Flood Risk Management Strategies Research Articles

Socio-hydrological modelling using participatory System Dynamics modelling for enhancing urban flood resilience through Blue-Green Infrastructure

Cities are complex systems characterised by interdependencies among infrastructural, economic, social, ecological, and human elements. Urban surface water flooding poses a significant challenge due to climate change, population growth, and ageing infrastructure, often resulting in substantial economic losses and social disruption. Traditional hydrological modelling approaches for flood risk management, while providing invaluable support in the analysis of hydrological dynamics of floods, lacks an understanding of the complex interplay between hydrological and non-hydrological (i.e., social, environmental, economic) aspects in an urban system, hindering effective flood risk management strategies. In this context, socio-hydrological modelling methods offer a complementary perspective to traditional hydrological models by integrating hydrological and social processes, thereby enhancing the understanding of the complex interactions driving flood resilience.The present work proposes a participatory socio-hydrological modelling approach based on System Dynamics (SD) to quantitatively analyse the interactions and feedback between flood risk and different aspects of the urban system. By combining scientific expertise with stakeholder knowledge, the modelling approach aims to provide decision-makers with a comprehensive understanding of flood dynamics and the effectiveness of resilience-building measures. Furthermore, the role of Blue-Green Infrastructure (BGI) in enhancing urban flood resilience, considering its interplay with grey infrastructure and interactions with various sub-systems, is explored.The results reveal i) the contribution of SD quantitative modelling in supporting the analysis of interactions between flood risk reduction measures and different sub-systems thus offering decision-makers actionable insights into the multifaceted nature of flood risk and resilience; ii) the added value provided by the combination of scientific and stakeholder knowledge in tailoring the model to the case study, quantifying socio-hydrological modelling dynamics limitedly explored in the scientific literature and supporting the selection of measures for increasing flood resilience; iii) the ability of BGI to provide not only hydrological benefits (mainly about the reduction of surface runoff) but also multiple social and environmental benefits (i.e., the co-benefits), especially when coupled with well-functioning grey infrastructure. Reference is made to one of the case studies of the CUSSH and CAMELLIA projects, namely Thamesmead (London, United Kingdom), a formerly inhospitable marshland currently undergoing a process of urban regeneration, with an increasing vulnerability to flooding.

Clustering of small watersheds in hilly areas based on complex network theory and similarity analysis

ABSTRACT Clustering analysis of small watersheds is an effective tool for identifying the similarity of runoff generation and concentration. In this paper, 545 small watersheds in the hilly areas of Shandong Province were investigated, and 12 indicators representing their climate and subsurface characteristics were selected to identify communities based on hydrological similarity. We further analyzed the hydrological connections among the small watersheds within each community using three indicators (network mean, centrality, and k-core). Finally, the clustering results were evaluated on the basis of the small watershed flood peak modulus. The results of this complex network method indicate that the study area contained six large communities and nine small communities. The community-clustering results were reasonable and showed the interconnectedness of the watersheds within each community. The three network indicators adequately described the degree of similarity, the representativeness of the watersheds, and the spatial scales of similar hydrological features. This method should be helpful for addressing the issue of parameter transplantation in ungauged watersheds and implementation of a flood risk management strategy.

Unlocking effective ice-jam risk management: Insights from agent-based modeling and comparative analysis of social theories in Fort McMurray, Canada

Ice jams pose a major flood hazard in communities along northern rivers, resulting in high backwater levels and overbank flooding during ice-cover breakup. Mitigation measures, including large-scale projects by government agencies and individual actions by asset owners, can help reduce flood risk and protect human life and assets. This study examines ice-jam flood risk and explores the effectiveness of adaptive strategies in mitigating such risks in Fort McMurry, Canada. It evaluates the effectiveness of top-down (government-led artificial breakup) and bottom-up (resident-led flood-proofing) strategies, comparing the Rational Choice Theory (RCT) and the Protection Motivation Theory (PMT) models. The objective is to explore the potential enhancements to the ice-jam flood risk model through the integration of the PMT as a decision-making framework under uncertainty. This study seeks to assess whether and to what extent such integration can improve the modeling of ice-jam flood risk. The findings highlight the benefits of incorporating socio-economic factors in the PMT model. Economic factors, such as income tax and the cost of flood-proofing, shape overall flood risk, especially when artificial breakup measures are not implemented. The study emphasizes the importance of considering heterogeneity in decision-making processes and diverse characteristics of individuals when designing flood risk management strategies. Response efficacy and self-efficacy coefficients are significant factors influencing flood risk and the adoption of flood-proofing measures. Enhancing individuals' belief in their actions' effectiveness and their confidence in self-protection contributes to more effective flood risk management. These findings inform the development of more effective flood risk management strategies.

Unraveling the interplay of human decisions and flood risk: An agent-based modeling approach

Flooding is a frequent and highly destructive natural disaster that requires effective flood management policies. This paper addresses the limitations of traditional flood risk assessment methods, focusing on the interplay between human decisions and flood risk using an integrated approach. Agent-Based Modeling (ABM) is employed to capture the social behavior of government, households, and farmers in three flood management scenarios. The study is conducted in the Fall River watershed, Idaho, USA, over a 30-year period. The results highlight the important influence of crop selection decisions on flood damage outcomes, with alfalfa emerging as the preferred crop due to its profitability and lower damage potential (around 67 % crop loss compared to more than 81 % for crops like corn, barley, and wheat). The levee scenario is identified as the most effective strategy, significantly reducing flood damage by more than 37 % and generating the highest average annual profit for farmers. The study emphasizes the need for comprehensive flood risk management strategies that address both frequent and rare flood occurrences in mitigating flood damage and promoting community resilience. By integrating human-flood interactions into flood risk assessment, this study offers valuable insights for Policymakers and stakeholders can utilize the research implications to develop effective strategies that mitigate damage and enhance community resilience.

Advancing Flood Risk Assessment through Integrated Hazard Mapping: A Google Earth Engine-Based Approach for Comprehensive Scientific Analysis and Decision Support

This study utilises a comprehensive, multi-layered approach to assess flooding susceptibility in a specific area, integrating diverse environmental datasets such as JRC Global Surface Water, Landsat 8 images, and SRTM elevation data. Employing the GEE FMA, a powerful tool leveraging Google Earth Engine capabilities, the analysis covers water occurrence, permanent water, elevation, distance to water, topographic hazard score, and vegetation indices (NDVI and NDWI). The Water Occurrence layer establishes a foundational understanding of water-body distribution’s correlation with flood vulnerability, while Permanent Water refines this understanding. Distance to Water measures proximity for targeted risk evaluation, and Elevation identifies vulnerable regions based on topography. The GEE FMA synthesises these layers into a Flood Hazard Susceptibility map, categorising vulnerability into Very Low, Low, Medium, High, and Very High. This nuanced understanding is crucial for prioritising interventions. The GEE FMA’s rapid processing speed makes it an invaluable tool for short-term decision support in flood hazard disaster management, offering insights for informed decision-making and resilient infrastructure development. The Topographic Hazard Score provides information on how topography influences flood risk, while the Wetness Hazard Score categorises moisture conditions for identifying flood-prone locations. Decision-makers rely on these values for quick and precise flood susceptibility assessments. In an era of climate uncertainties and urbanisation, the GEE FMA emerges as a reliable tool for decision-making, mitigating flood impacts, and developing effective flood risk management strategies.

Assessing flood risks and exploring opportunities for flood-based farming in the dry lowlands of Ethiopia

Food grown in the rainfed system in Ethiopia is frequently insufficient to meet household food needs due to recurrent drought, which causes severe food insecurity. Ethiopia’s drylands have also been hit by an increase in torrential floods. As a result, the ability to adapt to shocks and risks decreases. Despite the opportunity of highland-to-lowland to lowland connectivity, the opportunities for flood-recession farming are poorly understood. This study maps flood recession opportunities incorporating national flood occurrence information, flood images, and SMAP surface soil moisture from Soil Moisture Active Passive (SMAP) images in Omo Gibe basin and Mile sub-basin. The analysis demonstrates that during the past three decades, there have been substantial flood incidents in the country’s eastern, south-eastern, and southern regions. Notably, floods that happened in 1996, 2005, 2006, 2013, and 2018 affected 90, 91, 74, 74, and 69 locations, respectively. In 2020, flooding affected a considerable area (274 locations), which demonstrates the rise in flood hazards. Based on multi-criteria suitability analysis, about 32 million hectares of lowlands are highly suitable (61%) and moderately suitable (39%) for flood-based farming. In the Omo-Gibe and the Mile sub-basin, flood-recession zone mapping using a change detection approach revealed that Omo-Gibe basin has 107,359 ha and 29,550 ha of flood zones suitable for flood recession farming and Mile sub-basin of 8,048 ha and 88 ha, during the major and short rainy seasons, respectively. Our results highlight the extent of flood-prone areas and their suitability for flood farming and provide evidence of alternative strategies for managing flood risks. Consequently, identifying potential flood-prone areas using remote sensing technology aids decision-makers and subject-matter experts in introducing and demonstrating various types of flood-based farming. Further research is recommended to identify and validate appropriate flood farming practices under different biophysical and socio-economic contexts and explore complementary opportunities as well as support informed decision-making on flood risk management and recession flood strategies in the dry lowlands of Ethiopia.

A novel flood risk management approach based on future climate and land use change scenarios

Climate change and increasing urbanization are two primary factors responsible for the increased risk of serious flooding around the world. The prediction and monitoring of the effects of land use/land cover (LULC) and climate change on flood risk are critical steps in the development of appropriate strategies to reduce potential damage. This study aimed to develop a new approach by combining machine learning (namely the XGBoost, CatBoost, LightGBM, and ExtraTree models) and hydraulic modeling to predict the effects of climate change and LULC change on land that is at risk of flooding. For the years 2005, 2020, 2035, and 2050, machine learning was used to model and predict flood susceptibility under different scenarios of LULC, while hydraulic modeling was used to model and predict flood depth and flood velocity, based on the RCP 8.5 climate change scenario. The two elements were used to build a flood risk assessment, integrating socioeconomic data such as LULC, population density, poverty rate, number of women, number of schools, and cultivated area. Flood risk was then computed, using the analytical hierarchy process, by combining flood hazard, exposure, and vulnerability. The results showed that the area at high and very high flood risk increased rapidly, as did the areas of high/very high exposure, and high/very high vulnerability. They also showed how flood risk had increased rapidly from 2005 to 2020 and would continue to do so in 2035 and 2050, due to the dynamics of climate change and LULC change, population growth, the number of women, and the number of schools – particularly in the flood zone. The results highlight the relationships between flood risk and environmental and socio-economic changes and suggest that flood risk management strategies should also be integrated in future analyses. The map built in this study shows past and future flood risk, providing insights into the spatial distribution of urban area in flood zones and can be used to facilitate the development of priority measures, flood mitigation being most important.

Increasing risk of synchronous floods in the Yangtze River basin from the shift in flood timing

Floods are some of the most frequent and severe natural hazards worldwide. In the context of climate change, the risk of extreme floods is expected to increase in the future. While, the trends in flood timing and risk for flood synchronization remain unclear. In this study, the seasonality of flood peaks, annual maximum rainfall, and annual maximum soil moisture in the Yangtze River Basin were examined using observational and reanalysis data from 1949 to 2020. Changes in the timing of extreme events may increase the possibility of concurrent flooding, therefore the risk for synchronous floods were further explored. The results indicate that the seasonality of floods has a strong consistency with that of annual maximum rainfall. In the southern Yangtze River Basin, floods usually occur between early June and early July, with a delayed trend. However, they occur slightly later in the north, generally from late July to early August, with a tendency of advance. Overall, the timing of floods is positively correlated with rainfall and soil moisture peaks, and the correlation is much stronger for annual maximum rainfall. However, for more intense floods or for larger catchments, soil moisture plays an important role in modulating the variations in flood timing. Reverse latitudinal changes in flood timing are expected to result in more synchronous floods. The synchrony frequency exceeded 60 % for most of the stations, and the frequency was increasing for nearly half of the region, especially in the middle reaches, Poyang Lake and south of Dongting Lake. In addition, the flood synchrony scale in the south of the basin showed significant upward trends. These findings would provide important implications for flood risk management and adaptive strategy development.

A GIS-Based Flood Risk Assessment Using the Decision-Making Trial and Evaluation Laboratory Approach at a Regional Scale

This paper introduces an integrated methodology that exploits both GIS and the Decision-making Trial and Evaluation Laboratory (DEMATEL) methods for assessing flood risk in the Kosynthos River basin in northeastern Greece. The study aims to address challenges arising from data limitations and provide decision-makers with effective flood risk management strategies. The integration of DEMATEL is crucial, providing a robust framework that considers interdependencies among factors, particularly in regions where conventional numerical modeling faces difficulties. DEMATEL is preferred over other methods due to its proficiency in handling qualitative data and its ability to account for interactions among the studied factors. The proposed method is based on two developed causality diagrams. The first diagram is crucial for assessing flood hazard in the absence of data. The second causality diagram offers a multidimensional analysis, considering interactions among the criteria. Notably, the causality diagram referring to flood vulnerability can adapt to local (or national) conditions, considering the ill-defined nature of vulnerability. Given that the proposed methodology identifies highly hazardous and vulnerable areas, the study not only provides essential insights but also supports decision-makers in formulating effective approaches to mitigate flood impacts on communities and infrastructure. Validation includes sensitivity analysis and comparison with historical flood data. Effective weights derived from sensitivity analysis enhance the precision of the Flood Hazard Index (FHI) and Flood Vulnerability Index (FVI).

An Assessment of Flood Risk in the Gambia: A Data-Driven Approach to Flood Risk Management and Prevention

Coastal African countries, including Gambia, are increasingly vulnerable to a range of natural disasters, notably flooding, which poses significant challenges to sustainable development and safety. This study set out to comprehensively assess the flood risk in Gambia using a data-driven approach, aiming to enhance flood risk management and prevention strategies. Utilizing geospatial data and Geographic Information System (GIS) techniques, the research involved mapping and categorizing flood risk zones across various regions. The analysis focused on the identification and categorization of flood risk zones, assessment of the vulnerability of key infrastructures, health facilities, road networks, and settlements to flooding. The findings reveal that a total of 2,721 square kilometers of land is at flood risk categorized as either high or very high. This constitutes 26.89 percent of the country’s of terrestrial area. A total of 25 critical health infrastructure were in areas of this high flood risk categorization – ranging from general hospitals to clinics. Up to 24.5% of the nation's road network was found to be in this flood risk zone. Of the country’s five administrative regions, the West Coast Region which is home to most of its urban centers was most at risk of flooding. Here, of the 353 settlements in this region, 172 of them fell under the high to very high-risk flood zone categorization, constituting 48.7% of the settlements at a significant level of flood vulnerability. This study enhances the understanding of flood hazards in Gambia, providing essential perspectives for decision makers, city planners, and emergency response teams. This research highlights the need for using data-driven methods in identifying and managing environmental risks. It also advocates for the adoption of long-term strategic planning, infrastructure enhancements, and community-based activities to reduce the effects of floods.

Participatory Causal Loop Diagrams Building for Supporting Decision-Makers Integrating Flood Risk Management in an Urban Regeneration Process.

Several modeling tools commonly used for supporting flood risk assessment and management are highly effective in representing physical phenomena, but provide a rather limited understanding of the multiple implications that flood risk and flood risk reduction measures have on highly complex systems such as urban areas. In fact, most of the available modeling tools do not fully account for this complexity-and related uncertainty-which heavily affects the interconnections between urban systems evolution and flood risk, ultimately resulting in an ineffective flood risk management. The present research proposes an innovative methodological framework to support decision-makers involved in an urban regeneration process at a planning/strategic level, accounting for the multi-dimensional implications of flood risk and of different flood risk management strategies. The adopted approach is based on the use of System Thinking principles and participatory System Dynamics modeling techniques, and pursues an integration between scientific and stakeholder knowledge. Reference is made to one of the case studies of the CUSSH and CAMELLIA projects, namely Thamesmead (London), a formerly inhospitable marshland currently undergoing a process of urban regeneration, and perceived as being increasingly vulnerable to flooding. It represents an interesting opportunity for building a replicable modeling approach to integrate urban development dynamics with flood risk, ultimately supporting policy and decision-makers in identifying mitigation/prevention measures and understanding how they could help achieve multi-dimensional benefits (e.g., environmental, social and economic).

Identifying Irregular Rainfall Patterns Using Persistent Homology

Efficient investigation tools are required to elucidate the changes in climatic change caused by various climate processes, variables, and socioeconomic development activities around the world. In this study, we track the changes of daily rainfall at three flood-prone sites in Terengganu between 2012 to 2017. In recent years, topological data analysis (TDA) has been applied in many fields of data analytics to rank, classify, and cluster time series datasets. In this work, we employ Persistent Homology to quantify and identify topological patterns from a rainfall data. A sliding window (SW) approach is used for each 1D rainfall dataset to embed in higher dimensions before computing its Persistence Diagrams (PD). The topological information obtained from PD, namely connected components (H0) is then retrieved and vectorized in the form of Persistence Curves (Persistence Landscape (PL), Persistence lifetime Curve (PLC), and Persistence Lifetime Entropy (PLE)) to identify unusual rainfall patterns. We employ various types of L1-norms from these Persistence vectors to identify anomalies in rainfall data which can be used as an early warning flood system. The irregular pattern of Persistence lifetime and Persistence entropy mismatch the actual flood events suggesting that the irregular points may not be as closely related to flood risk. However, PL analysis of the irregular points shows match of about 59% to the flood events.It is expected that other determining factors, for example, land use, cloud cover, and wind information, which can be obtained via satellite gridded data may increase the predictability of flood events thus promotes an effective flood risk management strategies.

Flood Risk Assessment Focusing on Exposed Social Characteristics in Central Java, Indonesia

This study analyzes Indonesia, a country marked by significant socioeconomic diversity, to inform the development of holistic flood risk management strategies. We examine the relationship between flood-exposed populations and socioeconomic factors at the regency level, particularly in Central Java, using open data encompassing flood-prone areas, topography, population distribution, and socioeconomic indicators. Key findings include population exposure to flooding varies significantly across the 33 regencies and 7 cities, ranging from 1% to 61% in exposure rate. A notable 5.8-fold difference in average income exists among regencies, with income strongly correlating with higher education rates. Similarly, poverty rates correlate with low educational attainment; there was a very large range in the balance between the size of the exposed economy and the number of exposed poor population in each administrative division. Consequently, we propose a classification system that considers social vulnerability due to poverty, low-education, and economic impacts. The map reflecting these classifications is a risk map that facilitates the understanding of the risk characteristics and the relative risk magnitude of each administrative district. Our analysis underscores the importance of adapting flood risk management strategies to local socioeconomic characteristics and suggests the importance of the use of local wisdom.

Experiences and practices from application of rockfall remedial works at MKD national road network

Rockfalls, landslides and floods continue to have a growing share in the overall damages and losses, influencing considerably the national budget. Recent experiences and practices related to application of stabilisation measures and remediation of rockfall zones along MKD national road network are presented in the article. Analyses are based on case histories where the prognosis of geotechnical conditions is a key element in definition of remedial measures for rock slopes. Some issues related with Consequence Class, as suggested in Eurocode 7 202x, and the concepts of Acceptable Level of Risk are presented. Proposals in definition of Acceptable Level of Risk, through combining Probability of Failure, Reliability Index and Consequence Classes, are given in the article. These findings can help in development and/or improvement of rockfall, landslide and flood risk management strategies.

Uses and opportunities of emergency calls as a resource for flood risk management

Floods pose significant risks to both human lives and property, necessitating effective flood risk management strategies. One promising approach for enhancing flood risk management is the utilization of emergency calls as a valuable data source. This paper proposes two primary applications of these calls: 1) enhancing the cartography of flood risk areas and 2) improving risk communication systems. The disastrous flood event that took place in the Vega Baja region (Spain) in September 2019 is taken as a case study for this research. On the one hand, by analysing and geolocating emergency calls related to this flood event, we identify additional affected zones that are not represented in the existing cartography, leading to more comprehensive and accurate mapping. On the other hand, the analysis of the 112 calls is used to create a context-based catalogue of warning messages, tailored to address the unique requirements and challenges faced by the local population during the flood event under study. Thus, emergency calls serve as a direct link between the needs of affected population during a flood event and emergency response agencies. Overall, the outputs of this research underscore the importance of integrating emergency call data into flood management frameworks to enhance preparedness, response, and resilience in flood-prone regions in the framework of climate change.

FloodIntel: Advancing flood disaster forecasting through comprehensive intelligent system approach

<p><strong>Background:</strong> Every year, floods are the most devastating natural disaster that hits Malaysia, causing damage to people’s livelihoods, destroying property and infrastructure, and taking lives. Flood disasters are becoming more frequent and severe, necessitating the development of sophisticated forecasting and early warning systems to mitigate their effects. This study presents the design and implementation of a sophisticated flood forecasting and early warning system, utilizing intelligent technologies to enable timely prediction and proactive measures in high-risk areas.<strong> Methods:</strong> The proposed system incorporates Internet of Things (IoT) technology to collect real-time data on the river’s water level. The collected data are analyzed using an association rule technique to generate accurate forecasts of prospective flood occurrences. By using this intelligent flood disaster prediction system, users and authorities can receive early warnings and make informed decisions regarding evacuation, resource allocation, and infrastructure reinforcement. The system’s capability to provide early flood forecasts in high-risk areas can substantially enhance flood preparedness and response and save more life. <strong>Results:</strong> The findings of the study highlight the potential of the system to improve flood risk management strategies and reduce flood-related devastation and human suffering in vulnerable regions.<strong> Conclusions:</strong> In conclusion, it is important to implement IoT and AI technologies to improve flood prediction systems and reduce the negative effects of flood disasters.</p>

Social network analysis of EU flood risk management plans: Case Finland

AbstractFlood risks are increasing in Europe, and the EU Floods Directive is one of the main efforts to implement and harmonize effective flood risk governance and management among member states. At the same time, the ongoing shift from mere flood protection to multi‐functional flood risk governance calls for increased collaboration among a diversity of actors, typically with varying interests and responsibilities. In this study, we analyze the actor networks unfolding from flood risk management plans to visualize the diversity of roles and connections between actors participating in flood risk management measures. We introduce a new framework for analyzing flood risk management plans and related actor networks using Social Network Analysis and test it in the context of Finland. The framework helps to visualize the diversity of networks for different flood risk management strategies. The analysis also reveals the central roles that different types of actors—including those outside the public sector—occupy in the network. The findings suggest that Social Network Analysis can be used in the context of EU flood risk governance and that flood risk management plans offer an excellent opportunity for comparative governance studies in the EU.

FLOOD MODELLING USING CSIRO DATA61’S MODELLING TOOLKIT CFAST - A CASE STUDY OF THE RIVERVIEW FAMILY CARAVAN PARK IN VICTORIA

The Riverview Family Caravan Park (RFCP) sits on the eastern bank of the Barwon River Estuary in Ocean Grove, Victoria, Australia and is managed by the Barwon Coast Committee of Management (BCCM). The Park is susceptible to inundation during extreme weather events including catchment flooding from the Barwon River and storm surges from the Bass Straight. To protect from this river inundation there is a levee bank consisting of a rock revetment (seawall) with an elevated shared trail along its crest. This inundation risk will increase with climate change induced sea level rise (SLR), therefore, the BCCM was concerned with quantifying the long-term effectiveness of the elevated shared trail adaptation and to assess proposed flood risk management strategies such as further elevating the shared trail and the implementation of a detention basin.This study used detailed hydrodynamic and hydraulic modelling utilising the City-based Flood Adaptation Solutions Tool (CFAST) developed by CSIRO Data61 (Prakash et al., 2019) to determine the inundation risk posed by different scenarios with different adaptations applied.

Estimating the Socioeconomic Impacts of Flooding on Regional Economies With a Computable General Equilibrium Model

AbstractThe growing number of flood events in urban areas is causing far‐reaching economic losses and social disruptions. To implement suitable flood risk management strategies and measures for flood prevention and mitigation, it is paramount to adequately assess economy‐wide losses associated with potential flood events. The assessment of potential flood damage in the USA relies significantly on the Federal Emergency Management Agency (FEMA) Hazus Flood Model. A primary requirement to reduce uncertainty is to empirically gather user input data. Consequently, there is a need to integrate publicly accessible business data for accurate evaluation of losses in business activities. We present a novel approach for estimating both the direct losses in business activities during a flood and the total economy‐wide impact that arise from these disruptions. First, we integrate the FEMA Hazus Flood Model with Reference Solutions business‐level data to estimate the reduction in business activities (i.e., direct losses) following a 100‐year flood event in New Hanover County, North Carolina. Second, we estimate the broader economy‐wide impact these business activity losses have on total industry output, employment, value‐added and household income using a regional computational general equilibrium model. The advantage of our approach is the easy replicability of the business loss estimation process for US cities and regions of interest, which, when used in a follow‐up economic impact study, leads to estimating the total economy‐wide losses in economic activities. Policy makers relying solely on direct physical and economic damages would, by ignoring indirect losses, underestimate the benefits of investing in flood mitigation and protection.

Flood risk assessment of the river Benue catchment in Adamawa State Nigeria

Floods are one of the most devastating natural disasters that occur globally, causing loss of life and property. Adamawa, a state located in north-eastern Nigeria, is prone to flooding due to its location within the floodplains of the River Benue. The research examines the spatial distribution of flood risk, the role of altitude in determining flood risk levels, and the implications for population exposure. The investigation was carried out in the following local government areas, namely Yola-South, Demsa, Numan, Lamurde, Guyuk, Shelleng and Song. The study used a combination of remote sensing and geographic information systems (GIS) techniques to analyze the flood risks extent and exposure within the catchment. SRTM 30 Meters resolution Digital Elevation Model (DEM) data was used to derive catchment boundaries, stream networks and watershed characteristics. 500 years return period flood hazard raster along with a population raster of the settlements within the region of interest were also used. The findings reveal varying flood risk extents, with areas at lower elevations exhibiting higher flood risks. Additionally, the study identifies specific wards within each locality that are situated in different flood risk zones, highlighting the need for localized flood risk assessments. The results demonstrate a correlation between flood risk levels and the percentage of the population exposed to these risks. The study aligns with existing literature on flood risk, altitude, and population vulnerability, emphasizing the importance of considering these factors in flood risk management strategies. The findings contribute to a better understanding of flood risk patterns and can guide policymakers and stakeholders in implementing targeted interventions and developing effective disaster response plans. However, it is essential to contextualize the findings within the specific study areas and complement them with site-specific assessments and further research.