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The frequency of floods is predicted to increase in south-east Asia, and this may exacerbate the living conditions of poor people in flood-prone areas. Though much work has been conducted on the effects of poverty, there is a pressing need for more analysis on the local effects of floods. The work that does exist usually is based on qualitative analys...
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... studying the spatial distribution of respondents, we were able to examine the characteristics of people who suffer from severe floods. Fig. 2, Fig. 3 and Fig. 4 show the spatial distribution of income, education level, and house type respectively (where the flood depth threshold is 1 m). In Fig. 2, an average annual income of less than 400,000 Kyats is categorized as low, 400,000 to 1,000,000 Kyats as middle range, and greater than 1,000,000 Kyats as high. In Fig. 3 (education level), an ...
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... House type, education, number of family members, distance of house from the nearest river (distance) Flood depth DEM, distance, house type, number of family members range, and greater than 2.5 as high. In Fig. 4 (house type), 'good' stands for concrete and brick-nogging, 'middle' for wood, and 'bad' for bamboo. It is clear from Figs. 2-4 that people who experience floods of no more than 1 m in depth tend to live in better conditions than those who suffer deeper floods. The figures support the results in the regression ...
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... studying the spatial distribution of respondents, we were able to examine the characteristics of people who suffer from severe floods. Fig. 2, Fig. 3 and Fig. 4 show the spatial distribution of income, education level, and house type respectively (where the flood depth threshold is 1 m). In Fig. 2, an average annual income of less than 400,000 Kyats is categorized as low, 400,000 to 1,000,000 Kyats as middle range, and greater than 1,000,000 Kyats as high. In Fig. 3 (education level), an ...
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... House type, education, number of family members, distance of house from the nearest river (distance) Flood depth DEM, distance, house type, number of family members range, and greater than 2.5 as high. In Fig. 4 (house type), 'good' stands for concrete and brick-nogging, 'middle' for wood, and 'bad' for bamboo. It is clear from Figs. 2-4 that people who experience floods of no more than 1 m in depth tend to live in better conditions than those who suffer deeper floods. The figures support the results in the regression ...
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... These advanced models play a crucial role in identifying vulnerable regions, assessing potential flood impacts, and guiding the implementation of effective flood risk management strategies [50]. The link between poverty and vulnerability to flooding is complex and multifaceted [51][52][53]. Poverty increases vulnerability by limiting access to essential resources, infrastructure, and information, thereby affecting the ability of individuals and communities to prepare, cope, and recover from flood events [54][55][56][57]. ...
... Floods are considered to be one of the most serious global natural disasters, causing significant economic impacts, disruption of Flooding can exacerbate poverty by causing death, property damage, and economic disruption [12,[58][59][60][61] examined the impact of floods on household poverty in Nigeria [21]. provide a global estimate of the number of people at risk of intense river, pluvial, or coastal flooding [19,51]. examined the link between flooding and poverty at the household level. ...
... Poor households often lack the financial means to invest in flood-proof infrastructure or insurance, making them more vulnerable to the adverse effects of flooding [12]. In addition, poverty can limit access to education and awareness-raising and reduce the ability of communities to take resilience measures and respond effectively to flood events [51]. The complex interplay between flood vulnerability and poverty requires integrated approaches to disaster risk reduction and poverty alleviation. ...
... This paper showcased a strong relationship between floods and poverty at the household level, as reported elsewhere (Kawasaki et al. 2020;Mtapuri et al. 2018). In the sample, the mean annual per capita expenditure of the flooded farmers was greater than that of the non-flooded farmers. ...
Floods destroy crop production; nevertheless, the extent of their impact on farmers' livelihoods in developing countries has been poorly investigated. This paper contributes to the growing evidence-based assessment of the impacts of shocks on communities. It assessed the post-disaster livelihood of farmers affected by the 2012 flooding in the semi-arid zone of Benin. To this end, a survey was conducted on 228 farmers in two municipalities of the flood-prone part of the semi-arid zone of Benin (Malanville, Karimama). Information on the well-being of households was collected using semi-structured interviews. Data were analyzed using income and consumption approaches focusing on poverty and on subjective assessment using happiness approach. Additionally, a probit model was used for a poverty assessment. The survey revealed that flooded farmers were amongst the poorest in the study system. Seven variables determined poverty in this study: household size, location, the percentage of the farm size that was flooded, fishing, the farmer's gender, farm size, and "holding a secondary activity". Regarding happiness, 99% of the flooded farmers were unhappier after the flood in 2012. The results clearly show that being subjected to floods increases the incidence of poverty. The capacity of flood risk management and governance should be strengthened in the study system.
... People in poverty are disproportionately at risk of oods around the globe (Garbutt et al., 2015;Kawasaki et al., 2020;Mtapuri et al., 2018;Winsemius et al., 2018). This trend signi es that ood risk is not equally distributed (Wing et al., 2022). ...
The risk of floods from tropical storms is increasing due to climate change and human development. Maps of past flood extents can aid in planning and mitigation efforts to decrease flood risk. In 2021, Hurricane Ida slowed over the Mid-Atlantic and Northeast United States and released unprecedented rainfall. Satellite imagery and the Random Forest algorithm are a reliable combination to map flood extents. However, this combination is not usually applied to urban areas. We used Sentinel-2 imagery (10 m), along with derived indices, elevation, and land cover data, as inputs to a Random Forest model to make a new flood extent for southeastern Pennsylvania. The model was trained and validated with a dataset created with input from PlanetScope imagery (3 m) and social media posts related to the flood event. The overall accuracy of the model is 99%, and the flood class had a user’s and producer’s accuracy each over 99%. We then compared the flood extent to the Federal Emergency Management Agency (FEMA) flood zones at the county and tract level and found that more flooding occurred in the Minimal Hazard zone than in the 500-year flood zone. Our Random Forest model relies on publicly available data and software to efficiently and accurately make a flood extent map that can be deployed to other urban areas. Flood extent maps like the one developed here can help decision-makers focus efforts on recovery and resilience.
... It is widely recognized that an increase in flood risk may restrict the development of socioeconomic conditions and inhibit long-term sustainable development [76]. Individuals on a low income tend to live in marginal and flood-prone areas, where floods trap people in a cycle of poverty [77]. In reality, the relationship between flood risk and socioeconomic factors is complex. ...
In the context of urbanization, frequent flood event have become the most common natural disasters, posing a significant challenge to human society. Considering the effects of urbanization on flood risk is critical for flood risk reduction and reasonable land planning strategies at the city scale. This study proposes an integrated approach based on remote sensing data using CA, Markov, and simplified hydrodynamic (FloodMap) models to accurately and effectively assess flood risk under urbanization. Taking Chongqing City as a case study, this paper analyzes the temporal and spatial variations in land use/land cover (LULC) in 2010, 2015, and 2018 and predicts the LULC for 2030, based on historic trends. Flood risk is assessed by combining the hazard, exposure, and modified vulnerability. The results suggest that the area of built-up land will increase significantly from 19.56% in 2018 to 25.21% in 2030. From 2010 to 2030, the area of medium and high inundation depths will increase by 10 and 16 times, respectively. Flood damage varies remarkably according to the LULC and return period. The expected annual damage (EAD) has been estimated to increase from USD 68 million in 2010 to USD 200 million in 2030. Flood risk is proportional to population and is significantly inversely proportional to socioeconomic level. The approach used here can provide a comprehensive understanding of flood risk and is significant for land-use policymaking and the management of flood control facilities.
... The Lancang-Mekong section in Myanmar is relatively short and a border river, with only some small hydropower stations and indicators R5 (Installed capacity of hydropower dam), R6 (Reservoir capacity), and R7 (Hydropower dam density) scoring only 0.110, 0.218, and 0.200, respectively. The low water infrastructure development level has resulted in a weak response to changes in the water environment and water security conditions [81,82]. ...
Water security is crucial for the sustainable development of regional water resources. Here, we utilize the Driver-Pressure-State-Impact-Response (DPSIR) framework to construct an indicator system for assessing water security in the Lancang–Mekong River Basin (LMRB). This study also delves into the level of development in the coupling coordination between the economic and social systems and the water resources systems in the basin. The findings reveal that the overall water security situation in the LMRB is satisfactory, with three countries (China, Laos, and Vietnam) surpassing the “safe” threshold and three countries (Thailand, Cambodia, and Myanmar) “Basically safe”. However, water security issues persist, particularly in relation to water pollution and scarcity. Seasonal water shortages and water-related disasters arise due to uneven rainfall distribution throughout the year and inadequate regulating facilities such as wetlands and reservoirs. In addition, the overall coupling coordination level in the LMRB is low, ranging between 0.3 and 0.4, corresponding to a moderate imbalance level in the assessment criteria system. Specifically, Laos and China exhibit the highest coupling coordination level, with a degree of 0.36, whereas Thailand and Myanmar demonstrate the lowest level, with degrees of 0.33 and 0.31, respectively. Overall, our results offer a scientific foundation for the sustainable development of countries within the LMRB.
... Table 7 depicts that climate-induced shocks such as rainfall variability and drought exacerbate structural and stochastic poverty entry corroborating other studies (Letta et al., 2018;Maganga et al., 2021;Wossen & Berger, 2015). Floods can also heighten structural and stochastic poverty descents consistent with Kawasaki et al. (2020). Shocks aggravate poverty through decline in asset values, crop production, labor market participation, consumption, and income. ...
Whilst structurally poor households fall below the income and asset poverty line, stochastically poor households fall below the income poverty line but above the asset poverty line. This distinction suggests different challenges for the households in dealing with shocks and building the resilience to make a lasting escape from poverty. Accordingly, we examine the effect of shocks on structural and stochastic poverty, transitions, and the role of resilience as a mechanism for dealing with shocks and stochastic and structural poverty using the Ethiopian Socioeconomic Survey data. We find that recurrent and concurrent shocks adversely impact structural and stochastic poverty, whilst resilience capacities can curb poverty as shocks intensify. Access to irrigation, literacy, good vegetation cover, and non-farm economic activities help eradicate both structural and stochastic poverty. Rainfall variability, drought, conflict, input and output price volatility, and idiosyncratic shocks all drive both structural and stochastic poverty. However, the critical implication for policy is that reducing structural and stochastic poverty requires enhancing resilience capacity. This will require promoting symbiotic rural–urban links and rural revitalization to ensure a balanced mix of development. The findings suggest that two distinct sets of policies are required to protect against falling into poverty and sustain movements out of poverty, namely harmonizing cargo net and safety net policies.
... Flooding severely harms residential buildings in flood-prone areas of many developing countries [14][15][16][17]. Numerous researchers have reported flood damage assessment methods that connect flood damage to residential areas, focusing on flood damage to buildings [1,2,11,. ...
... It is evident from the field survey that many families are located next to the main river in Bangladesh. Kawasaki et al. [16] reported that it would be difficult to build embankments to reduce flood damage in the Bago River Basin since some homes are located next to the main river, implying that a large investment is needed for building embankments and relocating those residing near riverbanks. Therefore, better options to reduce the flood risk in residential areas include flood risk mapping, improving flood forecasting and early warning systems, effective use of existing structures, regulation of land use, and implementation of guidelines for building new houses with flood adaptation strategies. ...
... Therefore, better options to reduce the flood risk in residential areas include flood risk mapping, improving flood forecasting and early warning systems, effective use of existing structures, regulation of land use, and implementation of guidelines for building new houses with flood adaptation strategies. However, the choice of adaptation measures (structural or nonstructural) is also influenced by a number of factors, including finances, sociocultural context, and political concerns [ [16,59,60],]. The Teesta River Basin's residential regions are particularly vulnerable to flooding, which frequently results in damage to residential structures. ...
... The poor exhibit greater sensitivity to natural hazards and, in addition, tend to be overrepresented in disaster-prone areas in Malawi (Kawasaki et al., 2020;Longhurst & Sabates-Wheeler, 2019). ...
Social factors moderate the impacts of natural hazards, which means that households are affected differently when exposed to the same hazard. This differential impact of hazards can be explained by the concept of social vulnerability, which is commonly assessed to inform disaster preparedness and response action. Most of these assessments, however, focus their analyses on large administrative units and, consequently, neglect the heterogeneity of households within these units. This thesis leverages data from Malawi's social registry (the UBR) to construct a Household Social Vulnerability Index for Nsanje-one of the most disaster-prone districts in Malawi. In Nsanje, geocoded socioeconomic data was collected using a census-sweep approach with the goal of registering 100% of the district's residents. From this dataset, indicators are deductively selected and analyzed using Principal Component Analysis to produce a social vulnerability score for each household. These index scores are mapped at a spatial resolution of 0,01°. By repurposing a social registry to inform a new set of actors, including humanitarian and disaster risk management practitioners, the thesis highlights the considerable scope for collaboration within the realm of data and information by actors and policy fields that traditionally largely have operated in isolation from one another.
... It is the place where most villages are affected by flood. Flood-prone areas are the place where poor people tend to live, and that floods can cause and exacerbate poverty that tangible measures need to be taken to protect against floods and to help the poor escape from the vicious cycle that they are trapped [34]. There are three scenarios (T1, T2 and T3) that the outflow water could be diverted (See figure 3). ...
As significant catchment of flood water, this study examined important morphological structures of Lake Buhi (LB) catchment area which is a part of Bicol River Basin Area (BRBA). The information taken was used to derive a mathematical model which was then used in analysing the hydrologic event in the area. Different scenarios were investigated to provide solutions and avoid major risks and damages in BRBA during extreme precipitation. The method used to gather the data are Geographical Information System (GIS), digital elevation modelling (DEM), hydrologic modelling, site visit, and remote sensing. It was found that the morphology is well structured to collect and impound vast amount of rainwater flow and form huge body of water known as the LB. The area has immense number of streams that directs surface runoff into the impounded body of water having low drainage density. Enormous amount is needed to discharge away and prevent high water level rise and stop flood in the nearby villages. The capacity of the outflow river is sufficient in most cases of the forecasted precipitation while during the time of typhoon event was found insufficient. However, there are three options to divert away the accumulated floodwater and become a potential source of electrical energy.
... Table 4 presents the model fit of the final SEM based on the indices. As stated by Gefen et al. [67], the minimum value for a good model fit is 0. 80. The values of the Incremental Fit Index (0.871), Tucker Lewis Index (0.888), Comparative Fit Index (0.887), Goodness of Fit Index (0.855), and Adjusted Goodness of Fit Index (0.824) were greater than or equal to 0.80, as presented in Table 4. Steiger [68] suggested that the Room Mean Square Error of Approximation should be less than 0.07, whereas the parameter estimated was 0.055, which indicates a good model fit. ...
... According to Kurata et al. [23], if Filipinos participate in community preparedness activities and workshops, this will increase their intention to follow government directives in order to prevent and mitigate damages from flood disasters. Similarly, different related studies in the Philippines showed that people are trusting and believe in the mitigation plans, protocols, and effectiveness of local government units, which should be promoted [53,77,78,80]. ...
Natural hazards are one of the destructive phenomena that pose a significant hazard to humans, property, and the economy, among others. One of the most recurring natural hazards is flooding, which is caused by typhoons, monsoons, and heavy rainfall and has been one of the main concerns of the Philippines in recent years. The study's results will provide information on the factors affecting flood disaster preparedness by integrating the Theory of Planned Behavior (TPB) and the Protection Motivation Theory (PMT). A total of 509 individuals answered an online survey questionnaire with 52 adapted questions. Structural equation modeling (SEM) revealed that risk perception (RP), media (M), and personal experience (PE) had an effect on perceived severity (PS) and perceived vulnerability (PV), which consequently affected the attitude toward the behavior (ATB), social norms (SN), and perceived behavioral control (PBC). It was determined that ATB, SN, and PBC significantly affected adapted behavior (AB), which consequently led to intention to follow (ITF) and perceived prevention (PP). After analyzing the data, it was revealed that 56.2% of female respondents were said to be more disaster resilient compared to males. This is the first study to determine the perceived prevention of disaster preparedness and mitigation in flood-prone areas in the Philippines. The results will be beneficial to academicians and government officials in developing determining factors that affect flood disaster preparedness. Lastly, a deeper understanding of how AB is the most significant variable may be further researched to improve the paper.
