Search Results (2,866)

This study focused on the surface microtextures of quartz grains deposited by dam-break floods in Jiacha Gorge, situated in the middle reaches of the Yarlung Tsangpo River. Utilizing a scanning electron microscope (SEM), 200 quartz grains were observed and analyzed based on their microtextural features. Our findings reveal specific microtextures—such as angular outlines, high relief, flat cleavage surfaces, conchoidal fractures, and steps—that serve as clear indicators of the extreme energy associated with dam-break floods in Jiacha Gorge, distinguishing them from meteorological floods and smaller-scale dam-break events. These features also facilitate the differentiation of dam-break flood deposits from those of other sedimentary environments. This research demonstrates that quartz microtextures are a valuable tool for reconstructing sediment transport processes during extreme flood events. We recommend integrating microtextural analysis with complementary methods to refine interpretations and address methodological uncertainties in future studies. Full article

Flood modelling in snow-fed river basins is critical for understanding the impacts of climate change on hydrological extremes. The Zhabay River in northern Kazakhstan exemplifies a basin highly vulnerable to seasonal floods, which pose significant risks to infrastructure, livelihoods, and water resource management. Traditional flood forecasting in Central Asia still relies on statistical models developed during the Soviet era, which are limited in their ability to incorporate non-stationary climate and anthropogenic influences. This study addresses this gap by applying the Soil and Water Integrated Model (SWIM) to project climate-driven changes in the hydrological regime of the Zhabay River. The study employs a process-based, high-resolution hydrological model to simulate flood dynamics under future climate conditions. Historical hydrometeorological data were used to calibrate and validate the model at the Atbasar gauge station. Future flood scenarios were simulated using bias-corrected outputs from an ensemble of General Circulation Models (GCMs) under Representative Concentration Pathways (RCPs) 4.5 and 8.5 for the periods 2011–2040, 2041–2070, and 2071–2099. This approach enables the assessment of seasonal and interannual variability in flood magnitudes, peak discharges, and their potential recurrence intervals. Findings indicate a substantial increase in peak spring floods, with projected discharge nearly doubling by mid-century under both climate scenarios. The study reveals a 1.8-fold increase in peak discharge between 2010 and 2040, and a twofold increase from 2041 to 2070. Under the RCP 4.5 scenario, extreme flood events exceeding a 100-year return period (2000 m³/s) are expected to become more frequent, whereas the RCP 8.5 scenario suggests a stabilization of extreme event occurrences beyond 2071. These findings underscore the growing flood risk in the region and highlight the necessity for adaptive water resource management strategies. This research contributes to the advancement of climate-resilient flood forecasting in Central Asian river basins. The integration of process-based hydrological modelling with climate projections provides a more robust framework for flood risk assessment and early warning system development. The outcomes of this study offer crucial insights for policymakers, hydrologists, and disaster management agencies in mitigating the adverse effects of climate-induced hydrological extremes in Kazakhstan. Full article

Erosion is a significant environmental challenge in Serbia, shaped by natural and human factors. Pronounced relief, fragile geological substrate, a developed hydrographic network, and a climate characterized by an uneven distribution of precipitation throughout the year make this area prone to activating erosion processes and flash floods whenever there is a significant disruption in ecological balance, whether due to the removal of vegetation cover or inadequate land use. Researchers have recorded approximately 11,500 torrents in Serbia, most of which were activated during the 19th century, a period of significant social and political change, as well as intensive deforestation and the irrational exploitation of natural resources. By the mid-19th century, the effects of land degradation were impossible to ignore. As the adequate assessment of soil erosion intensity is the initial step in developing a prevention and protection strategy and the type and scope of anti-erosion works and measures, this article presents the path that the anti-erosion field in Serbia has taken from the initial observations of erosion processes through the first attempts to create the Barren Land Cadastre and Torrent Cadastre to the creation of the Erosion Potential Method (EPM) and its modification by Dr. Lazarević that resulted in the creation of the first Erosion Map of SR Serbia in 1971 (published in 1983). In 2020, a new Erosion Map of Serbia was created with the application of Geographic Information System (GIS) technologies and based on the original method by Professor Slobodan Gavrilović—the EPM—without the modifications introduced by Lazarević. We compared the 1983 and 2020 erosion maps in a GIS environment, where the change in soil erosion categories was analyzed using a confusion matrix. The updated erosion maps mirror the shift in methodology from a traditional approach (Lazarević’s modification) to the modern GIS-based method (Gavrilović’s original EPM) and reflect technological improvements and changes in land use, conservation practices, and environmental awareness. Full article

Under the combined influence of climate change, accelerated urbanization, and inadequate urban flood defense standards, urban pluvial flooding has become an increasingly severe issue. This not only poses significant challenges to social stability and economic development but also makes accurate flood risk assessment crucial for improving urban flood control and drainage capabilities. This study uses Jinan, a typical foothill plain city in Shandong Province, as a case study to compare the performance of differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO) in calibrating the SWMM. By constructing a hydrological–hydrodynamic coupled model using the SWMM and LISFLOOD-FP, this study evaluates the drainage capacity of the pipe network and surface inundation characteristics under both historical and design rainfall scenarios. An agent-based model (ABM) is developed to analyze the dynamic risks and vulnerabilities of population and building agents under different rainfall scenarios, capturing macroscopic emergent patterns from individual behavior rules and analyzing them in both time and space dimensions. Additionally, using multi-source remote sensing data, dynamic population vulnerability, and flood hazard processes, a quantitative dynamic flood risk analysis is conducted based on cloud models. The results demonstrated the following: (1) PSO performed best in calibrating the SWMM in the study area, with Nash–Sutcliffe efficiency (NSE) values ranging from 0.93 to 0.69. (2) Drainage system capacity was low, with over 90% of the network exceeding capacity in scenarios with return periods of 1 to 100 years. (3) The vulnerability of people and buildings increased with higher flood intensity and duration. Most affected individuals were located on roads. In Event 6, 11.41% of buildings were at risk after 1440 min; in the 20-year flood event, 26.69% of buildings were at risk after 180 min. (4) Key features influencing vulnerability included the DEM, PND, NDVI, and slope. High-risk areas in the study area expanded from 36.54% at 30 min to 38.05% at 180 min. Full article

Floods, as a form of disaster, significantly affect individuals and farmers in impacted areas, particularly through crop damage and the inability to harvest due to prolonged and extensive flooding. Among the most severely affected agricultural sectors are oil palm plantations, which regularly experience such disruptions annually. Current methods of assistance and relief during flooding rely on field surveys conducted manually by personnel, a process constrained by its time-intensive nature. Moreover, existing applications or platforms do not support the classification and inspection of oil palm plantations affected by floods during harvesting. This research aims to develop a method and application for inspecting oil palm plantations impacted by floods during harvesting. The approach utilizes deep learning and geographic information systems (GIS) to classify and analyze flood-affected areas and determine the ripeness of oil palm bunches on trees, enabling accurate and rapid identification of flood-affected areas. The study results demonstrate that the proposed method achieves a flood classification accuracy ranging from 96.80% to 98.29% and ripeness classification accuracy for oil palm bunches on trees ranging from 97.60% to 99.75%. These findings indicate that the proposed model effectively and efficiently monitors flood-affected areas. Additionally, the developed application serves as a valuable tool for flood management, facilitating timely assistance and relief for farmers impacted by flooding. Full article

On 29 October 2024, Spain suffered the impact of an Isolated Depression at High Levels (DANA) that caused severe human and material damage. As seen in cases of natural disasters of similar magnitudes, the impact on children requires sustained interventions, with educational communities being key settings for providing protection and accompaniment. Although numerous studies highlight the role of schools in preventing and mitigating the socio-emotional impact of natural disasters on children, the literature on concrete educational actions to address the consequences of flood disasters is limited. This study analyses the co-creation process of six actions developed between researchers and teachers from 18 schools in the most affected areas of Valencia. For this purpose, a communicative research methodology was used. The techniques used to co-create actions were six dialogic focus groups, one dialogical scientific gathering and one WhatsApp community with teachers affected by the DANA. The results provide information on the redevelopment of the following six evidence-based actions and their impacts in the first weeks after the DANA: (1) the mapping of educational communities; (2) the planning of dialogic gatherings; (3) the creation of solidarity networks; (4) the creation of optimal learning environments; (5) the preservation of violence-free networks; and (6) the giving of specific support to vulnerable groups. The study offers guidelines for educational practises in flood-related disaster interventions, focusing on enhancing community resilience. Full article

In this study, the correlations between milling conditions—namely, the cutting tool feed direction relative to the rolling direction, the milling type, the coolant application, as well as the cutting speed—and the surface residual stress of a selected aluminum alloy (2024 T351) were investigated. Determining the type and magnitude of residual stress is of paramount importance as this stress is among the primary causes of post-machining strain of thin-walled components. On the basis of the experimental results, it was found that all factors analyzed significantly affect the residual stress state. Specifically, milling in the parallel direction induces lower residual tensile stress compared to milling in the perpendicular direction. Analogously, up-milling yields lower tensile residual stress than down-milling, and flood cooling leads to lower tensile residual stress than MQL. It was clearly confirmed that as cutting speed increases, tensile residual stress also increases, but only up to a certain threshold; once the high-speed cutting regime is reached, tensile residual stress begins to decrease. Consequently, the proper selection of milling parameters is a crucial consideration for optimizing machining processes and minimizing machining-induced residual stress. Full article

This study details the design and implementation of a real-time river monitoring station established on the Sakarya River, capable of instantaneously tracking water levels and flow rates. The system comprises an ultrasonic distance sensor, a GSM module (Global System for Mobile Communications), which enables real-time wireless data transmission to a server via cellular networks, a solar panel, a battery, and a microcontroller board. The river monitoring station operates by transmitting water level data collected by the ultrasonic distance sensor to a server via a communication module developed on a microcontroller board using an Arduino program, and then sharing these data through a web interface. The developed system performs regular and continuous water level readings without the need for human intervention. During the installation and calibration of the monitoring station, laboratory and field tests were conducted, and the obtained data were validated by comparison with data from the hydropower plant located upstream. This system, mounted on a bridge, measures water levels twice per minute and sends these data to the relevant server via the GSM module. During this process, precipitation data were utilized as a critical reference point for validating measurement data for the 2023 hydrological year, with changes in precipitation directly correlated with river water levels and calculated flow values, which were analyzed accordingly. The real-time river monitoring station allows for instantaneous monitoring of the river, achieving a measurement accuracy of within 0.1%. The discharge values recorded by the system showed a high correlation (r² = 0.92) with data from the hydropower plant located upstream of the system, providing an accurate and comprehensive database for water resource management, natural disaster preparedness, and environmental sustainability. Additionally, the system incorporates early warning mechanisms that activate when critical water levels are reached, enabling rapid response to potential flood risks. By combining energy-independent operation with IoT (Internet Of Things)-based communication infrastructure, the developed system offers a sustainable solution for real-time environmental monitoring. The system demonstrates strong applicability in field conditions and contributes to advancing technologies in flood risk management and water resource monitoring. Full article

In recent years, flood hazards have occurred increasingly worldwide, posing significant threats to the safety of life and property in lacustrine and riverine environments. To mitigate the devastating impacts of floods, it is crucial to explore optimal strategies for joint flood diversion of flood diversion and storage measures (FDSM). The FDSM management of Poyang Lake in China focuses on studying semi-restoration polder areas (SR Polders) and flood storage and detention areas (FS Detentions), which are subjects of ongoing research. Existing studies primarily focus on SR Polders or FS Detentions, with limited research on the joint flood diversion potential of these two measures, particularly regarding optimal scheduling. This study takes 185 SR Polders and the Kangshan flood storage and detention area (KS Detention) as the primary research objects. By integrating hydraulic theory, numerical simulation techniques, and survey data, we develop a hydraulic model for the SR Polders and a hydrodynamic model for the KS Detention to carry out flood diversion simulation. The 1998 flood is chosen as a typical case to simulate and analyze their flood diversion processes under various schemes. The results indicate that altering the operation criteria for FDSM influences both the maximum diversion discharge and the timing of the main diversion period. For the SR Polders, under the current flood control scheme, raising the operation water level (OWL) of SR Polders-I by 1.0 m increases the maximum diversion discharge by 894 m³/s. Additionally, raising the OWL of SR Polders-II by 0.37 m delays the main diversion period by one day. For the KS Detention, higher flood diversion water levels correspond to greater discharge capacities. Furthermore, a fuzzy optimization method is applied to optimize nine joint schemes of the SR Polders and KS Detention. The results indicate that the optimal joint flood diversion strategy for Poyang Lake is operating SR Polders-I, SR Polders-II, and KS Detention at a Hukou water level of 21.65 m, 22.05 m, and 22.50 m, respectively. Finally, the study provides insights and recommendations for flood control management at Poyang Lake. The results of this study not only have important guiding significance for flood control management of large plain lakes but also provide references for the joint operation of flood diversion and storage areas in other regions. Full article

In an era of increasingly abundant and granular spatial and temporal data, the traditional divide between environmental GIS and building-centric BIM scales is diminishing, offering an opportunity to enhance natural hazard assessment by bridging the gap between territorial impacts and the effects on individual structures. This study addresses the challenge of integrating disparate data formats by establishing a centralised database as the foundation for a comprehensive risk assessment approach. A use case focusing on flood risk assessment for a public building in northwest Italy demonstrates the practical implications of this integrated methodology. The proposed TErritorial RIsk Management & Analysis Across Scale (TERIMAAS) framework utilises this centralised repository to store, process, and dynamically update diverse BIM and GIS datasets, incorporating real-time IoT-derived information. The GIS spatial analysis assesses risk scores for each hazard type, providing insights into vulnerability and potential impacts. BIM data further refine this assessment by incorporating building and functional characteristics, enabling a comprehensive evaluation of resilience and risk mitigation strategies tailored to dynamic environmental conditions across scales. The results of the proposed scalable approach could provide a valuable understanding of the territory for policymakers, urban planners, and any stakeholder involved in disaster risk management and infrastructure resilience planning. Full article

Floods are considered to be among the most dangerous and destructive geohazards, leading to human victims and severe economic outcomes. Yearly, many regions around the world suffer from devasting floods. The estimation of flood aftermaths is one of the high priorities for the global community. One such flood took place in northern Libya in September 2023. The presented study is aimed at evaluating the flood aftermath for Derna city, Libya, using high resolution GEOEYE-1 and Sentinel-2 satellite imagery in Google Earth Engine environment. The primary task is obtaining and analyzing data that provide high accuracy and detail for the study region. The main objective of study is to explore the capabilities of different algorithms and remote sensing datasets for quantitative change estimation after the flood. Different supervised classification methods were examined, including random forest, support vector machine, naïve-Bayes, and classification and regression tree (CART). The various sets of hyperparameters for classification were considered. The high-resolution GEOEYE-1 images were used for precise change detection using image differencing (pixel-to-pixel comparison and geographic object-based image analysis (GEOBIA) for extracting building), whereas Sentinel-2 data were employed for the classification and further change detection by classified images. Object based image analysis (OBIA) was also performed for the extraction of building footprints using very high resolution GEOEYE images for the quantification of buildings that collapsed due to the flood. The first stage of the study was the development of a workflow for data analysis. This workflow includes three parallel processes of data analysis. High-resolution GEOEYE-1 images of Derna city were investigated for change detection algorithms. In addition, different indices (normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed NDVI (TNDVI), and normalized difference moisture index (NDMI)) were calculated to facilitate the recognition of damaged regions. In the final stage, the analysis results were fused to obtain the damage estimation for the studied region. As the main output, the area changes for the primary classes and the maps that portray these changes were obtained. The recommendations for data usage and further processing in Google Earth Engine were developed. Full article

The Jiroft Dam, situated in Kerman province, Iran, serves as a crucial infrastructure for water management, flood control, and agricultural development in the region. However, the surrounding mountainous terrain presents considerable geotechnical challenges that threaten the stability of access roads and other essential infrastructure. This study is based on comprehensive field surveys and mapping, which have revealed significant ground displacements and evidence of slope instabilities in the area. The investigation identifies key factors, including soil composition, rock formations, groundwater flow, and seismic activity, that contribute to these shifts in the terrain. To ensure the accuracy of the elevation data, the study employed Monte Carlo simulation techniques to analyze the statistical distribution of the collected survey data. By simulating various possible outcomes, this study enhanced the precision of the elevation models, allowing for better identification of critical instability zones. Additionally, the Overall Equipment Effectiveness (OEE) was utilized to evaluate the effectiveness of the current monitoring equipment and infrastructure, providing a clearer understanding of operational efficiency and areas for improvement. The findings of this study highlight the immediate need for effective risk management strategies to mitigate the potential hazards of landslides and infrastructure failure. Addressing these challenges is essential to ensure the long-term sustainability of the region’s infrastructure. In response to these observations, this research proposes practical engineering solutions such as slope stabilization techniques and improved drainage systems to address the identified instabilities. Furthermore, this study underscores the necessity of the continuous monitoring and the implementation of early warning systems to detect further ground movements and mitigate associated risks.In addition to technical interventions, this research emphasizes the importance of integrating local knowledge and expertise into the risk management process. Full article

DPR refers to the fact that the reduction in the permeability of the water phase is much greater than the reduction in the permeability of the oil phase when a water-based gel seals porous media. In order to clarify the mechanisms of the gel-plugging pore pathway and the principle of the oil phase and water-phase permeability change, gel-plugging core replacement experiments and NMR T2 scanning experiments were conducted in this work. Based on the nuclear NMR T2 spectroscopy, the core blocked with Cr (III)–acetate–HPAM gel is divided into five stages (a plug injection and seal gel-formation stage, an oil-phase repulsion stage, a stopping-gel reabsorption stage, an oil-phase reinjection stage, and a subsequent water-drive stage) for displacement and scanning, and the signal changes of various phases in the displacement process are studied. The principle of an oil-phase permeability increase after Cr (III)–acetate–HPAM gel plugging, and the principle of a disproportionate decrease in water-phase permeability in the subsequent water-flooding stage were finally revealed. According to the results, the difference in the permeability leads to some diversity in NMR T2 curves, but the final conclusions for the mechanisms are consistent. They show a significant decrease in core permeability of 3.5 × 10⁻³ μm² and 0.8 × 10⁻³ μm² after gel plugging. With the injection of the oil phase, the permeability of the oil phase keeps increasing from 0.13 × 10⁻³ μm² to 0.76 × 10⁻³ μm² in the core permeability of 3.5 × 10⁻³ μm². Similarly, the permeability of the oil phase increases from 0.03 × 10⁻³ μm² to 0.19 × 10⁻³ μm² in the core permeability of 0.8 × 10⁻³ μm². During the oil-phase replacement phase, gel replacement in the large pores is the main cause of the increase in oil-phase permeability, and as the replacement process progresses, the mechanism for the increase in oil-phase permeability changes, and gel dehydration becomes the main mechanism for the increase in oil-phase permeability. Full article

Growing urbanization has increased impermeable surfaces, raising and polluting stormwater runoff, and exacerbating the risk of urban flooding. Effective stormwater management is essential to curb sedimentation, minimize pollution, and mitigate urban flooding. This systematic literature review from the Web of Science and Scopus between January 2000 and June 2024 presents hydrodynamic separation (HDS) technologies. It sheds light on the significant issues that urban water management faces. HDS is classified into four categories: screening, filtration, settling, and flotation, based on the treatment mechanisms. The results show a shift from traditional standalone physical separations to multi-stage hybrid treatment processes with nature-based solutions. The great advantage of these approaches is that they combine different separation mechanisms and integrate ecological sustainability to manage urban stormwater better. The findings showed that future research will examine hybrid AI-assisted separation technologies, biochar-enhanced filtration, and green infrastructure systems. When adopting an integrated approach, the treatment system will perform like natural processes to remove pollutants effectively with better monitoring and controls. These technologies are intended to fill existing research voids, especially in removing biological contaminants and new pollutants (e.g., microplastics and pharmaceutical substances). In the long term, these technologies will help to enforce Sustainable Development Goals (SDGs) and orient urban areas in developing countries towards meeting the circular economy objective. Full article

Global climate change has intensified flood disasters at the local scale. In response, this study constructs a flood resilience indicator system at the urban scale based on the “pressure-state-response” (PSR) model. Indicator weights were determined using the analytic hierarchy process–entropy weight method (AHP-EWM), the flood resilience of 35 cities within the Yellow River Basin from 2010 to 2021, and their spatiotemporal evolution patterns, spatial correlations, and determinants were analyzed. The results indicate that flood resilience shows an upward trend over time, with stronger resilience observed in upstream and downstream cities and weaker resilience in midstream cities. The spatial correlation of flood resilience was significantly positive in 2010, 2015, and 2021, while it exhibited non-significant, fluctuating declines in other years. Most cities in Henan and Shandong provinces are characterized as high–high agglomeration type, whereas most cities in Shanxi and Shaanxi provinces are classified as low–low type. Drainage efficiency, municipal investment, resilient planning, and urbanization have significant positive impacts on flood resilience, while the urban registered unemployment rate shows a positive spatial spillover effect. This study analyzed the flood resilience of cities in the Yellow River Basin from a comprehensive and logically coherent perspective and concluded with targeted recommendations aimed at enhancing flood resilience in the region. Full article