Adaptation of urban drainage networks to climate change: A review

Global perspectives on groundwater infiltration to sewer networks: A threat to urban sustainability

While existing studies on sewer networks have explored topics such as surface water inflow, limited research has delved into groundwater infiltration (GWI). This study aims to fill this void by providing a comprehensive overview of quantitative analyses of GWI in sewer networks plus current status, limitations and future perspectives, considering the most relevant peer-reviewed research, including 83 studies. We propose dividing the existing research into two main groups: (1) phreatic zone, and (2) vadose zone. Most research has focused on the latter, mainly considering Rainfall-Derived Inflow and Infiltration (RDII), including surface water inflow and GWI. The ratio of each is not frequently separated; otherwise, there may be some assumptions, e.g. in dry weather and assuming zero surface water inflow. We also divided the employed approaches in different categories from physically-based numerical models, to simpler ones, e.g. water budget analysis. In fact, a combination of approaches may be applied to find the intricate characteristics of 'urban groundwater' or 'urban karst.' The findings revealed a heightened vulnerability of sewer networks to GWI, due to climate change (CC) and its associated repercussions, e.g. sea level rise (SLR), making the coastal cities the most vulnerable regions. In future research, the criticality of pre-emptive measures and monitoring of networks, especially near the coastline, is emphasised to ensure the resilience and adaptability of sewer networks in the context of GWI amid the potential impacts of CC. However, current monitoring practices lack widespread evidence for spatiotemporal analysis of GWI quantity.

Exploring the network structure of coupled green-grey infrastructure to enhance urban pluvial flood resilience: A scenario-based approach focusing on 'centralized' and 'decentralized' structures

Urban pluvial floods pose a significant risk to cities, occurring when precipitation exceeds the carrying capacity of the urban drainage network. Coupled green-grey infrastructure has emerged as a sustainable solution for mitigating urban pluvial floods. This study aims to explore best practices in the network configuration of urban drainage systems coupled with low-impact development (LID) to enhance flow distribution and stormwater infiltration. To do so, we focused on two competing key concepts in network analysis: (1) Centralization and (2) Decentralization. We integrated a one-dimensional stormwater model with a rapid flood spreading model to assess the flood mitigation performance of various centralized and decentralized network configurations in the Gangnam region of Seoul, South Korea. To further assess the combined effects of green and grey infrastructure, we compared the performance of each drainage network configuration with and without identical mixed LID practices. Here we show that the centralized drainage network scenario performed best in reducing flood volume by 40.3%, the decentralized drainage network scenario performed best in shortening flood duration by 47.8%, and the LID practices scenario performed best in mitigating peak flooding rates by 4.2%, each as independent scenarios. When all three scenarios were coupled together, flood volume could be reduced by 73.5%, flood duration by 54.7%, and peak flooding rates by 19.8% in the study area. This exploratory study underscores the potential of network analysis in urban flood research, particularly the effectiveness of loosely-connected network topology. Our findings contribute to the development of best practices for coupled green-grey infrastructure, facilitating sustainable stormwater management and urban flood resilience.

Climate change impacts on magnitude and frequency of urban floods under scenario and model uncertainties

Many studies have confirmed that climate change leads to frequent urban flooding, which can lead to significant socioeconomic repercussions. However, most existing studies have not evaluated the impacts of climate change on urban flood from both event-scale and annual-scale dimensions. In addition, there are only few studies that simultaneously consider scenario and model uncertainties of climate change, and combine flood risk assessment and uncertainty analysis results to provide practical suggestions for urban drainage system management. This study uses the statistical downscaling method to calculate the design rainfall under ten rainfall return periods of four climate models and three climate change scenarios in 2040s, 2060s, and 2080s in various prefecture-level cities in China. The four climate models are HadGEM2- ES, MPI-ESM-MR, NorESM1-M and FGOALS-g2 models and the three climate change scenarios are constructed by different representative concentration pathways (RCP), namely RCP2.6, RCP4.5 and RCP8.5. On this basis, relying on the generated drainage systems using geographical information and other data, event-scale and annual-scale precipitation are combined to calculate the change ratio of annual flood volume expectation in prefecture-level cities in each future year compared with the current situation. Furthermore, the study evaluates scenario and model uncertainties of climate change, and then comprehensively integrates the flood risk and its uncertainties to provides suggestions for urban drainage system management.

Model parameter estimation with imprecise information

Model parameter estimation is a well-known inverse problem, as long as single-value point data are available as observations of system performance measurement. However, classical statistical methods, such as the minimization of an objective function or maximum likelihood, are no longer straightforward, when measurements are imprecise in nature. Typical examples of the latter include censored data and binary information. Here, we explore Approximate Bayesian Computation as a simple method to perform model parameter estimation with such imprecise information. We demonstrate the method for the example of a plain rainfall-runoff model and illustrate the advantages and shortcomings. Last, we outline the value of Shapley values to determine which type of observation contributes to the parameter estimation and which are of minor importance.

Method for analyzing urban waterlogging mechanisms based on a 1D-2D water environment dynamic bidirectional coupling model

Urban waterlogging is a significant global issue. To achieve precisely control urban waterlogging and enhance our understanding of its causes, a novel study method was introduced. This method is based on a dynamic bidirectional coupling model that combines 1D-2D hydrodynamic and water quality simulations. The waterlogging phenomenon in densely populated metropolitan areas of Changzhi city, China, was studied. This study focused on investigating the process involved in waterlogging formation, particularly overflow at nodes induced by the design of the topological structure of the pipe network, constraints on the capacity of the underground drainage system, and the surface runoff accumulation. The complex interplay among these elements and their possible influences on waterlogging formation were clarified. The results indicated notable spatial and temporal variation in the waterlogging formation process in densely populated urban areas. Node overflow in the drainage system emerged as the key influencing factor in the waterlogging formation process, accounting for up to 71% of the total water accumulation at the peak time. The peak lag time of waterlogging during events with short return periods was primarily determined by the rainfall peak moment. In contrast, the peak time of waterlogging during events with long return periods was influenced by the rainfall peak moment, drainage capacity and topological structure of the pipe network. Notably, the access of inflow from both upstream and downstream segments of the pipe network drainage system significantly impacted the peak time of waterlogging, with upstream water potentially delaying the peak time substantially. This study not only provides new insights into urban waterlogging mechanisms but also provides practical guidance for optimizing urban drainage systems, urban planning, and disaster risk management.

Contributions of climate change and urbanization to urban flood hazard changes in China's 293 major cities since 1980

The growing incidence of urban flood disasters poses a major challenge to urban sustainability in China. Previous studies have reported that climate change and urbanization exacerbate urban flood risk in some major cities of China. However, few assessments have quantified the contributions of these two factors to urban flood changes in recent decades at the nationwide scale. Here, surface runoff caused by precipitation extremes was used as the urban flood hazard to evaluate the impacts of climate change and urbanization in China's 293 major cities. This study assessed the contributions of these drivers to urban flood hazard changes and identified the hotspot cities with increased trends under both factors during the past four decades (1980-2019). The results showed that approximately 70% of the cities analyzed have seen an increase of urban flood hazard in the latest decade. Urbanization made a positive contribution to increased urban flood hazards in more than 90% of the cities. The contribution direction of climate change showed significant variations across China. Overall, the absolute contribution rate of climate change far outweighed that of urbanization. In half of the cities (mainly distributed in eastern China), both climate change and urbanization led to increased urban flood hazard over the past decade. Among them, 33 cities have suffered a consecutive increase in urban flood hazard driven by both factors.

Assessment of sustainable drainage strategies in urban water systems using urban water metabolism and multi-criteria decision analyses

The simulation of urban water metabolism (UWM) allows for the tracking of all water, energy, and material flows within urban water systems (UWSs) and the quantification of their performance, including emissions into the air, water, and soil. This study evaluates seven drainage strategies (DSs) within conventional and sustainable urban drainage systems (SUDSs) using UWM and multicriteria decision analysis (MCDA). The DSs were designed to assess their corresponding UWM performances, employing key performance indicators (KPIs) related to sewer system balance, energy consumption, greenhouse gas (GHG) emissions, acidification, eutrophication, contamination, and sludge production. The outcomes were ranked using the compromise programming MCDA model. The top three strategies were permeable pavements, green spaces, and infiltration trenches and sand filters. The approach used for the evaluation of DS can provide valuable insights for decision-makers, support the promotion of sustainable integrated UWS management and adaptation, and accommodate design variations in urban drainage. Sensitivity analysis on uncertain parameters and KPI selection also contributed to robust and sustainable urban drainage solutions.

From flood paths to floodways, an efficient method to map, identify and evaluate suitable floodways: A case study from Trondheim, Norway

Due to climate change and rapid urbanisation, many Norwegian cities and urban areas suffer from pluvial flooding caused by intense rainfall exceeding the capacity of the stormwater management system. This results in increased runoff rates, volumes and peak flows in the drainage network. In response to these challenges, the authors explore the potential of utilising the urban surface's ability to transport floodwater as an integral component of the stormwater infrastructure. When the capacity of the stormwater drainage system is exceeded, the overland flow paths transporting floodwater are considered a part of the stormwater management system, as floodways. The study proposes a spatial GIS method to map existing drainage lines and identify existing surface areas that function as floodways, combined with an automated process to identify which drainage lines could be implemented as stormwater management measures. Critical points are introduced to assess the floodways' potential hazards, combined with a classification method to evaluate and sort floodways. A case study from Trondheim, Norway, was used to demonstrate how drainage lines can be identified as floodways using the proposed method. The case study is also used to illustrate how a GIS-based analysis can be extended from identifying to evaluating floodways and whether GIS is sufficient for floodway evaluation. The method enables urban planners and municipalities to identify which areas of the urban surface already function as floodways during extreme events, and to prioritise measures to secure such areas and increase the city's flood resilience. The results highlight the need to assess existing areas that function as floodways, and to implement and design needed areas as floodways. GIS-based methods combined with an evaluation scheme can be an adequate tool to map and evaluate floodways in urban areas. When using GIS-based methods, however, the corresponding hazard potential, and also the uncertainty of the floodway's spatial placement, should be considered.

Matrix scenario-based urban flooding damage prediction via convolutional neural network

This study introduces a cutting-edge, high-resolution tool leveraging the predictive prowess of convolutional neural networks to advance the field of hazard assessment in urban pluvial flooding scenarios. The tool uniquely accounts for the high heterogeneity of urban space and the potential impact of complex climate scenarios, which are often underestimated by traditional data-reliant methods. Employing Shenzhen as a case study, the model showcased superior accuracy, resilience, and interpretability, illuminating potential flood hazards. The performance analysis shows that the model can accurately predict the vast majority of urban flood depths, but has errors in extreme flood predictions (depths greater than 35 cm). Findings underscore escalating flood impacts under enhanced scenario loads, with western and central Shenzhen-regions rife with construction-highlighted as particularly vulnerable. Under the most severe matrix scenario (Scenario 25), economic losses are estimated to be about $25,484 million. These commercial and residential hotspots are anticipated to suffer maximum economic loss, with these two areas accounting for 39.6% and 25.1% of the total losses, necessitating reinforced mitigation efforts, especially during extreme rainfall events and high soil saturation levels. In addition, the flooding control strategies should prioritize the reduction of flood inundation areas and integrate functionally oriented land use characteristics in their development. By aiding in the precise identification of flood-prone areas, this research expedites the development of efficient evacuation plans, bolsters urban sustainability, and augments climate resilience, ultimately mitigating flood-induced economic tolls.

Two-dimensional (2D) flood analysis and calibration of stormwater drainage systems using geographic information systems

In recent years, there has been severe flooding in urban areas as well as coastal and river flooding. Urban flooding is exacerbated by climate change, urbanization, growing population, and the increase of impervious surfaces in urban areas. Stormwater drainage systems that discharge stormwater to a safe location in urban areas are becoming increasingly important. The objective of this study is to analyze and calibrate the flood performance of stormwater drainage systems currently used in the central region of Malatya in a potential flood situation using geographic information systems and the InfoWorks ICM. The model was created using the land use type, buildings, and digital elevation model (DEM), and the analysis was performed by exposing stormwater drainage systems to rainfall events of 5, 10, and 15 min of duration for return periods of 2, 5, and 10 years. The model was then validated using field-observed rainfall and flood data and its performance was evaluated using R², NSE, RMSE, and MAE metrics. The results showed that the eight stormwater drainage systems currently in operation cannot fully convey stormwater and may pose a risk of loss of life and property in residential areas. In addition, the severity of the flooding was found to increase with an increasing return period.

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.

Optimization of in-pipe storage capacity use in urban drainage systems with improved DP considering the time lag of flow routing

The active control of urban drainage systems (UDSs) is playing an increasingly important role in the world threatened by urban flooding and associated disasters caused by insufficient drainage capacity. However, little research has recognized the importance of the optimal use of in-pipe storage space. To address this issue, the use of the in-pipe storage capacity was optimized in this study. A novel approach, that is, dynamic programming with successive approximation considering the time lag of flow routing (DPSA-TL), was developed to determine the control policies, in addition to the commonly used passive, rule-based control (RBC), and evolutionary algorithm (EA) strategies. A real-life urban catchment considering flooding control and combined sewer overflow (CSO) reduction was used as the case study. First of all, the potential benefit of maximizing the use of in-pipe storage space was tested using the four control strategies in three storm events, including a 3-year, 2-hour design (46.5 mm), a 5-year, 2-hour design (56.0 mm) and a 7-h historical (152.5 mm) storm events. Results indicate that DPSA-TL performed best in all cases. Without compromising the goal of flooding control, it provided 16.5%, 12.6%, and 3.0% reductions in CSO volume for the three storm events when compared with the passive strategy. Due to the limited capacity of in-pipe storage, the relative improvement diminished as the total rainfall depth increased. Then, control strategies were further applicated to the real-time operation. DPSA-TL was found to be the best alternative for CSO control, with the CSO volume reduced by 14.7%, 11.4%, and 2.5% in the three storm events, respectively. The findings suggest that the performance of UDS can be significantly improved by optimizing the use of in-pipe storage capacity, and the proposed method is effective in the offline optimization and real-time control of UDSs.

A framework for quantifying climate-informed heavy rainfall change: Implications for adaptation strategies

To understand the influence of climate change on heavy rainfalls and reduce the consequential multidimensional risks, we develop a climate-informed and adaptation strategies-related framework by using the information on heavy rainfalls and various socioeconomic factors. For this purpose, we firstly quantify the spatiotemporal characteristics of heavy rainfalls with various durations (1 h to multiple days) and return periods (2-year to 50-year) for the flood-prone country Cambodia, as a case study, during the historical period (1980-2005), mid-century (2040-2065), and late-century (2070-2095), using the latest three hourly climate model datasets under RCP 8.5 and 1 hourly ERA5 reanalysis datasets. A novel conditional artificial neural network (CANN) model is employed for temporal disaggregation to obtain the monthly maximum of 1 hourly rainfall in the future periods and subsequently, a zero-inflated generalized extreme value function (ZIGEV) is applied for extreme value analysis (EVA) to obtain rainfall intensity with different return periods. Secondly, the province-level flood risk change maps are developed based on a novel flood risk change index. The combination of CANN and ZIGEV performs better in EVA than traditional approaches by reducing the uncertainty from the stationarity assumption of temporal disaggregation and bias in the disaggregated rainfall. Rainfall intensity is projected to increase more in higher return periods and shorter durations towards the late-century, predominantly over Southern and Central Cambodia. Projected rainfall intensity-duration-frequency (IDF) curves in the capital city, Phnom Penh, reveal that the occurrence frequency of heavy rainfall in a given duration (e.g., 48 h) is likely to become ~10-fold in the mid-century. Results of province-level flood risk change maps indicate that Southeastern and Northwestern regions should be prioritized for employing adaption strategies. Our results will assist the policymakers in further mapping the flood susceptibility and vulnerability in different spatiotemporal scales across various communities and localities in the country and beyond.

Impacts of Rainfall Data Aggregation Time on Pluvial Flood Hazard in Urban Watersheds

Pluvial floods occur when heavy rainstorms cause the surcharge of the sewer network drainage, representing one of the most impacting natural hazard in urban watersheds. Pluvial flood hazard is usually assessed considering the effect of annual maxima rainfall of short duration, comparable with the typical concentration times of small urban watersheds. However, short duration annual maxima can be affected by an error of underestimation due to the time resolution as well as the aggregation time of the rainfall time series. This study aims at determining the impact of rainfall data aggregation on pluvial flood hazard assessment. Tuscany region (Central Italy) is selected as a case study to perform the assessment of the annual maxima rainfall underestimation error, since the entire region has the same temporal aggregation of rainfall data. Pluvial flood hazard is then evaluated for an urban watershed in the city of Florence (Tuscany) comparing the results obtained using observed (uncorrected) and corrected annual maxima rainfall as meteorological forcing. The results show how the design of rainfall events with a duration of 30 min or shorter is significantly affected by the temporal aggregation, highlighting the importance of correcting annual maxima rainfall for a proper pluvial flood hazard evaluation.

Towards the long term implementation of real time control of combined sewer systems: a review of performance and influencing factors

Real Time Control (RTC) is widely accepted as a cost-effective way to operate urban drainage systems (UDS) effectively. However, what factors influence RTC efficacy and how this might change in the long term remains largely unknown. This paper reviews the literature to understand what these factors likely are, and how they can be assessed in the future. Despite decades of research, inconsistent definitions of the performance of RTC are used, hindering an objective and quantitative examination of the benefits and drawbacks of different control strategies with regard to their performance and robustness. Furthermore, a discussion on the changes occurring and projected to occur to UDS reveals that the potential impact of these changes on the functioning of RTC systems can be significant and should be considered in the design stage of the RTC strategy. Understanding this 'best-before' characteristic of an RTC strategy is the key step to ensure long term optimal functioning of the UDS. Additionally, unexplored potential for RTC systems might exist in the transitions, rehabilitation and construction of drainage systems. The research gaps highlighted here could guide the way for further development of RTC strategies, and enabling more optimal, long term implementation of RTC for urban drainage systems.

Intensity-Duration-Frequency Curves at Ungauged Sites in a Changing Climate for Sustainable Stormwater Networks

Intensity-duration-frequency (IDF) curves representing the variation of the magnitude of extreme rainfall events with a return period and storm duration are widely used in hydrologic infrastructure design, flood risk management projects, and climate change impact studies. However, in many locations worldwide, short-duration rainfall-observing sites with long records do not exist. This paper introduces a new methodological framework for extracting IDF curves at ungauged sites transferring information from gauged ones with a relatively homogeneous extreme rainfall climate. This methodology is grounded on a simple scaling concept based on the multifractal behaviour of rainfall. A nonstationary Generalized Extreme Value (GEV) distribution fitted to annual rainfall monthly maxima at the ungauged site using a moving-time window approach is also applied to consider effects of a changing climate on IDF curve construction. An application is presented at the study site of Fourni, Crete, to derive IDF curves under changing climate conditions and present implications of the proposed methodology in the design of a sustainable stormwater network. The methodology introduced in this work results in increased rainfall extremes up to 20.5%, while the newly designed stormwater network is characterised by increased diameters of its primary conduits, compared to the ones resulting under fully stationary conditions.

Indices of Coastal Vulnerability to Climate Change: a Review

The objective of this paper is to identify and analyze relevant research of index-based methods for the evaluation of climate change vulnerability and resilience of coastal areas. We searched, retrieved, classified and reviewed papers on climate-change hazards, impacts, vulnerability and resilience of coastal water systems and relevant infrastructure. For this, Scopus, Science Direct, Thompson-Reuters Web of Science, Google Scholar, PubMed and other relevant databases were used. The analysis of the state-of-the-art presented in this paper acknowledges that using vulnerability and resilience indices in climate vulnerability research is effective, providing a solid, efficient and user-friendly framework. However, selection of index variables should be part of a holistic as well as dynamic approach to identify not only areas in danger, but also the level of social vulnerability.