Flood Risk Management Research Articles

Cataloging and Testing Flood Risk Management Measures to Increase the Resilience of Critical Infrastructure Networks

Cataloging and Testing Flood Risk Management Measures to Increase the Resilience of Critical Infrastructure Networks

Understanding bi-modal seas, shingle beach response and flood risk at Hayling island, UK

Beach management through nourishment and annual recycling has been applied for over 37 years to manage flood and coastal erosion risk to 1700 properties at Eastoke, Hayling Island, UK. This form of natural flood risk management has proved successful in avoiding the annual flooding that blighted this area previously. However there have been unexpected storm events which have caused beach erosion and localised flooding. Using long-term nearshore wave datasets and state-of-the-art statistical methods, new multivariate extreme wave conditions were derived. The conditions were applied to simulate beach profile response, estimated bimodal overtopping rates and resulting flood inundation, then validated against recent storm events. The Eastoke beach was found to meet its original design criteria of a 0.5% AEP standard of protection for unimodal wave conditions. However, it experiences greater roll back erosion, wave overwash and therefore flood inundation under certain bimodal wave conditions, causing uncertainty around the future standard of protection.It remains unclear if the unexpected beach response at Eastoke resulted from increased storminess due to climate change, additional loading by mixed sea/swell events, increasing bimodal events, or the assumptions within the original bivariate analysis, leading to incorrect AEP attribution.Communicating these issues to practitioners and a non-specialist audience is challenging as we tend to oversimplify, despite every beach and storm being unique. Better understanding of mixed beaches both in situ and through parametric and numerical models would reduce uncertainty, to ensure communities are resilient to climate change.

Geospatial Mapping and Meteorological Flood Risk Assessment: A Global Research Trend Analysis.

Flooding is a global threat causing significant economic and environmental damage, necessitating a policy response and collaborative strategy. This study assessed global research trends and advances in geospatial and meteorological flood risk assessment (G_MFRA), considering the ongoing debate on flood risk management and adaptation strategies. A total of 1872 original articles were downloaded in BibTex format using the Web of Science (WOS) and Scopus databases to retrieve G_MFRA studies published from 1985 to 2023. The annual growth rate of 15.48% implies that the field of G_MFRA has been increasing over time during the study period. The analysis of global trends in flood risk research and practice highlights the key themes, methodologies, and emerging directions. There exists a notable gap in data and methodologies for flood risk assessment studies between developed and developing countries, particularly in Africa and South America, highlighting the urgency of coordinated research efforts and cohesive policy actions. The challenges identified in the body of extant literature include technical expertise, complex communication networks, and resource constraints associated with the application gaps of the study methodologies. This study advocates for a holistic research approach to flood disaster management through ecosystem-based adaptation that underpins the Sustainable Development Goals to develop innovative flood techniques and models with the potential to influence global decision-making in the G_MFRA domain. Addressing these global challenges requires a networked partnership between the research community, institutions, and countries.

Influence of Rainfall Patterns on Rainfall–Runoff Processes: Indices for the Quantification of Temporal Distribution of Rainfall

To understand the influence of rainfall patterns on rainfall–runoff processes, we propose two indices: skewnessPEAK (skewp), representing the relative timing of peak rainfall, and the normalized root-mean-square error peak (NRMSEp), which quantifies the concentration of rainfall near the peak. By analyzing approximately 25,000 rainfall scenarios, we examined the relationship between these indices and peak flood discharge in the rainfall–runoff process. The analysis revealed that peak flood discharge positively correlates with the NRMSEp, indicating that concentrated rainfall near the peak substantially increases discharge. Conversely, a negative correlation with skewp suggests that earlier peak rainfall reduces discharge. These insights were synthesized into a three-dimensional solution space providing a comprehensive framework for predicting how variations in rainfall distribution affect flood discharge. The findings underscore the importance of incorporating these indices into real-time flood forecasting models and urban flood risk management strategies.

Integrated urban and riverine flood risk management in the Fujiang River Basin-Mianyang city

China’s monsoon climate, characterized by hot summers and substantial rainfall, leads to significant seasonal water accumulation in river basins. This often results in short-term heavy rainfall events, causing rapid water flow into river basins and subsequent flooding. River embankments, which narrow channels, exacerbate downstream flooding and pose severe safety threats. This study explores integrated urban and riverine flood risk management strategies within the Fujiang River Basin, focusing on Mianyang City. The research addresses the growing challenges of global climate change and rapid urbanization, which have intensified flood risks. Utilizing the one-dimensional Storm Water Management Model (SWMM) and two-dimensional hydrodynamic Rainfall-Runoff-Inundation (RRI) model, the study simulates flood events and urban flooding in Mianyang City and the Fujiang River Basin. These models were validated with data from hydrological stations and satellite imagery. The study evaluates extreme flood events in Mianyang City between 2015 and 2021. The findings highlight the significant role of sponge city initiatives and low-impact development (LID) facilities in mitigating flood impacts. Despite their benefits, these measures have limitations under extreme flood conditions. This study contributes to understanding the interactions between urban and riverine systems and provides a basis for improving flood risk management and resilience in rapidly urbanizing river basins. It supports ongoing efforts to implement sponge city concepts in China, offering crucial insights into effective strategies for managing flood risks in similar geographic and climatic contexts.

Flood Risks Analysis in Upton Upon Severn: Assessing the Baseline Conditions and Future Scenarios for 100 and 1000-year Flood Events with a 2050 Climate Change Perspective.

This paper investigates the flood risks in Upton upon Severn, a town in Worcestershire, England, which is prone to fluvial flooding from the River Severn. Historical events, such as the significant floods of 1947 and 2007, highlight the town’s vulnerability and underscore the importance of analysing current and future flood risks. This research uses a one-dimensional hydraulic model, using Flood Modeller and QGIS software, to assess baseline flood conditions and predict the potential impacts of severe and extreme flood events, including 1 in 100-year and 1 in 1000-year flood scenarios. Additionally, it examines the projected effects of climate change by incorporating a 20% increase in flood risk estimates for the year 2050. The model results reveal that the baseline scenario poses limited flood risks, with only 17 properties affected, and key infrastructure like the A4104 road remains passable. However, under the 1 in 100-year event, approximately 336 properties are at risk, with significant road blockages and a flooded area of 5.09 Km2. The 1 in 1000-year flood scenario predicts a peak flow of 1807 m3/s, threatening more properties and covering a larger area of land. When climate change is factored into the 1 in 100-year scenario, the risks become similar to those of the 1 in 1000-year event, with 500 properties and key infrastructure, including the fire station and primary school, at risk and the A4104 road becoming impassable. These findings provide vital insights for flood risk management, especially given the possible effects of climate change, and stress the need for long-term solutions to flooding.

Flood risk assessment for Mulde River catchment transferring data from an observed meteorological flood event

Extreme floods can cause significant damage to infrastructure, property, and human life and have long-lasting social, economic, and environmental impacts. Flood risk assessment is critical in areas where the frequency of flood events is altering due to climate change, anthropogenic warming, land use changes. Since the primary goal of flood risk management is to reduce the risk to a tolerable level, the risk assessment based on hydrological, hydrodynamic modeling, and damage analysis is essential to identify areas that are at risk of flooding, assessing the vulnerability of assets in the area, and evaluating the potential impacts on the human life, economy, and environment. The purpose of this study is developing a transferred approach between similar catchments to observe how the catchment response in terms of discharge and damage based on a particular extreme flood event in the past. To conduct the research, the meteorological variables from the Ahr River valley extreme flood event are acquired, and an approach is developed to transfer the same dataset into the Mulde River catchment to observe consequences of the event.

‘Don’t call us vulnerable!’: Rethinking flood vulnerability and community-led governance

ABSTRACT Traditional views on flood vulnerability often present a simplistic government-community relationship, portraying communities as passive recipients of aid. This perspective overlooks the dynamic agency of urban residents in shaping their own governance structures. Recent research challenges this binary thinking by revealing how communities actively manage flood risks. However, the intricate interplay between government absence and community-led actions, and their role in everyday governance, remains underexplored. Drawing on scholarship in flood vulnerability and community-led governance, this paper examines the experiences and management strategies of flood-prone residents in Dansoman, Accra, using interviews, focus groups, and “follow-along participant observation.” Three key themes emerged: “anticipate and navigate,” “collaborate and compensate for,” and “rupture and replace.” These themes illustrate how residents acquiesce to, reconfigure, or challenge government roles in flood management, establishing self-governance through community-led initiatives. These efforts often legitimize and strengthen community resilience, sometimes even usurping and replacing state authority. The findings underscore the need to recognize community agency in managing flood risks and compensating for insufficient government support through solidarity and self-help. Policymakers can enhance flood management by incorporating these community-driven insights, fostering more effective, inclusive, and sustainable strategies that reflect the fluid, evolving relationships between government and communities.

Passage efficiency through fishways of species of the family Cyprinidae and their management implications for fragmented rivers

The contemporary management of fragmented river systems is in a trade-off between the societal benefits of instream barriers (e.g. hydropower, flood risk management) and the ecological harms of their adverse impacts on fish populations. The consequent fragmentation can be mitigated through fishway construction, with mitigation performance measured using species-specific passage rates and efficiencies. There is, however, a bias in passage efficiency studies towards diadromous fishes and, although fish of the Cyprinidae family play a significant role in the fish assemblages of rivers worldwide, their passage efficiencies are poorly understood. Here, systematic review and meta-analyses assessed the passage efficiencies of cyprinid fishes through fishways that have been measured using telemetry methods. Passive integrated transponder (PIT) telemetry was the most common evaluation method of passage efficiency due to their high read rates and relatively low costs versus alternative telemetry methods. These methods revealed cyprinid passage efficiencies were highest through vertical slot fishways and lowest through nature-like constructions, with overall passage rates comparing favourably to anadromous salmonid fishes. Fish were most active during spring and summer, with passage and associated movements often related to spawning. Passage rates of non-native fishes were also higher than for native fishes. Despite the growing acknowledgment of how fishways influence potamodromous fish dispersal and distribution in rivers, passage data remain scarce, preventing managers and policy-makers from making informed decisions on optimal passage solutions for multiple fish species in highly fragmented rivers.

Preface: Advances in pluvial and fluvial flood forecasting and assessment and flood risk management

Preface: Advances in pluvial and fluvial flood forecasting and assessment and flood risk management

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.

Impacts of LULC changes on runoff from rivers through a coupled SWAT and BiLSTM model: A case study in Zhanghe River Basin, China

Changes in river runoff have a significant impact on the sustainable use of water resources in a watershed, and these changes are closely linked to variations in land use/land cover (LULC). This research explores an innovative approach in the Zhang River Basin (ZRB), China, by coupling a concept-based hydrological model, the Soil and Water Assessment Tool (SWAT), with a deep-learning model, the Bidirectional Long Short-Term Memory Network (Bi-LSTM), to improve the accuracy of river runoff simulations. By analyzing LULC changes in 2002, 2012, and 2022, this study developed three SWAT models and three coupled SWAT-BiLSTM models to quantitatively assess the impacts of these changes on river runoff through eight LULC scenarios. The findings revealed significant LULC changes from 2002 to 2022, with cropland and grassland areas decreasing while forest and urban land areas increased. The total area of grassland, forest, and cropland made up over 93 % of the basin, indicating active land type conversions. Calibration and validation results demonstrated that the SWAT-BiLSTM model outperformed the conventional SWAT model, yielding higher accuracy in runoff simulations. Specifically, the SWAT-BiLSTM model achieved R2 values of 0.89 and 0.90 during calibration and validation, compared to the SWAT model's R2 values of 0.76 and 0.79. Scenario analyses indicated that expansions in farmland, grassland, and urban areas were correlated with increased river runoff, while an expansion in forested areas led to reduced runoff. Notably, urban land changes had the most pronounced impact on runoff, emphasizing the need for careful runoff management and flood risk mitigation in urban planning. By combining SWAT and Bi-LSTM models, this study provides an innovative assessment of the impact of LULC changes on water resources in the ZRB. The results offer valuable insights for water resource management, LULC optimization, and flood risk management, highlighting the potential application of deep learning techniques in hydrological simulation. This research serves as a scientific basis for policy-making and sustainable land use planning in the ZRB and similar regions.

Coastal urban flood risk management: Challenges and opportunities − A systematic review

Generational mechanisms and spatio-temporal evolution patterns of Coastal urban flood risk involve complex interactions between climate change, sea level rise and human-induced factors, necessitating integrated adaptive flood management strategies to mitigate evolving vulnerabilities. This systematic review offers a thorough assessment of the challenges and strategic opportunities for sustainable adaptation in managing flood risk in Coastal urban areas. It integrates emerging innovative technologies and financial solutions to identify promising approaches to implement mitigation strategies and improve Coastal urban flood resilience. Enhancing governance and policy frameworks is crucial for the successful implementation of Coastal Urban Flood Risk Management (CUFRM) plans. An innovative participatory planning framework is developed to promote flood management practices which are socially inclusive and equitable. Funding for green infrastructure and nature-based solutions and the strategic use of public–private partnerships are effective methods for advancing sustainable flood risk management (FRM). The advancements in emerging technologies, such as artificial intelligence (AI), machine learning (ML), deep learning (DL), social media and digital twin technologies, provide dynamic and collaborative platforms for simulating flood scenarios and have potential to significantly improve CUFRM practices. In the end, a cross-country comparison of current practices in Australia, China, Netherlands, the UK and the USA reveals a diverse range of approaches and valuable insights derived from regional experiences. The review provides a comprehensive analysis for researchers, policymakers and practitioners aiming to improve flood resilience in coastal metropolitan regions by learning from effective UFRM approaches that enhance governance structures, infrastructure resilience and funding mechanisms.

Enhancing transparency in data-driven urban pluvial flood prediction using an explainable CNN model

Mitigating severe losses caused by pluvial floods in urban areas with dense population and property requires effective flood prediction for emergency measures. Physics-based models face issues with low computational efficiency for real‐time flood prediction. An alternative approach for rapid prediction instead of physics-based models is to predict from a data-driven perspective. However, data-driven approaches for urban flood prediction are often perceived as “black box” and might raise concerns. In this study, we propose an explainable deep learning (DL) approach for rapid urban pluvial flood prediction with enhanced transparency using a convolutional neural network (CNN) and the explainable artificial intelligence (AI) framework Shapley additive explanation (SHAP). We process a systematic stepwise feature selection process and establish a CNN model considering topography, drainage networks and rainfall to predict maximum inundation depths. Then, SHAP is applied to provide trustworthy explanations for the decision making in model results. The results show that: 1) Forward selection can offer insights into selecting effective input variables for improved predictions and promote understanding of DL modelling. The spatial pattern of inundation depths predicted by the proposed CNN model shows good agreement with those predicted by the physics-based model, demonstrated by average correlation coefficient (CC) and mean absolute error (MAE) values of 0.982 and 0.021 m, respectively. 2) The CNN model substantially outperforms the physics-based model in computational speed when using the same hardware, achieving speedups of 210 times on GPU and 51 times on CPU in the case study (575167 grid cells, 14.38 km2). Particularly, it can still achieve good performance on a CPU-only standard laptop without high-performance hardware, with only a modest increase in computational time. 3) The SHAP explainable analysis confirms that the CNN model correctly captures the relationships between input variables and water depth, revealing a reasonable decision-making process, enhancing its transparency. The explainable DL approach incorporating SHAP for rapid urban pluvial flood prediction is promising to build trust among stakeholders and provide a trustworthy reference for prompt measures aiming at saving lives and protecting assets during flood emergencies. Additionally, the proposed DL approach can potentially be further expanded to analyze the causes of urban flooding events and serve as a foundation for exploring the transferability of data-driven urban flood prediction, providing benefits for better urban flood risk management.

Flood management in the built environment: the micro-scale contribution to a distributed strategy

Abstract The built environment offers an opportunity to implement Flood Risk Management measures by spatially distributing them as a complementary alternative to large-scale protection schemes. Specifically, micro-scale solutions focus on buildings, open spaces, users, and small infrastructure, addressing various flood management strategies with different performance indicators. These measures encompass, for instance, private precautionary measures like floodproofing or elevation, and nature-based solutions. However, their adoption faces technology, economics, and social challenges. To effectively reduce flood risk and increase the built environment’s resilience to floods, a systemic approach is necessary, evaluating and jointly implementing diverse measures across the built environment. Drawing from a systematic literature review and expert interviews, this study explores micro-scale Flood Risk Management measures within the built environment, examining their barriers and drivers. Additionally, it introduces an atlas of multi-strategy measures, qualitatively comparing their performance. The paper concludes by emphasizing the need for a holistic perspective on flood adaptation and highlights opportunities to enhance building-level adaptation actions through collaboration with various stakeholders.

A novel framework for evidence-based assessment of flood resilience integrating multi-source evidence: A case study of the Yangtze River Economic Belt, China

Floods are extremely destructive to the society, economy and nature, and severely restrict sustainable urban development. There is an urgent need to assess the level of flood resilience, analyze its evolutionary characteristics and improve the flood resilience of the region. However, few previous studies have integrated multi-source evidence into a framework for assessing flood resilience over a longer time span. This study integrated multi-source evidence to analyze the Yangtze River Economic Belt, including literature-based evidence, policy texts and social media data. Subsequently, the Kruskal algorithm and Jaccard coefficient were used to construct collinear networks and identify the clustering dimensions of flood resilience. After evidence judgment, a flood resilience indicator system was constructed. The flood resilience index was calculated using the improved Critic method. The dynamic evolution characteristics and key influencing factors were illustrated by the Kernel density and Grey correlation methods, respectively. The principal contribution is to integrate multi-source evidence to assess flood resilience, with a newly constructed indicator system that systematically considers local public demand, policy priorities and common indicators from previous literature. The results show that: the flood resilience index of the Yangtze River Economic Belt is in the range of [0.16, 0.75] from 2000 to 2022, with higher values in the downstream compared to those in the midstream and upstream. The social dimension dominating the overall flood resilience. The kernel density curve of the YREB is characterized by a right trailing. The inter-regional differences tend to widen and have a polarization effect. The special funds for water conservancy finances and the length of flood prevention levees are key factors influencing the level of flood resilience. Finally, strategies for enhancing flood resilience were proposed. This study provides a feasible framework for constructing an indicator system and assessing flood resilience by integrating multi-source data. It is important for flood risk management and helps to the development of urban flood prevention and resilience strategies.

Mapping Research Trends in Global Climate Change Adaptation to Flood Disasters: A Bibliometric Analysis using VOSviewer

Using a bibliometric analysis, this study aims to map the research trends and thematic developments in climate change adaptation to flood disasters. Data from 812 publications, spanning from 1996 to 2023, were retrieved from the Scopus database and analyzed using VOSviewer software to visualize keyword co-occurrence networks. The analysis identified five key thematic clusters: adaptation strategies and resilience (180 publications), urban planning and green infrastructure (150 publications), flood risk management (85 publications), early warning systems (60 publications), and socio-economic impacts such as health and displacement (100 publications). Over time, research output has significantly increased, particularly after 2015, with publications peaking at 150 in 2023. Regionally, Asia leads the research output with 200 publications, followed by Europe (180 publications) and North America (120 publications). The findings highlight a shift toward sustainable, nature-based solutions and the integration of advanced technologies such as machine learning in flood management. The increasing focus on the socio-economic dimensions of flood adaptation, particularly in vulnerable communities, underscores the need for more inclusive approaches. Future research should prioritize underrepresented regions, such as Africa and Latin America, and foster interdisciplinary collaboration to address the complex challenges of climate-induced floods.

A nonstationary stochastic simulator for clustered regional hydroclimatic extremes to characterize compound flood risk

Traditional approaches to flood risk management assume flood events follow an independent, identically distributed (i.i.d.) random process from which static risk measures are computed. Modern risk accounting strategies also consider nonstationarity or long-term trends in the mean and moments of the associated flood probability distributions. However, few approaches consider how extreme hydroclimatic events cluster in both space and time, compounding damage risks. Here we introduce a compound flood risk simulator that models and conditionally forecasts future variability in regional flooding events that cluster in time, given trends and oscillations in a variable climate signal. A modular, novel integration of wavelet signal processing, nonstationary time series forecasting, k-nearest neighbor (KNN) bootstrapping, multivariate copulas, and modified Neyman-Scott (NS) event clustering process provides users the ability to model interannual and sub-annual clustering of flood risk. Our semi-parametric flood generator specifically targets the clustered temporal dynamics of jointly modeled flood intensity, duration, and frequency over a finite future period of a decade or more, thereby providing a foundation for adaptation approaches that integrate temporally clustered flood risk into planning, response and recovery.

FLOOD RISK MAPPING IN THE IRTYSH RIVER BASIN USING SATELLITE DATA

Floods are among the most frequent and devastating natural disasters, causing significant economic damage and loss of life worldwide. Effective flood risk management relies on accurate modeling techniques that can predict vulnerable areas and assess potential impacts. In this study, flood dynamics are simulated in the Irtysh River Basin near Ust-Kamenogorsk, a city in East Kazakhstan prone to seasonal flooding using high-resolution satellite imagery and digital elevation data. The primary objective is to visually model flood risks based on terrain characteristics. The study utilizes imagery sourced from the Mapbox platform, which combines data from MODIS, Landsat 7, Maxar, and the Google Earth Engine, providing access to Sentinel-2 surface reflectance imagery at 10-meter resolution. Elevation data from the Copernicus global digital elevation model, with a 30-meter resolution, is used to simulate flood progression. The flood simulation involves calculating flood depth relative to the terrain’s elevation, allowing for a pixel-by-pixel determination of submerged areas. Each simulation incrementally increases water levels to generate a sequence of images, showcasing the progression of flooding over time. The study describes hydraulic soil characteristics usage, and focuses on visualizing flood risk based on terrain data and water level changes. The simulation results indicate that flooding initially impacts riverbanks as water flow starts from the northwest of the city with critical infrastructure becoming vulnerable once water levels exceed 2 meters from the lowest elevation point. These findings highlight the potential of high-resolution satellite imagery and terrain data for flood risk assessment and improving urban flood preparedness. The results provide valuable insights into flood progression enabling more informed decision-making for disaster mitigation.

Rainwater Flood risk assessment in Zaatari Refugees Camp- Jordan: Towards Sustainable Solutions

Refugee settlements are frequently located in isolated and remote areas, characterized by poor-quality land and harsh climatic conditions. Zaatari camp in Jordan has suffered and continues to suffer from rainwater floods every winter. This study uses GIS analysis to assess flood risk in the camp, revealing that 61.1% of the area is moderately risky, while 0.84% and 22.42% are very high and high-risk zones respectively. The highest risk areas are near the camp boundary. This study recommends a structural intervention and first-class priority at the entrance area of the camp as it is located within a high-risk area and is of vital importance from a functional standpoint. These recommendations range to areas within high-risk zone in (1, 2, 7, 12) districts, to moderate risk zone in (4,8,11) districts with precautionary and preventive measures. In addition, some structural solutions can be implemented in several stages. This research is significant not only for managing flood risk in Zaatari but also for providing a replicable methodology for similar humanitarian settlements worldwide.