Search Results (185)

Identifying areas that frequently experience post-rainfall ponding is essential for effective flood mitigation and planning. This study integrates Sentinel-1 radar imagery and the Topographic Control Index (TCI) to identify 378 flood-prone urban depressions in Beaumont, Texas. Out of 159 major rainfall events, only six had Sentinel-1 radar imagery acquired within six hours of peak rainfall, and these were used to generate the flood frequency map; the ground-based flood sensor data were used to verify that these selected events corresponded to actual peak rainfall and to validate radar-detected water pixels. Validation results showed 100% precision, 70.87% recall, an F1-score of 82.95%, and 71.32% overall accuracy. Approximately 84% of medium-to-high TCI depressions overlapped with Beaumont’s two-year inundation map, confirming a strong relationship between TCI and observed flooding. A total of 124 depressions retained significant water, and after excluding 25 engineered detention ponds, 99 natural depressions remained flood vulnerable. Among these, 74 depressions with medium or high TCI were identified as the highest-priority nuisance flooding hotspots. The results demonstrate that combining TCI with radar imagery provides a reliable and cost-effective approach for identifying areas prone to frequent urban ponding. This framework supports practical decision-making for drainage improvements, hotspot identification, and early-warning system development in urban flood-prone regions. Full article

Reducing urban pluvial flooding susceptibility requires identifying dominant variables in different regions and offering quantitative management strategies, which remains a challenge for existing methodologies. To address this, this study delves into the characteristics of SHAP’s (Shapley Additive exPlanations) local interpretability and proposes a novel and concise framework based on explainable artificial intelligence (ensemble learning-SHAP) and applies it to the central urban area of Guangzhou as a case study. The research findings are as follows: (1) This framework captures the nonlinear and threshold effects of flood drivers, identifying specific inflection points where landscape features shift from mitigating to exacerbating flooding. (2) Anthropogenic variables, specifically impervious surface density (ISD) and vegetation (kNDVI), are identified as the dominant variables driving susceptibility in urban hotspots at the grid scale. (3) The interpretability results demonstrate high stability across model iterations. Finally, based on these findings, this study provides place-based and quantitative pluvial flooding management recommendations: for areas dominated by impervious surfaces and vegetation, maintaining the impervious surface density below 0.8 and the kNDVI above 0.25 can effectively reduce the susceptibility to urban flooding. This study provides a framework for achieving place-based and quantitative flooding management and offers valuable scientific insights for flooding management, urban planning, and sustainable urban development in the central district of Guangzhou, as well as in broader developing regions. Full article

Natural hazards pose an increasing threat to infrastructures, lives, and livelihoods in alpine regions due to climate change and the growing demand for settlement space. While grey protective structures are commonly deployed to provide immediate safety, their sustainability, and thus protective function, is limited by cost-intensive maintenance. Nature-based solutions (NbS) can alleviate these shortcomings by offering cost-effective, adaptive protection that strengthens over time, making their deployment a key factor in building resilience to climate-induced hazards. This paper introduces a systematic methodology for the strategic deployment of NbS to enhance climate resilience. It integrates a three-level hazard classification system with an expert-led assessment rating 74 NbS against 29 hazards. A subsequent Principal Component Analysis (PCA) synthesises these into six functional groupings based on their shared mitigation characteristics. The core of this framework introduces two key innovations: a novel Mitigation Score and a Hazard Mitigation Profile. Together, they evaluate NbS effectiveness dynamically through the different phases of natural hazards, surpassing traditional static ratings by evaluating NbS performance across the hazard management cycle—from predisposition to post-event recovery. Significant variation in mitigation scoring was observed for individual hazard classes and types. Erosion processes (e.g., sheet, rill, and gully erosion) achieved the highest mitigation scores (1.90), as they can be addressed by many highly effective NbS (21–33 types). Conversely, flood-related hazards, such as fluvial and pluvial floods, showed moderate scores (1.64–1.66) with a balanced mix of mitigative and supportive NbS, while options for mitigating impact floods and coastal floods were far more limited (1.00–1.42). The resulting methodology provides a crucial, practical link between specific climate-related threats and viable, nature-based responses, serving as a robust framework to guide the decisions of planners, engineers, and policymakers. By enabling a more strategic and temporally aware deployment of NbS, our findings inform the development of adaptive management strategies to ensure their long-term effectiveness. Full article

Floods, especially in urbanised areas, incur enormous economic and social losses. The structural flood management is often limited by urbanization and environmental issues. Following the catastrophic flood events of 1997 and 2010, a relatively large dry polder was constructed in Racibórz Dolny, Poland, with the highest flood retention capacity in Central Europe. During the 2024 flood in Czechia and Poland, the polder was filled to 80%, which significantly reduced the floodwave crest on the Odra River (by 1.65 m), halved the peak discharge, and delayed the floodwave passage by two days according to hydrological calculations. The operation of the polder enables multifunctional use of the river valley—ranging from agriculture and mineral extraction to environmental protection—without the need for permanent water impoundment. Aggregate extraction carried out within the basin contributed to shaping the reservoir, reducing the demand for transport and construction materials, while the overburden was reused for engineering and reclamation purposes. Mining activities between 2007 and 2023 increased the retention capacity of the polder by 13%, providing an example of rational environmental resource management combined with effective flood protection. The findings demonstrate that integrating retention functions with mineral resource management represents an efficient and sustainable approach to mitigating flood impacts in large European river valleys. Full article

Accelerating urbanization and the intensifying pace of climate change have heightened the occurrence of urban pluvial flooding, threatening urban sustainability. As the preferred approach to urban stormwater management, coupled gray and green infrastructure (GI–GREI) integrates GREI’s rapid runoff conveyance with GI’s infiltration and storage capacities, and their siting and scale can affect life-cycle cost (LCC) and urban drainage system (UDS) resilience. Focusing on Fengxi New City, China, this study develops a multi-objective optimization framework for the GI–GREI system that integrates GI suitability and pipe-network importance assessments and evaluates the Pareto set through entropy-weighted TOPSIS. Across multiple rainfall return periods, the study explores optimal trade-offs between UDS resilience and LCC. Compared with the scenario where all suitable areas are implemented with GI (maximum), the TOPSIS-optimal schemes reduce total life-cycle cost (LCC) by CNY 3.762–4.298 billion (53.36% on average), rebalance cost shares between GI (42.8–47.2%) and GREI (52.8–57.2%), and enhance UDS resilience during periods of higher rainfall return (P = 20 and 50). This study provides an integrated optimization framework and practical guidance for designing cost-effective and resilient GI–GREI systems, supporting infrastructure investment decisions and climate-adaptive urban development. Full article

With the increasing frequency of extreme rainfall events, pluvial flooding has become a critical challenge to the safety and sustainable development of megacities worldwide. This study proposes a multi-dimensional framework for assessing urban pluvial flood resilience (UPFR) by integrating a coupled hydrological-hydrodynamic model with system performance curves. The framework characterizes the dynamic evolution of resilience across three dimensions: rainfall characteristics, risk thresholds, and spatial scales. Results show that short-duration intense rainfall triggers instantaneous pipe overloading, whereas long-duration storms impose cumulative stress that leads to sustained systemic weakening, with the lowest resilience observed under extreme prolonged rainfall conditions. The specification of risk thresholds strongly influences resilience ranking, with the vehicle stalling risk (VSR) consistently showing the lowest resilience, followed by building inundation risk (BIR) and human instability risk (HIR). Spatially, pipes represent the weakest components, nodes maintain resilience under moderate stress, and the regional system exhibits a pattern of local weakness but overall stability, accompanied by delayed recovery. These findings highlight the importance of incorporating multi-threshold and multi-scale perspectives in flood resilience assessment and management. The proposed framework provides a scientific basis to support staged prevention measures and adaptive emergency response strategies, thereby enhancing urban flood resilience in megacities. Full article

The factors influencing urban pluvial flooding in cities with complex topography, such as hill–basin systems, are highly nonlinear and spatially heterogeneous due to the interplay between rugged terrain and intensive human activities. However, previous research has predominantly focused on plain, mountainous, and coastal cities. As a result, the waterlogging mechanisms in hill–basin areas remain notably understudied. In this study, we developed a geographically explainable artificial intelligence (GeoXAI) framework integrating Geographical Machine Learning Regression (GeoMLR) and Geographical Shapley (GeoShapley) values to analyze nonlinear impacts of flooding factors in Changsha, a typical hill–basin city. The XGBoost model was employed to predict flooding risk (validation AUC = 0.8597, R² = 0.8973), while the GeoMLR model verified stable nonlinear driving relationships between factors and flooding susceptibility (test set R² = 0.7546)—both supporting the proposal of targeted zonal regulation strategies. Results indicated that impervious surface density (ISD), normalized difference vegetation index (NDVI), and slope are the dominant drivers of flooding, with each exhibiting distinct nonlinear threshold effects (ISD > 0.35, NDVI < 0.70, Slope < 5°) that differ significantly from those identified in plain, mountainous, or coastal regions. Spatial analysis further revealed that topography regulates flooding by controlling convergence pathways and flow velocity, while vegetation mitigates flooding through enhanced interception and infiltration, showing complementary effects across zones. Based on these findings, we proposed tailored zonal management strategies. This study not only advances the mechanistic understanding of urban waterlogging in hill–basin regions but also provides a transferable GeoXAI framework offering a robust methodological foundation for flood resilience planning in topographically complex cities. Full article

Climate adaptation is becoming increasingly important for municipalities. Yet, key questions remain about why they engage with this agenda, particularly at different stages of the adaptation cycle and over time. This study examines how 17 different factors, grouped into four principal components (city size & scale; land use & compactness; socio-economics; and regional climate & exposure to extreme weather), influence municipal adaptation activities. It examines how these variables played out in 104 German cities, using the results of two assessment frameworks: one analysed municipal adaptation activities across five dimensions in 2018, while the other mapped them against three dimensions in both 2018 and 2022. Regression analysis indicates that larger, more compact and more exposed cities are generally more active in adaptation, whereas socio-economic factors have a minimal impact. City size & scale shows significant effects consistently across all assessment dimensions. All four components, including socio-economics, influence adaptation plan-related dimensions, whereas implementation of adaptation measures is primarily shaped by land use & compactness. The influence of city size & scale and regional climate & exposure declined between 2018 and 2022, suggesting a policy diffusion process. These findings reveal different nuances in factors influencing municipal adaptation, highlight the importance of including implementation in assessments of adaptation, and echo calls for further research into causal mechanisms and longitudinal studies. Full article

The rising urbanization and climate change have increased pluvial flood risks, especially in highly urbanized areas. This study focuses on the Lake Ganzirri area in Messina, Italy, where street-level floods have raised concerns for infrastructure resilience and public safety. This study aims to explore how to effectively represent key urban features, emphasizing buildings and low-impact development/sustainable urban drainage systems (LID/SUDS). For the buildings, a combination of referred approaches to represent buildings is compared against the widely used method to represent buildings as voids in a 2D mesh, ignoring them in the water balance calculations. For the LID/SUDS control elements, a 2D representation is presented and compared against the widely used 1D approach to model such elements. The study uses three well-known software packages—EPA-SWMM 5.2, HEC-RAS 6.2, and InfoWorks ICM 2021.9—applied to the Lake Ganzirri area, to explore the representation of buildings using the building void method (available in InfoWorks ICM 2021.9) against the proposed method (in HEC-RAS 6.2) to replicate runoff flow over a 2D model of a highly urbanized area. From scenario S0, three more scenarios were derived: S1 (S0 with pluvial drainage network), S2 (S1 with LID/SUDS control elements), and S3 (S0 with 2D representation of LID/SUDS), which were then compared against using four comparison schemes. Results show that the proposed method for representing buildings computed the propagation of the runoff comparable to when the building void method is used, with some shortcomings regarding mesh adjustments and computational times. Regarding the 2D representation of LID/SUDS, the effects were unperceivable on water depth maps (reduction in water depths of 1.5 mm on average for all the rainfall events). Still, they were reflected in the increase of 62% of the infiltration volume on average of all the rainfall scenarios and a decrease of 9.1% of water flowing outside the 2D area, therefore replicating the effect of capturing water. Full article

Ilha de Moçambique is an island off the northern coast of Mozambique, covering an area of 1.5 km². Recognized as a UNESCO World Heritage Site since 1991, the island is currently under threat due to the increasing frequency and intensity of extreme weather events caused by climate change. Cyclonic events and pluvial floods have led to the progressive degradation of buildings and are compromising the integrity of the site. Furthermore, the island’s economic and social vulnerability is also worsening. The article aims to critically review the strategic planning approaches adopted for climate adaptation on Ilha de Moçambique. The objective is to identify and assess the planning instruments implemented to protect coastal urban heritage in light of contemporary challenges. Methodologically, a literature review is conducted based on the analysis of a collection of plans dedicated to adapting to climate change and heritage preservation. The results reveal that current planning approaches remain fragmented and insufficient, reducing their practical impact. There is a notable absence of planning instruments specifically designed to integrate cultural heritage preservation with urban climate adaptation. In conclusion, although some initiatives are underway, significant gaps persist in the strategic planning framework, underscoring the urgent need for inclusive integrated and adaptive measures to safeguard the island’s urban heritage and community in the long term. Full article

Urban flooding caused by short and high-intensity rainfall events presents increasing challenges for cities, threatening infrastructure, public safety and economic activity. Accurately representing infiltration processes in hydrodynamic models is critical, as oversimplifying infiltration can lead to significant errors in predicted flood extents and water depths. This study systematically compares two widely used infiltration models—Green-Ampt and Curve Number—implemented within two leading 2D hydraulic models, HEC-RAS and IBER, to assess their influence on urban flood predictions. Simulations were conducted for 26 rainfall events, including both observed and synthetic hyetographs, across two urban neighbourhoods in Pamplona metropolitan area, Spain. Model performance was evaluated using root mean square error, mean absolute error and confusion matrix-derived metrics such as precision, accuracy, specificity, sensitivity and negative predictive value. Results indicate that the choice of infiltration method significantly affects both water depths and inundation extents: while Green-Ampt yields more conservative water depth estimates, Curve Number tends to underestimate flood extents. The comparison between the two hydraulic models has shown that IBER simulates broader flood extents and lower water depth errors compared to HEC-RAS. The findings highlight the importance of selecting appropriate infiltration methods and hydraulic models for reliable urban flood risk assessment, as well as providing guidance for model selection in urban inundation studies. Full article

Monitoring urban pluvial floods remains a challenge, particularly in dense city environments where drainage overflows are localized, and sensor-based systems are often impractical. Physical sensors can be costly, prone to theft, and difficult to maintain in areas with high human activity. To address this, we developed an innovative flood detection framework that utilizes publicly accessible CCTV imagery and large language models (LLMs) to classify flooding conditions directly from images using natural language prompts. The system was tested in Bandung, Indonesia, across 340 CCTV locations over a one-year period. Four multimodal LLMs, ChatGPT-4.1, Gemini 2.5 Pro, Mistral Pixtral, and DeepSeek-VL Janus, were evaluated based on classification accuracy and operational cost. ChatGPT-4.1 achieved the highest overall accuracy at 85%, with higher performance during the daytime (89%) and lower accuracy at night (78%). A cost analysis showed that deploying GPT-4.1 every 15 min across all locations would require approximately USD 59,568 per year. However, using compact models like GPT-4 nano could reduce costs by up to seven times, with minimal loss of accuracy. These results highlight the trade-off between performance and affordability, especially in developing regions. This approach offers a scalable, passive flood monitoring solution that can be integrated into early warning systems. Future improvements may include multi-frame image analysis, automated confidence filtering, and multi-level flood classification for enhanced situational awareness. Full article

Climate change is elevating temperatures, shifting weather patterns, and increasing frequency and severity of extreme weather events. Despite the urgency with which solutions are needed, relatively few studies comprehensively investigate the effectiveness of alternative flood risk management options under different climate conditions. Specifically, we are interested in a comparison of the effectiveness of resistance, nature-based, and managed retreat strategies. Using an integrated 1D-2D PCSWMM model, this paper presents a comprehensive investigation into the effectiveness of alternative adaptation strategies in reducing flood risks in Eastwick, a community of Philadelphia, PA, subject to fluvial, pluvial, and coastal flood hazards. While addressing the urgent public need to develop local solutions to this community’s flood problems, the research also presents transferable insights into the limitations and opportunities of different flood risk reduction strategies, manifested here by a levee, watershed-scale green stormwater infrastructure (GSI) program, and a land swap. The effectiveness of these options is compared, respectively, under compound climate change conditions, with the spatiotemporal patterns of precipitation and Delaware river tidal conditions based on Tropical Storm Isaias (2020). The hypothesis was that the GSI and managed retreat approaches would be superior to the levee, due to their intrinsic ability to address the compound climate hazards faced by this community. Indeed, the findings illustrate significant differences in the predicted flood extents, depths, and duration of flooding of the various options under both current and future climate scenarios. However, the ideal remedy to flooding in Eastwick is more likely to require an integrated approach, based on more work to evaluate cost-effectiveness, stakeholder preferences, and various logistical factors. The paper concludes with a call for integrating multiple strategies into multifunctional flood risk management. Full article

This study aims to analyse the effect of partially clogged inlets on the behaviour of urban drainage systems at the city scale, particularly regarding intercepted volumes and flood depths. The main challenges were to represent the inlet network in detail at a rather large scale and to avoid the effect of sewer network surcharging on the draining capacity of inlets. This goal has been achieved through a 1D/2D coupled hydraulic model of the whole urban drainage system in La Almunia de Doña Godina (Zaragoza, Spain). The model focuses on the interaction between grated drain inlets and the sewer network under partial clogging conditions. The model is fed with data obtained on field surveys. These surveys identified 948 inlets, classified into 43 types based on geometry and grouped into 7 categories for modelling purposes. Clogging patterns were derived from field observations or estimated using progressive clogging trends. The hydrological model combines a semi-distributed approach for micro-catchments (buildings and courtyards) and a distributed “rain-on-grid” approach for public spaces (streets, squares). The model assesses the impact of inlet clogging on network performance and surface flooding during four rainfall scenarios. Results include inlet interception volumes, flooded surface areas, and flow hydrographs intercepted by single inlets. Specifically, the reduction in intercepted volume ranged from approximately 7% under a mild inlet clogging condition to nearly 50% under severe clogging conditions. Also, the model results show the significant influence of the 2D mesh detail on flood depths. For instance, a mesh with high resolution and break lines representing streets curbs showed a 38% increase in urban areas with flood depths above 1 cm compared to a scenario with a lower-resolution 2D mesh and no curbs. The findings highlight how inlet clogging significantly affects the efficiency of urban drainage systems and increases the surface flood hazard. Further novelties of this work are the extent of the analysis (city scale) and the approach to improve the 2D mesh to assess flood depth. Full article

The increasing frequency of extreme weather events and rapid urbanization has exacerbated pluvial flood risks, underscoring the urgent need to strengthen the assessment of pluvial flood resilience in China’s southwestern mountainous regions. Kunming—a plateau basin city—was selected as a case study, and an urban pluvial flood resilience assessment system was developed based on the DPSIR model. The analytic hierarchy process (AHP), entropy method, and game theory-informed combination weighting were applied to determine indicator weights, while the extension cloud model was utilized to quantitatively assess resilience evolution from 2013 to 2022. The results reveal that: (1) Kunming’s pluvial flood resilience experienced a clear three-stage evolution—initial construction (Level II), resilience enhancement (Level III), and resilience reinforcement (Level IV)—reflecting a transition from rudimentary resilience to advanced adaptive capacity; (2) the ranking of primary indicator weights is as follows: Driving Forces > Pressure > State > Response > Impact, with Flood Disaster Risk (P6), Flood Disaster Early Warning Capability (R1), and Topographic and Geomorphological Characteristics (P7) identified as key influencing factors; (3) marked disparities exist across the five dimensions: the Driving Forces dimension demonstrates increasing economic support; the Pressure dimension reflects structural vulnerabilities and climate variability; the State and Impact dimensions advance incrementally through policy implementation; and the Response dimension has substantially improved due to smart city technologies, although persistent gaps in inter-agency emergency coordination remain. This research offers a scientific basis for enhancing pluvial flood resilience in southwestern mountainous cities. Full article