Search Results (83)

Despite a growing number of Earth Observation satellites, a critical observational gap persists for timely, high-resolution flood mapping, primarily due to infrequent satellite revisits and persistent cloud cover. To address this issue, we propose a novel framework that synergistically fuses complementary data from three public sensor types. Our methodology harmonizes these disparate data sources by using surface water fraction as a common variable and downscaling them with flood susceptibility and topography information. This allows for the integration of sub-daily observations from the Visible Infrared Imaging Radiometer Suite and the Advanced Himawari Imager with the cloud-penetrating capabilities of the Advanced Microwave Scanning Radiometer 2. We evaluated this approach on the February 2022 flood in Brisbane, Australia using an independent ground truth dataset. The framework successfully compensates for the limitations of individual sensors, enabling the consistent generation of detailed, high-resolution flood maps. The proposed method outperformed the flood extent derived from commercial high-resolution optical imagery, scoring 77% higher than the Copernicus Emergency Management Service (CEMS) map in the Critical Success Index. Furthermore, the True Positive Rate was twice as high as the CEMS map, confirming that the proposed method successfully overcame the cloud cover issue. This approach provides valuable, actionable insights into inundation dynamics, particularly when other public data sources are unavailable. Full article

The Clearwater River, located in western Washington, USA, is a free-flowing river with high precipitation rates impacted by spatially extensive logging throughout the 1900s. Declining salmon productivity within the watershed has been linked to the effects of legacy deforestation, including increased fine sediment loads, a lack of large wood and physical habitat complexity, and potential channel incision coupled with side channel and floodplain disconnection. To test a conceptual model positing that the river’s geomorphic diversity was declining, potentially due to anthropogenic incision, we employed a dual approach, combining historical geomorphic mapping and current-condition hydraulic modeling using SRH-2D. A dual approach allows us to identify mainstem river reaches with the greatest potential for floodplain and side channel reconnection by utilizing increased roughness as a proxy for large wood effects on the river stage. Based on our geomorphic mapping, the area occupied by the mainstem river and surrounding geomorphic units has remained relatively stable through time. However, there was a marked decrease in the side channel connections within the downstream-most 30 river kilometers, confirmed through the hydraulic modeling results. Between river kilometers 10 and 20, river stages at 2-year recurrence interval peak discharge are located over 2 m below young Holocene terraces and could indicate a recent anthropogenic incision contributing to side channel disconnection. A decrease in unvegetated alluvium through time also indicates that there could be less dynamic lateral channel movement and overbank inundation between 1980 and 2017, despite a similar history of high peak flows. Overall, even though the river is able to balance the loss of the active geomorphic unit area with the incorporation of new geomorphic units through lateral channel changes, this area is likely concentrated in a smaller number of individual channels and floodplains, specifically in the lower 30 river kilometers. This study provides a framework for a site-screening-level analysis in impacted watersheds, using a watershed impacted by legacy logging without flow regulation, where the impacts are often less pronounced than in dammed river systems. Full article

Accurate and detailed mapping of coastal wetlands is essential for effective wetland resource management. However, due to periodic tidal inundation, frequent cloud cover, and spectral similarity of land cover types, reliable coastal wetland classification methods remain limited. To address these issues, we developed an integrated pixel- and object-based hierarchical classification strategy based on multi-source remote sensing data to achieve fine-grained coastal wetland classification on Google Earth Engine. With the random forest classifier, pixel-level classification was performed to classify rough wetland and non-wetland types, followed by object-based classification to differentiate artificial and natural attributes of water bodies. In this process, multi-dimensional features including water level, phenology, variation, topography, geography, and geometry were extracted from Sentinel-1/2 time-series images, topographic data and shoreline data, which can fully capture the variability and dynamics of coastal wetlands. Feature combinations were then optimized through Recursive Feature Elimination and Jeffries–Matusita analysis to ensure the model’s ability to distinguish complex wetland types while improving efficiency. The classification strategy was applied to typical coastal wetlands in central Jiangsu in 2020 and finally generated a 10 m wetland map including 7 wetland types and 3 non-wetland types, with an overall accuracy of 92.50% and a Kappa coefficient of 0.915. Comparative analysis with existing datasets confirmed the reliability of this strategy, particularly in extracting intertidal mudflats, salt marshes, and artificial wetlands. This study can provide a robust framework for fine-grained wetland mapping and support the inventory and conservation of coastal wetland resources. Full article

Inundation dynamics in low-lying deltas are becoming increasingly important to monitor due to the impacts of climate change and human alterations to hydrological systems, which disrupt natural inundation patterns. In the Nemunas River Delta, where seasonal and extreme floods impact agricultural and natural landscapes, we used Sentinel-1 synthetic aperture radar (SAR) imagery (2015–2019), validated with drone data, to map flood extents. SAR provides consistent, 10 m resolution data unaffected by cloud cover, while drone imagery provides high-resolution (10 cm) data at 90 m flight height for validation during SAR acquisitions. Results revealed peak inundation during spring snowmelt and colder months, with shorter, rainfall-driven summer floods. Approximately 60% of inundated areas were low-lying agricultural fields, which experienced prolonged waterlogging due to poor drainage and soil degradation. Inundation duration was shaped by lithology, land cover, and topography. A consistent 5–10-day lag between peak river discharge and flood expansion suggests discharge data can complement SAR when imagery is unavailable. This study confirms SAR’s value for flood mapping in cloud-prone, temperate regions and highlights its scalability for monitoring flood-prone deltas where agriculture and infrastructure face increasing climate-related risks. Full article

Disaster risk reduction requires efficient flood control in lowland and flood-prone areas, especially in agricultural areas like the Bang Rakam model area in Phitsanulok province, Thailand. In order to improve flood prediction and response, this study proposes the creation of a low-cost, real-time water-level monitoring integrated with spatial data analysis using Geographic Information System (GIS) technology. Ten ultrasonic sensor-equipped monitoring stations were installed thoughtfully around sub-catchment areas to provide highly accurate water-level readings. To define inundation zones and create flood depth maps, the sensors gather flood level data from each station, which is then processed using a 1-m Digital Elevation Model (DEM) and Python-based geospatial analysis. In order to create dynamic flood maps that offer information on flood extent, depth, and water volume within each sub-catchment, an automated method was created to use real-time water-level data. These results demonstrate the promise of low-cost IoT-based flood monitoring devices as an affordable and scalable remedy for communities that are at risk. This method improves knowledge of flood dynamics in the Bang Rakam model area by combining sensor technology and spatial data analysis. It also acts as a standard for flood management tactics in other lowland areas. The study emphasizes how crucial real-time data-driven flood monitoring is to enhancing early-warning systems, disaster preparedness, and water resource management. Full article

Synthetic Aperture Radar (SAR) technology offers unparalleled advantages by delivering high-quality images under all-weather conditions, enabling effective flood monitoring. This capability provides massive remote sensing data for flood mapping, while recent rapid advances in deep learning (DL) offer methodologies for large-scale flood mapping. However, the full potential of deep learning in large-scale flood monitoring utilizing remote sensing data remains largely untapped, necessitating further exploration of both data and methodologies. This paper presents an innovative approach that harnesses convolutional neural networks (CNNs) with Sentinel-1 SAR images for large-scale inundation detection and dynamic flood monitoring in the Yangtze River Basin (YRB). An efficient CNN model entitled FloodsNet was constructed based on multi-scale feature extraction and reuse. The study compiled 16 flood events comprising 32 Sentinel-1 images for CNN training, validation, inundation detection, and flood mapping. A semi-automatic inundation detection approach was developed to generate representative flood samples with labels, resulting in a total of 5296 labeled flood samples. The proposed model FloodsNet achieves 1–2% higher F1-score than the other five DL models on this dataset. Experimental inundation detection in the YRB from 2016 to 2021 and dynamic flood monitoring in the Dongting and Poyang Lakes corroborated the scheme’s outstanding performance through various validation procedures. This study marks the first application of deep learning with SAR images for large-scale flood monitoring in the YRB, providing a valuable reference for future research in flood disaster studies. This study explores the potential of SAR imagery and deep learning in large-scale flood monitoring across the Yangtze River Basin, providing a valuable reference for future research in flood disaster studies. Full article

This study presents a novel bivariate-output deep learning framework based on LeNet-5 for the simultaneous prediction of urban flood susceptibility and inundation depth in Seoul, South Korea. Unlike previous studies that relied on single-output models, the proposed approach jointly learns classification and regression targets through a shared feature extraction structure, enhancing consistency and generalization. Among six tested architectures, the Le5SD_CBAM model—integrating a Convolutional Block Attention Module (CBAM)—achieved the best performance, with 83% accuracy, an Area Under the ROC Curve (AUC) of 0.91 for flood susceptibility classification, and a mean absolute error (MAE) of 0.12 m and root mean squared error (RMSE) of 0.18 m for depth estimation. The model’s spatial predictions aligned well with hydrological principles and past flood records, accurately identifying low-lying flood-prone zones and capturing localized inundation patterns influenced by infrastructure and micro-topography. Importantly, it detected spatial mismatches between susceptibility and depth, demonstrating the benefit of joint modeling. Variable importance analysis highlighted elevation as the dominant predictor, while distances to roads, rivers, and drainage systems were also key contributors. In contrast, secondary terrain attributes had limited influence, indicating that urban infrastructure has significantly altered natural flood flow dynamics. Although the model lacks dynamic forcings such as rainfall and upstream inflows, it remains a valuable tool for flood risk mapping in data-scarce settings. The bivariate-output framework improves computational efficiency and internal coherence compared to separate single-task models, supporting its integration into urban flood management and planning systems. Full article

Flood hazards have increasingly threatened the East Kazakhstan region in recent decades due to climate change and growing anthropogenic pressures, leading to more frequent and severe flooding events. This article considers an approach to modeling and forecasting river runoff using the example of the small Kurchum River in the East Kazakhstan region. The main objective of this study was to evaluate the numerical performance of the flood hazard model by comparing simulated flood extents with observed flood data. Two types of data were used as initial data: topographic data (digital elevation models and topographic maps) and hydrological data, including streamflow time series from stream gauges (hourly time steps) and lateral inflows along the river course. Spatially distributed rainfall forcing was not applied. To build the model, we used the software packages of HEC-RAS version 5.0.5 and MIKE version 11. Using retrospective data for 3 years (2019–2021), modeling was performed, the calculated boundaries of possible flooding were obtained, and the highest risk zones were identified. A dynamic map of depth changes in the river system is presented, showing the process of flood wave propagation, the dynamics of depth changes, and the expansion of the flood zone. Temporal flood inundation mapping and performance metrics were evaluated for each individual flood event (2019, 2020, and 2021). The simulation outcomes closely correlate with actual flood events. The assessment showed that the model data coincide with the real ones by 91.89% (2019), 89.09% (2020), and 95.91% (2021). The obtained results allow for a clarification of potential flood zones and can be used in planning measures to reduce flood risks. This study demonstrates the importance of an integrated approach to modeling, combining various software packages and data sources. Full article

Floods resulting from dam failures are highly destructive, characterized by intense impact forces, widespread inundation, and rapid flow velocities, all of which pose significant threats to public safety and social stability in downstream regions. To improve evacuation efficiency during such emergencies, it is essential to study flood evacuation route planning. This study aimed to minimize evacuation time and reduce risks to personnel by considering the dynamic evolution of dam-break floods. Using aerial photography from an unmanned aerial vehicle, the downstream road network of a reservoir was mapped. A coupled flood–road network coupling model was then developed by integrating flood propagation data with road network information. This model optimized evacuation route planning by combining the dynamic evolution of flood hazards with real-time road network data. Based on this model, a flood evacuation route planning method was proposed using Dijkstra’s algorithm. This methodology was validated through a case study of the Shanmei Reservoir in Fujian, China. The results demonstrated that the maximum flood level reached 18.65 m near Xiatou Village, and the highest flow velocity was 22.18 m/s near the Shanmei Reservoir. Furthermore, evacuation plans were developed for eight affected locations downstream of the Shanmei Reservoir, with a total of 13 evacuation routes. These strategies and routes resulted in a significant reduction in evacuation time and minimized the risks to evacuees. The life-loss risk was minimized in the evacuation process, and all evacuees were able to reach safe locations. These findings confirmed that the proposed method, which integrated flood dynamics with road network information, ensured the safety and effectiveness of evacuation routes. This approach met the critical needs of emergency management by providing timely and secure evacuation paths in the event of dam failure. Full article

Floods are among the most frequent and devastating climate-related hazards, causing significant environmental and socioeconomic impacts. This study integrates synthetic aperture radar (SAR)-based flood mapping via the Google Earth Engine (GEE) with hydraulic modeling in HEC-RAS to analyze flood dynamics downstream of the Gumara watershed, Upper Blue Nile (UBN) Basin, Ethiopia. A change detection approach using Sentinel-1 imagery was employed to generate flood inundation maps from 2017–2021. Among these events, flood events on 22 July, 3 August, and 27 August 2019 were used to calibrate the HEC-RAS model, achieving an F-score of 0.57, an overall accuracy (OA) of 86.92%, and a kappa coefficient (K) of 0.62 across the three events. Further validation using ground control points (GCPs) resulted in an OA of 86.33% and a K of 0.72. Using the calibrated HEC-RAS model, hydraulic simulations were performed to map flood inundation for return periods of 5, 10, 25, 50, and 100 years. Additionally, flood mapping was conducted for historical (1981–2005), near-future (2031–2055), and far-future (2056–2080) periods under extreme climate scenarios. The results indicate increases of 16.48% and 27.23% in the flood inundation area in the near-future and far-future periods, respectively, under the SSP5-8.5 scenario compared with the historical period. These increases are attributed primarily to deforestation, agricultural expansion, and intensified extreme rainfall events in the upstream watershed. The comparison between SAR-based flood maps and HEC-RAS simulations highlights the advantages of integrating remote sensing and hydraulic modeling for enhanced flood risk assessment. This study provides critical insights for flood mitigation and sustainable watershed management, emphasizing the importance of incorporating current and future flood risk analyses in policy and planning efforts. Full article

This paper proposes a flood risk visualization method that is (1) readily transferable (2) hyperlocal, (3) computationally inexpensive, and (4) geometrically accurate. This proposal is for risk communication, to provide high-resolution, three-dimensional flood visualization at the sub-meter level. The method couples a laser scanning point cloud with algorithms that produce textured floodwaters, achieved through compounding multiple sine functions in a graphics shader. This hyper-local approach to visualization is enhanced by the ability to portray changes in (i) watercolor, (ii) texture, and (iii) motion (including dynamic heights) for various flood prediction scenarios. Through decoupling physics-based predictions from the visualization, a dynamic, flood risk viewer was produced with modest processing resources involving only a single, quad-core processor with a frequency around 4.30 GHz and with no graphics card. The system offers several major advantages. (1) The approach enables its use on a browser or with inexpensive, virtual reality hardware and, thus, promotes local dissemination for flood risk communication, planning, and mitigation. (2) The approach can be used for any scenario where water interfaces with the built environment, including inside of pipes. (3) When tested for a coastal inundation scenario from a hurricane, 92% of the neighborhood participants found it to be more effective in communicating flood risk than traditional 2D mapping flood warnings provided by governmental authorities. 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

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

Based on statistical analyses applied to official data from the Digital Atlas of Disasters in Brazil over the last 25 years, we evidenced a consistent intensification in the annual occurrence of natural disasters in the state of Pará, located in the eastern Brazilian Amazon. The quantitative comparison between the averages of the most intense period of disasters (2017 to 2023) and the earlier years (1999 to 2016) revealed a remarkable percentage increase of 473%. Approximately 81% of the state’s municipalities were affected, as indicated by disaster mapping. A clear seasonal pattern was observed, with Hydrological disasters (Inundations, Flash floods, and Heavy rainfall) peaking between February and May, while Climatological disasters (Droughts and Forest fires) were most frequent from August to October. The catastrophic impacts on people and the economy were documented, showing a significant rise in the number of homeless individuals and those directly affected, alongside considerable material damage and economic losses for both the public and private sectors. Furthermore, we conducted a comprehensive composite analysis on the tropical ocean–atmosphere dynamic structure that elucidated the various triggering mechanisms of disasters arising from Inundations, Droughts, and Forest fires (on seasonal scale), and Flash floods and Heavy rainfall (on sub-monthly scale) in Pará. The detailed characterization of disasters on a municipal scale is relevant in terms of the scientific contribution applied to the strategic decision-making, planning, and implementation of public policies aimed at early risk management (rather than post-disaster response), which is critical for safeguarding human well-being and strengthening the resilience of Amazonian communities vulnerable to climate change. Full article

Inundation maps that show water depths that occur in the event of a flood are essential for protection. Especially information on timings is crucial. Creating a dynamic inundation map with depth data in temporal resolution is a major challenge and is not possible with physical models, as these are too slow for real-time predictions. To provide a dynamic inundation map in real-time, we developed a data-driven multi-step inundation forecast system for fluvial flood events. The forecast system is based on a convolutional neural network (CNN), feature-informed dense layers, and a recursive connection from the predicted inundation at timestep t as a new input for timestep t + 1. The forecast system takes a hydrograph as input, cuts it at desired timesteps (t), and outputs the respective inundation for each timestep, concluding in a dynamic inundation map with a temporal resolution (t). The prediction shows a Critical Success Index (CSI) of over 90%, an average Root Mean Square Error (RMSE) of 0.07, 0.12, and 0.15 for the next 6 h, 12 h, and 24 h, respectively, and an individual RMSE value below 0.3 m, for all test datasets when compared with the results from a physically based model. Full article