5.1. Significance and validation of results
To establish a flood risk assessment and management framework for reducing the risk of human and material damage, international researchers are employing numerous strategies to detect flood risks and flood locations. However, flood risk persists and is likely to increase in the future. Traditional conceptions of flood control must be replaced by holistic flood management strategies (Merz et al., 2010; Meyer et al., 2009a; Molinari et al., 2014). For example, the integration of of expert and community opinions aimed at reducing vulnerability instead of raising dikes (Hoang et al., 2018; Luu et al., 2018; Meyer et al., 2009b; Saleem Ashraf et al., 2017) has been successful in the Netherlands and the United Kingdom. These measures can be adapted to developing countries where there is a large low littoral zone. Population growth and the development of urbanization both influence the environment of the flood plain. Aging road networks and flood defense systems have many weak points. Repairing and rebuilding is expensive and time-consuming; therefore, a bottom-up approach is appropriate for developing countries and especially countries such as Vietnam. Several previous studies have emphasized that flood risk management decision-making must be supported by flood risk assessment and analysis of the adaptive capacity of populations (Vu and Ranzi, 2017). The results of this study are necessary, as they can support reducing the damage to human life and provide appropriate strategies to ensure sustainable socio-economic development. Although this study was applied to a study area in Vietnam, the results can be extrapolated to other affected regions.
In the introduction of this study, we examined factors such as land cover, population density, and socio-economic status and their relationship with flood risk and indicated that one of the research objectives was to analyze its roles in the risk. The damage to property and humans during floods can be reduced by scientifically integrating reliable information in the flood risk map. This is one of the primary objectives accomplished in this study. We identified the flood risk areas in Gianh River in 2020 and provided information on flood risk in these periods. The combination of high exposure (i.e. population density and urbanization) and vulnerability (poverty is high, and there are high number of agricultural workers, people with physical disability, children and older people) explains the high flood risk. In 2013, Quang Trach district was split into Ba Don town and Quang Trach district. Ba Don town is the northern administrative center of Quang Binh province. According to the provincial planning strategy, in 2030, the area will be fully urbanized. The urbanization process triggers increasing population and more social welfare facilities, especially schools and hospitals. These crucial services may be exposed to a high risk due to the consequences of floods, increasing the region's vulnerability as a whole if they are built in flood-prone areas without commensurate mitigating measures. Weaker and unwell people may have lower resilience to the flood effects.
The effect of urbanization on flood risk was noticeable in Ba Don, Quang Tho, Quang Phong, Quang Phuc, Quang Thua, Quang Van, Quang Hoa, Quang Loc, and Thanh Trach (Fig. 10). This trend is typical of many cities around the world. (Nirupama and Simonovic, 2007) analyzed the flood risk in London, Ontario (Canada). The authors pointed out that urbanization was the major cause of increased flood risk. This was confirmed by Agraw et al. in 2021 (Beshir and Song, 2021). The authors showed that increasing urbanization was the main cause of floods in Addis Ababa. Jianxiong et al. (Tang et al., 2021) reported that rapid urbanization had resulted in flood risk area spreading in the coastal watershed of southeastern China. In 2020, (Handayani et al., 2020) reported that population growth and urbanization were increasing flood risk near the northern coast of central Java, Indonesia. This was confirmed by the study of (Ferdous et al., 2020). The authors justified the growth of population and artificial surface area in the flood zone as the main causes.
Three major issues were addressed in this study. First, the components and sub-components of risk were weighted using the AHP method, based on both the authors' experiences and scientific literature. The hydrodynamic model in this study yielded reliable results with high calibration and validation values (over 90%). Flood depth and velocity values are reliable; however, these weights are used to construct the flood hazard map using AHP, which is still subjective (Table 1, 2). They are the same for flood exposure and vulnerability sub-components. Second, the size of agricultural sector and level of poverty are important elements that impact flood risk in a given area (Fig. 8). First, the agricultural sector is the most affected by floods, particularly in the context of climate change. At the same time, poverty reduction can improve resilience to floods. However, the weights are attributed to the agricultural and poverty level sectors in Table 1, 2, 3 and Table 4, which are hard to justify clearly. Because agricultural losses depend so much on the type of agriculture – e.g. aquaculture and livestock lose more than crops – the extent to which floods affects crops also varies (Klaus et al., 2016; Nguyen et al., 2017). In general, crop losses are controlled by flood characteristics (e.g. flood depth, duration, flood timing, flow velocity, water contamination, and sediment load), crop varieties (e.g. rice or cereal), and crop physical characteristics (e.g. growth stage, height, and submergence tolerance). For example, the effects of complete submergence during a crop’s vegetative stage causes more serious yield loss than when it is in the reproductive stage (Nguyen et al., 2021c). Third, while the sturdiness of buildings decreases flood damage, the city wealth can increase the ability to support residents after a flood reduces the flood risk. However, these factors are hard to find in developing countries (Nguyen et al., 2021a).
The findings of this study explored the ability of adaptive populations to fill in the gaps of the AHP method. The adaptive capacity of people in the Gianh River watershed depends on activities such as strengthening the capability to access natural, economic, human, and social resources, particularly inhabitants who find themselves in less favorable economic situations; improving risk memories; and increasing awareness of flood risks. Questionnaires completed by local inhabitants show the relationships between factors determining the adaptive capacity of population to floods. In particular, access to natural, human, and technical resources plays an important role in reducing the impacts of floods by enabling improved techniques for the land and residents. However, the ability to access these resources is uneven between populations. In the case of the Gianh River watershed, local authorities are helping widen access loans to reinforce housing. Although improved access to materials and technologies improve the adaptive capacity of populations, these resources are not available to everyone. Many residents lack the economic resources to implement the most effective strategies. Costs of materials and technology are growing increasingly rapidly; financial insecurity and inequitable labor limit the capacity to adapt and increase vulnerability in the study area. The leveraging of human resources is seen as a cheaper route to flood control.
In Vietnam, flood risk management at the provincial and district levels is mainly focused on directing and monitoring activities at lower levels, such as the reinforcement of dyke and drainage systems. At the commune level, the focus is mainly on prevention, emergency response and recovery. Mitigation remains limited to preparing facilities, storing food, and organizing meetings before the flood occurs. The decision-making process of provincial and district governments is a top-down one. At the commune-level, the trend is reversed. Officials report to the district, whereas steering committee make a summary and create a final action plan. These plans rely on the experience of commune staff and are tailored to the geographical characteristics of each place, but local people do not participate in developing these plans. Therefore, a bottom-up approach in flood risk management adds a very important layer, where involving local people in developing plans and acting proactively can significantly reduce the loss of life and property.
This study highlights the important role of combining flood risk assessment and adaptive capacity analysis in flood risk management. Mitigating flood risks using a bottom-up approach is essential to protect urban areas (such as the case study area, the Gianh River basin). This provides the opportunity to develop settlements on flood plains, which may face frequent natural disasters including extreme rain or water-related disasters, such as storms and floods.
5.2. Limitations and future research directions
This study faces general limitations due to the use of topographic data. This study uses DEM, extracted from a 1:100,000 topographic map, to simulate floods using a hydrodynamic model, however, in future research, we will use DEM from Lidar or UAV, as it detects more details on the ground, such as flow direction, slope, vegetation, and buildings. There are also limitations due to lack of available data; this is a significant problem in Vietnam. In general, studies show the need for the government to make socio-economic data access easier to more people. Here, this study has been limited because of this poor availability. Future studies can add vulnerability data, such as family income, education level, and agricultural land use area. However, this data does not exist for the study area yet. Another issue to bear in mind is that AHP is mainly based on subjective evaluations of experts (De Brito et al., 2018). Future research can use new methods including Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP), Integrated Determination of Objective Criteria Weights (IDOCRIW) and Step-Wise Weight Assessment Ratio Analysis (SWARA). Future research may be able to integrate all these changes with the novel elements of the current study; additional data on future vulnerability and climate change scenarios will also be needed.