Natural Hazards Research最新文献

The unique characteristics of drainage conditions in the Pagla river basin cause flooding and harm the socioeconomic environment. The main purpose of this study is to investigate the comparative utility of six machine learning algorithms to improve flood susceptibility and ensemble techniques' capability to elucidate the underlying patterns of floods and make a more accurate prediction of flood susceptibilities in the Pagla river basin. In the present scenario, the frequency of flood conditions in this study area becomes high with heavy and sudden rainfall, so it is essential to study flood mitigation and measure. At first, a spatial flood database was built with 200 flood locations and sixteen flood influencing factors, and its process with the help of the Geographic Information System (GIS) environment and build up different models applying the machine learning techniques. It has found different flood susceptibility zone using machine learning-based Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), Reduced Error Pruning Tree (REPTree), Logistic Regression (LR), and Bagging helping GIS environment and the model validation using the Receiver Operating Characteristic (ROC) Curve. Afterward, ensemble all the models to gate comparative accuracy of the flood zone. The calculated areas are under the very high flood susceptibility zone 8.69%, 14.92%, 14.17%, 12.98%, 14.65%, 13.24% and 13.41% for ANN, SVM, RF, REPTree, LR and Bagging, respectively. Finally, ROC curve, the Standard Error (SE), and the Confidence Interval (CI) at 95 per cent were used to assess and compare the performance of the models. The obtained results indicate that all models are highly accepted Area Under Curve (AUC) of ROC between 0.889 (LR) to 0.926 (Ensemble). From the estimation of the accuracy of the applied methods using ROC, it is found that the Ensemble model has the higher capability compared to the other applied models in projecting flood susceptibility in ​the ​study ​area. It has the highest area under the ROC curve the AUC values are 0.918 and 0.926, the SE (0.023, 034), and the narrowest CI (95 per cent) (0.873–0.962, 0.859–0.993) whereas highest area under Bagging (the ROC) curve (AUC) value (0.914, 0.919), for both the training and validation datasets. After ensembling, the result shows that the result is a highly flood susceptible area located at the lower part of the study area. In this area, the very high flood susceptibility zone values lie between 4.46 and 6.00 in the ensemble result. The areas comprise the low height and belong to Murarai I, Murarai II, Suti I and Suti II C.D. block of West Bengal. The current study will help the policymakers and the researcher determine the flood conditioning problems for prospects.

The Alboran Basin may be subject to tsunami hazards. If such an event were to occur, it is expected that the urbanised and densely populated areas of northern Moroccan coastline would be affected. Precise inundation hazard maps are needed for tsunami risk management in this region. In this article, we argue that the diversity of hazard mapping methods ensures the robustness of the scientific knowledge about the exposure of a territory. Hence, the main objective of this study is to analyse the exposure of the plain of Martil (north of Morocco), by using four hazard mapping methods to create inundation maps for two scenarios of tsunamis generated by extreme submarine mass failure (SMF) in the Alboran Sea, of 0.9 ​km³ and 3.8 ​km³ respectively. A digital terrain model of the plain was used to explore four methods of inundation mapping. The static method identified 4.32 ​km² and 19.83 ​km² of flooded areas for each scenario using water height values as inundation thresholds. The hybrid and the volumetric methods use the volume of water to determine the inundation extent. For the first scenario, 3.51 ​km² of the plain were inundated using the hybrid method, and 20.11 ​km² for the second scenario. The results of the volumetric methods are 2.32 ​km² and 7.82 ​km² respectively for the first and second scenario. Finally, the fourth method relies on numerical hydrodynamic modelling of tsunami inundation (Freshkiss3d® code). With this method, 4.55 ​km² of the plain were flooded in the first scenario, and 24.12 ​km² for the second. The comparison of the results highlights that the most sensitive areas to tsunami inundation are the lowest topographic ones, being the beaches and the wadis floodplains. This result raises questions on the current coastal development and the preparedness of its population, thus calling for more attention to engage on tsunami risk management related questions.

Identifying and understanding the value of citizen science to improve flood modeling is of importance to flood risk management. However, there are few studies that explore the value of citizen science data, with most studies focusing on evaluating the accuracy of the data. This research articulates the added value of citizen science data in flood modeling studies. During flood events, citizen scientists measured river water levels at selected sites along a main reach of the Big Akaki River in Addis Ababa, Ethiopia. They also provided information to estimate water discharge of the ungauged tributaries. The data acquired was used to force a one-dimensional (1D) HECRAS flood model, and to evaluate the model's sensitivity to inputs and parameters. Varying the downstream boundary condition caused a significant difference in the simulated water level (up to 3.5 ​km upstream of the downstream boundary site). Correcting the Digital Elevation Model and consideration of river tributary flows in the model simulation resulted in an underestimation of the observed stage by 0.08 ​m. The sensitivity analysis also showed that results were more sensitive to the Manning roughness values of the channel than that of the floodplain. Finally, this study identifies future flood modeling data collection priorities (e.g. flow data for the tributary). The flood modeling of the study area would not have been realized without the citizen science data.

Landslides pose a serious risk to life and property in the mountainous regions around the globe. Understanding the interplay of landslide conditioning and triggering factors is essential for lessening the impacts caused by the hazard. Cox's Bazar — a coastal mountainous district in Bangladesh is recurrently affected by rainfall-triggered landslides. Based on analysis of 14 experiential landslides and combination of gauged and satellite rainfall estimates for the period from 2003 to 2019, the present study determines three landslide-triggering rainfall thresholds for the Cox's Bazar District (CBD): 1. Intensity-Duration (ID) threshold derived in this study revealed that any rainfall event with an intensity of ≥4.04 ​mm/h if prolonging for ≥12h can cause slope failures; 2. Event-Duration (ED) threshold suggested that a normalized cumulative event rainfall (E_MAP) of 0.15 for one day is expected to trigger landslides; and 3. threshold calculated using randomly chosen antecedent rainfall expressed best distinction on 30-day rainfall and the equation of the threshold came out as R_th ​= ​64–0.02 R_a30. The recurrence probability of the derived antecedent rainfall threshold and likely landslides was determined through the Poisson distribution. Moreover, we assess the landslide susceptibility of the district with a coupled use of Frequency Ratio (FR) statistical measure and Geographic Information System (GIS). Considering the combined role of selected conditioning factors, the landslide susceptibility status of the CBD was quantified and classified into probability intervals. The accuracy of the susceptibility maps was assessed through the Relative Landslide Density Index (R-Index) that used a field landslide inventory, comprising well distributed 891 events. Moreover, gridded population data was superimposed on the derived susceptibility maps to understand the risk levels of people. The derivation of landslide-triggering rainfall thresholds and spatial susceptibility assessment has been useful to propose a low-cost Landslide Early Warning System (LEWS) which can contribute in alleviating the adverse effects of landslide hazard in the CBD.

Floods inflict devastating impacts worldwide. The frequency and severity of floods have notably increased as a result of climate change. Pakistan, in particular, has experienced two of the most catastrophic flooding events in recent years: the 2010 super floods and the 2022 monsoon floods, affecting millions of people. The tangible impacts of these events are still not fully understood. However, no study has been found that comprehensively explains the current state of knowledge, major research areas, and themes within the context of flood studies in Pakistan. To address this research gap, this study uses bibliometric and thematic analysis to conduct a state-of-the-art systematic literature review on Pakistan's floods. The dataset comprises 601 research articles obtained from the Scopus database. The bibliometric analysis reveals an upward trend in publications on flood-related topics, primarily within the scope of environmental, earth and planetary, and social sciences. These publications involve the contributions of 160 authors from 160 different institutions and 57 countries across the globe. The thematic analysis identifies several major research areas, including gender, social capital, livelihood, food security, risk perception, risk and vulnerability assessment, socioeconomic impacts, and the application of GIS and Remote Sensing. The study also presents indicators and quantitative methods to assess flood risk and vulnerability. The study recommends that future research examine the socioeconomic and institutional challenges associated with flood risk reduction. Additionally, integrating climate change adaptation and flood risk reduction can lead to developing policies and frameworks to promote flood-resilient communities.

Land degradation due to population growth, climate change and variability and other anthropogenic activities are main factors for the occurrence of flooding in Ethiopia. This study aimed to investigate flood incidence causes in the Ribb river. Possible causes of flooding were identified from literature. To recognize the causes, trend analysis of rainfall to know possible climate change, stream flow analysis to know possible land cover change and lake water level trend analysis to know backwater effect were conducted using the Mann-Kendall test. Land use land cover change review was also conducted. El Niño Southern Oscillation (ENSO) correspondence on rainfall extremes to know the climate pattern effect was conducted using correlation of Southern Oscillation Index (SOI) with summer rainfall anomaly. Structured interview with purposely and systematically selected flood plain community, focus group discussion with Woreda level early warning and response group leaders and natural resource conservation experts were used to confirm causes of flooding. At the end, landscape based mitigation measures from watershed morphometric analysis were proposed using integrated mission for sustainable development (IMSD) and FAO guidelines. The results indicated that the causes of flooding are combination of factors such as anthropogenic activities (expansion of cultivated land, significant lake water level rise and decreasing channel conveyance), climate change (significant increase in wet conditions), inadequate drainage and torrential rainfall. From the landscape based mitigation measures, a total of 7 check dams, 55 farm ponds, 29 percolation ponds and terracing were proposed.

There is a heightened sensitivity about the risks posed by the prospect of climate change as deduced from the results of the global-regional climate models. Notwithstanding the moderating, albeit feebly articulated, acknowledgement of variability as being an intrinsic attribute of climate, it is often claimed that intensification of large hydrological extremes such as floods is indeed emerging as a new looming reality. This has aptly given rise to fears of prospective exacerbated socio-economic vulnerability. In an attempt to investigate the veracity of whether the hydrological flood events are really intensifying across the Krishna River Basin, the present study has examined the historical floods in Krishna River Basin (KRB) using available streamflow and precipitation records along with published reports and featured news articles. The approach followed is based on quantitative and qualitative analysis of floods in KRB at the subsystem scale (K1 to K12). The quantitative analysis involved i) Development of unregulated flow series through hydrological modelling, ii) Frequency analysis of unregulated flows and precipitation, iii) Threshold selection for defining the small, medium, and large floods, iv) Identification of flood events in observed streamflow series and v) Identification of causal rainfall and its relationship with flood peak. The qualitative analysis focused on published reports and news articles to attempt a multivariate characterization of flood flow attributes and the accompanying losses. The study concludes that the hypothesis that flood events are intensifying is untenable for most subsystems of KRB except K7 (Lower Krishna Basin) being the sole exception where, in sharp contrast, flood events show signs of moderation. Interestingly, cyclic patterns analogous to Noah and Joseph Effect are seen in the case of small floods for all the subsystems except K7. Non-recurrent, standalone extremes that bear the classical signature of Erratic Noah and Joseph elements have also been observed in medium and large floods in all the subsystems except the K12 subbasin (Munneru Basin). The study confirms that qualitative analysis alone cannot lead to an incontrovertible determination of trend like features in flood records as every event in the historical record bears a unique multi-dimensional footprint arising from a mix flood characteristics, associated losses and perceived short and long-term impacts. This study can provide a guideline to identify changes in flood typology especially in basins with altered hydrologic regimes and serve as an aid in planning and formulating policies for ameliorative flood management strategies as well as in policy restructuring when deemed to be necessary.

In this study, flood susceptibility mapping was carried out for Chemoga watershed upper Abay River basin, Ethiopia. The main objective of this study is to identify the flood susceptibility areas using Frequency ratio and Information Values models. Based on Google Earth imagery and filed survey, about 168 flooding locations were identified and classified randomly into training flood locations datasets 70% (118) and the remaining 30% (50) of flooding locations datasets were used for validation purpose. Identified 12, flood conditioning factors such as slope, elevation, aspect, curvature, TWI, NDVI, distance from road, distance from river, soil texture, lithology, land use and rainfall were integrated with training flood locations datasets to determine the weights of each flood location conditioning factor and factor classes using both frequency ratio and information value models. The flood susceptibility maps were produced by overlay the weights of all the flood conditioning factors using raster calculator of the spatial analyst tool in ArcGIS 10.4. The final flood susceptibility maps were reclassified as very low, low, moderate, high and very high susceptibility classes both FR and IV models. This susceptibility maps were validated using flood location area under the curve (AUC). The results of AUC accuracy models showed that the success rates of the FR and IV models were 82.90% and 82.10%, while the prediction rates were 80.70% and 80.00% respectively. Past flood events are compared with the flood vulnerable database to validate the modeled output in the present study. This type of study will be very useful to the local government for future planning and decision on flood mitigation plans.

It has been more than three decades of disaster risk reduction education in the United Nations; however, no study has been conducted to explore its current intellectual landscape. That being so, the present study aimed to determine the existing research trends and gaps in disaster risk reduction education, including opportunities and challenges. The paper reports bibliometric research on disaster risk reduction education using publications extracted from the Scopus database. With 452 publications so far (as of writing), results revealed that although the trends in the number of publications per year are generally increasing, there is a considerably limited number of scholars, institutions, and countries that are involved in disaster risk reduction education research; not to mention its limited diversity. This may be because disaster risk reduction is an emerging field. Nevertheless, it may be necessary to invest in building the capacity and encouraging more scholars and institutions to conduct research along with various aspects/phases of disaster risk reduction, involving different stakeholders and vulnerable groups of people, using a variety of research designs in needs assessment and evaluation studies, as well as the assessment on the effectiveness of related strategies. The study contributes to the discourse on disaster risk reduction, specifically disaster risk reduction education. It attempted to establish the current disaster risk reduction education research landscape that led to determining existing trends and gaps. Consequently, findings informed the reflection on opportunities and challenges in disaster risk reduction education research.

This study analyzes the visibility characteristics in Beijing from 2015 to 2020 using the 10-min average horizonal visibility hourly data from 20 national meteorological stations (NMSs). We examine the visibility trends on different time and space scales, including year, month, day. Our findings reveal that the visibility of the Beijing area shows a noticeable change trend year by year. Overall, the number of days with high visibility (visibility ≥10 ​km) has increased, and the number of hours with low visibility (visibility < 1 ​km) has decreased. Low visibility in Beijing mainly occurs during winter, whereas high visibility occurs throughout the year. On a daily scale, low visibility mainly occurs around 6 a.m. and 8 p.m. Furthermore, the spatial distribution of high and low visibility in Beijing correlates with the topographic boundary between plains and mountains. We observed that mountainous areas have more days with increased visibility and fewer hours with low visibility, while plain areas have fewer days with high visibility and more hours with high visibility. These findings have implications for understanding the environmental hazards caused by poor visibility in Beijing, such as impaired air quality, increased traffic accidents, and reduced human mobility. By identifying the temporal and spatial patterns of visibility, this study provides valuable information that can be used to improve hazard mitigation strategies and promote public safety.