six indexes were identified by clustering heatmaps as key factors affecting the priority of SC planning, including topographic index, water pollution index, pollution rate based on the state standard of surface water environment quality, urbanization planning, urban levels, and vegetation index, which could guide SC planning in data-lacking regions. The approach and findings in this study cannot only provide helpful references for watershed managers and urban planners but also can be easily used in other regions.

Water Sensitive Cities: An Integrated Approach to Enhance Urban Flood Resilience in Parma (Northern Italy)

Climate change is globally causing more intense meteorological phenomena. Our cities experience increased rainfall intensity, more intense heat waves, and prolonged droughts providing economic, social, health and environmental challenges. Combined with population growth and rapid urbanization, the increasing impact of climate change will make our cities more and more vulnerable, especially to urban flooding. In order to adapt our urban water systems to these challenges, the adoption of newly emerging water management strategies is required. The complexity and scale of this challenge calls for the integration of knowledge from different disciplines and collaborative approaches. The water sensitive cities principles provide guidance for developing new techniques, strategies, policies, and tools to improve the livability, sustainability, and resilience of cities. In this study, the DAnCE4Water modeling approach promoting the development of water sensitive cities was applied to Parma, an Italian town that has faced serious water issues in the last few years. The city, indeed, had to face the consequences of flooding several times, caused by the inadequacy of both the network of open channels and the sewerage network due to the urban expansion and climate change of the last 30 years. Through the model, the efficiency of decentralized technologies, such as green roofs and porous pavement, and their integration with the existing centralized combined sewer system was assessed under a range of urban development scenarios. The obtained results show that the adoption of an integrated approach, including soft engineering hydraulic strategies, consisting in the use of natural and sustainable solutions, can increase resilience to urban flooding. Further, the study shows that there is a critical need for strategic investment in solutions that will deliver long-term sustainable outcomes.

An investigation into the impacts of water demand management and decentralized water recycling on excess sewer sediment deposition

Sewers are a critical part of the urban water system and represent a considerable investment due to the presence of extensive networks in many cities. Consequently, excess sewer sediment deposition, from changed inflow conditions or lack of appropriate sewer infrastructure, can lead to significantly increased maintenance and operational costs. The main aim of this manuscript is to quantify the potential impacts of reduced inflow and increased sediment concentrations from the implementation of sustainable water practices, such as Decentralized Water Recycling and Water Demand Management, on excess sediment deposition in gravity sewers. Experiments in a sewer pilot plant, with municipal wastewater, and modelling using a comprehensive local-scale sewer sediment model were used in conjunction to address this aim. Results from both these methods indicated that a reduction in inflows from the moderate implementation of sustainable water practices had a large impact on the quantity of sediment deposited in gravity sewers. However, further modelling showed that the reduction in bed erosion during peak flows for the same implementations of sustainable water practices occurred more gradually. Overall, our findings showed that in existing gravity sewer mains with reasonable slope and flow velocities, a moderate decrease in peak flow velocity of around 15% due to the implementation of Decentralized Water Recycling and Water Demand Management was unlikely to result in a net increase of sediment deposition. Future work in this area could focus on confirming these findings through case studies in the field or on long-term pilot studies with detailed bed height and density measurements.

Site-scale Urban Water Mass Balance Assessment (SUWMBA) to quantify water performance of urban design-technology-environment configurations

Historically, little consideration has been given to water performance of urban developments such as "hydrological naturalness" or "local water self-sufficiency". This has led to problems with increased stormwater runoff, flooding, and lack of local contributions to urban water security. Architectural design, water servicing technologies and environmental conditions are each known to influence water performance. However, most existing models have overlooked the integration of these factors. In this work, we asked 'how the water performance of urban developments at site-scale can be quantified, with joint consideration of architectural design, water servicing technologies, and environmental context (i.e. climate and soil)'. Answering this question led to the development of a new method and tool called Site-scale Urban Water Mass Balance Assessment (SUWMBA). It uses a daily urban water mass balance to simulate design-technology-environment configurations. Key features include: (i) a three-dimensional boundary focussed on the "entity" of development (ii) a comprehensive water balance accounting for all urban water flows, (iii) methods that include key variables capturing the interactions of natural, built-environment and socio-technological systems on water performance. SUWMBA's capabilities were demonstrated through an evaluation of a residential infill development case study with alternative design-technology-environment configurations, combining three dwelling designs, seven water technologies and three environmental contexts. The evaluation showed how a configuration can be identified that strikes a balance between the conflicting objectives of achieving the desired dwelling densities whilst simultaneously improving water performance. For two climate zones, the optimal configuration increases the total number of residents by 300% while reducing the imported water per capita and stormwater discharge by 45% and 15%, respectively. We infer that SUWMBA could have strong potential to contribute to performance-based urban design and planning by enabling the water performance of dwelling designs to be quantified, and by facilitating the setting of locally-specific water performance objectives and targets.

Citizens' preferences and attitudes towards urban waterfront spaces: a case study of Qiantang riverside development

Many cities are facing various environmental problems, where performance-based planning and nature-based solutions have been proposed to address such problems. As a natural landscape in the city, urban waterfront space has efficient ecological benefits, high-quality landscape vision and environment, and it is an important carrier of citizens' activities. However, existing studies have mainly focused on coping with environmental problems, while social functions and strategies have been neglected. Therefore, this study aims to fill such research gaps by understanding the social performance of urban waterfront spaces. Field observation and questionnaire survey were conducted in a famous urban waterfront space, Qiantang riverside walkway, in Hangzhou, China. The results indicate that the Qiantang riverside walkway serves as a space for tourism, leisure and entertainment, as the visitors who lived more than 5 km away from here accounted for about 50% and the local people also accounted for about 50%. People's activities exhibited significantly temporal differences, where the occupation of the Qiantang riverside walkway reached a peak at night. For the people who lived far from here, they mainly depended on self-driving, which led to two critical problems relevant to transport linkage with the city and parking lot. Results also indicate that the landscape, supporting facilities and road functions could perform well among the mind of 102 respondents. However, public service and main facilities should be improved to meet more people's requirements. Moreover, importantly, the results indicate the phenomenon of stratification and agglomeration so that the similar aspects (e.g. rest seat quantity, rest seat style) could be merged into the same cluster (e.g. rest seat) for consideration in the urban waterfront space planning and design. This study also generates some implications for the renovation of urban waterfront spaces. Overall, this study provides people with basic understanding of the social performance of existing urban waterfront spaces, which can further promote urban planners and designers to comprehensively build sustainable, resilient and healthy water-based living environments.

A Review of Volatile Organic Compound Contamination in Post-Industrial Urban Centers: Reproductive Health Implications Using a Detroit Lens

Volatile organic compounds (VOCs) are a group of aromatic or chlorinated organic chemicals commonly found in manufactured products that have high vapor pressure, and thus vaporize readily at room temperature. While airshed VOCs are well studied and have provided insights into public health issues, we suggest that belowground VOCs and the related vapor intrusion process could be equally or even more relevant to public health. The persistence, movement, remediation, and human health implications of subsurface VOCs in urban landscapes remain relatively understudied despite evidence of widespread contamination. This review explores the state of the science of subsurface movement and remediation of VOCs through groundwater and soils, the linkages between these poorly understood contaminant exposure pathways and health outcomes based on research in various animal models, and describes the role of these contaminants in human health, focusing on birth outcomes, notably low birth weight and preterm birth. Finally, this review provides recommendations for future research to address knowledge gaps that are essential for not only tackling health disparities and environmental injustice in post-industrial cities, but also protecting and preserving critical freshwater resources.

Modeling Water Quality in Watersheds: From Here to the Next Generation

In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in-stream water quality and process interactions, soil health and land management, and (peri-)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches, and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure, and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.

Using Complex Network Analysis for Optimization of Water Distribution Networks

The optimization of water networks supports the decision-making process by identifying the optimal trade-off between costs and performance (e.g., resilience and leakage). A major challenge in the domain of water distribution systems (WDSs) is the network (re)design. While the complex nature of WDS has already been explored with complex network analysis (CNA), literature is still lacking a CNA of optimal water networks. Based on a systematic CNA of Pareto-optimal solutions of different WDSs, several graph characteristics are identified, and a newly developed CNA design approach for WDSs is proposed. The results show that obtained designs are comparable with results found by evolutionary optimization, but the CNA approach is applicable for large networks (e.g., 150,000 pipes) with a substantially reduced computational effort (runtime reduction up to 5 orders of magnitude).

Framework, Procedure, and Tools for Comprehensive Evaluation of Sustainable Stormwater Management: A Review

To better evaluate and enhance the performance and benefit of sustainable stormwater management (SSWM) in developing countries, this study proposes a comprehensive evaluation framework based on thorough literature review. This framework re-classifies evaluation goals and indicators into four aspects—stormwater system, integrated management, social engagement, and urban development. The purpose of this review is to provide a guideline for decision makers to choose appropriate goals and indicators according to different regional context. Meanwhile, a structured procedure for comprehensive evaluation of SSWM is proposed to guide a well-organised decision-making process. Furthermore, pros and cons of eight decision support tools, as well as their functional focus, are compared, aiming to provide references for SSWM in developing countries. Outcomes presented in this review are expected to support decision makers in the process of screening optimal SSWM strategies and monitoring SSWM projects.

A new model framework for sponge city implementation: Emerging challenges and future developments

Sponge City concept is emerging as a new kind of integrated urban water systems, which aims to address urban water problems. However, its implementation has encountered a variety of challenges. The lack of an integrated comprehensive model to assist Sponge City planning, implementation and life cycle assessment is one of the most challenging factors. This review briefly analyses the opportunity of existing urban water management models and discusses the limitation of recent studies in the application of current integrated models for Sponge City implementation. Furthermore, it proposes a new Sponge City model framework by integrating four main sub-models including MIKE-URBAN, LCA, W045-BEST, and MCA in which environmental, social, and economic aspects of Sponge City infrastructure options are simulated. The new structure of Sponge City model that includes the sub-model layer, input layer, module layer, output layer, and programing language layer is also illustrated. Therefore, the proposed model could be applied to optimize different Sponge City practices by not only assessing the drainage capacity of stormwater infrastructure but also pays attention to multi-criteria analysis of urban water system (including the possibility of assessing Sponge City ecosystem services for urban areas and watershed areas) as well. Balancing between simplification and innovation of integrated models, increasing the efficiency of spatial data sharing systems, defining the acceptability of model complexity level and improving the corporation of multiple stakeholders emphasizing on possible future directions of a proper Sponge City design and construction model.

Urban Hydroinformatics: Past, Present and Future

Hydroinformatics, as an interdisciplinary domain that blurs boundaries between water science, data science and computer science, is constantly evolving and reinventing itself. At the heart of this evolution, lies a continuous process of critical (self) appraisal of the discipline’s past, present and potential for further evolution, that creates a positive feedback loop between legacy, reality and aspirations. The power of this process is attested by the successful story of hydroinformatics thus far, which has arguably been able to mobilize wide ranging research and development and get the water sector more in tune with the digital revolution of the past 30 years. In this context, this paper attempts to trace the evolution of the discipline, from its computational hydraulics origins to its present focus on the complete socio-technical system, by providing at the same time, a functional framework to improve the understanding and highlight the links between different strands of the state-of-art hydroinformatic research and innovation. Building on this state-of-art landscape, the paper then attempts to provide an overview of key developments that are coming up, on the discipline’s horizon, focusing on developments relevant to urban water management, while at the same time, highlighting important legal, ethical and technical challenges that need to be addressed to ensure that the brightest aspects of this potential future are realized. Despite obvious limitations imposed by a single paper’s ability to report on such a diverse and dynamic field, it is hoped that this work contributes to a better understanding of both the current state of hydroinformatics and to a shared vision on the most exciting prospects for the future evolution of the discipline and the water sector it serves.

Using Real-Time Data and Unsupervised Machine Learning Techniques to Study Large-Scale Spatio–Temporal Characteristics of Wastewater Discharges and their Influence on Surface Water Quality in the Yangtze River Basin

Most worldwide industrial wastewater, including in China, is still directly discharged to aquatic environments without adequate treatment. Because of a lack of data and few methods, the relationships between pollutants discharged in wastewater and those in surface water have not been fully revealed and unsupervised machine learning techniques, such as clustering algorithms, have been neglected in related research fields. In this study, real-time monitoring data for chemical oxygen demand (COD), ammonia nitrogen (NH3-N), pH, and dissolved oxygen in the wastewater discharged from 2213 factories and in the surface water at 18 monitoring sections (sites) in 7 administrative regions in the Yangtze River Basin from 2016 to 2017 were collected and analyzed by the partitioning around medoids (PAM) and expectation–maximization (EM) clustering algorithms, Welch t-test, Wilcoxon test, and Spearman correlation. The results showed that compared with the spatial cluster comprising unpolluted sites, the spatial cluster comprised heavily polluted sites where more wastewater was discharged had relatively high COD (>100 mg L−1) and NH3-N (>6 mg L−1) concentrations and relatively low pH (<6) from 15 industrial classes that respected the different discharge limits outlined in the pollutant discharge standards. The results also showed that the economic activities generating wastewater and the geographical distribution of the heavily polluted wastewater changed from 2016 to 2017, such that the concentration ranges of pollutants in discharges widened and the contributions from some emerging enterprises became more important. The correlations between the quality of the wastewater and the surface water strengthened as the whole-year data sets were reduced to the heavily polluted periods by the EM clustering and water quality evaluation. This study demonstrates how unsupervised machine learning algorithms play an objective and effective role in data mining real-time monitoring information and highlighting spatio–temporal relationships between pollutants in wastewater discharges and surface water to support scientific water resource management.

Effects of Urban Forms on Separate Drainage Systems: A Virtual City Perspective

The development of urban drainage systems is challenged by rapid urbanization; however, little attention is paid to the urban form and its effects on these systems. This study develops an integrated city-drainage model that configures typical urban forms and their associated drainage infrastructures, specifically domestic wastewater and rainwater systems, to analyze the relationship between them. Three typical types of urban forms were investigated: the square, the star, and the strip. Virtual cities were designed first, with the corresponding drainage systems generated automatically and then linked to a model herein called the Storm Water Management Model (SWMM). Evaluation was based on 200 random configurations of wastewater/rainwater systems with different structures or attributes. The results show that urban forms play more important roles on three dimensions of performance, namely economic efficiency, effectiveness, and adaptability, of the rainwater systems than of the wastewater systems. Cost is positively correlated to the effectiveness of rainwater systems among the different urban forms, while adaptability is negatively correlated to the other two performance dimensions. Regardless of the form, it is difficult for a city to make its drainage systems simultaneously cost-effective, efficient, and adaptable based on the virtual cities we investigated. This study could inspire the urban planning of both built-up and to-be-built areas to become more sustainable with their drainage infrastructure by recognizing the pros and cons of different macroscale urban forms.

Water Quality Evaluation of the Yangtze River in China Using Machine Learning Techniques and Data Monitoring on Different Time Scales

Unlike developed countries, China has a nationally unified water environment standard and a specific watershed protection bureau to perform water quality evaluation. It is a major challenge to assess the water quality of a large watershed at a wide spatial scale and to make decisions in a scientific way. In 2016, weekly and real-time data for four monitoring indicators (pH, dissolved oxygen, permanganate index, and ammonia nitrogen) were collected at 21 surface water sections (sites) of the Yangtze River Basin, China. Results showed that one site had a relatively low Site Water Quality Index and was polluted for 12 weeks meanwhile. By using expectation-maximization clustering and hierarchical clustering algorithms, the 21 sites were classified. Variable spatiotemporal distribution characteristics for water quality and pollutants were found; some sites exhibited similar water quality variations on the weekly scale, but had different yearly grades. The results revealed polluted water quality for short periods and abrupt anomalies, which imply potential pollution sources and negative effects on water ecosystems. Potential spatio-temporal water quality characteristics, explored by machine learning methods and evidenced by time series and statistical models, could be applied in environmental decision support systems to make watershed management more objective, reliable, and powerful.

Future trajectories of urban drainage systems: A simple exploratory modeling approach for assessing socio-technical transitions

In this work, we present a novel approach to explore future trajectories in urban drainage systems, emphasizing the adoption and implementation of sustainable 'nature-based' stormwater management strategies. The focus is on the development and long-term assessment of socio-technical pathways to create a multifunctional stormwater system at the city scale. The innovation is to identify and represent the socio-technical pathways by means of adoption curves for such transition processes. We combine urban planning policies and state-of-the-art urban engineering approaches with societal aspects and analyze them with traditional biophysical models (hydrologic-hydraulic sewer modeling). In doing so, different pathways from a current to a future system state are investigated under a variety of political, population and climate scenarios. Results allow for strategy screening by addressing the spatial and temporal implementation of decentralized stormwater control measures, to enable a successful transition to a sustainable future city. The model is applied to an ongoing transition of Kiruna, a city in Sweden, considering 36 different future trajectories over a transition period of 23 years. Results show that the trajectory of raingarden implementation under a sustainability policy can alleviate the adverse effects of urbanization (growth scenario). While this trajectory resulted in, for example, nearly the same sewer surcharge performance as that characterized by declining urbanization (stagnation) and a business-as-usual policy (with expected raingarden uptake rates approximately one-third lower), significantly better ecological performances (e.g. runoff treatment ratios up to 50%) are achieved.

Integrated modelling of stormwater treatment systems uptake

Nature-based solutions provide a variety of benefits in growing cities, ranging from stormwater treatment to amenity provision such as aesthetics. However, the decision-making process involved in the installation of such green infrastructure is not straightforward, as much uncertainty around the location, size, costs and benefits impedes systematic decision-making. We developed a model to simulate decision rules used by local municipalities to install nature-based stormwater treatment systems, namely constructed wetlands, ponds/basins and raingardens. The model was used to test twenty-four scenarios of policy-making, by combining four asset selection, two location selection and three budget constraint decision rules. Based on the case study of a local municipality in Metropolitan Melbourne, Australia, the modelled uptake of stormwater treatment systems was compared with attributes of real-world systems for the simulation period. Results show that the actual budgeted funding is not reliable to predict systems' uptake and that policy-makers are more likely to plan expenditures based on installation costs. The model was able to replicate the cumulative treatment capacity and the location of systems. As such, it offers a novel approach to investigate the impact of using different decision rules to provide environmental services considering biophysical and economic factors.