Strategic planning of the integrated urban wastewater system using adaptation pathways

Multi-stage planning of LID-GREI urban drainage systems in response to land-use changes

Long-term planning of urban drainage systems aimed at maintaining the sustainability of urban hydrology remains challenging. In this study, an innovative multi-stage planning framework involving two adaptation pathways for optimizing hybrid low impact development and grey infrastructure (LID-GREI) layouts in opposing chronological orders was explored. The Forward Planning and Backward Planning are adaptation pathways to increase LID in chronological order based on the initial development stage of an urban built-up area and reduce LID in reverse chronological order based on the final development stage, respectively. Two resilience indicators, which considered potential risk scenarios of extreme storms and pipeline failures, were used to evaluate the performance of optimized layouts when land-use changed and evolved over time. Compared these two pathways, Forward Planning made the optimized layouts more economical and resilient in most risk scenarios when land-use changed, while the layouts optimized by Backward Planning showed higher resilience only in the initial stage. Furthermore, a decentralized scheme in Forward Planning was chosen as the optimal solution when taking costs, reliability, resilience, and land-use changes into an overall consideration. Nevertheless, this kind of reverse optimization order offers a novel exploration in planning pathways for discovering the alternative optimization schemes. More comprehensive solutions can be provided to decision-makers. The findings will shed a light on the exploration of optimized layouts in terms of spatial configuration and resilience performance in response to land-use changes. This framework can be used to support long-term investment and planning in urban drainage systems for sustainable stormwater management.

The role of deep learning in urban water management: A critical review

Deep learning techniques and algorithms are emerging as a disruptive technology with the potential to transform global economies, environments and societies. They have been applied to planning and management problems of urban water systems in general, however, there is lack of a systematic review of the current state of deep learning applications and an examination of potential directions where deep learning can contribute to solving urban water challenges. Here we provide such a review, covering water demand forecasting, leakage and contamination detection, sewer defect assessment, wastewater system state prediction, asset monitoring and urban flooding. We find that the application of deep learning techniques is still at an early stage as most studies used benchmark networks, synthetic data, laboratory or pilot systems to test the performance of deep learning methods with no practical adoption reported. Leakage detection is perhaps at the forefront of receiving practical implementation into day-to-day operation and management of urban water systems, compared with other problems reviewed. Five research challenges, i.e., data privacy, algorithmic development, explainability and trustworthiness, multi-agent systems and digital twins, are identified as key areas to advance the application and implementation of deep learning in urban water management. Future research and application of deep learning systems are expected to drive urban water systems towards high intelligence and autonomy. We hope this review will inspire research and development that can harness the power of deep learning to help achieve sustainable water management and digitalise the water sector across the world.

Exploring the use of water resource recovery facility instrument data to visualise dynamic resilience to environmental stressors

Water resource recovery facilities (WRRF) face increasingly dynamic stressors, such as higher rainfall intensity and extended dry periods, which can exert stress on ageing water infrastructure and processes. These events can generate process stresses, which lead to wastewater process failures which result in pollution events that could be identified from instrument data used for operational/compliance monitoring. This extraction can be performed on two levels (1) for discrete processes that generate data to monitor process control variables and (2) at the WRRF process boundary (global), which is mainly used for compliance. Both levels of data hold valuable information on the dynamic influence of environmental stressors (cause) and the resulting process stress or resilience (effect) as 'dynamic resilience'. This paper proposes a novel methodology that uses actual water company instrument data to evaluate the 'discrete' (unit processes) and 'global' (WRRF boundary) dynamic resilience of a WRRF in the south of the UK. Dynamic resilience is presented as a four-stage methodology, which; (1) cleans WRRF data and extracts a standard operating condition; (2) identifies dynamic high and low flow environmental stressor events (one in five years); (3) models the process stresses and resilience generated by the imposed dynamic stressor before; (4) generating a contoured heat map of process-related stresses or resilience as a self ordering window. These methods demonstrate the possibility of visualising the dynamics of WRRF resilience (dynamic stressors and process stresses/resilience) resulting from high and low flow dynamic environmental stressors. Despite some challenges experienced with self ordering window scaling, the results demonstrate the possibility of identifying zones of process stress and resilience. It may also be possible to expand the methods developed to incorporate storm flows and combined sewer discharges.

Gazing into the Crystal Ball: A Review of Futures Analysis to Promote Environmental Justice in the UK Water Industry

Water is a vital and multifunctional resource for our society, economy and ecosystems; thus, how water is managed now and into the future has wide-reaching consequences. Sustainable water management and environmental justice therefore become key topics; a discussion of these terms is explored in the context of the UK water industry, which provides the focus for this study. This systematic review explores how considerations of the future have been applied in water research. The literature is reviewed with respect to (1) defining the end goal, (2) the use of futures analysis and (3) possible evaluation methods, including a discussion on the boundaries applied to each of the studies. A growing body of research associated with decision-making applying future scenarios was identified. However, the methods of application varied substantially, with holistic analyses largely lacking. The formulation of methods appears to be specific to the goal that is sought as well as the cultural influence of the region in which the analysis was developed and deployed. This paper presents a case for the visualisation of catchment characteristics and interdependencies to enable transparency in decision-making. This should reflect not only the current system but also a range of potential futures to enable appraisal of impacts.

Integrating Urban Agriculture and Stormwater Management in a Circular Economy to Enhance Ecosystem Services: Connecting the Dots

Due to the rapid urbanization in the context of the conventional linear economy, the vulnerability of the urban ecosystem to climate change has increased. As a result, connecting urban ecosystem services of different urban land uses is imperative for urban sustainability and resilience. In conventional land use planning, urban agriculture (UA) and urban stormwater management are treated as separate economic sectors with different-disconnected-ecosystem services. Furthermore, few studies have synthesized knowledge regarding the potential impacts of integration of UA and stormwater green infrastructures (GIs) on the quantity and quality of urban ecosystem services of both economic sectors. This study provides a detailed analysis of the imperative question—how should a city integrate the developments of both urban agriculture and stormwater green infrastructure to overcome barriers while enhancing the ecosystem services? To answer this question, we conducted an extensive literature review. The results show that integrating UA with GIs can enhance urban food production while protecting urban water quality. This paper provides an initial context and mechanisms for future researchers and city planners regarding the manner in which the synergies between UA and stormwater GIs can create greater value for the wellbeing of urban ecosystems and resilience in the circular economy.

Exploring the Spatial Impact of Green Infrastructure on Urban Drainage Resilience

This paper explores the spatial impact of green infrastructure (GI) location on the resilience of urban drainage systems by the application of exploratory spatial data analysis (ESDA). A framework that integrates resilience assessment, location sensitivity analysis and ESDA is presented and applied to an urban catchment in the United Kingdom. Three types of GI, namely a bioretention cell, permeable pavement, and green roof, are evaluated separately and simultaneously. Resilience is assessed using stress-strain tests, which measure the system performance based on the magnitude and duration of sewer flooding and combined sewer overflows. Based on the results of a location sensitivity analysis, ESDA is applied to determine if there is spatial autocorrelation, spatial clusters, and spatial outliers. Results show a stronger spatial dependency using sewer flooding indicators. Different GI measures present differences in spatial autocorrelation and spatial cluster results, highlighting the differences in their underlying mechanisms. The finding of conflicting spatial clusters indicates that there are trade-offs in the placement of GI in certain locations. The proposed framework can be used as a tool for GI spatial planning, helping in the development of a systematic approach for resilience-performance orientated GI design and planning.