Low impact development techniques to mitigate the impacts of climate-change-induced urban floods: Current trends, issues and challenges

Modeling the effect of land use change to design a suitable low impact development (LID) system to control surface water pollutants

Growth in urbanization has led to increased impervious surfaces, exacerbating flood risks and water quality degradation. This study investigated the impact of land use change and Low-Impact Development (LID) systems on urban runoff quality and quantity in the second region of Tehran. Pioneering an innovative approach, the integration of the Land Change Modeler (LCM) with the Stormwater Management Model (SWMM) signifies a paradigm shift in urban water management. Combined with other hydrological models, this new approach provides a comprehensive method for assessing the future effectiveness of LID practices. The Event Mean Concentration Method (EMC) was used in this study to measure Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), Total Phosphorus (TP), and Zinc (Zn) in urban runoff from five land uses. Results pinpointed transportation land uses as the primary source of pollutants. Using LCM, the study forecasted a surge in urban runoff pollutants by 2030, particularly in the Northwest area of the region due to anticipated land use shifts towards commercial and residential land uses. Model results showed an 11 % increase in TSS over a decade, highlighting the importance of land use change in runoff quality. The study used three types of LIDs to reduce contaminants in dense urban areas. Assessing the impact of LID scenarios on runoff pollutants using SWMM revealed that the bio-retention cell had the best performance, reducing TSS by 20.92 %, and the vegetative swale had the worst performance, reducing TSS by 8.43 %. The study also concluded that combining LIDs would be more effective than using them separately. The results of this study suggest that LID systems can be an effective way to reduce urban runoff pollutants and improve water quality in the second region of Tehran. However, more research is needed to optimize the design and placement of LID systems in different urban areas.

Flood susceptibility mapping of Cheongju, South Korea based on the integration of environmental factors using various machine learning approaches

Floods are natural occurrences that pose serious risks to human life and the environment, including significant property and infrastructure damage and subsequent socioeconomic challenges. Recent floods in Cheongju County, South Korea have been linked to river overflow. In this study, we created flood susceptibility maps of Cheongju, South Korea using machine learning techniques including support vector regression (SVR), boosted tree (BOOST), and long short-term memory (LSTM) algorithms, based on environmental factors. Potentially influential variables were selected based on flood data gathered through field surveys; these included the slope, aspect, length-slope factor, wind exposition index, terrain wetness index, plan curvature, normalized difference water index, geology, soil drainage, soil depth, soil texture, land use type, and forest density. To improve the robustness of the flood susceptibility model, the most influential factors were identified using the frequency ratio method. Implementing machine learning techniques like SVR and BOOST produced encouraging outcomes, achieving the area under the curve (AUC) of 83.16% and 86.70% for training, and 81.65% and 86.43% for testing, respectively. While, the LSTM algorithm showed superior flood susceptibility mapping performance, with an AUC value of 87.01% for training and 86.91% for testing, demonstrating its robust performance and reliability in accurately assessing flood susceptibility. The results of this study enhance our understanding of flood susceptibility in South Korea and demonstrate the potential of the proposed approach for informing and guiding crucial regional policy decisions, contributing to a more resilient and prepared future.

'Climate Healing Stones': Common Minerals Offer Substantial Climate Change Mitigation Potential

This review proposes that mineral-based greenhouse gas (GHG) mitigation could be developed into a substantial climate change abatement tool. This proposal was evaluated via three objectives: (1) synthesise literature studies documenting the effectiveness of geological minerals at mitigating GHG emissions; (2) quantify, via meta-analysis, GHG magnitudes that could be abated by minerals factoring-in the carbon footprint of the approach; and (3) estimate the global availability of relevant minerals. Several minerals have been effectively harnessed across multiple sectors-including agriculture, waste management and coal mining-to mitigate carbon dioxide/CO2 (e.g., olivine), methane/CH4 (e.g., allophane, gypsum) and nitrous oxide/N2O (e.g., vermiculite) emissions. High surface area minerals offer substantial promise to protect soil carbon, albeit their potential impact here is difficult to quantify. Although mineral-based N2O reduction strategies can achieve gross emission reduction, their application generates a net carbon emission due to prohibitively large mineral quantities needed. By contrast, mineral-based technologies could abate ~9% and 11% of global CO2 and CH4 anthropogenic emissions, respectively. These estimates conservatively only consider options which offer additional benefits to climate change mitigation (e.g., nutrient supply to agricultural landscapes, and safety controls in landfill operations). This multi-benefit aspect is important due to the reluctance to invest in stand-alone GHG mitigation technologies. Minerals that exhibit high GHG mitigation potential are globally abundant. However, their application towards a dedicated global GHG mitigation initiative would entail significant escalation of their current production rates. A detailed cost-benefit analysis and environmental and social footprint assessment is needed to ascertain the strategy's scale-up potential.

Analysis of Primary and Secondary Metabolites, Physical Properties, Antioxidant and Antidiabetic Activities, and Chemical Composition of Rosmarinus officinalis Essential Oils under Differential Water Stress Conditions

This study investigated the effects of varying water stress levels on Rosmarinus officinalis essential oils (EO). Three samples (S1, S2, and S3) were cultivated under different stress levels (40, 60, and 80%). Increased water stress led to changes in primary and secondary metabolites, EO contents, and physical properties. Antioxidant activity varied, with S2 exhibiting the highest IC50 value. In terms of antidiabetic activity, S2 showed robust α-amylase inhibition, while S3 displayed a commendable influence. For α-galactosidase inhibition, S3 had a moderate effect, and S2 stood out with increased efficacy. Gas chromatography-mass spectrometry analysis revealed stress-induced changes in major compounds. The study enhances the understanding of plant responses to water stress, with potential applications in antioxidant therapy and diabetes management. The findings emphasize the importance of sustainable water management for optimizing the EO quality in its various uses.

Modeling benefits and tradeoffs of green infrastructure: Evaluating and extending parsimonious models for neighborhood stormwater planning

Green infrastructure is often proposed to complement conventional urban stormwater management systems that are stressed by extreme storms and expanding impervious surfaces. Established hydrological and hydraulic models inform stormwater engineering but are time- and data-intensive or aspatial, rendering them inadequate for rapid exploration of solutions. Simple spreadsheet models support quick site plan assessments but cannot adequately represent spatial interactions beyond a site. The present study builds on the Landscape Green Infrastructure Design (L-GrID) Model, a process-based spatial model that enables rapid development and exploration of green infrastructure scenarios to mitigate neighborhood flooding. We first explored how well L-GrID could replicate flooding reports in a neighborhood in Chicago, Illinois, USA, to evaluate its potential for green infrastructure planning. Although not meant for prediction, L-GrID was able to replicate the flooding reported and helped identify strategies for flood control. Once evaluated for this neighborhood, we extended the model to include water quality through the representation of dispersion and settling mechanisms for two pollutant surrogates-total nitrogen and total suspended solids. With the extended model, Landscape Green Infrastructure Design Model-Water Quality (L-GrID-WQ), we examined benefits, costs, and tradeoffs for different green infrastructure strategies. Bioswales were slightly more effective than other green infrastructure types in reducing flooding extent and downstream runoff and pollution, through increased infiltration and settling capacity. Permeable pavers followed in effectiveness and are suggested where spatial constraints may limit the installation of bioswales. Although green infrastructure supports both flooding and pollution control, small tradeoffs between these functions emerged across spatial layouts: strategies based on only curb-cuts better controlled pollution, while layouts that followed the path of water flow better controlled flooding. By illuminating such tradeoffs, L-GrID-WQ can support green infrastructure planning that prioritizes unique concerns in different areas of a landscape.

Informing Urban Flood Risk Adaptation by Integrating Human Mobility Big Data During Heavy Precipitation

Understanding the impact of heavy precipitation on human mobility is critical for finer-scale urban flood risk assessment and achieving sustainable development goals #11 to build resilient and safe cities. Using ∼2.6 million mobile phone signal data collected during the summer of 2018 in Jiangsu, China, this study proposes a novel framework to assess human mobility changes during rainfall events at a high spatial granularity (500 m grid cell). The fine-scale mobility map identifies spatial hotspots with abnormal clustering or reduced human activities. When aggregating to the prefecture-city level, results show that human mobility changes range between -3.6 and 8.9%, revealing varied intracity movement across cities. Piecewise structural equation modeling analysis further suggests that city size, transport system, and crowding level directly affect mobility responses, whereas economic conditions influence mobility through multiple indirect pathways. When overlaying a historical urban flood map, we find such human mobility changes help 23 cities reduce 2.6% flood risks covering 0.45 million people but increase a mean of 1.64% flood risks in 12 cities covering 0.21 million people. The findings help deepen our understanding of the mobility pattern of urban dwellers after heavy precipitation events and foster urban adaptation by supporting more efficient small-scale hazard 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.

Modeling the impact of future rainfall changes on the effectiveness of urban stormwater control measures

The convergence of urban expansion, deteriorating infrastructure, and a changing climate will escalate the risks of stormwater pollution and urban flooding in the coming decades. Using outputs from an ensemble of global climate models to drive a high spatial resolution stormwater model, we analyzed climate change impacts on urban stormwater runoff and control measures for 23 cities across the United States. Runoff model outputs for two future emissions scenarios ending in 2055 were compared against a historical scenario to assess changes. All cities showed increases in average annual stormwater runoff, with changes up to 30% over the next 30 years due to a greater frequency of high intensity storm events. Runoff model outputs showed substantial variation across cities with untreated stormwater runoff increasing by as much as 48%. Patterns of future runoff impacts within cities will affect the performance of distributed treatment strategies such as Green Stormwater Infrastructure (GSI) to meet municipal water quality improvement and runoff reduction goals. Results indicate that adoption of adaptable design standards and decision support tools that readily accommodate projected precipitation changes are critical for supporting more resilient designs of stormwater control measures.

Influence of landscape patterns on nitrate and particulate organic nitrogen inputs to urban stormwater runoff

This study investigated the effect of the landscape pattern of permeable/impermeable patches on NO3--N and particulate organic nitrogen (PON) concentrations during stormwater runoff transport and their source contributions. Six landscape pattern indices, namely, mean proximity index (MPI), largest patch index (LPI), mean shape index (MSI), landscape shape index (LSI), connect index (CONNECT), and splitting index (SPLIT), were selected to reflect the fragmentation, complexity, and connectivity of permeable patches in urban catchments. The results show that lower fragmentation, higher complexity, and greater connectivity can reduce NO3--N concentrations in road runoff and drainage flow (i.e., the flow in the stormwater drainage network), as well as PON concentrations in road runoff. Further, the above landscape pattern is effective for mitigating the contributions of NO3--N and PON from road runoff. Low impact development (LID) can be incorporated with the landscape pattern of permeable/impermeable patches to mitigate nitrogen pollution in urban stormwater at the catchment scale by optimizing the spatial arrangement.

Evaluating the response and adaptation of urban stormwater systems to changed rainfall with the CMIP6 projections

Climate change is altering urban rainfall characteristics, leading to extreme urban stormwater and, particularly, more frequent flooding. Due to the uncertainty of climate change, the responses of urban drainage systems to climate change are becoming more complicated. This complexity makes it difficult for decision makers to assess whether urban infrastructure is sufficiently resilient to cope with flood risks. In this study, the Xiao Zhai area, a high-density urban area of China, was used as an example. A quantitative method for assessing these risks and the resilience of urban drainage systems to future urban stormwater was developed. First, based on the Coupled Model Intercomparison Project Phase 6 (CMIP6), the variation and uncertainty of future rainfall in the study area were analysed. A high-fidelity hydro-hydraulic model was developed to analyse the influence of climate change on future urban stormwater. Finally, the relationship between urban flood risk and the resilience of urban drainage systems was evaluated. The results show that the temporal distribution of future rainfall from 2023 to 2100 is relatively uniform. However, the number of heavy rainfall events increases significantly during this period. The flood risk caused by future rainfall was one level higher than the historical flood risk. For example, the flood risk caused by future 5a rainfall is equal to the flood risk from historical 10a rainfall. The correlations between the spatial distributions of flood risk and resilience are 0.49-0.63. Urban drainage systems urgently need to be improved and refined in areas with flood risk and low resilience to become more resilient to climate change. Rational planning of grey-green rainwater facilities in flood risk and low resilience areas can improve the rainwater system's resilience to 0.67-0.95 for climate change.

Assessing runoff control of low impact development in Hong Kong's dense community with reliable SWMM setup and calibration

Low impact development (LID) is a sustainable practice to managing urban runoff. However, its effectiveness in densely populated areas with intense rainfall, such as Hong Kong, remains unclear due to limited studies with similar climate conditions and urban patterns. The highly mixed land use and complicated drainage network present challenges for preparing a Storm Water Management Model (SWMM). This study proposed a reliable framework for setting up and calibrating SWMM by integrating multiple automated tools to address these issues. With a validated SWMM, we examined LID's effects on runoff control in a densely built catchment of Hong Kong. A designed full-scale LID implementation can reduce total and peak runoffs by around 35-45% for 2, 10 and 50-year return rainfalls. However, LID alone may not be adequate to handle the runoff in densely built areas of Hong Kong. As the rainfall return period increases, total runoff reduction increases, but peak runoff reduction remains close. Percentages of reduction in total and peak runoffs decline. The marginal control diminishes for total runoff while remaining constant for peak runoff when increasing the extent of LID implementation. In addition, the study identifies the crucial design parameters of LID facilities using global sensitivity analysis. Overall, our study contributes to accelerating the reliable application of SWMM and deepening the understanding of the effectiveness of LID in ensuring water security in densely built urban communities located near the humid-tropical climate zone, such as Hong Kong.

Probabilistic assessment of failure of infiltration structures under model and parametric uncertainty

We focus on the quantification of the probability of failure (PF) of an infiltration structure, of the kind that is typically employed for the implementation of low impact development strategies in urban settings. Our approach embeds various sources of uncertainty. These include (a) the mathematical models rendering key hydrological traits of the system and the ensuing model parametrization as well as (b) design variables related to the drainage structure. As such, we leverage on a rigorous multi-model Global Sensitivity Analysis framework. We consider a collection of commonly used alternative models to represent our knowledge about the conceptualization of the system functioning. Each model is characterized by a set of uncertain parameters. As an original aspect, the sensitivity metrics we consider are related to a single- and a multi-model context. The former provides information about the relative importance that model parameters conditional to the choice of a given model can have on PF. The latter yields the importance that the selection of a given model has on PF and enables one to consider at the same time all of the alternative models analyzed. We demonstrate our approach through an exemplary application focused on the preliminary design phase of infiltration structures serving a region in the northern part of Italy. Results stemming from a multi-model context suggest that the contribution arising from the adoption of a given model is key to the quantification of the degree of importance associated with each uncertain parameter.

Can atmospheric reanalysis datasets reproduce flood inundation at regional scales? A systematic analysis with ERA5 over Mahanadi River Basin, India

The prime challenges limiting efficient flood management, especially over large regions, are concurrently related to limited hydro-meteorological observations and exorbitant economics with computational modeling. Reanalysis datasets are a valuable alternative, as they furnish relevant variables at high spatiotemporal resolutions. In recent times, ERA5 has gained significant recognition for its applications in hydrological modeling; however, its efficacy at the inundation scale needs to be understood. The advent of "global flood models" has ensured flood inundation and hazard modeling over large regions, otherwise obscure with regional models. For the first time, the present study explores the fidelity of ERA5 reanalysis at the inundation scale over the Mahanadi River basin, a severely flood-prone region in India. The biases in the discharges within ERA5 are ascertained by comparing them with station-level data at the nascent and extreme levels (i.e., 95th and 99th percentiles). Later, ERA5 is fed to LISFLOOD-FP, an acclaimed global flood model, to reenact the 2006, 2008, 2011, and 2014 flood events. Hit rates exceeding 0.8 compared to MODIS satellite imageries affirm the suitability of ERA5 in accurately capturing flood inundation. Distributed design discharges for 50 yr and 100 yr are derived using a set of extreme value distributions and fed to LISFLOOD-FP to derive design flood inundation and hazards in terms of both "depth" and "product of depth and velocity" of flood waters. Results derived from the study provide vital lessons for efficient land-use planning and adaptation strategies linked to flood protection and resilience.

Developing nitrogen removal models for stormwater bioretention systems

Bioretention systems have the potential of simultaneous runoff volume reduction and nitrogen removal. Internal water storage (IWS) layers and real-time control (RTC) strategies may further improve performance of bioretention systems. However, optimizing the design of these systems is limited by the lack of effective models to simulate nitrogen transformations under the influences of IWS design and environment conditions including soil moisture and temperature. In this study, nitrogen removal models (NRMs) are developed with two complexity levels of nitrogen cycling: the Single Nitrogen Pool (SP) models and the more complex 3 Nitrogen Pool (3P) models. The 0-order kinetics, 1st order kinetics, and the Michaelis-Menten equations are applied to both SP and 3P models, creating six different NRMs. The Storm Water Management Model (SWMM), in combination with each NRM, is calibrated and validated with a lab dataset. Results show that 0-order kinetics are not suitable in simulating nitrogen removal or transformations in bioretention systems, while 1st order kinetics and Michaelis-Menten equation models have similar performances. The best performing NRM (referred to as 3P-m) can accurately predict nitrogen event mean concentrations in bioretention effluent for 20% more events when compared to SWMM. When only calibrated with soil moisture conditions in bioretention systems without internal storage layers, 3P-m was sufficiently adaptable to predict cumulative nitrogen mass removal rates from systems with IWS or RTC rules with less than ±7% absolute error, while the absolute error from SWMM prediction can reach -23%. In general, 3P models provide higher prediction accuracy and improved time series of biochemical reaction rates, while SP models improve prediction accuracy with less required user input for initial conditions.

Climate change impacts on wastewater infrastructure: A systematic review and typological adaptation strategy

Wastewater infrastructures play an indispensable role in society's functioning, human production activities, and sanitation safety. However, climate change has posed a serious threat to wastewater infrastructures. To date, a comprehensive summary with rigorous evidence evaluation for the impact of climate change on wastewater infrastructure is lacking. We conducted a systematic review for scientific literature, grey literature, and news. In total, 61,649 documents were retrieved, and 96 of them were deemed relevant and subjected to detailed analysis. We developed a typological adaptation strategy for city-level decision-making for cities in all-income contexts to cope with climate change for wastewater structures. 84% and 60% of present studies focused on the higher-income countries and sewer systems, respectively. Overflow, breakage, and corrosion were the primary challenge for sewer systems, while inundation and fluctuation of treatment performance were the major issues for wastewater treatment plants. In order to adapt to the climate change impact, typological adaptation strategy was developed to provide a simple guideline to rapidly select the adaptation measures for vulnerable wastewater facilities for cities with various income levels. Future studies are encouraged to focus more on the model-related improvement/prediction, the impact of climate change on other wastewater facilities besides sewers, and countries with low or lower-middle incomes. This review provided insight to comprehensively understand the climate change impact on wastewater facilities and facilitate the policymaking in coping with climate change.

Enhancing stormwater management with low impact development (LID): a review of the rain barrel, bioretention, and permeable pavement applicability in Indonesia

Low impact development (LID) is a sustainable land use and planning strategy that aims to minimize the environmental impacts of development. A community can enhance their water resources and create sustainable and resilient neighbourhoods. This approach has demonstrated success in managing stormwater and promoting water reuse globally, however, its suitability in developing countries like Indonesia remains uncertain and requires further investigation. The implementation of LID in developing countries may face several challenges including high density and complex drainage networks, combined sewer usage, clay soil type, irregular housing layouts, community socio-economic characteristics, affordability, cost, and the availability of regulations and policies. With proper planning and site-specific strategies, LID can be implemented effectively in Indonesia. Clear regulations, secured funding source and community-based LID are all essential for successful LID deployment. This paper can be used as a starting point for considering LID implementation in Indonesia and other countries with similar characteristics.

Urban stormwater management at the meso-level: A review of trends, challenges and approaches

Cities worldwide are facing a significant threat of stormwater hazards caused by the increase in extreme downpours and urbanization. Meso-level urban stormwater management focuses on alleviating the detrimental impacts of urban flooding and enhancing water resource utilization at the block or community scale, typically through 1) specific policies and management rules; 2) catchment-scale scenario simulation, optimization and evaluation; 3) the group of stormwater control measures implementation. It may effectively coordinate macro-level urban stormwater management planning and micro-level distributed stormwater control facilities. This study conducts a review of Urban Stormwater Management at Meso-level (USM-M) with a view to research publication trends, citation analysis, geographic spread and subject category, as well as content analysis, including temporal progression and research gaps. The Web of Science database and CiteSpace are used for the bibliometric analysis of 66 articles from 2006 to 2021. The results show that the number of USM-M topic articles generally has an upward trend over the years. Whilst the United States and China are leading research on this topic, the European countries have diverse local research and dense cooperation. Research foci have generally shifted from theoretical frameworks to multi-element subdivided topics and specific technical roadmaps. Moreover, the spatial layout optimization and multi-functional integration are, or will be, potential research directions in terms of enhancing stormwater utilization and co-benefits of USM-M. This systematic review concludes trends, challenges and potential approaches of USM-M, and aims to provide recommendations for researchers and policymakers on the development of a more advanced and comprehensive USM-M.

A review of compaction effect on subsurface processes in soil: Implications on stormwater treatment in roadside compacted soil

Sustainable cities require spacious infrastructures such as roadways to serve multiple functions, including transportation and water treatment. This can be achieved by installing stormwater control measures (SCM) such as biofilters and swales on the roadside compacted soil, but compacted soil limits infiltration and other functions of SCM. Understanding the effect of compaction on subsurface processes could help design SCM that could alleviate the negative impacts of compaction. Therefore, we synthesize reported data on compaction effects on subsurface processes, including infiltration rate, plant health, root microbiome, and biochemical processes. The results show that compaction could reduce runoff infiltration rate, but adding sand to roadside soil could alleviate the negative impact of compaction. Compaction could decrease the oxygen diffusion rate in the root zone, thereby affecting plant root activities, vegetation establishment, and microbial functions in SCM. The impacts of compaction on carbon mineralization rate and root biomass vary widely based on soil type, aeration status, plant species, and inherent soil compaction level. As these processes are critical in maintaining the long-term functions of SCM, the analysis would help develop strategies to alleviate the negative impacts of compaction and turn road infrastructure into a water solution in sustainable cities.

Cost-effectiveness analysis of extensive green roofs for urban stormwater control in response to future climate change scenarios

Green roof, as a popular low impact development practice, has become important to mitigate adverse impacts of future climate change on urban stormwater. However, there is limited information regarding assessment of the effectiveness of green roofs in response to uncertain future climate change challenges. In this study, the validated model was used to simulate the reduction performance of green roofs on urban catchment outflow and assess their cost-effectiveness in response to design storms under climate change scenarios. Results showed that the median runoff volume of urban catchments increased by 12.5 %-14.6 % and 15.5 %-18.1 % and the median peak flow rate increased by 14.4 %-17.8 % and 17.9 %-22.1 % under SSP2-4.5 and SSP5-8.5 scenarios, respectively. This indicated the variability of runoff volume and peak flow changes for short return storm events caused by climate change was relatively high. Green roof implementation had reasonable mitigation effects on runoff volume and peak flow amplification in urban catchments caused by climate change. The median runoff volume reduction of green roofs for the 1-year storm was 15.2 % under SSP2-4.5 scenario. As rainfall intensity increased, the median runoff volume reduction of green roofs significantly declined to 5.6 % for the 100-year storm. However, the variations of runoff volume and peak flow reduction of green roofs were relatively smaller for longer return periods under climate change scenarios. Runoff reduction percentages of green roofs increased linearly with their implementation cost. The average value of the cost-effectiveness (C/E) index for green roofs was 91.2 %/million $ under base climate condition, and it decreased to 88.9 %/million $ and 88.4 %/million $ for SSP2-4.5 and SSP5-8.5 scenarios, respectively. The C/E values decreased with increasing storm return period, and the values were relatively lower in SSP5-8.5 scenarios. These results could help to understand the potential role of green roofs to mitigate the impacts of future climate change.

Quantitative calculation of stormwater regulation capacity and collaborative configuration of sponge facilities in urban high-density built-up areas

Implemented in the frequent extreme rainstorms, aggravation of non-point source pollution, and complex drainage system of urban built-up area, sponge facility practices often intertwine with a very large number of hydro-environmental and socio-economic considerations and constraints. Due to the lack of basic and measured data, fragmented engineering design, more systematic and strategic approaches to address this multi-scale, and multi-parameter problem of practice allocation should be planned and optimized. In this study, a practical quantitative calculation method of stormwater regulation capacity in urban built-up areas is proposed. The details are as follows: the sub-catchment areas are divided by using the drainage pipe section editing function of Auto CAD and the regional analysis function of ArcGIS, and the stormwater regulation capacity in study area to be reconstructed is divided into four grades to define the water-sensitive areas according to the comprehensive runoff coefficient (α), which were excellent (α < 0.6), good (0.6 ≤ α < 0.7), medium (0.7 ≤ α < 0.8), and poor (α ≥ 0.8). The stormwater regulation capacity of green spaces is determined by empirical model calculation and soil moisture hydrograph field measurement. The measured soil saturated hydraulic conductivity in the study area was about 110 mm/h. The drainage capacity of the pipe networks was determined by the length of the overloaded pipe section. Under the conditions of 10a and 50a rainfall return period and 3-h rainfall duration simulated by SWMM, the length of the overloaded pipe section accounts for 33.0% and 40.8% of the total, respectively. Based on the identification of water-sensitive areas in urban high-density built-up areas, the quantitative calculation method of stormwater runoff regulation capacity was constructed through the calculation of the coupling coordination degree of "source-midway-terminal" infrastructures and the layout of storage tanks in the overloaded pipe section. These can estimate the current situation of stormwater regulation capacity of different sizes in urban built-up areas and formulate the optimized planning scheme of sponge reconstruction projects.

Impact of climate change induced future rainfall variation on dynamics of arid-humid zone transition in the western province of India

The transition of the Earth's climate from one zone to another is one of the major causes behind biodiversity loss, rural-urban migration, and increasing food crises. The rising rate of arid-humid zone transition due to climate change has been substantially visible in the last few decades. However, the precise quantification of the climate change-induced rainfall variation on the climate zone transition still remained a challenge. To solve the issue, the Representative Grid Location-Multivariate Adaptive Regression Spline (RGL-MARS) downscaling algorithm was coupled with the Koppen climate classification scheme to project future changes in various climate zones for the study area. It was observed that the performance of the model was better for the humid clusters compared to the arid clusters. It was noticed that, by the end of the 21^st century, the arid region would increase marginally and the humid region would rise by 24.28-36.09% for the western province of India. In contrast, the area of the semi-arid and semi-humid regions would decline for the study area. It was observed that there would be an extensive conversion of semi-humid to humid zone in the peripheral region of the Arabian sea due to the strengthening of land-sea thermal contrast caused by climate change. Similarly, semi-arid to arid zone conversion would also increase due to the inflow of dry air from the Arabian region. The current research would be helpful for the researchers and policymakers to take appropriate measures to reduce the rate of climate zone transition, thereby developing the socioeconomic status of the rural and urban populations.

Battle of centralized and decentralized urban stormwater networks: From redundancy perspective

Recent research underpinned the effectiveness of topological decentralization for urban stormwater networks (USNs) during the planning stage in terms of both capital savings and resilience enhancement. However, how centralized and decentralized USNs' structures with various degrees of redundancy (i.e., redundant water flow pathways) project resilience under functional and structural failure remains an unresolved issue. In this work, we present a systemic and generic framework to investigate the impact of adding redundant flow paths on resilience based on three strategies for optimal centralized versus decentralized USNs. Furthermore, a tailored graph-theory based measure (i.e., eigenvector centrality) is proposed to introduce redundant paths to the critical locations of USNs. The proposed framework is then applied to a real large-scale case study. The results confirm the critical role of layout decentralization under both functional (e.g., extreme precipitation events), and structural failure (e.g., pipe collapse). Moreover, the findings indicate that the implementation of redundant paths could increase resilience performance by up to 8% under functional failure without changing the network's major structural characteristics (i.e., sewer diameters, lengths, and storage capacity), only by leveraging the effective flow redistribution. The scheme proposed in this study can be a fruitful initiative for further improving the USNs' resilience during both planning and rehabilitation stages.

Inundation Resilience Analysis of Metro-Network from a Complex System Perspective Using the Grid Hydrodynamic Model and FBWM Approach: A Case Study of Wuhan

The upward trend of metro flooding disasters inevitably brings new challenges to urban underground flood management. It is essential to evaluate the resilience of metro systems so that efficient flood disaster plans for preparation, emergency response, and timely mitigation may be developed. Traditional response solutions merged multiple sources of data and knowledge to support decision-making. An obvious drawback is that original data sources for evaluations are often stationary, inaccurate, and subjective, owing to the complexity and uncertainty of the metro station’s actual physical environment. Meanwhile, the flood propagation path inside the whole metro station network was prone to be neglected. This paper presents a comprehensive approach to analyzing the resilience of metro networks to solve these problems. Firstly, we designed a simplified weighted and directed metro network module containing six characteristics by a topological approach while considering the slope direction between sites. Subsequently, to estimate the devastating effects and details of the flood hazard on the metro system, a 100-year rainfall–flood scenario simulation was conducted using high-precision DEM and a grid hydrodynamic model to identify the initially above-ground inundated stations (nodes). We developed a dynamic node breakdown algorithm to calculate the inundation sequence of the nodes in the weighted and directed network of the metro. Finally, we analyzed the resilience of the metro network in terms of toughness strength and organization recovery capacity, respectively. The fuzzy best–worst method (FBWM) was developed to obtain the weight of each assessment metric and determine the toughness strength of each node and the entire network. The results were as follows. (1) A simplified three-dimensional metro network based on a complex system perspective was established through a topological approach to explore the resilience of urban subways. (2) A grid hydrodynamic model was developed to accurately and efficiently identify the initially flooded nodes, and a dynamic breakdown algorithm realistically performed the flooding process of the subway network. (3) The node toughness strength was obtained automatically by a nonlinear FBWM method under the constraint of the minimum error to sustain the resilience assessment of the metro network. The research has considerable implications for managing underground flooding and enhancing the resilience of the metro network.

Wastewater System Inflow/Infiltration and Residential Pluvial Flood Damage Mitigation in Canada

Pluvial flooding in urban areas is one of the most significant drivers of disaster loss in Canada. Damages during pluvial flood events are associated with overwhelmed urban drainage (stormwater and wastewater) systems. During the period from 2013 to 2021, Canadian property and casualty insurers reported approximately CAD 2 billion in personal property (residential) pluvial sewer backup claims during flood catastrophes. There has been growing interest in managing pluvial urban flood risk, notably through newly funded national programs focused on climate change adaptation. These programs have included the development of new guidelines and standards focused on managing the underlying factors contributing to urban and basement flooding. Inflow and infiltration (I/I) has received limited attention in the pluvial flood literature, however. Informed by significant engagement with practitioners in Canada, this paper provides a review of the issue of I/I into wastewater systems and its relation to pluvial flooding. The paper will address concerns related to private property engagement in I/I and urban pluvial flood reduction programs. Both improved technical standards and administrative support are needed to ensure that wastewater infrastructure is less susceptible to I/I over its lifecycle.

Remote Sensing for Development of Rainfall Intensity–Duration–Frequency Curves at Ungauged Locations of Yangon, Myanmar

This study aims to develop the intensity–duration–frequency (IDF) curves for Yangon, the economic center of Myanmar, using four satellite precipitation datasets, namely GPM IMERG, TRMM, GSMaP_NRT, and GSMaP_GC. Different probability distribution functions were used to fit the annual rainfall maximum series to determine the best-fit distribution. The estimated parameters of the best-fit distribution were used to fit the rainfall intensities of 2, 5, 10, 25, 50, and 100-year return periods for generating IDF curves using the Sherman equation. The IDF curves were bias-corrected based on the daily rainfall data available only at a location in Yangon. The bias correction factors were then used to estimate IDF curves from satellite rainfall at ungauged locations of Yangon. The results showed that the Generalized Extreme Value Distribution best fit the hourly rainfall distribution of satellite data. Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) is the most suitable for constructing Yangon’s IDF curves. The bias-corrected IDF curve generated at four locations of greater Yangon indicates higher rainfall intensity at the coastal stations than the inland stations. The methodology presented in this study can be used to derive IDF curves for any location in Myanmar.

Exploring sustainable solutions for the water environment in Chinese and Southeast Asian cities

Water is essential for human activities and economic development, and the water environment significantly influences ecological balance and global climate. China and Southeast Asia are the most populous areas in the world, and their water resources are deteriorating day by day. We focus on five representative cities such as, Beijing, Jakarta, Hanoi, Kathmandu and Manila to investigate water-environmental problems with the ultimate goal of providing recommendations for sustainable urban water management. The study found that (1) the water environment of all cities has been polluted to varying levels, while the pollution has improved in Beijing and Jakarta, and the situation in other regions is severe. (2) The aquatic biodiversity has reduced, and its pollution is mainly caused by organic pollutants and decreasing river flow. In addition, numerous people live in megacities without access to clean surface water or piped drinking water, which greatly increases the use of groundwater. Further, frequent floods in the world leads to serious damage to urban infrastructure and further deterioration of water environment quality. To address these problems, countries and organizations have begun to construct wastewater treatment plants and develop water-saving technology to ensure healthy and sustainable development of water environment. The results and practical recommendations of this study can provide scientific insights for future research and management strategies to address water quality challenges during ongoing policy debates and decision-making processes.

After the Epidemic, Is the Smart Traffic Management System a Key Factor in Creating a Green Leisure and Tourism Environment in the Move towards Sustainable Urban Development?

The purpose of this study is to explore, after the epidemic, the intelligent traffic management system, which is the key to creating a green leisure tourism environment in the move towards sustainable urban development. First, quantitative research, snowballing, and convenience sampling methods are used to analyze 750 questionnaires with a basic statistical test, t-test, ANOVA test, and the Pearson product–moment correlation coefficient (PPMCC) method. Qualitative research and a semi-structured interview method are used to collect the opinions of six experts on the data results. Finally, the results are discussed with the multivariate inspection method. Although the current electric bicycle system is convenient, the study found that the service quality of the airport is sufficient; that the fare of the subway is low and popular with students if the system can ease the crowd during peak hours; and that the login and security check time can be shortened, which can help improve the operating convenience of the system interface and link the information of leisure and tourism activities. On the other hand, adjusting fares, increasing seats, planning for women-only ticketing measures and travel space, providing disinfection or cleaning facilities in public areas, and improving passenger’s public health literacy and epidemic prevention cooperation will further enhance the student travel experience, improve the smart city and green tourism network, and help achieve sustainable urban tourism.

New framework for assessing urban stormwater management measures in the context of climate change

Global climate change has necessitated the update of urban stormwater management measures (SMMs), but this task is extremely difficult due to the deficiency of evaluation caused by discreteness and the limitation of selected storm events, the ignorance of antecedent dry day (ADD) and lack of suitable stochastic storm generation method. In this study, a new framework that considers both stochastic precipitation and ADD is introduced to evaluate urban SMMs more adequately. Gamma distribution fitting, the space discarding method, the production of probability density distribution maps and multiple nonlinear regression were combined with a physical-based model to assess the effectiveness of SMMs under changing climates. Taking low impact development practices (LIDs) as an example of SSMs, the case study showed that the proposed framework provided effectiveness probability density distribution map and regression equations with more evaluation details by increasing the number and type of storm events compared with current monitoring. Moreover, it is demonstrated that ADD should be considered as one important factor in the design of LIDs, especially for controlling urban non-point source pollution. The value of ADD will significantly affect the control effect of LIDs on pollutant loads and event mean concentration in runoff, which varied for different pollutants. Through case study, it shows there is a risk that LIDs would be less effective at controlling runoff and non-point source pollution in future climate scenarios, especially for RCP 8.5 which is more extreme. Therefore, adaptation capacity of climate change should be considered in the design of SMMs. The proposed framework will be a useful tool in the assessment, design and planning of urban SMMs considering climate change.

Investigating the Performance of Green Roof for Effective Runoff Reduction Corresponding to Different Weather Patterns: A Case Study in Dublin, Ireland

This article aims to analyse the performance of green roof in runoff reduction. A case study has been conducted through a deployed green roof at the custom house quay building in Dublin, Ireland. Modular green roofs have been deployed which have IoT scales associated to it for measuring the effective reduction in runoff. Hydro-meteorological variables such as rainfall, temperature, relative humidity and wind speed values were corresponded to the amount of runoff reduction by means of a regression-based relationship. Comparison of the observed runoff reduction from a modular green roof and that estimated based on the developed regression relationship yielded a R2 value of 0.874. Through this research, a pattern was identified which established that longer records and better weather variables data have the potential to improve the performance of the regression model in predicting the amount of runoff reduction corresponding to different rainfall and weather patterns. In general, performance of green roof was found to be highly positively correlated to the amount of rainfall received; however, low correlation between rainfall and the percentage of runoff reduction indicate that saturated soil in green roofs considerably deteriorates the performance in runoff reduction. Overall, this study can help in identification of locations where installation of green roofs can help mitigate floods at a city scale.

Assessment of Water Resources Availability in Amu Darya River Basin Using GRACE Data

Water is diminishing in many places of the globe due to human intervention and climate variability. This study was conducted to assess water sustainability in the Amu Darya basin, the largest river catchment of central Asia, using two Gravity Recovery and Climate Experiment (GRACE) satellite solutions with a spatial resolution of 0.5°. Spatial variability of water sustainability was estimated by integrating reliability, resiliency and vulnerability. In addition, the Modified Mann–Kendall (MMK) test was utilized to detect the significant trends in water availability. Findings show a significant decline in the basin’s water supply, especially after 2010. Water availability was more variable in the east and a small area in the south. Trend analysis revealed higher declination in water availability in the range of −0.04 to −0.08 cm/year in the tundra and warm dry continental climate zones and the delta region of the basin ending in the Aral Sea in the cold desert climate zone. Water resources in the cold semi-arid (steppe) and most parts of the cold desert climate are more sustainable than the rest of the basin. Overall, the results indicate that water resources availability in a large-scale basin with climate diversity could be well assessed using the method used in this study.

Willingness to Pay for the Maintenance of Green Infrastructure in Six Chinese Pilot Sponge Cities

Due to the increasingly devastating impact of pluvial flooding on human beings’ lives and properties in cities, the use of green infrastructure to manage stormwater onsite is becoming more popular worldwide. The maintenance of green infrastructure to ensure its function has become one of the most pressing tasks facing policy makers. However, there is limited research regarding the willingness to pay the stormwater fee as a form of maintenance funding. This study utilized contingent valuation data obtained from a survey of 1101 respondents living in six pilot sponge cities in China to estimate the willingness to pay for the green infrastructure maintenance. The findings indicated that two-thirds of all respondents were willing to pay, 17% would like to pay around 6–10 RMB/month (0.95–1.59 US dollars/month), and 17.8% would like to pay more than 20 RMB/month (3.2 US dollars/month). The educational level and age of the respondents were significant determinants of the probability of willingness to pay and the amount they would like to pay. Knowledge of the concept of sponge cities was another significant influencing factor for the willingness to pay, but it did not influence the amount of payment. The findings could help policy makers make better strategies regarding the maintenance of green infrastructure and its costs.

Selection of the gridded temperature dataset for assessment of thermal bioclimatic environmental changes in Amu Darya River basin

Assessment of the thermal bioclimatic environmental changes is important to understand ongoing climate change implications on agriculture, ecology, and human health. This is particularly important for the climatologically diverse transboundary Amy Darya River basin, a major source of water and livelihood for millions in Central Asia. However, the absence of longer period observed temperature data is a major obstacle for such analysis. This study employed a novel approach by integrating compromise programming and multicriteria group decision-making methods to evaluate the efficiency of four global gridded temperature datasets based on observation data at 44 stations. The performance of the proposed method was evaluated by comparing the results obtained using symmetrical uncertainty, a machine learning similarity assessment method. The most reliable gridded data was used to assess the spatial distribution of global warming-induced unidirectional trends in thermal bioclimatic indicators (TBI) using a modified Mann-Kendall test. Ranking of the products revealed Climate Prediction Center (CPC) temperature as most efficient in reconstruction observed temperature, followed by TerraClimate and Climate Research Unit. The ranking of the product was consistent with that obtained using SU. Assessment of TBI trends using CPC data revealed an increase in the T_min in the coldest month over the whole basin at a rate of 0.03-0.08 °C per decade, except in the east. Besides, an increase in diurnal temperature range and isothermally increased in the east up to 0.2 °C and 0.6% per decade, respectively. The results revealed negative implications of thermal bioclimatic change on water, ecology, and public health in the eastern mountainous region and positive impacts on vegetation in the west and northwest.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00477-022-02172-8.

Research Progress of Urban Floods under Climate Change and Urbanization: A Scientometric Analysis

Urban floods research has been attracting extensive attention with the increasing threat of flood risk and environmental hazards due to global climate change and urbanization. However, there is rarely a comprehensive review of this field and it remains unclear how the research topics on urban floods have evolved. In this study, we analyzed the development of urban floods research and explored the hotspots and frontiers of this field by scientific knowledge mapping. In total, 3314 published articles from 2006 to 2021 were analyzed. The results suggest that the number of published articles in the field of urban floods generally has an upward trend year by year, and the research focus has shifted from exploring hydrological processes to adopting advanced management measures to solve urban flood problems. Moreover, urban stormwater management and low impact development in the context of climate change and urbanization have gradually become research hotspots. Future research directions based on the status and trends of the urban floods field were also discussed. This research can not only inspire other researchers and policymakers, but also demonstrates the effectiveness of scientific knowledge mapping analysis by the use of the software CiteSpace and VOSviewer.

Can Electric Bicycles Enhance Leisure and Tourism Activities and City Happiness?

Using Meizhou City as an example, the aim of this study was to verify whether e-bike sharing systems can improve the convenience of engaging in leisure and tourism activities in cities, and thus increase residents’ well-being. First, quantitative research was carried out to collect data on users’ opinions. Then, qualitative research was carried out to obtain the opinions of experts. Finally, the investigation was conducted using multivariate review analysis. The results of the research showed that e-bikes should be classified based on electric quantity, the rental fee should be charged based on mileage, and the relevant corporate information should be integrated. It is also necessary to develop a software interface with functions for users to rent e-bikes and search for information related to consumption, leisure, and travel activities, and make promotion plans. Finally, people need to be encouraged to cycle to obtain the benefits of leisure and exercise. In this manner, the convenience of participation in leisure and tourism activities can be increased, people’s physical and mental health will be substantially improved, and the happiness index of the people and the city will increase.

A process-driven and need-oriented framework for review of technological contributions to disaster management

An escalation in the frequency and intensity of natural disasters is observed over the last decade, forcing the community to develop innovative technological solutions to reduce disaster impact. The multidisciplinary nature of disaster management suggests the collaboration between different disciplines for an efficient outcome; however, any such collaborative framework is found lacking in the literature. A common taxonomy and interpretation of disaster management related constraints are critical to develop efficient technological solutions. This article proposes a process-driven and need-oriented framework to facilitate the review of technology based contributions in disaster management. The proposed framework aims to bring technological contributions and disaster management activities in a single frame to better classify and analyse the literature. A systematic review of benchmark disruptive technology based contributions to disaster management has been performed using the proposed framework. Furthermore, a set of basic requirements and constraints at each phase of a disaster management process have been proposed and cited literature has been analysed to highlight corresponding trends. Finally, the scope of computer vision in disaster management is explored and potential activities where computer vision can be used in the future are highlighted.

Information Disclosure, Coal Withdrawal and Carbon Emissions Reductions: A Policy Test Based on China’s Environmental Information Disclosure

How to better explore a diversity of emissions reduction paths has become the key to China achieving carbon peak and carbon neutralization goals as well as transforming the existing energy structure as soon as possible. Based on this, from the perspective of information flow, this study used the differences-in-differences method (DID) to identify the “net effect” of the carbon emissions reduction caused by China’s environmental information disclosure. The results showed the following: first, environmental information disclosure could effectively promote regional carbon emissions reductions and had a better effect on the central and western regions and low carbon emissions density regions. Second, the achievement of carbon emissions reduction targets was mainly attributed to the positive impact of information disclosure in the process of “coal withdrawal.” Finally, this study also found that environmental information disclosure helped to promote the positive effect of clean energy development on “coal withdrawal,” and the promotion of public awareness regarding environmental supervision helped to strengthen the external impact of environmental information disclosure on regional carbon emissions reduction.

Investigating the Role of Green Infrastructure on Urban WaterLogging: Evidence from Metropolitan Coastal Cities

Urban green infrastructures (UGI) can effectively reduce surface runoff, thereby alleviating the pressure of urban waterlogging. Due to the shortage of land resources in metropolitan areas, it is necessary to understand how to utilize the limited UGI area to maximize the waterlogging mitigation function. Less attention, however, has been paid to investigating the threshold level of waterlogging mitigation capacity. Additionally, various studies mainly focused on the individual effects of UGI factors on waterlogging but neglected the interactive effects between these factors. To overcome this limitation, two waterlogging high-risk coastal cities—Guangzhou and Shenzhen, are selected to examine the effectiveness and stability of UGI in alleviating urban waterlogging. The results indicate that the impact of green infrastructure on urban waterlogging largely depends on its area and biophysical parameter. Healthier or denser vegetation (superior ecological environment) can more effectively intercept and store rainwater runoff. This suggests that while increasing the area of UGI, more attention should be paid to the biophysical parameter of vegetation. Hence, the mitigation effect of green infrastructure would be improved from the “size” and “health”. The interaction of composition and spatial configuration greatly enhances their individual effects on waterlogging. This result underscores the importance of the interactive enhancement effect between UGI composition and spatial configuration. Therefore, it is particularly important to optimize the UGI composition and spatial pattern under limited land resource conditions. Lastly, the effect of green infrastructure on waterlogging presents a threshold phenomenon. The excessive area proportions of UGI within the watershed unit or an oversized UGI patch may lead to a waste of its mitigation effect. Therefore, the area proportion of UGI and its mitigation effect should be considered comprehensively when planning UGI. It is recommended to control the proportion of green infrastructure at the watershed scale (24.4% and 72.1% for Guangzhou and Shenzhen) as well as the area of green infrastructure patches (1.9 ha and 2.8 ha for Guangzhou and Shenzhen) within the threshold level to maximize its mitigation effect. Given the growing concerns of global warming and continued rapid urbanization, these findings provide practical urban waterlogging prevention strategies toward practical implementations.

A Cross-Sectional Study on the Flood Emergency Preparedness among Healthcare Providers in Saudi Arabia

This study used a descriptive cross-sectional methodology to measure healthcare workers’ knowledge, attitudes, perceptions, and willingness to respond to a flood scenario in Saudi Arabia. A validated survey was distributed to collect data using a convenience sampling technique through multiple social media platforms. A total of 227 participants were included in this study: 52% of them were aged between 26 to 34 years, 74% were residents from Riyadh, and 52.4% worked in nursing divisions. A significant number of respondents (73.2%) had positive perceptions towards their hospitals’ ability to provide an effective response to a flood, 89% were willing to report to work following a flood, and 90% of participants reported the need to develop both guidelines and training for flood disaster preparedness. Preparation and successful flood mitigation in the hospital setting requires staff that have both knowledge and training in emergency management. One way to obtain such readiness is through competency-based training, including both table-top and full-scale live exercises. Although the willingness to respond to such a flooding emergency was high among staff, the development of guidelines and educational programs is needed in order to develop the competencies and skills sets to improve disaster preparedness response and preparedness efforts.

Integrated Methodology for Urban Flood Risk Mapping at the Microscale in Ungauged Regions: A Case Study of Hurghada, Egypt

Flood risk mapping forms the basis for disaster risk management and the associated decision-making systems. The effectiveness of this process is highly dependent on the quality of the input data of both hazard and vulnerability maps and the method utilized. On the one hand, for higher-quality hazard maps, the use of 2D models is generally suggested. However, in ungauged regions, such usage becomes a difficult task, especially at the microscale. On the other hand, vulnerability mapping at the microscale suffers limitations as a result of the failure to consider vulnerability components, the low spatial resolution of the input data, and the omission of urban planning aspects that have crucial impacts on the resulting quality. This paper aims to enhance the quality of both hazard and vulnerability maps at the urban microscale in ungauged regions. The proposed methodology integrates remote sensing data and high-quality city strategic plans (CSPs) using geographic information systems (GISs), a 2D rainfall-runoff-inundation (RRI) simulation model, and multicriteria decision-making analysis (MCDA, i.e., the analytic hierarchy process (AHP)). This method was implemented in Hurghada, Egypt, which from 1996 to 2019 was prone to several urban flood events. Current and future physical, social, and economic vulnerability maps were produced based on seven indicators (land use, building height, building conditions, building materials, total population, population density, and land value). The total vulnerability maps were combined with the hazard maps based on the Kron equation for three different return periods (REPs) 50, 10, and 5 years to create the corresponding flood risk maps. In general, this integrated methodology proved to be an economical tool to overcome the scarcity of data, to fill the gap between urban planning and flood risk management (FRM), and to produce comprehensive and high-quality flood risk maps that aid decision-making systems.