Assessing urban flooding risk in response to climate change and urbanization based on shared socio-economic pathways

Unraveling urban hydro-environmental response to climate change and MCDA-based area prioritization in a data-scarce developing city

Climate change leads to more frequent and intense heavy rainfall events, posing significant challenges for urban stormwater management, particularly in rapidly urbanizing cities of developing countries with constrained infrastructure. However, the quantitative assessment of urban stormwater, encompassing both its volume and quality, in these regions is impeded due to the scarcity of observational data and resulting limited understanding of drainage system dynamics. This study aims to elucidate the present and projected states of urban flooding, with a specific emphasis on fecal and organic contamination caused by combined sewer overflow (CSO). Leveraging a hydrological model incorporating physical and biochemical processes validated against invaluable observational data, we undertake simulations to estimate discharge, flood volume, and concentrations of suspended solids (SS), Escherichia coli (E. coli), and chemical oxygen demand (COD) within the drainage channel network of Phnom Penh City, Cambodia. Alterations in flood volumes, and pollutant concentrations and loads in overflow under two representative concentration pathways (RCPs 4.5 and 8.5) for extreme rainfall events are projected. Furthermore, we employ a multi-criteria decision analysis (MCDA) framework to evaluate flood risk, incorporating diverse indicators encompassing physical, social, and ecological dimensions. Our results demonstrate the exacerbating effects of climate change on flood volumes, expansion of flooded areas, prolonged durations of inundation, elevated vulnerability index, and heightened susceptibility to pollutant contamination under both scenarios, underscoring increased risks of flooding and fecal contamination. Spatial analysis identifies specific zones exhibiting heightened vulnerability to flooding and climate change, suggesting priority zones for investment in flood mitigation measures. These findings provide crucial insights for urban planning and stormwater management in regions with limited drainage infrastructure, offering essential guidance for decision-making in locales facing similar challenges.

Synergistic assessment of multi-scenario urban waterlogging through data-driven decoupling analysis in high-density urban areas: A case study in Shenzhen, China

Extreme meteorological events and rapid urbanization have led to serious urban flooding problems. Characterizing spatial variations in flooding susceptibility and elucidating its driving factors are essential for preventing damages from urban pluvial flooding. However, conventional methods, limited by spatial heterogeneity and the intricate mechanisms of urban flooding, frequently demonstrated a deficiency in precision when assessing flooding susceptibility in dense urban areas. Therefore, this study proposed a novel framework for an integrated assessment of urban flood susceptibility, based on a comprehensive cascade modeling chain consisting of XGBoost, SHapley Additive exPlanations (SHAP), and Partial Dependence Plots (PDP) in combination with K-means. It aimed to recognize the specific influence of urban morphology and the spatial patterns of flooding risk agglomeration under different rainfall scenarios in high-density urban areas. The XGBoost model demonstrated enhanced accuracy and robustness relative to other three benchmark models: RF, SVR, and BPDNN. This superiority was effectively validated during both training and independent testing in Shenzhen. The results indicated that urban 3D morphology characteristics were the dominant factors for waterlogging magnitude, which occupied 46.02 % of relative contribution. Through PDP analysis, multi-staged trends highlighted critical thresholds and interactions between significant indicators like building congestion degree (BCD) and floor area ratio (FAR). Specifically, optimal intervals like BCD between 0 and 0.075 coupled with FAR values between 0.5 and 1 have the potential to substantially mitigate flooding risks. These findings emphasize the need for strategic building configuration within urban planning frameworks. In terms of the spatial-temporal assessment, a significant aggregation effect of high-risk areas that prone to prolonged duration or high-intensity rainfall scenarios emerged in the old urban districts. The approach in the present study provides quantitative insights into waterlogging adaptation strategies for sustainable urban planning and design.

Projecting urban flood risk through hydrodynamic modeling under shared socioeconomic pathways

Under future climate change, accurate risk assessment of urban flooding disasters is paramount for effective adaptation and mitigation strategies. However, conventional indicator-based assessment methods often fall short of accurately capturing the complexity of flooding dynamics. Current research predominantly focuses on predicting future hazard shifts while overlooking changes in other critical indicators. In this study, we establish a comprehensive index system for risk assessment, and quantified future changes in most indicators, utilizing the InfoWorks ICM model for hazard simulation and the CLUMondo model for land use predictions. Based on risk assessment results and regional characteristics, we further analyze the key factors driving future risk and discuss corresponding measures. The results indicate an exacerbation of future urban flood risk, with an 18% increase in high risk areas, primarily concentrated in the center of the study area. The dominant indicators are inundation depth and land use over the whole study area. However microtopography significantly affects risk in low-lying areas. Overall, under higher emission scenarios, the influence of GDP and population rises. These findings offer methodological insights for future urban flood risk assessment research and provide policymakers with valuable guidance to develop targeted adaptation measures in response to climate change.

Flood simulation using LISFLOOD and inundation effects: A case study of Typhoon In-Fa in Shanghai

Urban flooding threatens residents and their property, necessitating timely and accurate flood simulations to enhance prevention measures. However, as a megacity, Shanghai presents a complex underlying surface that proves challenging to assess accurately in existing studies. To simulate the dynamic flooding caused by Typhoon In-Fa in Shanghai from July 23rd to 28th 2021, we employed the LISFLOOD hydrodynamic model with multi-source data and validated the flooded area using the S1FLOOD deep learning model with Sentinel-1 satellite imagery. Based on simulated flood results and a flood depth classification system, we quantified the impacts of flood inundation on population, land use, and buildings. Key findings include: (1) The most severe flooding period in Shanghai occurred on July 25th and 26th 2021. (2) The LISFLOOD model effectively captured the extent of inundation, with the very-high flood depth zone covering 98.07 % of the area identified as flooded by the S1FLOOD and Sentinel-1. (3) Peak-affected individuals were recorded on July 25th 2021. (4) Farmland experienced the most extensive flooding among land use types, while residential buildings were notably affected among building types. Our study reconstructed the spatiotemporal dynamics of Typhoon In-Fa-induced flooding in Shanghai. We mapped the spatial extent and water depths, revealing the dynamic impacts of inundation on population, land use, and buildings across urban areas. This comprehensive framework for flood simulation and inundation impact analysis offers a valuable approach to improve urban flood emergency response.

Urban flood-bearing vulnerability evaluation based on the moment estimate weighting and improved gray target model

Increasingly severe flooding seriously threatens urban safety. A scientific urban flood-bearing vulnerability assessment model is significant to improve urban risk management capacity. The gray target model (GTM) has advantages in urban flood-bearing vulnerability assessment. However, indicator correlation and single bull's-eye are commonly neglected, leading to defective evaluation results. By integrating the four base weights, an improved weighting method based on the moment estimate was proposed. Then, the marginal distance was used to quantify the indicator correlation, and the TOPSIS model was introduced to define the relative bull's-eye distance. Thus, an improved gray target evaluation method was established. Finally, an urban flood-bearing vulnerability evaluation model was presented based on the moment estimate weighting-improved GTM. In this study, Zhengzhou City, China, was taken as an example. The spatial and temporal changing characteristics of the flood-bearing vulnerability of Zhengzhou from 2006 to 2020 were investigated. The results show that: (1) On the temporal scale, the disaster-bearing vulnerability of Zhengzhou City showed an upward trend during the 15 years; (2) On the spatial scale, Guancheng District of Zhengzhou City had the relatively highest vulnerability to urban flooding. This study is expected to provide a scientific reference for urban flood risk management.

Impact of climate change on future flood susceptibility projections under shared socioeconomic pathway scenarios in South Asia using artificial intelligence algorithms

This study investigated the impact of climate change on flood susceptibility in six South Asian countries Afghanistan, Bangladesh, Bhutan, Bharat (India), Nepal, and Pakistan-under two distinct Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6 and SSP5-5.8, for 2041-2060 and 2081-2100. To predict flood susceptibility, we employed three artificial intelligence (AI) algorithms: the K-nearest neighbor (KNN), conditional inference random forest (CIRF), and regularized random forest (RRF). Predictions were based on data from 2452 historical flood events, alongside climatic variables measured over monthly, seasonal, and annual timeframes. The innovative aspect of this research is the emphasis on using climatic variables across these progressively condensed timeframes, specifically addressing eight precipitation factors. The performance evaluation, employing the area under the receiver operating characteristic curve (AUC) metric, identified the RRF model as the most accurate, with the highest AUC of 0.94 during the testing phase, followed by the CIRF (AUC = 0.91) and the KNN (AUC = 0.86). An analysis of variable importance highlighted the substantial role of certain climatic factors, namely precipitation in the warmest quarter, annual precipitation, and precipitation during the wettest month, in the modeling of flood susceptibility in South Asia. The resultant flood susceptibility maps demonstrated the influence of climate change scenarios on susceptibility classifications, signalling a dynamic landscape of flood-prone areas over time. The findings revealed variable trends under different climate change scenarios and periods, with marked differences in the percentage of areas classified as having high and very high flood susceptibility. Overall, this study advances our understanding of how climate change affects flood susceptibility in South Asia and offers an essential tool for assessing and managing flood risks in the region.

Climate change and urban sprawl: Unveiling the escalating flood risks in river deltas with a deep dive into the GBM river delta

River deltas, such as the Ganges-Brahmaputra-Meghna (GBM) delta, are highly vulnerable to flooding, exacerbated by intense human activities and rapid urban growth. This study explores the evolution of urban flood risks in the GBM delta under the combined impacts of climate change and urban expansion. Unlike traditional assessments that focus on a single flood source, we consider multiple sources-coastal, fluvial, and pluvial. Our findings indicate that future urban expansion will significantly increase flood exposure, with a substantial rise in flood risk from all sources by the end of this century. Climate change is the main driver of increased coastal flood risks, while urban growth primarily amplifies fluvial, and pluvial flood risks. This highlights the urgent need for adaptive urban planning strategies to mitigate future flooding and support sustainable urban development. The extreme high emissions future scenario (SSP5-8.5) shows the largest urban growth and consequent flood risk, emphasizing the necessity for preemptive measures to mitigate future urban flooding. Our study provides crucial insights into flood risk dynamics in delta environments, aiding policymakers and planners in developing resilience strategies against escalating flood threats.

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.

Characteristics and effects of global sloping land urbanization from 2000 to 2020

Cities usually expand on flat land. However, in recent decades, the increasing scarcity of available flat land has compelled many cities to expand to sloping land (sloping land urbanization, SLU), and the understanding for global SLU is still unclear. This study, based on the currently available high-precision global Digital Elevation Model (FABDEM) and global land cover dataset (GlobeLand30), investigated the characteristics and impacts of SLU in 26,402 urban residential areas worldwide from 2000 to 2020. Results show that the total area of SLU globally is 16,383 km2, accounting for 9.54 % of the overall urban expansion. This phenomenon is widespread globally and relatively concentrated in a few countries, with 42.78 %, 24.35 %, and 21.83 % of the area coming from cultivated land, forest, and grassland respectively. Global SLU has accommodated 34.78 million urban population, and indirectly protected 8922 km2 of flat cultivated land, while causing a net loss of 4373 km2 of green ecological land. Deliberately balancing the dual effects of SLU is crucial for advancing sustainable global urbanization.

The influence of rainfall events on the toxicity of urban wastewaters to freshwater mussels Elliptio complanata

The cumulative impacts of rainfall frequency and intensity towards the ecotoxicity of urban pollution is gaining more and more attention in these times of climate change. The purpose of this study was to examine the ecotoxicological impacts of combined sewers overflows and municipal effluent discharge sites during 3 periods (years) of varying intensity precipitations to freshwater mussels Elliptio complanata. Mussels were placed in benthic cages for 3 months during the summer at 2 overflow discharge and 8 km downstream sites including an upstream site for three consecutive years with low (164 mm), medium (182 mm) and high (248 mm) amounts of rain. The results revealed that the effects were mainly influenced by suspended matter loadings and to the dissolved components to a lesser extent. Impacts at the downstream and overflow sites were noticeable at the reproduction (vitellogenin), genotoxicity, neurotoxicity (dopamine and serotonin changes) levels in addition to xenobiotic biotransformation revealed by glutathione S-transferase activity and metallothioneins for organic and heavy metals respectively. The site downstream the effluent produced most of the effects compared to the overflow sites in the Saint-Lawrence River. However, the impacts of combined sewers overflows could become problematic in low dilution systems such as small river and lakes.

Optimized strategies of green and grey infrastructures for integrated control objectives of runoff, waterlogging and WWDP in old storm drainages

Frequent waterlogging occurs in old high-density urban areas where the sewage is inappropriately connected to storm drainages, resulting in serious wet weather discharge pollution (WWDP). To address urban waterlogging and runoff, the optimization of green infrastructures (GIs) and grey infrastructures (GRs) has been proposed to improve rainwater management efficiency. However, most strategies neglect WWDP and fail to achieve integrated control of runoff, waterlogging, and discharge pollution. In the present study, a new optimization method was introduced to identify optimal solutions for renovating outdated storm drainage systems, considering the management of discharge pollution in wet weather. A case study in Shanghai, China was conducted to demonstrate the application of the method. The cost-benefit index (CBI) of optimized GIs (0.06) was lower than that of optimized GRs (2.78) under 22.2 mm rainfall (no runoff and WWDP), but the costs of the former were only half those of the latter. In a 5-year return period storm (no waterlogging), optimized GIs had a significantly higher CBI (2.85 times) compared to optimized GRs, costing only 44 % of the latter. When WWDP reached the control objective (COD≤70 mg/L), the optimized GIs needed to be further optimized with GRs. The CBI of optimized GI-GRs was higher than GRs by 2.50, and the cost was 58% of the latter. In areas with frequent low-intensity rainfall, optimized GIs and GRs should be selected based on local cost or benefit requirements for drainage reconstruction. In high-intensity storm-prone areas, the optimized GI-GR combination should be selected for drainage reconstruction. The proposed method can compensate for the shortcomings of existing optimization methods in controlling WWDP for the reconstruction of old storm drainages.

Investigating non-point pollution mitigation strategies in response to changing environments: A cross-regional study in China and Germany

Climate change and urbanization have altered regional hydro-environments. Yet, the impact of future changes on the pollution risk and associated mitigation strategies requires further exploration. This study proposed a hydraulic and water-quality modeling framework, to investigate the spatiotemporal characteristics of pollution risk mitigation by low impact development (LID) strategies under future Representative Concentration Pathways (RCP) and Shared Socioeconomic Pathways (SSP) scenarios. Results demonstrated that the LID strategies exhibited an effective performance of pollutant removal in the current hydro-environment, with the removal rates ranging from 33% to 56%. In future climate and urbanization scenarios, the LID performance declined and turned to be uncertain as the greenhouse gas (GHG) emissions increased, with the removal rates ranging from 12% to 59%. Scenario analysis suggested that the LID performance was enhanced by a maximum of 73% through the diversified implementation of LID practices, and the performance uncertainty was reduced by a maximum of 67% through the increased LID deployment. In addition, comparative analysis revealed that the LID strategies in a well-developed region (Dresden, Germany) were more resilient in response to changing environments, while the LID strategy in a high-growth region (Chaohu, China) exhibited a better pollutant removal performance under low-GHG scenarios. The methods and findings in this study could provide additional insights into sustainable water quality management in response to climate change and urbanization.

Comparative toxicity of urban wastewater and rainfall overflow in caged freshwater mussel Elliptio complanata

Municipal effluents are well-recognized as disrupting sexual differentiation and reproduction in mussels. However, the contribution to this problem made by rainfall combined with sewer overflow (increased by rain due to climate change) is not well understood. The purpose of this study was to compare the neuroendocrine effects of municipal discharge and rainfall overflow on caged endemic mussel Elliptio complanata. To this end, mussels were experimentally caged and placed for 3 months at a municipal effluent dispersion plume site and at overflow sites. Data revealed that downstream surface water contained some pharmaceuticals (caffeine and carbamazepine) and accumulated significant levels of heterotrophic bacteria, but these effects were not observed at the overflow sites. The principal effects observed at the downstream site were increased soft tissue mass (and gonad index), inflammation, and Vtg proteins in male mussels as determined by a novel immunostaining methodology. The rainfall overflow sites had no effects on these markers, but were specifically associated with reduced Vtg proteins in females, dopamine (Dop), gonad lipids, and DNA strand breaks, with increased metallothioneins. In conclusion, the observed feminizing effects of municipal effluent were not additionally observed in mussels caged at rainfall overflow sites, although the latter exhibited a different pattern of toxicity.