Imagining flood futures: risk assessment and management in practice

Author(s):  
Stuart N. Lane ◽  
Catharina Landström ◽  
Sarah J. Whatmore

The mantra that policy and management should be ‘evidence-based’ is well established. Less so are the implications that follow from ‘evidence’ being predictions of the future (forecasts, scenarios, horizons) even though such futures define the actions taken today to make the future sustainable. Here, we consider the tension between ‘evidence’, reliable because it is observed, and predictions of the future, unobservable in conventional terms. For flood risk management in England and Wales, we show that futures are actively constituted, and so imagined, through ‘suites of practices’ entwining policy, management and scientific analysis. Management has to constrain analysis because of the many ways in which flood futures can be constructed, but also because of commitment to an accounting calculus, which requires risk to be expressed in monetary terms. It is grounded in numerical simulation, undertaken by scientific consultants who follow policy/management guidelines that define the futures to be considered. Historical evidence is needed to deal with process and parameter uncertainties and the futures imagined are tied to pasts experienced. Reliance on past events is a challenge for prediction, given changing probability (e.g. climate change) and consequence (e.g. development on floodplains). So, risk management allows some elements of risk analysis to become unstable (notably in relation to climate change) but forces others to remain stable (e.g. invoking regulation to prevent inappropriate floodplain development). We conclude that the assumed separation of risk assessment and management is false because the risk calculation has to be defined by management. Making this process accountable requires openness about the procedures that make flood risk analysis more (or less) reliable to those we entrust to produce and act upon them such that, unlike the ‘pseudosciences’, they can be put to the test of public interrogation by those who have to live with their consequences.

2011 ◽  
pp. 234-248
Author(s):  
Enid Mumford

Participative systems design has, in the past, been seen as a positive group process of thinking through needs and problems and arriving at solutions for making the situation better. This improved situation then continues until new technology or new solutions provide an opportunity for making the situation better still. So far this book has concentrated on how to make the best use of the positive factors assisting change, especially change that involves the introduction and use of technology. It has described the importance of getting a clear understanding of the change problem and its complexity, of developing effective strategies to address this complexity, and of the creation of structures, often organizational, to facilitate the subsequent use of the new system. This last requires always keeping in mind the need to meet the dual objectives of achieving operating efficiency and a good quality of working life. This is often described as job satisfaction. Most of all there has been a continual stress on the importance of participation. This involves sharing the design tasks with those who will be affected by them and taking account of their opinions in design decisions. This chapter addresses the reverse of this positive objective. It considers the negative factors in a change situation which are likely to cause problems and to threaten the success of the change programme and of the new system. There are very many of these kinds of problems and it is only possible to discuss a few here. The ones I have selected are criminal threats which affect the future viability of the company, technical problems which reduce efficiency, unpleasant and stressful work that threatens employee health, and problems of morale which affect the individual’s happiness in the workplace. A consideration of negative factors brings us into the challenging areas of uncertainty and risk. Uncertainty is when we do not know what is going to happen and often contains an element of surprise. This is especially true today when so many decisions depend on forecasts of the future. A contributing factor here can be an overemphasis on the present as a means of forecasting the future. Uncertainty is also often a result of the behaviour of others rather than of events. This is hard to predict. Experts tell us that today we are living in a risk society (Beck, 1992). Complex design problems can have a high degree of uncertainty and easily become risks. They often have a subjective element, for what one person considers a problem or a risk, another will see as an opportunity. Complex problems also require information for their solution and this may be difficult to find. It requires the ability to search for, analyse and synthesise, relevant intelligence and relate it to past, current and future events. Threats to important institutions from terrorists are of a different nature and scale to those that have been experienced before. Many will take us completely by surprise. Bernstein (1996) suggests that the essence of risk management lies in maximising the areas which we have some control over while minimising those areas where we have no control over the outcome and the linkage between cause and effect is hidden. When we take a risk we are making a bet that a particular outcome will result from the decision we have made although we have no certainty that this will happen. Risk management usually starts with risk analysis, which attempts to establish and rank the most serious risks to be avoided so far as these are known. Here many companies attempt to achieve a balance between the benefits of greater security and the costs involved. Too high a level of security, while providing good protection, can result in a system that is both difficult to use and expensive to operate (Mumford, 1999). Risk analysis next moves on to risk assessment. This is an analysis of the seriousness of different risks by determining the probability and potential damage of each one. For example, major risks can come from a large concentration of data in one place that is accessed by many different people, not all of whom are known. There can be relationships between risks. Clifford Stoll’s (1990) book The Cuckoo’s Egg shows how the ability of a German hacker to enter a university laboratory computer made it possible for him to later enter into the computers of United States military bases. Risk analysis identifies the risks; risk assessment tries to estimate how likely they are to happen and how serious the consequences will be. Risk priorisation recognises that all companies cannot be protected from all risks and choices must be made. Risk impact is the likely magnitude of the loss if a system break-in, fraud or other serious problem occurs. Risk control involves further actions to reduce the risk and to trigger further defensive actions if a very serious problem occurs. Risk control also covers the monitoring of risk on a regular basis to check that existing protection is still effective. This can lead to risk reassessment. Very serious risks such as those coming from terrorist attack or criminal activity require monitoring. This, together with the detailed documentation of any problems or illegal activities when they occur, is essential to avoid complacency. An effective system must both prevent problems and detect when they have occurred. All of these activities to design security into a system require human vigilance if they are to be effective. All employees should accept some responsibility for checking that the system they work with continues to maintain its integrity and security. This chapter will place its main focus on protective problem solving and design directed at avoiding or minimising very serious risks. Today, it is unwise for managers to neglect this. Because of its growth in recent years and its prevalence today criminal activity will be examined first in some detail. Particular attention will be paid to how the involvement of employees in problem solving can play a part in reducing or avoiding this.


2020 ◽  
Author(s):  
Jeroen Aerts

<p>Despite billions of dollars of investments in disaster risk reduction (DRR), data over the period 1994- 2013 show natural disasters caused 1.35 million lives. Science respond with more timely and accurate information on the dynamics of risk and vulnerability of natural hazards, such as floods. This information is essential for designing and implementing effective climate change adaptation and DRR policies. However, how much do we really know about how the main agents in DRR (individuals, businesses, government, NGO) use this data? How do agents behave before, during, and after a disaster, since this can dramatically affect the impact and recovery time. Since existing risk assessment methods rarely include this critical ‘behavioral adaptation’ factor, significant progress has been made in the scientific community to address human adaptation activities (development of flood protection, reservoir operations, land management practices) in physically based risk models.</p><p>This presentation gives an historic overview of the most important developments in DRR science for flood risk. Traditional risk methods integrate vulnerability and adaptation using a ‘top- down’ scenario approach, where climate change, socio economic trends and adaptation are treated as external forcing to a physically based risk model (e.g. hydrological or storm surge model). Vulnerability research has made significant steps in identifying the relevant vulnerability indicators, but has not yet provided the necessary tools to dynamically integrate vulnerability in flood risk models.</p><p>However, recent research show novel methods to integrate human adaptive behavior with flood risk models. By integrating behavioral adaptation dynamics in Agent Based Risk Models, may lead to a more realistic characterization of the risks and improved assessment of the effectiveness of risk management strategies and investments. With these improved methods, it is also shown that in the coming decades, human behavior is an important driver to flood risk projections as compared to other drivers, such as climate change. This presentation shows how these recent innovations for flood risk assessment provides novel insight for flood risk management policies.</p>


Author(s):  
T. K. J. McDermott ◽  
S. Surminski

Urban areas already suffer substantial losses in both economic and human terms from climate-related disasters. These losses are anticipated to grow substantially, in part as a result of the impacts of climate change. In this paper, we investigate the process of translating climate risk data into action for the city level. We apply a commonly used decision-framework as our backdrop and explore where in this process climate risk assessment and normative political judgements intersect. We use the case of flood risk management in Cork city in Ireland to investigate what is needed for translating risk assessment into action at the local city level. Evidence presented is based on focus group discussions at two stakeholder workshops, and a series of individual meetings and phone-discussions with stakeholders involved in local decision-making related to flood risk management and adaptation to climate change, in Ireland. Respondents were chosen on the basis of their expertise or involvement in the decision-making processes locally and nationally. Representatives of groups affected by flood risk and flood risk management and climate adaptation efforts were also included. The Cork example highlights that, despite ever more accurate data and an increasing range of theoretical approaches available to local decision-makers, it is the normative interpretation of this information that determines what action is taken. The use of risk assessments for decision-making is a process that requires normative decisions, such as setting ‘acceptable risk levels' and identifying ‘adequate’ protection levels, which will not succeed without broader buy-in and stakeholder participation. Identifying and embracing those normative views up-front could strengthen the urban adaptation process—this may, in fact, turn out to be the biggest advantage of climate risk assessment: it offers an opportunity to create a shared understanding of the problem and enables an informed evaluation and discussion of remedial action. This article is part of the theme issue ‘Advances in risk assessment for climate change adaptation policy’.


2015 ◽  
Vol 51 (8) ◽  
pp. 6399-6416 ◽  
Author(s):  
B. Merz ◽  
S. Vorogushyn ◽  
U. Lall ◽  
A. Viglione ◽  
G. Blöschl

Author(s):  
Toon Haer ◽  
W. J. Wouter Botzen ◽  
Vincent van Roomen ◽  
Harry Connor ◽  
Jorge Zavala-Hidalgo ◽  
...  

Many countries around the world face increasing impacts from flooding due to socio-economic development in flood-prone areas, which may be enhanced in intensity and frequency as a result of climate change. With increasing flood risk, it is becoming more important to be able to assess the costs and benefits of adaptation strategies. To guide the design of such strategies, policy makers need tools to prioritize where adaptation is needed and how much adaptation funds are required. In this country-scale study, we show how flood risk analyses can be used in cost–benefit analyses to prioritize investments in flood adaptation strategies in Mexico under future climate scenarios. Moreover, given the often limited availability of detailed local data for such analyses, we show how state-of-the-art global data and flood risk assessment models can be applied for a detailed assessment of optimal flood-protection strategies. Our results show that especially states along the Gulf of Mexico have considerable economic benefits from investments in adaptation that limit risks from both river and coastal floods, and that increased flood-protection standards are economically beneficial for many Mexican states. We discuss the sensitivity of our results to modelling uncertainties, the transferability of our modelling approach and policy implications. This article is part of the theme issue ‘Advances in risk assessment for climate change adaptation policy’.


Author(s):  
Michalis I. Vousdoukas ◽  
Dimitrios Bouziotas ◽  
Alessio Giardino ◽  
Laurens M. Bouwer ◽  
Evangelos Voukouvalas ◽  
...  

Abstract. An upscaling of flood risk assessment frameworks beyond regional and national scales has taken place during recent years, with a number of large-scale models emerging as tools for hotspot identification, support for international policy-making and harmonization of climate change adaptation strategies. There is, however, limited insight on the scaling effects and structural limitations of flood risk models and, therefore, the underlying uncertainty. In light of this, we examine key sources of epistemic uncertainty in the Coastal Flood Risk (CFR) modelling chain: (i) the inclusion and interaction of different hydraulic components leading to extreme sea-level (ESL); (ii) inundation modelling; (iii) the underlying uncertainty in the Digital Elevation Model (DEM); (iv) flood defence information; (v) the assumptions behind the use of depth-damage functions that express vulnerability; and (vi) different climate change projections. The impact of these uncertainties to estimated Expected Annual Damage (EAD) for present and future climates is evaluated in a dual case study in Faro, Portugal and in the Iberian Peninsula. The ranking of the uncertainty factors varies among the different case studies, baseline CFR estimates, as well as their absolute/relative changes. We find that uncertainty from ESL contributions, and in particular the way waves are treated, can be higher than the uncertainty of the two greenhouse gas emission projections and six climate models that are used. Of comparable importance is the quality of information on coastal protection levels and DEM information. In the absence of large-extent datasets with sufficient resolution and accuracy the latter two factors are the main bottlenecks in terms of large-scale CFR assessment quality.


2018 ◽  
Vol 94 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Haixing Liu ◽  
Yuntao Wang ◽  
Chi Zhang ◽  
Albert S. Chen ◽  
Guangtao Fu

Sign in / Sign up

Export Citation Format

Share Document