Validation of flood risk models: Current practice and possible improvements

The socioeconomic impacts of flooding are huge. Between 1980 and 2013, flood losses exceeded $1 trillion globally, and resulted in approximately 220,000 fatalities. To reduce these negative impacts of floods, effective flood risk management is required. Reducing risk globally is at the heart of two recent international agreements: the Sendai Framework for Disaster Risk Reduction and the Warsaw International Mechanism for Loss and Damage Associated with Climate Change Impacts. Prerequisites for effective risk reduction are accurate methods to assess hazard and risk, based on a thorough understanding of underlying processes. Due to the paucity of local scale hazard and risk data in many regions, several global flood hazard and flood risk models have been developed in recent years. More and more, these global models are being used in practice by an ever‐increasing range of users and practitioners. In this chapter, we provide an overview of recent advances in global flood hazard and risk modeling. We then discuss applications of the models in high‐level advocacy in disaster risk management activities, international development organizations, the reinsurance industry, and flood forecasting and early warning. The chapter concludes with several remarks on limitations in global flood risk models and the way forward for the future.

A Technical Assistance project funded by the European Investment Bank has been undertaken to develop a programme of flood risk management measures for Moldova that will address the main shortcomings in the present flood management system, and provide the basis for long-term improvement. Areas of significant flood risk were identified using national hydraulic and flood risk modelling, and flood hazard and flood risk maps were then prepared for these high risk areas. The flood risk was calculated using 12 indicators representing social, economic and environmental impacts of flooding. Indicator values were combined to provide overall estimates of flood risk. Strategic approaches to flood risk management were identified for each river basin using a multi-criteria analysis. Measures were then identified to achieve the strategic approaches. A programme of measures covering a 20-year period was developed together with a more detailed Short-Term Investment Plan covering the first seven years of the programme. Arrangements are now being made to implement the programme. The technical achievements of the project included national hydrological and hydraulic modelling covering 12,000 km of river, the development of 2-dimensional channel and floodplain hydraulic models from a range of topographic and bathymetric data, and an integrated flood risk assessment that takes account of both economic and non-monetary impacts.

In previous assessments of UK flood risk, flood hazard has often been poorly represented by low skill models. Meanwhile, assessments of future flood risk have lacked a detailed representation of future flood exposure. This study represents a significant step forward in both flood hazard and future exposure representation. Using a state-of-the-art climate-conditioned hydrodynamic model for fluvial, pluvial and coastal flooding paired with the latest UK shared socioeconomic projections of population (UK-SSP), we explore how climate-induced changes in flood hazard interact with increasing exposure due to population changes. Using a detailed exposure dataset, we are also able to explore discrepancies in current and future flood risk between residential property types.Our model values current yearly residential economic floods losses at £894 million, with fluvial flooding accounting for approximately 75% of these. This flood risk is not borne equally between the devolved British nations, however. In Scotland and Wales approximately 30% of local authorities have over £100 of annual flood losses per property, whereas only 4% of local authorities in England experience such levels. There are also significant discrepancies in flood risk by property type. Flats and terraced housing experience almost twice the rate of yearly inundations than detached and semi-detached housing. Under a high emissions climate scenario (RCP 8.5), by 2070 we estimate flood risk will increase by 29% whilst under a low emissions scenario (RCP 4.5) flood risk will increase by 10%. Similarly to current flood risk, these increases too are not borne equally across regions and property types.Whilst climate change is set to drive an increase in flood risk over the next 50 years, our study suggests that population dynamics may have potential for offsetting relative flood risk rises. In all SSP projections studied here (SSP1, SSP2 and SSP5), an increase in population drives an increase in the yearly rate of property inundations. As such, the highest increases in flood risk due to population change are seen in SSP5 (+50%). However, due to population growth predominantly occurring in low flood risk areas, relative flood risk decreases most in SSP5 (-10%). Similar changes, although smaller in magnitude, are seen under SSP1 and SSP2. Despite these decreases in relative flood risk due to population change, when paired to climate change, we still see significant increase in flood risk over the next 50 years. Consequently, low emissions scenarios, such as SSP1/RCP2.6, see the lowest increases in flood risk by 2070 (+5%). These results show a clear need for climate change mitigation actions in order to limit flood risk increases. However, this study presents a promising signal where population growth may limit the burden of increased flood risk, due to climate change.

CLIM-SEG: A generalizable segmentation model for heat and flood risk mapping

Floods are recognized as one of the main risk factors to cultural heritage sites. However, flood risks in these areas are mainly considered in flood protection schemes from a civil engineering point of view. Changgyeong Palace is one of the major palaces of Seoul, Korea, and is home to the national architectural heritage. It experienced a flood on July 28, 2011, when extreme rainfall struck Seoul. This study examined the flood events at Changgyeong and analyzed the flood risk based on flood risk analysis models, using a map of the observed inundation. Flood risk analysis models, including Hydrologic Engineering Centre–Hydrologic Modeling System, Storm Water Management Model, and Geographic Information System, were used for further analysis of flood risk scenarios (100-, 200-, and 500-year return periods). The flood risk analysis carried out in this study revealed that flooding which occurred at Changgyeong Palace was due to an insufficient urban drainage network enclosing the site. The flood risk model was used to simulate possible flooding areas based on flood risk scenarios. Although the architectural heritage would be safe from inundation in all flood risk scenarios, the lower part of Changgyeong Palace has a high risk of flooding.

This research aimed to evaluate flood hazards and risk areas in the Gidabo Watershed using remote sensing (RS), Geographic Information Systems (GIS), and analytical hierarchy process (AHP). Six main factors were considered to identify flooding hazard zones: drainage density (DD), soil, elevation, rainfall, slope, and land use land cover (LULC). Population density, flood hazard zone, and LULC were considered for mapping the flood risk zone in the Gidabo watershed. A weighted overlay analysis tool has been utilized to integrate the thematic layers to identify both flood hazard and flood risk zones. The findings indicated that about 41.6% (337 km2) of the watershed falls within the high and very high flooding hazard zones. Conversely, 31.11% (252 km2) of the watershed is categorized under very-low and low flooding hazards. Moreover, the study identified five flood risk zones in the area viz; very high, high, moderate, low, and very low. The result of the flood risk map revealed that 199.5 km2 (24.5%) of the watershed has a higher and very higher risk of flooding. These zones were validated using the receiver operating characteristic (ROC) curve, showing a correlation coefficient of 0.943. These results emphasize the need to implement prediction of floods, early warning systems, and effective management practices on a regular and sustainable basis.

Flood events are expected to increase in their frequency and severity, which results in higher flood risk without additional adaptation measures. The information gained from flood risk models is essential in effective disaster risk management. However, vulnerability estimations are often a large driver of uncertainty, and flood damage is rarely estimated due to a lack of empirical damage data from flood events. This study uses a unique data set with experienced damages and the implementation of flood damage mitigation (FDM) measures on the household level, collected after the flood event in the Netherlands in 2021. Flood damage models that control for several hazard, exposure, and vulnerability indicators are estimated and allow for additional input in flood risk models. Previous estimates of the effectiveness of FDM measures are prone to a selection bias, as households that do, and do not implement FDM measures systematically differ in their risk profiles. By using an instrumental variable‐estimation, this study overcomes this selection bias and finds significant reductions in flood damage due to FDM measures. These reductions can be incorporated in multivariate flood vulnerability estimations, which indicate that FDM measures significantly reduce flood damage. Providing information on flood hazard, as well as implementing early warning systems, is crucial for ensuring effective flood risk management.

The sensitivity of simulated flood depth and area to DEM resolution are acknowledged, but their effects on flood damage and risk estimates are less well understood. This study sought to analyse the relative benefits of using global DEMs of different resolution sizes, 5 m AW3D Standard, 12.5 m ALOS PALSAR and 30 m SRTM, to simulate flood inundation, damage and risk. The HEC-RAS 2D model was adopted for flood simulations, and the Toba River in the Klang River Basin in Malaysia was chosen for the case study. Simulated inundation areas from AW3D coincide the most with reported flooded areas, but the coarser-resolution DEMs did capture some of the reported flooded areas. The inundation area increased as the resolution got finer. As a result, AW3D returned almost double flood damage and risk estimates compared to ALOS PALSAR, and almost quadruple compared to SRTM for building-level damage and risk analysis. The findings indicate that a finer-resolution DEM improves inundation modelling and could provide greater flood damage and risk estimates compared to a coarser DEM. However, DEMs of coarser resolution remain useful in data-scarce regions or for large-scale assessments in efforts to manage flood risk.

This study contributed to the comprehensive assessment of flood risk in the Huaihongnanpian flood control protected area (simplified as the HHNP) of the Huaihe River Basin in China. Flood risk analyses were performed by incorporating flood hazard and vulnerability. Flood hazard was simulated by a 1D–2D coupled hydrodynamic model. Flow velocity, inundation duration, and inundation depth were taken as hazard indicators, while agricultural population proportions, female population proportions, GDP per unit area, GDP per person, population density, residential density, shelter density, the land-use sensitivity index, road network density, and river network density were used as vulnerability indicators. Based on these indicators, a regional flood risk assessment model was put forward, which coupled fuzzy c-means clustering, factor analysis, and a clustering validity function. As an example, a proposed model was applied to evaluate the degree of flood risk for 15 townships in the HHNP. The research results showed that (1) flood risk in the HHNP was closely related to three main factors: socioeconomic factor, land cover factor, and flood factor; (2) the degree of risk was objectively divided into six zones: especially high, high, relatively high, medium, relatively low and low; and (3) in the 15 townships, Xiaobengbu (XB), Chengguan (CG), and Wuxiaojie (WX) fell into the especially high, high, and relatively high zones, respectively. Xinji (XJ), Toupu (TP), Daxin (DX), Caoguzhang (CGZ), Meiqiao (MQ), Caolaoji (CL), and Mohekou (MH) fell into the medium-risk zone. Linbeihuizu (LB) was categorized into the relatively low-risk zone, and Xinmaqiao (XM), Wangzhuang (WZ), Kuainan (KN), and Weizhuang (WZ) fell into the low-risk zone. The research results revealed the main driving factors and the spatial distribution of flood risk in the HHNP; therefore, it is highly significant for us to understand the main flood risk sources to provide guidance for flood control and management in the HHNP.

This chapter presents all-important discussions relating to flood risk analysis which arguably is a subject of overwhelming significance within the context of less developed societies, for example Nigeria. Whilst a possible means of eradicating flooding from human environment is inconceivable, debates for more effective flood risk reduction methodologies for critical infrastructure protection must continue. Increased population and urbanisation scenarios drive worsened flood risk which trigger increased efforts for corporate adaptability to flooding. To ensure that social systems can cope with floods, it is important to investigate why best practices in flood risk reduction are not fully applicable. This chapter explores these issues drawing from extant dialogues on flood risk management (FRM). Arguably, the current flood modelling techniques and assessment of vulnerability operations largely do not support a realistic analysis of flood risk. Funnelled through an interpretative research paradigm, the chapter conceives that these limitations fall under five cardinal issues – (1) data, (2) theories and concepts, (3) existing flood risk analyses methods, (4) legislation and policy, and (5) sustainable development. It argues that the realisation of a more effective flood risk reduction for the poorer and less developed societies will depend on effective tackling of these issues which creates opportunities for flood risk analyses through simplified approaches, and use of free and open geospatial data infrastructure.

Flood risk mapping and analysis have gained much attention globally due to the increasing rate of flood disasters in most coastal and riverine areas. As a key element of flood risk study, mapping and delineation of flood hazard areas are very important for risk management and disaster reduction. We analysed potential flood hazards zones in Ogunpa river basin using integrated hydraulic modelling, the height above nearest drainage (HAND) model and multi criteria decision analysis (MCDA). An event-based flood simulation using FLO-2D model was done for the 28 h of the flooding event, while HAND model was employed in modelling the terrain to delineate inundation extent in the basin. In addition, seven flood causative factors were integrated using the Analytical Hierarchical Process (AHP) to delineate potential hazard zones in the study area. Flood inundation model result from FLO-2D revealed inundation depths between 0 and 5.24 m, an indication of intense flood hazards capable of causing lots of damages. MCDA results were classified into least, low, moderate, high and extreme flood hazard zones, while the classified HAND model include none, very low, low, moderate and high flood hazard zones. The MCDA results revealed that the downstream portions of the basin are mostly susceptible to moderate flood hazard compared to low vulnerability observed majorly in the upriver parts of the catchment. The comparison between the estimated flood depths ranges of 0.3–5.2 m by the FLO-2D model and the HAND model value range of 0–5 m (high flood hazard zone) revealed a strong similarity. In all, areas closed to the river course are highly vulnerable to flooding. The output of this study can serve as policy and decision-making tools for future flood hazard analysis, prevention and reduction plans at the research location.

Calibrating a High-Performance Hydrodynamic Model for Broad-Scale Flood Simulation: Application to Thames Estuary, London, UK

ABSTRACTSiberia is a huge and relatively sparsely populated territory in the centre of Eurasia with a harsh climate and diverse flow formation conditions. Nevertheless, it is characterized by a high flood hazard, as most of the settlements and industrial centres are located along river banks. The flood hazard can vary across space and time against a background of climatic and anthropogenic changes, as well as socio-economic factors. We carried out a geographical analysis of floods as a tool to identify the present status of flood hazard in Siberia. The analysis included: analysis of flood hazard during a period of climatic change (1985–2016); analysis of ice jams and ice dams as a specific natural factor causing floods; and identification of areas with different flood hazards using multi-scale mapping. Rainfall floods, mixed floods (due to rainfall and snowmelt) and floods resulting from ice dams are the most dangerous in Siberia. They have the highest rate of recurrence, strength of impact, and number of fatalities and displaced people. The locations of areas with different flood hazards were identified by multi-scale mapping. The highest flood hazard in Siberia is in the most populated and economically developed southern regions along the Ob, Lena, Yenisei rivers and in the Lake Baikal basin. In areas with a high flood hazard, complex measures are needed, taking into account regional peculiarities.

Availability and applications of open-source data for disaster risk reductions are increasing. Flood hazards are a constant threat to local communities and infrastructures (e.g., built-up environment and agricultural areas) in Nepal. Due to its negative consequences on societies and economic aspects, it is critical to monitor and map those risks. This study presents the open access earth observation (EO) data, geospatial products, and different analytical models available for flood risk assessment (FRA) and monitoring in Nepal. The status of flood risk knowledge and open-source data was reviewed through a systematic literature review. Multispectral optical data are widely used, but use of microwave data is extremely low. With the recent developments in this field, especially optical and microwave data, the monitoring, mapping, and modeling of flood hazards and risk have been more rapid and precise and are published in several scientific articles. This study shows that the choice of appropriate measurements and data for a flood risk assessment and management involves an understanding of the flood risk mechanism, flood plain dynamics, and primary parameter that should be addressed in order to minimize the risk. At the catchments, floodplains, and basin level, a variety of open data sources and models may be used under different socioeconomic and environmental limitations. If combined and analyzed further, multi-source data from different models and platforms could produce a new result to better understand the risks and mitigation measures related to various disasters. The finding of this study helps to select and apply appropriate data and models for flood risk assessment and management in the countries like Nepal where the proprietary data and models are not easily accessible.

Private precaution is an important component in contemporary flood risk management and climate adaptation. However, quantitative knowledge about vulnerability reduction via private precautionary measures is scarce and their effects are hardly considered in loss modeling and risk assessments. However, this is a prerequisite to enable temporally dynamic flood damage and risk modeling, and thus the evaluation of risk management and adaptation strategies. To quantify the average reduction in vulnerability of residential buildings via private precaution empirical vulnerability data (n = 948) is used. Households with and without precautionary measures undertaken before the flood event are classified into treatment and nontreatment groups and matched. Postmatching regression is used to quantify the treatment effect. Additionally, we test state‐of‐the‐art flood loss models regarding their capability to capture this difference in vulnerability. The estimated average treatment effect of implementing private precaution is between 11 and 15 thousand EUR per household, confirming the significant effectiveness of private precautionary measures in reducing flood vulnerability. From all tested flood loss models, the expert Bayesian network‐based model BN‐FLEMOps and the rule‐based loss model FLEMOps perform best in capturing the difference in vulnerability due to private precaution. Thus, the use of such loss models is suggested for flood risk assessments to effectively support evaluations and decision making for adaptable flood risk management.