Depth-damage Functions Research Articles

Flood damage models and flood damage factors in a data-scarce river basin, Nigeria

ABSTRACT To evaluate the effectiveness of various adaptation strategies, it is critical to develop a flood damage function that considers building characteristics for future risk evaluation. This study analysed the contributions of some important flood and household characteristics in flood damage processes and developed a new, more accurate, multi-variable flood loss model for flood damage assessment for residential buildings and contents, considering different house types in Ogun River Basin, Nigeria. A detailed household questionnaire survey was conducted to collect data (149 samples). Depth-damage functions were developed to estimate direct flood damage to building structures and contents using the nonlinear logistic and power (concave) models, and the model parameters were estimated using the Gauss–Newton algorithm. The performance of all flood damage regression analysis functions for building structures and building contents under different construction materials and basement floors was evaluated by comparing predicted damages and after-flood survey damages, which demonstrated acceptable comparativeness. In the event of a flood disaster in the area, the models can be used to forecast future damages and devise risk-mitigation strategies. The study’s findings will aid in developing flood protection adaptation measures.

Effects of damage initiation points of depth-damage function on flood risk assessment

The flood depth in a structure is a key factor in flood loss models, influencing the estimation of building and contents losses, as well as overall flood risk. Recent studies have emphasized the importance of determining the damage initiation point (DIP) of depth-damage functions, where the flood damage is assumed to initiate with respect to the first-floor height of the building. Here we investigate the effects of DIP selection on the flood risk assessment of buildings located in Special Flood Hazard Areas. We characterize flood using the Gumbel extreme value distribution’s location (μ) and scale (α) parameters. Results reveal that average annual flood loss (AAL) values do not depend on μ, but instead follow an exponential decay pattern with α when damage initiates below the first-floor height of a building (i.e., negative DIP). A linear increasing pattern of the AAL with α is achieved by changing the DIP to the first-floor height (i.e., DIP = 0). The study also demonstrates that negative DIPs have larger associated AAL, thus contributing substantially to the overall AAL, compared to positive DIPs. The study underscores the significance of proper DIP selection in flood risk assessment.

Assessment of Flood Economic Losses Under Climate Change: A Case Study in the Ngan Sau River Basin, Ha Tinh Province and Vietnam

The Ngan Sau River basin, which is situated in Ha Tinh Province of Vietnam, experiences flooding during the rainy season, resulting in significant loss of property and human life. This research aimed to investigate the impact of climate change and land-use variation on flood losses. The study began by simulating the heavy rainfall events in August 2007 using the Weather Research and Forecast model with an ensemble method. Future rainfall was examined through numerical simulation based on pseudo-global warming constructed using six CMIP5 models (MIROC-ESM, MRI-CGM3, GISS-E2-H, HadGEM2-ES, HadGEM2-ES, and CNRM-CM5), and the variation in land-use was obtained from local authorities. Inundations caused by rainfall in 2007 and rainfall in the future were determined by the rainfall-runoff-inundation model. Finally, based on flood maps, land-use, and flood depth-damage functions, the economic losses were computed. The results of the average flood economic loss were $380 million in CTL, whereas the local authorities report an estimated loss of over $300 million. Under the impact of climate change and land-use variation, economic losses ranged from $380 million to $526 million in six CMIP5 models. The result of INMCM4 showed the highest value of $526 million, the results of MRI-CGM3, GISS-E2-H, HadGEM2-ES, and CNRM-CM5 fluctuated around $500 million, and the MIROC-ESM recorded the lowest at $380 million. The damage maps showed that the losses would be highest in urban areas, followed by forest areas, and lowest in agricultural areas. This information is essential for decision-makers to improve solutions for preventing economic losses caused by floods.

Expert-opinion depth-damage functions: what's the variability introduced by the survey setup?

Modelers and policy analysts need accurate depth-damage functions to forecast and mitigate potential flood damages. Often, however, these functions are either unavailable or project constraints and information barriers make it unpracticable to create them. A most frequent choice to overcome this deficiency is to repurpose existing functions developed for other contexts. Nevertheless, although expeditious, this practice introduces confidence problems to the forecasts. In these cases, expert-opinion may provide data to develop depth-damage functions that are worthy of confidence – but exactly how much? Herein we try to quantify the survey-dependent variability of expert-opinion depth-damage functions. For this it is assumed that these are but instances of a stochastic process and several synthetic surveys were simulated varying number of experts and number of calibration questions. The classical model of expert data elicitation is well suited to this experiment. Some key insights of the study were that the number of calibration questions contributes more variability than number of experts to damage functions; survey-dependent damage variability ranged, as a minimum, from ± 10 % to almost 50 %; the classical model is better for characterizing damage uncertainty than equally weighting experts’ estimates. Although further studies are needed to confirm the generalizability of these observations, these insights hopefully are useful to better appraise the implications of expert-opinion depth-damage functions for disaster management policy studies.

Non-structural flood mitigation optimization at community scale: Middle Cedar Case Study

Flooding is the leading natural hazard in Iowa and has resulted in billions of dollars of damage to properties and critical infrastructure over the past couple of decades. Land alterations, urbanization, and changing precipitation regimes increase the magnitude and frequency of flood events. Considering the increasing risk, flood mitigation efforts are significant to reduce future losses. In this study, we present a comprehensive flood mitigation assessment for the cities of Cedar Falls, Cedar Rapids, and Waterloo in Iowa, utilizing various datasets such as property information, flood inundation maps, mitigation costs, and depth-damage functions. The research revealed that flooding has a minimal impact on Waterloo below the 200-year return period flood scenario, but Cedar Falls and Cedar Rapids are significantly vulnerable, requiring more mitigation investments and planning. The study conducted a benefit-cost analysis, indicating that dry floodproofing is the most feasible option to reduce flood impacts in all studied communities. Moreover, the research conducted a climate data-driven analysis, which found that elevating structures significantly increases the number of feasible mitigation options, regardless of various long-term climate projections. The study also analyzed predetermined mitigation budgets, revealing potential avoided losses and benefit-cost ratios for properties with the highest BCRs and prioritizing them to maximize the total benefit to the communities. The study findings offer crucial insights and recommendations to guide decision-makers in the community on prioritizing cost-effective flood mitigation strategies and minimizing flood impact in the studied regions.

An urban waterlogging footprint accounting based on emergy: A case study of Beijing

The waterlogging disaster has become a major threat to the resilient development of cities, which needs a unified and systematic accounting framework to estimate potential waterlogging effects in terms of forming disaster prevention strategies. In this paper, we developed an emergy-based waterlogging footprint accounting framework applying depth-damage functions, input–output (IO) analysis and ecological footprint, considering both economic and environmental factors. Taking the old town Beijing as a case study, we simulated the urban waterlogging distribution with a hydrodynamic model (InfoWorks ICM) over different return periods (1, 2, 3, 5, 10, 20, and 50 years) at high resolution. This study first considered the economic impact caused by urban waterlogging to evaluate the direct economic loss and indirect economic loss by using the depth-damage function and IO model, respectively. The environmental loss was assessed with an ecological footprint and was unified by emergy with economic loss. Then, four emergy-based indicators were established to quantify the specific impacts of waterlogging on urban resilience. The results show that the waterlogging footprints grew constantly by 284.57% from 1 year to 50 years. The waterlogging footprints driven by environmental loss, which were overlooked, accounted for 13.02% of the total. The waterlogging footprint density and waterlogging resilience index showed high spatial heterogeneity, indicating that the sensitivity of subdistricts to waterlogging varies greatly. Our study provides a useful energy-based tool for assessing urban waterlogging losses and supports decision-making for waterlogging disaster mitigation and risk zoning management.

Sea-Level Rise Effects on Changing Hazard Exposure to Far-Field Tsunamis in a Volcanic Pacific Island

Coastal flooding exacerbated by climate change is recognised as a major global threat which is expected to impact more than a quarter of all people currently residing in Pacific Island countries. While most research in the last decade has focused on understanding the dynamics and impacts of future coastal flooding from extreme sea levels, the effects of relative sea level rise (RSLR) on exacerbating tsunami hazards are not well understood. Far-field or distant sourced tsunamis tend to have relatively lower impacts in Pacific Island states compared with locally sourced events, but there is limited understanding of how the impact of far-field tsunamis changes over time due to RSLR. Using the hydrodynamics software BG-Flood, we modelled the Tōhoku-oki tsunami from propagation to inundation in Samoa under incremental SLR to examine the effects that RSLR has on changing the exposure of the built environment (e.g., buildings) to a far-field tsunami. Outputs of maximum tsunami inundation and flow depth intensities which incorporate incremental SLR were then combined with digital representations of buildings and depth-damage functions in the RiskScape multi-hazard risk modelling software to assess the changes in building exposure over time. Results suggest that the impacts of Tōhoku-oki-type far-field tsunamis become significant once RSLR reaches 1 m above present levels. Present-day building exposure will increase by approx. 500% with 1 m RSLR by 2080–2130, and approx. 2350% with 2 m RSLR by as early as 2130–2140. These findings provide useful insights for application to tsunami hazard risk assessments under changing sea level conditions in analogous island environments.

GIS-based fuzzy comprehensive evaluation of urban flooding risk with socioeconomic index system development.

Due to the climate change-induced extreme rainfall, urban flooding risk is one of the major concerning risks in the near future with accelerating occurrence frequency and intensity. To systematically evaluate the socioeconomic impacts induced by urban flooding, this paper proposed a GIS-based spatial fuzzy comprehensive evaluation (FCE) framework for local government to efficiently take contingency measures especially under urgent rescue conditions. The risk-assessing procedure could be investigated in 4 aspects: 1) application of the hydrodynamic model to simulate the depth and extent of inundation; 2) quantification of the impact of flooding with 6 methodically picked evaluation indexes concerning the transportation attenuation, residential security, and tangible and intangible monetary losses according to depth-damage functions; 3) implementing FCE method: comprehensive evaluation of urban flooding risk with the diverse socioeconomic indexes by fuzzy theory; and 4) presenting the risk maps of single and multiple impact factors intuitively in ArcGIS platform. The detailed case study in SA city validates the effectiveness of the adopted multiple index evaluation framework, which could help detect higher risk areas with low transport efficiency, high economic loss, high social impact, and high intangible damage. The results of single-factor analysis can also provide feasible suggestions for decision-makers and other stakeholders. Theoretically, the proposed method tends to improve the evaluation accuracy as the inundation distribution can be simulated by hydrodynamic model rather than subjective prediction with hazard factors, while the impact quantification with flood-loss models can also directly reflect the vulnerability of involved factors instead of empirical weight analysis of traditional methods. Besides, the results illustrate that the areas with higher risk levels reasonably coincide with severe inundation situations and dense hazard-bearing bodies. This systematic evaluation framework can support applicable references for further extension to other similar cities.

Development and Demonstration of an Interactive Tool in an Agent-Based Model for Assessing Pluvial Urban Flooding

Urban pluvial floods (UPFs) are a threat that is expected to increase with economic development, climate change, and the proliferation of urban cover worldwide. Methods to assess the spatiotemporal magnitude of UPFS and their impacts are needed to research and explore mitigation measures. This study presents a method for the assessment of UPFs and their impacts by combining a hydrodynamic sewer system model with a GIS-based overland diffusive flow algorithm. The algorithm is implemented in the software GIS-based Agent-based Modeling Architecture (GAMA) along with the depth-damage functions and land use data to estimate financial impacts. The result is a dynamic and interactive model that allows the user to monitor the events in real-time. Functionality is demonstrated in a case study in Dresden, Germany and with ten to 100-year design storms. The majority of flood extents and damages occur in the early stages of the event. Sewer surcharge emerges from few of the manholes, suggesting early action vitally reduces flood risks and interventions at a few hot spots, largely reducing impacts. Flood protection barriers were interactively implemented as a potential response measure in the hot spot areas reducing the damage by up to 90%. The user can compare different parameters in a visually compelling way that can lead to a better understanding of the system and more efficient knowledge transfer.

Estimating Pluvial Depth–Damage Functions for Areas within the United States Using Historical Claims Data

Flooding has been the most costly natural disaster over the last 2 decades within the US. Therefore, recent research has focused on more accurately predicting economic losses from flooding to aid decision makers and mitigate economic exposure. For this, depth–damage functions have commonly been employed to predict the relative or absolute damage to buildings caused by different magnitudes of flooding. Although depth–damage functions, such as those adopted by the US Army Corps of Engineers, are widely available for fluvial and coastal flooding, less work has been done to develop functions for pluvial-induced flooding. Here, we use a database containing 13.5 million claims to develop pluvial depth–damage functions. For this, recently released flood hazard data are utilized to identify claims within the database that are likely related to pluvial flooding. We employed two types of regression models to fit the depth–damage functions. Secondarily, we developed an automated valuation model (AVM) to estimate building values across the state of New Jersey. These building values were then combined with flood hazard layers in order to apply the depth–damage functions and compute an aggregate annualized loss for New Jersey. The results indicated moderate agreement between the observed damage within the state of New Jersey and that computed by applying the study-developed depth–damage curves to buildings within the state using pluvial flood hazard layers. It is anticipated that the depth–damage functions developed by this research will aid future work in more accurately quantifying the economic risks associated with flooding across the US.

Quantitative risk assessment of typhoon storm surge for multi-risk sources

Typhoon storm surge (TSS) is a complex marine disaster affected by multi-risk sources. Quantitative risk assessment is an important prerequisite for identifying risk areas and designing risk reduction strategies. This paper aims to propose a rapid, accurate, and comprehensive quantitative risk assessment method for TSS under multi-risk sources, including disaster occurrence probability and severity. First, identify the primary risk sources according to the disaster-causing mechanism of TSS. Then, based on the official public data from 1989 to 2020, the dependence structure among multi-risk sources is constructed using Copulas to calculate the probability of each superposition scenario. Meanwhile, build visual scenario databases employing Geographical Information System (GIS) techniques. Subsequently, the extent and depth of inundation are translated into economic risk and population risk using GIS and depth-damage functions. Finally, taking the “Mangkhut” as a case study, the method’s feasibility and accuracy are verified. The results show that the primary risk sources of TSS are storm tide, astronomical tide and coastal waves. The Gumbel Copula is optimal, with OLS (ordinary least squares) and D of 0.0186 and 0.1831, respectively. The probability assessment under different superposition scenarios indicates that the greatest threat of TSS in Guangzhou comes from the storm tide and the astronomical tide. As for the “Mangkhut” case study in Jiangmen City, the assesses occurrence probability is 0.0355%, the accuracy of economic risk assessment (except mariculture) is 95.28%, and the accuracy of population risk assessment is 98.60%. Residences and the disaster-bearing bodies in 0–3 m inundation depth are most severely affected by TSS disasters. Measures such as locating residential and important buildings away from the shoreline (at least 10 km) and ground (above 3 m), formulating disaster emergency plans, and developing the forecast and prevention of storm tides and astronomical tides will help ensure the safety of residents’ life and property. This paper provides an efficient and accurate method, which is of great significance for disaster control, sustainable development, and decision-making.

The utilization of physically based models and GIS techniques for comprehensive risk assessment of storm surge: A case study of Huizhou

Quantitative analyses of storm surge risk, which are mostly focused on physical vulnerability, have been widely used to help coastal communities mitigate impacts and damage. Such assessments, however, overlook the social aspect of vulnerability in storm surge risk. By considering both the community’s social vulnerability and buildings’ physical vulnerability, this study proposed a methodology that incorporates social vulnerability into the framework for making quantitative risk assessments of storm surge using a coupled hydrodynamic and wave model, Geographical Information System (GIS) techniques, and the Principal Component Analysis (PCA) method. The coastal area of Huizhou was chosen as the case study due to its high concentration of oil and gas infrastructure in China’s southern Guangdong Province. By combining hazard, exposure, physical vulnerability, and social vulnerability, it was possible to explore the effect of social vulnerability on the physical vulnerability-based risk assessment of storm surge and determine the overall risk level. First, the Gumbel distribution was utilized to establish five representative and plausible hypothetical typhoon events with different return periods (10, 20, 50, 100, and 1000 years) for the study area. Then, using the well-validated fully-coupled model, the Simulating Waves Nearshore (SWAN) model and the ADvanced CIRCulation (ADCIRC) model, storm surge simulations for defined return periods were run, and the geographical distribution of the maximum surge elevations displayed on a GIS platform was used to assess hazard levels. In terms of the physical aspect, the depth-damage functions for buildings were established to estimate direct economic losses and assign risk levels accordingly. For the social vulnerability of a community, a composite score was computed using the PCA method by combining and aggregating indicators representing various characteristics of the social group. The results show that the overall risk level, taking into account both social vulnerability and physical vulnerability, has decreased on average. It suggests that social vulnerability-based risk assessment may account for a significant portion of the overall risk assessment, which is frequently overlooked in traditional storm surge risk assessment. Additionally, the comprehensive and precise risk maps can assist local policymakers in identifying areas at different risk levels and developing evacuation plans, thereby minimizing potential losses, especially in high-risk areas.

Economically optimizing elevation of new, single-family residences for flood mitigation via life-cycle benefit-cost analysis

Construction with freeboard—vertical height of a structure above the minimum required—is commonly accepted as a sound investment for flood hazard mitigation. However, determining the optimal height of freeboard poses a major decision problem. This research introduces a life-cycle benefit-cost analysis (LCBCA) approach for optimizing freeboard height for a new, single-family residence, while incorporating uncertainty, and, in the case of insured homes, considering the costs from losses, insurance, and freeboard (if any) to the homeowner and National Flood Insurance Program (NFIP) separately. Using a hypothetical, case study home in Metairie, Louisiana and U.S. Army Corps of Engineers design depth-damage functions for generic inland flooding, results show that adding 2 ft of freeboard at the time of construction might be considered the optimal option given that it yields the highest net benefit, but the highest net benefit-cost ratio occurs for the 1 ft freeboard. Even if flood loss reduction is not considered when adding freeboard, the savings in annual insurance premiums alone are sufficient to recover the construction costs paid by the homeowner if at least one foot of freeboard is included at construction. Collectively, these results based on conservative assumptions suggest that at the time of construction, even a small amount of freeboard provides a huge savings for the homeowner and (especially) for the financially-strapped National Flood Insurance Program. For community planners, the results suggest that wise planning with reasonable expectations on the front end makes for a more sustainable community.

Development of Time–Depth–Damage Functions for Flooded Flexible Pavements

The first step toward building pavement structures that are resilient to flooding is to have a proper understanding of the impact of inundation on the pavement. Depth-damage functions have been developed and are widely used to quantify flood-induced damage to buildings. However, such damage functions do not exist for roadway pavements. The objective of this study is to develop a methodological framework to model postflooding road damage by identifying the importance of several parameters including flood duration, flood depth, flood pattern (including real flood data), transfer functions, pavement materials, and analysis location. Pavement serviceability and costs are introduced into the evaluation as well. The long-term goal is a tool for decision makers to use in planning and management of flooding events for more resilient pavements and allocation of budgets. It is established that the most important parameters that should be accounted for by decision makers are the flood duration, combination of the materials, critical location on the roadway (both vertical and lateral), and use of appropriate transfer functions. Opening the roadway to traffic immediately after the floodwater recedes will lead to earlier and more significant deterioration of the pavement and more costly maintenance and reconstruction.

Commercial Real-Estate at Risk: An Examination of Commercial Building and Economic Impacts in the United States Using a High-Precision Flood Risk Assessment Tool

Environmental changes are predicted to exacerbate changes in flood events, resulting in consequences for exposed systems. While the availability and quality of flood risk analyses are generally increasing, very little attention has been paid to flood impacts related to the commercial market. This is notable given that the commercial market is often made up of the most valuable physical structures in communities, employs much of the local labor force, and generally plays a key role in the sustainability of economies. This study provides the first national spatial model of flood risk for commercial and multi-unit residential buildings at a property level resolution within the United States. This is achieved through the use of high-resolution inputs (hazard and property data), flood hazard information for the four major flood types, multi-return period hazard information, component-based depth-damage functions, GDP and economic multipliers information, and future facing projections. This study estimates that over the next 30 years, the absolute count of commercial and multi-unit buildings with risk will increase 8%, structural damage costs will increase 25.4%, downtime days will increase 29.1%, and economic impacts will increase 26.5%. Additionally, these impacts are concentrated in certain spatial locations. A high resolution model capturing flood risk as related to these commercial buildings is important for a comprehensive understanding of overall flood risk within the United States.Classification CodesJEL C30, E00, G17, M20, R10, R30

The proposal of the Coast-RiskBySea: COASTal zones RISK assessment for Built environment bY extreme SEA level, based on the new Copernicus Coastal Zones data

Natural hazards and the long-term effects of climate change could have disastrous consequences for urban settlements, in this context, risk assessment is a key factor in supporting urban designers and planners. The research proposes a simplified model for the assessment of coastal risk on built environment expressed in terms of potential, direct and tangible, economic damages. The Coast-RiskBySea (COASTal zones RISK assessment for Built environment bY extreme SEA level) is developed in GIS and the risk is evaluated based on the IPCC AR5 and AR6 frameworks through an equation that combines exposure, vulnerability and hazard. The first step is the creation of the land use grid derived from the Copernicus Coastal Zones database, after which then damage scenarios are calculated using depth-damage functions that enable to translate climate impacts into economic damages (exposure). Vulnerability is then assessed in relation to coastal elevation through the DTM and, finally, climate projections of Extreme Sea Level (ESL) are introduced (hazard). To test and verify the approach, the model is applied to the metropolitan city of Naples, Italy, the results show multiple areas at medium and high risk for ESL events with significant economic impacts. From the output maps it is possible to identify the higher risk areas and to understand how, with significant mitigation actions, the risks could be significantly reduced. In conclusion, the Coast-RiskBySea, as it has been developed with input data characterised by a homogeneous spatial coverage, it can be replicable in all EU coastal zones that dispose of a DTM with adequate vertical accuracy.

Development of flood vulnerability curves for Sri Lankan hospitals

PurposeThe government-led public healthcare services in Sri Lanka became a major strength in managing the COVID-19 comparatively well. However, natural hazards are a major threat to this healthcare system, as they cause severe damages, especially to curative healthcare infrastructures such as hospitals. Floods have been the major contributor to the economic loss of the Sri Lankan healthcare system. Therefore, the purpose of this study is to develop a proper flood risk assessment framework for Sri Lankan hospitals.Design/methodology/approachThis research study has attempted to develop a flood vulnerability assessment tool for hospitals using the concept of Depth Damage Functions (DDFs). Flood vulnerability curves have been developed for identified critical units of hospitals considering the damage caused to building contents which are predominantly expensive medical equipment. The damage caused only by wetting was considered in generating vulnerability curves. Structured interviews were conducted with government officials in the healthcare sector to gather details on the cost and damages of medical equipment. Pilot studies were carried out in two hospitals identified as located in flood-prone areas and have previous experiences of flooding, to acquire data regarding building contents of the critical units.FindingsThe developed vulnerability curves indicate that no major damage would occur to building contents in critical units (other than the labor room) until the inundation depth reaches a value of 0.6–0.9 m (varies for each type of unit). It is also noteworthy that after a certain range in the inundation depth, the damage increases drastically, and building contents would incur total damage if the inundation depth passes a value of 1.2–1.5 m.Originality/valueThis study explains the initial phase of developing a flood vulnerability assessment framework for Sri Lankan hospitals. Not many studies had been carried out to assess the vulnerability of hospitals specifically for floods using vulnerability curves. The study recommends a zoning system with pre-defined vulnerability levels for critical units during a flood, which can be associated with evacuation planning as well. Further studies must be carried out to verify this system for hospitals in Sri Lanka.

Flood depth-damage and fragility functions derived with structured expert judgment

Depth-damage relationships of residential typologies have an important bearing on the reliability of flood risk assessments. Yet, the information for creating adequate functions is frequently missing in places where they are needed the most: developing countries where floods inflict severe losses. To overcome the lack of site-specific functions, modelers usually adopt functions from foreign typologies, extrapolate from limited flood damage data, and conduct expert-elicitation surveys. The latter, although not exempt from challenges, are a helpful resource in situations in which there are sufficient experts available. The classical model is a structured expert judgment technique which is one of the most used because it minimizes the number of subjective elements in the ranking of experts’ experience and its results can be compared with similar studies. This paper presents depth-damage and fragility functions developed with the classical model conducted with experts with extensive knowledge in the flood damage mechanism of typologies in northeast Argentina—a region under a severe flood hazard. The depth-damage functions were partially validated with damage data and exhibited good agreement, also they are consistent with the fragility functions. Hence, these functions can be helpful for probabilistic flood risk assessments, provided that the underlying assumptions are deemed acceptable.

Uncertainty and Bias in Global to Regional Scale Assessments of Current and Future Coastal Flood Risk.

This study provides a literature‐based comparative assessment of uncertainties and biases in global to world‐regional scale assessments of current and future coastal flood risks, considering mean and extreme sea‐level hazards, the propagation of these into the floodplain, people and coastal assets exposed, and their vulnerability. Globally, by far the largest bias is introduced by not considering human adaptation, which can lead to an overestimation of coastal flood risk in 2100 by up to factor 1300. But even when considering adaptation, uncertainties in how coastal societies will adapt to sea‐level rise dominate with a factor of up to 27 all other uncertainties. Other large uncertainties that have been quantified globally are associated with socio‐economic development (factors 2.3–5.8), digital elevation data (factors 1.2–3.8), ice sheet models (factor 1.6–3.8) and greenhouse gas emissions (factors 1.6–2.1). Local uncertainties that stand out but have not been quantified globally, relate to depth‐damage functions, defense failure mechanisms, surge and wave heights in areas affected by tropical cyclones (in particular for large return periods), as well as nearshore interactions between mean sea‐levels, storm surges, tides and waves. Advancing the state‐of‐the‐art requires analyzing and reporting more comprehensively on underlying uncertainties, including those in data, methods and adaptation scenarios. Epistemic uncertainties in digital elevation, coastal protection levels and depth‐damage functions would be best reduced through open community‐based efforts, in which many scholars work together in collecting and validating these data.

Assessing the economic impacts of future fluvial flooding in six countries under climate change and socio-economic development

Floods are among the most frequent and costliest natural hazards. Fluvial flood losses are expected to increase in the future, driven by population and economic growth in flood-prone areas, and exacerbated in many regions by effects of climate change on the hydrological cycle. Yet, studies assessing direct and indirect economic impacts of fluvial flooding in combination with climate change and socio-economic projections at a country level are rare. This study presents an integrated flood risk analysis framework to calculate total (direct and indirect) economic damages, with and without socio-economic development, under a range of warming levels from < 1.5 to 4 °C in Brazil, China, India, Egypt, Ethiopia, and Ghana. Direct damages are estimated by linking spatially explicit daily flood hazard data from the Catchment-based Macro-scale Floodplain (CaMa-Flood) model with country- and sector-specific depth-damage functions. These values input into an economic Input-Output model for the estimation of indirect losses. The study highlights that total fluvial flood losses are largest in China and India when expressed in absolute terms. When expressed as a share of national GDP, Egypt faces the largest total losses under both the climate change and climate change plus socio-economic development experiments. The magnitude of indirect losses also increased significantly when socio-economic development was modelled. The study highlights the importance of including socio-economic development when estimating direct and indirect flood losses, as well as the role of recovery dynamics, essential to provide a more comprehensive picture of potential losses that will be important for decision makers.