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• W1807359909 abstract "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." @default.
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• W1807359909 title "Editorial: steps towards global flood risk modelling" @default.
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