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.

Optimization of Manning's roughness coefficient using 1-dimensional hydrodynamic modelling in the perennial river system: A case of lower Narmada Basin, India.

This research bears significant implications for river management, flood forecasting, and ecosystem preservation in the Lower Narmada Basin. A more precise estimation of Manning's Roughness Coefficeint (n) will enhance the accuracy of hydraulic models and facilitate informed decision-making regarding flood risk management, water resource allocation, and environmental conservation efforts. Ultimately, this study aspires to contribute to the sustainable management of perennial river systems in India and beyond by offering a robust methodology for optimizing Manning's n tailored to the complex hydrological dynamics of the Lower Narmada Basin. Through a synthesis of empirical evidence and computational modelling, it seeks to empower stakeholders with actionable insights toward preserving and enhancing these invaluable natural resources. Using the new HEC-RAS v 6.0, a one-dimensional hydrodynamic model was developed to predict overbank discharge at different points along the basin. The study analyzes water levels, stream discharges, and river stage, optimizing Manning's n and required flood risk management. The model predicted a strong output agreement with R2, NSE, and RMSE for the 2020 event as 0.83, 0.81, and 0.36, respectively, with an optimum Manning's n of 0.03. The lower Narmada Basin part near the coastal zone (validation point) appears inundated frequently. The paper aims to provide insights into optimizing Manning's coefficient, which can ultimately lead to better water flow predictions and more efficient water management in the region.

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.

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.

Freshwater wetlands for flood control: How manipulating the hydroperiod affects plant and invertebrate communities.

Thoughtfully managed hydroperiods in natural and artificial wetlands could potentially provide a combination of desirable flood control services and high ecological functions. To explore how managed freshwater wetlands typical of the Houston, Texas area would respond to different hydrological regimes that might occur if wetlands were drained in anticipation of a heavy rain that did not materialize, we conducted a mesocosm experiment with six flooding depths and seven drought durations, followed by seven months of recovery. We found that the speed in which mesocosms dried out was a function of initial water depth, with mesocosms initially set with greater water depths (30 cm) taking ~ 38 days to dry out versus zero days for wetlands that were completely drained. Individual plant species (14 species planted; 8 species common at the end of the recovery period) were affected by drought length, flooding depth, or their interaction, although details of these responses varied among the species. The composition of the plant community at the end of the drought period was strongly affected by drought length, and the effect of the drought length treatment persisted through seven months of post-drought recovery, with the 80- and 160-day drought treatments diverging most strongly from shorter drought treatments. Above- and below-ground biomass of plants was not affected by the treatments, but above-ground dead biomass (litter) decreased with increasing drought length. Densities of mosquito larvae, snails and tadpoles were temporally variable, and were affected more during the treatment period and early in recovery than after a disturbance event late in recovery. Our results indicate that managed wetlands in southeast Texas would be quite resilient to dry periods of up to 40 days in duration, especially if water was not completely drained at the beginning of the drought. In addition, many species would persist in managed wetlands even with droughts of up to 160 days. This indicates considerable potential for managing the hydroperiods of artificial detention ponds by retaining water longer to increase ecological function, with little to no loss of flood control services, and for managing the hydroperiods of natural wetlands by draining them in advance of anticipated rains to increase flood control services, with little to no loss of ecological function.

Risk Factors of Wasting-Based Malnutrition in the Flood-Affected Areas of Pakistan: A Cross-Sectional Study.

The current study aimed to find the risk factors of wasting in flood-affected areas of Khyber Pakhtunkhwa, Pakistan. Sociodemographic and anthropometric data was collected. Children living in large family are 2.59 times more likely to be wasted (AOR = 2.59, 95% confidence interval (CI): 1.10, 6.10; p value = .029) and children living in medium size family are 2.23 times more likely to be wasted (AOR = 2.23, 95% CI: 1.03, 4.80; p value = .04) as compared to children in small family size. The study underscores the need for targeted interventions to address the identified risk factors and mitigate the impact of flooding on child nutrition.

Adaptation of the Invasive Plant Sphagneticola trilobata to Flooding Stress by Hybridization with Native Relatives.

Hybridization is common between invasive and native species and may produce more adaptive hybrids. The hybrid (Sphagneticola × guangdongensis) of Sphagneticola trilobata (an invasive species) and S. calendulacea (a native species) was found in South China. In this study, S. trilobata, S. calendulacea, and Sphagneticola × guangdongensis were used as research materials to explore their adaptability to flooding stress. Under flooding stress, the ethylene content and the expression of key enzyme genes related to ethylene synthesis in Sphagneticola × guangdongensis and S. calendulacea were significantly higher than those in S. trilobata. A large number of adventitious roots and aerenchyma were generated in Sphagneticola × guangdongensis and S. calendulacea. The contents of reactive oxygen species and malondialdehyde in Sphagneticola × guangdongensis and S. calendulacea were lower than those in S. trilobata, and the leaves of S. trilobata were the most severely damaged under flooding stress. The results indicate that hybridization catalyzed the tolerance of Sphagneticola × guangdongensis to flooding stress, and the responses of Sphagneticola × guangdongensis to flooding stress were more similar to that of its native parent. This suggests that hybridization with native relatives is an important way for invasive species to overcome environmental pressure and achieve invasion.

Contrasting diversity patterns and drivers of aquatic macroinvertebrates in floodplain and non-floodplain wetlands.

Understanding diversity patterns and underlying drivers is one of the central topics in the fields of biogeography and community ecology. Aquatic macroinvertebrates are widely distributed in various wetlands and play vital ecological roles. Previous studies mainly have focused on macroinvertebrate diversity in a single type of wetland. Our understanding of the differences in diversity patterns and underlying drivers between different wetland types remains limited. Here, we compared diversity patterns and community assembly of floodplain wetlands (FWs) and non-floodplain wetlands (NWs) in the Sanjiang Plain, Northeast China. We found that the taxonomic richness and abundance were higher in NWs than those in FWs. Nineteen taxa were identified as habitat specialists in the NWs, whereas only four taxa were designated as habitat specialists in the FWs. In addition, the FW and NW assemblages exhibited contrasting compositions. Spatial and environmental variables explained the largest variations in the macroinvertebrate assemblages of NWs and FWs, respectively. Normalised stochasticity ratios and Sloan neutral models confirmed that the macroinvertebrate community assembly of both wetland types was driven largely by stochastic processes. Stochastic processes were more prominent in shaping macroinvertebrate communities of FWs, whereas a stronger dispersal limitation was detected in NWs. Our results revealed contrasting diversity patterns and assembly mechanisms of macroinvertebrate communities in FWs and NWs. We underscore the importance of flood disturbance in shaping wetland ecosystems in the Sanjiang Plain and highlight that conservation and restoration actions cover different types of wetland habitats.

Assessment of vulnerability to flood risk in the Padma River Basin using hydro-morphometric modeling and flood susceptibility mapping.

An evaluation of flood vulnerability is needed to identify flood risk locations and determine mitigation methods. This research introduces an integrated method combining hydro-morphometric modeling and flood susceptibility mapping to assess Padma River Basin's flood risk. Flood zoning, flooding classes, and resource flood risk were explicitly analyzed in this river basin study. Flood risk was calculated using GIS-based hydro-morphometric modeling. Using Horton's and Strahler's methods, drainage density, stream density, and stream order of the Padma River Basin were determined. The Padma River Basin has five sub-basins: A, B, C, D, and E, with stream densities of 0.53 km-2, 0.13 km-2, 0.25 km-2, 0.30 km-2, and 0.28 km-2 and drainage densities of 0.63 km-1, 0.16 km-1, 0.29 km-1, 0.35 km-1, and 0.33 km-1, respectively. Sub-basin A is the most prone to floods due to its high stream and drainage density, whereas B and C are the least susceptible. This study used elevation, TWI, slope, precipitation, NDVI, distance from road, drainage density, distance from river, LU/LC, and soil type to create a flood vulnerability map incorporating GIS and AHP with pair-wise comparison matrix (PCM). The study's flood zoning shows that the northeastern part of this basin is more likely to flood than the southwestern part due to its elevation and high-order streams. Moderate River Flooding, the region's most hazardous flood class, covers 48.19% of the flooding area, including 1078.30 km2 of agricultural land, 94.86 km2 of bare soil, 486.39 km2 of settlements, 586.42 km2 of vegetation cover, and 39.34 km2 of water bodies. The developed hydro-morphometric model, the flood susceptibility map, and the analysis of this data may be utilized to offer long-term advance alarm insight into areas potentially to be invaded by a flood catastrophe, boosting hazard mitigation and planning.

Combustion-Derived Pollutants Linked with Kidney Disease in Low-Lying Flood-Affected Areas in the Balkans.

Tens of thousands of people in southern Europe suffer from Balkan endemic nephropathy (BEN), and four times as many are at risk. Incidental ingestion of aristolochic acids (AAs), stemming from the ubiquitousAristolochia clematitis(birthwort) weed in the region, leads to DNA adduct-induced toxicity in kidney cells, the primary cause of BEN. Numerous cofactors, including toxic organics and metals, have been investigated, but all have shown small contributions to the overall BEN relative to non-BEN village distribution gradients. Here, we reveal that combustion-derived pollutants from wood and coal burning in Serbia also contaminate arable soil and test as plausible causative factors of BEN. Using a GC-MS screening method, biomass-burning-derived furfural and coal-burning-derived medium-chain alkanes were detected in soil samples from BEN endemic areas levels at up to 63-times and 14-times higher, respectively, than in nonendemic areas. Significantly higher amounts were also detected in colocated wheat grains. Coexposure studies with cultured kidney cells showed that these pollutants enhance DNA adduct formation by AA, - the cause of AA nephrotoxicity and carcinogenicity. With the coincidence of birthwort-derived AAs and the widespread practice of biomass and coal burning for household cooking and heating purposes and agricultural burning in rural low-lying flood-affected areas in the Balkans, these results implicate combustion-derived pollutants in promoting the development of BEN.

Group living in highland tuco-tucos (Ctenomys opimus) persists despite a catastrophic decline in population density.

Identifying the factors that favor group living is central to studies of animal social behavior. One demographic parameter that is expected to substantially shape spatial and social relationships is population density. Specifically, high population densities may favor group living by constraining opportunities to live alone. In contrast, low densities may allow individuals to spread out within the habitat, leading to a reduction in the prevalence or size of social groups. Abrupt changes in density following natural catastrophic events provide important opportunities to evaluate the effects of population density on patterns of spatial and social organization. As part of long-term studies of the behavioral ecology of a population of highland tuco-tucos (Ctenomys opimus) at Monumento Natural Laguna de los Pozuelos, Jujuy Province, Argentina, we monitored the demographic and behavioral consequences of a flood that inundated our study site during December 2012. Unlike most species of Ctenomys studied to date, highland tuco-tucos are group living, meaning that multiple adults share burrow systems and nest sites. Despite a post-flood reduction in population density of ~75%, animals present on the study site during the 2013 breeding season continued to live in multi-adult social units (groups). No differences between pre- and post-flood home range sizes were detected and although between-unit spatial overlap was reduced in 2013, overlap within social units did not differ from that in pre-flood years. Animals assigned to the same social unit in 2013 had not lived together during 2012, indicating that post-flood groups were not simply the remnants of those present prior to the flood. Collectively, these findings indicate that group living in highland tuco-tucos is not driven by the density of conspecifics in the habitat. In addition to enhancing understanding of the adaptive bases for group living in Ctenomys, our analyses underscore the power of catastrophic events to generate insights into fundamental aspects of social behavior.

Investigating the impacts of climate change on hydroclimatic extremes in the Tar-Pamlico River basin, North Carolina.

Evaluating the forthcoming impacts of climate change is important for formulating efficient and flexible approaches to water resource management. General Circulation Models (GCMs) are primary tools that enable scientists to study both past and potential future climate changes, as well as their impacts on policies and actions. In this work, we quantify the future projected impacts of hydroclimatic extremes on the coastal, risk-prone Tar-Pamlico River basin in North Carolina using GCMs from the Sixth International Coupled Model Intercomparison Project (CMIP6). These models incorporate projected future societal development scenarios (Shared Socioeconomic Pathways, SSPs) as defined in the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Specifically, we have utilized historical residential expansion data, the Soil and Water Assessment Tool Plus (SWAT+), the Standardized Precipitation Index (SPI), and the Interquartile Range (IQR) method for analyzing extremes from 2024 to 2100. Our findings include: (1) a trend toward wetter conditions is identified with an increase in flood events toward 2100; (2) projected increases in the severity of flood peaks are found, quantified by a rise of 21% compared to the 2000-2020 period; (3) downstream regions are forecast to experience severe droughts up to 2044; and (4) low-lying and coastal regions are found as particularly susceptible to higher flood peaks and more frequent drought events between 2045 and 2100. This work provides valuable insights into the anticipated shifts in natural disaster patterns and supports decision-makers and authorities in promoting adaptive strategies and sustainable policies to address challenges posed by future climate changes in the Tar-Pamlico region and throughout the state of North Carolina, United States.

[Effects of water level on soil seed bank in the floodplain wetland of Juzhang River, China]. / 水位变化对沮漳河河漫滩湿地土壤种子库的影响.

Aiming to understand the responses of soil seed bank to different water levels, we investigated vegetation and soil seed bank along a water level gradient (frequently flooded area, unflooded area) on the floodplain wetland of Juzhang River. We used the structural equation model to explore the direct and indirect effects of water level on soil seed bank, and used non-metric multidimensional scaling (NMDS) to assess the role of soil seed bank for vegetation regeneration. The results showed that the density of transient and persistent seed banks at unflooded area was 36.9% and 7.8% higher than that of frequently flooded area, respectively. Shannon index and Pielou index of seed bank and vegetation were significantly affected by water level and sampling location. Water level significantly affected the similarity between seed bank and aboveground vegetation, and the similarity of persistent seed bank with aboveground vegetation was significantly higher than that with transient seed bank. Structural equation model showed that water level had a direct effect on seed bank density, and indirect effects on density and richness of seed bank via affecting soil pH and NH4+-N content. NMDS results showed that there was no significant difference in the composition of the persistent seed bank and vegetation community in autumn under different water levels, but water level significantly changed the community composition of transient seed bank. Transient seed bank was affected by the vegetation and soil property, while persistent seed bank was determined by aboveground vegetation and water level. Although soil seed bank had low regeneration potential for the vegetation communities in floodplain wetlands, soil seed bank could not be neglected during the restoration of propagule diversity after disturbance in wetlands. Persistent seed bank would be an importance source of diversity of propagules for floodplain wetlands restoration following disturbance.

Prediction of flood sensitivity based on Logistic Regression, eXtreme Gradient Boosting, and Random Forest modeling methods.

Floods are one of the most destructive disasters that cause loss of life and property worldwide every year. In this study, the aim was to find the best-performing model in flood sensitivity assessment and analyze key characteristic factors, the spatial pattern of flood sensitivity was evaluated using three machine learning (ML) models: Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). Suqian City in Jiangsu Province was selected as the study area, and a random sample dataset of historical flood points was constructed. Fifteen different meteorological, hydrological, and geographical spatial variables were considered in the flood sensitivity assessment, 12 variables were selected based on the multi-collinearity study. Among the results of comparing the selected ML models, the RF method had the highest AUC value, accuracy, and comprehensive evaluation effect, and is a reliable and effective flood risk assessment model. As the main output of this study, the flood sensitivity map is divided into five categories, ranging from very low to very high sensitivity. Using the RF model (i.e., the highest accuracy of the model), the high-risk area covers about 44% of the study area, mainly concentrated in the central, eastern, and southern parts of the old city area.