Flood Risk Management Research Articles

Communities’ Perceptions and Coping Strategies to Flood Hazard in a Peri-Urban Catchment: The Case Study of the Zio River Basin, Togo, West Africa

Climate and land use changes have exacerbated the frequency and intensity of floods in West Africa. In Togo, the communities of the Zio river sub-basin are regularly affected by flood hazard with severe socio-economic consequences. The main objective of this article is to contribute to the improvement of the resilience of these populations through a better understanding of their perceptions and coping strategies to flood hazards. To achieve this goal, a mixed method consisting of household surveys, focus groups and direct observation is used. In addition, the data collected at the household level focused on the perceptions of the flood (origin and cause), the socio-economic impacts as well as the coping strategies. The results indicate that more than 80% of the households are affected by floods and the main negative consequences are the destruction of houses (40.8%), and interruption of socio-economic activities (58.4%). In addition, the efforts to cope with the severe impacts of floods include embankment (71.6%), storage on occasional shelves (52.3%), digging ditches (26.6% and the construction of cement houses with fairly raised foundations (13.1%). This study shows that is important to integrate indigenous knowledge and local population’s opinions in the flood risk management plans in order to increase their resilience.

Mobility behaviors shift disparity in flood exposure in U.S. population groups

The current characterization of flood exposure is largely based on residential location of populations; however, location of residence only partially captures the extent to which populations are exposed to flood hazards. An important, yet under-recognized aspect of flood exposure is associated with human mobility patterns and population visitation to places located in flood prone areas. In this study, we analyzed large-scale, high-resolution location-intelligence data collected from anonymous mobile phone users to characterize human mobility patterns and the resulting flood exposure in coastal counties of the United States. We developed the metric of mobility-based exposure based on dwell time in places located in the 100-year floodplain. The results of examining the extent of mobility-based flood exposure across demographic groups reveal significant disparities across race, income, and education level groups. The results show that Black and Asian, economically disadvantaged, and undereducated populations in US coastal cities are disproportionally exposed to flood due to their daily mobility activities, indicating a pattern contrary to that of residential flood exposure. The results suggest that mobility behaviors play an important role in extending flood exposure reach disproportionally among different socio-demographic groups. The results highlight that urban flood risk assessments should not only focus on the level of flood exposure to residences, but also should consider mobility-based exposure to better learn the disparities in flood exposure among social groups. Mobility-based flood exposure provides a new perspective regarding the extent to which floods could disrupt people's life activities and enable a better characterization of disparity in populations' exposure to flood hazards beyond their place of residence. The findings of this study have important implications for urban planners, flood managers, and city officials in terms of accounting for mobility-based flood exposure in flood risk management plans and actions.

A comparison of global flood models using Sentinel-1 and a change detection approach

Advances in numerical algorithms, improvement of computational power and progress in remote sensing have led to the development of global flood models (GFMs), which promise to be a useful tool for large-scale flood risk management. However, performance and reliability of GFMs, especially in data-scarce regions, is still uncertain, as they are difficult to validate. Here we aim at contributing to develop alternative, more flexible, and consistent methods for GFM validation by applying a change detection analysis on synthetic aperture radar (CD-SAR) imagery obtained from the Sentinel-1 imagery, on a cloud-based geospatial analysis platform. The study addresses two main objectives. First, to validate four widely adopted GFMs with flood maps generated through the proposed CD-SAR approach. This exercise was conducted for eight different large river basins on four continents, to account for a diverse range of hydro-climatic environments. Second, to compare CD-SAR-derived flood maps with those obtained from alternative remote sensing sources. These comparative results offer valuable insights into the reliability of CD-SAR data as a validation tool, more specifically how it stacks up against flood maps generated by other remote sensing techniques.

Does flood risk funding protect the most vulnerable? A case study of England

Not only has the frequency and severity of flooding increased under climate change, but which communities are at-risk and how those risks are distributed within communities has changed too. This realisation points to an important interplay between exposure (risk) and social vulnerability. Who is vulnerable? Answering this question is far from straightforward as different vulnerability indicators identify different groups across different scales. This paper addresses this issue. Using the empirical case of England, the spatial distribution of flood risk at different administrative scales is analysed in relation to the Environment Agency (EA) allocation of flood funding between 2015 and 2021. We find that Upper-tier Local Authority Districts with higher levels of both flood risk and social vulnerability received more funding in the period considered. Yet, that is not the scale at which flood scheme funding decisions are made. Flood schemes are decided at the local level, but funding data for smaller administrative scales is not available. Different understandings of vulnerability, the Modifiable Areal Unit Problem (MAUP), and issues around data transparency and availability in flood risk management, need to be urgently addressed.

Two Decades of Integrated Flood Management: Status, Barriers, and Strategies

Losses from flood disasters are increasing globally due to climate-driven forces and human factors such as migration and land use changes. The risks of such floods involve multiple factors and stakeholders, and frameworks for integrated approaches have attracted a global community of experts. The paper reviews the knowledge base for integrated flood risk management frameworks, including more than twenty bibliometric reviews of their elements. The knowledge base illustrates how integrated strategies for the reduction of flood risk are required at different scales and involve responses ranging from climate and weather studies to the construction of infrastructure, as well as collective action for community resilience. The Integrated Flood Management framework of the Associated Programme on Flood Management of the World Meteorological Organization was developed more than twenty years ago and is explained in some detail, including how it fits within the Integrated Water Resources Management concept that is managed by the Global Water Partnership. The paper reviews the alignment of the two approaches and how they can be used in tandem to reduce flood losses. Success of both integrated management approaches depends on governance and institutional capacity as well as technological advances. The knowledge base for flood risk management indicates how technologies are advancing, while more attention must be paid to social and environmental concerns, as well as government measures to increase participation, awareness, and preparedness. Ultimately, integrated flood management will involve solutions tailored for individual situations, and implementation may be slow, such that perseverance and political commitment will be needed.

A review of nature‐based infrastructures and their effectiveness for urban flood risk mitigation

AbstractAnthropogenic climate change and rapid urbanization are contributing to more frequent and intense urban flooding. There is widespread agreement that traditional gray infrastructure, a single‐purpose solution, fails to address the problem properly and contributes to adverse direct and indirect environmental impacts. As such, Nature‐based Solutions (NbS) can provide improved outcomes to flood risk management along with co‐benefits to society and the economy, as they have numerous benefits incuding often a smaller carbon footprint or even sometimes sequestering carbon. However, there is ambiguity and misconception about NbS and the uptake of NbS for flood management, which is still inadequate compared to traditional gray infrastructure. This research seeks to explore various nature‐based infrastructures including their present status of application for flood risk management to build resilience to urban flooding through a systematic literature review. The robustness of some NbS is questionable and varies across different spatial scales from plot to watershed. NbS can work stand‐alone in many cases as well as supplement traditional gray infrastructure to achieve wider benefits. The review provides a comparison of nature‐based solutions with gray infrastructure, identifies flood mitigation infrastructures that include nature‐based elements, and provides an overview of their effectiveness across different scales. The research findings should contribute to a better understanding of appropriate and diverse options of NbS, gray, and hybrid designs by policymakers and decision‐makers to enable them in effectively designing and implementing urban flood risk mitigation measures.This article is categorized under: Climate and Development > Urbanization, Development, and Climate Change Vulnerability and Adaptation to Climate Change > Learning from Cases and Analogies

Hydrogeomorphological approach for flood analyses at high- detailed scale: Narrow rivers with broad complex alluvial plains

Fluvial flood risk management requires precise knowledge of flooding processes; thus, there is a need to carry out a detailed hydrological, hydraulic, and geomorphologic analysis to understand floods and the factors that influence them. Although the integration of geomorphological methods and hydrological-hydraulic modelling is known to be crucial for flood risk analysis, it remains a challenge to apply these approaches to the understanding of floods in small and medium-sized catchments, focusing on complex fluvial systems that have been highly modified. In this work, a hydrogeomorphological methodology was developed to analyse fluvial floods in narrow rivers with broad complex alluvial plains. The procedure comprises different phases that combine hydrologic-hydraulic modelling and a geomorphological break-down. Critically, this allows for a detailed analysis of existing relationships among erosional and depositional fluvial landforms and hydraulic parameters, comprising geometric sections, discharges, depths, flood extents and, therefore, flood hazards. The approach involves the analysis of specific flood events beforehand and the study of fluvial floods associated with different probabilities of occurrence afterwards. The information employed in the present study incorporates high- detailed inputs comprising hourly time series of hydrometeorological data, LiDAR data to extract high-precision terrain information, fluvial geomorphic maps and flood event extents based on Sentinel-2 imagery. We demonstrate this methodology in the Carrión River, located in the province of Palencia, Spain, whose characteristics include a wide complex alluvial plain, reaches highly modified by anthropic interventions and recurrent flood episodes. This hydrogeomorphological approach allowed the integration of geomorphological knowledge and hydrologic-hydraulic modelling in flood analysis. As a result, it led to a deeper comprehension of the processes of inundation, which would be challenging to thoroughly understand without incorporating a holistic method, an essential factor for defining effective flood management strategies.

Can geomorphic flood descriptors coupled with machine learning models enhance in quantifying flood risks over data-scarce catchments? Development of a hybrid framework for Ganga basin (India).

Quantifying flood risks through a cascade of hydraulic-cum-hydrodynamic modelling is data-intensive and computationally demanding- a major constraint for economically struggling and data-scarce low and middle-income nations. Under such circumstances, geomorphic flood descriptors (GFDs), that encompass the hidden characteristics of flood propensity may assist in developing a nuanced understanding of flood risk management. In line with this, the present study proposes a novel framework for estimating flood hazard and population exposure by leveraging GFDs and Machine Learning (ML) models over severely flood-prone Ganga basin. The study incorporates SHapley Additive exPlanations (SHAP) values in flood hazard modeling to justify the degree of influence of each GFD on the simulated floodplain maps. A set of 15 relevant GFDs derived from high-resolution CartoDEM are forced to five state-of-the-art ML models; AdaBoost, Random Forest, GBDT, XGBoost, and CatBoost, for predicting flood extents and depths. To enumerate the performance of ML models, a set of twelve statistical metrics are considered. Our result indicates a superior performance of XGBoost (κ = 0.72 and KGE = 82%) over other ML models in flood extent and flood depth prediction, resulting in about 47% of the population exposure to high-flood risks. The SHAP summary plots reveal a pre-dominance of Height Above Nearest Drainage during flood depth prediction. The study contributes significantly in comprehending our understanding of catchment characteristics and its influence in the process of sustainable disaster risk reduction. The results obtained from the study provide valuable recommendations for efficient flood management and mitigation strategies, especially over global data-scarce flood-prone basins.

Participatory Framework for Urban Pluvial Flood Modeling in the Digital Twin Era

The recent advancement in digital twin technology, which creates virtual replicas of real-world processes, offers an interactive testbed for understanding and predicting environmental changes. As pluvial flood damage escalates globally in urban areas, there remains a gap in understanding the most effective collaboration between governments and local residents for sustainable flood risk management. To address this gap, we develop a participatory framework for urban pluvial flood modeling, incorporating open source software, virtual reality, minimum viable product, and gamification components. This framework engages citizens in every phase of the participatory modeling process, from input data preparation, through hydrological model construction, to model verification, and experiments. We present a case study on the recurring pluvial flood damages in South Korea's Gangnam region, demonstrating the practical implications of an interactive, web-based crowdsourcing platform to leverage community engagement and local knowledge. The results underscore the evidence of a proactive role for citizens, not merely as recipients of disaster information but as key contributors collaborating with a range of stakeholders in stormwater management and modeling. Combining digital twin technology with citizen participation can empower informed decision-making and collective actions in the evolving digital era, leading to a disaster-resilient community.

Seasonal Variation Of Wave Attenuation Capacity Of Canadian Saltmarsh Vegetation

Nature-based Solutions (NbS) for coastal protection have been widely recognized as sustainable, economical, and eco-friendly alternatives to conventional grey structures, particularly under the threat of climate change (Temmerman et al., 2013). Living shorelines are a form of NbS, which incorporate natural elements (such as saltmarshes) that provide flood and erosion risk management benefits. Climate change impacts, such as rising sea levels and reducing sea-ice cover (Savard et al., 2016), are increasingly motivating communities in Canada to consider incorporating living shorelines in coastal protection schemes. The efficacy of wave energy dissipation by vegetation depends on both hydrodynamic conditions and plant characteristics. However, plant parameters, such as standing biomass exhibit seasonal fluctuations, leading to corresponding variations in attenuation capacity (Schulze et al., 2019). Hence, the design of NbS utilizing saltmarsh vegetation must account for seasonal variations to ensure sustained efficacy, especially within the context of Canadian regional climates, which are typically characterized by extended, stormy winters and shorter summer seasons. Few studies have quantified wave attenuation by real saltmarsh vegetation in large-scale laboratory facilities (Möller et al., 2014; Maza et al., 2015; Ghodoosipour et al., 2022), particularly for species native to the east coast of Canada. There is a knowledge gap on how seasonality affects wave attenuation by saltmarsh vegetation and how attenuation varies from the lower marsh to the higher marsh depending on species-specific plant traits. Research is needed to bridge this gap and develop technical guidance for the design of performant living shorelines in Canada.

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, lack 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.

A two-stage robust decision-making framework (2S-RDM) for flood risk adaptation under deep uncertainty

Flood is one of the major challenges facing human societies. Adapting to future flood risks involves deep uncertainty, especially when long-term projections of climate change are considered. This study proposed a Two-stage Robust Decision Making (2S-RDM) framework to help devise flexible and robust strategies capable of addressing the inherent deep uncertainty associated with managing flood risks. Taking the Yangtze River Basin in China as a case study, we simulated flood risks across ∼0.6 million scenarios until 2050. This analysis considered four types of uncertain factors, i.e., future climate change, socio-economic growth, industrial structure transformation, and population aging. We then examined the effectiveness of four adaptation measures and their combinations, i.e. building elevation, tunnel construction, people relocation, and river basin conservation. Our projections show that without immediate adaptation, an estimated 0.9 to 27.3 million people will be impacted by floods until 2050, accompanied with $33.8 to $198.5 billion economic losses in the entire basin. When defining the goal as limiting the affected population <0.05% and ensuring economic losses <0.02%, we identified 24 global robust strategies capable of meeting this criterion in >80% of scenarios. Then, we compared the 24 global robust strategies regarding their relative costs and performances in each of the future scenario pools. The final recommended solutions are hybrid strategies that integrate engineering-based measures with ‘soft’ adaptation options (e.g. Elevation++, Tunnel++, and Relocation). This study provides tools to design flood adaptation strategies not only robust across diverse scenarios but also flexible for decision-makers to customize and refine their strategies based on specific needs.

Flood resilience: a review of evolving definitions

Flooding is one of the most complicated and prolific natural hazards that communities face. Added to this, more people will be affected by this hazard than any other in the future. Within recent years, there has been a notable shift in flood risk management from risk-based approaches to resilience-based. Considered a novel and necessary approach, yet no single definition of flood resilience exists. Leading to confusion surrounding the applicability of the concept. A systematic review of flood resilience definitions was hence conducted, covering a 5-year period from 2017 to 2021, resulting in 65 papers, supplemented by a narrative review (to include papers outside of the scope of the study), which added a further 11 papers. Results indicated that whilst there is no singular definition for flood resilience, there are similarities between definitions through the use of synonymous language. Whilst there is evidence of these definitions evolving over time, there is still confusion over the definition. Further research is required to further comprehend the definitions of resilience, helping to develop the use of resilience within flood sciences and corresponding flood risk management practices.

Flood Water Depth Prediction with Convolutional Temporal Attention Networks

Robust and accurate flood hazard maps are essential for early warning systems and flood risk management. Although physically based models are effective in estimating pluvial flooding, the computational burden makes them difficult to use for real-time flood prediction. In contrast, data-driven models can provide faster flood predictions if trained offline. While most studies have focused on predicting maximum water depth, in this study, we predict pixel-wise water depth maps for entire catchments at a lead time of 2 h. To that end, we propose a deep learning approach that uses a sequence encoding network with temporal self-attention. We also adapt the popular hydrological performance metric Nash–Sutcliffe efficiency (NSE) as our loss function. We test the effectiveness and generalizability of our method using a new dataset called SwissFlood, which consists of 100 catchments and 1500 rainfall events extracted from real observations in Switzerland. Our method produces 2 m spatial resolution flood maps with absolute error as low as 27 cm for water depth exceeding 1 m.

APPLICATION OF REMOTE SENSING DATA AND GEOGRAPHIC INFORMATION SYSTEM FOR FLOOD MODELING IN WAI RUHU WATERSHED AMBON CITY BASED ON GEOGLE EARTH ENGINE

Flood hazard modeling in watersheds is an important step in natural disaster risk mitigation, especially in vulnerable areas such as Ambon City. This research focused on the Wai Batu Gantung, Wai Batu Gajah, Wai Tomu, Wai Batu Merah, and Wai Ruhu watersheds, using JRC Global Surface Water Mapping Layers data, NASA SRTM Digital Elevation 30 m data, and USGS Landsat 8 Level 2, Collection 2, Tier 1 data analyzed on the Google Earth Engine (GEE) platform. Prediction of built-up land in flood-prone areas was conducted by utilizing flood history analysis, hydrological modeling, and flood zone mapping. The results show that flood hazard modeling provides a better understanding of flood risk, assists in the development of safer land use planning, and increases public awareness of flood risk in Ambon City. It is hoped that the results of this research can contribute to flood risk management and sustainable regional development in the future.

MODELING FLOOD HAZARDS IN AMBON CITY WATERSHEDS: CASE STUDIES OF WAI BATU GANTUNG, WAI BATU GAJAH, WAI TOMU, WAI BATU MERAH AND WAI RUHU

Flood hazard modeling in watersheds is an important step in natural disaster risk mitigation, especially in vulnerable areas such as Ambon City. This research focused on the Wai Batu Gantung, Wai Batu Gajah, Wai Tomu, Wai Batu Merah, and Wai Ruhu watersheds, using JRC Global Surface Water Mapping Layers data, NASA SRTM Digital Elevation 30 m data, and USGS Landsat 8 Level 2, Collection 2, Tier 1 data analyzed on the Google Earth Engine (GEE) platform. Prediction of built-up land in flood-prone areas was conducted by utilizing flood history analysis, hydrological modeling, and flood zone mapping. The results show that flood hazard modeling provides a better understanding of flood risk, assists in the development of safer land use planning, and increases public awareness of flood risk in Ambon City. It is hoped that the results of this research can contribute to flood risk management and sustainable regional development in the future.

Developing more useful equity measurements for flood-risk management

Developing more useful equity measurements for flood-risk management

Flood Risk Perception and Government’s Role in Implementing Regulations

This study investigates the relationship between public perceptions of flood risk, participation in government action, satisfaction with government efforts, knowledge of flood prevention, and institutional trust in Freetown, Sierra Leone. Employing a quantitative approach, the research utilizes structural equation modeling to analyze the relationships among these variables across six disaster-prone communities in Freetown from 2017 to 2022. Data were collected from 1,500 respondents using STATSL2020 questionnaire, covering flood risk perception, public participation, satisfaction with government actions, flood knowledge, and institutional trust. The analysis reveals that higher public participation and institutional trust correlate with lower perceived flood risks, while a greater perception of flood risk negatively affects views on government efforts. Furthermore, the findings indicate that knowledge and trust influence the impact of public engagement on satisfaction with government actions, with demographic variables such as gender, age, income, and education playing a role in these relationships. This study empirically demonstrates the relationship between institutional trust and public participation in flood risk perception, highlighting the importance of clear communication and participatory strategies in building trust and satisfaction in government flood risk management. The research underscores the significance of involving communities in disaster prevention efforts and the need for transparent communication to enhance public trust and satisfaction with government regulations for flood disaster prevention.

A dam or a polder? Stakeholders' dispute over the "right" flood-protection measure in the Czech Republic.

This study focuses on the Skalička Waterwork(SWW), a largely debated and media-covered water-related/flood-protection project in the Czech Republic. Relying primarily on stakeholder interviews, we traced back and reconstructed the project's development, including its key tipping points reflecting the changing societal preferences for particular measures, yet also the involvement of individual actors/stakeholders, and their differing views. The case eventually crystallized into the "dam versus polder" dispute; concerned by the repercussions for the local landscape, a joint initiative of NGOs, local activists, and politicians not only opposed the dam variant proposed by the state river basin administration but also succeeded in pushing through the alternative scheme of side dry polder. While in many ways specific (e.g. not entailing local resistance), the case exemplifies recent shifts (and respective struggles) within flood risk management, including the increasing importance attributed to complex, catchment-wide perspectives, joint local and scientific knowledge, participatory decision-making processes, or implementation of nature-based and hybrid solutions.

ENSEMBLE MODELS FOR FORECASTING FLOODS IN GREAT BRITAIN BASED ON SOLAR ACTIVITY

This study aims to uncover hidden dependencies between streams of solar particles emanating from the Sun and flood events caused by precipitation in the UK. The uniqueness of this work lies in applying solar activity data as input parameters for machine learning and correlating them with real data on precipitation intensity and floods in the UK, which serve as output data. The analysis covered 20 significant floods from October 2001 to December 2019. The analysis was performed on a daily basis, taking into account the time shift between solar emissions and their impact on terrestrial weather conditions, which ranged from 0 to 9 days. The study employs correlation analysis to determine the degree of interrelation between time series of solar activity and floods, laying the foundation for a deeper understanding of possible cause-and-effect relationships. Subsequently, using predictive modeling methods, including decision tree algorithms and ensemble classification models, the potential connection between changes in solar activity and terrestrial floods was explored. An ensemble of models was created using the hard voting method, which takes into account various indicators such as proton density, differential proton flux, and ion temperature, with a time shift of up to 9 days. The analysis showed that these key parameters could effectively predict the occurrence of floods with up to 92% accuracy for the specified period. The results underscore the importance of integrating space weather and solar activity into the models for predicting terrestrial weather conditions, particularly in the context of floods in England. This not only allows for a better understanding of the causes of natural disasters but also enhances the effectiveness of emergency planning and reduces potential damages from them. Thus, this work makes a significant contribution to the field of space weather research and its impact on Earth, particularly in the areas of hydrometeorology and flood risk management.