Search Results (2,114)

Submergence during germination (SG) is a major constraint during sowing, severely limiting the promotion and application of direct-seeded rice. Recent studies have revealed the adaptive mechanisms by which rice responds to SG. At the physiological level, flood-tolerant varieties effectively maintain energy supply and cellular homeostasis by enhancing amylase activity, improving glycolysis and ethanolic fermentation efficiency, promoting embryo sheath elongation, and activating antioxidant enzyme systems; at the molecular level, multiple key genes and signalling pathways have been identified, including SUB1A, OsTPP7, OsGF14h, etc., participating in hypoxia perception, metabolic reprogramming, and hormone signal integration to regulate SG under flooding. In addition, the interactions among plant hormones, such as ethylene, gibberellin, abscisic acid, and cytokinin, also play key roles in the SG process. Future research should prioritize breeding strategies that pyramid multiple genes by integrating gene editing, whole-genome selection, and high-throughput phenotyping to improve seed germination under flood stress. Full article

This study investigates flood risk trends using rainfall data collected from 13 districts of Diyarbakır Province, Turkey, with a focus on supporting sustainability-oriented climate adaptation. Both annual and seasonal precipitation variations were examined, with particular emphasis on the role of maximum daily rainfall in driving flood potential. In addition, the analysis integrates extreme precipitation patterns with regional hazard characteristics to provide a more comprehensive flood risk assessment framework. Non-parametric statistical methods, including the Mann–Kendall trend test and Spearman’s Rho correlation, were applied to detect trends in annual and seasonal datasets. Flood magnitudes were estimated using the Generalized Extreme Value (GEV) and Peaks Over Threshold (POT) approaches. The dataset covers varying periods between 2009 and 2023, depending on station availability. The results show a statistically significant increase in both annual and winter precipitation at Bismil, and a significant winter increase at Çermik. Other stations displayed upward trends that were not statistically significant. Çüngüş, Lice, and Kulp were identified as particularly susceptible to extreme rainfall. Although the relatively short observation period poses a limitation, consistent patterns of intensified precipitation were detected. Previous studies in Turkey have demonstrated that such events often cause severe infrastructure damage and displacement of vulnerable communities. The findings of this study provide practical insights for national and regional authorities, including the Disaster and Emergency Management Authority (AFAD), the General Directorate of State Hydraulic Works (DSİ), and the Ministry of Environment, Urbanization, and Climate Change, to strengthen sustainable climate adaptation planning and disaster risk reduction strategies. Overall, this research highlights the importance of integrating extreme precipitation analysis into sustainable flood management, resilient infrastructure development, and long-term sustainability policies, thereby reinforcing the connection between hydrological risk assessment and sustainability science. Full article

Leveraging ubiquitous wireless signals for environmental sensing provides a highly promising pathway toward constructing low-cost and high-density flood monitoring systems. However, in real-world flood scenarios, the wireless channel is simultaneously affected by rainfall-induced signal attenuation and complex multipath effects caused by surface runoff (water accumulation). These two physical phenomena become intertwined in the received signals, resulting in severe feature ambiguity. This not only greatly limits the accuracy of environmental sensing but also hinders communication systems from performing effective channel compensation. How to disentangle these combined effects from a single wireless link represents a fundamental scientific challenge for achieving high-precision wireless environmental sensing and ensuring communication reliability under harsh conditions. To address this challenge, we propose a novel signal processing framework that aims to effectively decouple the effects of rainfall and surface runoff from Channel State Information (CSI) collected using commercial Wi-Fi devices. The core idea of our method lies in first constructing a two-dimensional CSI spatiotemporal spectrogram from continuously captured multicarrier CSI data. This spectrogram enables high-resolution visualization of the unique “fingerprints” of different physical effects—rainfall manifests as smooth background attenuation, whereas surface runoff appears as sparse high-frequency textures. Building upon this representation, we design and implement a Dual-Decoder Convolutional Autoencoder deep learning model. The model employs a shared encoder to learn the mixed CSI features, while two distinct decoder branches are responsible for reconstructing the global background component attributed to rainfall and the local texture component associated with surface runoff, respectively. Based on the decoupled signal components, we achieve simultaneous and highly accurate estimation of rainfall intensity (mean absolute error below 1.5 mm/h) and surface water accumulation (detection accuracy of 98%). Furthermore, when the decoupled and refined channel estimates are applied to a communication receiver for channel equalization, the Bit Error Rate (BER) is reduced by more than one order of magnitude compared to conventional equalization methods. Full article

Flooding events have increased in frequency and severity worldwide in recent years, a trend that has been made worse by human activity and climate change. Floods are one of the world’s most dangerous natural catastrophes because of the serious risks they represent to property, human life, and cultural heritage. The necessity for efficient flood management techniques to reduce the growing dangers is what motivated this study. It specifically examines the flood risk in the Garyllis River Basin in Cyprus, a region recognized for it high susceptibility to extreme weather conditions Adopting an integrates approach that combines modeling tools and techniques, such as remote sensing, Geographic Information Systems (GIS) and hydraulic modeling, along with multiple data types of data and in situ measures, this study evaluates flood risk and proposed shelters and escapes routes for the worst-case scenarios. The research utilizes the open-access software HEC-RAS to simulate the spatio-temporal progression of surface water depth and water velocity for different return periods. The vulnerability levels are enumerated through a weighted linear combination of relevant factors, in specific population density and age distribution, according to the last official government reports. Exposure levels were calculated in terms of land value. For each flood component, all factors are assigned equal weighting coefficients. Subsequently, flood risk levels are assessed for each location as the product of hazard, vulnerability, and exposure levels. The validity of the proposed methodology is assessed by comparing the critical points identified during in situ visits with the flood risk level estimates. As a result, escape routes and refuge areas were proposed for the worst-case scenario. Full article

Recently, disaster damages have become more widespread due to climate change and the interaction between disasters, and the complexity of solving this problem is increasing. Consequently, many buildings have been severely affected, with some houses being razed or flooded, losing their residential function. The damage to housing facilities not only destroys the life-cycles of individuals and households but also causes functional loss and productivity decrease in local communities. As a countermeasure, the central and local governments provide their citizens with housing facilities, such as temporary housing, to make their lives stable. This study conducted interviews with disaster victims who experienced housing damage from various natural disasters, from the victims of the earthquake in Pohang in 2017 to the landslide in Yecheon and Bonghwa in 2023, and victims who lived in temporary housing between 2017 and 2024 immediately after they suffered such disasters. It then investigated the housing facilities themselves. The study conducted in-depth interviews through one-on-one meetings with the disaster victims directly and their satisfaction levels with their temporary housing facilities were investigated. This study also explored certain issues to be improved on and inconveniences in housing through the statements and experiences of the disaster victims. Based on the interviews, the study identified and gathered the actual problems in and of the housing facilities. Furthermore, based on the results of these investigations, this study developed modular temporary housing units which reflect the various needs and demands of different households. This study contributes to the stability in the living situations of disaster victims. It increases the disaster resilience of the local communities. Full article

Carbon dioxide (CO₂) has been widely applied in gas flooding for reservoir development due to its remarkable oil recovery potential. However, because its viscosity is lower than that of water and most crude oils, severe channeling often occurs during the flooding process, resulting in a significant reduction in the sweep efficiency. To address this issue, foam flooding has attracted considerable attention as an effective method for controlling CO₂ mobility. In this study, a compound foam system was developed with alpha-olefin sulfonate (AOS) as the primary foaming agent, alcohol ethoxylate (AEO) and cetyltrimethylammonium bromide (CTAB) as co-surfactants, and partially hydrolyzed polyacrylamide (HPAM) as the stabilizer. The optimal system was screened through evaluations of comprehensive foam index, salt tolerance, oil resistance, and shear resistance. Results indicate that the AOS+AEO formulation exhibits superior foaming ability, salt tolerance, and foam stability compared with the AOS+CTAB system, with the best performance achieved at a mass ratio of 2:1 (AOS:AEO), balancing both adaptability and economic feasibility. A heterogeneous reservoir model was constructed using parallel core flooding to investigate the displacement performance and blocking capability of the system. Nuclear magnetic resonance (NMR) imaging was employed to monitor in situ oil phase migration and clarify the recovery mechanisms. Experimental results show that the compound foam system demonstrates excellent conformance control performance, achieving a blocking efficiency of 84.5% and improving the overall oil recovery by 4.6%. NMR imaging further reveals that the system effectively mobilizes low-permeability zones, with T₂ spectrum analysis indicating a 4.5% incremental recovery in low-permeability layers. Moreover, in reservoirs with larger permeability ratio, the system exhibits enhanced blocking efficiency (up to 86.5%), though the incremental recovery is not strictly proportional to the blocking effect. Compared with previous AOS-based CO₂ foam studies that primarily relied on pressure drop and effluent analyses, this work introduces NMR imaging and T₂ spectrum diagnostics to directly visualize pore-scale fluid redistribution and quantify sweep efficiency within heterogeneous cores. The NMR data provide mechanistic evidence that the enhanced recovery originates from selective foam propagation and the mobilization of residual oil in low-permeability channels, rather than merely from increased flow resistance. This integration of advanced pore-scale imaging with macroscopic displacement analysis represents a mechanistic advancement over conventional CO₂ foam evaluations, offering new insights into the conformance control behavior of AOS-based foam systems in heterogeneous reservoirs. Full article

Floods seriously threaten public health by promoting the spread of antimicrobial-resistant (AMR) bacteria, particularly in urban areas with poor sanitation. In May 2024, the state of Rio Grande do Sul, Brazil, experienced the most severe flood in its history, affecting over 2.3 million people and resulting in extensive dissemination of sewage, contaminating the environment. This study aimed to investigate the presence of Enterobacteriaceae and clinically relevant carbapenemase genes (bla_KPC and bla_NDM) in floodwaters from Porto Alegre using molecular methods. Seventy-nine water samples were collected during four sampling campaigns conducted between May and June 2024. Samples were obtained from flooded areas and points across Guaíba Lake. DNA was extracted with the DNeasy PowerWater Kit, and qPCR was performed using TaqMan assays targeting Enterobacteriaceae, bla_KPC and bla_NDM. Of the 79 samples, 75 yielded sufficient DNA for analysis. Enterobacteriaceae were detected in 100% of the samples, across all collections. The bla_KPC gene was detected in 100% of the first collection pools, and in 94.7%, 94.7%, and 85.7% of samples from the second, third, and fourth collections, respectively. The bla_NDM gene was present in 81.3% of the first collection pools, and in 78.9%, 89.4%, and 80.9% of samples from the subsequent collections. The high prevalence of Enterobacteriaceae and carbapenemase genes in floodwaters reveals an alarming environmental dissemination of AMR genetic markers. These findings underscore the need for environmental AMR surveillance, especially in disaster settings, and support the implementation of the One Health approach to mitigate the spread of resistance genes across human, animal, and environmental interfaces. Full article

Floods are among the most frequent and destructive natural hazards worldwide, with increasingly severe socioeconomic consequences due to rapid urbanization, land use changes, and climate variability. While the combination of Geographic Information Systems (GIS) with models such as HEC-RAS has been extensively explored for flood risk management, many existing studies remain limited to one-dimensional (1D) models or use coarse-resolution terrain data, often underestimating flood risk and failing to produce critical multivariate flood characteristics in densely built urban areas. This study applies a two-dimensional (2D) hydraulic modeling framework in HEC-RAS combined with GIS-based spatial analysis, using a high-resolution (1 × 1 m) LiDAR-derived Digital Terrain Model (DTM) and a hybrid mesh refined between 2 × 2 m and 8 × 8 m, with the main contributions represented by the specific application context and methodological choices. A key methodological aspect is the direct integration of synthetic hydrographs with defined exceedance probabilities (10%, 1%, and 0.1%) into the 2D model, thereby reducing the need for extensive hydrological simulations and defining a data-driven approach for resource-constrained environments. The primary novelty is the application of this high-resolution urban modeling framework to a Romanian urban–peri-urban setting, where detailed hydrological observations are scarce. Unlike previous studies in Romania, this approach applies detailed channel and floodplain discretization at high spatial resolution, explicitly incorporating anthropogenic features like buildings and detailed land use roughness for the accurate representation of local hydraulic dynamics. The resulting outputs (inundation extents, depths, and velocities) support risk assessment and spatial planning in the Ungheni locality (Iași County, Romania), providing a practical, transferable workflow adapted to data-scarce regions. Scenario results quantify vulnerability: for the 0.1% exceedance probability scenario (with a calibration accuracy of ±15–30 min deviation for peak flow timing), the flood risk may affect 882 buildings, 42 land parcels, and 13.5 km of infrastructure. This framework contributes to evidence-based decision-making for climate adaptation and disaster risk reduction strategies, improving urban resilience. Full article

Floods are among the most destructive natural disasters, and accurate flood susceptibility mapping (FSM) is crucial for disaster prevention and mitigation amid climate change. The Poyang Lake basin, characterized by complex flood formation mechanisms and high spatial heterogeneity, poses challenges for the application of FSM models. Currently, the use of machine learning models in this field faces several bottlenecks, including unclear model applicability, limited sample quality, and insufficient machine interpretation. To address these issues, we take the 2020 Poyang Lake flood as a case study and establish a high-precision flood inundation sample database. After feature screening, the performance of three hybrid models optimized by Particle Swarm Optimization (PSO)—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Convolutional Neural Network (CNN) is compared. Furthermore, the Shapley Additive exPlanations (SHAP) framework is employed to interpret the contributions and interaction effects of the driving factors. The results demonstrate that the ensemble learning models exhibit superior performance, indicating their greater applicability for flood susceptibility mapping in complex basins such as Poyang Lake. The RF model has the best predictive performance, achieving an area under the receiver operating characteristic curve (AUC) value of 0.9536. Elevation is the most important global driving factor, while SHAP local interpretation reveals that the driving mechanism has significant spatial heterogeneity, and the susceptibility of local depressions is mainly controlled by the terrain moisture index. A nonlinear phenomenon is observed where the SHAP value was negative under extremely high late rainfall, which is preliminarily attributed to the “spatial transfer that is prone to occurrence” mechanism triggered by the backwater effect, highlighting the complex nonlinear interactions among factors. The proposed “high-precision sampling, model comparison, SHAP explanation” framework effectively improves the accuracy and interpretability of FSM. These research findings can provide a scientific basis for smart flood control and precise flood risk management in basins. Full article

Highway culverts are essential components of transportation infrastructure, designed to convey water beneath highways and protect embankments from flooding. However, extreme flood events often impose hydraulic loads, overtopping, and debris accumulation that can trigger erosion, scour, blockage, and in severe cases, catastrophic washout. This paper presents a comprehensive review of highway culvert behavior under flooding conditions, integrating insights from hydraulics, geotechnical engineering, and structural performance. The review is organized around four themes: (1) types of flooding and their interactions with culverts; (2) hydraulic performance during flood events; (3) common failure modes, including scour, debris blockage, and structural instability; and (4) mitigation strategies to enhance resilience. Advances in hydraulic modeling, including 1D, 2D, 3D, and CFD approaches, are summarized, with attention to their accuracy, applicability limits, and validation needs. Representative experimental, numerical, and empirical studies are grouped by common properties to highlight key findings and constraints. Finally, emerging research opportunities are discussed, including the need for quantitative relationships between culvert geometry and flood intensity, methods to assess structural capacity loss during flooding, and the integration of artificial intelligence and computer vision for rapid post-flood inspection. This synthesis establishes a foundation for more robust evaluation, design, and maintenance strategies, supporting the long-term resilience of highway culverts in an era of increasingly frequent and severe floods. Full article

Dam collapse is a catastrophic event involving an artificial reservoir usually filled with water for hydropower or irrigation purposes. Several cases of dam collapses have overwhelmed entire valleys, reconfiguring their geomorphology, redesigning their landscape, and causing several thousand casualties. These episodes led to more careful regulations and the activation of more effective monitoring and mitigation strategies. A fundamental tool in defining appropriate procedures for alert and risk scenarios is the Dam Emergency Plan (PED), an operational document that establishes the actions and procedures required to manage potential hazards (e.g., geo-hydrological and seismic risk). The aim of this study is to describe a reference methodology for identifying geo-hydrological criticalities based on historical and geomorphological data, applied to civil protection activities. A further objective is to provide a structured inventory of Italian reservoirs, assigning each a potential risk index based on an analytical approach considering several factors (age and construction methodology of the dam, morphological and environmental settings, anthropized environment, and exposed population). The approach identifies that the most significant change in risk over time is not only the dam itself but also the transformation of the territory. This methodology does not incorporate probabilistic forecasting of flood or climate change; instead, it objectively characterizes the exposed territory, offering insights into existing vulnerabilities on which to base effective mitigation strategies. Full article

Climate change is associated with an increase in the frequency of extreme weather that threatens emotional well-being, with some research pointing to increased vulnerability among older adults. We investigated how age relates to depression and anxiety following adversities due to extreme weather or natural disaster. Socioemotional selectivity theory (SST) posits that older age buffers against emotional distress. The strength and vulnerability integration model (SAVI) posits that this age-related advantage is attenuated during periods of acute stress. Members (n = 9761, M age = 52.22, SD = 16.36 yrs) of a nationally representative, probability-based US internet panel, the Understanding America Study (UAS), reported their experience with extreme weather or natural disaster (e.g., severe storms, tornado, flood), associated adversities (e.g., property loss), and depression and anxiety over the past month. Of the 1075 respondents experiencing extreme weather or natural disaster, 216 reported related adversity. Those experiencing adversity reported more anxiety and depression than those with no events, while extreme weather or disaster alone made no significant difference. Consistent with SST, older age was associated with less depression and anxiety. This age-related benefit was most apparent among those experiencing weather- or disaster-related adversity, even when controlling for socio-demographic correlates. Findings highlight age-related emotional resilience with implications for climate change policy and practice. Full article

Extensive practice has demonstrated that the continuous pursuit of high yields in the black soil region of Northeast China resulted in imbalances in soil nutrients and declines in both soil quality and water use efficiency. Alternate wetting and drying (AWD) irrigation offers a promising solution for increasing rice yield and maintaining soil fertility. However, the success of this irrigation method largely depends on its scheduling. This study examined the threshold effects of AWD on rice growth, yield, and soil nutrient availability in the Sanjiang Plain, a representative black soil region in Northeast China. A two-year trial was conducted from 2023 to 2024 at the Qixing National Agricultural Science and Technology Park. “Longjing 31”, a local cultivar, was selected as the experimental material. The lower limit of soil water content under AWD was set as the experimental factor, with three levels: −10 kPa (LA), −20 kPa (MA), and −30 kPa (SA). The local traditional irrigation practice, continuous flooding, served as the control treatment (CK). Indicators of rice growth and soil nutrient content were measured and analyzed at five growth stages: tillering, jointing, heading, milk ripening, and yellow ripening. The results showed that, compared to CK, AWD had minimal impact on rice plant height and tiller number, with no significant differences (p > 0.05). However, AWD affected leaf area index (LAI), shoot dry matter (SDM), yield, and soil nutrient availability. In 2023, control had little effect on rice plant height and tiller number among the different irrigation treatments. The LAI of LA was 11.1% and 22.5% higher than that of MA and SA, respectively, while SDM in LA was 10.5% and 17.2% higher than in MA and SA. Significant differences were found between LA and MA, as well as between LA and SA, whereas no significant differences were observed between MA and SA. The light treatment is beneficial to the growth and development of rice, while the harsh growth environment caused by the moderate and severe treatments is unfavorable to rice growth. The average contents of nitrate nitrogen (NO₃⁻-N), available phosphorus (AP), and available potassium (AK) in LA were 11.4%, 8.4%, and 9.3% higher than in MA, and 16.7%, 11.5%, and 15.0% higher than in SA, respectively. Significant differences were observed between LA and SA. This is because the light treatment facilitates the release of available nutrients in the soil, while the moderate and severe treatments hinder this process. Although panicle number per unit area and grain number per panicle in LA were 7.5% and 2.3% higher than in MA, and 10.8% and 2.2% higher than in SA, these differences were not statistically significant. Seed setting rate and thousand-grain weight showed little variation across irrigation treatments. The yield of LA was 10,233.3 kg hm⁻², 9.1% and 14.1% higher than that of MA and SA, respectively, with significant differences observed. Compared with the moderate and severe treatments, the light treatment increases indicators such as the number of panicles per unit area, grains per panicle, thousand-grain weight, and seed setting rate, resulting in significant differences among the treatments. Water use efficiency (WUE) decreased as the control level increased. The WUE of all AWD irrigation treatments was significantly higher than that of the control treatment (CK). Compared with CK, AWD reduces evaporation, percolation, and other water losses, leading to a significant decrease in water consumption. Meanwhile, the yield remains basically unchanged or even slightly increases, thus resulting in a higher WUE than CK. The trends in rice growth, soil nutrient indicators, and WUE in 2024 were generally consistent with those observed in 2023. In 2024, the yield of LA was 9832.7 kg hm⁻², 14.9% and 17.3% higher than that of MA and SA, respectively, with significant differences observed. Based on the results, the following conclusions are drawn: (1) AWD irrigation can affect the growth of rice, alter the status of available nutrients in the soil, and thereby cause changes in yield and WUE; (2) LA is the optimal treatment for increasing rice yield, improving the availability of soil available nutrients, and improving WUE; (3) Both MA and SA enhanced WUE; however, these practices negatively impacted rice growth and the concentration of soil available nutrients, leading to a concurrent decline in yield. To increase rice yield and maintain soil fertility, LA, with an irrigation upper limit of 30 mm and a soil water potential threshold of −10 kPa, is recommended for the Sanjiang Plain region. Full article

Accurately estimating the time of concentration (Tc) is critical for hydrological modelling, flood forecasting, and hydraulic infrastructure design. However, conventional methods often overlook the combined effects of rainfall intensity and antecedent soil moisture, thereby limiting their applicability under changing climates. This study presents a novel approach that integrates data-driven techniques with remote sensing data to improve Tc estimation. This method was successfully applied in the Kalu River Basin, Sri Lanka, demonstrating its performance in a tropical catchment. While an overall inverse relationship between rainfall intensity and Tc was observed, deviations in several events underscored the influence of initial soil moisture conditions on catchment response times. To address this, a modified kinematic wave-based equation incorporating both rainfall intensity and soil moisture was developed and calibrated, achieving high predictive accuracy (calibration: R² = 0.97, RMSE = 1.1 h; validation: R² = 0.96, RMSE = 0.01 h). A hydrological model was developed to assess the impacts of Tc uncertainties on design hydrographs. Results revealed that underestimating Tc led to substantially shorter lag times and significantly increased peak flows, highlighting the sensitivity of flood simulations to Tc variability. This study highlights the need for improved $T_C$ estimation and presents a robust, transferable methodology for enhancing hydrological predictions and climate-resilient infrastructure planning. Full article

Flood inundation mapping (FIM), which is essential for effective disaster response and management, requires rapid and accurate delineation of flood extent and depth. Remote sensing FIM, especially using satellite imagery, offers certain capabilities and advantages, but also faces challenges such as cloud and canopy obstructions and flood depth estimation. This research developed a novel hybrid approach, named FLDSensing, which combines remote sensing imagery with the FLDPLN (pronounced “floodplain”) flood inundation model, to improve remote sensing FIM in both inundation extent and depth estimation. The method first identifies clean flood edge pixels (i.e., floodwater pixels next to bare ground), which, combined with the FLDPLN library, are used to estimate the water stages at certain stream pixels. Water stage is further interpolated and smoothed at additional stream pixels, which is then used with an FLDPLN library to generate flood extent and depth maps. The method was applied over the Verdigris River in Kansas to map the flood event that occurred in late May 2019, where Sentinel-2 imagery was used to generate remote sensing FIM and to identify clean water-edge pixels. The results show a significant improvement in FIM accuracy when compared to a HEC-RAS 2D (Version 6.5) benchmark, with the metrics of CSI/POD/FAR/F1-scores reaching 0.89/0.98/0.09/0.94 from 0.55/0.56/0.03/0.71 using remote sensing alone. The method also performed favorably against several existing hybrid approaches, including FLEXTH and FwDET 2.1. This study demonstrates that integrating remote sensing imagery with the FLDPLN model, which uniquely estimates stream stage through floodwater-edges, offers a more effective hybrid approach to enhancing remote sensing-based FIM. Full article