Search Results (1,391)

Recently, the impact of climate change has led to an increase in the scale and frequency of extreme rainfall and flash floods. Due to this, the occurrence of floods and various river disasters has increased, necessitating the acquisition of technologies to prevent river disasters. Owing to the nature of rivers, areas with poor accessibility exist, and obtaining information over a wide area can be time-consuming. Artificial intelligence technology, which has the potential to overcome these limits, has not been broadly adopted for river detection. Therefore, the current study conducted a performance analysis of artificial intelligence for automatic river path setting via the YOLOv8 model, which is widely applied in various fields. Through the augmentation feature in the Roboflow platform, many river images were employed to train and analyze the river spatial information of each applied image. The overall results revealed that the models with augmentation performed better than the basic models without augmentation. In particular, the flip and crop and shear model showed the highest performance with a score of 0.058. When applied to rivers, the Wosucheon stream showed the highest average confidence across all models, with a value of 0.842. Additionally, the max confidence for each river was extracted, and it was found that models including crop exhibited higher reliability. The results show that the augmentation models better generalize new data and can improve performance in real-world environments. Additionally, the RivDet artificial intelligence model for automatic river path configuration developed in the current study is expected to solve various problems, such as automatic flow rate estimation for river disaster prevention, setting early flood warnings, and calculating the range of flood inundation damage. Full article

The 2018 Sulawesi Earthquake and Tsunami serves as a backdrop for this work, which employs simple and straightforward remote sensing techniques to determine the extent of the destruction and indirectly evaluate the region’s vulnerability to such catastrophic events. Documenting damage from tsunamis is only meaningful shortly after the disaster has occurred because governmental agencies clean up debris and start the recovery process within a few hours after the destruction has occurred, deeming impact estimates unreliable. Sentinel-2 and Maxar WorldView-3 satellite images were used to calculate well-known environmental indices to delineate the tsunami-affected areas in Palu, Indonesia. The use of NDVI, NDSI, and NDWI indices has allowed for a quantifiable measure of the changes in vegetation, soil moisture, and water bodies, providing a clear demarcation of the tsunami’s impact on land cover. The final tsunami inundation map indicates that the areas most affected by the tsunami are found in the urban center, low-lying regions, and along the coast. This work charts the aftermath of one of Indonesia’s recent tsunamis but may also lay the groundwork for an easy, handy, and low-cost approach to quickly identify tsunami-affected zones. While previous studies have used high-resolution remote sensing methods such as LiDAR or SAR, our study emphasizes accessibility and simplicity, making it more feasible for resource-constrained regions or rapid disaster response. The scientific novelty lies in the integration of widely used environmental indices (dNDVI, dNDWI, and dNDSI) with threshold-based Decision Tree classification to delineate tsunami-affected areas. Unlike many studies that rely on advanced or proprietary tools, we demonstrate that comparable results can be achieved with cost-effective open-source data and straightforward methodologies. Additionally, we address the challenge of differentiating tsunami impacts from other phenomena (et, liquefaction) through index-based thresholds and propose a framework that is adaptable to other vulnerable coastal regions. Full article

This study focuses on the analysis of sediment core retrieved from the deepest part (25 m) of Pirovac Bay. A long sedimentary sequence (7.45 m) supplemented by a shorter sediment core (1.45 m) from a shallower part of the bay was analyzed for sedimentological, mineralogical, geochemical, and micropaleontological (ostracod) parameters. The sediment thickness above the underlying karst paleorelief (karstic bedrock) is up to 12 m. Sediments recorded a transition from a freshwater to a marine environment starting from post-Neapolitan Yellow Tuff tephra sedimentation. First, the floodplain developed in Pirovac Bay, with intermittent pools and ponds, followed by wetland environment. The formation of a shallow freshwater paleolake during the Middle Holocene at 10 cal kyr BP was enabled by the rising sea level and high freshwater input from the karstified underground from the adjacent Lake Vrana (Biograd na Moru). The onset of marine intrusions through the karstified underground is evident with formation of a brackish lake in the Pirovac Bay basin. Marine transgression and flooding of the bay occurred at 7.3 cal kyr BP, evidenced by the geochemical and ostracod parameters, providing crucial insights into the dynamics of coastal inundation under past climate change. Intriguingly, freshwater ostracod species were still present in the marine sediments, brought into the bay from Lake Vrana through surficial canal Prosika and groundwater discharge (numerous estavelles) along the northeastern shores of the bay, proving their mutual influence. This submerged Holocene freshwater paleolake, reported here for the first time, underlines the sensitivity of coastal karst systems to the rise in sea level and serves to stress how important understanding of these processes is for effective management in coastal zone and climate change adaptation strategies. The findings provided evidence supporting the existence of coastal marine basins as freshwater lakes prior to being flooded by seawater as a consequence of the Holocene post-glacial sea level rise. Full article

Compound mitigation systems, integrations of natural and engineering structures against the high inundating current from tsunamis or storm surges, have garnered significant interest among researchers, especially following the Tohoku earthquake and tsunami in 2011. Understanding the complex flow phenomena is essential for the resilience of the mitigation structures and effective energy reduction. This study conducted a flume experiment to clarify flow characteristics and fluid force dissipation in a compound defense system. Vegetation models (V) with different porosities (Φ) were placed at three different positions downstream of an embankment model (E). A single-layer emergent vegetation model was considered, and a short-layer vegetation with several values of Φ was incorporated to increase its density (decreased Φ). Depending on Φ and the spacing (S) between the E and V, hydraulic jumps occurred in the physical system. The findings demonstrated that a rise in S allowed a hydraulic jump to develop inside the system and contributed to reducing the fluid force in front and downstream of V. Due to the reduced porosity of the double-layer vegetation, the hydraulic jump moved upstream and terminated within the system, resulting in a uniform water surface upstream of V and downstream of the system. As a result, the fluid force in front of and behind V reduced remarkably. Full article

This study explores the application of numerical simulation in debris flow disaster early warning, using the Shiyang Gully in China as a case study. Using both the HEC-HMS and FLO-2D, the 18 June 2017 debris flow event was reconstructed to analyze the impacts of cumulative rainfall, rainfall intensity, and rainfall range on debris flow hazards. Simulation results showed that cumulative rainfall exceeding 90 mm or rainfall intensity surpassing 200 mm/8 h significantly increases debris flow depth, impact force, and affected areas, leading to severe structural damage. Expanding the rainfall range to the entire basin further amplifies disaster risks, increasing both inundation depth and exposed elements. Based on these findings, a four-tier debris flow early warning system was developed: (1) blue (IV) warning for cumulative rainfall of up to and including 20 mm or intensity of 200 mm/24 h, indicating preparation and monitoring; (2) yellow (III) warning for rainfall exceeding 20 mm but below 60 mm, requiring enhanced inspections and safety measures; (3) orange (II) warning for rainfall between 60 and 90 mm or intensity of 200 mm/12 h, necessitating immediate evacuation preparations; and (4) red (I) warning for rainfall over 90 mm or intensity of 200 mm/8 h, demanding full evacuation and emergency responses. This study demonstrates the value of numerical simulation in refining early warning systems by integrating multi-scenario analyses of rainfall parameters. The proposed system offers scientific and practical insights for enhancing debris flow disaster management, particularly in small, high-risk watersheds, providing a framework for cross-regional disaster mitigation strategies. Full article

The development of the mass rearing technique for the egg parasitoid Telenomus podisi has been under study for about 20 years, with increasing attention on the development of quality control. Here, we evaluated the behavior, biological parameters, morphometrics and presence of endosymbionts of T. podisi produced in cryopreserved eggs compared to those produced in traditional fresh stink bug eggs. Parasitoids reared from cryopreserved eggs showed similar parasitism and emergence rates, sex ratios, longevity, morphometrics, and proportions of flyers compared to those originating from fresh eggs. Slight differences, including an increase in egg-to-adult development time and differences in the presence of endosymbionts, were observed. Despite these differences, we conclude that the use of cryopreserved eggs is suitable for T. podisi mass rearing, allowing more options for timed inundative parasitoid releases for biological control. Full article

Riverine coastal megacities, particularly in semi-arid South Asian regions, face escalating environmental challenges due to rapid urbanization and climate change. While previous studies have examined urban growth patterns or environmental impacts independently, there remains a critical gap in understanding the integrated impacts of land use/land cover (LULC) changes on both ecosystem vulnerability and sustainable development achievements. This study addresses this gap through an innovative integration of multitemporal Landsat imagery (5, 7, and 8), SRTM-DEM, historical land use maps, and population data using the MOLUSCE plugin with cellular automata–artificial neural networks (CA-ANN) modelling to monitor LULC changes over three decades (1990–2020) and project future changes for 2025, 2030, and 2035, supporting the Sustainable Development Goals (SDGs) in Karachi, southern Pakistan, one of the world’s most populous megacities. The framework integrates LULC analysis with SDG metrics, achieving an overall accuracy greater than 97%, with user and producer accuracies above 77% and a Kappa coefficient approaching 1, demonstrating a high level of agreement. Results revealed significant urban expansion from 13.4% to 23.7% of the total area between 1990 and 2020, with concurrent reductions in vegetation cover, water bodies, and wetlands. Erosion along the riverbank has caused the Malir River’s area to decrease from 17.19 to 5.07 km² by 2020, highlighting a key factor contributing to urban flooding during the monsoon season. Flood risk projections indicate that urbanized areas will be most affected, with 66.65% potentially inundated by 2035. This study’s innovative contribution lies in quantifying SDG achievements, showing varied progress: 26% for SDG 9 (Industry, Innovation, and Infrastructure), 18% for SDG 11 (Sustainable Cities and Communities), 13% for SDG 13 (Climate Action), and 16% for SDG 8 (Decent Work and Economic Growth). However, declining vegetation cover and water bodies pose challenges for SDG 15 (Life on Land) and SDG 6 (Clean Water and Sanitation), with 16% and 11%, respectively. This integrated approach provides valuable insights for urban planners, offering a novel framework for adaptive urban planning strategies and advancing sustainable practices in similar stressed megacity regions. Full article

Tidal marshes play a globally critical role in carbon and hydrologic cycles by sequestering carbon dioxide from the atmosphere and exporting dissolved organic carbon to connected estuaries. These ecosystems provide critical habitat to a variety of fauna and also reduce coastal flood impacts. Accurate characterization of tidal marsh inundation dynamics is crucial for understanding these processes and ecosystem services. In this study, we developed remote sensing-based inundation classifications over a range of tidal stages for marshes of the Mid-Atlantic and Gulf of Mexico regions of the United States. Inundation products were derived from C-band and L-band synthetic aperture radar (SAR) imagery using backscatter thresholding and temporal change detection approaches. Inundation products were validated with in situ water level observations and radiometric modeling. The Michigan Microwave Canopy Scattering (MIMICS) radiometric model was used to simulate radar backscatter response for tidal marshes across a range of vegetation parameterizations and simulated hydrologic states. Our findings demonstrate that inundation classifications based on L-band SAR—developed using backscatter thresholding applied to single-date imagery—were comparable in accuracy to the best performing C-band SAR inundation classifications that required change detection approaches applied to time-series imagery (90.0% vs. 88.8% accuracy, respectively). L-band SAR backscatter threshold inundation products were also compared to polarimetric decompositions from quad-polarimetric Phased Array L-band Synthetic Aperture Radar 2 (PALSAR-2) and L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) imagery. Polarimetric decomposition analysis showed a relative shift from volume and single-bounce scattering to double-bounce scattering in response to increasing tidal stage and associated increases in classified inundated area. MIMICS modeling similarly showed a relative shift to double-bounce scattering and a decrease in total backscatter in response to inundation. These findings have relevance to the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission, as threshold-based classifications of wetland inundation dynamics will be employed to verify that NISAR datasets satisfy associated mission science requirements to map wetland inundation with classification accuracies better than 80% at 1 hectare spatial scales. Full article

Accurate and near-real-time flood monitoring is crucial for effective post-disaster relief efforts. Although extensive research has been conducted on flood classification, efficiently and automatically processing multi-source imagery to generate reliable flood inundation maps remains challenging. In this study, a new automatic flood monitoring method, utilizing optical and Synthetic Aperture Radar (SAR) imagery, was developed based on the Google Earth Engine (GEE) cloud platform. The Normalized Difference Flood Vegetation Index (NDFVI) was innovatively combined with the Edge Otsu segmentation method, utilizing SAR imagery, to enhance the initial accuracy of flood area mapping. To more effectively distinguish flood areas from non-seasonal water bodies, such as lakes, rivers, and reservoirs, pre-flood Landsat-8 imagery was analyzed. Non-seasonal water bodies were classified using multi-index methods and water body probability distributions, thereby further enhancing the accuracy of flood mapping. The method was applied to the catastrophic floods in Poyang Lake, Jiangxi Province, in 2020, and East Dongting Lake, Hunan Province, China, in 2024. The results demonstrated classification accuracies of 92.6% and 97.2% for flood inundation mapping during the Poyang Lake and East Dongting Lake events, respectively. This method offers efficient and precise information support to decision-makers and emergency responders, thereby fully demonstrating its substantial potential for practical applications. Full article

The Mekerra Basin in northern Algeria is highly vulnerable to severe flood events, such as those in October 1986 and September 1994, which caused significant damage to infrastructure and the environment. To address flood risk, this study applied the Rainfall–Runoff–Inundation (RRI) model to simulate hydrological processes and flood extents. The model was calibrated and validated using discharge data from these historical events. The sensitivity analyses identified hydraulic conductivity, suction head, and channel roughness as key parameters influencing flood peaks. The RRI model demonstrated a strong performance, achieving correlation coefficients of 0.97 and 0.94 for the 1986 and 1994 events, respectively. The model also produced R² values of 0.94 (calibration) and 0.89 (validation), with Percent Bias (PBIAS) values of 0.006 and 0.013, indicating minimal bias. Nash–Sutcliffe Efficiency (NSE) scores of 0.93 (calibration) and 0.86 (validation) confirmed its robustness in simulating event flows. This study represents the first application of the RRI model in the Mekerra Basin and highlights its utility for flood risk assessment in arid and semi-arid regions, offering critical insights for flood management and mitigation strategies. Full article

Accurate river hydraulic characterization is fundamental to assess flood risk, parametrize flood forecasting models, and develop river maintenance workflows. River hydraulic roughness and riverbed/floodplain geometry are the main factors controlling inundation extent and water levels. However, gauging stations providing hydrometric observations are declining worldwide, and they provide point measurements only. To describe hydraulic processes, spatially distributed data are required. In situ surveys are costly and time-consuming, and they are sometimes limited by local accessibility conditions. Satellite earth observation (EO) techniques can be used to measure spatially distributed hydrometric variables, reducing the time and cost of traditional surveys. Satellite EO provides high temporal and spatial frequency, but it can only measure large rivers (wider than ca. 50 m) and only provides water surface elevation (WSE), water surface slope (WSS), and surface water width data. UAS hydrometry can provide WSE, WSS, water surface velocity and riverbed geometry at a high spatial resolution, making it suitable for rivers of all sizes. The use of UAS hydrometry can enhance river management, with cost-effective surveys offering large coverage and high-resolution data, which are fundamental in flood risk assessment, especially in areas that difficult to access. In this study, we proposed a combination of UAS hydrometry techniques to fully characterize the hydraulic parameters of a river. The land elevation adjacent to the river channel was measured with LiDAR, the riverbed elevation was measured with a sonar payload, and the WSE was measured with a UAS radar altimetry payload. The survey provided 57 river cross-sections with riverbed elevation, and 8 km of WSE and land elevation and took around 2 days of survey work in the field. Simulated WSE values were compared to radar altimetry observations to fit hydraulic roughness, which cannot be directly observed. The riverbed elevation cross-sections have an average error of 32 cm relative to RTK GNSS ground-truth measurements. This error was a consequence of the dense vegetation on land that prevents the LiDAR signal from reaching the ground and underwater vegetation, which has an impact on the quality of the sonar measurements and could be mitigated by performing surveys during winter, when submerged vegetation is less prevalent. Despite the error of the riverbed elevation cross-sections, the hydraulic model gave good estimates of the WSE, with an RMSE below 3 cm. The estimated roughness is also in good agreement with the values measured at a gauging station, with a Gauckler–Manning–Strickler coefficient of M = 16–17 m^1/3/s. Hydraulic modeling results demonstrate that both bathymetry and roughness measurements are necessary to obtain a unique and robust hydraulic characterization of the river. Full article

The wetting of compacted clays and their subsequent swelling often result in damage to structures and infrastructures. Estimations of the swell that is expected to develop during wetting are usually based on standard laboratory tests. The standard procedure requires inundating the test specimens; this procedure represents an extreme wetting condition and provides an upper limit to the swell. However, wetting may result from less extreme conditions, for example by the absorption of water due to suction forces, which may result in a smaller swell. This paper describes a laboratory investigation of the swell difference in high-plasticity clay that may result from different wetting conditions. Swell tests were carried out on specimens prepared at different initial conditions and wetted under different wetting conditions of inundation or absorption. The results indicate that as the initial void ratio decreases and the degree of saturation increases, it is more likely that different wetting conditions will result in different swell magnitudes, where inundation may create a larger swell than absorption. The soil at a low initial void ratio and high degree of saturation seems to be characterized by mono-modal pore size distributions in the micropore range. This unique pore size distribution may be the explanation of the different swell magnitudes. Full article

In this research, a numerical model for simulating dam break floods was developed utilizing ArcGIS 10.8, 3ds Max 2021, and Flow-3D v11.2 software, with the aim of accurately representing the dam break disaster at Tianchi Lake in Changbai Mountain. The study involved the construction of a Triangulated Irregular Network (TIN) terrain surface and the application of 3ds Max 2021 to enhance the precision of the three-dimensional terrain data, thereby optimizing the depiction of the region’s topography. The finite volume method, along with multi-block grid technology, was employed to model the dam break scenario at Tianchi Lake. To evaluate the severity of the dam break disaster, the research integrated land use classifications within the study area with the simulated flood depths resulting from the dam break, applying the natural breaks method for hazard level classification. The findings indicated that the computational fluid dynamics (CFD) numerical model developed in this study significantly enhanced both the efficiency and accuracy of the simulations. Furthermore, the disaster assessment methodology that incorporated land use types facilitated the generation of inundation maps and disaster zoning maps across two scenarios, thereby effectively assessing the impacts of the disaster under varying conditions. Full article

The coast is the most dynamic part of the Earth’s surface due to its strategic position at the interface of the land and the sea. It is, therefore, exposed to hazards and specific risks because of the geography as well as the geological and environmental characteristics of different countries. The coastal environment is essentially dynamic and evolving in time and space, marked by waves, tides, and seasons; moreover, it is subjected to many marine and continental processes (forcing). This succession of events significantly influences the frequency and severity of coastal hazards. The present paper aims at describing and characterizing the hazards and vulnerabilities on the Cameroonian coast. Cameroon possesses 400 km of coastline, which is exposed to various hazards. It is important to determine the probabilities of these hazards, the associated effects, and the related vulnerabilities. In this study, in this stable intraplate setting, the methodology used was diverse and combined techniques for the study of the shore and methods for the treatment of climatic data. Also, historical data were collected during field observations and from the CRED website for all the natural hazards recorded in Cameroon. In addition, documents on climate change were consulted. Remotely sensed data, combined with GIS tools, helped to determine and assess the associated risks. A critical grid combining a severity and frequency analysis was used to better understand these hazards and the coastal vulnerabilities of Cameroon. The results show that Cameroon’s coastal margins are subject to natural processes that cause shoreline changes, including inundation, erosion, and accretion. This study identified seven primary hazard types (earthquakes, volcanism, landslides, floods, erosion, sea level rise, and black tides) affecting the Cameroonian coastline, with the erosion rate exceeding 1.15 m/year at Cape Cameroon. Coastal populations are continuously threatened by these natural or man-induced hazards, and they are periodically subjected to catastrophic disasters such as floods and landslides, as experienced in Cameroon. In addition, despite the existence of the National Contingency Plan devised by the Directorate of Civil Protection, National Risk, and Climate Change Observatories, the implementation of disaster risk reduction and mitigation strategies is suboptimal. Full article

Research on hurricane impacts in Florida’s coastal regions has been extensive, yet there remains a gap in comparing the effects and potential damage of different hurricanes within the same geographical area. Additionally, there is a need for reliable discussions on how variations in storm surges during these events influence evacuation accessibility to hurricane shelters. This is especially significant for rural areas with a vast number of aging populations, whose evacuation may require extra attention due to their special needs (i.e., access and functional needs). Therefore, this study aims to address this gap by conducting a comparative assessment of storm surge impacts on the evacuation accessibility of southwest Florida communities (e.g., Lee and Collier Counties) affected by two significant hurricanes: Irma in 2017 and Ian in 2022. Utilizing the floating catchment area method and examining Replica’s OD Matrix data with Geographical Information Systems (GISs)-based technical tools, this research seeks to provide insights into the effectiveness of evacuation plans and identify areas that need enhancements for special needs sheltering. By highlighting the differential impacts of storm surges on evacuation accessibility between these two hurricanes, this assessment contributes to refining disaster risk reduction strategies and has the potential to inform decision-making processes for mitigating the impacts of future coastal hazards. Full article