Urban stormwater is sometimes a risk, sometimes a resource. To address both aspects, the Three Points Approach (3PA) is advocated. This research applied the 3PA to map stormwater approaches in two Chinese and four European cities. While all cities have targets for the Technical Design Domain, none have targets for all domains; and thereby lack interventions to maximize benefits of frequent small events in the Day-to-Day Domain or minimize flood risks of rare large events in the Extreme Domain. Using open global precipitation data cities’ stormwater targets were expressed as more comparable rain depths, demonstrating that event depths within the Day-to-Day Domain in Chinese cities, cover much of the same range as those within the Technical Design Domain in the European cities. Expressing targets as depths in the context of the 3PA framework may facilitate transdisciplinary and transnational knowledge sharing, paving the way for management strategies covering all three domains.

The rapid growth of impervious areas in urban basins worldwide has increased the number of impermeable surfaces in cities, leading to severe flooding and significant economic losses for civilians. This trend highlights the urgent need for methodologies that assess flood hazards and specifically address the direct impact on pedestrians, which is often overlooked in traditional flood hazard analyses. This study aims to evaluate a methodology for assessing the risk to pedestrians from hydrodynamic forces during urban floods, with a specific focus on Cúcuta, Colombia. The methodology couples research outcomes from other studies on the impact of floodwaters on individuals of different ages and sizes with 1D/2D hydrological modeling. Advanced computational algorithms for image recognition were used to measure water levels at 5-s intervals on November 6, 2020, using drones for digital elevation model data collection. In Cúcuta, where flood risk is high and drainage infrastructure is limited, the PCSWMM (Computer-based Urban Stormwater Management Model) was calibrated and validated to simulate extreme flood events. The model incorporated urban infrastructure details and geomorphological parameters of Cúcuta's urban basin. Four return periods (5, 10, 50, 100), with extreme rainfall of 3 h, were used to estimate the variability of the risk map. The output of the model was analyzed, and an integrated and time-varying comparison of the results was done. Results show that the regions of high-water depth and high velocity could vary significantly along the duration of the different extreme events. Also, from 5 to 100 years return period, the percentage of area at risk increased from 9.6% to 16.6%. The pedestrian sensitivity appears much higher than the increase in velocities or water depth individually. This study identified medium to high-risk locations, which are dynamic in time. We can conclude dynamics are spatiotemporal, and the added information layer of pedestrians brings vulnerability information that is also dynamic. Areas of immediate concern in Cúcuta can enhance pedestrian safety during flash flood events. The spatiotemporal variation of patterns requires further studies to map trajectories and sequences that machine learning models could capture.

In response to pressing global challenges like climate change, rapid population growth, and an urgent need for sustainable infrastructure, cities face an immediate and crucial necessity to transition swiftly toward an integrated approach to managing urban water resources. This shift is not merely an option but an imperative, driven by the rapidly evolving urban landscape. In addressing this imperative, a crucial decision support tool that has emerged as an asset in the domain of urban water planning and management is the Urban Water Use (UWU) tool. This tool offers an integrated approach for strategic planning, promoting urban water conservation and environmental health through the investigation of interventions in urban infrastructure under different scenarios. In this study, the latest version of this UWU tool was deployed in a case study conducted in Almirante Tamandaré, Brazil. The objective was to evaluate how an integrated decision-making approach concerning urban water systems influences the efficiency and effectiveness of interventions, ultimately contributing to achieve widespread adoption, accessibility, and relevance of urban water services. The refined UWU tool evaluates a spectrum of measures across diverse scenarios, incorporating various drivers, focusing on the stakeholders' visions for the locality. These visions are composed of sustainability indicators, specifying different sets of target values and importance weights for each indicator. The approach followed in this study demonstrates how the effectiveness indexes can vary based on stakeholders' perception. Measures under Water Sensitive Urban Design and Water Demand Management strategies were deployed to simulate the response of urban water systems under three distinct scenarios, embracing the complexities of social dynamics and of climate change. The findings of the study emphasize that realizing a desired vision through selected measures relies significantly on the adoption of an integrated approach within the decision-making process. The stakeholders' perception of how indicators should be weighted while defining the vision was found to significantly impact the effectiveness range of these measures.

With rapid urbanization, the types of land in China’s cities are continuously evolving, irreversibly impacting the habitat patches within urban areas. However, the development of park cities has reversed this trend to some extent, particularly in Chengdu, China. To investigate the influence of land use type changes on habitat quality in Chengdu Tianfu New District, the research team selected remote sensing imagery data from the Landsat satellite for three distinct periods: 2014, 2019, and 2024. By employing a comprehensive approach that includes land cover trajectory analysis, land transfer matrices, FRAG-STATS landscape pattern indices, and the habitat quality module within the In-VEST model, this study analyzes the spatial and temporal evolution of land use patterns and the dynamics of habitat quality categories. The findings reveal: (1) the coverage of trees and shrubs in the study area initially declined but later increased, primarily driven by anthropogenic construction activities. Specifically, the land use types in the built-up areas on the northern side of Tianfu New District underwent notable fluctuations, whereas those on the southern side, adjacent to the Longquan Mountain Range, remained relatively stable. (2) From 2014 to 2019, high-quality habitats were predominantly distributed in the southeast of Tianfu New District, characterized by a robust ecological foundation, high landscape integrity, and strong connectivity of ecological land. In contrast, the areas with the poorest habitat quality were situated in the northern built-up areas of Tianfu New District, exhibiting highly fragmented habitat patches, simple edge shapes, and low connectivity. However, between 2019 and 2024, the overall habitat quality within the study area improved, characterized by an increase in the number of high-quality habitats and continuous expansion of habitat areas. The research findings offer valuable insights into future urban planning, ecological restoration, and conservation efforts in Chengdu Tianfu New District, providing critical guidance for the implementation and strategic development of the park city policy.

This chapter assesses ACM’s potential as a pathway to address the flooding problem of Greater Jakarta, significantly exacerbated by land subsidence and climate change. It is based on a thought experiment by the authors to envision application of this approach to the problem and is not the result of empirical work. A background of Jakarta’s flooding is first provided and subsequently its framing as a ‘wicked problem’. Results of the thought experiment are then discussed, focusing on three questions: (i) Can ACM be applied, given Jakarta’s flooding governance structure? (ii) Will ACM’s social learning work for the flooding problem? And (iii) if ACM were applicable to Jakarta’s case, what operational indicators would apply? The chapter concludes with recommending a two-step ACM pathway: (1) adjusting the current flooding governance structure, for which leadership is needed with a long-term vision and the application of adaptive governance at the river basin scale; (2) shaping the enabling conditions for learning that stimulate creativity in and discovery of new problem framings and solutions outside the policy system. While the authors recognise the considerable challenges when applying ACM to the flooding of Greater Jakarta, the crisis stage it has reached necessitates adaptation approaches that can break the cycle of narrow, longstanding paradigms, policy beliefs, and maladaptive pathways..

Saturated hydraulic conductivity (K_s) of the filler layer in grassed swales are varying in the changing environment. In most of the hydrological models, K_s is assumed as constant or decrease with a clogging factor. However, the K_s measured on site cannot be the input of the hydrological model directly. Therefore, in this study, an Ensemble Kalman Filter (EnKF) based approach was carried out to estimate the K_s of the whole systems in two monitored grassed swales at Enschede and Utrecht, the Netherlands. The relationship between K_s and possible influencing factors (antecedent dry period, temperature, rainfall, rainfall duration, total rainfall and seasonal factors) were studied and a Multivariate nonlinear function was established to optimize the hydrological model. The results revealed that the EnKF method was satisfying in the K_s estimation, which showed a notable decrease after long-term operation, but revealed a recovery in summer and winter. After the addition of Multivariate nonlinear function of the K_s into hydrological model, 63.8% of the predicted results were optimized among the validation events, and compared with constant K_s. A sensitivity analysis revealed that the effect of each influencing factors on the K_s varies depending on the type of grassed swale. However, these findings require further investigation and data support.

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.

Flash Flood Guidance (FFG) is a rainfall threshold which initiates flooding in streams. It merely provides a binary output (yes or no) which has large uncertainties in forecasting. In this paper, we propose a new method by combining FFG with the Frequentist method to present the probability of flash flood occurrence based on historical rainfall events. We first calculated deviation from the log transform rainfall data leading to flash floods. Kernel Density Estimation (KDE) was used to describe the deviation. Normal Distribution Function (NDF) was chosen to fit the KDE output and to calculate probabilities of flooding as per the Frequentist FFG. In order to aid decision making, three probability thresholds (10, 20 and 60%) were used for defining four flood risk classes, namely very low, low, significant and high, and were colour coded respectively as green, yellow, orange and red. The proposed Frequentist FFG method was then applied to the Posina River basin in Italy. Comparison of forecasts from the conventional FFG (with probability 0 or 1) and Frequentist FFG for 94 6-hourly rainfall events, including 23 flood events, shows that the Frequentist FFG presented a probability of flooding varying from 0 to 100% and the corresponding risk class can be used to reduce false alarms while still reducing the disaster risk. The application of the developed approach to the Posina basin shows that decision making regarding flash forecasting is easier with the presented approach compared to the traditional FFG approach.