Flood Risk-Related Research Trends in Latin America and the Caribbean

Coastal Erosion and Flood Coping Mechanisms in Southern Thailand: A Qualitative Study

Communities in coastal regions are affected by the impacts of extreme climatic events causing flooding and erosion. Reducing the impacts of flood and erosion in these areas by adopting coping strategies that fortify the resilience of individuals and their localities is desirable. This study used summative content analysis to explore the coping mechanisms of coastal communities before, during, and after various dangers relating to flooding and erosion. The findings from the study show that effective surveillance systems, disaster preparedness, risk mapping, early warning systems, availability of databases and functional command systems, as well as reliable funding are essential to efficiently cope with hazards of coastal flooding and erosion. As flooding and erosion have been predicted to be more severe due to climate change in the coming years, the adoption of effective natural and artificial mechanisms with modern technologies could help coastal regions to be more resilient in coping with the dangers associated with flooding and erosion. Pragmatic policies and programs to this end by actors are critical to averting crises induced by flooding and erosion in coastal areas.

Flood Hazard Index Application in Arid Catchments: Case of the Taguenit Wadi Watershed, Lakhssas, Morocco

During the last decade, climate change has generated extreme rainfall events triggering flash floods in short periods worldwide. The delimitation of flood zones by detailed mapping generally makes it possible to avoid human and economic losses, especially in regions at high risk of flooding. The Taguenit basin, located in southern Morocco, is a particular case. The mapping of the flood zones of this basin by the method of the Flood Hazard Index (FHI) in a GIS geographic information systems environment was based on the multi-criteria analysis, taking into consideration the seven parameters influencing these extreme phenomena, namely rainfall, slope, flow accumulation, drainage network density, distance from rivers, permeability, and land use. Average annual rainfall data for 37 years (1980 to 2016) was used in this study for floodplain mapping. A weight was calculated for each parameter using the Analytical Hierarchy Process (AHP) method. The combination of the maps of the different parameters made it possible to draw up a final map classified into five risk intervals: very high, high, moderate, lower and very lower presenting, respectively, 8.04%, 20.63%, 31.47%, 15.36%, and 24.50% of the area of the basin. The reliability of this method was tested by a Flood susceptibility analysis. The results generated by the Flood Hazard Index (FHI) model are similar to those of previous historical events. Realistic and applicable solutions have been proposed to minimize the impact of these floods as much as possible.

Recovering from Financial Implications of Flood Impacts—The Role of Risk Transfer in the West African Context

In many West African river basins, households regularly experience floods and the associated impacts. In the absence of widely accessible formal risk transfer mechanisms (e.g., insurance), households often have to cope with financial impacts. Only a few studies have explored the financial effects of floods on agriculture-dependent households in the region and the role formal and informal risk transfer plays in their mitigation. This study addresses this gap, explores flood impacts with financial implications for households, and researches the existing strategies to mitigate them. Moreover, it aims to better understand how different measures influence the recovery process. The study draws on primary data from a household survey (n = 744) in the Lower Mono River basin, combined with stakeholder workshops and semi-structured interviews, and applies a generalized linear model to the survey data. The results reveal four flood impact types with financial implications: agricultural, material, health, and trade. Moreover, a shortened recovery time is significantly associated with assistance from savings groups and cooperatives—groups originally not formed to help during floods. In light of the severe and frequent flood impacts, effective and publicly accepted adaptation measures are needed to enable favorable conditions for creating sustainable and accessible risk transfer mechanisms.

Potential Flood Risk in the City of Guasave, Sinaloa, the Effects of Population Growth, and Modifications to the Topographic Relief

The coastal city of Guasave, Sinaloa, located on the Mexican Pacific coast, is subject to extreme precipitation events, which have caused flooding with damage to the city’s infrastructure. The factors that influence flooding are vegetation, geology, degree of soil saturation, drainage characteristics of the watershed, and the shape of the topographic relief. Of the above factors, the topographic relief, which is the subject of the study, has been partially modified in some areas by infrastructure works (from 20.2 m to 17.6 m), and the population of the urban area has grown by 51.8% in 17 years (2004–2021); therefore, the objective is to evaluate the potential flood risk due to changes in this factor and the growth of the urban area. When using this method, the potential flood risk was determined considering four extreme events, 1982, 1990, 1998, and 2019. It was found that the potential risk increases for the whole city, being more intense in sector III, which, before the modification of the topographic relief, was the area with the lowest risk of flooding. In an extreme event such as Hurricane Paul in 1982, practically the entire city would be flooded.

Development and Application of a Real-Time Flood Forecasting System (RTFlood System) in a Tropical Urban Area: A Case Study of Ramkhamhaeng Polder, Bangkok, Thailand

In urban areas of Thailand, and especially in Bangkok, recent flash floods have caused severe damage and prompted a renewed focus to manage their impacts. The development of a real-time warning system could provide timely information to initiate flood management protocols, thereby reducing impacts. Therefore, we developed an innovative real-time flood forecasting system (RTFlood system) and applied it to the Ramkhamhaeng polder in Bangkok, which is particularly vulnerable to flash floods. The RTFlood system consists of three modules. The first module prepared rainfall input data for subsequent use by a hydraulic model. This module used radar rainfall data measured by the Bangkok Metropolitan Administration and developed forecasts using the TITAN (Thunderstorm Identification, Tracking, Analysis, and Nowcasting) rainfall model. The second module provided a real-time task management system that controlled all processes in the RTFlood system, i.e., input data preparation, hydraulic simulation timing, and post-processing of the output data for presentation. The third module provided a model simulation applying the input data from the first and second modules to simulate flash floods. It used a dynamic, conceptual model (PCSWMM, Personal Computer version of the Stormwater Management Model) to represent the drainage systems of the target urban area and predict the inundation areas. The RTFlood system was applied to the Ramkhamhaeng polder to evaluate the system’s accuracy for 116 recent flash floods. The result showed that 61.2% of the flash floods were successfully predicted with accuracy high enough for appropriate pre-warning. Moreover, it indicated that the RTFlood system alerted inundation potential 20 min earlier than separate flood modeling using radar and local rain stations individually. The earlier alert made it possible to decide on explicit flood controls, including pump and canal gate operations.

Climatic and Hydrological Factors Affecting the Assessment of Flood Hazards and Resilience Using Modified UNDRR Indicators: Ayutthaya, Thailand

This research aims to investigate the effect of climatic and hydrological factors on flood hazards and assess flood resilience in Ayutthaya, Thailand, using the 10 essentials for making cities resilient modified by the United Nations Office for Disaster Risk Reduction (UNDRR). Flood resilience assessment was performed based on a multi-criteria decision-making approach or the analytical hierarchy process (AHP) of pairwise comparison. The results indicate that runoff is considered the most influential factor in flood hazards, followed by land use, rainfall, and historical flood events, sequentially. Regarding the flood incident management concept, a questionnaire survey (n = 552) was conducted to understand the impacts of flood on local communities. The findings reveal that 50% of respondents had never received any flood information or participated in training sessions on flood preparedness. Most reported their concerns about the inadequate supply of drinking water during a flood. Spearman’s correlation coefficient shows positive correlations between flood disaster relief payments, preparedness training, access to flood hazard mapping, emergency health services, and their flood preparation actions. According to the modified UNDRR indicators, the top three highest AHP values in building community resilience to flood hazards in Ayutthaya are flood risk scenario identification, the effectiveness of emergency flood disaster response, integrated urban planning, and disaster risk reduction. The policy implications of this research include the need for national authorities to better understand the role cities can play a vital role in supporting both national and international climate resilience frameworks, especially Thailand’s National Disaster Management Plan, the Sendai Framework for Disaster Risk Reduction (SFDRR), and the global Sustainable Development Goals (SDGs).

Flood Resilience and Adaptation in the Built Environment: How Far along Are We?

Cities are experiencing an increased rate of climate-related extreme events threats derived from climate change. Floods are one of the most challenging issues to address to reduce damages and losses in urban areas. Building resilience through adaptation to these changing conditions has become a common goal for different disciplines involving planning for the future. Adaptation planning is widely recognized as generally applicable to any field. However, there are current limitations to overcome for architectural and urban planning to switch from theory to practice. This paper proposes a critical overview of literature works on flood mitigative strategies and adaptive approaches considering uncertainties, linking strategies for the Built Environment (BE) to mitigate the effects of floods, and operative frameworks to pursue adaptation under changing environmental conditions. The literature selection accounts for the pivotal components of the BE: open spaces (OSs), buildings, and users. Next, we provide an overview of the most relevant adaptive methodologies that have emerged in literature, and, lastly, the planning strategies are discussed, considering the climate-related uncertainties that might undermine the effectiveness of the designed action. The present paper aimed to provide a contribution to the discussion regarding the necessity of making architectural and urban planning adaptive, providing a base for future studies for operative adaptation.

Developing Robust Flood Susceptibility Model with Small Numbers of Parameters in Highly Fertile Regions of Northwest Bangladesh for Sustainable Flood and Agriculture Management

The present study intends to improve the robustness of a flood susceptibility (FS) model with a small number of parameters in data-scarce areas, such as northwest Bangladesh, by employing machine learning-based sensitivity analysis and an analytical hierarchy process (AHP). In this study, the nine most relevant flood elements (such as distance from the river, rainfall, and drainage density) were chosen as flood conditioning variables for modeling. The FS model was produced using AHP technique. We used an empirical and binormal receiver operating characteristic (ROC) curves for validating the models. We performed Sensitivity analyses using a random forest (RF)-based mean Gini decline (MGD), mean decrease accuracy (MDA), and information gain ratio to find out the sensitive flood conditioning variables. After performing sensitivity analysis, the least sensitivity variables were eliminated. We re-ran the model with the rest of the parameters to enhance the model’s performance. Based on previous studies and the AHP weighting approach, the general soil type, rainfall, distance from river/canal (Dr), and land use/land cover (LULC) had higher factor weights of 0.22, 0.21, 0.19, and 0.15, respectively. The FS model without sensitivity and with sensitivity performed well in the present study. According to the RF-based sensitivity and information gain ratio, the most sensitive factors were rainfall, soil type, slope, and elevation, while curvature and drainage density were less sensitive parameters, which were excluded in re-running the FS model with just vital parameters. Using empirical and binormal ROC curves, the new FS model yields higher AUCs of 0.835 and 0.822, respectively. It is discovered that the predicted model’s robustness may be maintained or increased by removing less relevant factors. This study will aid decision-makers in developing flood management plans for the examined region.

Flood Hazard and Risk Mapping by Applying an Explainable Machine Learning Framework Using Satellite Imagery and GIS Data

Flooding is one of the most destructive natural phenomena that happen worldwide, leading to the damage of property and infrastructure or even the loss of lives. The escalation in the intensity and number of flooding events as a result of the combination of climate change and anthropogenic factors motivates the need to adopt real-time solutions for mapping flood hazards and risks. In this study, a methodological framework is proposed that enables the assessment of flood hazard and risk levels of severity dynamically by fusing optical remote sensing (Sentinel-1) and GIS-based data from the region of the Trieste, Monfalcone and Muggia Municipalities. Explainable machine learning techniques were utilised, aiming to interpret the results for the assessment of flood hazard. The flood inventory was randomly divided into 70%, used for training, and 30%, employed for testing. Various combinations of the models were evaluated for the assessment of flood hazard. The results revealed that the Random Forest model achieved the highest F1-score (approx. 0.99), among others utilised for generating flood hazard maps. Furthermore, the estimation of the flood risk was achieved by a combination of a rule-based approach to estimate the exposure and vulnerability with the dynamic assessment of flood hazard.

Using ARC-D Toolkit for Measuring Community Resilience to Disasters

Increased levels of resilience will reduce the negative consequences of any disaster and develop the capacities of communities to mitigate future disasters. The main objective of this study was to measure the level of resilience of two different communities in two different study areas and compare the resilience levels in terms of a flood. The study used the Analysis of Resilience of Communities to Disasters (ARC-D) toolkit. The study was conducted in two different areas to compare the level of community resilience. Both quantitative and qualitative methods were used in the study. A structured questionnaire was developed by using the toolkit. Results of the study indicated that communities in study area 1 were more resilient than communities in study area 2. Communities from study area 1 were more aware of their risk(s) and problem(s) and ensured proper strategies and actions to solve problems. On the other hand, communities in study area 2 were less aware of their risk(s). The strategies and actions implemented by the communities of study area 1 focused on the short-term problem(s), which reduced their level of resilience. Measuring resilience is very important in terms of developing disaster risk reduction (DRR) plans and incorporating DRR in the development process in lower-income countries and developing countries. As data scarcity is one of the major issues in developing countries, introducing a community resilience assessment mechanism can be a great help to reduce gaps in the planning and implementation process.