The increasing global population and in-country migration have a significant impact on global land use land cover (LULC) change, which reduces green spaces and increases built-up areas altering the near-surface radiation and energy budgets, as well as the hydrological cycle over an urban area. The LULC change can lead to a combination of hazards such as increasing urban temperatures and intensified rainfall, ultimately resulting in increased flooding. This present study aims to discuss the changing pattern in urban temperature, daily rainfall, and flooding in Jakarta. The daily urban temperature and daily rainfall were based on a 30-year dataset from three meteorological stations of Jakarta in the period between 1987 and 2013. The changing trend was analyzed by using the Mann–Kendall and the Pettitt’s tests. The relation between daily rainfall and flooding was analyzed using a 30-year flooding dataset collected from several sources including the international disaster database, research, and newspaper. The results show that there was an increasing trend in the daily temperature and the daily rainfall in Jakarta. The annual maximum daily temperature showed that an increasing trend started in 2001 at the KMY station, and in 1996 at the SHIA station. In general, the highest annual maximum daily temperature was about 37 °C, while the lowest was about 33 °C. Moreover, the maximum daily rainfall started increasing from 2001. An increase in the maximum daily rainfall was observed mainly in January and February, which coincided with the flood events recorded in these months in Jakarta. This indicates that Jakarta is not only vulnerable to high urban temperature but also to flooding. While these two hazards occur in distinct timeframes, there is potential for their convergence in the same geographical area. This study provides new and essential insights to enhance urban resilience and climate adaptation, advocating a holistic approach required to tackle these combined hazards.@en

Microbial pathogens in urban floodwaters pose risks to human health, potentially causing diseases such as diarrhea. However, the disease burden related to urban traffic exposure from citizens passing through floodwaters is not easily quantified and therefore not included in many studies. Notably, this problem has received little attention in low-to-middle-income countries, with frequent flood events and the heavy diarrheal disease burden. This article calculates the infection risks and disease burden, considering traffic associated with exposure to floodwater contaminated with rotavirus for the first time in Ninh Kieu District, Can Tho city. Can Tho city in the Vietnamese Mekong Delta is well known to have many flood events every year, with many diarrheal cases during the flood season. The methodology comprises two steps. First, we applied quantitative microbial risk assessment that proposes the inclusion of exposure to traffic due to rotavirus in floodwater. Second, the disease burden was expressed in disability-adjusted life years (DALYs). The exposed groups are child pedestrians, adult pedestrians, motorcyclists, and cyclists. We used video footage to monitor the traffic. The results show that total DALYs per flood event were 1.35 × 10⁴ for 63,390 exposed people (i.e., 2129 DALYs per 10,000 cases). Motorcyclists are the strongest contributors to the DALYs (95%), followed by cyclists (2.8%), adult pedestrians (2%), and child pedestrians (0.2%). The population in Ninh Kieu District may suffer from waterborne diseases through traffic activities during flooding times. Our approach can be applied in other areas worldwide and helps identify main risk groups and focus areas for interventions.

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Risk-informed flood risk management requires a comprehensive and quantitative risk assessment, which often demands multiple (thousands of) river and flood model simulations. Performing such a large number of model simulations is a challenge, especially for large, complex river systems (e.g., Mekong) due to the associated computational and resource demands. This article presents an efficient probabilistic modeling approach that combines a simplified 1D hydrodynamic model for the entire Mekong Delta with a detailed 1D/2D coupled model and demonstrates its application at Can Tho city in the Mekong Delta. Probabilistic flood-hazard maps, ranging from 0.5 to 100 year return period events, are obtained for the urban center of Can Tho city under different future scenarios taking into account the impact of climate change forcing (river flow, sea-level rise, storm surge) and land subsidence. Results obtained under present conditions show that more than 12% of the study area is inundated by the present-day 100 year return period of water level. Future projections show that, if the present rate of land subsidence continues, by 2050 (under both RCP 4.5 and RCP 8.5 climate scenarios), the 0.5 and 100 year return period flood extents will increase by around 15- and 8-fold, respectively, relative to the present-day flood extent. However, without land subsidence, the projected increases in the 0.5 and 100 year return period flood extents by 2050 (under RCP 4.5 and RCP 8.5) are limited to between a doubling to tripling of the present-day flood extent. Therefore, adaptation measures that can reduce the rate of land subsidence (e.g., limiting groundwater extraction), would substantially mitigate future flood hazards in the study area. A combination of restricted groundwater extraction and the construction of a new and more efficient urban drainage network would facilitate even further reductions in the flood hazard. The projected 15-fold increase in flood extent projected by 2050 for the twice per year (0.5 year return period) flood event implies that the “do nothing” management approach is not a feasible option for Can Tho.

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Flood risk reduction strategies play an important role in flood risk management (FRM) and these strategies are being co-designed with the engagement of the stakeholder through multiple consultations and co-designing sessions. Effective participation of stakeholders in interactive work sessions requires fast and accurate modeling systems with a user-friendly interface, which can simulate the impact due to various flood reduction measures selected by the stakeholders and also generate outputs that can be understood by all stakeholders, especially those who are not FRM specialists. Presenting an easy-to-understand tool with easy inputs and outputs for a variety of stakeholders and at the same time providing reliable and accurate results for a range of scenarios and interventions is a challenge. Seven requirements that are essential for a user-friendly flood risk tool were used to develop an instant flood risk modeling tool. This paper presents a web-based hydraulic tool, i.e., instant flood risk model (Inform), to support FRM in the urban center of Can Tho city (Ninh Kieu district), Mekong Delta, Vietnam. Inform was developed based on (i) a simplified 1D model for the entire Mekong Delta; and (ii) flood hazard and damage maps, and estimated flood damage for Ninh Kieu district in Can Tho city obtained directly from the 1D/2D coupled model for Ninh Kieu district. Inform rapidly generates flood levels, flood hazard and damage maps, estimated damages. Pilot testing with experts confirmed that Inform qualifies as a reliable co-design tool for developing FRM strategies as it features an inbuilt input library, comprises flexible options, easy to use, produces quick results and has a user-friendly interface. With the help of an interactive web-based tool such as Inform presented here, it is possible to co-design FRM strategies for Can Tho or any other city that is subject to flood risk.

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Urbanization is changing land use–land cover (LULC) transforming green spaces (GS) and bodies of water into built-up areas. LULC change is affecting ecosystem services (ES) in urban areas, such as by decreasing of the water retention capacity, the urban temperature regulation capacity and the carbon sequestration. The relation between LULC change and ES is still poorly examined and quantified using actual field data. In most ES studies, GS is perceived as lumped areas instead of distributed areas, implicitly ignoring landscape patterns (LP), such as connectivity and aggrega-tion. This preliminary study is one of the first to provide quantitative evidence of the influence of landscape pattern changes on a selection of urban ecosystem services in a megacity as Jakarta, In-donesia. The impact of urbanization on the spatiotemporal changes of ES has been identified by considering connectivity and aggregation of GS. It reveals that LP changes have significantly de-creased carbon sequestration, temperature regulation, and runoff regulation by 10.4, 12.4, and 11.5%, respectively. This indicates that the impact of GS on ES is not only determined by its area, but also by its LP. Further detailed studies will be needed to validate these results.

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Infrastructure Asset Management (IAM) is the process by which decisions are made and resources allocated to ensure organisational or societal assets continue to deliver, as required. IAM is an evolving field. We discuss this evolution and present our perspectives on the future direction of IAM. IAM was born as a response to the poor state of maintenance of infrastructure, largely due to lack of resources, and emphasizes the need to prioritize maintenance and renewal using risk-based approaches. The demands on IAM have also continued to evolve as asset systems have become more complex, with multifunctionality, adaptative capacity and nature-based infrastructure, all issues that IAM must now consider. These challenges underpin the changing context of Water Infrastructure Asset Management (WIAM) and the opportunity for WIAM to harness new technical developments from other IAM domains. WIAM will need to continue to evolve, responding to these challenges and take advantage of these opportunities through research and application in collaboration with a relevant education and capacity development agenda.

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This paper investigates the contamination of floodwaters in the urban center of Can Tho city, Vietnam. We sampled water from sewers, surface water bodies, and flood, before, during, and after specific flooding events. Total nucleic acid was extracted from the samples and subjected to a quantitative polymerase chain reaction (qPCR) to detect specific enteric pathogens. The difference between pathogen concentrations in floodwater and sewer water was compared by using the Mann Whitney U test. Correlations between the different pathogens were determined using the non-parametric Spearman test. E. coli and Rotavirus-A were the most prevalent pathogens in floodwater. We observed a weak association between E. coli and Rotavirus in flood-related waters (r < 0.5). Floodwater quality showed no difference to sewer water quality in terms of the E. coli and Rotavirus A concentrations (p > 0.05). Our results indicate that floodwater poses a significant urban public health risk due to the presence of enteric pathogens.@en

Urbanization continues to trigger massive land-use land-cover change that transforms natural green environments to impermeable paved surfaces. Fast-growing cities in Asia experience increased urban temperature indicating the development of urban heat islands (UHIs) because of decreased urban green space, particularly in recent decades. This paper investigates the existence of UHIs and the impact of green areas to mitigate the impacts of UHIs in Colombo, Sri Lanka, using UrbClim, a boundary climate model that runs two classes of simulations, namely urbanization impact simulations, and greening simulations. The urbanization impact simulation results show that UHIs spread spatially with the reduction of vegetation cover, and increases the average UHI intensity. The greening simulations show that increasing green space up to 30% in urban areas can decrease the average air temperature by 0.1 °C. On the other hand, converting entire green areas into urban areas in suburban areas increases the average temperature from 27.75 °C to 27.78 °C in Colombo. This demonstrates the sensitivity of UHI to vegetation cover in both urban and suburban areas. These seemingly small changes are average grid values and may indicate much higher impacts at sub-grid levels.

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Probabilistic flood forecasting requires flood models that are simple and fast. Many of the modelling applications in the literature tend to be complex and slow, making them unsuitable for probabilistic applications which require thousands of individual simulations. This article focusses on the development of such a modelling approach to support probabilistic assessment of flood hazards, while accounting for forcing and system uncertainty. Here, we demonstrate the feasibility of using the open-source SWMM (Storm Water Management Model), focussing on Can Tho city, Mekong Delta, Vietnam. SWMM is a dynamic rainfall-runoffsimulation model which is generally used for single event or long-term (continuous) simulation of runoffquantity and quality and its application for probabilistic riverflow modelling is atypical. In this study, a detailed SWMM model of the entire Mekong Delta was built based on an existing ISIS model containing 575 nodes and 592 links of the same study area. The detailed SWMM model was then systematically reduced by strategically removing nodes and links to eventually arrive at a level of detail that provides sufficiently accurate predictions of water levels for Can Tho for the purpose of simulating urban flooding, which is the target diagnostic of this study. After a comprehensive assessment (based on trials with the varying levels of complexity), a much reduced SWMM model comprising 37 nodes and 40 links was determined to be able to provide a sufficiently accurate result while being fast enough to support probabilistic future flood forecasting and, further, to support flood risk reduction management.@en

In the context of climate adaptation planning there are relationships between adaptation drivers and adaptation measures, which makes the selection and implementation of the adaptation measures a challenging task. This challenge may be addressed by: structuring the adaptation problem using a multiple perspective adaptation framework; and applying a context specific precedence grammar logic for selecting and evaluating adaptation measures. Precedence grammar logic is a set of rule based algorithms (grammar) that are based on the relationships in a local adaptation context. This paper demonstrates the application of a context specific precedence grammar logic in an adaptation context in Can Tho, Vietnam. Adaptation pathways comprising flood adaptation measures (i.e. dike heightening) for this case were generated using rule based algorithms based on the relationships between the drivers and the adaptation measures. The results show that complex adaptation issues that are structured, can be resolved using a context specific adaptation grammar approach.@en