Search Results (14)

Extreme rainfall events characterized by small catchments with high-velocity flows pose critical challenges to infrastructure resilience, particularly the rail infrastructure, due to its partial location near rivers and in mountainous regions, and the limited availability of alternative routes. This can lead to severe damages, often resulting in long-term route closures. To mitigate flash flood damage, detailed information about affected structures and damage processes is necessary. Therefore, this study presents a newly developed multi-criteria flash flood damage assessment framework for the rail infrastructure and a QGIS-based analysis of the most frequent damages. Applying the framework to Eifel route damages in Western Germany after the July 2021 flood disaster shows that nearly 45% of the damages affected the track superstructure, especially tracks and bedding. Additionally, power supply systems, sealing and drainage systems, as well as railway overpasses or bridges, were impacted. Approximately 30% of the railway section showed washout of ballast, gravel and soil. In addition, deposit of wood or stones occurred. Most damages were classified as minor (47%) or moderate (34%). Furthermore, damaged track sections were predominantly located within a 50 m distance to the Urft river, whereas undamaged track sections are often located at a greater distance to the Urft river. These findings indicate that the proposed framework is highly applicable to assess and classify damages. Critical elements and relations could be identified and can help to adapt standards and regulations, as well as to develop preventive measures in the next step. Full article

The frequency and intensity of floods increase with global climate change. Strengthening the resilience of farmers to disasters, in particular to mitigate flood risks, has become an important policy issue. Increasing the livelihood resilience of farmers to enhance their disaster preparedness has become the main form of coping with flood risk. However, few studies have explored the correlation between farmers’ livelihood resilience and disaster preparedness. Using data from a survey of 540 rural households conducted in July 2021 across nine towns in three counties in Sichuan Province, we construct an indicator system for evaluating the farmers’ livelihood resilience in flood risk areas. The relationship between farmers’ livelihood resilience and their disaster preparedness is studied using the tobit model. The results show that farmers’ livelihood resilience is composed of multiple dimensions, with self-organization capacity scoring the highest (0.541), followed by learning ability (0.303), and buffer capacity scoring the lowest (0.223). Additionally, the level of trust in society and the possibility of suffering from floods in the research area have a noticeable positive effect on farmers’ decision-making related to disaster preparedness. The more farmers trust in society and the greater the likelihood of exposure to flood risk is, the more they tend to be prepared for risk avoidance. Furthermore, farmers’ livelihood resilience is positively associated with their overall disaster preparedness. Specifically, both buffer capacity and learning ability influence emergency disaster preparedness and knowledge and skill preparation; self-organization capacity affects only knowledge and skill preparation. These results suggest procedures to enhance farmers’ livelihood resilience and further strengthen preparedness for disasters such as floods. Full article

Floods stand as one of the most harmful natural disasters, which have become more dangerous because of climate change effects on urban structures and agricultural fields. This research presents a comprehensive flood mapping approach that combines multi-sensor satellite data with a machine learning method to evaluate the July 2021 flood in the Netherlands. The research developed 25 different feature scenarios through the combination of Sentinel-1, Landsat-8, and Radarsat-2 imagery data by using backscattering coefficients together with optical Normalized Difference Water Index (NDWI) and Hue, Saturation, and Value (HSV) images and Synthetic Aperture Radar (SAR)-derived Grey Level Co-occurrence Matrix (GLCM) texture features. The Random Forest (RF) classifier was optimized before its application based on two different flood-prone regions, which included Zutphen’s urban area and Heijen’s agricultural land. Results demonstrated that the multi-sensor fusion scenarios (S18, S20, and S25) achieved the highest classification performance, with overall accuracy reaching 96.4% (Kappa = 0.906–0.949) in Zutphen and 87.5% (Kappa = 0.754–0.833) in Heijen. For the flood class F1 scores of all scenarios, they varied from 0.742 to 0.969 in Zutphen and from 0.626 to 0.969 in Heijen. Eventually, the addition of SAR texture metrics enhanced flood boundary identification throughout both urban and agricultural settings. Radarsat-2 provided limited benefits to the overall results, since Sentinel-1 and Landsat-8 data proved more effective despite being freely available. This study demonstrates that using SAR and optical features together with texture information creates a powerful and expandable flood mapping system, and RF classification performs well in diverse landscape settings. Full article

Flood disasters often result in significant losses of life and property, making them among the most devastating natural hazards. Therefore, reliable and accurate water level forecasting is critically important. Rainfall-runoff modeling, which is a complex and nonlinear time series process, plays a key role in this endeavor. Numerous studies have demonstrated that data-driven methods, particularly deep learning approaches such as convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and transformers, have shown promising performance in water level prediction tasks. This study introduces the Conformer, a novel deep learning architecture that integrates the strengths of CNNs and transformers for rainfall-runoff modeling. The framework uses self-attention mechanisms combined with convolutional computations to extract essential features—such as water levels, precipitation, and meteorological data—from multiple stations, which are then aggregated to predict subsequent water level series. This study utilized data spanning from 1 April 2006 to 25 July 2021, totaling 5595 days (134,280 h), which were divided into training, validation, and test sets in an 8:1:1 ratio to train the model, adjust parameters, and evaluate performance, respectively. The effectiveness and feasibility of the proposed model are evaluated in the Lanyang River Basin, with a focus on predicting 7-day-ahead water levels. The results obtained from ablation experiments indicate that convolutional computations significantly enhance the ability of the model to capture the local relationships between water levels and other parameters. Additionally, performing convolution computations after executing self-attention operations yields even better results. Compared with other models in simulations, the Conformer model markedly outperforms the CNN, LSTM, and traditional transformer models in terms of the coefficient of determination (R²) and Nash–Sutcliffe efficiency (NSE) indicators. These findings highlight the potential of the Conformer model to replace the commonly used deep learning methods in the field of hydrology. Full article

This study aimed to explore the differences in various aspects of community disaster resilience and how to enhance disaster resilience tailored to different community types. The evaluation results were validated using the flood event that occurred in Zhengzhou on 20 July 2021 (hereinafter referred to as the “7.20” rainstorm disaster). The main results of the analysis showed that the respondents’ overall evaluation of their community’s resilience to the “7.20” disaster was relatively high. Commercial housing communities performed the best, followed by urban village communities, and employee family housing communities performed the worst. Specifically, commercial housing communities scored highest in three dimensions: human capital, physical infrastructure, and adaptation. Urban village communities scored highest in the three dimensions of social capital, institutional capital, and community competence, while employee family housing communities consistently ranked the lowest in each dimension. The most significant disparities were found in human capital, followed by community competence and social capital, adaptation, and, lastly, institutional capital and physical infrastructure. Targeted improvement strategies and measures are suggested for each type of community, offering valuable recommendations for relevant government agencies aiming to enhance community disaster resilience and disaster risk reduction. Full article

Under climate change, the frequency of drought-flood abrupt alternation (DFAA) events is increasing in Southeast China. However, there is limited research on the evolution characteristics of DFAA in this region. This study evaluated the effectiveness of the drought and flood indexes including SPI (Standardized Precipitation Index), SPEI (Standardized Precipitation Evapotranspiration Index), and SWAP (Standardized Weighted Average Precipitation Index) in identifying DFAA events under varying days of antecedent precipitation. Additionally, the evolution characteristics of DFAA events in Fujian Province from 1961 to 2021 were explored. The results indicate that (1) SPI-12d had the advantages of high effectiveness, optimal generalization accuracy, and strong generalization ability of identification results, and it can be used as the optimal identification index of DFAA events in Southeast China. (2) There was an overall increase in DFAA events at a rate of 1.8 events/10a. The frequency of DFAA events showed a gradual increase from the northwest to the southeast. (3) DTF events were characterized by moderate drought to flood, particularly in February, July, and August, while FTD events were characterized by light/moderate flood to drought, with more events occurring from June to October. (4) DTF event intensity increased in the northern and western regions from 1961 to 2021. For FTD events, the intensity notably increased in the western region from 1961 to 2001, while a significant increase occurred in all regions except the central region from 2001 to 2021. These findings emphasize the need for precautionary measures to address the increasing frequency and severity of DFAA events in Southeast China. Full article

The Diannong River, a valuable river and lake resource of the northern Ningxia Yellow River Irrigation Area, plays an instrumental role in regional flood control, drought resistance, climate regulation, and biodiversity conservation. Phytoplankton and zooplankton, as crucial elements of the aquatic ecosystem, have their distribution patterns evaluated and potential influencing factors identified, thereby enhancing the understanding of community distribution patterns. Nested structures and interspecies interaction relationships bear significant implications for community distribution patterns, functions, and stability. The upstream section of the Diannong River in Yinchuan City was chosen as the study object. Water samples were collected in January, April, July, and October 2021, and the community composition of phytoplankton and zooplankton was analyzed using relative abundance, density, and biomass. The distribution matrix temperature and bipartite network methodologies were deployed to investigate their nested pattern and interaction network seasonal characteristics. The findings indicate that the water environment of the Diannong River’s upstream section displays pronounced spatiotemporal heterogeneity, characterized by weak alkalinity and high fluoride content. The plankton community composition and relative abundance showed marked differences among the distinct sampling periods. The temperature of the random distribution matrix shows a significant difference compared to the zero-sum model, revealing a notable nested pattern in plankton in the Diannong River’s upstream section. The bipartite network suggests that the plankton composition was the simplest in January and the most complex in July, with the fiercest species competition observed in January and the lowest levels of species specificity, vulnerability, and generality. Water temperature (WT), dissolved oxygen (DO), total phosphorus (TP), available phosphorus (AP), COD_Cr, F⁻, and Cl⁻ constitute the environmental parameters influencing the overall structure of the phytoplankton community in the Diannong River’s upstream section, whereas zooplankton did not present a significant correlation with water environmental factors. Full article

Surface water flood risk is projected to increase worldwide due to the growth of cities as well as the frequency of extreme rainfall events. Flood risk modelling at high resolution in megacities is now feasible due to the advent of high spatial resolution terrain data, fast and accurate hydrodynamic models, and the power of cloud computing platforms. Analysing the flood exposure of urban features in these cities during multiple storm events is essential to understanding flood risk for insurance and planning and ultimately for designing resilient solutions. This study focuses on London, UK, a sprawling megacity that has experienced damaging floods in the last few years. The analysis highlights the key role of accurate digital terrain models (DTMs) in hydrodynamic models. Flood exposure at individual building level is evaluated using the outputs from the CityCAT model driven by a range of design storms of different magnitudes, including validation with observations of a real storm event that hit London on the 12 July 2021. Overall, a novel demonstration is presented of how cloud-based flood modelling can be used to inform exposure insurance and flood resilience in cities of any size worldwide, and a specification is presented of what datasets are needed to achieve this aim. Full article

In July 2021, a flooding event, which attracted the attention of the whole country and even the world, broke out in Henan, resulting in dramatic losses across multiple fields (e.g., economic and agricultural). The basin at the junction of Hebi, Xinxiang, and Anyang was the most affected region, as the spread of water from the Wei river submerged surrounding agricultural land (e.g., corn-dominated). To comprehensively evaluate the flooding impacts, we proposed a framework to detect the flooding area and evaluated the degree of loss using satellite time series data. First, we proposed a double-Gaussian model to adaptively determine the threshold for flooding extraction using Synthetic Aperture Radar (SAR) data. Then, we evaluated the disaster levels of flooding with field survey samples and optical satellite images. Finally, given that crops vary in their resilience to flooding, we measured the vegetation index change before and after the flooding event using satellite time series data. We found the proposed double-Gaussian model could accurately extract the flooding area, showing great potential to support in-time flooding evaluation. We also showed that the multispectral satellite images could potentially support the classification of disaster levels (i.e., normal, slight, moderate, and severe), with an overall accuracy of 88%. Although these crops were temporarily affected by this flooding event, most recovered soon, especially for the slightly and moderately affected regions. Overall, the distribution of resilience of these affected crops was basically in line with the results of classified disaster levels. The proposed framework provides a comprehensive aspect to the retrospective study of the flooding process on crops with diverse disaster levels and resilience. It can provide rapid and timely flood damage assessment and support emergency management and disaster verification work. Full article

Due to extreme weather phenomena, precipitation-induced flooding has become a frequent, widespread, and destructive natural disaster. Risk assessments of flooding have thus become a popular area of research. In this study, we studied the severe precipitation-induced flooding that occurred in Zhengzhou, Henan Province, China, in July 2021. We identified 16 basic indicators, and the random forest algorithm was used to determine the contribution of each indicator to the Zhengzhou flood. We then optimised the selected indicators and introduced the XGBoost algorithm to construct a risk index assessment model of precipitation-induced flooding. Our results identified four primary indicators for precipitation-induced flooding in the study area: total rainfall for three consecutive days, extreme daily rainfall, vegetation cover, and the river system. The Zhengzhou storm and flood risk evaluation model was constructed from 12 indicators: elevation, slope, water system index, extreme daily rainfall, total rainfall for three consecutive days, night-time light brightness, land-use type, proportion of arable land area, gross regional product, proportion of elderly population, vegetation cover, and medical rescue capacity. After streamlining the bottom four indicators in terms of contribution rate, it had the best performance, with an accuracy rate reaching 91.3%. Very high-risk and high-risk areas accounted for 11.46% and 27.50% of the total area of Zhengzhou, respectively, and their distribution was more significantly influenced by the extent of heavy rainfall, direction of river systems, and land types; the medium-risk area was the largest, accounting for 33.96% of the total area; the second-lowest-risk and low-risk areas together accounted for 27.09%. The areas with the highest risk of heavy rainfall and flooding in Zhengzhou were in the Erqi, Guanchenghui, Jinshui, Zhongyuan, and Huizi Districts and the western part of Xinmi City; these areas should be given priority attention during disaster monitoring and early warning and risk prevention and control. Full article

The labor relationship developed by the Amazonian riverside dwellers is weakened due to changes in temperature, the flood pulse, the ebb tide of the rivers, and precipitation. In this context, this research aimed to evaluate the impacts of climate change on the socio-biodiversity chains in the region of Médio Juruá-Amazonas. Collections were carried out in two communities located in the Sustainable Development Reserve (RDS) Uacari, in July 2022, through participatory workshops. The communities affirm that the extreme flood events of the Juruá River are more intense in recent years, both concerning the extreme levels of the river and in periodicity and speed of flooding. The large floods have impacted the productive calendar, generating losses for farmers. In addition, rubber trees and cassava plantations have been dying with the large floods, and oil seeds are being carried by the water before harvest. The physical data of the Juruá River shows a trend of increasing extreme floods over the last 40 years for the period November to April, highlighting the years 2013 to 2015 and 2021 with the largest positive anomalies. Farmers have adapted their calendars, modified some planting areas to locations with higher altitudes and farther from the river banks, and have sought new rubber matrices. The results point to the need for mitigation and adaptation measures promoted by local governments. Full article

By relying on the advantages of a uniform site distribution and continuous observation of the Continuously Operating Reference Stations (CORS) system, real-time high-precision Global Navigation Satellite System/Precipitable Water Vapor (GNSS/PWV) data interpretation can be carried out to achieve accurate monitoring of regional water vapor changes. The study of the atmospheric water vapor content and distribution changes is the basis for the realization of rainfall forecasting and water vapor circulation research. Such research can provide data support for the effective forecasting of regional precipitation in megacities and the construction of a more sensitive flood prevention and warning system. Nowadays, a single model is often adopted for GNSS/PWV time series. This makes it challenging to match the high randomness characteristic of water vapor change. This study proposes a hybrid model that takes into account the linear and nonlinear aspects of water vapor data by using complete empirical mode decomposition (CEEMDAN) of adaptive noise, differential autoregressive integrated moving average (ARIMA), and the long-short-term memory network (LSTM). The CEEMDAN is used to decompose the water vapor data series. Then, the high- and low-frequency data are modeled separately, reducing the sequence’s complexity and non-stationarity. In selecting the prediction model, we use the ARIMA model for the high-frequency series and the ARIMA–GWO–LSTM ensemble model for the low-frequency sub-series and residual series. The model is verified using GNSS/PWV time series data collected at the Hong Kong CORS station in July 2021. The results show the following: (1) The LSTM model optimized by the grey wolf optimization algorithm (GWO) is comparable with the single LSTM model in the low-frequency sequence prediction process, and the error items are reduced by 30% after calculation. (2) During the process from CEEMDAN decomposition to the use of the combination model for prediction, the accuracy evaluation indexes of the station increase by more than 20%. The interpolation method can accurately determine the regional water vapor spatial variation, which is of practical significance for local rainfall forecasting. High-frequency data obtained by CEEMDAN decomposition demonstrate the dramatic changes in water vapor before and after the rainfall, which can provide ideas for improving the accuracy of rainfall forecasting. Full article

The wildfires of summer 2021 in Greece were among the most severe forest fire events that have occurred in the country over the past decade. The conflagration period lasted for 20 days (i.e., from 27 July to 16 August 2021) and resulted in the devastation of an area of more than 3600 Km². Forest fire events of similar severity also struck other Mediterranean countries during this period. Apart from their direct impacts, forest fires also render an area more susceptible to runoff erosion by massively removing its vegetation, among other factors. It is clear that immediately after a forest fire, most areas are much more susceptible to erosion. In this paper, we evaluate the erosion hazard of Attica, Northern Euboea, and the Peloponnese that were devastated by forest fires during the summer of 2021 in Greece, in comparison with their geological and geomorphological structures, as well as land cover and management. Given that a very significant part of these areas were burnt during the major conflagrations of this summer, erosion risk, as well as flood risk, are expected to be very high, especially for the coming autumn and winter. For the evaluation of erosion risk, the burnt areas were mapped, and the final erosion-risk maps were constructed through GIS software. The final maps suggest that most of the burnt areas are highly susceptible to future surface runoff erosion events. Full article

Flood is a kind of natural disaster that is extremely harmful and occurs frequently. To reduce losses caused by the hazards, it is urgent to monitor the disaster area timely and carry out rescue operations efficiently. However, conventional space observers cannot achieve sufficient spatiotemporal resolution. As spaceborne GNSS-R technique can observe the Earth’s surface with high temporal and spatial resolutions; and it is expected to provide a new solution to the problem of flood hazards. During 19–21 July 2021, Henan province, China, suffered a catastrophic flood and urban waterlogging. In order to test the feasibility of flood disaster monitoring on a daily basis by using GNSS-R observations, the CYGNSS (Cyclone Global Navigation Satellite System) Level 1 Science Data were processed for a few days before and after the flood to obtain surface reflectivity by correcting the analog power. Afterwards, the flood was monitored and mapped daily based on the analysis of changes in surface reflectivity from spaceborne GNSS-R mission. The results were evaluated based on the image from MODIS (Moderate Resolution Imaging Spectroradiometer) data, and compared with the observations of SMAP (Soil Moisture Active Passive) in the same period. The results show that the area with high CYGNSS reflectivity corresponds to the flooded area monitored by MODIS, and it is also in high agreement with SMAP. Moreover, CYGNSS can achieve more detailed mapping and quantification of the inundated area and the duration of the flood, respectively, in line with the specific situation of the flood. Thus, spaceborne GNSS-R technology can be used as a method to monitor floods with high temporal resolution. Full article