ABSTRACT Atlantic‐facing coastal communities of western Ireland face recurrent flood hazards driven by intense precipitation, storm surges, and tidal influences. Existing national flood mapping efforts provide valuable hydrodynamic simulations but rarely integrate multi‐criteria decision analysis with data‐driven machine learning to capture local‐scale flood susceptibilities. This study provides a first integrated Analytical Hierarchy Process (AHP) and Machine Learning (ML) for Atlantic‐facing of Connemara and Aaran Islands, County Galway. The integrated AHP‐ML approach outperforms standalone ML models by integrating transparent, expert‐driven weights that reflect local geomorphology and data constraints often missed by non‐linear ML algorithms. Nine flood influencing parameters: rainfall, elevation, slope, curvature, land cover, distance to rivers, soil type, and normalised differential vegetation indexand four ML models (Logistic Regression, Random Forest, XGBoost, and Support Vector Machines) are considered for the study. The model performances were qualified using the area under the receiver operating characteristic (AUC), precision and recall. The probabilistic benchmarking validation was also performed against the GloFAS 100‐year flood hazard product, DEM‐based Sea Level Rise (+1 m, +2 m, +5 m and +10 m) scenarios, and OPW flood records. The AHP results indicate that 1.36% and 33.13% of the study area falls within ‘very high’ (16.92 km 2 ) and ‘high’ (410 Km 2 ) flood susceptibility zones. A spatially separated evaluation of the ML models, showed highest predictive accuracy for XGBoost (73%) closely followed by SVM (68%), Random Forest (64%) and Logistic Regression (63%). Discrimination and calibration were moderate (ROC‐AUC≈0.70, PR‐AUC≈0.70, PR‐AUC≈0.75, Brier ≈0.206) for the best‐performing XGBoost Model. The socio‐demographic data overlaid on the final susceptibility classes reveals that a total population of 11,318 people (with approximately 24% elderly and 19% children age groups) were vulnerable to high flood susceptibility. The study findings demonstrate the need for integrating complementary modelling approaches to enhance flood susceptibility assessments in mid‐latitude Irish coastal and remote regions.
ABSTRACT Many coastal and riverine communities lack dedicated flood protection and face growing flood risk from climate change and urban development. In such areas, flood insurance is one of few mechanisms to manage financial exposure, yet how adaptation measures, participation structures, and infrastructure policy jointly shape long‐term insurability is poorly understood. We develop an integrated framework that combines hydrodynamic modelling, damage assessments, and premium estimates to quantify flood risk and insurance viability under coupled climate and socioeconomic change. Applied to unembanked areas in the Rotterdam Metropolitan Region, the Netherlands, we evaluate sea‐level rise scenarios (0–2 m), urban development projections and adaptation strategies. Currently, flood insurance is unavailable for unembanked residents in the Netherlands. We therefore investigate whether such a scheme could be viable, what premiums it would imply, and how adaptation would shape its long‐term sustainability. Under full participation, risk‐based annual premiums covering expected damages and cost of capital would average approximately €50 per household today, but without adaptation could rise to nearly €3000 under high sea‐level rise. Beyond 1.0 m sea level rise, the dominant risk driver is no longer gradual climate change itself but its second‐order effect on infrastructure policy: storm surge barrier closure thresholds that must rise with the sea. We find that building‐level adaptation reduces aggregate damages by up to 60%, but under risk‐based participation structures increases individual premiums by shrinking the pool faster than it reduces losses. Our results indicate that flood insurance for unembanked areas could be viable under current conditions, though sustaining manageable premiums over time depends on combining building‐level and regional adaptation with risk transfer mechanisms before climate impacts accelerate. The framework is transferable to other coastal and riverine settings facing similar challenges, though flood risk and premium trajectories will vary with local hydrodynamic conditions and how flood damages are allocated among insurers, governments, and households.
ABSTRACT Reliable design precipitation estimation is vital for flood risk management and infrastructure design. In Japan, the Depth‐Area‐Duration (DAD) method is widely used for operational Probable Maximum Precipitation (PMP) estimation, but it lacks a probabilistic link to rainfall frequency and does not yield the spatiotemporal rainfall fields needed for hydrological modeling. This study applies Stochastic Storm Transposition (SST), the first application of SST in Japan, to 35 years of high‐resolution Radar/Rain gauge‐Analyzed Precipitation (RA) data to generate probabilistic design precipitation estimates for the Arakawa and Akagawa watersheds. SST‐based precipitation depths are consistently lower than DAD‐based PMP estimates and operational design values adopted by the Ministry of Land, Infrastructure, Transport and Tourism (MLIT), used operationally as conservative, roughly 1000‐year benchmarks, while aligning more closely with recent dynamical model‐based PMP studies. At an annual exceedance probability (AEP) of 10 −5 , the SST‐based 48‐h estimate for Arakawa is 9% lower than the DAD‐based value and 16% lower than the MLIT design value; for Akagawa, the SST‐based 12‐h estimate is 20% and 23% lower, respectively. SST‐based results also align closely with recent dynamical model‐based estimates, differing by 3% for Akagawa at AEP = 10 −5 and showing consistent behavior for Arakawa at AEP = 2 × 10 −3 .
ABSTRACT Visualizing and communicating flood inundation depth and extent can be valuable for informing decisions related to flood preparedness and disaster response. In the United States, several different types of flood inundation mapping (FIM) exist and a new National Water Model (NWM) FIM product is being rolled out across the country (expected to be fully deployed by the end of 2026). A mixed‐methods social science research study investigated how select FIM products are used for decision support, what barriers exist to using their information, and what improvements to design and delivery could improve their usefulness. Professional and residential participants in scenario‐based focus groups and surveys in the Mid‐Atlantic and West Gulf River Forecast Center regions provided feedback on FIM use and design that was incorporated into redesigned versions. These revised FIM products were tested in a follow‐up online survey with professionals gathering more detailed feedback on what elements of the maps and design were most helpful for their needs. Many of the proposed changes were reviewed positively, including color transparency, localization (labeling/geographic context), delineation of the ordinary high‐water channel, and inclusion of critical infrastructure and evacuation routes. Findings also describe user needs for FIM products to offer historical context, probabilistic information, and mobile accessibility. Overall, the study highlights the value of flood inundation mapping to decision‐making and the desire for this type of information for both professional and public audiences and provides actionable design‐related recommendations to improve the usefulness and accessibility of these products.
ABSTRACT Flooding remains one of the most frequent and destructive natural hazards globally, causing major economic and social losses each year. In response to increasing climate extremes, Nature‐based Solutions (NbS) have emerged as a sustainable and multifunctional approach to flood risk management (FRM), integrating ecological, social, and engineering benefits. While NbS have been widely implemented across the globe, their large‐scale adoption in Australia remains limited. This study therefore aims to identify the underlying barriers constraining NbS uptake and the enabling mechanisms that can support their mainstreaming within Australia's FRM framework. A systematic literature review (SLR) was conducted, revealing six major barrier clusters: (B1) Institutional and Governance Fragmentation, (B2) Financial, Economic, and Market Constraints, (B3) Technical, Knowledge, and Data Limitations, (B4) Socio‐Cultural and Perceptual Barriers, (B5) Physical, Environmental, and Spatial Constraints, and (B6) Conceptual, Methodological, and Epistemic Challenges. Conversely, six enabler clusters were identified: (E1) Policy, Legislation and Institutional Frameworks, (E2) Collaboration, Intermediaries and Multi‐Level Partnerships, (E3) Community Awareness, Social Licence and Co‐Design, (E4) Finance, Incentives and Business Cases, (E5) Evidence, Data, Tools and Decision Frameworks, and (E6) Design Integration, Multifunctionality and Implementation/Pilots. Building on these findings, this study provides a structured synthesis of fragmented Australian NbS literature by integrating governance, financial, technical, socio‐cultural, and environmental dimensions influencing implementation pathways for FRM. The study also proposes a conceptual relationship framework linking the six barrier and enabler clusters, illustrating how enabling mechanisms can systematically address implementation challenges. Rather than advancing a new theoretical model, the framework serves as an integrated and implementation‐oriented synthesis tool to support policymakers, planners, and practitioners in overcoming institutional fragmentation, strengthening coordination, and advancing NbS integration within Australia's future flood adaptation strategies.
ABSTRACT Flood inundation mapping has become a critical reference for flood risk management, particularly for determining insurance premiums and formulating mitigation strategies in rapidly urbanizing areas with high population density. With recent advancements in mapping technologies and high computing capacity, high‐resolution spatiotemporal datasets can be integrated into flood simulation models. In this study, a hydrological digital elevation model (HyDEM) was implemented at the city scale for Kaohsiung City, Taiwan, to improve urban flood simulations. The HyDEM incorporated specialized datasets, including high‐resolution DEM, building layer, bankline layer, and seadike layer. Using the Delft3D FM 1D–2D modeling platform, 10 basin‐scale models were constructed across Kaohsiung City, and multiresolution computational meshes were employed to achieve a balance between simulation accuracy and computational efficiency. Model performance was validated using two historical flood events, during which it achieved an overall accuracy of ~84% for the June 5 Rainstorm event and 80% for the Typhoon Gaemi (2024) event. These results indicate that flood inundation maps (FIMs) can be considerably improved by preserving critical topographic features, particularly building footprints and hydraulic structures such as river and sea dikes. The proposed method thus addresses limitations of earlier generations of FIMs and provides a framework for advancing next‐generation flood inundation maps.
ABSTRACT The increasing severity and frequency of urban pluvial floods has prompted numerous research studies on pluvial flood risk assessment. However, the literature reveals significant variability in the data resolutions and model types employed. In this study, we aim to provide quantitative insights on the data and modelling requirements for pluvial flood risk practitioners and scientists. We analyse combinations of various data resolutions, ranging from 2 to 50 m, and model complexities, including cellular automata (CA), 2D hydrological models, and coupled 2D–1D models. We evaluate each combination's performance against a benchmark, assessing accuracy in flood mapping, hazard, damage, and risk quantification. Our findings indicate that data resolution affects accuracy more than model complexity. Lower data resolutions lead to underpredictions in flood depth, which subsequently affect hazard, damage, and risk assessment. Thus, we recommend using a digital elevation model (DEM) resolution between 2 and 5 m for accurate flood modelling. With respect to model complexity, we show that combining CA models with high‐resolution data (e.g., 2 m DEM) offers substantial computational time savings while maintaining high F‐statistic values (between 0.76 and 0.78). Additionally, we found that all models were able to estimate relative residential damage with errors between +10% to −20% when combined with DEMs with 2 and 5 m resolution, while lower DEM resolutions were associated to higher errors (above −30%). The results presented in this study, and their discussion, serve as a guide in selecting appropriate models and data resolutions for pluvial flood risk modelling, and in understanding the loss in accuracy related to multiple model–data combinations.
ABSTRACT Knowing the status of emergency capacity for disaster risk reduction helps the government and stakeholders to minimize vulnerabilities and disaster risk. However, there is no widely applied methodology for emergency capacity assessment. This study develops a multidimensional framework integrating vulnerability, susceptibility, and adaptability assessments to evaluate regional flood emergency capacity. The information quantity method, the Maxent model, and the entropy‐weighted TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) are used to assess the regional vulnerability, susceptibility, and adaptability of disaster emergency capacity, respectively. These capacity features are then integrated into the overall disaster capacity using the analytic hierarchy process (AHP)‐entropy method. The exemplary application in Zengcheng District (ZCD), China shows the effectiveness of the proposed method. The identified vulnerability hotspots of flood over ZCD are located in Xintang and southern Yongning, driven by their high road density, GDP per capita, residential disposable income, and high population density. Susceptibility modeling revealed land use, Vegetation Index, and elevation as the dominant drivers, with Xintang exhibiting the highest risk. Adaptability assessments highlighted superior resilience in Paitan, Zhongxin, and Shitan, driven by enhanced disaster‐preparedness investments. Integrated analysis prioritized emergency resource storage capacity, drainage capacity, and emergency rescue capacity as critical determinants of flood response capacity. Lower emergency capacity is concentrated in Zhongxin, Xiaolou, Zhenguo, and Zhucun. This paper constructs a multidimensional assessment framework covering the entire chain of pre‐disaster prevention, in‐disaster response, and post‐disaster recovery, to systematically evaluate a region's comprehensive emergency capacity, providing potentially feasible insights for urban areas vulnerable to flood disasters.
ABSTRACT Preparing for unprecedented natural hazard events is challenging because the lack of records and experience makes it impossible to know how such events will unfold. Repeated record‐breaking floods in central Europe have increased awareness of the need for proactive planning beyond observed extremes. In a participatory approach involving firefighters and civil protection officers, we developed a method to identify locations where mobile levees can be deployed to protect critical infrastructure during extreme flood events. The approach is based on event storylines derived from synthetic design hydrographs, reforecast‐based precipitation scenarios, and physical modeling of river discharge, inundation, and flood impacts. Optimized locations for mobile levee installation were identified by evaluating their effectiveness in reducing flood impacts and delaying inundation to gain time for evacuations. The approach shows that rapid and anticipatory deployment of mobile levees at selected weak points can delay flooding by several hours, providing valuable time for evacuation and the protection of critical infrastructure. The participatory approach proved to be valuable for decision‐makers to exclude or prioritize strategies, raise awareness among actors, and train for event interventions with emergency services.
ABSTRACT Urban drainage systems play an essential role in stormwater management. However, conventional evaluation methods typically focused on individual performance indicators and failed to account for the compound effects of multiple environmental factors. To address this gap, an integrated probabilistic‐hydrodynamic framework was proposed, combining dynamic hydraulic simulation (SWMM) with Bayesian network (BN) inference. A total of 150 scenario simulations were conducted, covering six rainfall return periods, five imperviousness levels, and five downstream water levels. Flooding volumes were classified into three severity levels using the 25th and 75th percentiles. Three complementary metrics were adopted: Risk Ratio (RR), Information Gain (IG), and Relative Contribution (RC). The framework was applied to a typical urban area in Yuanjiang City. The results indicated that rainfall was the most dominant factor (IG = 0.45 bits, 71%), followed by downstream water level (IG = 0.07 bits, 11%) and imperviousness (IG = 0.11 bits, 18%). Extreme rainfall events (50–100 years) exhibited the highest RR (2.12) and contributed 35% of the excess flood risk, whereas high water level contributed 32% and high imperviousness contributed 22%. Low‐severity floods were associated with low rainfall intensities and low water levels, while high‐severity floods required the simultaneous occurrence of extreme rainfall, high imperviousness, and elevated downstream water levels. The proposed framework facilitates a transition from single‐factor to multi‐factor assessment and provides a scientific basis for prioritizing drainage system improvements.
ABSTRACT Urban stormwater waterlogging poses significant threats to public safety and property, making scientific and effective risk management a crucial element in China's whole‐area sponge city construction. This study evaluates waterlogging hazards in Nanning's Chaoyang River Basin using a SWMM–LISFLOOD coupled model to simulate 11 design storm scenarios (0.25–100‐year return periods). A multicriteria decision analysis (MCDA) method integrating six hazard indicators (inundation depth, inundation duration, inundation area, elevation, slope, and land use) was employed through a game theory‐optimized composite weighting method combining AHP and CRITIC approaches. The results indicate that the waterlogging simulation outcomes align well with the actual conditions. The risk contributions of urban waterlogging hazard factors are generally higher than those of underlying surface disaster‐predisposing environmental factors, with inundation depth exhibiting the highest risk contribution (weight: 0.405). The coupling waterlogging hazards across different storm scenarios in the study area exhibited significant spatial heterogeneity. Spatial hazard patterns transition from Low and Negligible Risk under 0.25–2‐year storms to Moderate and High Risk dominance at 20–100‐year scenarios. This study achieved coupled waterlogging hazard zoning mapping at a 12.5 m spatial resolution across the study area, providing critical scientific support for the sustainable development of systematic and whole‐area sponge city construction in Nanning City, as well as for waterlogging hazard early warning systems and contingency planning.
ABSTRACT Floods are among the most frequent and damaging natural hazards in India, particularly affecting low‐lying urban areas in the eastern regions such as Cuttack, Odisha. This study aims to develop a flood hazard susceptibility map for the Cuttack district in Odisha, India, using a combined Analytical Hierarchy Process (AHP) and fuzzy logic framework. Eight spatial parameters were used to generate weighted flood susceptibility surfaces. The AHP method is used for assigning weights to various flood hazard criteria: slope (16%), elevation (17.7%), drainage density (14%), rainfall (15.7%), distance from roads (2.8%), distance from rivers (6.2%), Topographic Wetness Index (TWI) (16.5%), and Land Use Land Cover (LULC) (10.2%). The AHP‐derived weights were integrated with fuzzified inputs using a fuzzy gamma overlay, resulting in a composite flood hazard map, which was classified into five susceptibility zones. Model validation using 125 ground truth flood points and Receiver Operating Characteristic (ROC) analysis yielded an Area Under the Curve (AUC) value of 0.85, indicating strong predictive accuracy. Additional performance metrics, including an accuracy of 0.82, F1‐score of 0.83, precision of 0.84, and recall of 0.81, further confirmed the reliability of the generated map. Sensitivity analysis, performed by varying key parameter weights by ±10%, showed less than 5% variation in high and very high hazard zones, confirming the model's robustness. The results show that approximately 44% of the study area falls under high or very high flood susceptibility zones, including densely populated and rapidly urbanizing regions such as Cuttack Sadar, Naraj, and Mundali. This approach demonstrates the utility of semi‐quantitative spatial modeling techniques for flood risk assessment in data‐constrained urban watersheds.
ABSTRACT Early warning messages (EWMs) are a crisis communication tool to prepare citizens to take action, save lives, and prevent damage in case of an approaching flood. They should be simple, but at the same time sufficient, inform recipients about the magnitude, timing, and possible impacts, and offer recommendations on actions to take. Past flood events in Germany have revealed communication problems with the distributed EWMs, as although citizens did receive one, the magnitude was not well communicated, leading to confusion and either no or maladaptive behavior. To better communicate such information, visual cues can be added to EWMs. In an experimental online survey, 1280 participants were confronted with flood EWMs including different warning symbols or a map displaying a fluvial flood scenario (riverine flooding with several hours of lead time). The influence of the warning symbology on the perceived event magnitude served as a proxy to gain deeper insights into the effectiveness of including visual cues. Survey results show that participants estimated the magnitude generally well, specifically for extreme events, while for medium events the magnitude could not be determined as clearly. There was only a small significant difference between the usage of a generic warning symbol and that of a hazard‐specific symbol. Maps had a significant effect and worked best in comparison to the magnitude we assigned to the described flood scenarios. Along with the name of the area at risk, the time of inundation, recommendations on safe behavior, and an evaluation of the severity of the situation, maps are the most favored item to be included in EWMs. Visual cues in general offer the opportunity to communicate across language barriers, but in the future there needs to be international standardization of color schemes and symbology.
ABSTRACT Flooding is one of the most devastating natural disasters worldwide. It is also expected to become more severe as climate change impacts are realised. Two‐dimensional (2D) hydrodynamic models are used to obtain reliable inundation estimations. However, these models are computationally expensive and time‐consuming, making rapid and ensemble flood scenario assessment challenging. This study investigates the application of hybrid hydrodynamic‐machine learning techniques to develop two rapid flood inundation scenario assessment models in a study catchment in Aotearoa New Zealand. This approach aims to reduce the numerical modelling load to rapidly make robust predictions of potential flooding events from an ensemble of previously assessed events. The proposed framework is based on an innovative combination of a flexible climate‐informed synthetic storm approach and a hybrid hydrodynamic (BG‐Flood 2D hydrodynamic model)—machine learning (Random Forest algorithm) approach. A catalogue of synthetic storms was created based on the characteristics of the main inundation driver (heavy rainfall) and the synoptic conditions in the New Zealand region using statistical techniques. Two hybrid models based on the Random Forest algorithm were developed to emulate the BG‐Flood outputs and efficiently predict flood/non‐flood and maximum inundation depth in the floodplain catchment. The storms' features and the geographic characteristics of the catchment were used as predictor features, and the inundation maps produced by BG‐Flood were used as the target data to develop the hybrid models. Validation results show that the hybrid models can rapidly (under 1 s) and accurately predict flood/non‐flood (mean Recall = 0.878, Precision = 0.895 and F1 score = 0.886) and maximum inundation depth (mean RMSE = 0.0610 m and NSE = 0.866) in the catchment, producing reliable and valuable flood hazard information for flood risk management.
ABSTRACT When estimating future flood events using a global hydrological model (GHM), the large uncertainties associated with general circulation models (GCMs) and bias in the GHM model pose significant challenges. In the meantime, most future flood estimations are conducted only at specific gauge stations due to limited data availability and are unable to support basin‐wide water resources planning and management. To address these issues, we propose a spatiotemporal‐pattern‐based machine learning method, DSGPR‐EOF, which is a combination of Dual‐stage Sparse Gaussian Process Regression (DSGPR) and Empirical Orthogonal Function (EOF) analysis. DSGPR‐EOF is developed to improve the accuracy of basin‐wide flood estimations, including flood peak discharge, flood peak time, and flood volume. We apply the proposed method to the Brahmaputra River Basin (BRB), known for its topographical and climatic diversity, to evaluate the effectiveness and efficiency of the method. DSGPR‐EOF is shown to lead to higher accuracy in flood peak discharge estimation than the widely used multi‐GCMs ensemble mean method and several mainstream machine learning methods, including Support Vector Regression (SVR), Artificial Neural Network (ANN), and Long Short‐Term Memory (LSTM). Comprehensive comparisons reveal that DSGPR‐EOF achieves the lowest relative error of peak discharge (3.36%) among all compared methods, with particularly notable advantages in capturing higher‐order temporal patterns of flood dynamics. The errors in the estimated 10 and 100‐year flood peak discharges of DSGPR‐EOF are reduced by 68.6% and 54.5%, respectively, compared to SGPR‐EOF method. The accuracy of flood peak and volume estimated by DSGPR‐EOF method is highly consistent spatially. Furthermore, when model‐derived reference discharge data are substituted by observed data, flood peak estimation is shown to further improve over the entire basin even though the substitution is made only at locations of gauging stations. These findings underscore the practical significance of the DSGPR‐EOF method for basin‐wide flood estimation.
ABSTRACT In recent times, the development of algorithms to delineate water surface maps has significantly boosted flood monitoring and mitigation efforts by utilizing dual polarization, multi‐temporal Sentinel‐1 synthetic aperture radar (SAR) data. The Sentinel‐1 mission, with its global land monitoring capability, has been widely employed for SAR‐based flood mapping. Compared to single‐image flood algorithms, change‐detection methods offer superior results by deriving flood extent from classified changes, requiring data‐based parameterization. This study critically evaluates the effectiveness of three cutting‐edge thresholding algorithms—Edge Otsu, Bmax Otsu, and Kittler–Illingworth (KI)—for automated flood water detection using dual polarization, multi‐temporal Sentinel‐1 SAR data, focusing on the September 2019 flood event in North‐eastern Thailand. Utilizing Google Earth Engine for preprocessing and image correction, the study examines three Sentinel‐1 change detection models—Difference Image, Normalized Difference Flood Index (NDFI), and Normalized Difference Sigma‐naught Index (NDSI). Among 27 combinations of inputs, change detection methods, and thresholding algorithms, the “Harmonic data‐S1GBM (2016–2017)” input paired with the KI thresholding algorithm and the NDSI change detection method achieved the highest overall accuracy of 86.29% (calculated using user accuracy, producer accuracy, and overall accuracy metrics against 2000 validation samples from GISTDA flood maps and Sentinel‐2 NDWI data). This combination proved most effective in distinguishing flooded from non‐flooded areas, underscoring the importance of selecting optimal data inputs and algorithms for accurate flood inundation mapping. The results highlight the superiority of the KI thresholding algorithm, particularly when used with harmonic data inputs, and establish a robust framework for future flood monitoring applications using Sentinel‐1 SAR data. Furthermore, the study emphasizes that for global and automatic flood services, algorithms should not depend on locally optimized parameters, as these cannot be automatically estimated and vary spatially, significantly affecting mapping accuracy.
ABSTRACT Adaptation measures play a crucial role in mitigating the increasing severity of flood disasters driven by climate change. While various strategies are implemented globally, their geographical characteristics and long‐term effectiveness remain uncertain. This study addresses this gap by evaluating and comparing the regional effectiveness levels of six major flood adaptation measures in Japan, thereby considering the interplay of climate change and demographic decline. Given the inherent uncertainties in long‐term climate and socioeconomic projections, this study focuses on a relative evaluation of damage reduction rates rather than providing absolute damage predictions. Our findings reveal that while stilt houses constitute the most effective measure at the national level, their effectiveness varies significantly at the prefectural level, highlighting that a one‐size‐fits‐all national strategy is suboptimal and that tailored, region‐specific adaptation portfolios based on local geographical and socioeconomic conditions are essential. In the near future, the impacts of climate change are projected to overwhelm the benefits of most individual adaptation measures, leading to increased economic damage. However, toward the end of the century, the decline in the population of Japan may significantly reduce flood exposure, potentially outweighing climate change impacts and creating a risk of maladaptive overinvestment in infrastructure if not planned dynamically. This complex dynamic necessitates the development of flexible, adaptive pathways for flood risk management rather than static, long‐term plans. Furthermore, we find that measures with notable co‐benefits, such as those contributing to climate change mitigation, hold strategic importance despite their lower direct flood‐reduction effectiveness. These findings provide crucial insights for policy‐makers to design sustainable and efficient flood risk management strategies that are spatially explicit, dynamically adjusted over time, and integrate both adaptation and mitigation goals.
ABSTRACT Tidal flooding in estuaries is expected to worsen as sea‐level rise (SLR) continues to accelerate and increases storm surge height. Conventional structural defences are often unsustainable, while nature‐based solutions like managed realignment require extensive land to be repurposed. Although estuary entrance geometry significantly influences tidal propagation, its role in flood mitigation remains underexplored. This study evaluates a simplified mitigation strategy inspired by natural entrance flow constriction using a calibrated 2D hydrodynamic model of the Clyde Estuary, Scotland, under 2100 and 2300 SLR projections. Simulated flow constrictions reduced tidal flood extents by up to 28% and 23%, dampened tidal range by 0.95 and 0.83 m under 2100 and 2300 SLR, respectively, and both delayed peak flooding by 30 min. Sensitivity analysis revealed that wider interventions placed closer to the estuary mouth are more effective at mitigating the effects of 2100 SLR. However, as a larger tidal prism caused by 2300 SLR is more concentrated in the middle estuary, placing interventions in the middle estuary, rather than towards the mouth, is more effective under the 2300 SLR scenario. These findings suggest that strategically placed flow constrictions could offer a scalable and effective tidal flood mitigation option, addressing pressing climate emergency challenges across urban estuaries around the world where alternative approaches to adaptation are not possible.
ABSTRACT Flooding is among the most frequent natural hazards threatening cultural heritage sites, yet current flood hazard studies often operate at urban or regional scales. While building‐scale damage models exist, they generally rely on flood depth inputs from large‐scale inundation models, inputs that may fail to capture the internal complexity of heritage buildings. This paper presents a 2D building‐scale flood hazard modelling approach designed to improve risk assessment, management, and adaptation for cultural heritage buildings. The method incorporates detailed architectural and structural features—such as basements; openings; uneven floor levels; and interior spatial layout—to simulate internal flood dynamics. The methodology is applied to the Marini Museum in Florence, Italy. An offline‐coupled hydraulic model is used in conjunction with a 2D urban‐scale flood model to simulate floodwater ingress, internal flow patterns, and the effects of mitigation measures. Our results indicate that relying solely on urban‐scale flood maps leads to substantial overestimation of internal flood depths, whereas the building‐scale model represents inundation processes within exhibition spaces. Such approach provides a more robust foundation for risk assessment and mitigation planning that supports heritage managers in the correct placement and display mode for vulnerable artworks, thanks to a risk classification of exhibition spaces. Future work will address model validation and extend this approach to heritage buildings with multiple levels under a variety of flood scenarios.
ABSTRACT Global warming increases the potential risks of hydrological extremes, such as extreme precipitation and flood. Limited attention has been given to the integrated effects of climate change, land‐use change, and socioeconomic advancement on flood risk under global warming of 1.5°C and 2.0°C threshold outlined in the Paris Agreement. Here, utilizing the latest coupled model Intercomparison Project 6 (CMIP6), the new shared socioeconomic pathway scenarios (SSPs), hydrological model and future land use simulation (FLUS) model, we perform a comprehensive assessment of the flood risk in the Huai River Basin (HRB) under the global warming of 1.5°C and 2.0°C scenarios. The results reveal that (1) more intense extreme precipitation events will occur in the HRB under two global warming scenarios. The increases in extreme precipitation are approximately twice as high under 2.0°C than under 1.5°C global warming scenario; (2) under global warming of 1.5°C and 2.0°C scenarios, future 100‐year floods will increase by 18.4% and 19.2%, respectively, in the HRB; and (3) high flood‐risk areas are expected to primarily locate in regions with unfavorable flood regimes, with increases of 4.3% and 17.8%, and very high flood‐risk areas are projected to expand by 2% and 4.3%, respectively. Considering the holistic effects of future environmental changes on the flood risk, it is imperative to incorporate flood control management and prevention measures into regional adaptation strategies.
