Search Results (326)

This study focuses on the Guangdong-Hong Kong-Macao Greater Bay Area and employs a multi-model coupling method of InVEST-Bathtub-GeoSOS-FLUS to predict and analyze the impacts of sea level rise and rapid urbanization on ecological source areas by the year 2100. The InVEST model is used to delineate areas with higher habitat quality scores as ecological source areas. The Bathtub inundation model predicts the impact ranges under three different sea level rise scenarios by 2100. The FLUS model simulates the land-use pattern of the Greater Bay Area in 2100. Finally, the raster calculator is used to conduct overlay analysis and accurately calculate the impact on ecological source areas under the combined effects of sea level rise and urban expansion. The results show that by 2100, the proportion of cultivated land in the Greater Bay Area is expected to decrease from 24.95% to 10.55%, while the proportion of urban land will increase from 7.69% to 26.84%. Under the dual impacts of the three sea level rise scenarios and urbanization, the affected areas of ecological source areas will reach 109.88 km², 125.05 km², and 255.10 km², respectively. This study provides an important basis and decision-making support for the sustainable planning and scientific management of ecological source areas in the Greater Bay Area. Full article

Existing drainage systems have limited capacity to mitigate future climate change-induced flooding problems effectively. However, some studies have evaluated the effectiveness of integrating Water-Sensitive Urban Design (WSUD) with existing drainage systems in mitigating flooding in tropical regions. This study examined the performance of drainage systems and integrated WSUD options under current and future climate scenarios in a sub-catchment of Saltwater Creek, a tropical catchment located in Cairns, Australia. A combination of one-dimensional (1D) and two-dimensional (1D2D) runoff generation and routing models (RORB, storm injector, and MIKE+) is used for simulating runoff and inundation. Several types of WSUDs are tested alongside different climate change scenarios to assess the impact of WSUD in flood mitigation. The results indicate that the existing grey infrastructure is insufficient to address the anticipated increase in precipitation intensity and the resulting flooding caused by climate change in the Engineers Park sub-catchment. Under future climate change scenarios, moderate rainfall events contribute to a 25% increase in peak flow (95% confidence interval = [1.5%, 0.8%]) and total runoff volume (95% confidence interval = [1.05%, 6.5%]), as per the Representative Concentration Pathway 8.5 in the 2090 scenario. Integrating WSUD with existing grey infrastructure positively contributed to reducing the flooded area by 18–54% under RCP 8.5 in 2090. However, the efficiency of these combined systems is governed by several factors such as rainfall characteristics, the climate change scenario, rain barrel and porous pavement systems, and the size and physical characteristics of the study area. In the tropics, the flooding problem is estimated to increase under future climatic conditions, and the integration of WSUD with grey infrastructure can play a positive role in reducing floods and their impacts. However, careful interpretation of results is required with an additional assessment clarifying how these systems perform in large catchments and their economic viability for extensive applications. Full article

This study presents a novel bivariate-output deep learning framework based on LeNet-5 for the simultaneous prediction of urban flood susceptibility and inundation depth in Seoul, South Korea. Unlike previous studies that relied on single-output models, the proposed approach jointly learns classification and regression targets through a shared feature extraction structure, enhancing consistency and generalization. Among six tested architectures, the Le5SD_CBAM model—integrating a Convolutional Block Attention Module (CBAM)—achieved the best performance, with 83% accuracy, an Area Under the ROC Curve (AUC) of 0.91 for flood susceptibility classification, and a mean absolute error (MAE) of 0.12 m and root mean squared error (RMSE) of 0.18 m for depth estimation. The model’s spatial predictions aligned well with hydrological principles and past flood records, accurately identifying low-lying flood-prone zones and capturing localized inundation patterns influenced by infrastructure and micro-topography. Importantly, it detected spatial mismatches between susceptibility and depth, demonstrating the benefit of joint modeling. Variable importance analysis highlighted elevation as the dominant predictor, while distances to roads, rivers, and drainage systems were also key contributors. In contrast, secondary terrain attributes had limited influence, indicating that urban infrastructure has significantly altered natural flood flow dynamics. Although the model lacks dynamic forcings such as rainfall and upstream inflows, it remains a valuable tool for flood risk mapping in data-scarce settings. The bivariate-output framework improves computational efficiency and internal coherence compared to separate single-task models, supporting its integration into urban flood management and planning systems. Full article

The Gulf of Guinea is undergoing accelerated sea-level rise (SLR), with localized rates surpassing 10 mm yr⁻¹, more than double the global mean. Integrating GRACE/FO ocean mass data, reanalysis products, and machine learning, we identify a regime shift in the regional sea-level budget post-2015. Over 60% of observed SLR near major riverine outlets stems from ocean mass increase, driven primarily by intensified terrestrial hydrological discharge, marking a transition from steric to barystatic and manometric dominance. This shift coincides with enhanced monsoonal precipitation, wind-forced equatorial wave adjustments, and Atlantic–Pacific climate coupling. Piecewise regression reveals a significant 2015 breakpoint, with mean coastal SLR rates increasing from 2.93 ± 0.1 to 5.4 ± 0.25 mm yr⁻¹ between 1993 and 2014, and 2015 and 2023. GRACE data indicate extreme mass accumulation (>10 mm yr⁻¹) along the eastern Gulf coast, tied to elevated river discharge and estuarine retention. Dynamical analysis reveals the reorganization of wind field intensification, which modifies Rossby wave dispersion and amplifies zonal water mass convergence. Random forest modeling attributes 16% of extreme SLR variance to terrestrial runoff (comparable to wind stress at 19%), underscoring underestimated land–ocean interactions. Current climate models underrepresent manometric contributions by 20–45%, introducing critical projection biases for high-runoff regions. The societal implications are severe, with >400 km² of urban land in Lagos and Abidjan vulnerable to inundation by 2050. These findings reveal a hybrid steric–manometric regime in the Gulf of Guinea, challenging existing paradigms and suggesting analogous dynamics may operate across tropical margins. This calls for urgent model recalibration and tailored regional adaptation strategies. Full article

The compound effects of extreme climate change and intensive urban development have led to more frequent urban inundation, highlighting the urgent need for the fine-scale evaluation of stormwater drainage system performance in high-density urban built-up areas. A typical basin, located in Shenzhen, was selected, and a pipe–river coupled SWMM was developed and calibrated via a genetic algorithm to simulate the storm drainage system. Design storm scenario analyses revealed that regional inundation occurred in the central area of the basin and the enclosed culvert sections of the midstream river, even under a 0.5-year recurrence period, while the downstream open river channels maintained a substantial drainage capacity under a 200-year rainfall event. To systematically identify bottleneck zones, two novel metrics, namely, the node cumulative inundation volume and the conduit cumulative inundation length, were proposed to quantify the local inundation severity and spatial interactions across the drainage network. Two critical bottleneck zones were selected, and strategic improvement via the cross-sectional expansion of pipes and river culverts significantly enhanced the drainage efficiency. This study provides a practical case study and transferable technical framework for integrating hydraulic modeling, spatial analytics, and targeted infrastructure upgrades to enhance the resilience of drainage systems in high-density urban environments, offering an actionable framework for sustainable urban stormwater drainage system management. Full article

Effective flood management in agricultural fields has become increasingly important due to the rising frequency and intensity of rainfall events driven by climate change. This study investigates the applicability of urban flood analysis models—SWMM (1D) and K-Flood (2D)—to irrigated agricultural fields with artificial drainage systems. A case study was conducted in a rural area near the Sindae drainage station in Cheongju, South Korea, using rainfall data from an extreme weather event in 2017. The models simulated inland flooding and were validated against flood trace maps provided by the Ministry of the Interior and Safety (MOIS). Receiver Operating Characteristic (ROC) analysis showed a true positive rate of 0.565, a false positive rate of 0.21, and an overall accuracy of 0.731, indicating reasonable agreement with observed inundation. Scenario analyses were also conducted to assess the effectiveness of three improvement strategies: reducing the Manning coefficient, increasing pump station capacity, and widening drainage channels. Among them, increasing pump capacity most effectively reduced flood volume, while channel widening had the greatest impact on reducing flood extent. These findings demonstrate the potential of urban flood models for application in agricultural contexts and support data-driven planning for rural flood mitigation. Full article

Recent years have seen frequent heavy rainfall events in Japan, often linked to Baiu fronts and typhoons. These events are exacerbated by global warming, leading to an increased frequency and intensity. As floods represent a serious threat to sustainable urban development and community resilience, this study contributes to sustainability-focused risk reduction through integrated analysis. This study focuses on the 2 June 2023 heavy rain disaster in Toyohashi City, Japan, which caused extensive damage due to flooding from the Yagyu and Umeda Rivers. Using numerical models, this study accurately reproduces flooding patterns, revealing that high tides amplified the inundation area by 1.5 times at the Yagyu River. A resident questionnaire conducted in collaboration with Toyohashi City identifies key trends in evacuation behavior and disaster information usage. Traditional media such as TV remain dominant, but younger generations leverage electronic devices for disaster updates. These insights emphasize the need for targeted information dissemination and enhanced disaster preparedness strategies, including online materials and flexible training programs. The methods and findings presented in this study can inform local and regional governments in building adaptive disaster management policies, which contribute to a more sustainable society. Full article

Flood control infrastructure is essential for the development of cities and the population’s well-being. The goal is to protect human and economic resources by reducing the inundation area and controlling the flood level and peak discharges. Detention basins can do this by storing a large volume of water to be released after the peak discharge. By doing this, a large amount of energy is stored, which can be recovered via micro-hydropower. In addition, as the release flow is controlled and almost constant, Pumps as Turbines (PAT) could be a feasible and economic option in these cases. Thus, this study investigates the feasibility of micro-hydropower (MHP) in urban detention basins, using the Santa Lúcia detention basin in Belo Horizonte as a case study. The methodology involved hydrological modeling, hydraulic analysis, and economic and environmental assessment. The results demonstrated that PAT selection has a crucial role in the feasibility of the MHP, and exploiting rainfall with lower intensities but higher frequencies is more attractive. Using multiple PATs with different operating points also showed promising results in improving energy production. In addition to the economic benefits, the MHP in the detention basin produces minimal environmental impact and, as it exploits a wasted energy source, it also reduces the carbon footprint in the urban water cycle. Full article

With global warming and accelerated urbanization, urban flooding became one of the top ten international natural disasters in 2024. In order to accurately and efficiently simulate the impact of upstream river water transport on downstream river inundation under heavy rainfall scenarios, this study proposes a river inundation water depth calculation model based on a deep ensemble learning approach. The model integrates flood inundation data from hydrodynamic models with machine learning techniques, introducing a matrix-based deep ensemble learning method. The results demonstrate superior prediction accuracy, with an RMSE of 0.04 and R² of 0.95. Validation using typical rainfall data from 6 July 2022 shows that the model achieves a prediction error of less than 0.15 m across 99.8% of the domain, outperforming standalone models. These findings confirm that the deep ensemble model effectively captures the complex relationships between rainfall, terrain, and flow dynamics, providing reliable water depth predictions in data-scarce regions through multi-coupling modeling based on river characteristics. Full article

Increasing frequency and severity of urban flooding, driven by climate change and urban population growth, present major challenges. Traditional flood control infrastructure alone cannot fully prevent flood damage, highlighting the need for a comprehensive and multi-dimensional disaster management approach. This study proposes the Flood Risk Index for Building (FRIB)—a building-level assessment framework that integrates vulnerability, hazard, and exposure. FRIB assigns customized risk levels to individual buildings and evaluates the effectiveness of a multi-dimensional method. Compared to traditional indicators like flood depth, FRIB more accurately identifies high-risk areas by incorporating diverse risk factors. It also enables efficient resource allocation by excluding low-risk buildings, focusing efforts on high-risk zones. For example, in a case where 5124 buildings were targeted based on 1 m flood depth, applying FRIB excluded 24 buildings with “low” risk and up to 530 with “high” risk, reducing unnecessary interventions. Moreover, quantitative metrics like entropy and variance showed that as FRIB levels rise, flood depth distributions become more balanced—demonstrating that depth alone does not determine risk. In conclusion, while qualitative labels such as “very low” to “very high” aid intuitive understanding, FRIB’s quantitative, multi-dimensional approach enhances precision in urban flood management. Future research may expand FRIB’s application to varied regions, supporting tailored flood response strategies. Full article

Extreme rainfall events driven by climate change are increasing flood risks. Addressing flood mitigation solely from either a hydraulic engineering or urban planning perspective may overlook both feasibility and effectiveness. This study focuses on Tainan City and the Tainan Science Park in Taiwan, applying the NbS framework to assess flood mitigation strategies. From an urban planning perspective, Agricultural Development Zone Type II (Agri-DZII), parks, green spaces, and Taiwan Sugar Corporation (TSC) land were selected as flood detention sites. Hydraulic modeling was used to evaluate their effectiveness under both current and climate-change-induced rainfall conditions. Simulation results show that under current rainfall conditions, flood mitigation measures reduced inundated areas with depths exceeding 2.0 m by up to 7.8% citywide and 20.8% within the Tainan Science Park Special District Plan Area. However, under climate change scenarios, the reduction effects declined significantly, with maximum reductions of only 1.6% and 17.8%, respectively. Results indicate that, even when utilizing all available detention areas, the overall flood reduction in Tainan City remains limited. However, TSC agri-land within the Tainan Science Park overlaps with high-flood-risk zones, demonstrating significant local flood mitigation potential. This study recommends integrating hydrological analysis into urban planning to prevent high-density residential and economic zones from being designated in flood-prone areas. Additionally, policymakers should consider reserving appropriate land for flood detention to enhance climate resilience. By combining urban planning and hydraulic engineering perspectives, this study highlights the flexibility of NbS in disaster management, advocating for the integration of Natural Water Detention Measures into flood adaptation strategies to improve urban water management and climate adaptability. Full article

Sea level rise (SLR) and increased urbanisation of coastal areas have exacerbated coastal flood threats, making them even more severe in important cultural sites. In this context, the role of hard coastal defences such as promenades and embankments needs to be carefully assessed. Here, a thorough investigation is conducted in Grado, one of the most significant coastal and historical towns in the Friuli Venezia Giulia region of Italy. Grado is located on a barrier island of the homonymous lagoon, the northernmost of the Adriatic Sea, and is prone to flooding from both the sea and the back lagoon. The mean and maximum sea levels from the historical dataset of Venice (1950–2023) were analysed using the Gumbel-type distribution, allowing for the identification of annual extremes based on their respective return periods (RPs). Grado and Trieste sea level datasets (1991–2023) were used to calibrate the statistics of the extremes and to calculate the local component (subsidence) of relative SLR. The research examined the occurrence of annual exceedance of the minimum threshold water level of 110 cm, indicating Grado’s initial notable marine ingression. The study includes a detailed analysis of flood impacts on the urban fabric, categorised into sectors based on the promenade elevation on the lagoon side, the most vulnerable to flooding. Inundated areas were obtained using a high-resolution digital terrain model through a GIS-based technique, assessing both the magnitude and exposure of the urban environment to flood risk due to storm surges, also considering relative SLR projections for 2050 and 2100. Currently, approximately 42% of Grado’s inhabited area is inundated with a sea level threshold value of 151 cm, which occurs during surge episodes with a 30-year RP. By 2100, with an optimistic forecast (SSP1-2.6) of local SLR of around +53 cm, the same threshold will be met with a surge of ca. 100 cm, which occurs once a year. Thus, extreme levels linked with more catastrophic events with current secular RPs will be achieved with a multi-year frequency, inundating more than 60% of the urbanized area. Grado, like Venice, exemplifies trends that may impact other coastal regions and historically significant towns of national importance. As a result, the generated simulations, as well as detailed analyses of urban sectors where coastal flooding may occur, are critical for medium- to long-term urban planning aimed at adopting proper adaptation measures. Full article

This study assesses the exposure of the 155 Greek seaports to marine flooding and extreme heat under climate change. Flood exposure was estimated through a threshold approach that compared projected mean and extreme sea levels to high-resolution port quay elevation data. It was found that while relatively few ports will face quay inundation, the majority will experience operational disruptions due to insufficient freeboard for berthing of commercial vessels under both the mean (80%) and extreme sea (96%) levels by 2050. For selected ports, 2-D flood modelling was undertaken that showed that the used ‘static’ flood threshold approach likely underestimates flood exposure. Future heat exposure was studied through the comparison of extreme temperature and humidity projections to operational and health/safety thresholds. Port infrastructure and personnel/users will be exposed to large material, operational and health risks, whereas energy demand will rise steeply. Deadly heat days (due to mean temperature/humidity combination) will increase, particularly at island ports: 20% of Greek ports might face more than 50 such days annually by end-century. As ports are associated with large urban clusters, these findings suggest a broader health risk. Our findings suggest an urgent climate adaptation need given the strategic socio-economic importance of ports. Full article

The October 2024 cut-off low event triggered one of the most catastrophic floods recorded in the Valencia Metropolitan Area, exposing significant vulnerabilities in urban planning, infrastructure resilience, and emergency preparedness. This study presents a novel comprehensive assessment of the event, using a multi-sensor satellite approach combined with socio-economic and infrastructure data at the metropolitan scale. It provides a comprehensive spatial assessment of the flood’s impacts by integrating of radar Sentinel-1 and optical Sentinel-2 and Landsat 8 imagery with datasets including population density, land use, and critical infrastructure layers. Approximately 199 km² were inundated, directly affecting over 90,000 residents and compromising vital infrastructure such as hospitals, schools, transportation corridors, and agricultural lands. Results highlight the exposure of peri-urban zones and agricultural areas, reflecting the socio-economic risks associated with the rapid urban expansion into flood-prone plains. The applied methodology demonstrates the essential role of multi-sensor remote sensing in accurately delineating flood extents and assessing socio-economic impacts. This approach constitutes a transferable framework for enhancing disaster risk management strategies in other Mediterranean urban regions. As extreme hydrometeorological events become more frequent under changing climatic conditions, the findings underscore the urgent need for integrating remote sensing technologies, early warning systems, and nature-based solutions into regional governance to strengthen resilience, reduce vulnerabilities, and mitigate future flood risks. Full article

The flood risk of urban buildings has been continuously increasing, owing to the increasing frequency and severity of floods. There is an urgent need to implement precise mitigation strategies to address the unique characteristics of urban residential structures. In this study, an indicator system consisting of 17 indicators in four dimensions (extent of hazard, degree of exposure, vulnerability, and response ability) was developed for the flood risk of residential buildings. The assessment was conducted in Katsushika Ward, Tokyo, and the ANALYTIC HIERARCHY PROCESS(AHP)—Criteria Importance Through Intercriteria Correlation (CRITIC) method was integrated with Geographic Information System(GIS) technology. The spatial distribution of residential flood risk exhibits marked heterogeneity, with ‘extremely high’ and ‘high’ risk areas concentrated in northwestern and southwestern riverine zones. These regions exhibit dense populations, substantial assets, deep immersion depths, prolonged inundation durations, high proportions of wooden houses, and narrow roads impeding rescue operations. The mitigation priorities are the following: Enhance flood-resistant building heights and quality in riverside areas, strengthen vacant house management, widen rescue access routes, promote mid-/high-rise buildings, and optimize subsidies for tenants and single-person households to minimize losses. Full article