Validating self-reported Toxic Release Inventory data using Benford's Law: investigating toxic chemical release hazards in floodplains.

Introduction: Acute and long-term health impacts from flooding related toxic chemical releases are a significant local health concern and can disproportionately impact communities with vulnerable populations; reliable release data are needed to quantify this hazard. Methods: In this paper, we analyze US Federal Emergency Management Agency designated floodplain data and US Environmental Protection Agency Toxic Release Inventory (TRI) data to determine if geographically manipulated databases adhere to Benford's Law. Results: We investigated multiple variants and discovered pollution releases adhere to Benford's Law and tests which thereby validates the self-reported toxic release dataset. Discussion: We find that Benford's Law applies to self-reported toxic chemical release and disposal data, indicating a lack of widespread data errors or manipulation.

Enhanced resilience in urban stormwater management through model predictive control and optimal layout schemes under extreme rainfall events.

Optimizing the layout of urban stormwater management systems is an effective method for mitigating the risk of urban flooding under extreme storms. However, traditional approaches that consider only economic costs or annual runoff control rates cannot dynamically respond to the uncertainties of extreme weather, making it difficult to completely avoid large accumulations of water and flooding in a short period. This study proposes an integrated method combining system layout optimization and Model Predictive Control(MPC)to enhance the system's resilience and effectiveness in flood control. An optimization framework was initially built to identify optimal system layouts, balancing annual average life cycle cost (AALCC) and resilience index. The MPC was then applied to the optimal layout selected using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, aiming to alleviate inundation cost-effectively. The adaptability of MPC to varying sets of control horizons and its efficacy in managing the hydrograph and flood dynamics of urban drainage system were examined. Conducted in Yubei, Chongqing, this study revealed patterns in optimal layout fronts among various extreme design rainfalls, showing that peak position rate and return period significantly influence system resilience. The contribution of MPC to the optimal system layout was particularly notable, resulting in improved instantaneous and overall flood mitigation. The application of MPC increased the resilience index by an average of 0.0485 and offered cost savings of 0.0514 million yuan in AALCC. Besides, our findings highlighted the importance of selecting an optimal set of control horizons for MPC, which could reduce maximum flood depth from 0.43m to 0.19m and decrease conduit peak flow by up to 14% at a flood-prone downstream location.

Application of a multi-criteria decision support system for assessing development potential in flood risk areas - Case study of the Warta River.

The paper discusses the concept of a proprietary decision-making model that allows for determining whether the planned development in areas not protected by flood embankments and at risk of flooding can be shaped in a safe manner. The model was used to evaluate the possibility of shaping the development in flood-risk areas on the example of one of the types of Oleder villages - a dispersed village located in the floodplains of the Warta River (western Poland). So far, there has been no comprehensive evaluation method supporting proper spatial planning for flood-risk areas. The use of multi-criteria analysis methods enabled to specify the key criteria of this evaluation, which form the basis of the decision-making support system. Additionally, the elaborated method enables to determine whether the localities under study can retain their current functional nature and if there is a potential for further spatial development based on the characteristics of an Oleder village. The presented methodology can be easily adapted to other cultural areas located in countries with different levels of development, traditions, landscape or climate.

Climate change impacts on magnitude and frequency of urban floods under scenario and model uncertainties.

Many studies have confirmed that climate change leads to frequent urban flooding, which can lead to significant socioeconomic repercussions. However, most existing studies have not evaluated the impacts of climate change on urban flood from both event-scale and annual-scale dimensions. In addition, there are only few studies that simultaneously consider scenario and model uncertainties of climate change, and combine flood risk assessment and uncertainty analysis results to provide practical suggestions for urban drainage system management. This study uses the statistical downscaling method to calculate the design rainfall under ten rainfall return periods of four climate models and three climate change scenarios in 2040s, 2060s, and 2080s in various prefecture-level cities in China. The four climate models are HadGEM2- ES, MPI-ESM-MR, NorESM1-M and FGOALS-g2 models and the three climate change scenarios are constructed by different representative concentration pathways (RCP), namely RCP2.6, RCP4.5 and RCP8.5. On this basis, relying on the generated drainage systems using geographical information and other data, event-scale and annual-scale precipitation are combined to calculate the change ratio of annual flood volume expectation in prefecture-level cities in each future year compared with the current situation. Furthermore, the study evaluates scenario and model uncertainties of climate change, and then comprehensively integrates the flood risk and its uncertainties to provides suggestions for urban drainage system management.

Enhancing dynamic flood risk assessment and zoning using a coupled hydrological-hydrodynamic model and spatiotemporal information weighting method.

Climate change and intensified human activities are exacerbating the frequency and severity of extreme precipitation events, necessitating more precise and timely flood risk assessments. Traditional models often fail to dynamically and accurately assess flood risks due to their static nature and limited handling of spatiotemporal variations. This study confronts these challenges head-on by developing a novel coupled hydrological-hydrodynamic model integrated with a Block-wise use of the TOPMODEL (BTOP) and the Rainfall-Runoff-Inundation (RRI) model. This integrated approach enables the rapid acquisition of high-precision flood inundation simulation results across large-scale basins, addressing a significant gap in dynamic flood risk assessment and zoning. A critical original achievement of this research lies in developing and implementing a comprehensive vertical-horizontal combined weighting method that incorporates spatiotemporal information for dynamic evaluation indicators, significantly enhancing the accuracy and rationality of flood risk assessments. This innovative method successfully addresses the challenges posed by objective and subjective weighting methods, presenting a balanced and robust framework for flood risk evaluation. The findings from the Min River Basin in China, as a case study, demonstrate the effectiveness of the BTOP-RRI model in capturing the complex variations in runoff and the detailed simulations of flood processes. The model accurately identifies the timing of these peaks, offering insights into the dynamic evolution of flood risks and providing a more precise and timely assessment tool for policymakers and disaster management authorities. The flood risk assessment results demonstrate good consistency with the actual regional conditions. In particular, high-risk areas exhibit distinct characteristics along the river channel, with the distribution area significantly increasing with a sudden surge in runoff. Intense precipitation events expand areas classified as moderate and high risk, gradually shrinking as precipitation levels decrease. This study significantly advances flood risk assessment methodologies by integrating cutting-edge modeling techniques with comprehensive weighting strategies. This is essential for improving the scientific foundation and decision-making processes in regional flood control efforts.

Flood resilience assessment of metro station entrances based on the PSR model framework: A case study of the Donghaochong Basin, Guangzhou.

Increasingly frequent intense rainfall events have caused flood disasters of metro systems worldwide. Flood management based on flood resilience is a novel strategy for dealing with floods. Nevertheless, limited research has been conducted on the correlation between metro systems and flood resilience. In this study, an index system-based framework is proposed to evaluate the flood resilience of metro station entrances, the selected indices are multidimensional, emcompassing external environment, entrance structure, socio-economic, and post-disaster response factors. The flood resilience assessment of metro station entrances is carried out via a case study of the Donghaochong Basin, Guangzhou, China. The pressure-state-response (PSR) model is used to establish the index system for the assessment of the flood resilience. Indices of inundation at the entrance sites are simulated and extracted using the InfoWorks ICM model. Two rainfall scenarios are chosen to simulate inundation, and the resilience results of metro station entrances in each scenario are compared. The results suggest that in the 200-year (200a) return period scenario, 70% of the entrances are at the high and highest resilience levels, whereas in the Zhengzhou "720" scenario, the proportion of that is 43.33%. The resilience of metro station entrances is significantly reduced under the higher rainfall scenario. However, factors unrelated to rainfall are found to mitigate the extent to which total resilience is reduced. Before and during a flood disaster, the indices indicate the resistance and adaptability of metro entrances to the disaster. After a flood disaster, they indicate the ability of entrances to recover to regular functioning. Specific steps should be implemented before and after a disaster to effectively enhance the flood resilience of metro entrances. This study provides valuable insights into enhancing the pertinence and effectiveness of flood disaster management at metro station entrances, and reducing flood damage to metro systems.

Seasonal dynamics, tidal influences, and anthropogenic impacts on microplastic distribution in the Yangtze River estuary: A comprehensive characterization and comparative analysis.

Microplastics (MPs) are emerging contaminants with significant ecological and human health implications. This study examines the abundance, characteristics, and distribution of MPs in the Yangtze River estuary, focusing on seasonal variations, tidal cycles, and anthropogenic influences. Surface samples were collected using the Manta trawl method to ensure consistency with previous marine MP research. The study found an average MP concentration of 1.01 (± 0.65) n m-3, predominantly comprising low-density polymers such as polystyrene (38 %), polypropylene (33 %), and polyethylene (29 %). MPs were mainly fragments (34.9 %) and foam (30.7 %), with a prevalence of white particles. Seasonal analysis indicated significantly higher MP concentrations during flood seasons (1.32 ± 1.09 n m-3), nearly 1.9 times higher than during non-flood seasons (0.70 ± 0.28 n m-3). Tidal cycles also impacted MP distribution, with ebb tides showing increased concentrations (2.44 ± 1.30 n m-3) compared to flood tides (1.48 ± 2.07 n m-3). Furthermore, MP abundance showed a decreasing trend with increasing distance from urban centers, with significant correlations (0.52 < R2 < 0.65, P < 0.001). These findings underscore the necessity for seasonally adjusted monitoring and robust management strategies to combat MP pollution. The study advocates for the integration of diverse sampling methods and the consideration of environmental factors in future MP assessments, laying the groundwork for understanding the MP transport mechanism in the Yangtze River estuary and similar estuarine systems worldwide.

Integrating machine learning and geospatial data analysis for comprehensive flood hazard assessment.

Flooding is a major natural hazard worldwide, causing catastrophic damage to communities and infrastructure. Due to climate change exacerbating extreme weather events robust flood hazard modeling is crucial to support disaster resilience and adaptation. This study uses multi-sourced geospatial datasets to develop an advanced machine learning framework for flood hazard assessment in the Arambag region of West Bengal, India. The flood inventory was constructed through Sentinel-1 SAR analysis and global flood databases. Fifteen flood conditioning factors related to topography, land cover, soil, rainfall, proximity, and demographics were incorporated. Rigorous training and testing of diverse machine learning models, including RF, AdaBoost, rFerns, XGB, DeepBoost, GBM, SDA, BAM, monmlp, and MARS algorithms, were undertaken for categorical flood hazard mapping. Model optimization was achieved through statistical feature selection techniques. Accuracy metrics and advanced model interpretability methods like SHAP and Boruta were implemented to evaluate predictive performance. According to the area under the receiver operating characteristic curve (AUC), the prediction accuracy of the models performed was around > 80%. RF achieves an AUC of 0.847 at resampling factor 5, indicating strong discriminative performance. AdaBoost also consistently exhibits good discriminative ability, with AUC values of 0.839 at resampling factor 10. Boruta and SHAP analysis indicated precipitation and elevation as factors most significantly contributing to flood hazard assessment in the study area. Most of the machine learning models pointed out southern portions of the study area as highly susceptible areas. On average, from 17.2 to 18.6% of the study area is highly susceptible to flood hazards. In the feature selection analysis, various nature-inspired algorithms identified the selected input parameters for flood hazard assessment, i.e., elevation, precipitation, distance to rivers, TWI, geomorphology, lithology, TRI, slope, soil type, curvature, NDVI, distance to roads, and gMIS. As per the Boruta and SHAP analyses, it was found that elevation, precipitation, and distance to rivers play the most crucial roles in the decision-making process for flood hazard assessment. The results indicated that the majority of the building footprints (15.27%) are at high and very high risk, followed by those at very low risk (43.80%), low risk (24.30%), and moderate risk (16.63%). Similarly, the cropland area affected by flooding in this region is categorized into five risk classes: very high (16.85%), high (17.28%), moderate (16.07%), low (16.51%), and very low (33.29%). However, this interdisciplinary study contributes significantly towards hydraulic and hydrological modeling for flood hazard management.

Assessing the performance of blue-green solutions through a fine-scale water balance model for an urban area.

Blue and Green Infrastructures (BGIs) are natural or semi-natural systems that are considered an efficient solution to enhance stormwater management. To assess the performance of BGIs in mitigating floods and droughts in an urban area, a water balance model was developed in this study to simulate runoff formation and propagation. The developed model features fine spatial and temporal resolutions and flexibly integrates BGIs. Combining the conceptual single reservoir approach and the empirical continuous Soil Conservation Service Curve Number (SCS-CN) method, the model achieves computational efficiency, enabling long-term simulations that capture both short-term extreme events and long-term water balance. Its high transferability allows for easy incorporation of local datasets, making it adaptable to various urban contexts. Applied on a university campus located in Belgium, the model was used to simulate the water balance components of feasible BGIs for the study area, which were green roofs, permeable surfaces and rainwater tanks. Scenario analysis of both single BGI and combined BGI implementations was conducted, and all BGI scenarios were evaluated based on peak flow and runoff volume reduction and water balance analysis. Results demonstrate that the implementation of a combination of several BGIs with different functions is an effective solution for both flood control and drought mitigation, as these solutions can significantly reduce runoff flows, increase infiltration and provide considerable rainwater reuse.

Sources of nutrients fuelling post-flood phytoplankton biomass in a subtropical bay.

Extreme rainfall from an ex-tropical cyclone caused a major flood event in the Logan River system in southeast Queensland, Australia. This resulted in a significant flood plume, containing nutrients and sediment, being discharged into the adjacent estuary/Bay system. The spatial extent of higher phytoplankton biomass (Chl a) matched the distribution of higher nutrient and sediment concentrations post-flood, suggesting nutrients fuelled phytoplankton production. Particulate nitrogen (PN) constituted over 50 % of total nitrogen in floodwaters, with lower proportions of dissolved inorganic nitrogen (DIN) and phosphate (PO4-P). Phytoplankton utilised DIN rapidly but may have maintained growth due to the release of ammonia from suspended sediments and microbial mineralisation of particulate organic nitrogen. Ammonia release from intertidal sediments contributed minimally (0.85 %) to daily phytoplankton nitrogen demands. Our study highlights the need to understand the fate of particulate nitrogen in coastal systems and its role in stimulating phytoplankton growth.

Satellite imagery-based tropical cyclone impact assessment on LULC and vegetation: a case study of cyclone Biparjoy.

Cyclones pose significant threats to coastal regions, triggering widespread ecological and hydrological changes. This study presents an impact assessment of cyclone Biparjoy, which originated in the Arabian Sea and made landfall on the Gujarat coast of India on June 16, 2023. The research encompasses flood delineation and vegetation impact assessment in the Kachchh and Devbhoomi Dwarka districts of Gujarat, India. Sentinel-1A (VV polarized) imagery is used to precisely map the extent of inundation caused by cyclone Biparjoy. The total flooded area for Kachchh and Devbhoomi Dwarka was calculated to be 6556.73 km2 and 104.49 km2, respectively. The most affected LULC class in Kachchh is found to be bare ground (38.95%) and rangeland (38.94%) which is the major part of the Northeastern Rann region. In Dwarka, most waterlogging has been seen in the cropland (33.04%). The classification of the water and non-water pixels for the pre- and post-images is validated using the ROC curve. The accuracy was 93.2% and 89.5% for pre- and post-images classifications, respectively. Furthermore, vegetation impact was investigated to estimate the cyclone's ecological consequences. Alterations in vegetation density and overall health were estimated by calculating Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from both pre- and post-cyclone Landsat-8 OLI images. The cyclone-induced damage is further assessed for the mangrove trees in Kori Creek. This work contributes to understanding the ecological repercussions of such extreme weather events.

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

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

Understanding the Spectrum of Flood Syndrome: A Case Series.

Flood syndrome refers to ruptured umbilical hernias in patients with chronic ascites with underlying liver cirrhosis. These ruptures may introduce infection into the abdomen and hence require emergency surgery. However, these patients are at high risk during these procedures owing to coagulopathy, hypotension and electrolyte imbalances. In our series, we describe six patients who presented with varying degrees of severity and were treated with a standardised protocol of primary anatomic repair and drain placement. Furthermore, we assessed the Child-Turcotte-Pugh (CTP) and Model for End-Stage Liver Disease (MELD) scores in these patients and correlated them to postoperative outcomes. This surgical technique has a good outcome in patients whose CTP and MELD scores predict a safe postoperative period.

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

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

Transformation and environmental risk of 90Sr and 137Cs under extreme rainstorm at a proposed nuclear facility site in China.

This paper explores the environmental hazards associated with nuclear facilities in arid regions, focusing on the rapid migration of radionuclides facilitated by flood runoff resulting from extreme rainstorms. Through a case study of a proposed nuclear facility site in China, the study developed a comprehensive model to calculate the transformation of 90Sr and 137Cs in flood and subsurface water during accidents. The methodology employs a combination of field tests, radionuclide adsorption tests, the SWAT model, and the HGS model to create a fully integrated model. This approach allows for the several complex couplings (radionuclide-flood runoff-subsurface water) that have not been previously examined in the reactive solute transport. The findings reveal that despite groundwater movement being relatively sluggish, 90Sr and 137Cs migrate downstream rapidly due to their transportation by floods, which permeate the Upper Pleistocene gravel aquifer along the route. The study underscores the importance of considering the migration of radionuclides carried by floods generated by extreme rainstorms, as it poses a significant risk that cannot be ignored.

Global insights from local actions: a case study on enhancing flood disaster management efficiency in China's Greater Bay Area.

This study examines the flood disaster management network within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2015 to 2021, identifying government department involvement and influence shifts. Key findings indicate a decrease in the centrality of the Public Security Office and Department of Transportation, suggesting a strategic shift toward more specialized, technology-driven disaster management. Conversely, the Science Bureau's increased engagement, from 8.43% to 12.84%, highlights a policy shift toward scientific research and technological innovation in managing flood risks. The analysis reveals underutilized communication between the Central Committee, the Poverty Alleviation Office, and the Publicity Department, highlighting opportunities for improved integration in disaster management and public communication strategies. To address these issues, the study suggests strengthening inter-departmental collaboration to leverage technological advancements in disaster management. It also recommends integrating flood disaster management with poverty alleviation initiatives to support affected populations comprehensively. Increasing the involvement of the Publicity Department is crucial for improving timely and transparent communication of flood-related data to the public. The conclusions advocate for an adaptive, strategically planned network approach to flood disaster management in the GBA, aiming to bolster responsiveness and preparedness for future flood events.

Examining direct and indirect flood damages in residential and business sectors through an empirical lens.

Investment to reduce flood risk for social and economic wellbeing requires quantitative evidence to guide decisions. Direct and indirect flood damages at individual household and business building levels were assessed in this study using multivariate analysis with three groups of flood damage attributes, i.e., flood characteristics, socioeconomic conditions, and building types. A total of 172 and 45 respondents from residential and commercial buildings were gathered through door-to-door interviews at areas in Peninsular Malaysia that were pre-identified to have frequently flooded. Two main findings can be drawn from this study. First, flood damage is greatly contributed by high-income households and businesses, despite them being less exposed to floods than low-income earners. This supports the current use of mean economic damage in engineering-based flood intervention analysis. Second, indirect damages increase with the increase in family size, indicating the importance of strengthening preparedness and social support to those with great social responsibility. Overall, the study highlights the importance of holistic flood management accounting for both direct and indirect losses.

Integrated modeling of urban mobility, flood inundation, and sewer hydrodynamics processes to support resilience assessment of urban drainage systems.

With the increasing frequency of extreme weather events and a deepening understanding of disasters, resilience has received widespread attention in urban drainage systems. The studies on the resilience assessment of urban drainage systems are mostly indirect assessments that did not simulate human behavior affected by rainfall or semi-quantitative assessments that did not build simulation models, but few research characterizes the processes between people and infrastructure to assess resilience directly. Our study developed a dynamic model that integrates urban mobility, flood inundation, and sewer hydrodynamics processes. The model can simulate the impact of rainfall on people's mobility behavior and the full process including runoff generation, runoff entering pipes, node overflow, flood migration, urban mobility, and residential water usage. Then, we assessed the resilience of the urban drainage system under rainfall events from the perspectives of property loss and urban mobility. The study found that the average percentage increase in commuting time under different return periods of rainfall ranged from 6.4 to 203.9%. Calculating the annual expectation of property loss and traffic obstruction, the study found that the annual expectation loss in urban mobility is 9.1% of the annual expectation of property loss if the rainfall is near the morning commuting peak.

NPCC4: Climate change and New York City's health risk.

This chapter of the New York City Panel on Climate Change 4 (NPCC4) report considers climate health risks, vulnerabilities, and resilience strategies in New York City's unique urban context. It updates evidence since the last health assessment in 2015 as part of NPCC2 and addresses climate health risks and vulnerabilities that have emerged as especially salient to NYC since 2015. Climate health risks from heat and flooding are emphasized. In addition, other climate-sensitive exposures harmful to human health are considered, including outdoor and indoor air pollution, including aeroallergens; insect vectors of human illness; waterborne infectious and chemical contaminants; and compounding of climate health risks with other public health emergencies, such as the COVID-19 pandemic. Evidence-informed strategies for reducing future climate risks to health are considered.