floods - Hydrological risk, Exams of Hydrology

Download floods - Hydrological risk and more Exams Hydrology in PDF only on Docsity! 198 3.4 Hydrological risk: floods Hannah Cloke, Giuliano di Baldassarre, Owen Landeg, Florian Pappenberger, Maria-Helena Ramos 3.4.1 Introduction: flood hazards and impacts In principle, flooding is a natural phe- nomenon that affects all river basins around the world in more or less reg- ular intervals and that fulfils essential functions in the natural ecosystem. However, owing to human settle- ments being established within flood- plains and common development practices not leaving room for rivers under flood conditions, flooding is mostly considered for its negative rather than its positive effects (Watson and Adams, 2010). Alfieri et al. (2016) estimate flood impact at the Europe- an Union level to be ≈EUR 6 billion per year, affecting 250 000 people per year. Although flood impact assess- ment is an essential step by which to optimise flood mitigation measures, there are many sources of uncertain- ty that affect such complex estimates. For example, uncertainty may come from sparse and short datasets, poor knowledge of hydraulic structures such as dams and weirs along rivers, assumptions and extrapolations in statistical analyses of extreme floods, and depth-damage functions. The es- timation of flood damages also de- pends on several assumptions (Merz et al., 2010). It involves challenges in defining damages for different el- ements at risk (e.g. houses, public spaces, industries), and transferring solutions in space (from one region to another) and in time (from one flood event to another). Flooding causes long-term damage to health, with immediate impacts such as drowning, physical trauma, infec- tions and chemical hazards, and also affects well-being, livelihoods and social cohesion. It is also not always easy to identify the local consequenc- es of flooding, such as the effects caused by displacement, the destruc- tion of homes, delayed recovery and the disruption of access to health services (WHO, 2013). Flooding can also cause damage to critical infra- structure and can interrupt health and social care service delivery and busi- ness supply chains (National Flood Resilience Review, 2016; Landeg and Lawson, 2014). Finally, flooding is also frequently associated with power outages, which themselves can have a detrimental impact on health and businesses (Klinger et al., 2014) and a knock-on effect on other critical infrastructure such as railways and wastewater services. Flood disasters affect a large number of people across the world every year, with severe social and economic impacts. Severe flooding repeatedly affects European populations, with trans-national events often being the most damaging. CHAPTER 3 UNDERSTANDING DISASTER RISK: HAZARD RELATED RISK ISSUES - SECTION II 199 The vulnerability of riverside com- munities around the world is particu- larly worrying in the light of migra- tion pressures, socioeconomic drivers and climatic change. Even those who live flood-adapted lifestyles are not resilient to severe floods that occur only rarely, particularly when the last big flood was beyond living memory (Garde-Hansen et al., 2016) and in light of the impacts of future climate change. In this subchapter, the main drivers of flood hazard are introduced and flood hazard and risk mapping are discussed, particularly at the region- al scale. Flood predictability is then considered, along with a review of the added value of flood monitoring, flood forecasting and EWSs. 3.4.2 Living with floods Learning to live with flooding means that we recognise that flooding will continue to happen, as it is a natural phenomenon. There are many uncer- tainties in knowing when and where a flood will happen, both in the imme- diate term and in terms of probable climate change timescales, and when it does flood there is inevitably some disruption to our lives. However, there are many things that we can do to pre- pare better for floods and manage the risk, including strengthening compo- nents of flood prevention, flood pre- paredness, flood response and flood recovery, which are part of the dis- aster cycle (Figure 3.). Interventions can be taken during a flood to limit the impact of the disaster, including the evacuation of settlements or the creation of additional flood relief space through the opening of dykes or dams. This response is followed by a recovery phase after the disaster has passed, which includes relief meas- Hazards and risk event cycle Source: courtesy of authors FIGURE 3.24 Hazards and risk Event Cycle Emergency provisions Management Warning and alert systems Resources for intervention Emergency planning Training and exercise Prevention Legal bases Land use planning Technical & Environmental measures Biological measures Organisational measures Reconstruction Reconstruction and strengthening of resilience Financing Event analysis Documentation Lesson learnt Recondition Energy & Transport systems Communications Supply and disposal Intervention Alert Rescue Damage mitigation Emergency measuresPreparations for Intervention Early warning Raised readiness Preparedness Response Recovery 202 od and a chain of models and assess- ments (Sampson et al., 2014, Dottori et al., 2016), although simpler map- ping based solely on flood events or other historical information can also be useful (Boudou et al., 2015). Flood hazard and flood risk maps are required for land use planning, floodplain management, disaster response planning and financial risk planning. They can be produced at increasingly higher resolutions using flood modelling tools. Uncertainties can be taken into account by using probabilistic methods. A focus on flood hazard impacts can enhance communication to the public. For fluvial floods, a full risk mapping requires long-term series of hydrome- teorological data, satellite data on the flood extent for the assimilation of spatial information, large datasets on population/asset exposure and flood protection standards (Scussolini et al., 2016), and commercially sensitive damage data from insurance compa- nies, which are often not openly ac- cessible. Longer timescale changes in flood risk are usually assessed through scenarios of climate change and soci- oeconomic development (Apel et al., 2008; Winsemius et al., 2013). These can take into account flood policies, such as the implementation of flood protection measures, as well as the interaction of human and physical systems, such as the adaptation effect and the failed levee effect (Di Bal- dassare et al., 2015; Collenteur et al., 2015). Flood hazard maps can be produced by using hydraulic models to simulate water flow along rivers, over flood- plains and in urban surface water accumulation zones. Simulations are often combined with Geographic In- formation System (GIS) techniques to build flood maps. This ideally requires substantial observed data for model calibration and validation. For fluvial floods, hydraulic models can use time series of historical river flows, histori- cal rainfalls or time series of synthetic design rainfall events, in conjunction with catchment hydrology rain- fall-runoff models. However, even the most sophisticated approaches have difficulty producing robust esti- mates of extreme events (Sampson et al., 2014), which can be problematic if these maps are the only resourc- es used to support decision-making processes, such as urban planning. Describing flood inundation hazard and risk using probabilistic methods is therefore encouraged (Romanow- icz and Beven, 2003; Pappenberger et al., 2006). For example, flood in- undation hazard can be mapped from the development and set-up of flood inundation models, a sensitivity analy- sis using observations, the use of the multiple acceptable (‘behavioural’) model parameter sets to perform ‘en- semble’ (multiple) simulations using an uncertain synthetic design event, or an ensemble of scenarios, as input to the flood inundation models (Di Baldassarre et al., 2010). Probabilistic methods can be used, as they assume that, whichever model is chosen, it will not perfectly represent all flood propagation and inundation process- es involved. This can be very impor- tant when modelling flood inundation in changing environments, when they are subject either to strong land use changes or to climate changes. Regional-scale fluvial flood hazard mapping has been improved by the use of satellite data assimilation and flood models to map flood inunda- tion pathways. Global flood hazard maps can also be useful in the assess- ment of flood risk in a number of different applications, including (re) insurance and large-scale flood pre- paredness. These maps can be creat- ed using large-scale computer models of rainfall-runoff processes in river catchments and river routing. They may, however, require the use of a variety of post-processing methods to better adjust simulations to local measurements (Pappenberger et al., 2012; Ward et al., 2013; Winsemius et al., 2013; Dottori et al., 2016). At the local scale, surface water flood hazard mapping (pluvial flooding) has benefited from recent improvements to fine-scale surface water modelling, particularly in cities, on 1-metre or 2-metre grids, integrating topography, land use, urban structures and poten- tially also subterranean drainage and flooding impacts (Tyrna et al., 2016; Palla et al., 2016). All numerically produced flood haz- ard maps, regardless of their spatial scale, require validation in order to be useful. This can be very challenging because of a lack of robust observed data. On local, regional or national scales, validation can be undertaken, CHAPTER 3 UNDERSTANDING DISASTER RISK: HAZARD RELATED RISK ISSUES - SECTION II 203 at least to some extent, on the basis of past observations of inundation ex- tents, from satellite, ground-based ob- servations or community-based data sources, as well as from river stage and discharge measurements from river gauges. In contrast, the accuracy of global maps is far more challenging, as globally consistent observations can rarely be obtained. Trigg et al. (2016), for instance, describe several differ- ent global flood hazard maps, which have been individually validated with- in a limited context. The estimates of global flood hazard obtained are com- pared to analyse their consistency and to provide an estimate of model un- certainty. In Africa, the agreement be- tween the different models is relatively low (30-40 %), with major differences in magnitude and spatial extent par- ticularly observed for deltas, arid/ semi-arid zones and wetlands, which are all areas that suffer from a lack of data for validation. Such discrepan- cies can have significant impact: for example, the models showed a large discrepancy in the Nile delta, where approximately 95 % of the popula- tion of Egypt lives. This highlights the fact that any global flood hazard map should be used with caution and that multimodel products may be use- ful (Trigg et al., 2016). The role of databases and post-event analyses is key to improve our understanding of global flood hazard and risk (de Moel et al., 2015). 3.4.5 Flood monitoring, forecasting and early warning systems The predictability of hydrological systems varies because of the large number of non-linearities in these systems, the challenges in the observ- ability of the state of the hydrologi- cal variables, the presence of outliers (rare occurrences), the variability of external forcing and the numerous interactions among processes across scales (Bloschl and Zehe, 2005; Ku- mar et al., 2011; Peña et al., 2015; La- vers et al., 2011). Different types of floods are predictable with different time ranges. Flash floods driven by convective rainfall are notoriously challenging to predict ahead in time to produce effective early warnings (Collier, 2007; Berenguer et al., 2005), whereas slower developing floods in large catchments can be predicted several days ahead of time with the use of probabilistic flood forecasting systems (Emerton et al., 2016). The use of satellites and EWSs based on computer-intensive forecasts has re- cently enabled distinct improvements in our ability to provide effective in- formation on the likelihood and se- verity of upcoming flooding and the extent of the affected area (Alfieri et al., 2013; Revilla-Romero et al., 2015). This information can be provided to agencies, responders, stakeholders and the public in various forms, in- cluding interactive watch or warning maps and flood guidance statements (e.g. FFC, n.d.; Vigicrues, 2017). However, there is substantial uncer- tainty in predicting floods, which stems from the uncertainty in the atmosphere, the complexity of the land-surface processes and the imper- fection in the computer models used to represent them (Cloke and Pap- penberger, 2009; Rodríguez-Rincón et al., 2015). Ensemble techniques can be used to represent the main sources of predictive uncertainty. These use multiple simulations based on different model set-ups, model pa- rameters, initial conditions, data, etc. Rather than just providing one ‘best guess’ prediction, ensembles provide a whole range of model realisations and equally possible predictions for the future. Information can be ob- tained on which scenarios are most likely to happen and on the worst possible scenario (given our current knowledge of initial conditions and process representation). This can be useful to communicate forecast un- certainty and to help stakeholders to take more informed decisions (Cloke and Pappenberger, 2009; Stephens and Cloke, 2014; Zsótér et al., 2016). The HEPEX initiative (Hydrologic Ensemble Prediction Experiment, n.d.) seeks to advance the science and practice of hydrologic ensemble pre- diction and its use in risk-based deci- sion-making by engaging researchers, forecasts and users in several commu- nity activities. Real-time monitoring and rapid map- ping of floods based on satellite data have been implemented at a variety of scales and by a number of different actors to detect flooding severity and extent in affected areas. For instance, the Copernicus Emergency Man- agement Service—Mapping (2017) integrates satellite remote sensing and available in situ data to provide stakeholders with timely and accu- rate geospatial information in emer- gency situations and humanitarian crises (not just for floods, but also other hazards). It operates for the full emergency management cycle and can be broadly divided into (1) a Rap- id Mapping component, which pro- vides on-demand information within 204 hours or days, usually immediately in response to a disaster event, and (2) a risk and recovery mapping to sup- port activities in the area of preven- tion, preparedness and disaster risk reduction. Another activity in the area of monitoring flooding from space and their impacts is the Dartmouth Flood Observatory (n.d.). Maps are published to provide an overview of flooding impact and extent, and a day- to-day record of flooding occurrenc- es is built for analyses at a later stage. The use of space-based information facilitates international flood detec- tion, response, future risk assessment, and community-wide hydrological re- search. Improvements in rainfall data assimilation to meteorological mod- els (e.g. Ballard et al., 2016) and soil moisture, discharge and water level data or flood inundation characteris- tics to flood models (e.g. Garcia-Pin- tado et al., 2015; Alvarez-Garreton et al., 2015) have also provided improve- ments in flood forecasting and hazard mapping. Many other vital data have emerged, derived from ground-based imagery flood monitoring, crowd- sourcing, unmanned aerial vehicles, rapid flood mapping and post-event data collection by authorities, re- searchers and local communities (e.g. Walker et al., 2016; Le Coz et al., 2016; Perks et al., 2016). Numerical weather prediction models have now improved to the point that operational centres can set up hydro- meteorological systems that are able to forecast river flow and flooding on larger catchments several days, and even weeks, ahead of an upcoming flood event at global scales (Emerton et al., 2016). Transnational forecasting and warning systems can be of par- ticular benefit, as they provide con- sistent and comparable information for rivers that cross national bound- aries. They can also be useful as sup- port information for all nations that do not have adequate flood forecast- ing and warning capabilities (Alfieri et al., 2012; Thiemig et al., 2015). As Emerton et al. (2016) argue: Flood forecasting and EWSs are identified as key preparedness actions for flood risk management and can be implemented at local scales through to continental and global scales. Radar and numerical weather forecasting systems can be used as inputs to flood forecasts, but uncertainties should be taken into account using ensemble (probabilistic) forecasting techniques. Operational systems currently have the capability to produce coarse- scale discharge forecasts in the medi- um-range and disseminate forecasts and, in some cases, early warning products in real time across the globe, in support of national forecasting capabilities. With improvements in seasonal weather forecasting, future advances may include more seamless hydrological forecasting at the glob- al scale alongside a move towards multi-model forecasts and grand en- semble techniques, responding to the requirement of developing multi-haz- ard EWSs for disaster risk reduction. Flood magnitude and return period (or average frequency of occurrence) can be assessed for single points on a river. However, for those applications that require a measure of flood sever- ity across an entire region, or ‘flood- iness’, as, for example, in the case of initiating and forecasting the need for humanitarian actions, floodiness indi- ces can be used to provide a spatial view of the risk of flooding (Stephens et al., 2015). Although several applica- tions still rely on rainfall forecasts as a proxy for imminent flood hazard, Stephens et al. (op. cit.) have shown that monthly floodiness is not well correlated with precipitation, which demonstrates the need for hydrome- teorological EWSs at such scales. 3.4.6 Copernicus Emergency Management Service: floods (EFAS and GloFAS) The European Flood Awareness Sys- tem (EFAS, 2016; operational since 2012) and GloFAS (GloFAS, 2017; due to become operational in ear- ly 2017) aim to provide early flood information to national authorities to support national capabilities, par- ticularly with earlier and probabilis- tic information. EFAS additionally provides information to the Europe- an Commission’s ERCC to support flood disaster response. The EFAS project was initiated fol- lowing the severe 2002 flooding that took place across Europe and has CHAPTER 3 UNDERSTANDING DISASTER RISK: HAZARD RELATED RISK ISSUES - SECTION II 207 is ensured at all stages and that essen- tial information for decision-making is not lost (see Chapter 4). Communi- cation not only targets decision-mak- ers at public or private companies, but also involves communication to the public and to experts (Environ- ment Agency, 2015) who may prefer information to be described in terms of possible impacts. The visualisation of model outputs and maps is part of the communication process (Pap- penberger et al., 2013). Usually, com- munication will cover information on alerts, watches and warnings, risk maps and vulnerable areas that can be potentially affected by floods of different magnitudes and return pe- riods (100-year flood, 10-year flood, etc.), but also guidance on using and interpreting maps. It is important that communication follows Open Ge- ospatial Consortium (OGC) stand- ards, such as providing information as Web Mapping Services (WMS) or WaterML, so that it can be easily in- tegrated into other systems and be more effective. The communication of flood hazard and risk and the asso- ciated uncertainties should be a strong focus at all stages in the prevention, preparedness, response and recovery cycle. It should also be active during recovery in order to facilitate post- event surveys, to speed up recovery with the help of local communities or to convey lessons learned (Marchi et al., 2009; Stephens and Cloke, 2014; Javelle et al., 2014). Efficient communication is also de- pendent on how users perceive risk and understand uncertainty, and tend to act in the face of uncertain infor- mation (Ramos et al., 2010; Bubeck et al., 2012). A two-way approach can enhance, and even modify, established links between modelling outputs (haz- ard and risk maps) and social actions. Through an increased understanding of user needs and institutional and so- cial vulnerability drivers (Rufat et al., 2015, Daupras et al., 2015), existing bottlenecks in flood response, such as areas of difficult access or with high rates of injuries and fatalities, can be detected and targeted in the maps. With time, behaviour changes can even bring modifications to the vul- nerability zones and can modify flood risk maps that cross flood vulnerabil- ity with hazard. In this process, build- ing trust and confidence is essential. Uncertainties are not necessarily unwelcome by the public and stake- holders (McCarthy et al., 2007), and explicitly acknowledging uncertainty in flood risk mapping is also valuable for decision-makers (Michaels, 2015). The communication of uncertainty can help modellers and forecasters by strengthening a relationship of confi- dence between them and the users of their products. Flood forecasts and flood risk maps have associated uncertainties and are useful if decision-makers can understand and act upon the information provided, so forecasting and mapping must be in harmony with user needs and requirements to bring added value to the whole process of flood hazard and risk management. One uncertainty that it is essential to consider in all aspects of flood risk management is the projected future changes in flooding risks to commu- nities, businesses and infrastructure. This means considering adaptive management approaches in the design of flood risk management policy and infrastructure (Gersonius et al., 2013). The degree of uncertainty in the im- pacts of climate change projections requires the consideration of flexi- ble adaptation pathways. Regardless of the sources of uncertainties, more needs to be done in flood risk man- agement policy and practice to make our societies resilient to future flood risk (CCC, 2017; EEA, 2017). 3.4.8 Conclusions and key messages Flood disasters affect a large num- ber of people across the world every year, with severe social and economic impacts. Severe flooding repeatedly affects European populations, with trans-national events often being the most damaging. Partnership Our best strategy for flood manage- ment is to learn to live with flooding, that is, to prepare ourselves today to be better adapted for flood risks to- morrow. The combination of strong flood management policy, advanced early warning technology and in- creased international collaboration has the potential to reduce flood risk and improve disaster response from the local to the global scale. This re- quires stakeholders from different disciplines, scientists, policymakers 208 and practitioners to work closely to- getherin partnership. Knowledge Flood hazard and flood risk maps are required for land use planning, flood- plain management, disaster response planning and financial risk planning. They can be produced at increasingly high resolution for fluvial and surface water flooding (and coastal flooding) using flood modelling tools. Uncer- tainties can be taken into account by using probabilistic methods. A focus on flood hazard impacts can enhance communication to the public. Innovation Flood forecasting and EWSs are in- novations that are key preparedness actions for flood risk management and can be implemented at local scales through to continental and global scales. Radar and numerical weather forecasting systems can be used as inputs to flood forecasts, but uncertainties should be taken into ac- count using ensemble (probabilistic) forecasting techniques. There is probably a substantial mon- etary benefit in cross-border conti- nental-scale flood EWSs. In Europe, transnational flood early warning is undertaken by the Copernicus Emer- gency Management Service: Floods, which consists of EFAS and its global twin system, GloFAS. 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