Corrigendum: The credibility challenge for global fluvial flood risk analysis (vol 11, 094014, 2016)
MA TriggCE BirchJeffrey NealPaul BatesAndrew SmithCC SampsonDai YamazakiYukiko HirabayashiFlorian PappenbergerEmanuel DutraPJ WardH.C.M. WinsemiusP SalamonFrancesco DottoriRoberto RudariKappesAL SimpsonG HadzilacosTJ Fewtrell
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<p>Floods are extreme hydro-meteorological hazards that pose significant risks to the economy and society. Reducing the risk associated with floods and better adapting to them is a daunting task because flood risk dynamics are influenced by different factors. Flood risk is usually defined as the product of three components: hazard, exposure and vulnerability. Global Flood Risk Models (GFRM) represent the underlying physical hazard, the exposure of people, properties or other assets to the hazard, and the losses that may occur following a flood event. &#160;Consequently, they are used by governmental agencies, risk reduction organisations, global investors and the (re)insurance industry to help manage the societal and financial risks associated with floods. GFRMs are subject to many sources of uncertainty, including uncertainty in processes representation, model parameters and input data; however, the relative importance of these different sources is poorly understood. Currently, no evidence exists on which uncertain input factor mostly control the final uncertainty in predicted losses in different places and circumstances. In this project, we use JBA&#8217;s (a leading flood risk modelling company) Global Flood Model and Open Exposure Data (OED) to develop an appropriate methodological approach to analyse the sensitivity of loss predictions in a structured way. This is particularly challenging as input uncertainties exhibit complex spatially distributed and spatially-structured (correlated) patterns. We apply the methodology to the Rhine river basin, covering regions with different physical and socio-economic characteristics. We pursue the following objectives; (1) Identify and quantify the various sources of uncertainty e.g. associated to rainfall data, extraction of flood events sets, defence database, vulnerability curves, exposure portfolios (2) Analyse their relative importance on flood losses predictions across places along the river (3) Understand which of them are most important at each place. We aim to produce scientifically robust evidence about the importance of different sources of uncertainty across places with different climate, hydrology and socio-economic characteristics and try to address questions related to exposure and vulnerability dynamics, flood losses modelling and adaptation strategies. Such evidence base will help prioritise efforts for uncertainty reduction of the case study model, as well as other flood risk models used by (re)insurers and government agencies, ultimately contributing to more informed decisions for flood risk mitigation.</p>
Vulnerability
Flood risk assessment
Natural hazard
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In 2002, when colleagues at HR Wallingford, Halcrow and my team (then at the University of Bristol) undertook the first National Flood Risk Assessment (NaFRA) for the Environment Agency (EA), it was a breathtaking exercise. The methodology was hot off the press from the EA's RASP (Risk Assessment for Strategic Planning) project. The Department of the Environment Food and Rural Affairs (DEFRA) had conducted a National Appraisal of Assets at Risk from Flooding and Coastal Erosion in 2001, but the analysis did not take account of the effect of flood defence systems. The EA had then taken the hugely significant step of developing a National Flood and Coastal Defence Database. On top of this novel dataset, we built a flood defence system reliability calculation that took into account the spatial dependence in water levels. We piloted in the Parrett catchment (somewhere that became infamous for flooding last winter) and then pressed the button on the whole of England and Wales. Less than a year later, we were running climate change scenarios and scenarios of socio-economic change as part of the government's Foresight Future Flooding Project, and before long Sir David King was taking the results to the US, Russia, India and China. The race was on for massively broad-scale flood risk analysis. Next stop the world. The Catastrophe (Cat) modellers were making huge steps too, armed with back rooms full of analysts who seldom saw the light of day. One of their team leaders describes the tactic as ‘shock and awe’, a poignant phrase at the time of the 2003 invasion of Iraq. In the Cat modeller's vernacular, this meant releasing a model of some part of the world nobody at the time thought you could model (China, Vietnam), and then fixing the bugs while everyone else recovered from the shock. It is a trick that seemed to work, with a captive market and nobody allowed to look under the bonnet. By 2006, the team at the European Joint Research Centre (JRC) at Ispra was using their LISFLOOD broad-scale hydrological and flood inundation model to develop the first European-scale flood risk estimates, driven by ensembles of regional climate model (RCM) scenarios. Even then, the NaFRA and Foresight results for England and Wales were the only national risk estimate available for comparison with the JRC's results for Europe. The JRC's work was just the start. The EU's WATCH project, which ran from 2007 to 2011, provided new gridded hydrological datasets for the 20th century and multi-model ensembles of RCM and hydrological models. The process of model intercomparison has now taken hold in the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP): the first assessment of changes in global flood hazard (reported by Rutger Dankers et al. in a paper that appeared online in Proceedings of the National Academy of Sciences in December 2013) compared nine global hydrology and land surface models, along with five climate models. That work modelled hydrology and flood hazard, but global population and economic datasets are also enabling global mapping of vulnerability. The framework published by Philip Ward and colleagues from VU University in Amsterdam in 2013 brought together flood hydrology with socio-economic vulnerability in the latest major step towards global flood risk analysis. A similar path has been followed on the coast, where successive developments of the DIVA model, by Jochen Hinkel and Robert Nicholls, have steadily improved understanding of the global risks of coastal flooding and sea level rise. In global risk analysis, scarcity of information about the location, protection standard and condition of flood defence systems (including dikes, flood control reservoirs, channel modifications and beaches) means that for the time being risk assessments are based upon assumptions, in all but the few places in the world where national flood protection databases exist. I gather that a global database of flood protection standards may be published before too long. Other crucial human adaptations to flood risk are not mapped globally. For example, we do not know about the extent and enforcement of floodplain zoning, nor do we have complete information about the coverage of flood warning systems, even though these are some of the most cost-effective adaptations to flood risk. There remains the fundamental problem of validating flood risk estimates. Risk is not an observable quantity, so risk estimates cannot be validated directly. One route to validation is to scrutinise each element in the calculation and assemble evidence to validate the risk estimate piece by piece. At an aggregate level, in the long run, and all other things being equal, the average of observed damages should approximate to the expected annual damage (EAD). But all other things are not equal. The baseline flood hazard and human vulnerability are moving. Nonetheless, we should expect observed damages and model estimates to be comparable, a challenge that has been laid down by the giant of flood hazard research, Edmund Penning-Rowsell. In his controversial paper, published this year in the Transactions of British Geographers, he interpreted more than 20 years of flood damage data to reach the conclusion that ‘NaFRA appears to overestimate the economic risk by between four and five-fold (i.e. at c.£1.1 bn p.a., as opposed to a central estimate here of £0.25 bn p.a.)’. That is an outcome that does not surprise me – the uncertainties in risk estimates should not be underestimated. Edmund's estimate falls outside the uncertainty range we quoted in the 2002 NaFRA analysis (£0.6–2.2 bn), but that range was based only on uncertainty bounds on the flood defence fragility curves and the depth-damage functions, neglecting other uncertainties. A serious issue is that the effect of water level and dike crest level uncertainties in well-protected locations is asymmetric, but hugely influential. A small upward error in the crest level or downward error in the water level will take an already low risk estimate close to zero, while an error in the opposite direction will yield a large contribution to EAD. There are many other subtle sources of uncertainty. The aim that DEFRA originally had, to use NaFRA to monitor the benefits of its investments in risk reduction, has proven, for the time being, to be hampered by data uncertainties and improvements in methodology (another shifting baseline). That does not, however, mean that models like NaFRA are without merit – on the contrary, NaFRA still provides the best means we have of comparing risks and targeting scarce resources. For sure, it provides a more efficient way of managing risk than waiting for floods to happen and throwing money at the problem after the event. Yet perhaps the most significant observation is that the comparison with a long and reasonably reliable time series of flood damages, in the way that Penning-Rowsell has done for England and Wales, is only feasible in relatively few parts of the world. The Dartmouth Flood Observatory and EM-DAT teams are doing a great job in recording floods and their impacts, but underreporting, especially of relatively small and frequent floods, is a great obstacle to reliable validation of risk estimates. With uncertainties so endemic and model-dependent, it is a relief that the modelling process is becoming more open. Exercises like ISI-MIP, and associated online databases, are reducing the barriers to entry for researchers. Even the black boxes of Cat modelling are opening up in the OASIS open architecture loss modelling framework. The journey is far from over, but the pace of change is remarkable. If we look at engineering hydraulics, or even hydrological science, I would challenge anyone to point to a really significant advance that has been made in the last decade. Yet, in flood modelling, a revolution has been taken place. It is, frankly, a relief to see the first steps we took in 2002 being superseded by better methodologies and datasets. Those methodologies have in a short time made it out of universities and into the offices of analysts and consultants who are now tooled up to do the large numbers of runs that risk analysis requires. Hugely exciting, the steps have been taken, in just a few years, to move right up to the global scale, which presumably is the end of the road. Now all we have to do is fill in the gaps.
Futures studies
Flood risk management
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The socioeconomic impacts of flooding are huge. Between 1980 and 2013, flood losses exceeded $1 trillion globally, and resulted in approximately 220,000 fatalities. To reduce these negative impacts of floods, effective flood risk management is required. Reducing risk globally is at the heart of two recent international agreements: the Sendai Framework for Disaster Risk Reduction and the Warsaw International Mechanism for Loss and Damage Associated with Climate Change Impacts. Prerequisites for effective risk reduction are accurate methods to assess hazard and risk, based on a thorough understanding of underlying processes. Due to the paucity of local scale hazard and risk data in many regions, several global flood hazard and flood risk models have been developed in recent years. More and more, these global models are being used in practice by an ever-increasing range of users and practitioners. In this chapter, we provide an overview of recent advances in global flood hazard and risk modeling. We then discuss applications of the models in high-level advocacy in disaster risk management activities, international development organizations, the reinsurance industry, and flood forecasting and early warning. The chapter concludes with several remarks on limitations in global flood risk models and the way forward for the future.
Disaster risk reduction
Natural hazard
Reinsurance
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Estimates of flood risk involve using the statistical moments of peak flow series data, i.e. mean, standard deviation, to estimate the parameters of a distribution. The fitted distribution relates the probability of exceedance to a peak flow discharge. The fitted distribution, or flood frequency curve, is then used to inform the design of structures and water management and planning. However, flood risk estimation requires key assumptions, some of which have come under scrutiny in recent years. The first, stationarity, assumes that the moments used to fit a probability distribution are time invariant; e.g. the mean is constant throughout the observed record. The second assumption, homogeneity, is defined as the spatial invariance of flood moments. Homogeneity is a critical assumption when using regional information to inform a statistic of interest. In regional flood frequency estimation, commonly employed techniques such as quantile regression, regional skew estimation and the index flood method work under the assumption of homogeneity. Homogeneity assumes that all sites within a defined region will have the same flood frequency curve, indicating that given the same climatic disturbance, all sites will behave similarly. However as climate and landscape change, these assumptions can be violated. Alterations to precipitation and temperature have occurred, producing subsequent changes to associated flood risk. Landscape changes have also altered how runoff is translated through the watershed, ultimately impacting peak flows. This analysis considers several cases under which the assumptions of flood risk are violated. Chapter 1 analyzes the regional water balance, defined by the Baseflow Index (BFI), on the flood frequency curve indexed at key return periods. Chapter 2 assesses how storage in a case study watershed, the Suwannee River Basin, impacts the assumptions of homogeneity. Results from Chapter 2 guide the development of a new regional skew for the Suwannee River Basin which is documented in Chapter 3. Finally, Chapter 4 addresses using hydrological models to assess flood risk. First, different bias correction methods are applied to peak flows modeled using the Soil and Water Assessment Tool (SWAT), and then the impacts of climate and landscape change on these peak flows are compared to determine how each uniquely alter flood risk.
Quantile
Statistic
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Flood risk management
River management
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Vulnerability
Flood risk management
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Flood risk management
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Flooding is a natural hazard with the potential to cause damage at the local, national, and global scale. Flooding is a natural product of heavy precipitation and increased runoff. It may also arise from elevated groundwater tables, coastal inundation, or failed drainage systems. Flooded areas can be identified as land beyond the channel network covered by water. Although flooding can cause significant damage to urban developments and infrastructure, it may be beneficial to the natural environment. Preemptive actions may be taken to protect communities at risk of inundation that are not able to relocate to an area not at risk of flooding. Adaptation measures include flood defenses, river channel modification, relocation, and active warning systems. Natural flood management (NFM) interventions are designed to restore, emulate, or enhance catchment processes. Such interventions are common in upper reaches of the river and in areas previously transformed by agriculture and urban development. Natural techniques can be categorized into three groups: water retention through management of infiltration and overland flow, managing channel connectivity and conveyance, and floodplain conveyance and storage. NFM may alter land use, improve land management, repair river channel morphology, enhance the riparian habitat, enrich floodplain vegetation, or alter land drainage. The range of natural flood management options allows a diverse range of flood hazards to be considered. As a consequence, there is an abundance of NFM case studies from contrasting environments around the globe, each addressing a particular set of flood risks. Much of the research supporting the use of NFM highlights both the benefits and costs of working with natural processes to reduce flood hazards in the landscape. However, there is a lack of quantitative evidence of the effectiveness of measures, both individually and in combination, especially at the largest scales and for extreme floods. Most evidence is based on modeling studies and observations often relate to a specific set of upstream measures that are challenging to apply elsewhere.
Flood Mitigation
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