Extreme Storm Research Articles

Northeastern American Forests: Natural Disturbances, Climate Change Impact, and the Utilization of Increasingly Damaged Forest Trees for Biofuel Production

Forests and forestry-related industries and ecosystem services play a critical role in the daily life of all societies, including in cultural, ecological, social, economic, and environmental aspects. Globally, there are about 4.1 billion hectares of forestland. In the United States, there are about 304 million hectares of forestland, covering about 34% of the total land area, and the forest product industry produces over USD 200 billion worth of forestry products annually. Evidence suggests these precious resources may be negatively impacted by climate change via direct and indirect processes, including wildfires, insect/pest pressure, drought, extreme storm events, increased air temperature, solar radiation, vapor pressure deficit, and other factors and variables that can be detrimental. All these can not only cause significant changes in the health and productivity of the forests, but can also cause the extinction, migration, and/or re-distribution of different tree species. Thus, humankind has the paramount responsibility to take policy, technologic, economic, environmental, and management decisions and actions to protect this vital resource for current and future generations, plants, and animals. This paper provides an overview of some of the important characteristics of forest environmental services, climate–environment–forest interactions with respect to forest health and productivity, climate change’s impacts on forest species, and the utilization of forest biomass for high-value products.

Disaster risk in Caribbean fisheries: How vulnerability is shaped and how it can be reduced in Dominica and Antigua and Barbuda

Hurricanes and tropical storms have a substantial and sustained influence on fisheries globally. These threats present particularly significant challenges in Caribbean islands, where fisheries contribute towards economies, food security, and social and cultural identities. Yet, storm impacts on coastal communities and fisheries are a relatively neglected area of disaster risk reduction. In response, this paper reports on a novel application and adaptation of the Pressure and Release model (PAR) focused on Caribbean Island fisheries. The PAR is a well-established framework used to understand how vulnerability manifests and to identify appropriate policy and management options to reduce vulnerability and build resilience in the longer-term. This research highlights how this approach can expose underlying social, cultural, and economic factors that can either reduce or exacerbate vulnerability in the Caribbean island fisheries sector following extreme weather events using Dominica and Antigua and Barbuda as case studies. This study combines a literature review compiling data on underlying factors of vulnerability for Caribbean Island fisheries, with in-person interviews with fisheries managers from Dominica, and Antigua and Barbuda. It showcases the utility of the PAR in fisheries-focused recovery, and provides empirical evidence that fisheries play an important role in supporting immediate and medium-term coping and recovery after an extreme storm event. This approach has broader relevance for climate change adaptation as it highlights strategies for building resilience for fisheries-dependent societies.

The Extreme Space Weather Event of 1872 February: Sunspots, Magnetic Disturbance, and Auroral Displays

We review observations of solar activity, geomagnetic variation, and auroral visibility for the extreme geomagnetic storm on 1872 February 4. The extreme storm (referred to here as the Chapman–Silverman storm) apparently originated from a complex active region of moderate area (≈ 500 μsh) that was favorably situated near disk center (S19° E05°). There is circumstantial evidence for an eruption from this region at 9–10 UT on 1872 February 3, based on the location, complexity, and evolution of the region, and on reports of prominence activations, which yields a plausible transit time of ≈29 hr to Earth. Magnetograms show that the storm began with a sudden commencement at ≈14:27 UT and allow a minimum Dst estimate of ≤ −834 nT. Overhead aurorae were credibly reported at Jacobabad (British India) and Shanghai (China), both at 19.°9 in magnetic latitude (MLAT) and 24.°2 in invariant latitude (ILAT). Auroral visibility was reported from 13 locations with MLAT below ∣20∣° for the 1872 storm (ranging from ∣10.°0∣–∣19.°9∣ MLAT) versus one each for the 1859 storm (∣17.°3∣ MLAT) and the 1921 storm (∣16.°2∣ MLAT). The auroral extension and conservative storm intensity indicate a magnetic storm of comparable strength to the extreme storms of 1859 September (25.°1 ± 0.°5 ILAT and −949 ± 31 nT) and 1921 May (27.°1 ILAT and −907 ± 132 nT), which places the 1872 storm among the three largest magnetic storms yet observed.

A coupled approach for planning in vulnerable coastal landscapes

One of the most problematic steps in planning and management of vulnerable coastal systems under high level of uncertainties is anticipating potential design scenarios where multiple spatial scales interact. The mutual interference between beach morphodynamics and infrastructural needs of the wide territory poses several questions to urban planners and architects. In this study we assess the ability of the proposed coupled approach of MIKE 21 Spectral Wave and XBeach to significantly improve our understanding of vulnerable barrier beaches in urbanized areas, and hence to contribute to improved system design and management. Specifically, to improve the result accuracy and the time-computational efficiency of the proposed coupled approach, an additional module was developed. Located in the West Metropolitan Area of Cagliari city (Sardinia, IT), the barrier beach of La Playa is an important evacuation route that has experienced a coastal squeeze process under the increase in magnitude and frequency of sea storms. From an extensive beach survey, the “whole model composition” supports the selection of the territorial scale as the reference for defining a potential design scenario at the local scale in La Playa beach. While ordinary (10-year return time) storms create a nearshore sand berm with a protective function for the barrier beach and evacuation route against future storms, extreme (140-year return time) storms significantly erode the beach with a shoreline moving approximately 15 m landward and an overwash of Qcrest >20 l/m/s. Acting as a rigid impermeable obstacle against landward barrier migration, the evacuation route is destroyed. Urban planners and architects should consider the downgrade of the evacuation route as a part of a design potential scenario capable of minimizing the territorial vulnerability and reducing the local vulnerability. Rebuilt with environment-friendly functional road materials, this design scenario can also define a new flexible landward limit of the barrier beach and restore an eventual migration.

Subgrid modeling of urban flooding incorporating buildings' effects

Coastal cities at low-lying elevations face extensive inundation risks when attacked by extreme storm surges or tsunamis. Efficient and accurate modeling of flooding is critical for reducing losses in economic and lives. Therefore, the paradox arises that the computational cost is usually too high for rapid forecasts when considering the complex topography and buildings. The present contribution presents a subgrid-based approach, which combines the subgrid model and a drag force module, to realize efficient modeling of urban flooding at a high resolution. The volume-occupation effects of buildings are incorporated naturally in the subgrid treatment, and the blockage, sheltering, and frictional effects of buildings acting on the flows are considered through a simple, bulk parameterization in the form of drag forces. The model is validated with a laboratory experiment and then applied to an ideal numerical model at a real city scale. The proposed model is compared to three other models using coarse grids, with high-resolution simulation results serving as the benchmark for evaluation. The results demonstrate that the presence of buildings induces noticeable alterations in the distribution of water level and velocity fields within the urban area. The proposed model yields comparable predictions to the benchmark in terms of inundation extent, elevation, and velocity. Furthermore, the model exhibits minor sensitivity to grid size, allowing for a significant reduction in computational time compared to high-resolution simulations. The model achieves a well-balanced combination of high resolution, accuracy, and efficiency for large-scale simulations of coastal urban flooding.

Modeling and Evaluating Power Grid Resilience Under Extreme Space Weather

AbstractThe geomagnetically induced current (GIC) produced during extreme geomagnetic storms can easily lead to large‐scale blackouts in China due to the increase in the scale of its electric power grid. A power grid's resilience is its capability to resist various natural hazards, withstand primary failures, and quickly resume normal operation. To avoid power grid damages, this study developed a resilient power grid, incorporating failure, power flow calculation and recovery models under a uniform induced geoelectric field. We chose a system's performance loss as the resilience evaluation indicator, which intuitively reflected a system's loss under GIC. In addition, the recovery model was optimized using a genetic algorithm, and two resilience improvement measures were proposed. The IEEE‐RTS‐79 system, consisting of 10 generators, 24 buses and 5 transformers, was chosen as an example to verify the feasibility of this study. The results show that the genetic algorithm and optimization measures effectively enhanced the system's resilience indicator and provided a reference for preventing system damages under GIC and quick recovery after possible failures.

Assessing the Risk of Extreme Storm Surges from Tropical Cyclones under Climate Change Using Bidirectional Attention-Based LSTM for Improved Prediction

Accurate prediction of storm surges is crucial for mitigating the impact of extreme weather events. This paper introduces the Bidirectional Attention-based Long Short-Term Memory (LSTM) Storm Surge Architecture, BALSSA, addressing limitations in traditional physical models. By leveraging machine learning techniques and extensive historical and real-time data, BALSSA significantly enhances prediction accuracy. Utilizing a bidirectional attention-based LSTM framework, it captures complex, non-linear relationships and long-term dependencies, improving the accuracy of storm surge predictions. The enhanced model, D-BALSSA, further amplifies predictive capability through a doubled bidirectional attention-based structure. Training and evaluation involve a comprehensive dataset from over 70 typhoon incidents in Macao between 2017 and 2022. The results showcase the outstanding performance of BALSSA, delivering highly accurate storm surge forecasts with a lead time of up to 72 h. Notably, the model exhibits a low Mean Absolute Error (MAE) of 0.0287 m and Root Mean Squared Error (RMSE) of 0.0357 m, crucial indicators measuring the accuracy of storm surge predictions in water level anomalies. These metrics comprehensively evaluate the model’s accuracy within the specified timeframe, enabling timely evacuation and early warnings for effective disaster mitigation. An adaptive system, integrating real-time alerts, tropical cyclone (TC) chaser, and prospective visualizations of meteorological and tidal measurements, enhances BALSSA’s capabilities for improved storm surge prediction. Positioned as a comprehensive tool for risk management, BALSSA supports decision makers, civil protection agencies, and governments involved in disaster preparedness and response. By leveraging advanced machine learning techniques and extensive data, BALSSA enables precise and timely predictions, empowering coastal communities to proactively prepare and respond to extreme weather events. This enhanced accuracy strengthens the resilience of coastal communities and protects lives and infrastructure from the escalating threats of climate change.

Refining the history of extreme coastal events in southern Newfoundland, NW Atlantic, with lake sediment archives

Extreme events, including tsunamis, storm surges and hurricanes, affect the western North Atlantic coast, threatening communities and impacting ecosystems. Although these extreme coastal events (ECEs) are currently thoroughly monitored in the area, older ECEs are known only from short historical evidence. Therefore, establishing a reliable ECE history that extends more than several decades into the past requires data from geological records. This study aims to refine the existing record of ECEs on the southern Burin Peninsula (Newfoundland, Canada) that extends back to the mid-18th century by providing the first lake-sediment-based reconstruction of the event history of the Canadian province of Newfoundland and Labrador. We investigated short sediment cores collected from four shallow coastal lakes. The identification of ECEs relied mainly on lithostratigraphic evidence and peak detection of sand grain counts. Diatoms, dry bulk density, and loss-on-ignition were also analyzed. The age-depth models of the cores were based on 210Pb and 137Cs dating. The correlation of our records with major historical events confirmed that statistically significant sand count peaks, sand layers, and redeposited peat clasts are indicative of major ECEs. We found that the type and characteristics of ECE records depended mainly on the availability of sediment, distance from the seashore, and magnitude of the event. Although some of our ECE markers are associated with marine inundations, only slight diatom community changes were observed, contrary to what is commonly expected from the disturbance of brackish and freshwater ecosystems by major saltwater intrusions. Combined, our records indicate that at least eight ECEs occurred since the late 17th century, three of which might correlate with historically documented events, namely the 1755 CE Lisbon tsunami, the 1775 CE Great Independence Hurricane, and the 1929 CE Newfoundland tsunami. Although further investigations are required to better constrain the type and magnitude of particular ECEs, our study confirms the suitability of geological archives for improving hazard assessment in coastal regions.

Statistical and hydrodynamic numerical modeling to quantify storm surge hazard: Comparison of approaches applied to U.S. North Atlantic coast

Estimating the storm surge magnitude and annual exceedance probability is a key element in the siting and design of coastal nuclear power plants in both the U.S. and France. However, differences in storm climatology, specifically the relative importance of tropical cyclones (TCs) versus extratropical storms (XTCs), have driven differences in estimation method development. This work compares purely statistical modeling with combined statistical and numerical simulation modeling approaches for extreme storm surge applied to the U.S. North Atlantic coast which is subject to both tropical and extratropical storms. Two frequency analysis methods are applied to observed water levels and compared to a copula-based joint probability analysis of TCs and automated frequency analysis of XTCs that is enriched with numerically simulated storms. One frequency analysis method is applied using (1) hourly at-site data and (2) hourly at-site data enriched with additional data from a homogeneous region. The other frequency analysis method is applied using (1) hourly at-site data and (2) hourly at-site data enriched with monthly water level maxima. Variables of interest used in the comparison are skew storm surge, maximum instantaneous storm surge, non-tidal residual and maximum seal level. The performance of the methods (mean surge and water level estimates and confidence intervals) depend on the variable of interest and, to some extent, on return period. Inclusion of additional information (e.g., regional water levels, and monthly maxima) in the frequency analysis methods does not have a large impact on estimated mean surge and water levels, but significantly reduces resulting confidence intervals (over 40% reduction in some cases). However, the confidence intervals still grow with increasing return period. Inclusion of simulated storms in the joint probability analysis results in significantly different mean surge and water level estimates (up to 25% higher than the frequency analysis in some cases). The joint probability analysis confidence intervals are wider than those for the frequency analysis methods lower return periods (e.g., 60%–80% wider at 100 years), but they grow much more slowly and are significantly narrower for higher return periods (e.g., 40%–60% narrower at 1 000 years). Although there are appreciable differences between the results documented in this paper, these are reasonable due to differences in the data and methods used in this comparison.

Analyses of Equatorward Auroral Extensions during the Extreme Geomagnetic Storm on 15 July 1959

Abstract Intense solar eruptions occasionally trigger extreme geomagnetic storms, expand the boundaries of the auroral oval, and facilitate equatorward extensions of the auroral visibility. It is important to analyse such events, to better understand the extremity of space weather and its impact on the technological infrastructure of the modern civilization. However, unlike other extreme geomagnetic storms, little is known about the auroral activity associated with the extreme geomagnetic storm on 15/16 July 1959, the second largest geomagnetic storm in the space age. This study acquired and analysed two Chinese accounts and one Russian account of auroral visibility at low (≤40°) magnetic latitudes (MLATs). These records allowed us to conservatively reconstruct the equatorward boundaries of the auroral visibility and the auroral oval at 27.4° MLAT and 35.4° invariant latitude, respectively. Our analysis chronologically contextualized these auroral records slightly before the peak of the extreme geomagnetic storm. Moreover, their coloration indicates the excitations of, at least, nitrogen at 427.8 nm and oxygen at 557.7 nm at these low MLATs. Our results allow us to contextualize this extreme geomagnetic storm within other extreme events, based on the equatorward boundary of the auroral oval, thereby facilitating the improvement in existing empirical models for correlations of the auroral extension and the storm intensity.

Disentangling Dynamic from Thermodynamic Summer Ice Area Loss from Observations (1979–2021): A Potential Mechanism for a “First-Time” Ice-Free Arctic

Abstract In recent decades, the Arctic minimum sea ice extent has transitioned from a predominantly thick multiyear ice cover to a thinner seasonal ice cover. We partition the total (observed) Arctic summer area loss into thermodynamic and dynamic (convergence, ridging, and export) sea ice area loss during the satellite era from 1979 to 2021 using a Lagrangian sea ice tracking model driven by satellite-derived sea ice velocities. Results show that the thermodynamic signal dominates the total summer ice area loss and the dynamic signal remains small (∼20%) even in 2007 when dynamic loss was largest. Sea ice loss by compaction (within pack ice convergence) dominates the dynamic area loss, even in years when the export is largest. Results from a simple (Ekman) free-drift sea ice model, supported by results from the Lagrangian model, suggest that nonlinear effects between dynamic and thermodynamic area loss can be important for large negative anomalies in sea ice extent, in accord with previous modeling studies. A detailed analysis of two all-time record minimum years (2007 and 2012)—one with a semipermanent high in the southern Beaufort Sea and the other with a short-lived but extreme storm in the Pacific sector of the Arctic in late summer—shows that compaction by Ekman convergence together with large thermodynamic melt in the marginal ice zone dominated the sea ice area loss in 2007 whereas, in 2012, it was dominated by Ekman divergence amplified by sea–ice albedo feedback—together with an early melt onset. We argue that Ekman divergence from more intense summer storms when the sun is high above the horizon is a more likely mechanism for a “first-time” ice-free Arctic.

An extreme storm decreases reef fish abundance and richness but does not impact spatial heterogeneity

An extreme storm decreases reef fish abundance and richness but does not impact spatial heterogeneity

Evaluating the extent and impact of the extreme Storm Gloria on Posidonia oceanica seagrass meadows

Extreme storms can trigger abrupt and often lasting changes in ecosystems by affecting foundational (habitat-forming) species. While the frequency and intensity of extreme events are projected to increase under climate change, its impacts on seagrass ecosystems remain poorly documented. In January 2020, the Spanish Mediterranean coast was hit by Storm Gloria, one of the most devastating recent climate events in terms of intensity and duration. We conducted rapid surveys of 42 Posidonia oceanica meadows across the region to evaluate the extent and type of impact (burial, unburial and uprooting). We investigated the significance of oceanographic (wave impact model), geomorphological (latitude, depth, exposure), and structural (patchiness) factors in predicting impact extent and intensity. The predominant impact of Storm Gloria was shoot unburial. More than half of the surveyed sites revealed recent unburial, with up to 40 cm of sediment removed, affecting over 50 % of the meadow. Burial, although less extensive, was still significant, with 10–80 % of meadow cover being buried under 7 cm of sediment, which is considered a survival threshold for P. oceanica. In addition, we observed evident signs of recently dead matte in some meadows and large amounts of detached drifting shoots on the sea bottom or accumulated as debris on the beaches. Crucially, exposed and patchy meadows were much more vulnerable to the overall impact than sheltered or continuous meadows. Given how slow P. oceanica is able to recover after disturbances, we state that it could take from decades to centuries for it to recoup its losses. Seagrass ecosystems play a vital role as coastal ecological infrastructure. Protecting vulnerable meadows from anthropogenic fragmentation is crucial for ensuring the resilience of these ecosystems in the face of the climate crisis.

Impacts of Extratropical Cyclone Fiona on a sensitive coastal lagoon ecosystem

AbstractOceanic storms can strongly disturb the physical and biogeochemical conditions of transitional coastal waters. Impacts of extreme oceanic storms on coastal ecosystems have received limited attention worldwide, with no studies at higher latitudes (> 45°) where tropical cyclones have usually abated. This study investigates the combined impacts from marine and atmospheric forcing on a coastal lagoon in Prince Edward Island, Canada, during and after Extratropical Cyclone Fiona in September 2022. Physical (water levels and temperature) and biogeochemical (dissolved oxygen [DO], electrical conductivity, pH, nitrate–nitrogen concentrations, total suspended solids [TSS]) datasets from the lagoon and the watershed's tributaries, groundwater springs, and piezometers were used to assess ecosystem disturbance and recovery timelines following the storm. Fiona resulted in a 1.6 m storm surge into the lagoon that elevated water temperatures by up to 6°C, disturbed the density‐dependent stratification of salinity and temperature, and reduced the diel amplitude of DO, indicating a reduction in plant respiration due to ecosystem disturbance. The freshwater tributaries revealed sharp changes in flow (30‐fold increase), nitrate–nitrogen (NO3‐N) concentrations and loading (70‐fold increase), and TSS loading (40‐fold increase) to the lagoon during and immediately following the storm. The lagoon rapidly recovered (hours) from the hydraulic disturbance of the storm surge, but elevated nutrient levels persisted for months. The intensity and frequency of extratropical cyclones is projected to increase in the Northwest Atlantic, making field‐based studies of cyclone impacts on coastal waters critical for understanding future coastal ecosystem disturbance and recovery periods relative to the timing of future storms.

Large regional variability in geomagnetic storm effects in the auroral zone

A digital society is fragile and vulnerable to space-originated electromagnetic disturbances. Global geomagnetic conditions have been actively monitored since the invention of the magnetometer in 1833. However, regional changes in the magnetic environment have been widely left unstudied because of the sparsity of the observing networks. The Scandinavian Magnetometer Array (SMA) was the densest magnetometer network in history, and it was in operation in Fennoscandia during the International Magnetospheric Study (IMS) in 1976–1979. The data has been left mainly unstudied because it was recorded on 35 mm films, which are difficult to use for scientific studies. We used the DigiMAG digitization method to digitize magnetic data from all 32 SMA stations for a geomagnetic storm on 10–12 December 1977. Using these digitized values and modern magnetic data, we found large regional differences about up to 2 nT/km during strong geomagnetic storms (Dst 100–200 nT) and 7 nT/km for major scale Halloween geomagnetic storm, which correspond to 400 and 1400 nT difference for a typical 200 km station separation, respectively. The average size of substorms is 400 nT in the auroral zone. We conclude that the sparse magnetometer network can cause an underestimation of the regional magnetic disturbances and their effects. Misestimation of regional disturbances during extreme storms like the Carrington event may lead to insufficient planning of mitigation procedures and strategies.

Geomagnetically Induced Current Mitigation in New Zealand: Operational Mitigation Method Development With Industry Input

AbstractReducing the impact of Geomagnetically induced currents (GICs) on electrical power networks is an essential step to protect network assets and maintain reliable power transmission during and after storm events. In this study, multiple mitigation strategies are tested during worst‐case extreme storm scenarios in order to investigate their effectiveness for the New Zealand transmission network. By working directly with our industry partners, Transpower New Zealand Ltd, a mitigation strategy in the form of targeted line disconnections has been developed. This mitigation strategy proved more effective than previous strategies at reducing GIC magnitudes and durations at transformers at most risk to GIC while still maintaining the continuous supply of power throughout New Zealand. Under this mitigation plan, the average 60‐min mean GIC decreased for 27 of the top 30 at‐risk transformers, and the total network GIC was reduced by 16%. This updated mitigation has been adopted as an operational procedure in the New Zealand national control room to manage GIC. In addition, simulations show that the installation of 14 capacitor blocking devices at specific transformers reduces the total GIC sum in the network by an additional 16%. As a result of this study Transpower is considering further mitigation in the form of capacitor blockers. We strongly recommend collaborating with the relevant power network providers to develop effective mitigation strategies that reduce GIC and have a minimal impact on power distribution.

Rice farming practices and their responses to environmental changes during the mid-Holocene on the Ningshao Plain, eastern China

The cultivation of rice, which serves as a dietary staple for more than half of the global population, exerts a profound impact on both the national economy and people's livelihood. However, there remains a lack of clarity regarding the developmental stages of rice agriculture and its response to environmental change. Here, we present a multi-proxy analysis encompassing pollen, phytolith, grain size, and magnetic susceptibility derived from the Xiawangdu site of the Ningshao Plain, eastern China. Our findings indicate that climatic conditions transitioned from warm and humid to cool and dry between 6000 and 4300 cal a BP. The sedimentary environment within this region can be categorized into three evolutionary sequences, viz. marine transgression (6000–5500 cal a BP), salt marsh environment (5500–5000 cal a BP), and freshwater environment (5000–4300 cal a BP). Notably, extreme storm events occurred around ∼5300–5100 cal a BP. Concurrently, rice farming practices underwent three distinct stages from a slow period (6000–5500 cal a BP), to a developing period (5500–5000 cal. a BP), through to a flourishing period (5000–4300 cal a BP) on the Ningshao Plain. Marine transgression likely served as the primary inhibiting factor hindering and delaying rice farming practices during 6000–5500 cal a BP, whereas extreme storm events may have contributed to asynchronous agricultural activities on the Ningshao Plain at 5500–5000 cal a BP.

An Integrated Scheme for Forecasting and Controlling Ramps in Offshore Wind Farms Considering Wind Power Uncertainties during Extreme Storms

Global warming-induced extreme tropical storms disrupt the operation of offshore wind farms, causing wind power ramp events and threatening the safety of the interconnected onshore grid. In order to attenuate the impact of these ramps, this paper proposes an integrated strategy for forecasting and controlling ramps in offshore wind farms. First, the characteristics of wind power ramps during tropical storms are studied, and a general ramp control framework is established. Second, a wind power ramp prediction scheme is designed based on a minimal gated memory network (MGMN). Third, by taking into account the wind power ramp prediction results and wind power uncertainties, a chance-constraint programming-based optimal ramp control scheme is developed to simultaneously maximize wind power absorption and minimize ramp control costs. Finally, we use real-world offshore wind farm data to validate the effectiveness of the proposed strategy.

An XGBoost-SHAP approach to quantifying morphological impact on urban flooding susceptibility

Urban flooding risks, often overlooked by conventional methods, can be profoundly affected by city configurations. However, explainable Artificial Intelligence could provide insights into how urban configurations affected urban flooding. This study, taking entered on Shenzhen City, deploys an XGBoost, integrating SHapley Additive exPlanation and Partial Dependency Plots, to assess how urban morphology influences urban flooding susceptibility. The models and strategies presented in this study aimed to adapt to extreme storms from the perspective of city spatial configuration planning. The findings underscore the varying impact of disaster variables on urban flooding, with morphological attributes becoming highly significant during severe inundations. In the analysis, mean building volume emerged as a pivotal parameter, with a mean SHAP value of 0.0107 m and a contribution ratio of 9.70 %. The study indicates that building configurations should be optimized to minimize urban flooding risks. It is recommended that the Mean Building Volume (MBV) be maintained within the range of 1.25 km3 to 2.5 km3, and the Standard Deviation of Building Volume (SDBV) be kept below 2.814 km3. By harnessing explainable Artificial Intelligence algorithms, this study offers insights into the intricate relationship between urban forms and flood risk, thereby informing the development of effective urban flooding adaptation strategies.

Comparative Analysis of BALSSA and Conventional NWP Methods: A Case Study in Extreme Storm Surge Prediction in Macao

In coastal regions, accurate storm surge prediction is crucial for effective disaster management and risk mitigation. This study presents a comparative analysis between BALSSA (Bidirectional Attention-based LSTM for Storm Surge Architecture) and the Japan Meteorological Agency (JMA) numerical storm surge model, focusing on the Saola-induced storm surge in Macao, September 2023. To train and assess the model, we leverage an extensive dataset comprising meteorological and tide level information from more than 80 typhoon occurrences in Macao spanning the period from 2017 to 2023. The results provide evidence of BALSSA’s effectiveness in capturing the complex spatio-temporal dynamics of storm surges, with a lead time of up to 72 h, as reflected by its MAE of 0.019 and RMSE of 0.024. It demonstrates reliable accuracy in predicting storm surge magnitude, timing, and spatial extent, potentially contributing to more precise and timely warnings for coastal communities. Furthermore, the real-time data assimilation feature of BALSSA ensures up-to-date information, aligned with the latest observations, which is essential for effective emergency preparedness and response. The high-resolution grids enhance risk assessment, highlighting BALSSA’s potential for early warnings, emergency planning, and coastal risk management. This study contributes valuable insights to the broader field of storm surge prediction, guiding decision-making processes and supporting the development of effective strategies to enhance coastal resilience.