Search Results (19)

Flood disasters pose significant threats to human life and infrastructure, creating an urgent need for reliable vision-based environmental sensing technologies for rapid floodwater identification. Vision-based platforms such as unmanned surface vehicles (USVs) provide an effective solution for monitoring inland water environments; however, accurate floodwater segmentation remains challenging due to complex water boundaries, reflections, and background interference. To address these issues, we propose RFD-BiSeNet V2, a lightweight semantic segmentation network. Building upon BiSeNet V2, our model integrates an edge-aware learning strategy to track dynamic contours, a feature refinement module to suppress reflection noise, and a multi-scale feature fusion module to accommodate varying morphological scales. Evaluated on a comprehensive dataset incorporating USV data, UAV imagery, and diverse real-world scenes, RFD-BiSeNet V2 achieves an mIoU of 97.10%, outperforming the baseline by 6.68%. Crucially, the results demonstrate the practical implications of our architectural advancements: the edge-aware and feature refinement modules successfully sharpen ambiguous water boundaries and effectively filter out severe surface reflections, directly driving the segmentation accuracy. With a compact size of 5.95M parameters and real-time inference capabilities, the model offers a robust and highly efficient solution suitable for resource-constrained deployments across diverse intelligent environmental sensing systems. Full article

Floodplains are key components of inland river systems of Australia with floodplain vegetation playing important roles in habitat provision, nutrient cycling, and supporting strong cultural values. These vegetation communities are highly dynamic, particularly in response to flooding. However, decades of water development and highly managed water resources are linked to wetland habitat decline in this region. We explored patterns of vegetation response to flooding over twenty years at the Narran Lakes Ramsar site, a terminal floodplain wetland system in the northern Murray–Darling Basin, Australia. We collated data from previous monitoring efforts and resampled permanent plots for understorey vegetation structure and composition. Three flood events were surveyed over a 20-year period, with each event surveyed on two occasions first, following initial drawdown (minimal standing water) and a second survey under dry or drier conditions (~6 months after the recession of floodwaters). Overall, we observed a high diversity of native plant species (~110 species) in understorey communities across the wetland and high compositional turnover both between flood events and within years (i.e., paired surveys). Notably, vegetation cover, but not species richness, was greatest in the 2023 survey following the largest of the three flood events investigated. Understorey composition was strongly driven by inundation regimes, particularly the duration of recent inundation, and the number of wet and dry years prior. Large flood events are critical for supporting vegetation resilience in these systems, increasingly so under a drier climate and with stretched water resources. Continued long-term monitoring of vegetation through flood cycles at the Narran Lakes will be critical to understanding ecological responses to longer-term changes in climate and hydrology to inform adaptive water management and maintain the values of this Ramsar site. Full article

Background: Extreme weather events, such as storms, may evoke intense fear in individuals and impair their daily functioning, resulting in significant distress. In Brazil, recent climate-related disasters have highlighted the need to assess storm fear in the population. Objective: This study aimed to translate, adapt, and validate the Storm Fear Questionnaire (SFQ) for the Brazilian context. Methods: Translation and adaptation were conducted, followed by back-translation, review by an expert panel, and acceptability assessment by the target population. For the psychometric evaluation, a sample of 268 postpartum women exposed to a flood in southern Brazil completed the SFQ and the following questionnaires: the Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5), Beck Depression Inventory II (BDI-II), and the Pregnancy Experience Scale—Brief Version (PES-Brief). Results: The instrument showed excellent acceptability in the target population and good content validity. Regarding criterion validity, Pearson correlations indicated high convergence between the SFQ and PCL-5 and moderate convergence with the BDI-II. Regarding construct validity, SFQ scores were significantly higher among postpartum women who had to leave their homes due to the flood or had their houses affected by floodwaters. The first factor generated in the factor analysis explained 35.2% of the variance, with 14 out of 15 items presenting loadings greater than 0.40. Internal consistency was high (α = 0.88). Conclusions: The Brazilian version of the SFQ is a valid and reliable instrument for assessing fear of storms. Future studies are needed to evaluate the instrument’s applicability in diverse populations across the country. Full article

Climate-induced flooding is a major issue throughout the globe, resulting in damage to infrastructure, loss of life, and the economy. Therefore, there is an urgent need for sustainable flood risk management. This paper assesses the effectiveness of the hybrid defense system using advanced artificial intelligence (AI) techniques. A data series of energy dissipation (ΔE), flow conditions, roughness, and vegetation density was collected from literature and laboratory experiments. Out of the selected 136 data points, 80 points were collected from literature and 56 from a laboratory experiment. Advanced AI models like Random Forest (RF), Extreme Boosting Gradient (XGBoost) with Particle Swarm Optimization (PSO), Support Vector Regression (SVR) with PSO, and artificial neural network (ANN) with PSO were trained on the collected data series for predicting floodwater energy dissipation. The predictive capability of each model was evaluated through performance indicators, including the coefficient of determination (R²) and root mean square error (RMSE). Further, the relationship between input and output parameters was evaluated using a correlation heatmap, scatter pair plot, and HEC-contour maps. The results demonstrated the superior performance of the Random Forest (RF) model, with a high coefficient of determination (R² = 0.96) and a low RMSE of 3.03 during training. This superiority was further supported by statistical analyses, where ANOVA and t-tests confirmed the significant performance differences among the models, and Taylor’s diagram showed closer agreement between RF predictions and observed energy dissipation. Further, scatter pair plot and HEC-contour maps also supported the result of SHAP analysis, demonstrating greater impact of the roughness condition followed by vegetation density in reducing floodwater energy dissipation under diverse flow conditions. The findings of this study concluded that RF has the capability of modeling flood risk management, indicating the role of AI models in combination with a hybrid defense system for enhanced flood risk management. Full article

Global warming and intensified human activities increase flood disasters, causing annual casualties and economic losses. Mountain shadows are a major source of interference in floodwater extraction from SAR imagery, severely impacting the accuracy of water body detection. This study proposes an innovative approach based on the Inverted Exponential Shadow Removal Model (IESRM). This model can adaptively and dynamically adjust the slope threshold according to the terrain characteristics. It is easy to use, eliminating the need for manual parameter setting. The experimental results demonstrate the following: (1) Water body detection tests across diverse terrains (mountains, plains, and foothill plains) show robust results even in complex foothill regions, with an overall accuracy of 94.51% and a Kappa coefficient of 0.86. (2) A comparative analysis with the shadow formation mechanism method and the HAND (Height Above Nearest Drainage) method revealed that the inverted exponential function model achieved the highest accuracy, with an overall accuracy of 96.46% and a Kappa coefficient of 0.89. The IESRM provides an innovative solution for removing mountain shadows, enhancing SAR imagery-based flood monitoring in complex terrains. It offers timely and accurate data support for flood disaster management agencies. Full article

We present a novel methodology for generating and filtering synthetic Unmanned Aerial Vehicle (UAV) flood imagery to enhance the generalization capabilities of segmentation models. Our framework combines text-to-image synthesis and image inpainting, using curated prompts and real-world segmentation masks to produce diverse and realistic flood scenes. To overcome the lack of human annotations, we employ an unsupervised pseudo-labeling method that generates segmentation masks based on floodwater appearance characteristics. We further introduce a filtering stage based on outlier detection in feature space to improve the realism of the synthetic dataset. Experimental results on five state-of-the-art flood segmentation models show that synthetic data can closely match real data in training performance, and combining both sources improves model robustness by 1–7%. Finally, we investigate the impact of prompt design on the visual fidelity of generated images and provide qualitative and quantitative evidence of distributional similarity between real and synthetic data. Full article

River nodes play a crucial role in regulating water and sediment transport within river networks. The SiXianJiao (SXJ) node serves as a key exchange point between the West River (WR) and North River (NR) in the Pearl River Delta (PRD), South China. Understanding the differences in flood diversion dynamics between X-shaped and H-shaped configurations under varying geomorphic conditions is essential for flood management. This study employs the Delft3D-Flow model to investigate the flood diversion mechanisms of these composite river nodes. Results revealed distinct hydrodynamic behaviors: the X-shaped node facilitates greater water exchange due to a shared channel segment, whereas the H-shaped node experiences restricted exchange due to flow resistance in the connecting branch. Both configurations exhibit self-regulating flood diversion processes that significantly reduce flood risks. A critical flow fraction of approximately 75.9% [WR/(WR + NR)] is identified, at which water levels (WLs) at both ends of the SXJ node almost equalize. When the WR flow fraction exceeds this threshold, floodwaters are diverted toward the NR. Below it, the diversion direction reverses. Additionally, flood diversion synchronizes asynchronous flood waves, stabilizing the discharge fraction at Makou (Sanshui), which fluctuates around 75.8% (24.2%) for the X-shaped node and 76.6% (23.4%) for the H-shaped node. These findings enhance our understanding of flood diversion dynamics and provide valuable insights for optimizing flood mitigation strategies and hydraulic infrastructure planning in the PRD and comparable river systems worldwide. Full article

The INterpolated FLOod Surface (INFLOS) tool was developed to meet the operational needs of the Copernicus Emergency Management Service (CEMS) Rapid Mapping (RM) component, which delivers critical crisis information within hours during and after disasters. With increasing demand for accurate and real-time flood depth estimates, INFLOS provides a rapid, adaptable solution for estimating floodwater depth across diverse flood scenarios, using remotely sensed data and high-resolution Digital Terrain Models (DTMs). INFLOS calculates flood depth by interpolating water surface elevation from sample points along flooded area boundaries, derived from satellite imagery. This tool is capable of delivering flood depth estimates in a rapid mapping context, leveraging a multistep interpolation and filtering process for improved accuracy. Tested across fourteen regions in Europe and South America, INFLOS has been successfully integrated into CEMS RM operations. The tool’s computational optimisations further enhance efficiency, improving computation times by up to 15-fold, compared to similar techniques. Indeed, it is able to process areas of up to 6000 ha in a median time of 5.2 min, and up to 30 min at most. In conclusion, INFLOS is currently operational and consistently generates flood depth products quickly, supporting real-time emergency management and reinforcing the CEMS RM portfolio. Full article

The Linhuaigang flood control project (LFCP), situated on the Huaihe River, China, uses the river channels upstream of the LFCP, together with the hinterland areas outside the channels, to retain and store fluvial floodwaters that exceed the downstream channel’s discharge capacity. The hinterland areas are split into seven flood storage areas, three floodplain areas, eight polder areas, and three flood-control-protected areas, and they are connected to the river in various ways. A coupled hydrodynamic model was established to simulate the hydrodynamic and water volume exchange between the river channels and the hinterland areas. The flood storage area, under the control of a flood diversion sluice, was simulated with a 2D hydrodynamic model, and the inflow process initiated by the flood diversion sluice was simulated as a control structure. The polder area was generalized as a reservoir that would be filled in several hours once put into use because of its small size. The uncontrolled inflow process between the flood-control-protected areas and the channel was simulated by means of a dam break model, which could simulate levee breaching. The flooding within the flood-control-protected area, which represents a vast space, was simulated with a 2D hydrodynamic model. The floodplain area was laterally connected to the river channel along the river levee. The difference between the simulated and the measured flood peak water stage did not exceed 0.2 m in 2003 and 2007, indicating that the accuracy of the model was relatively high. In the scenario of a design flood with a return period of 100 years, the flood storage areas and the LFCP were used in the following order: Mengwa, Qiujiahu, Nanrunduan, Shouxihu, Jiangtanghu, Chengxihu, Chengdonghu, and the LFCP. When the Huaihe River encounters a flood with a return period of 1000 years that exceeds the design standard, the highest water stage upstream of the LFCP and Zhengyangguan shall not exceed 29.30 m and 27.96 m after the use of all the flood storage areas, floodplain areas, and flood-control-protected areas. The results of this research can provide technical support for the flood risk management of the LFCP. Full article

In this study, we examine the possibility of proactive floodwater diversion to fields via backwater in numerical experiments using multiple elevation data products with different spatial resolutions and explore the optimal timing of water diversion from the perspective of crop calendars. This study targeted the Ida River System Land Improvement District, which has beneficiary lands on both banks of the Ida River, one of the tributaries of the Jinzu River that flows through Toyama and Gifu Prefectures in the Hokuriku and Chubu Regions of Japan. First, a comparison of the elevation data products revealed that photogrammetric data can capture microtopography, such as the footpaths between rice paddies and drainage channels around a field. Numerical experiments using two elevation data products, 5m DEM and LP-derived approximately 5m DEM, showed that flood peaks were reduced downstream in both cases using 5m DEM and LP approximately 5m DEM by directing floodwaters. Interviews with land improvement districts and a review of previous studies revealed that the ear-burst period is particularly vulnerable to flooding. Although the effect of flood peak reduction is reduced due to flooding of the field, it is possible that floodwater can be channeled during the ripening period in August and in late September and October when the ears have been harvested. Full article

Climate change is contributing to extreme weather conditions, which transform the scale and degree of flood events. Therefore, it is important for relevant government agencies to effectively respond to both extreme climate conditions and their impacts by providing more efficient asset management strategies. Although international research projects on water-sensitive urban design and rural drainage design have provided partial solutions to this problem, road networks commonly serve unique combinations of urban-rural residential and undeveloped areas; these areas often have diverse hydrology, geology, and climates. Resultantly, applying a one-size-fits-all solution to asset management is ineffective. This paper focuses on data-driven flood modelling that can be used to mitigate or prevent floodwater-related damage in Western Australia. In particular, a holistic and coherent view of data-driven asset management is presented and multi-criteria analysis (MCA) is used to define the high-risk hotspots for asset damage in Western Australia. These state-wide hotspots are validated using road closure data obtained from the relevant government agency. The proposed approach offers important insights with regard to factors influencing the risk of damage in the stormwater management system. Full article

As the global climate continues to change, extreme weather events such as hurricanes and heavy rainfall are becoming more frequent. Subsequently, flooding and standing water disrupt and negatively impact many communities. The use of nature-based solutions (NBS) is an innovative and sustainable approach to flood mitigation. Geospatial research and applications have developed rapidly to identify and map broad regions in the world, as well as specific locations for NBS. We conducted a geospatial analysis in ArcGIS Pro to identify areas where NBS, referred to as “FloodWise” practices in this study, could be sited in the North Carolina Coastal Plain to strategically reduce flooding and provide water quality and habitat improvement. The study provides a spatially explicit application of integrated remote sensing, scientific and professional knowledge, and extant databases to screen diverse variables and identify potential specific NBS opportunities and sites. The practices modeled in this study are wetland restoration, afforestation, agroforestry, “water farming” (which uses a combination of dry dams and berms), and stream restoration. Maps of specific areas and tracts in the county for the NBS practices in Robeson County, North Carolina were developed based on the land ownership size, biophysical characteristics, current land uses, and water management opportunities. Land suitability locations revealed in these maps can be used in future resilience planning initiatives to reduce floodwaters on North Carolina’s rural landscapes. The geospatial analysis methodologies employed in this study can be followed to model NBS locations for flood reduction and water storage opportunities in other counties in Eastern North Carolina or other regions with similar topographies and land-type characteristics. Full article

The provision of detailed information on the impact of potential fluvial floods on urban population health, quantifying the impact magnitude and supplying the location of areas of the highest risk to human health, is an important step towards (a) improvement of sustainable measures to minimise the impact of floods, e.g., by including flood risk as a design parameter for urban planning, and (b) increase public awareness of flood risks. The three new measures of the impact of floods on the urban population have been proposed, considering both deterministic and stochastic aspects. The impact was determined in relation to the building’s function, the number of residents, the probability of flood occurrence and the likely floodwater inundation level. The building capacity concept was introduced to model population data at the building level. Its proposed estimation method, an offshoot of the volumetric method, has proved to be successful in the challenging study area, characterised by a high diversity of buildings in terms of their function, size and density. The results show that 2.35% of buildings and over 122,000 people may be affected by 500-year flooding. However, the foreseen magnitude of flood impact on human health is moderate, i.e., on average ten persons per residential building over the 80% of flood risk zones. Such results are attributed to the low inundation depth, i.e., below 1 m. Full article

The western region of Jilin Province is an ecologically fragile area with scarce water resources. The effective allocation of the limited water resources in order to obtain a higher ecological service value is an urgent requirement. In this paper, an interval fuzzy, double-sided chance-constrained, stochastic programming (IFDCP) model was used based on the interconnected river system network project in the western Jilin Province. With the objective of maximizing the value of regional ecological services, the water diversion and supplement schemes were optimized and adjusted. The model results showed that the restored water surface area of all lakes and ponds in the western region of Jilin Province was higher than the initially planned scheme in the high flow year. The water surface area fulfilled the minimum constraints, but did not fulfill the initial scheme in the normal flow year. In the low flow year, the lower limit of some of the regions had to be decreased in order to meet the allocation of the limited water resources. The proportion of floodwater resource utilization gradually increased with an increase in the flood amount. The ecological service value produced in the normal and high flow years was found to be higher than the initial scheme. The marsh wetland can produce higher ecological service value. Therefore, the core of the model optimization was introducing more water to the marsh wetland after fulfilling the basic consumption of ponds and the reed wetland. In addition, the IFDCP model was more flexible in water diversion and supplement as compared to other models that had been developed previously. Full article

The Poyang water system in Jiangxi Province, China, is important for floodwater storage, diversity maintenance, and the economy of the Poyang Lake watershed. In recent years, pollution has destroyed the ecosystem and impacted human health and the related economy. The water quality of the Poyang Lake watershed and the impact of human interference must be assessed. Conventional analysis and high-throughput sequencing were used to evaluate the structure of both zooplankton and fungi in six sub-lakes of the Poyang Lake watershed under different anthropogenic influences. The sub-lakes included were Dahuchi Lake (in natural preserve, DHC), Shahu Lake (in natural reserve, SH), Nanhu Lake (out of natural preserve, NH), Zhelinhu Lake (artificial reservoir, ZLH), Sixiahu Lake (agricultural lake artificially isolated from Poyang Lake, SXH), and Qianhu Lake (urban lake, QH). The densities and biomass of the zooplankton in DHC, SH, NH were higher compared with those in SXH, ZLH and QH (p < 0.05). Zooplankton distribution of SXH was the most strongly associated with total nitrogen (TN), total phosphorus (TP) and chlorophyll a (Chl a), while QH was highly associated with pH, conductivity (Cond), and water temperature (WT). For fungal diversity, a large number of beneficial fungi, Basidiomycota (phylum level) and Massarina (genus level) were obtained from DHC (55.3% and 27.5%, respectively), SH (54.4% and 28.9%, respectively), and NH (48.6% and 1.4%, respectively), while a large number of pathogenic Chytridiomycota (at phylum level) were identified from SXH (21.0%), ZLH (5.5%), and QH (7.5%). Manmade pollutants have impacted the natural hydrology and water quality and promoted variation between the zooplankton and fungi in the six sub-lakes, reducing the relative abundance of beneficial fungi and increasing the number of pathogens in the environment, which threatens human health and economic production. Understanding the diversity among the zooplankton and fungi in the six sub-lakes of the Poyang Lake watershed may help guide future water management practices. Full article