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Global exposure to river and coastal flooding: Long term trends and changes

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  • IVM - VU & GFDRR - World Bank
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... They concluded the increase in global flood exposure is due mainly to increased exposure in many low-latitude regions, particularly Asia and Africa. In the study by Jongman et al. (2012), the global total population exposed to a 1 in 100-year flood is simulated to reach 1.05 billion in 2050 and this number could well be underestimated as they assumed the hazard area remains constant over time. Milly et al. (2002) found that future changes of 1 in 100-year flood will increase in almost all the 29 major river basins in the world. ...
... The risks associated with the projected changes in flood hazard to human society were estimated by calculating present and future populations exposed to flood hazard. In line with previous studies (Jongman et al. 2012;Hirabayashi et al. 2013;Winsemius et al. 2016;Arnell et al. 2018) we calculated for each climate scenario the sum of the population living in the modelled inundation areas in which annual maximum discharge exceeds the Q100-20C flood. Since future exposure to fluvial flooding will be driven by both socioeconomic and climate changes also used population changes according to one of the Shared Socioeconomic Pathways (SSP2, (Jones and O'Neill 2016) as given in the spatial population scenarios dataset. ...
... Hence, our projection provides the potential risk of flooding, irrespective of non-climatic factors such as land-use changes, river improvements or flood mitigation efforts (Hirabayashi et al. 2013). For places that have sufficient protection against 1 in 100-year flood, this means that some of the modelled flooding areas will in reality not be flooded and can lead to overestimation of inundation areas (Jongman et al. 2012) and population exposure. ...
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We project climate change induced changes in fluvial flood risks for six global warming levels between 1.5 and 4 °C by 2100, focusing on the major river basins of six countries. Daily time series of precipitation, temperature and monthly potential evapotranspiration were generated by combining monthly observations, daily reanalysis data and projected changes in the five CMIP5 GCMs also selected in the ISI-MIP fast track project. These series were then used to drive the HBV hydrological model and the CaMa-Flood hydrodynamic model to simulate river discharge and flood inundation. Our results indicate that return periods of 1 in 100-year floods in the late twentieth century (Q100-20C) are likely to decrease with warming. At 1.5 °C warming, 47%, 66%, 27%, 65%, 62% and 92% of the major basin areas in Brazil, China, Egypt, Ethiopia, Ghana and India respectively experience a decrease in the return period of Q100-20C, increasing to 54%, 81%, 28%, 82%, 86% and 96% with 4 °C warming. The decrease in return periods leads to increased number of people exposed to flood risks, particularly with 4 °C warming, where exposure in the major river basin areas in the six countries increases significantly, ranging from a doubling (China) to more than 50-fold (Egypt). Limiting warming to 1.5 °C would avoid much of these increased risks, resulting in increases ranging from 12 to 1266% for the 6 countries.
... The flooding and intensity of the floods will likely grow over time (Ali et al., 2019;Bresinger et.al, 2016;Mukherjee et al., 2018). Between 2010 and 2050, research estimate a twofold to threefold increase in exposure to river and coastal flooding (Huong & Pathirana, 2013;Januriyadi et al., 2018;Jongman et al., 2012). Increases in population and rampant construction of buildings and roads to address the growing population needs regularly contribute to water logging after a heavy downpour. ...
... As past research show (Hallegatte et al., 2013;Jongman et al., 2012;Sundermann et al., 2014), factors such as rising sea levels, urbanization, especially along coastlines and large water bodies, deforestation, aging infrastructure, and ruralto-urban population shifts increase the occurrences of flood. In urban areas, causes of flooding also include improper design of drainage and wastewater systems (Dewan, 2013;Swan, 2010). ...
Article
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Flooding is a critical issue affecting many countries worldwide with severe consequences on the lives of their residents. In this paper, we conduct a case study of the flood management policies of India by evaluating their implementation in six Indian states that are affected by recurrent flooding every year. The states selected have major cities located near water bodies and have experienced flooding leading to deaths and displacement besides slowing down economic and community development. We evaluate how each of the states align their policies to the national level disaster management guidelines for flood management. We find that for policies established at the state level, implementation within the various regions can vary with some urban areas going beyond the state and national guidelines. We find that not all Indian states follow the established national guidelines, and this poses questions on the challenges on having uniform flood management policies for addressing a complex issue.
... There has been a growing need to build a range of different structures in the ocean to support the blue economy and coastal population growth, with these structures expected to be increasingly vulnerable to the impacts of climate change, including sea level rise and the changing frequency and intensity of extreme storms [1][2][3][4][5][6][7]. As a result, studies on the dynamic interactions between wave-driven ocean processes and coastal/offshore structures are vital, given how wave impacts affect coastal protection and the safety of marine structures. ...
... Wave Impact Types >2.5 Impulsive 1.2-2.5 Dynamic <1. 2 Quasi-static For a given wave's time series, the total number N of individual wave impacts was grouped into the three wave impact types, N = A + B + C, where A is the number of impulsive wave impacts, B is the number of dynamic wave impacts, and C is the number of quasi-static wave impacts in series. Thus, the probability of three such types of wave impact could be calculated as follows: PROim = A/N, PROdyn = B/N, and PROqs+ = C/N, where PROim, PROdyn, PROqs+ represent the probability of impulsive, dynamic, and quasi-static wave impact, respectively. ...
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When the fundamental natural frequency of marine structures is comparable to the dominant frequency of incident waves, the response of the load on the structure will be amplified. Accurately quantifying how wave loads can be amplified by incident wave conditions must thus be considered in any structural analysis, given how sensitive these characteristics are to different wave impact types. Systematic physical model tests of wave impacts on the simple horizontal plate and the vertical wall with a horizontal overhanging cantilever slab were performed. By first comparing quasi-static wave load estimates along a simple horizontal plate (obtained by low-pass filtering the pressure time series at different cutoff frequencies) with quasi-static uplift pressures from established predictive formulations, a cutoff frequency of 7 Hz was found to accurately separate the quasi-static component from impulsive wave impacts. By applying the low-pass filtering approach with the selected cutoff frequency to the pressure measurements for the vertical wall with a horizontal cantilever slab case, the impulsive and quasi-static peaks were attained, which were then used to quantify the probabilities of individual impulsive, dynamic, and quasi-static wave impacts. Incoming wave conditions and structural clearance had a significant effect on the probabilities of different wave impacts. With the increasing wave height and wave steepness, wave impacts on the horizontal slab and vertical wall were increasingly of the impulsive type and less frequently of the quasi-static type, while the probability of dynamic impact types were relatively stable. As the overhanging slab was shifted from elevated to submerged, the dominant type of wave impact on the structure was variable, ranging from impulsive to dynamic to quasi-static as its elevation was lowered. The results indicated that up to 90% of the impacts were of the impulsive type when the overhanging slab was on or slightly over the still water level. Moreover, the presence of the vertical wall increased the magnitude of wave loads and the occurring frequency of impulsive wave impacts for the horizontal slab.
... For the HYDE vulnerability method, we follow the approach outlined in Ward et al. (2013). Maximum damages for each country are calculated using a GDP normalization equation from Jongman et al. (2012) applied to a maximum damage value from the Damagescanner model (Klijn et al., 2007). To convert the maximum damage values from 2005 USD into 2010 EUR (to ensure consistency with the Huizinga et al. (2017) database), we use the average annual inflation from 2005 to 2010 and the average USD to EUR exchange rate for 2010. ...
... Maximum damage values are calculated for each country following the approach ofJongman et al. (2012) which uses a country's GDP to normalize maximum damages obtained from the Damagescanner tool. Damages are calculated exclusively for urban areas which are derived from the HYDE (Klein Goldewijk et al., 2010) fractional urban landcover data set at 5 arc min resolution (∼10 km at the equator). ...
Article
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Over the last two decades, several data sets have been developed to assess flood risk at the global scale. In recent years, some of these data sets have become detailed enough to be informative at national scales. The use of these data sets nationally could have enormous benefits in areas lacking existing flood risk information and allow better flood management decisions and disaster response. In this study, we evaluate the usefulness of global data for assessing flood risk in five countries: Colombia, England, Ethiopia, India, and Malaysia. National flood risk assessments are carried out for each of the five countries using six data sets of global flood hazard, seven data sets of global population, and three different methods for calculating vulnerability. We also conduct interviews with key water experts in each country to explore what capacity there is to use these global data sets nationally. We find that the data sets differ substantially at the national level, and this is reflected in the national flood risk estimates. While some global data sets could be of significant value for national flood risk management, others are either not detailed enough, or too outdated to be relevant at this scale. For the relevant global data sets to be used most effectively for national flood risk management, a country needs a functioning, institutional framework with capability to support their use and implementation.
... Several populations living in low-lying areas around the world (e.g., deltaic regions) are protected by flood protection such as levees, seawalls, and deliberately raised structures (e.g., buildings on stilts; Scussolini et al. 2016;Nicholls et al. 2019). Just like previous flood exposure studies (Neumann et al. 2015;Hanson et al. 2011;Kulp and Strauss 2019;McGranahan et al. 2007;Jongman et al. 2012;Lichter et al. 2011), our exposure estimates do not account for these protection tactics because they can be overtopped and breached. While this omission is common practice in exposure assessment (McClean et al. 2020), it is not always apparent to policy makers, stakeholders, and decision-makers who must interpret such information. ...
Article
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Estimates of changes in the frequency or height of contemporary extreme sea levels (ESLs) under various climate change scenarios are often used by climate and sea level scientists to help communicate the physical basis for societal concern regarding sea level rise. Changes in ESLs (i.e., the hazard) are often represented using various metrics and indicators that, when anchored to salient impacts on human systems and the natural environment, provide useful information to policy makers, stakeholders, and the general public. While changes in hazards are often anchored to impacts at local scales, aggregate global summary metrics generally lack the context of local exposure and vulnerability that facilitates translating hazards into impacts. Contextualizing changes in hazards is also needed when communicating the timing of when projected ESL frequencies cross critical thresholds, such as the year in which ESLs higher than the design height benchmark of protective infrastructure (e.g., the 100-year water level) are expected to occur within the lifetime of that infrastructure. We present specific examples demonstrating the need for such contextualization using a simple flood exposure model, local sea level rise projections, and population exposure estimates for 414 global cities. We suggest regional and global climate assessment reports integrate global, regional, and local perspectives on coastal risk to address hazard, vulnerability and exposure simultaneously. Supplementary information: The online version contains supplementary material available at 10.1007/s10584-021-03288-6.
... Many studies focused on the flood risk assessment at the local, regional or national scale, while future climate change concerns have increased the need for the flood risk assessment with both spatial and temporal dynamics [43]. In other words, results will be various when multi-criteria analysis methods are applied at different temporal-spatial scales for flood risk assessment. ...
Article
China suffers the most serious loss of life and property with the most floods in the world. In this study, a multi-criteria analysis model with the combined analytic hierarchy process and Entropy weight method (AHP-Entropy) was proposed to assess the long and short-term flood risk in Poyang Lake basin, and results were verified by several flood events that happened on July 2020. Considering multi-factors of flood risk, six flood hazard factors (namely, maximum three-day rainfall (RMAX3), annual average rainstorm frequency (RF), annual average rainstorm amount (ARA), drainage density (DD), slope, elevation (DEM)) and four flood vulnerability factors (namely, population density (PD), land use pattern (LUP), GDP, normalized difference vegetation index (NDVI)) were selected and weights of them were derived from the AHP-Entropy method. Results show that PD (0.168), RMAX3 (0.163), LUP (0.146), GDP (0.129), and RF (0.111) play a vital role in the results of flood risk assessment. Spatially, the long and short-term flood risk maps are shown to have similar characteristics with correlation coefficient of 0.9056. Areas with high risk and very high risk account for 19.6% of the total area in the long-term flood risk map and increased to 22.2% in the short-term flood risk map. Overall, the northeastern parts of the Poyang Lake basin are more prone to floods and the flood risk gradually decreases from the Poyang Lake towards the surrounding areas. Verification of the results with Sentinel-1 synthetic aperture radar data shows that the flood risk assessment model has an accuracy of more than 50% in very high risk zones for floods, and more than 90% for high and very high risk floods, which showed that the presented model is reliable in flood risk assessment.
... In the coastal-estuarine region, coastal cities are widely distributed because the flat terrain, accessible transportation and adequate water resources provide convenient conditions for urban human settlements [9]. As those coastal cities have experienced much higher economic growth than the inner cities, together with higher population density, coastal flood damages in coastal-estuarine regions are expected to increase significantly [10][11][12]. Therefore, reliable flood risk estimate is demanded in coastal-estuarine regions [13]. ...
Article
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In coastal areas of southeastern China, multiple flood drivers such as river flow, precipitation and coastal water level can lead to compound flooding which is often much greater than flooding simulated by one flood driver in isolation. Bivariate probability distributions accounting for compound flooding from river discharge and sea level were constructed based on MvCAT (Multivariate Copula Analysis Toolbox) combined with goodness of fit tests in 15 coastal-estuarine regions of Southeastern China. Flood typing-based bivariate probability distributions considering multiple flood-generating mechanisms were also built. Our results indicated that the performance of flood typing-based bivariate distribution was not significantly better than the bivariate probability distribution in coastal-estuarine regions based on the Mann–Whitney U test; the compounding effects of river discharge and sea level had limited impact on bivariate return periods, but had greater impact on coastal flooding risk in terms of design values. Ignoring compounding effects of river discharge and sea level leads to significant underestimation of design values. The results suggest that the compounding effect of river discharge and sea level should be considered when calculating design values in coastal flood risk assessment.
... UNISDR recently noted that the impact on flood affected economies is increasing in all regions of the world [53]. However, the risk of flooding in developed countries is decreasing due to their increasing incomes and increased capacity for disaster prevention and mitigation. ...
Article
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Flash floods are devastating natural disasters worldwide. Understanding their spatiotemporal distributions and driving factors is essential for identifying high risk areas and predicting hydrological conditions. In this study, several methods were used to analyze the changing patterns and driving factors of flash floods in the Altay region. Results indicate that the number of flash floods each year increased in 1980–2015, with two sudden change points (1996 and 2008), and April, June, and July presented the highest frequency of events. Habahe and Jeminay were known to have high flash flood incidences; however, currently, Altay City, Fuhai, Fuyun, and Qinghe are most affected. In terms of driving force analysis, precipitation and altitude performance have a key impact on flash flood occurrence in this settlement compared to other subregions, with a high percentage increase in the mean squared error value of 39, 37, 37, 37, and 33 for 10 min precipitation in a 20-year return period, elevation, 60 min precipitation in a 20-year return period, 6 h precipitation in a 20-year return period, and 24 h precipitation in a 20-year return period, respectively. The study results provide insights into spatial–temporal dynamics of flash floods and a scientific basis for policymakers to set improvement targets in specific areas.
... Of this percentage, the majority of the population at risk is concentrated in developing countries, most located in sub-Saharan Africa and in South and East Asia. The number of people exposed to flood risk has increased enormously in recent decades, surpassing, already in 2020, the estimates made for 2050 (Jongman et al., 2012;Rentschler and Salhab, 2020). This situation is due to various factors, among which climate change plays a fundamental role. ...
... The frequency of severe floods as well as the damages they cause have been increasing in the last decades globally (Jongman et al. 2012; European Environment Agency (EEA) 2019). According to projections, they are going to further rise with our climate changing and precipitation events getting more extreme both in terms of intensity and frequency in many parts of the world (Frei et al. 2006;IPCC 2014;EEA 2019). ...
Article
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Riverine floods cause increasingly severe damages to human settlements and infrastructure. Ecosystems have a natural capacity to decrease both severity and frequency of floods. Natural flood regulation processes along freshwaters can be attributed to two different mechanisms: flood prevention that takes place in the whole catchment and flood mitigation once the water has accumulated in the stream. These flood regulating mechanisms are not consistently recognized in major ecosystem service (ES) classifications. For a balanced landscape management, it is important to assess the ES flood regulation so that it can account for the different processes at the relevant sites. We reviewed literature, classified them according to these mechanisms, and analysed the influencing ecosystem characteristics. For prevention, vegetation biomass and forest extent were predominant, while for mitigation, the available space for water was decisive. We add some aspects on assessing flood regulation as ES, and suggest also to include flood hazard into calculations.
... Studies that use relatively coarse (by current standards) spatial resolution flood hazard data tend to only represent major fluvial floodplains. This means they are unable to capture pluvial flood risk and flooding along secondary rivers, and thus drastically underestimate exposure 3,5,20,21 . One study projects that the global number of flood-exposed people will reach 1.3 billion by 2050 20 , but our study shows that this threshold has already been exceeded by at least 39%. ...
Article
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Flooding is among the most prevalent natural hazards, with particularly disastrous impacts in low-income countries. This study presents global estimates of the number of people exposed to high flood risks in interaction with poverty. It finds that 1.81 billion people (23% of world population) are directly exposed to 1-in-100-year floods. Of these, 1.24 billion are located in South and East Asia, where China (395 million) and India (390 million) account for over one-third of global exposure. Low- and middle-income countries are home to 89% of the world’s flood-exposed people. Of the 170 million facing high flood risk and extreme poverty (living on under $1.90 per day), 44% are in Sub-Saharan Africa. Over 780 million of those living on under $5.50 per day face high flood risk. Using state-of-the-art poverty and flood data, our findings highlight the scale and priority regions for flood mitigation measures to support resilient development.
... Floods are one of the most devastating hazards, regularly causing considerable damage to goods, infrastructure, human well-being, and livelihoods (Nguyen et al., 2020; UNDRR -UN Office for Disaster Risk Reduction, and CRED -Centre for Research on the Epidemiology of Disasters, 2020). Damages and losses due to flooding are predicted to further increase in the future (Jongman et al., 2012;Hirabayashi et al., 2013;Dottori et al., 2018; Intergovernmental Panel on Climate Change, 2021) calling for knowledge-based solutions to reduce current and prevent future flood risks. Understanding the root causes, drivers, patterns, and dynamics of flood risks for people, assets, sectors and systems, and associated uncertainties is important to inform adequate risk management (Adger et al., 2018;Hagenlocher et al., 2020). ...
Article
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River floods are a common environmental hazard, often causing severe damages, loss of lives and livelihood impacts around the globe. The transboundary Lower Mono River Basin of Togo and Benin is no exception in this regard, as it is frequently affected by river flooding. To enable adequate decision-making in the context of flood risk management, it is crucial to understand the drivers of risk, their interconnections and how they co-produce flood risks as well as associated uncertainties. However, methodological advances to better account for these necessities in risk assessments, in data-scarce environments, are needed. Addressing the above, we developed an impact chain via desk study and expert consultation to reveal key drivers of flood risk for agricultural livelihoods and their interlinkages in the Lower Mono River Basin of Benin. Particularly, the dynamic formation of vulnerability and its interaction with hazard and exposure is highlighted. To further explore these interactions, an alpha-level Bayesian Network was created based on the impact chain and applied to an exemplary what-if scenario to simulate changes in risk if certain risk drivers change. Based on the above, this article critically evaluates the benefits and limitations of integrating the two methodological approaches to understand and simulate risk dynamics in data-scarce environments. The study finds that impact chains are a useful model approach to conceptualize interactions of risk drivers. Particularly in combination with a Bayesian Network approach, the method enables an improved understanding of how different risk drivers interact within the system and allows for dynamic simulations of what-if scenarios, for example, to support adaptation planning.
... The situation will be worsened because of the increasing exposure of population and human assets along the coastal area. The number of people living in low elevation coastal zones and their exposure to coastal flooding is highest in Asia and China, India, Bangladesh, Indonesia and Viet Nam [5][6][7] and this is likely to remain unchanged in future [7,8]. In Bangladesh, about 46% of its total population live within 10 m above sea level and approximately 50 million people live below 5 m above the mean sea level [9]. ...
Article
Bangladesh is as a low-lying country, susceptible to various Sea Level Rise (SLR) induced impacts. Previous studies have separately explored SLR effects on Bangladesh's coastal ecosystems and livelihoods, across multiple spatial and temporal scales. However, empirical studies acknowledging local population's perceptions on the causal factors to different SLR induced physiographic impacts, their effects at societal scale and ongoing adaptation to these impacts of SLR have not been able to establish a causal-linkage relationship between these impacts and their potential effects. Our study explores how SLR has already impacted the lives and livelihoods of coastal communities in Bangladesh and how these have been responded by adopting different adaptative measures. We applied a qualitative community-based multistage sampling procedure, using two Participatory Rural Appraisal (PRA) tools, namely Focus Group Discussions (FGDs) and Community Meetings (CM), to collect empirical data about SLR effects on livelihoods and implemented adaptation responses. Our study found that both man-made and natural causes are responsible for different physiographic impacts of SLR, and which seem to vary between place and context. Five major SLR induced impacts were identified by coastal communities, namely: salinity increase, rising water levels, land erosion, waterlogging and the emergence of char land. Salinity increase and land erosion are the two most severe impacts of SLR resulting in the largest economic losses to agriculture. Our results highlight how coastal communities in Bangladesh perceive the impacts of SLR and the benefits of different adaptation processes set in motion to protect them, via development projects and other local interventions.
... Although the period 1992-2008 showed some ups and downs, after that Shannon diversity index becomes very stable. Flood exposure is anticipated to triple by 2050 as a result of population growth and economic development in flood prone areas (Jongman, Ward, and Aerts, 2012). ...
Thesis
Scientific research creates substantial quantities of peer-reviewed literature on a wide variety of constantly growing topics and sub-topics. Scientists and practitioners find it more arduous to assimilate this massive collection of literature. This study explores topic modelling using Latent Dirichlet Allocation (LDA) as a form of unsupervised learning and applies it to 11,187 articles’ abstracts, keywords, and titles from the Web of Science database to provide maximum contextual analyses of hydrology and flood-related literature published since 1939. We identified several essential topics in the corpus. The work structured the body of literature into its principal components, allowing researchers a quick grasp on which topics constitute hydrology and flood research. Relationships between specific individual topics were at times more prominent than others with implications on the interdisciplinary character and extensive impact of the topics such as 'Modeling and Forecasting'. In contrast, the topics 'Wetland and Ecology' and 'Urban Risk Management' are studied largely independently from other topics within hydrology and flood research. Also, trends in research themes were identified. Research on the topics 'Precipitation and Extremes' and 'Coastal hydrology' increased significantly which was not the case for all topics in hydrology and flood. In contrast to the manual literature review, this study used quantitative and qualitative methods and employed labelled topics to examine topic trends, inter topic relationships, and topic diversity. This thesis's methodology and findings may be advantageous to scientists and researchers seeking contextual knowledge of the present condition of flood-related literature. In the long run, we see topic modelling as a tool that will be used to enhance the efficiency of literature reviews, scientific communication, and can benefit science-informed legislation and decision making.
... Consequently, flooding becomes a severe threat for the transport networks by damaging roads, railway lines, and other infrastructural failures, e.g., bridge collapse, massive obstruction of traffic, and such events are leading to increase the repairing cost and significantly affect the regional economy and livelihood by reducing serviceability and accessibility (World Bank 2016; Taylor 2017). Jongman et al. (2012) has projected that the global exposure to river and coastal flooding is 46 trillion USD in 2010, which might be increased by 158 trillion USD by 2050, due to systematic and unplanned socioeconomic development within the flood-prone areas. Recent researched data from the World Resource Institute (WRI) shows that by 2030 the number of people affected by the flood will be doubled from 65 to 132 million due to riverine floods and the loss of urban property will increase by threefold from 157 million USD to 535 million USD (Ward et al. 2020). ...
Chapter
This study combines machine learning (ML) algorithms with statistical models to generate new hybrid models for flood susceptibility mapping (FSM) in the Teesta River basin of Bangladesh (LR). Two-hybrid ML algorithms, such as ANN-LR and RF-LR models for FSM, have been created by combining two ML techniques, such as artificial neural network (ANN) and random forest (RF), with one statistical approach, such as logistic regression. The FSMs were then validated using parametric and non-parametric receiver operating characteristic curves (ROC), such as empirical and binormal ROC. We evaluated the impact of the parameters on FSM using a Random forest-based sensitivity analysis. The extremely high (1023–1120 km2) and high flood vulnerability zones were estimated using three methods and two hybrid models (521–674 km2). Based on the ROC’s area under the curve, the ANN-LR model (ROCe-AUC: 0.883; ROCb-AUC: 0.936) outperformed other models (AUC). According to the validation results, two hybrid models outperformed three machine learning and statistical models. The findings of this research will aid FSMs in building long-term flood control strategies by increasing their efficiency.
... Floods are devastating natural disasters, costing billions in damages annually. Flood risk is projected to increase as a consequence of driving forces such as socioeconomic development (Winsemius et al., 2016), population growth (Jongman et al., 2012), and climate change (IPCC, 2014). Although human adaptation responses can limit trends in flood risks, risk projections often do not consider the interplay between the flood risk environment and the dynamic adaptive behav-cope with flood losses as it provides financial compensation for those impacted during a flood event. ...
Article
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Coastal flood risk is expected to increase as a result of climate change effects, such as sea level rise, and socioeconomic growth. To support policymakers in making adaptation decisions, accurate flood risk assessments that account for the influence of complex adaptation processes on the developments of risks are essential. In this study, we integrate the dynamic adaptive behavior of homeowners within a flood risk modeling framework. Focusing on building‐level adaptation and flood insurance, the agent‐based model (DYNAMO) is benchmarked with empirical data for New York City, USA. The model simulates the National Flood Insurance Program (NFIP) and frequently proposed reforms to evaluate their effectiveness. The model is applied to a case study of Jamaica Bay, NY. Our results indicate that risk‐based premiums can improve insurance penetration rates and the affordability of insurance compared to the baseline NFIP market structure. While a premium discount for disaster risk reduction incentivizes more homeowners to invest in dry‐floodproofing measures, it does not significantly improve affordability. A low interest rate loan for financing risk‐mitigation investments improves the uptake and affordability of dry‐floodproofing measures. The benchmark and sensitivity analyses demonstrate how the behavioral component of our model matches empirical data and provides insights into the underlying theories and choices that autonomous agents make.
... This study focused on the spatial analysis of flood hazard and flood risk at the 100-year flood recurrence interval, with 1% of Annual Exceedance Probability (AEP) scenario, which is commonly used in flood hazard research and policy documents Burton & Cutter, 2008;J. Chakraborty et al., 2014;Grineski et al., 2015;Jongman et al., 2012;Ludy & Kondolf, 2012;Maantay & Maroko, 2009). For simplicity and consistency, the remainder of the paper uses the term "100-year flood hazard" to represent the magnitude of combined fluvial, pluvial, and coastal flooding, which has a 1-in-100 (1%) chance of occurring in any given year. ...
Article
This study presents the first nationwide spatial assessment of flood risk to identify social vulnerability and flood exposure hotspots that support policies aimed at protecting high-risk populations and geographical regions of Canada. The study used a national-scale flood hazard dataset (pluvial, fluvial, and coastal) to estimate a 1-in-100-year flood exposure of all residential properties across 5721 census tracts. Residential flood exposure data were spatially integrated with a census-based multidimensional social vulnerability index (SoVI) that included demographic, racial/ethnic, and socioeconomic indicators influencing vulnerability. Using Bivariate Local Indicators of Spatial Association (BiLISA) cluster maps, the study identified geographic concentration of flood risk hotspots where high vulnerability coincided with high flood exposure. The results revealed considerable spatial variations in tract-level social vulnerability and flood exposure. Flood risk hotspots belonged to 410 census tracts, 21 census metropolitan areas, and eight provinces comprising about 1.7 million of the total population and 51% of half-a-million residential properties in Canada. Results identify populations and the geographic regions near the core and dense urban areas predominantly occupying those hotspots. Recognizing priority locations is critically important for government interventions and risk mitigation initiatives considering socio-physical aspects of vulnerability to flooding. Findings reinforce a better understanding of geographic flood-disadvantaged neighbourhoods across Canada, where interventions are required to target preparedness, response, and recovery resources that foster socially just flood management strategies.
... Coastal zones are extremely vulnerable to extreme sea levels (ESLs; Kron, 2013). Exposure to coastal flooding damage is 20 projected to increase in the future (Jongman et al., 2012) due to higher frequency, magnitude, and duration of extreme sea levels (Tebaldi et al., 2021;Devlin et al., 2021). Relevant causes are the mean sea level rise (Menéndez and Woodworth, 2010;Marcos et al., 2009), and increases in storm surges intensity (Cid et al., 2016;Vousdoukas et al., 2016). ...
Preprint
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Coastal flooding caused by extreme sea levels (ESLs) is one of the major impacts related to the climate change. It is expected to increase in the future due to sea level rise and storm surge intensification. Estimates of return levels obtained under the framework provided by extreme events theory might be biased under climatic non-stationarity. Additional uncertainty is related to the choice of the model. In this work, we fit several extreme values models to a long-term (96 years) sea level record from the city of Venice (NW Adriatic Sea, Italy): a Generalized Extreme Value distribution (GEV), a Generalized Pareto Distribution (GPD), a Point Process (PP), and the Joint Probability Method (JPM) under different detrending strategies. We model non-stationarity with a linear dependence of the model’s parameters from the mean sea level. Our results show that non-stationary GEV and PP models fit the data better than stationary models even with detrended data. The non-stationary PP model is able to reproduce the rate of extremes occurrence fairly well. Actualized estimates of the return levels for non-stationary models are generally higher than estimates from stationary models. Thus, projections of return levels in the future might be significantly different from those calculated using stationary models. Overall, we show that non-stationary extremes analyses can provide more robust estimates of return levels to be used in coastal protection planning.
... L'urbanisation rapide peut provoquer non seulement des débits de pointe importants mais également un volume d'eau élevé des inondations (Al-Ghamdi, Elzahrany, Mirza, & Dawod, 2012); ce qui est à l'origine de la plupart des inondations torrentielles comme à Bamako en 2019 et périodiquement dans la ville de Makkah (Dawod, Mirza, & Al-Ghamdi, 2011). L'expansion des surfaces bâties a conduit à la susceptibilité aux hasards naturels, en particulier les inondations diluviennes (Theilen-Willige & Wenzel, 2019) ; la forte concentration et l'accroissement des villes dans les plaines côtières inondables (Jongman, Ward, & Aerts, 2012 ;Jongman, Koks, Husby, & Ward, 2014). ...
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... The risk from coastal flooding generally increases due to the sea level rise and climate changes (Jongman et al., 2012;Muis et al., 2017). The sea level can reach especially high values where tidal amplitudes are large. ...
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Accurate parameter estimation for the Global Tide and Surge Model (GTSM) benefits from observations with long time-series. However, increasing the number of measurements leads to a large computation demand and increased memory requirements, especially for the ensemble-based methods that assimilate the measurements at one batch. In this study, a memory-efficient parameter estimation scheme using model order reduction in time patterns is developed for a high-resolution global tide model. We propose using projection onto empirical time-patterns to reduce the model output time-series to a much smaller linear subspace. Then, to further improve the estimation accuracy, we introduce an outer-loop, similar to Incremental 4D-VAR, to evaluate model-increments at a lower resolution and subsequently reduce the computational cost. The inner-loop optimizes parameters using the lower-resolution model and an iterative least-squares estimation algorithm called DUD. The outer-loop updates the initial output from the high-resolution model with updated parameters from the converged inner-loop and then restarts the inner-loop. We performed experiments to adjust the bathymetry with observations from the FES2014 dataset. Results show that the time patterns of the tide series can be successfully projected to a lower dimensional subspace, and memory requirements are reduced by a factor of 22 for our experiments. The estimation is converged after three outer iterations in our experiment, and tide representation is significantly improved, achieving a 34.5% reduction of error. The model’s improvement is not only shown for the calibration dataset, but also for several validation datasets consisting of one year of time-series from FES2014 and UHSLC tide gauges.
... Floods are particularly devastating, causing the largest cumulative global economic losses of any natural disaster since 1950 (Podlaha et al. 2020). Furthermore, it is estimated that global exposure to floods will more than triple by 2050, not only because climate change is expected to intensify floods but also because populations and economic assets in flood-prone areas are expanding (Jongman et al. 2012;McGranahan et al. 2007). Since climate change worsens the exposure of the poor in particular (Winsemius et al. 2018), there are concerns that future floods will lead to greater inequality. ...
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... Population density in low-lying urban areas, which are particularly facing the risk of flooding, is growing rapidly in many parts of the world, making cities more vulnerable to the impacts of floods (Filho et al., 2019;Güneralp et al., 2015;Sterzel et al., 2020). At present, more than a half of the world's population lives in cities and by 2050 this figure is estimated to be increased profoundly (Angel et al., 2011;Jongman et al., 2012). There will be nearly a 66% of population living in urban areas by that time and expected to be growing (Leeson, 2018). ...
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... Consequently, flooding becomes a severe threat for the transport networks by damaging roads, railway lines, and other infrastructural failures, e.g., bridge collapse, massive obstruction of traffic, and such events are leading to increase the repairing cost and significantly affect the regional economy and livelihood by reducing serviceability and accessibility (World Bank 2016; Taylor 2017). Jongman et al. (2012) has projected that the global exposure to river and coastal flooding is 46 trillion USD in 2010, which might be increased by 158 trillion USD by 2050, due to systematic and unplanned socioeconomic development within the flood-prone areas. Recent researched data from the World Resource Institute (WRI) shows that by 2030 the number of people affected by the flood will be doubled from 65 to 132 million due to riverine floods and the loss of urban property will increase by threefold from 157 million USD to 535 million USD (Ward et al. 2020). ...
... In relation to this, exposure to risk (in this case flooding) is considered to be the total number of people or the total amount of material goods that may be affected by a given event (torrential rainfall). In this regard, as stated by Jongman et al. (2012), a considerable increase in the population exposed to floods is estimated up to 2050. ...
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In recent years, floods have become one of the natural hazards that generate the greatest economic and human losses on the planet. As is well known, torrential rainfall events are the triggering factor for flooding processes; nevertheless, it is worth examining the responsibility of the human factor, such as urban development, in the occurrence of these potential natural disasters. To this end, rainfall observations obtained during different precipitation events have been analysed. The evolution and urban development from the growth of the number of buildings was also examined. The information obtained has been crossed with the digital cartography of flooded areas (National System of Flood Zones Cartography, SNCZI in Spanish acronym). The results obtained show that the last two extraordinary rainfall events (December 2016 and September 2019) that occurred in the municipalities of Los Alcázares and San Javier (Region of Murcia, SE Spain) exceeded 200 mm, and quantified very high hourly intensities (> 50 mm/h). On the other hand, the number of buildings constructed and the built-up area in both municipalities has increased notably, with an evolution between 1950 and 2019 from 1057 to 15,969 buildings constructed, increasing from 16.09 ha. to 450.06 ha. occupied. This real estate development has caused the number of buildings exposed to flooding to reach 3840 in 2019 for a 10-year RP (return period) and 5941 for a 500-year RP. It can be concluded by indicating the clear influence of territorial transformation on the increase of exposure and economic losses generated by flood events.
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Climate change is presenting an ongoing and eminent threat to various regions, communities and infrastructure worldwide. In this study, the current and future climate impacts faced by Viet Nam due to Tropical Cyclones (TCs), specifically wind and surge, are evaluated, and different adaptation measures to manage this risk are appraised. The level of wind and storm surge risk was assessed focusing on three categories of assets: residential houses, agriculture, and people. The expected damage to these assets was then evaluated based on their exposure to the hazard under current and future climate scenarios. Physical adaptation measures such as mangroves, sea dykes, and gabions, and financial adaptation measures such as risk transfer via insurance were applied to the expected future risk and evaluated based on a socio-economic cost–benefit analysis. The output will give decision-makers the ability to make more informed decisions, prioritize the most cost-effective adaptation measures and increase physical and financial resilience. The results indicated significant TC exposure in future climate scenarios due to economic development and climate change that almost doubles the current expected damage. Surge-related damage was found to be many times higher than wind damage, and houses had more exposure (value in total) than agriculture on a national scale. The physical adaptation measures are successful in significantly reducing the future wind and especially surge risk and would form a resilient strategy along with risk transfer for managing TC risks in the region.
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Flood-induced damage in transport infrastructure (TI) is very prominent and growing rapidly with the increased establishment of human amenities within the flood-prone region of India. The estimation of actual damage of TI for flooding is now an essential task for sustainable planning in future development by reducing financial losses. The Government of India has significantly increased the share of the total GDP from 0.30 to 0.98% in the last ten years (2007–2017) for the development of the transport sector. The regional level statistical investigation has been done to show the temporal development of road networks for the past 60 years. After normalising with the Wholesale Price Index (WPI), the financial damages by flood events have been also presented here for the same period. The result shows that the flooding area of the country has been reduced by ~40% in comparison with the severe phase of the Indian Flood (1970–1980s), whereas the damage of public utility or primarily transport infrastructure loss has been significantly increased by ~240%. Maps are also showing a positive association between higher flood-affected areas and maximum losses of public utility mainly in the states of Lower Gangetic Basin and Eastern Coast of India.
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Green infrastructure (GI) is a strategic planning infrastructure that uses the functions of ecosystems. Under an increased river flood risk, flood-risk management utilizing GI is gaining attention from managers and ecologists in Japan. Flood-control basins are facilities that temporarily store river water in adjacent reservoirs to mitigate flood peaks and gradually drain the water back to the main channels after a flood. GI is expected to provide multiple functions, such as flood-risk reduction and habitat provisions. However, there are limited studies on the ecological functions of flood-control basins. In this article, we first introduce the characteristics of flood-control basins constructed in Japan. Next, we show the ecological importance of flood-control basins in terms of wetland organism biodiversity conservation. Finally, to aid the integration of GI into conventional flood-control measures, we highlight ecological and social issues about introducing and managing flood-control basins.
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Flood damage assessments provide critical insights on processes controlling building damage and loss. Here, we present a novel damage assessment approach to develop an empirical residential building damage database from five flood events in New Zealand. Object‐level damage data was collected for flood hazard and building characteristics, along with relative building component and sub‐components damage ratios. A Random Forest Model and Spearman's Rank correlation test were applied to analyse damage data variable importance and monotonic relationships. Model and test results reveal flood inundation depth above first finished floor level is highly important and strongly correlated with total building damage ratios while flow velocity is important for structure component damage. Internal finishes components contribute highly to total building damage ratios as higher value sub‐component materials are susceptible to direct damage from water contact and indirect damage during repair. The empirical damage data has several implications for damage model development due to the limited heterogeneity of flood hazard intensities and building attributes observed. Extending empirical damage data with synthetic damage data in future would support development of more representative object‐specific damage models to evaluate direct tangible damages for local contexts.
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To assess the potential of radar rainfall nowcasting for early warning, nowcasts for 659 events were used to construct discharge forecasts for 12 Dutch catchments. Four open‐source nowcasting algorithms were tested: Rainymotion Sparse (RM‐S), Rainymotion DenseRotation (RM‐DR), Pysteps deterministic (PS‐D), and probabilistic (PS‐P) with 20 ensemble members. As benchmark, Eulerian Persistence (EP) and zero precipitation input (ZP) were used. For every 5‐min step in the available nowcasts, a discharge forecast with a 12‐hr forecast horizon was constructed. Simulations using the observed radar rainfall were used as reference. Rainfall and discharge forecast errors were found to increase with both increasing rainfall intensity and spatial variability. For the discharge forecasts, this relationship depends on the initial conditions, as the forecast error increases more quickly with rainfall intensity when the groundwater table is shallow. Overall, discharge forecasts using RM‐DR, PS‐D, and PS‐P outperform the other methods. Threshold exceedance forecasts were assessed by using the maximum event discharge as threshold. Compared to benchmark ZP, an exceedance is, on average, forecast 223 (EP), 196 (RM‐S), 213 (RM‐DR), 119 (PS‐D), and 143 min (PS‐P) in advance. The EP results are counterbalanced by both a high false alarm ratio (FAR) and inconsistent forecasts. Contrarily, PS‐D and PS‐P produce lower FAR and inconsistency index values than all other methods. All methods advance short‐term discharge forecasting compared to no rainfall forecasts at all, though all have shortcomings. As forecast rainfall volumes are a crucial factor in discharge forecasts, a future focus on improving this aspect in nowcasting is recommended.
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The combined effect of global sea level rise and land subsidence phenomena poses a major threat to coastal settlements. Coastal flooding events are expected to grow in frequency and magnitude, increasing the potential economic losses and costs of adaptation. In Italy, a large share of the population and economic activities are located along the low-lying coastal plain of the North Adriatic coast, one of the most sensitive areas to relative sea level changes. Over the last half a century, this stretch of coast has experienced a significant rise in relative sea level, the main component of which was land subsidence; in the forthcoming decades, climate-induced sea level rise is expected to become the first driver of coastal inundation hazard. We propose an assessment of flood hazard and risk linked with extreme sea level scenarios, under both historical conditions and sea level rise projections in 2050 and 2100. We run a hydrodynamic inundation model on two pilot sites located along the North Adriatic coast of Emilia-Romagna: Rimini and Cesenatico. Here, we compare alternative extreme sea level scenarios accounting for the effect of planned and hypothetical seaside renovation projects against the historical baseline. We apply a flood damage model to estimate the potential economic damage linked to flood scenarios, and we calculate the change in expected annual damage according to changes in the relative sea level. Finally, damage reduction benefits are evaluated by means of cost–benefit analysis. Results suggest an overall profitability of the investigated projects over time, with increasing benefits due to increased probability of intense flooding in the near future.
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Two-dimensional model test of a vertical wall with overhanging horizontal cantilever slab has been carried out with the scale factor of 1:30. Different influences of wave condition and structural geometry on wave load are investigated. Wave pressure of cantilever slab highly depends on structural clearance and reaches the peak when clearance close to 0.2 times wave height. Larger wave steepness is responsible for larger wave pressure for vertical wall. With the increasing length of slab, wave forces are reduced on slab while enlarged on vertical wall. Besides, a negative correlation between rise frequency and rise time is observed and corresponding empirical formula has been proposed. Through finite element analysis, the structural response of a simple plate under wave load has been studied. In general, the cut-off frequency has a dramatic effect on the dynamic magnification factor, where the actual dynamic response keeps stable when cut-off frequency larger than 4 times structural natural frequency. Finally, it is adoptable to determine the adequacy of the structure under wave load by considering the quasi-static wave load and corresponding impact factor from 2.0 to 6.0.
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We investigate here the effects of geometric properties (channel depth and cross-sectional convergence length), storm surge characteristics, friction, and river flow on the spatial and temporal variability of compound flooding along an idealized, meso-tidal coastal-plain estuary. An analytical model is developed that includes exponentially convergent geometry, tidal forcing, constant river flow, and a representation of storm surge as a combination of two sinusoidal waves. Non-linear bed friction is treated using Chebyshev polynomials and trigonometric functions, and a multi-segment approach is used to increase accuracy. Model results show that river discharge increases the damping of surge amplitudes in an estuary, while increasing channel depth has the opposite effect. Sensitivity studies indicate that the impact of river flow on peak water level decreases as channel depth increases, while the influence of tide and surge increases in the landward portion of an estuary. Moreover, model results show less surge damping in deeper configurations and even amplification in some cases, while increased convergence length scale damps surge waves with time scales of 12 h–72 h along an estuary. For every modeled scenario, there is a point where river discharge effects on water level outweigh tide/surge effects. As a channel is deepened, this cross-over point moves progressively upstream. Thus, channel deepening may alter flood risk spatially along an estuary and reduce the length of a river-estuary, within which fluvial flooding is dominant.
Chapter
Des inondations ont été régulièrement signalées dans le Bassin du Fleuve Congo (BFC) avec des dommages importants pour la vie humaine, les systèmes de production alimentaire et infrastructures. Les pertes encourues par ces dommages sont énormes et représentent un défi majeur pour les économiques des nations en développement. Dans le BFC, où la disponibilité de données de terrain est un défi important, de nouvelles approches sont nécessaires pour étudier les risques d'inondation et mettre en place des stratégies de gestion efficaces. Cette étude utilise des données globales de prévision des inondations récemment développées afin de produire des cartes de risque d'inondation pour le BFC, où l'information sur les inondations n'existe pas. Les cartes des aléas d'inondation qui déterminent les inondations fluviales à une résolution spatiale de ~90 m, les données de densité de population d'une résolution spatiale de ~30 m, et la couche spatiale de données d'infrastructure sont utilisées pour évaluer le risque d'inondation à l’échelle du BFC. Les données globales sur les inondations fournissent différentes périodes de retour des inondations et leurs étendues pour le BFC. Les résultats de l'analyse sont présentés en termes de pourcentage de la population et des infrastructures exposées aux inondations pour six périodes de retour (5, 10, 20, 50, 75 et 100 ans). Sur les 525 territoires administratifs, 374 sont exposés aux inondations fluviales, et 38 (10 %) d'entre eux sont classés comme zones à haut risque. L'analyse montre que les territoires les plus exposés représentent 1 % de l'exposition totale, qui est estimée à 2.65 % de la population du bassin. Cette étude est la première et la plus importante étape dans la mise en place des réponses aux inondations en déterminant les zones de risque d'inondation et la population qui serait exposée. Les cartes des risques d'inondation produites dans cette étude fournissent les informations nécessaires pour soutenir les décisions politiques pour la prévention des catastrophes liées aux inondations, y compris la priorisation des interventions pour réduire les risques d'inondation dans le BFC.
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Flood disasters have regularly been reported in the Congo Basin with significant damage to human lives, food production systems, and infrastructure. Losses incurred by these damages are huge and represent a major challenge for economic expansion in developing nations. In the Congo River Basin, where availability of in situ data is a significant challenge, new approaches are needed to investigate flood risks and enable effective management strategies. This study uses recently developed global flood prediction data in order to produce flood risk maps for the Congo River Basin, where flood information currently does not exist. Flood hazard maps that estimate fluvial flooding at a grid cell resolution of 3 arc‐seconds (~90 m), gridded population density data of 1 arc‐second (~30 m) spatial resolution, and a spatial layer of infrastructure data set are used to address flood risk at the scale of the Congo Basin. The global flood data provides different return periods of exposure to flooding in the Congo Basin and identifies flood extents. The risk analysis results are presented in terms of the percentage of population and infrastructure at flood risk for six return periods (5, 10, 20, 50, 75, and 100 years). Of the 525 administrative territories, 374 are exposed to fluvial flood, and 38 (10%) of them are categorized as risk hotspots. Analysis shows that the most exposed territories represent 1% of total exposure, which is estimated at 2.65% of the basin's population. This study demonstrates the first and potentially most important stage in developing flood responses by determining the flood hazard areas and the population that would be exposed. The flood risk maps produced in this study provide information necessary to support policy decisions of flood disaster prevention, including prioritization of interventions to reduce flood risk in the CRB.
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Plain Language Summary Flood risks are expected to increase in the future due to the combined effects of climate change, land use change and population growth. New approaches are needed to complement conventional flood risk management (FRM) based on engineering solutions and project‐based approaches. In this Commentary we present the findings of the LAND4FLOOD project, which is based on 4 years of research by academics and stakeholders from diverse backgrounds and disciplines: engineering, societal and environmental. We identify three main issues that should be considered to gain support from different stakeholders for the successful implementation of flood risk measures. First, more process orientation in planning and preparing measures is needed. Second, a comprehensive and inclusive land policy is crucial for flood retention. Third, it is important to start at the local scale.
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A growing concern of coastal communities is increased flood risk and non‐monetary consequences due to climate‐induced impacts such as sea level rise (SLR). Previous efforts have discussed the importance of future flood risk quantification using broad aggregations of monetary loss with “bathtub” SLR models rather than more physically based modeling approaches. Here we quantify actual impacts to coastal communities at the census block level using a dynamic, high‐resolution, bio geophysical modeling framework for four SLR scenarios for the year 2100. This framework accounts for future sea‐levels, landscape change, and urbanization to quantify the 1% and 0.2% annual exceedance probability (AEP) water levels. The computed AEP water levels were used to quantify building damage and populations of displaced people and people requiring long‐term shelter across the Northern Gulf of Mexico (NGOM) (Mississippi, Alabama, and the Florida panhandle). The increase in damaged buildings under SLR is linear, with an increase of 16,367 damaged buildings per 1 m of SLR (R² = 0.96) for the 1% AEP flood. The rate increases to 24,981 damaged buildings per 1 m of SLR (R² = 0.96) for the 0.2% AEP, on average. The increase in displaced people across the NGOM is 8,056 people per meter of SLR, and people requiring shelter is 300 per meter of SLR. The results in this work highlight the varying levels of risk across the NGOM and the change in risk under climate change‐induced impacts.
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The increased frequency and intensity of flooding and related disasters result from changing climatic conditions and other socio-economic factors. As flooding can be highly destructive and negatively impact human lives, this study attempts to estimate the population, capital stock and disparities in exposure to flooding hazards in Nigeria using GIS and Statistical methodologies. First, the study assessed the spatial distribution of the population and capital stock exposed to flood by utilising population and socio-economic datasets. Then, the distribution of the vulnerable groups affected is estimated by superimposing the population and socio-economic datasets onto the flood hazard maps. The results show that approximately 24.7 million (8.3%) of Nigeria’s Population were exposed to floods in 2015. Most exposed groups were primarily in urban areas irrespective of the income class. Additionally, the clusters of communities within the high-risk flood hazard zones had significantly increased, evident in the number of residents exposed to flood within the 15 years (2000-2015) growing exponentially. These findings further highlight a disturbing state of localities where people are generally less responsive to climate change and natural hazards. Overall, this study provides essential information for disaster risk management and policy formation at different levels of administration and identifies areas where varied and informal strategies are needed to mitigate flood risk and climate change in regions with diverse socio-economic conditions. In addition, this study provides empirical proof of the socio-economic disparities associated with flood exposure in Nigeria and presents valuable insights into the underlying factors.
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The impacts of rainstorms and its induced floods (RAIF) are substantial and rising, and the coastal regions in southeastern China may suffer more because of the frequent occurrence of RAIF. Therefore, research on the vulnerability of the affected population to RAIF is vital. This study presents an assessment framework for vulnerability in Zhejiang Province, China, at county level. Based on data related to loss records, precipitation, population, economy, topography and hydrology, relatively important variables were first selected by random forest regression and agglomerative hierarchical clustering to avoid multicollinearity and over-fitting. And then we established and validated a vulnerability model and analyzed the importance score and response curve of each variable. The results indicated the following: (1) The counties suffering more from RAIF were mostly distributed in the southeast and west of Zhejiang Province. Approximately 1.42 million people were affected per year. (2) The R² of the vulnerability model based on random forest regression was 0.41, and the largest multiple-day rainfall was the most import driver of the population affected by RAIF. (3) The response curve of the largest multiple-day rainfall showed a trend of first increasing and then stabilizing; GDP per capita first decreased sharply and tended to become stable; population first increased, then decreased and showed an increasing trend again. An innovative aspect of this work was the use of machine learning to analyze the vulnerability and the non-linear relationships between variables and the affected population, and these results may help policymakers develop suitable mitigation strategies against RAIF.
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System-wide functionality failure emerges when a collection of components collapse simultaneously. Aiming to detect the early warning of the rapid critical facilities accessibility loss after flooding, this paper presents a network-theory-based detection model to identify the critical point after which the rate of road access loss to critical facilities starts to accelerate. The road network of Harris County, Texas during Hurricane Harvey 2017 flooding and simulated 500-year flooding (1,000 flooding sequences) is used for the case study. We also considered the travel radius for reaching critical facilities (e.g., 5 miles travel radius for hospitals, and 4 miles for groceries and pharmacies) in the accessibility assessment to show the flood impact on daily life. Additionally, we link the critical threshold to the spatial network to identify the corresponding critical roads whose disruption will likely trigger the rapid critical facility accessibility loss. This result will facilitate the decision-making on targeted infrastructure protection. Moreover, we map the number of critical facilities that each network component has access to within reach and reveal the resource redundancy inequity and risk disparity across the network. Integrating with the social vulnerability mapping, our result can help inform the resilience planning toward an equitable community.
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Changes in the severity and likelihood of flooding events are typically associated with changes in the intensity and frequency of streamflows, but temporal adjustments in a river's conveyance capacity can also contribute to shifts in flood hazard. To assess the relative importance of channel conveyance to flood hazard, we compare variations in channel conveyance to variations in the flow magnitude of moderate (1.2 years) floods at 50 river gauges in western Washington State between 1930 and 2020. In unregulated rivers, moderate floods have increased across the region, but in regulated rivers this trend is suppressed and in some cases reversed. Variations in channel conveyance are ubiquitous, but the magnitude and timing of adjustments are not regionally uniform. At 40% of gages, conveyance changes steadily and gradually. More often, however, conveyance variability is nonlinear, consisting of multidecadal oscillations (36% of gages), rapid changes due to unusually large sediment‐supply events (14% of gages), and increases or decreases to conveyance following flow regulation (10% of gages). The relative importance of conveyance variability for flood risk depends on the mode of adjustment; in certain locations with historic landslides, extreme floods, and flow regulation, the influence of conveyance changes on flood risk matches or exceeds that of streamflow at the same site. Flood hazard management would benefit from incorporating historic long‐term and short‐term conveyance changes in predictions of future flood hazard variability.
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With the rapid development of urbanization and global climate change, urban pluvial floods have occurred more frequently in urban areas. Despite of the increasing urban pluvial flood risk, there is still a lack of comprehensive understanding of the physical and social influencing factors on the process. To fill this knowledge gap, this paper proposes a novel approach to calculate the comprehensive urban pluvial flooding risk index (PFRI) and investigates the interplay impacts from different components at catchment level. To be more specific, PFRI is determined by two components, Exposure Index (EI) and Social Vulnerability Index (SoVI). EI is evaluated based on two indicators, the depression-based Topographic Control Index (TCI) and impervious area ratio. SoVI is measured based on a set of demographic and socio-economic indicators. Our results demonstrated the spatial heterogeneity of urban pluvial flood exposure and social vulnerability, as well as the composite flooding risk across the study area. Our catchment-based urban pluvial flooding risk assessment method can provide a comprehensive understanding of urban flooding and promote the formulation of effective flood mitigation strategies from the catchment perspective.
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Floods have occurred frequently all over the world. During 2000-2020, nearly half (44.9%) of global floods occurred in the Belt and Road region because of its complex geology, topography, and climate. Therefore, providing an insight into the spatial distribution characteristics of flood susceptibility in this region is essential. Here, a database was established with 11 flood conditioning factors, 1500 flooded points, and 1500 non-flooded points selected by an improved method. Subsequently, a rare combination of logistic regression and support vector machine, integrated by heterogeneous framework, was applied to generate an ensemble flood susceptibility map. Based on it, the concept of ecological vulnerability synthesis index in the ecological field was introduced into this study, and the flood susceptibility comprehensive index (FSCI) was proposed to quantify the degree of flood susceptibility of each country and sub-region. At the results, the ensemble model has an excellent accuracy, with the highest AUC value of 0.9342. The highest and high flood susceptibility zones are mainly located in the southeastern part of Eastern Asia, most of Southeast Asia and South Asia, account for 12.22% and 9.57% of the total study area respectively. From the regional perspective, it can be found that Southeast Asia had the highest flood susceptibility with the highest FSCI of 4.69, while East Asia and Central and Eastern Europe showed the most significant spatial distribution characteristics. From the national perspective, of the 66 countries in this region, 20 of the countries have the highest flood susceptibility level (FSCIn > 0.8), which face the greatest threat of flooding. These results are able to facilitate reasonable flood mitigation measures develop at the most critical locations in the Belt and Road region, and lays a theoretical basis for quantifying flood susceptibility at national or regional scale.
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Global flood hazard maps can be used in the assessment of flood risk in a number of different applications, including (re)insurance and large scale flood preparedness. Such global hazard maps can be generated using large scale physically based models of rainfall-runoff and river routing, when used in conjunction with a number of post-processing methods. In this study, the European Centre for Medium Range Weather Forecasts (ECMWF) land surface model is coupled to ERA-Interim reanalysis meteorological forcing data, and resultant runoff is passed to a river routing algorithm which simulates floodplains and flood flow across the global land area. The global hazard map is based on a 30 yr (1979-2010) simulation period. A Gumbel distribution is fitted to the annual maxima flows to derive a number of flood return periods. The return periods are calculated initially for a 25 × 25 km grid, which is then reprojected onto a 1 × 1 km grid to derive maps of higher resolution and estimate flooded fractional area for the individual 25 × 25 km cells. Several global and regional maps of flood return periods ranging from 2 to 500 yr are presented. The results compare reasonably to a benchmark data set of global flood hazard. The developed methodology can be applied to other datasets on a global or regional scale.
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Part I of this two-part paper provided an overview of the HAZUS-MH Flood Model and a discussion of its capabilities for characterizing riverine and coastal flooding. Included was a discussion of the Flood Information Tool, which permits rapid analysis of a wide variety of stream discharge data and topographic mapping to determine flood-frequencies over entire floodplains. This paper reports on the damage and loss estimation capability of the Flood Model, which includes a library of more than 900 damage curves for use in estimating damage to various types of buildings and infrastructure. Based on estimated property damage, the model estimates shelter needs and direct and indirect economic losses arising from floods. Analyses for the effects of flood warning, the benefits of levees, structural elevation, and flood mapping restudies are also facilitated with the Flood Model.
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Recent improvements in mapping of global population distribution makes it possible to estimate the number and distribution of people near coasts with greater accuracy than previously possible, and hence consider the potential exposure of these populations to coastal hazards. In this paper, we combine the updated Gridded Population of the World (GPW2) population distribution estimate for 1990 and lighted settlement imagery with a global digital elevation model (DEM) and a high resolution vector coastline. This produces bivariate distributions of population, lighted settlements and land area as functions of elevation and coastal proximity. The near-coastal population within 100 km of a shoreline and 100 m of sea level was estimated as 1.2 × 109 people with average densities nearly 3 times higher than the global average density. Within the near coastal-zone, the average population density diminishes more rapidly with elevation than with distance, while the opposite is true of lighted settlements. Lighted settlements are concentrated within 5 km of coastlines worldwide, whereas average population densities are higher at elevations below 20 m throughout the 100 km width of the near-coastal zone. Presently most of the near-coastal population live in relatively densely-populated rural areas and small to medium cities, rather than in large cities. A range of improvements are required to define a better baseline and scenarios for policy analysis. Improving the resolution of the underlying population data is a priority.
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This paper applies the DIVA model to assess the risk of and adaptation to sea-level rise for the European Union in the 21st century under the A2 and B1 scenarios of the Intergovernmental Panel on Climate Change. For each scenario, impacts are estimated without and with adaptation in the form of increasing dike heights and nourishing beaches. Before 2050, the level of impacts is primarily determined by socio-economic development. In 2100 and assuming no adaptation, 780Ã-10<sup>3</sup> people/year are estimated to be affected by coastal flooding under A2 and 200Ã-10<sup>3</sup> people/year under B1. The total monetary damage caused by flooding, salinity intrusion, land erosion and migration is projected to be about US$ 17Ã-10<sup>9</sup> under both scenarios in 2100; damage costs relative to GDP are highest for the Netherlands (0.3% of GDP under A2). Adaptation reduces the number of people flooded by factors of 110 to 288 and total damage costs by factors of 7 to 9. In 2100 adaptation costs are projected to be US$ 3.5Ã-10<sup>9</sup> under A2 and 2.6Ã-10<sup>9</sup> under B1; adaptation costs relative to GDP are highest for Estonia (0.16% under A2) and Ireland (0.05% under A2). These results suggest that adaptation measures to sea-level rise are beneficial and affordable, and will be widely applied throughout the European Union. © 2010 Springer Science+Business Media B.V
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In this letter we analyse the temporal development of physical population-driven water scarcity, i.e. water shortage, over the period 0 AD to 2005 AD. This was done using population data derived from the HYDE dataset, and water resource availability based on the WaterGAP model results for the period 1961–90. Changes in historical water resources availability were simulated with the STREAM model, forced by climate output data of the ECBilt–CLIO–VECODE climate model. The water crowding index, i.e. Falkenmark water stress indicator, was used to identify water shortage in 284 sub-basins. Although our results show a few areas with moderate water shortage (1000–1700 m3/capita/yr) around the year 1800, water shortage began in earnest at around 1900, when 2% of the world population was under chronic water shortage (<1000 m3/capita/yr). By 1960, this percentage had risen to 9%. From then on, the number of people under water shortage increased rapidly to the year 2005, by which time 35% of the world population lived in areas with chronic water shortage. In this study, the effects of changes in population on water shortage are roughly four times more important than changes in water availability as a result of long-term climatic change. Global trends in adaptation measures to cope with reduced water resources per capita, such as irrigated area, reservoir storage, groundwater abstraction, and global trade of agricultural products, closely follow the recent increase in global water shortage.
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This paper presents the Climate Framework for Uncertainty, Negotiation and Distribution (FUND), an integrated assessment model of climate change, and discusses selected results. FUND is a nine-region model of the world economy and its interactions with climate, running in time steps of one year from 1990 to 2200. The model consists of scenarios for economy and population, which are perturbed by climate change and greenhouse gas emission reduction policy. Each region optimizes its net present welfare. Policy variables are energy and carbon efficiency improvement, and sequestering carbon dioxide in forests. It is found that reducing conventional air pollution is a major reason to abate carbon dioxide emissions. Climate change is an additional reason to abate emissions. Reducing and changing energy use is preferred as an option over sequestering carbon. Under non-cooperation, free riding as well as assurance behaviour is observed in the model. The scope for joint implementation is limited. Under cooperation, optimal emission abatement is (slightly) higher than under non-cooperation, but the global coalition is not self-enforcing while side payments are insufficient. Optimal emission control under non-cooperation is less than currently discussed under the Framework Convention on Climate Change, but higher than observed in practice.
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Despite growing recognition of the importance of climate change adaptation, few global estimates of the costs involved are available for the water supply sector. We present a methodology for estimating partial global and regional adaptation costs for raw industrial and domestic water supply, for a limited number of adaptation strategies, and apply the method using results of two climate models. In this paper, adaptation costs are defined as those for providing enough raw water to meet future industrial and municipal water demand, based on country-level demand projections to 2050. We first estimate costs for a baseline scenario excluding climate change, and then additional climate change adaptation costs. Increased demand is assumed to be met through a combination of increased reservoir yield and alternative backstop measures. Under such controversial measures, we project global adaptation costs of $12 bn p.a., with 83–90% in developing countries; the highest costs are in Sub-Saharan Africa. Globally, adaptation costs are low compared to baseline costs ($73 bn p.a.), which supports the notion of mainstreaming climate change adaptation into broader policy aims. The method provides a tool for estimating broad costs at the global and regional scale; such information is of key importance in international negotiations.
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Aim This paper presents a tool for long-term global change studies; it is an update of the History Database of the Global Environment (HYDE) with estimates of some of the underlying demographic and agricultural driving factors. Methods Historical population, cropland and pasture statistics are combined with satellite information and specific allocation algorithms (which change over time) to create spatially explicit maps, which are fully consistent on a 5′ longitude/latitude grid resolution, and cover the period 10,000 bc to ad 2000. Results Cropland occupied roughly less than 1% of the global ice-free land area for a long time until ad 1000, similar to the area used for pasture. In the centuries that followed, the share of global cropland increased to 2% in ad 1700 (c. 3 million km2) and 11% in ad 2000 (15 million km2), while the share of pasture area grew from 2% in ad 1700 to 24% in ad 2000 (34 million km2) These profound land-use changes have had, and will continue to have, quite considerable consequences for global biogeochemical cycles, and subsequently global climate change. Main conclusions Some researchers suggest that humans have shifted from living in the Holocene (emergence of agriculture) into the Anthropocene (humans capable of changing the Earth's atmosphere) since the start of the Industrial Revolution. But in the light of the sheer size and magnitude of some historical land-use changes (e.g. as result of the depopulation of Europe due to the Black Death in the 14th century and the aftermath of the colonization of the Americas in the 16th century) we believe that this point might have occurred earlier in time. While there are still many uncertainties and gaps in our knowledge about the importance of land use (change) in the global biogeochemical cycle, we hope that this database can help global (climate) change modellers to close parts of this gap.
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In recent years, through the availability of remotely sensed data and other national datasets, it has become possible to conduct national-scale flood risk assessment in England and Wales. The results of this type of risk analysis can be used to inform policy-making and prioritisation of resources for flood management. It can form the starting point for more detailed strategic and local-scale flood risk assessments. The national-scale risk assessment methodology outlined in this paper makes use of information on the location, standard of protection and condition of flood defences in England and Wales, together with datasets of floodplain extent, topography, occupancy and asset values. The flood risk assessment was applied to all of England and Wales in 2002at which point the expected annual damage from flooding was estimated to be approximately 1 billion. This figure is comparable with records of recent flood damage. The methodology has subsequently been applied to examine the effects of climate and socio-economic change 50 and 80years in the future. The analysis predicts increasing flood risk unless current flood management policies, practices and investment levels are changed – up to 20-fold increase in real terms economic risk by the 2080s in the scenario with highest economic growth. The increase is attributable primarily to a combination of climate change (in particular sea level rise and increasing precipitation in parts of the UK) and increasing economic vulnerability.
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Coastal flooding poses serious threats to coastal areas, and the vulnerability of coastal communities and economic sectors to flooding will increase in the coming decades due to environmental and socioeconomic changes. It is increasingly recognised that estimates of the vulnerability of cities are essential for planning adaptation measures. Jakarta is a case in point, since parts of the city are subjected to regular flooding on a near-monthly basis. In order to assess the current and future coastal flood hazard, we set up a GIS-based flood model of northern Jakarta to simulate inundated area and value of exposed assets. Under current conditions, estimated damage exposure to extreme coastal flood events with return periods of 100 and 1,000years is high (€4.0 and €5.2 billion, respectively). Under the scenario for 2100, damage exposure associated with these events increases by a factor 4–5, with little difference between low/high sea-level rise scenarios. This increase is mainly due to rapid land subsidence and excludes socioeconomic developments. We also develop a detemporalised inundation scenario for assessing impacts associated with any coastal flood scenario. This allows for the identification of critical points above which large increases in damage exposure can be expected and also for the assessment of adaptation options against hypothetical user-defined levels of change, rather than being bound to a discrete set of a priori scenarios. The study highlights the need for urgent attention to the land subsidence problem; a continuation of the current rate would result in catastrophic increases in damage exposure. KeywordsFlood–Damage–Jakarta–GIS–Model
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A new global coastal database has been developed within the context of the DINAS-COAST project. The database covers the world's coasts, excluding Antarctica, and includes information on more than 80 physical, ecological, and socioeconomic parameters of the coastal zone. The database provides the base data for the Dynamic Interactive Vulnerability Assessment modelling tool that the DINAS-COAST project has produced. In order to comply with the requirements of the modelling tool, it is based on a data model in which all information is referenced to more than 12,000 linear coastal segments of variable length. For efficiency of data storage, six other geographic features (administrative units, countries, rivers, tidal basins or estuaries, world heritage sites, and climate grid cells) are used to reference some data, but all are linked to the linear segment structure. This fundamental linear data structure is unique for a global database and represents an efficient solution to the problem of representing and storing coastal data. The database has been specifically designed to support impact and vulnerability analysis to sea-level rise at a range of scales up to global. Due to the structure, consistency, user-friendliness, and wealth of information in the database, it has potential wider application to analysis and modelling of the world's coasts, especially at regional to global scales.
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Our aim is to produce a world map of flooded areas for a 100 year return period, using a method based on large rivers peak flow estimates derived from mean monthly discharge time-series. Therefore, the map is supposed to represent flooding that affects large river floodplains, but not events triggered by specific conditions like coastal or flash flooding for instance. We first generate for each basin a set of hydromorphometric, land cover and climatic variables. In case of an available discharge record station at the basin outlet, we base the hundred year peak flow estimate on the corresponding time-series. Peak flow magnitude for basin outlets without gauging stations is estimated by statistical means, performing several regressions on the basin variables. These peak flow estimates enable the computation of corresponding flooded areas using hydrologic GIS processing on digital elevation model.
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Damage assessments of natural hazards supply crucial information to decision support and policy development in the fields of natural hazard management and adaptation planning to climate change. Specifically, the estimation of economic flood damage is gaining greater importance as flood risk management is becoming the dominant approach of flood control policies throughout Europe. This paper reviews the state-of-the-art and identifies research directions of economic flood damage assessment. Despite the fact that considerable research effort has been spent and progress has been made on damage data collection, data analysis and model development in recent years, there still seems to be a mismatch between the relevance of damage assessments and the quality of the available models and datasets. Often, simple approaches are used, mainly due to limitations in available data and knowledge on damage mechanisms. The results of damage assessments depend on many assumptions, e.g. the selection of spatial and temporal boundaries, and there are many pitfalls in economic evaluation, e.g. the choice between replacement costs or depreciated values. Much larger efforts are required for empirical and synthetic data collection and for providing consistent, reliable data to scientists and practitioners. A major shortcoming of damage modelling is that model validation is scarcely performed. Uncertainty analyses and thorough scrutiny of model inputs and assumptions should be mandatory for each damage model development and application, respectively. In our view, flood risk assessments are often not well balanced. Much more attention is given to the hazard assessment part, whereas damage assessment is treated as some kind of appendix within the risk analysis. Advances in flood damage assessment could trigger subsequent methodological improvements in other natural hazard areas with comparable time-space properties.
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Traditional flood design methods are increasingly supplemented or replaced by risk-oriented methods which are based on comprehensive risk analyses. Besides meteorological, hydrological and hydraulic investigations such analyses require the estimation of flood impacts. Flood impact assessments mainly focus on direct economic losses using damage functions which relate property damage to damage-causing factors. Although the flood damage of a building is influenced by many factors, usually only inundation depth and building use are considered as damage-causing factors. In this paper a data set of approximately 4000 damage records is analysed. Each record represents the direct monetary damage to an inundated building. The data set covers nine flood events in Germany from 1978 to 1994. It is shown that the damage data follow a Lognormal distribution with a large variability, even when stratified according to the building use and to water depth categories. Absolute depth-damage functions which relate the total damage to the water depth are not very helpful in explaining the variability of the damage data, because damage is determined by various parameters besides the water depth. Because of this limitation it has to be expected that flood damage assessments are associated with large uncertainties. It is shown that the uncertainty of damage estimates depends on the number of flooded buildings and on the distribution of building use within the flooded area. The results are exemplified by a damage assessment for a rural area in southwest Germany, for which damage estimates and uncertainty bounds are quantified for a 100-year flood event. The estimates are compared to reported flood damages of a severe flood in 1993. Given the enormous uncertainty of flood damage estimates the refinement of flood damage data collection and modelling are major issues for further empirical and methodological improvements.
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One important prerequisite for a comparable quantitative risk assessment for different types of hazards (e.g., earthquakes, windstorms and floods) is the use of a common database about and financial appraisal of the assets at risk. For damage assessments it is necessary to represent the values at risk on a regional disaggregated scale and to intersect them with hazard scenarios. This paper presents a methodology and results of a financial appraisal of residential buildings for all communities in Germany. The calculated values are defined as replacement values for the reference year 2000. The resulting average replacement costs for residential buildings per inhabitant amount to EUR 46 600, with considerable differences between communities. The inventory can be used for the calculations of direct losses from various natural disasters within the project "Risk Map Germany''.
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This paper presents a model of factors influencing levels of human losses from natural hazards at the global scale, for the period 1980–2000. This model was designed for the United Nations Development Programme as a building stone of the Disaster Risk Index (DRI), which aims at monitoring the evolution of risk. Assessing what countries are most at risk requires considering various types of hazards, such as droughts, floods, cyclones and earthquakes. Before assessing risk, these four hazards were modelled using GIS and overlaid with a model of population distribution in order to extract human exposure. Human vulnerability was measured by crossing exposure with selected socio-economic parameters. The model evaluates to what extent observed past losses are related to population exposure and vulnerability. Results reveal that human vulnerability is mostly linked with country development level and environmental quality. A classification of countries is provided, as well as recommendations on data improvement for future use of the model.
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This paper provides the results from the modeling framework presented by Cai and Rosegrant (2002), including projections of water demand and supply for domestic, industrial, livestock, and irrigation water use at the basin or country level in the global scope, during 1995 to 2025. Water demand is projected to grow rapidly for domestic and industrial uses, and relatively slowly for agriculture. The developing world is projected to have much higher growth in total water demand than the developed world, and about 93 percent of the additional demand will occur in developing countries. Moderate increases are projected for water supply capacity expansion, management improvement, and irrigation development. It is found that for the developing world, there will be increasing scarcity of water for irrigation, with a declining fraction of potential irrigation demand being met over time. Particularly large declines are found in dry basins that face rapid growth in domestic and industrial sectors. Variability in irrigation water supply due to climate variability tends to increase over time. Following presentation of the "best-estimate" baseline scenario, alternative scenarios are examined for changes in infrastructure investment, non-irrigation water demand growth, and groundwater pumping.
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The following values have no corresponding Zotero field: Research Notes: This is a deliverable from the EU funded FLOODsite project, and reviews the methods used to estimate flood damages. CORFU, ideally, shouldn't repeat this work A2 - Project, FLOODsite ID - 1
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Worldwide, flooding is probably the number one cause of losses from natural events. No region in the world is safe from being flooded. As the flood risk is a function of the flood hazard, the exposed values and their vulnerability, the increase in flood losses must be attributed to changes in each of these aspects. While flood protection measures may reduce the frequency of inundation losses, appropriate preparedness measures lessen the residual financial risk considerably. Besides public and private measures, insurance is a key factor in reducing the financial risk for individuals, enterprises, and even whole societies. In recent years, the demand for flood insurance has been growing. This is forcing the insurance industry to develop appropriate solutions. At the same time it is vital for the insurers to know the probable maximum losses they might face as the result of an extreme event.
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Pa577 Times Cited:40 Cited References Count:25 , The following values have no corresponding Zotero field: Author Address: Smith, Di Australian Natl Univ,Ctr Resource & Environm Studies,Canberra 0200,Australia Australian Natl Univ,Ctr Resource & Environm Studies,Canberra 0200,Australia ID - 2 , The following values have no corresponding Zotero field: Author Address: Smith, Di Australian Natl Univ,Ctr Resource & Environm Studies,Canberra 0200,Australia Australian Natl Univ,Ctr Resource & Environm Studies,Canberra 0200,Australia ID - 2 , The following values have no corresponding Zotero field: ID - 25
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This work presents a flexible system called GIS-based Flood Information System (GFIS) for floodplain modeling, flood damages calculation, and flood information support. It includes two major components, namely floodplain modeling and custom designed modules. Model parameters and input data are gathered, reviewed, and compiled using custom designed modules. Through these modules, it is possible for GFIS to control the process of flood-plain modeling, presentation of simulation results, and calculation of flood damages. Empirical stage-damage curves are used to calculate the flood damages. These curves were generated from stage-damage surveys of anthropogenic structures, crops, etc., in the coastal region of a frequently flooded area in Chia-I County, Taiwan. The average annual flood damages are calculated with exceedance probability and flood damages for the designed rainfalls of 2, 5, 10, 25, 50, 100, and 200 year recurrence intervals with a duration of 24 hours. The average annual flood depth in this study area can also be calculated using the same method. The primary advantages of GFIS are its ability to accurately predict the locations of flood area, depth, and duration; calculate flood damages in the floodplain; and compare the reduction of flood damages for flood mitigation plans.
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A new version of the PAGE model, PAGE2002, has been used to calculate the marginal impacts of CO2, CH4 and SF6 emissions based on Scenario A2 of the IPCC. The mean marginal impact of CO2 is found to be US$19 per tonne of carbon (or about US$5 per tonne of CO2), for methane it is US$105 per tonne, and for SF6 it is US$200,000 per tonne. For each gas, the range between the 5% and 95% points is about an order of magnitude. The climate change impacts of methane are a significant proportion of its market price, and for SF6 the climate change impacts are much larger than the market price. The economics of schemes to reduce the leakage of SF6 are transformed once the climate change impacts are properly counted.
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A frequently predicted consequence of global climate change is an increased effect of coastal hazards on the world's human population. The impact of coastal hazards depends on the proximity of human population to the coastal zone. Recently compiled population estimates are combined with a new continental digital elevation model in an attempt to quantify the global distribution of human population and occupied land area with respect to elevation and coastal proximity. The limited spatial resolution of the census data allows one to quantify some of the uncertainty in the spatial distribution of population. This provides a lower bound on the uncertainty in the resulting distributions but does not account for uncertainty in the census data or elevation data. Long-term records of relative sea level rise, tidal heights, and storm surge heights can be combined with global sea level rise estimates for a variety of climate change scenarios to estimate the approximate magnitude of vertical changes in local sea level. It is verified that large numbers of people live at low elevations near coasts but the uncertainties are too large to provide meaningful estimates of the number of people who reside in so-called “coastal zones” worldwide. The principal conclusion is that both the spatial distribution and the resolution of global data must be significantly improved before realistic quantitative assessments of the global impact of coastal hazards can be made.
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This article argues that urban spatial expansion results mainly from three powerful forces: a growing population, rising incomes, and falling commuting costs. Urban growth occurring purely in response to these fundamental forces cannot be faulted as socially undesirable, but three market failures may distort their operation, upsetting the allocation of land between agricultural and urban uses and justifying criticism of urban sprawl. These are the failure to account for the benefits of open space, excessive commuting because of a failure to account for the social costs of congestion, and failure to make new development pay for the infrastructure costs it generates. Precise remedies for these market failures are two types of development taxes and congestion tolls levied on commuters. Each of these remedies leads to a reduction in the spatial size of the city.
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Damages from weather related disasters are projected to increase, due to a combination of increasing exposure of people and assets, and expected changes in the global climate. Only few studies have assessed in detail the potential range of losses in the future and the factors contributing to the projected increase. Here we estimate future potential damage from river flooding, and analyse the relative role of land-use, asset value increase and climate change on these losses, for a case study area in The Netherlands. Projections of future socioeconomic change (land-use change and increase in the value of assets) are used in combination with flood scenarios, projections of flooding probabilities, and a simple damage model. It is found that due to socioeconomic change, annual expected losses may increase by between 35 and 172% by the year 2040, compared to the baseline situation in the year 2000. If no additional measures are taken to reduce flood probabilities or consequences, climate change may lead to an increase in expected losses of between 46 and 201%. A combination of climate and socioeconomic change may increase expected losses by between 96 and 719%. Asset value increase has a large role, as it may lead to a doubling of losses. The use of single loss estimates may lead to underestimation of the impact of extremely high losses. We therefore also present loss–probability curves for future risks, in order to assess the increase of the most extreme potential loss events. Our approach thus allows a more detailed and comprehensive assessment than previous studies that could also be applied in other study areas to generate flood risk projections. Adaptation through flood prevention measures according to currently planned strategies would counterbalance the increase in expected annual losses due to climate change under all scenarios.
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Worldwide, flooding is probably the number one cause of losses from natural events. No region in the world is safe from being flooded. As the flood risk is a function of the flood hazard, the exposed values and their vulnerability, the increase in flood losses must be attributed to changes in each of these aspects. While flood protection measures may reduce the frequency of inundation losses, appropriate preparedness measures lessen the residual financial risk considerably. Besides public and private measures, insurance is a key factor in reducing the financial risk for individuals, enterprises, and even whole societies. In recent years, the demand for flood insurance has been growing. This is forcing the insurance industry to develop appropriate solutions. At the same time it is vital for the insurers to know the probable maximum losses they might face as the result of an extreme event.
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IFPRI and IWMI's report uses computer modeling to project water demand and availability through to 2025 and predicts the likely impact of changes in water policy and investment, making specific recommendations for specific locations around the globe. The report argues that if current water policies continue, farmers will find it difficult to meet the world’s food needs. Hardest hit will be the world’s poorest people. The authors call for: International commitment to sustainable use of water, through appropriate policies and investments; Wider application of existing water saving technologies; The removal of inappropriate incentives and reform of institutions which hinder effective use of water.
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This paper provides the results from the modeling framework presented by Cai and Rosegrant (2002), including projections of water demand and supply for domestic, industrial, livestock, and irrigation water use at the basin or country level in the global scope, during 1995 to 2025. Water demand is projected to grow rapidly for domestic and industrial uses, and relatively slowly for agriculture. The developing world is projected to have much higher growth in total water demand than the developed world, and about 93 percent of the additional demand will occur in developing countries. Moderate increases are projected for water supply capacity expansion, management improvement, and irrigation development. It is found that for the developing world, there will be increasing scarcity of water for irrigation, with a declining fraction of potential irrigation demand being met over time. Particularly large declines are found in dry basins that face rapid growth in domestic and industrial sectors. Variability in irrigation water supply due to climate variability tends to increase over time. Following presentation of the “best-estimate” baseline scenario, alternative scenarios are examined for changes in infrastructure investment, non-irrigation water demand growth, and groundwater pumping.
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This work presents a flexible system called GIS-based Flood Information System (GFIS) for floodplain modeling, flood damages calculation, and flood information support. It includes two major components, namely floodplain modeling and custom designed modules. Model parameters and input data are gathered, reviewed, and compiled using custom designed modules. Through these modules, it is possible for GFIS to control the process of flood-plain modeling, presentation of simulation results, and calculation of flood damages. Empirical stage-damage curves are used to calculate the flood damages. These curves were generated from stage-damage surveys of anthropogenic structures, crops, etc., in the coastal region of a frequently flooded area in Chia-I County, Taiwan. The average annual flood damages are calculated with exceedance probability and flood damages for the designed rainfalls of 2, 5, 10, 25, 50, 100, and 200 year recurrence intervals with a duration of 24 hours. The average annual flood depth in this study area can also be calculated using the same method. The primary advantages of GFIS are its ability to accurately predict the locations of flood area, depth, and duration; calculate flood damages in the floodplain; and compare the reduction of flood damages for flood mitigation plans.