Flood Disaster Risk Management Research Articles

Hydrological streamflow modeling at the inlet of Gilgel Gibe III dam of Ethiopia using soil and water assessment tool

AbstractComprehensive approach is a pre‐requisite to improve the estimation of variability in streamflow data for designing water supply systems, generating hydropower, determining environmental flows, allocating water, and conducting pollution studies that need a comprehensive approach. There is a need to model streamflow data to develop effective water policy plans when sufficient data are unavailable. This study aims at assessing the accuracy and applicability of the Soil and Water Assessment Tool (SWAT) model in predicting streamflow at the inlet of the Gilgel Gibe III dam watershed in Ethiopia. Historical meteorological data from nine available stations between 1987 and 2017 were used to predict the streamflow. The SWAT model was calibrated and validated for two different time periods (1997–1999 and 2003–2004) using SWATCUP program and SUFI‐2 technique. Calibration and validation were based on monthly observed discharges at the Abelti and Gojeb stations. The results showed acceptable values of R2, Nash–Sutcliffe modeling efficiency (NSE), and percent bias (PBIAS). At the Abelti station and Gojeb station, the calibration and validation results were 0.82, 0.79, and −10.1%; 0.88, 0.84, and −14.4%; and 0.86, 0.79, and 14.1% for calibration and 0.88, 0.8, and 11.1% for validation, respectively. The prediction uncertainty of 95% was consistent with the observed discharge. The mean annual runoff was 440.08 m3/s, demonstrating the model's effectiveness in simulating streamflow at the dam's inlet. The calibrated and validated model can be used to study the effects of climate and land use changes; analyze water quality and sediment yield; and inform future water policy plans, dam construction planning, and flood disaster risk management.

FLOOD SUSCEPTIBILITY MAPPING USING MACHINE LEARNING IN KENING RIVER, SUB WATERSHED OF BENGAWAN SOLO, TUBAN

Floods are natural occurrences with the potential to cause damage to ecosystems and pose significant threats to human life, resulting in the destruction of property, infrastructure, and socioeconomic challenges. In recent times, flooding in the Sub-Watershed of Bengawan Solo has been linked to the overflowing Kening River in Tuban County. Aim: This study aims to produce a flood susceptibility map to mitigate the frequency of flood occurrences as well as facilitate effective planning for flood disaster risk management. Methodology and results: Flood data is collected from 2016 to 2023 through field surveys, Sentinel-1 satellite imagery, and data from the Development Planning Agency, Tuban County. Integrating remote sensing data from satellite imagery (PlanetScope, Sentinel-2), geographic information systems (GIS), and spatial modeling techniques, a flood susceptibility map is developed for the Kening River catchment. The occurrence of floods in the Kening River area is associated with various factors (11 variables) assessed through the frequency ratio approach, including profil curvature, LS factor, aspect, rainfall, river distance, road distance, building density 100 m, road density 100 m, vegetation type, normalized difference water index (NDWI), and soil adjusted vegetation index (SAVI). The results show flood susceptibility maps utilizing frequency ratio (FR) and convolutional neural network (CNN) techniques. The flood susceptibility map obtained through the CNN method demonstrates a notably high AUC value. The model development generated a validation AUC value of 0.857 for training and 0.856 for testing. Conclusion, significance and impact study: This research provides an understanding into the factors that influence the occurrence of floods in the Kening River catchment area. It also emphasizes the benefit of advanced machine learning approaches in mapping the susceptibility of floods. Furthermore, this study has the potential to be helpful in guiding regional policy decisions and result in enhanced flood risk management measures in Tuban County.

Flood Affected Building Footprints 3D Model from LiDAR and Aerial Photogrammetry (Case Study: Pandugo Street, Surabaya)

Frequent rainfall events in Surabaya occurred due to tall silt within the channel, which prevent water flow and at that point flooding onto the street. Information about the events, including the simulation and prediction are needing accurate visualization to be major consideration for infrastructure and public facilities decision-making. 3D modelling are accurate enough as flood disaster risk management for buildings, using LiDAR and aerial photogrammetry that can provide strong visual representations accurately. DSM and DTM derived from aerial photogrammetry survey are then used to get DSM LiDAR. DSM&DTM from LiDAR was taken in 2016 and DSM&DTM from aerial photogrammetry was taken in 2022. The only objects that are updated are buildings from DSM aerial photogrammetry then applied to DSM LiDAR. Buildings footprint, road network, nDSM, and flood inundation data are then processed to get the 3D Model of buildings and floods. The model are then evaluated by comparing the total of affected buildings with manual methods. Validation on buildings height also performed and results in 0.86233 RMSe that achieved the LOD2 standard. Potential further studies could focus on improving the LOD and considering other parameters such as rainfall data or gutter data.

What drives vulnerability? Explaining the institutional context of flood disaster risk management in Sub-Saharan Africa

This paper analyzes institutional vulnerability drivers in Flood Disaster Risk Management (FDRM). Using Ghana as case study, we explain the distinct institutional context of vulnerability to natural hazards in Sub-Sahara Africa (SSA). Addressing a research gap, the limited consideration of the embeddedness of institutional vulnerability, we broaden Papathoma-Köhle et al.'s (2021) institutional vulnerability framework for application in SSA. This combines the analysis of institutions that directly or indirectly impact disaster management and of the capacity to respond to impacts of hazardous events. We explore four pillars, each with specific drivers (socio-cultural, socio-political, fiscal-economic and legislative-regulatory), to answer two research questions: 1) What drives institutional vulnerability in Ghana? and 2) How do these drivers foster or hamper FDRM in Ghana? Specifically, we analyze the drivers of institutional vulnerability and how they hamper FDRM, revealing causal links between the institutional context and FDRM policy implementation. Our qualitative application of the framework enables us to identify the interconnectedness between drivers of institutional vulnerability and underlying causes. Our results show that in an SSA country such as Ghana, the institutional context can be understood as a sensitive interplay of formal and informal institutional drivers, behavioral pitfalls, a culture of impunity, and deficits in necessary resources and risk transfer mechanisms.

Cost-Benefit Analysis of the Wuxikou Integrated Flood Management Project Considering the Effects of Flood Risk Reduction and Resettlement

AbstractEvaluation of the economic costs and benefits of flood disaster risk management projects is crucial. However, current cost-benefit analyses (CBA) often lack reliable estimates of the expected loss reduction from flood control measures and ignore quantitative assessments of resettlement. To address these limitations, this study incorporated a probabilistic risk analysis method and quantitative resettlement benefits assessment into the CBA framework, using the Wuxikou Integrated Flood Management Project (WIFMP) in Jiangxi Province, China, as a case study. The direct economic benefits of flood control were estimated by integrating hydrological statistics, numerical flood inundation simulation, and quantitative damage analysis with exposure and vulnerability data. Furthermore, the resettlement benefits were quantified by measuring the annual income growth of migrants based on assumptions about household employment. Our analysis shows that the total WIFMP investment is RMB 3546.1 million yuan (USD 1 = RMB 6.976 yuan), including loan principal and interest of 244.4 million yuan, and operations and maintenance of 605.5 million yuan at 2020 prices. Annual project benefits are estimated at 351.3 million yuan in flood risk reduction, 155.7–191.9 million yuan from increased resettlement income, and 42.7 million yuan in power and water revenues. Considering the costs and benefits across the entire project lifecycle, the internal rate of return ranges from 13.7 to 14.2%, and the net present value ranges from 31.8 to 352.6 billion yuan. Through improved benefit estimation methodology, this research enables a more reliable and holistic evaluation of costs and benefits for flood risk management projects. It provides insights for policymakers and practitioners involved in similar projects, contributing to more informed decision making and better allocation of resources in flood disaster risk management.

Navigating the ecosystem flood regulation service flows from subbasin to catchment in the Han River Basin

With the increasing cross-regional impact of climate change increasing in recent years, the Han River Basin, as a vital water resource supply and densely populated area in China, faces severe cross-regional flood threats and challenges. The systematic consideration of flood regulation throughout the entire upstream and downstream of the basin has become imminent. Our research aims to gain a deeper understanding of the ecosystem flood regulation service flows from upstream to downstream within subbasins and catchments scales of the Han River Basin. The results showed that the overflow path from upstream to downstream of the flood was basically consistent with the trend of the stream and the main tributaries of the Han River. It emerged that subbasins c, e, g, h and k were the key areas for the overall regulation of the upstream and downstream floods at subbasin scale. A total of 11 catchments overflow into their adjacent downstream catchments across the subbasins, a fact which is critical for catchment scale flood regulation. What’s more, there is evident interaction not only between adjacent subbasin and catchment units but also substantial exchange of service flows between non-adjacent units. Notably, catchment c’s flood regulation service flow was primarily contributed by its non-adjacent units. These findings not only contribute to filling the current knowledge gap in cross-watershed flood overflow and flood regulation service flows, but also provide support for the integrated response of upstream and downstream flood disaster risk management at the whole basin scale.

Spatial heterogeneity and impact scales of driving factors of precipitation changes in the Beijing-Tianjin-Hebei region, China

Changes in land surface properties during urbanization have a significant impact on variations in precipitation. Little research has been carried out on spatial heterogeneity and influence strength of the driving factors of precipitation changes at different urbanization scales. Using a trend analysis and multi-scale geographically weighted regression, this study analysed the spatial heterogeneity and impact scale of driving factors of precipitation changes in 156 urban units in the Beijing-Tianjin-Hebei urban agglomeration region (Jing-Jin-Ji). In summer, RAD (radiation), RHU (relative humidity), WIN (wind speed), and POP (urban population density) were found to act on a small regional scale, AOD (aerosol optical depth) on a medium regional scale, and NDVI (normalized difference vegetation index), NLI (night time light intensity), UHI (urban heat island intensity), and AREA (urban area size) on a global scale. In winter, AREA and WIN acted on a medium regional scale, UHI on a large regional scale, and AOD, NDVI and NLI on a global scale. Across the whole year, NDVI and AREA had a medium regional impact and NLI a large regional one. Variations in natural factors, such as RAD and RHU, had a great influence on the spatial heterogeneity of precipitation changes, whereas human factors, such as NLI and UHI, had a small influence. In summer, AOD mainly affected Tangshan and Qinhuangdao in the northeast and Cangzhou in the southeast of the Jing-Jin-Ji. RHU and AREA primarily affected the cities of Handan and Xingtai. In winter, NLI, AREA, WIN, and UHI had significant effects in the cities of Handan and Xingtai, with AREA being the most important factor. In the Shijiazhuang-Hengshui area, RAD and NLI played a significant role; in the Beijing-Zhangjiakou-Chengde area, the most important factor affecting precipitation changes was the variation in POP. These results provide a scientific basis for flood disaster risk management in the Jing-Jin-Ji and the establishment of differentiated climate policies in different cities.

Review on Urban Flood Risk Assessment

Under the background of rapid urban development and continuous climate change, frequent floods around the world have caused serious economic losses and social problems, which has become the main reason for the sustainable development of cities. Flood disaster risk assessment is an important non-engineering measure in urban disaster prevention and mitigation, and scientific flood disaster risk assessment is the premise and foundation of flood disaster risk management. This paper summarizes the current situation of flood risk assessment by analyzing the international literature in recent 20 years. The mechanism of flood disaster is mainly discussed. The flood disaster assessment methods are summarized, including historical disaster statistics method, multi-criteria index system method, remote sensing and GIS (Geographic Information System) coupling method, scenario simulation evaluation method and machine learning method. Furthermore, the development status of flood risk analysis and forecasting is summarized. Finally, the development trend and direction of flood risk assessment are put forward.

Resilience Assessment of Traffic Networks in Coastal Cities under Climate Change: A Case Study of One City with Unique Land Use Characteristics

How to assess the risk of flood disasters and improve the resilience of coastal cities has become a scientific problem that must be solved urgently. This paper aims to construct a resilience assessment model for transport systems in the context of climate change based on an analysis of the spatial characteristics of regional transport networks and complex network theory, using the Pudong New Area in Shanghai, China as the empirical object. Other objectives of the developed model are to establish a system of homogeneity, efficiency, and stability indicators and to assess the impact of flood depth (up to 7 m) on the resilience of transport networks in terms of static network structure and dynamic network performance by designing flood inundation disturbance scenarios. Finally, the characteristics, change trends, and conceptual connotations of the resilience of transport networks in coastal cities are condensed. The results of this study provide a solid scientific basis for future flood disaster risk management in global coastal cities.

Rediscovering South Africa: Flood disaster risk management through ecosystem-based adaptation

The emergence of ecosystem-based adaptation (EbA) has created a shift from the conventional adaptation approaches often accompanied by high costs, including conflicting interests associated with the dense urban fabric and inflexibility. Therefore, EbA presents a potential for more comprehensive, cost-efficient, multifunctional and multidimensional measures in flood disaster risk management. Essentially, EbA integrates biodiversity and ecosystem services as an approach to climate change adaptation and disaster risk reduction. This study adopts descriptive and exploratory analytical approaches aimed at assessing the trends of flood events in South Africa and their potential risk management through EbA. The study reveals that between 1959 and 2019, several major floods have occurred, resulting in losses of life and livelihoods and significant infrastructure damages among others. Recovery has taken years and needed continuous investment. Unfortunately, many communities have not been able to “bounce back/bounce forward”, despite all these efforts. This study revealed that EbA strategies can be instituted regionally and nationally to offer efficient flood disaster adaptation or mitigation in flood-prone areas. In general, utilising EbA can stabilise or reduce flood impacts, minimise environmental impacts and damages associated with flood disasters by developing innovative EbA strategies. Therefore, the study recommends an increased awareness of EbA strategies to enhance universal adoption in ameliorating flood risk.

Using CHIRPS Satellite-Based Data for Spatio-Temporal Variability of Rainfall in Dashti Hawler District – Kurdistan Region of Iraq 1982 – 2019

Abstract Rainfall is one of the most essential climatic indicators of climate change, The changes in rainfall in space and time would impact runoff, soil moisture, and groundwater reserves. The study of the climate change effects on water resource planning and management necessitates the analysis of precipitation changes (Khayyat, et. al, p. 35, 2019; M. Nawaz, et. al., 2020, p.2). The availability of long-term and temporally consistent rainfall time series is one of the most important criteria for undertaking a detailed study of regional and temporal variation and trends in rainfall in any location. Agriculture activities, flood disaster risk management and reduction, drought, water harvesting, water supply to communities, and other human activities related to the spatiotemporal distribution of rainfall in any region benefit from the analysis of long-term climate time-series data at a high temporal and spatial resolution. Water resource management, agricultural productivity, and climate change mitigation benefit from understanding the spatial distribution of rainfall and its temporal trends (Morales-Acuña, et al., 2021, p. 1). Rainfall data and measurements from traditional ground weather stations have traditionally been the major sources of such climate data; however, due to limited or nonexistent station networks in many regions of the world, historical records from station observations are insufficient. As a result, satellite-based rainfall data are increasingly being utilized to supplement or replace station observations.

Weighted Linear Combination Procedures with GIS and Remote Sensing in Flood Vulnerability Analysis of Abeokuta Metropolis in Nigeria

The study offered the opportunity for an evaluation of the role of Remote Sensing and Geospatial techniques in flood disaster risk management and development of spatial decision support system for flood risk assessment and management in Abeokuta metropolis. Datasets used includes cloud free high resolution satellite images and Shuttle Radar Topographic Mission (SRTM) data downloaded from earth explorer site. Soil data used was obtained from Food and Agriculture Organization (FAO’s) Harmonised World Soil Database, while rainfall data was obtained from the Climate Hazards Group InfraRed Precipitation Station. Maps of flood enhancing factors namely: soil types, rainfall intensity, drainage density and topography were created in Geographic Information Systems using same scale of 1: 50,000 and Geographic coordinate system (WGS 1984). All maps were produced in raster format with the same cell grid cell size of 0.0028 mm. They were then subjected to weighting by ranking and Multi-Criteria Analysis (MCA) using the Weighted Linear Combination. The study identified topography and land use as key factors contributing to flooding within Abeokuta metropolis. Obstruction of natural drainage channels by buildings aggravates disasters from flash flood events.

Looking through the Lens of schools: Children perception, knowledge, and preparedness of flood disaster risk management in Pakistan

Schools serve as a hub for children to learn and involve in disaster mitigation activities and to connect with the community. The government of Pakistan has initiated different capacity building and safety training programs to increase the preparedness level among school children. However, the effectiveness of such capacity building training programs in disaster-prone areas is yet to be evaluated. In this perspective, this study examines the school children's perception, knowledge, and preparedness of flood disaster risk management in Pakistan. The analysis is based on a primary survey covering 20 primary schools across four severely flood-affected districts of Khyber Pakhtunkhwa province of Pakistan. By using a structured questionnaire, we collected data from 100 students of grade-5. Pearson's Chi-square test is used to determine the statistical significance of the differences in preparedness across districts. The findings show that, concerning perception, overall 40% of the total sample children believe that the flood disaster would destroy the houses of residents (including theirs), 39% said that it would damage the large buildings and 31% reported that persons near the river source are facing a higher risk. Regarding flood disaster risk-related knowledge, 68% of respondents experienced flood disasters between 2010 and 2014, and houses were flooded and damaged by 65% and 46% of the respondents, respectively. The findings further indicated that preparedness level is generally low in the study districts as 12% have a family emergency plan, however, only 5% practice emergency plans at the household level. In addition, only 18% of the schools show emergency exit, assembly areas, and places to meet or leave important messages in an emergency. Moreover, the study found that awareness and training programs are needed at the school level to increase knowledge and preparedness for future floods.

The role of local community leaders in flood disaster risk management strategy making in Accra

Flood disasters have upward trends and devastating impacts in cities worldwide. This problem could be due to lack of participation of flood prone community leaders in flood risk management strategy making. Institutional challenges, lack of technical expertise and conflicts could exclude stakeholders from flood management. This research investigates the participation of flood-prone community leaders in flood risk management strategy making in Accra. Flood-prone communities bear direct impacts of floods. Community leaders can influence flood risk management in their neighbourhoods if they participate in the strategy making of the city authorities. However, it is unclear if leaders of flood-prone communities participate in strategy making for flood disaster risk management in Accra. This study seeks to answer the following research questions: Do flood prone community leaders in the city of Accra participate in flood risk management strategy making? How are the local community leaders involved in the flood risk management strategy making? This qualitative research obtained data from secondary and primary sources. Documents analysis provided the secondary data whereas the primary data were obtained from face-to-face interviews and focus group discussion. The research findings revealed that flood prone community leaders participate only in the implementation stage of flood risk management strategies. The paper concludes that an effective flood risk management strategy making, and implementation require active participation and role play of flood prone community leaders.

Research on Chongqing Mountain Flood Disaster Risk Assessment System Based on AHP-GIS

The risk of mountain flood disasters is the fundamental factor restricting the occurrence of disasters. The analysis and assessment of disaster risks is the basis for disaster risk management. Through the investigation of mountain flood disasters from 2013 to 2015, the basic information such as hydrometeorology, topography, socio-economic and mountain flood disaster prevention and control situation in mountain flood disaster prevention and control areas were found out. Taking the city of mountains-Chongqing as an ex-ample, this paper considered the results of mountain flood disaster investigation and evaluation in recent years, and divided the risk evaluation indicators into two types of first-level indicators: rainfall and under-lying surface, and the secondary indicators were subdivided into comprehensive rainstorm indicators, critical rainfall, topographic relief, land use convergence capacity, soil texture and river network density. Using AHP to set the weight coefficient of each level indicators, those showed that heavy rain conditions, critical rainfall and topographic relief were important factors impacting the mountain flood disasters, and the land use convergence capacity had the minimum weight coefficient. Through GIS superposition analysis, the risk index of mountain flood disasters in the region were obtained. The western, southeastern and northeastern Chongqing were high risk areas for mountain flood disasters, which provided decision-making basis for mountain flood disaster risk management.

Evaluation of regional flood quantiles at ungauged sites by employing nonlinearity-based clustering approaches.

Water resource engineers extensively use regional flood frequency analysis to compute the discharge at ungauged sites with limited flow records in the river basins. Frequency and magnitude are the two important factors required to be analyzed for an effective assessment of flood disaster risk management. Globally, many linear clustering techniques are employed to categorize the watershed which are ineffective when dealing with noise and outliers. The present study overcomes this by proposing a relatively new nonlinear clustering algorithm based on hierarchical estimation of densities (NLCAHD) for the Cauvery basin, where the Homogeneity test (H) is enforced to identify the group of stations with same populations. Discordancy measure is carried out for screening the data in order to eliminate the conflicting sites from the group. The whole basin is classified into six homogeneous clusters, while the goodness of fit measure tests the data to distinguish the preferred distribution for the purpose of calculating the growth curves. A comparative study is made with the other linear algorithms such as K-means and C-means, which reveals the better performance of the proposed nonlinear model for identifying the homogeneous regions, in arriving at precise estimates of flood quantiles for various return periods up to 160 years.

Comparative analyses of flood damage models in three Asian countries: towards a regional flood risk modelling

The use of different approaches in the development of flood damage models in various countries is expected to affect flood damage modelling at a regional or global scale. Since these models are often used as tools for disaster management and decision making, it is very needful to understand the comparative similarity and differences in countries’ loss models; this can help in the overall integration for developing regional risk models and cross-country risk assessment. In this study, empirically generated generalised loss models in three Asian countries (Sri Lanka, Thailand and Japan) were compared and applied to estimate potential flood damages in two different urban river basins. For each case study, each model was normalised using cost prices and floor areas (as applied to each country) and were integrated within the Geographic Information Systems (GIS) to estimate damages for the flood events. Using the mean vulnerability index of corresponding building types for the selected countries, a single model for regional flood risk assessment was created. However, the study showed that there are variations in the vulnerability and the potential flood damage estimates of similar global building types from the three countries, despite being developed by the same approach. These are attributed to the country’s specific conditions such as building regulations and codes, GDP per capita, cost price of building materials. Our results suggest that the average vulnerability index from the countries however reduced potential errors in the estimates. Moreover, it is proposed that the average regional vulnerability model derived with empirical data inputs from all the countries for regional risk assessment and cross-country comparison. Therefore, it can predict near accurate potential flood damages, which can serve as measures for regional flood disaster risk management plans.

Quantitative Agricultural Flood Risk Assessment Using Vulnerability Surface and Copula Functions

Agricultural flood disaster risk assessment plays a vital role in agricultural flood disaster risk management. Extreme precipitation events are the main causes of flood disasters in the Midwest Jilin province (MJP). Therefore, it is important to analyse the characteristics of extreme precipitation events and assess the flood risk. In this study, the Multifractal Detrended Fluctuation Analysis (MF-DFA) method was used to determine the threshold of extreme precipitation events. The total duration of extreme precipitation and the total extreme precipitation were selected as flood indicators. The copula functions were then used to determine the joint distribution to calculate the bivariate joint return period, which is the flood hazard. Historical data and flood indicators were used to build an agricultural flood disaster vulnerability surface model. Finally, the risk curve for agricultural flood disasters was established to assess the flood risk in the MJP. The results show that the proposed approaches precisely describe the joint distribution of the flood indicators. The results of the vulnerability surface model are in accordance with the spatiotemporal distribution pattern of the agricultural flood disaster loss in this area. The agricultural flood risk of the MJP gradually decreases from east to west. The results provide a firm scientific basis for flood control and drainage plans in the area.

Uncertainty analysis of designed flood on Bayesian MCMC algorithm: a case study of the Panjiakou Reservoir in China

Estimation of the magnitude of designed flood is a fundamental task crucial for the determination of scale of engineering construction and for the development of flood disaster risk management projects. Due to a high level of uncertainty in observed data, selection of frequency distribution model, and estimation of model parameters, the process of designed flood has uncertainties consequently. A Bayesian flood frequency analysis method is adopted for designed flood estimation with P-III probability distribution as its flood frequency model. In the Bayesian method, the adaptive metropolis Markov Chain Monte Carlo (AM-MCMC) sampling algorithm is employed to estimate posterior distributions of parameters, upon which estimation of expectations and credible intervals of designed floods is obtained. With analyzing the drawback of likelihood function expressed with the product of probability of occurrence of each sample individual, four likelihood functions expressed on residuals are presented, and then based on Bayesian AM-MCMC method, performance of presented likelihood functions is compared with that of the classical likelihood function, with taking peak flow uncertainty analysis of Panjiakou Reservoir as a case study. The results show that expectations of flood peak quantiles estimation with likelihood functions based on residuals between observed/censored and calculated values of flood peaks are almost the same, but there are obvious differences between likelihood function based on occurrence probability of flood sample and those based on residuals with respect to expectation of quantiles estimation and also show that expectation and credible interval of quantiles estimation with Bayesian AM-MCMC method based on the whole likelihood function are more reasonable than those acquired with maximum likelihood function. Finally, some relevant flood frequency analyses issues based on Bayesian AM-MCMC algorithm which need to be further studied are also presented.

Coastal Risk Assessment Framework: Comparison of modelled fluvial and marine inundation impacts, Bocca di Magra, Ligurian coast, Italy

The identification and classification of critical coastal areas is becoming more and more important from a coastal management point of view, especially considering future climate change. The standardized assessment of multiple hazards and their potential impacts is crucial, in terms of risk management, for those coastal areas where both marine and fluvial hazards can occur.Nevertheless, in Bocca di Magra (Liguria Region, Italy), where both coastal and fluvial flooding can occur, up until now the potential impacts from marine flooding have not been thought to be of importance; only the impact of fluvial flooding has been systematically analysed. Now, however, the Liguria Regional stakeholders have become interested in understanding the potential impact of marine inundations compared to fluvial inundations, applying the CRAF (Coastal Risk Assessment Framework) methodology developed inside the RISC-KIT project.The hazard modelling of coastal and fluvial inundations was used, together with exposure data, to evaluate the direct and systemic impacts generated by both flooding mechanisms separately. An End-User-driven Multi-Criteria Analysis was implemented to compare coastal and fluvial impacts on the same area.For an event with a 1 in 200 year return period, the CRAF predicts that fluvial inundation generates higher impacts, in comparison to the marine one. Even though the impacts in the coastal area are less, the impacted exposed elements are different from those impacted by fluvial inundation and none of them can be excluded from the analysis.This work highlights the need for regional managers to develop combined coastal-fluvial flooding assessments; such actions should be seen as a priority for flood disaster risk management in locations affected by both marine flooding and riverine flash flooding.