Urban centres and flood risk

A recent BBC article (Shanghai ‘most vulnerable to flood risk’) reports on a paper published in Natural Hazards by a team of researchers. The paper ‘A flood vulnerability index for coastal cities and its use in assessing climate change impacts’ in the journal Natural Hazards by Balcia, Wright and van der Meulen, follows in a tradition of trying to quantify risk using a set of key variables. (I think the paper is on open access so you should be able to read it via the link). The authors develop what they call the Coastal City Flood Vulnerability Index (CCFVI) that is composed of three parts: the hydro-geologic, the socio-economic and the politico-administrative. These parts represent the three key interacting subsystems that affect coastal flooding, the natural subsystem, the social-economic subsystem and the administrative and institutional subsystem. Within each of these the authors identify variables that indicate the degree of exposure to hazard, the susceptibility to the hazard and the resilience to the hazard. The hydro-geologic part only has indicators of exposure whilst the ‘human’ parts have indicators of all three.

Exposure is defined as the predisposing of a system to be disrupted by a flood event due to its location. Susceptibility is defined as the elements exposed within the system that influence the probability of being damaged during the flood event. Resilience is defined as the ability of a system, community or society to adapt to a hazard. This term is assessed through political, administrative, environmental and social organisational evaluation. Variables selected include sea-level rise, storm surge, number of cyclones in last five years, river discharge, foreshore slope, soil subsidence for the hydrogeologic subsystem. For the socio-economic subsystem the population close to the shoreline, the growing coastal population as well as cultural heritage are included as exposure factors whilst uncontrolled planning zones are an exposure variable for the political and administrative subsystem.  Susceptibility variables include the percentage of the population disabled or young or old and flood hazard maps. Resilience variables include shelters, level of awareness, institutional organisations and flood protection.

The paper carries out a detailed analysis of each subsystem and then combines the indicators into a single equation to determine overall vulnerability.  The selection of variables is well argued and the complexity and issues of using such indexes is discussed well, so the authors do not have a simplistic interpretation of hazards and vulnerability. Any paper that tries to squeeze and freeze the complex and dynamic concept of risk into a single index will always have the problem of simplification. Simplification, not only of the subsystems but also of the interpretation by others of the index itself.

The variables selected may reflect the data readily available plus a particular view of how the flood hazard should be alleviated. The focus on institutional organisations as resilience does imply a rather hierarchical view of hazard management and prevention (maybe a valid argument with a set of large urban areas with low social cohesion). Interpretation of the index, as in the BBC report, tends to focus on the final product rather than on the variables used in its construction and the ratings of the subsystems. Discussions could be made as to the appropriateness of the same variables for cities across the globe or for the selection of those variables anyway. Looking in detail at the breakdown of the index, it is clear that Shanghai is the most ‘vulnerable’ city on variables used to determine the hydro-geologic subsystem because of its high length of coastline and high river discharge (plus high soil subsidence). Manila, however, is ranked second because of its exposure to tropical cyclones and flooding – can both the same index combine both types of exposure? Does this mean that Manila is a more vulnerable location, as tropical cyclones are more frequent than high discharges? Can degrees of difference in vulnerability or rather exposure be assessed using a combined index? Shanghai is not, however, the top ranked city for all subsystems. For the economic variables, Shanghai is ranked fourth meaning that it is likely to recover quickly, economically at least, from the affects of a flood event.

An index like the one presented in this paper are very, very useful. They can be used, as the authors have done, to try to predict how changes in climate could impact on hazards and as such can be of great use in planning and management. A single index should, however, be used with caution, particularly if the choice of variables reflects a particular view of hazard management. Similarly, understanding how the index is constructed and how different parts of the index contribute to the whole is vital in understanding where vulnerability (and resilience) lie and how these might be improved.

More Mash-ups: Mapping A Century of Earthquakes

A recent posting on the AGU linkedin site drew my attention to a map tat plotted all magnitude 4 and above earthquakes that have occurred since 1898. The map in the Herald Sun clearly shows the distribution and the ‘hotpots’ you might expect around the Pacific ‘ring of fire’ as well as some intra-plate bursts of colour that suggest even the interior of continents are not immune from these hazards.
Although a nice image, the map represents a key trend that I mentioned in a earlier blog – mash-ups. The map was produced by John Nelson of IDV Solutions  a US software company specialising in visualising data. The maps combine data from the US Advanced National Seismic System and the United States Geological Survey to produce a map that spatially locates each piece of data. IDV Solutions understand the importance and power of such mash-ups and Deborah Davis published an article in Directions magazine (25th February 2010) on the importance of mash-ups for security. Although their observations about mash-ups are directed at security the observations in the articles are as useful for trying to understand and manage hazards and the risks associated with them.

Mash-ups provide a means of consolidating data from diverse sources into a single, comprehensible map and in a visual context that has some meaning for the observer. The map produced can be made relevant to the customer or user by ensuring that it contains additional information relevant to their interpretation of the information. A map of landslides combined with topographic data provides a context for helping to understand why the landslides might have occurred. Adding surface geology as another layer improves the context of interpretation for a landslide specialist, adding the road network improves the context of interpretation for a hazard manager. Once data has a context it is easier to spot relationships between phenomena. With this single, common map available to all parties there is a common basis for discussion and for decision-making. Having a common source of reference may even encourage discussion and debate. In addition, it may be easy to see where data is lacking and what other data these parties may require to aid their decision-making. The cost-effectiveness of such mapping should not be neglected either. Using existing data and producing a new product is very cost-efficient.

Haddon Matrix: Getting The Message Across

The Haddon Matrix is a tried and trusted tool for thinking about managing risk, particularly in public health. The basic matrix contains 3 key elements: the host, the agent (or vehicle) and the environment (subdivided into physical and social). These form the columns of the matrix. The rows refer to pre-event, the event and post-event activities.

	HOST	AGENT	ENVIRONMENT
PRE-EVENT
EVENT
POST-EVENT

In a previous post I illustrated this with a road traffic accident example as below.

This is useful but does it get across two important aspect when trying to manage risk: which of the cells in the matrix is the most significant AND which of the cells is it most effective to try to influence?

Not all the cells and their contents affect the risks of a hazard equally. From public health take the example of smoking - it could be argued that the social and cultural norms that an individual grows up in have a huge impact on their propensity to smoke. The host or individual smokes but what chance did they have given their environment. In other words the environment cells, particularly pre-event (the event being the cancer caused by smoking) has a massive impact on the risk. Likewise, survival of the event depends greatly on the level of health care available including catching the cancer early on, so both the event and post-event are greatly influenced by the environment. Visually the Haddon Matrix might look as below.

	HOST	AGENT	ENVIRONMENT
PRE-EVENT
EVENT
POST-EVENT

This alteration of cell size to match the perceived level of influence of host, agent and environment can help to get across the message as to which of the three is most important.

A second aspect, however, is which of the different elements to try to influence. It could be argued that changing a social or cultural environment is a long-term and difficult process but the one that has the greatest imapct on smoking levels. Effectiveness cna be defined in many ways and it maybe that in the short term targeting the host to change their behaviour is much easier (and cheaper) to produce and places responsibility firmly in the lap of the person smoking. As well as being cheap for health authorities and potentially politically more palatable as it highlights individual responsibility (depending on your political persuasion) altering cells sizes to reflect this, as below, does highlight to the individual that they do have potential control over their fate (whether this is an illusion or not is another question).

	HOST	AGENT	ENVIRONMENT
PRE-EVENT
EVENT
POST-EVENT

So for risk analysis and management maybe it is worthwhile changing the sizes of the cells when discussing both degree of influence and effectiveness of potential actions however this is defined. This may help in targeting resources effectively for the ends in mind.

Risk and Heuristics

The perception of risk affects how people behave. People tend to simplify the world; to use simple heuristics to help them understand risk and how they should behave in the face of risks. These simple rules affect how much insurance they buy, where they live, how dangerous they believe modern life is. Quoting facts and figures may do little to alter people’s behaviour or the hold of these heuristics. Studying the types of heuristics people have about risk has been a fruitful are of research since the 1970s when Tversky and Kahneman undertook their early studies (e.g. 1974, Judgment under uncertainty, Science, 185, 1124-1131). Amongst the numerous heuristics that have been researched I want to consider just three in this blog – representativeness, availability and anchoring – in the light of environmental risk.

Representativeness refers to people’s ability or tendency to view risk in one area as comparable to risk in another if the two areas, at least to them, resemble each other. Crime, whatever, its complexion may provide a convenient category for people to fear even if the causes of terrorism are different from bag snatching. The classing of both as crimes may connect the different activities as comparable in people’s minds. A previous blog discussed the media hype behind the BP oil spill. Media reports kept comparing the spill to the Exxon Valdez, forming a comparability connection in people’s minds. Both are oil spills, so they must be comparable. A closer examination of the causes and characteristics of each casts some doubt on their comparability. One was tanker spill, the other a massive, destructive blowout; one occurred in a confined water body, the other a dynamic ocean; one was associate with stark images and immediate of dying wildlife, the other with less obvious and visually striking losses of livelihoods. Yet, calling each an oil spill implies similarities in nature and similarities in response. Pointing out differences may do little to make people think that the things are different.

The ongoing floods in Pakistan are another example. Third world floods, again, may be the immediate response of some readers and viewers. The same sort of floods seems to happen every year, somewhere over there, surely by now they should know what to do? Classifying the event as a flood brings with it the risk of comparison with other events in the same class. By comparison the death toll seems small, by comparison the event seems slow, by comparison it happens a long way away. Such comparisons can become a convenient short-hand to explaining or justifying a lack of action or the vigorousness of a response. Classing an event may help to understand it but there is also a danger that we assume that as it is a member of that sort of event, we understand what it should do and how we should behave towards it. At the crudest level, for example, how many people should be dead to make it an important flood, rather than looking at the individuality of each event. Floods are different in causes, consequences and solutions; one size fitting all is as inappropriate for environmental hazards as it is for understanding most things.

The flood example is also an illustration of availability bias. Availability bias refers to the tendency for people to respond to risks more vigorously when examples of that type of risk are readily available to them. Availability may be from individual or community memory, from the media, from their beliefs about the world and any number of other sources. The Pakistan floods are compared to the impact of other floods we call to mind most readily whatever their cause. Similarly, the BP oil spill is contrasted in the media with the Exxon Valdez, as the latter is viewed as a key environmental event and so a sort of benchmark for other events, however inappropriate or appropriate the comparison might be.

On a more personal level, the fact that you may have experience a flood of your home in the last two or three years, may make you more wary of the flood risk and so more likely to purchase insurance or to try to at least as insurance companies using the same bias may raise premiums to match the increased perception of risk in your local area. Statistically, the local flood may not alter the probability of future flooding by much, if at all, but does it feel like that to you as you wade through your sodden possessions?

Anchoring refers to an individual’s or community’s starting point for assessing risk. Usually people start from a particular value that they belie is associated with a particular type of risk or event and then adjust their estimation of the risk (or its seriousness) in the light of further information. The adjustment will, however, always be in relation to that initial starting value. In other words, for the same physical risk or event, two individuals, one with a low initial estimate, the other with a high one, will interpret any further information about the risk or event in the light of their initial starting values. After the event, it is likely that both will have moved from their initial estimates but the person with the initial low value will still have a lower estimation of the risk than the person with the initial high estimation.

Once again the two recent disasters of the BP oil spill and the floods in Pakistan can be interpreted as examples of anchoring. How do you judge the impact of the BP oil spill? Initial estimates by the company and environmental groups varied. BP trying to downplay the incident, some environmental groups proclaiming nightmarish scenarios for the future of the Gulf. As the event has unfolded how have each side changed its rhetoric? BP has slowly admitted the spill was worst than initial thought, at least in terms of the amount of oil released into the ocean. Images of environmental annihilation of the Gulf have not emerged. So do you adjust your assessment of the damage wrought by the oil spill up or down as evidence and opinion have increased? Does it depend on where you started – as a committed environmentalist or as a company supporter? Does it really matter where you start, doesn’t the evidence speak for itself? Evidence is always interpreted so these heuristics are important.

Aid for the floods in Pakistan may have suffered from an anchoring effect. The areal extent of the disaster is huge and the impact and suffering caused by the floods is both massive and real, but the initial death toll seemed minor in comparison to other disasters in recent memory, such as the Haitian earthquake or the Boxing Day tsunami. It may be simplistic but impact and death toll may be related in people’s mind and a low death toll anchors the flood disaster relatively low down in a mental pecking order or recent disasters. Subsequent media coverage, celebrity appeals and governmental and UN urging for aid may be interpreted in the light of this initial anchor point.

As an additional thought, what is your individual anchor point in the ongoing ‘discussions’ about the need to reduce expenditure on public spending to clear public deficits? The debate seems to have moved beyond do we need to? The debate only seems to be how severely do we need to? Accepting the need is as much an anchor as setting an amount. I may be overly cynical but if leaks suggest a 40% cut in the spending of a government department and a review finally recommends only 30%, then you can’t help but feel a little relieved it is lower than you expected. Anchoring is a very strong tool in setting agendas both for environmental issues and for politics in general.

HAZARDS AND RISK

Risk is a tricky thing to pin down and, as with most things in hazards analysis, open to a wide range of interpretations. A useful website for discovering just how open to debate this term is is John Adams website (http://john-adams.co.uk/). We all encounter risk everyday. The financial markets have just collapsed under the weight of risks. We drive along the motorway aware of the risk of other drivers (at least I hope everyone else does as I do!). We weigh up the risk to our health, the length of our life, of another drink, another cigarette, another burger or pie. Or do we?

Risk can seem to be such an easy thing to define. You can work out the probability of something occurring- the probability of dying from smoking a specific number of cigarettes per day, the probability of a specific amount of alcohol per day giving you cancer, the probability of contracting cancer given a specific level of exposure to radiation. Trouble is people often act as if they don’t know these probabilities exist.

Risk can be defined accurately, mathematically and scientifically using statistical analysis. Risk can be defined as the chance of a particular defined hazard or event occurring. If you now the frequency of occurrence of a particular level of flow in a river then you can work out the probability in any one year that there will be a flow of a given magnitude. Leaving aside problems of how long does the record of flows need to be to be representative, how well are extreme events represented in that record and many other factors, the key point is that, in theory, risk can be calculated from such records. Risk can be given a number: a fixed value that informs people and what they should do. Btu why should risk bother you? Risk only becomes important because you feel you might have something to loss. Risk can only be defined in relation to loss, so only within a context of fear or loss. It can be refined as a simple equation of:

RISK = [Hazard (probability) x Loss (expected)]/ Preparedness (loss mitigation)

You can see how each bit of the equation could be given a number. The hazard from scientific analysis of the geophysical nature of the hazard or rather the probability of the hazardous event. Loss can be calculated by the amount of money you would need to replace what you could lose if the event happened. Preparedness, more tricky, but maybe how much you can pay to insure against you loss by that hazardous event. But is this all risk really is?

There are other ways of looking at risk.

• Risk can be defined as being a real thing, out there and so subject to scientific and mathematical analysis and calculations that are common across experts.
  
• Risk can be seen as a cultural and social phenomenon created by the society we live in and so subject to change as that society changes
• Risk can be defined legally as a responsibility or a failure of expected conduct
• Risk can be defined psychologically as a set of behaviours and understandings about the world
  
• Risk can be defined within the humanities as an emotional phenomena and as a story or narrative

  Each of these different definitions illuminates different aspects of risk and may ring true with individuals in different circumstances. When watching news reports about floods in Pakistan, for example, I am seeing risk as a story or narrative dictated by the media and its beliefs about how I expect the disaster to unfold. Never underestimate the tight constraints of such storylines in affecting how we see things. Risk of injury on a building site could be viewed through the lens of legal definitions of risk and responsbility. The reactions of individuals to flooding and flood risk could be viewed through the lens of psychology. Some people believe in the risk and insure, others don’t and save their money – are the first group risk averse, the second risk takers or is it more complicated than that?

  There is another way of defining risk, either singularly or in combination.

  • Real: the calculation approach as above plus objective below
  • Objective: the risk is real, a thing and it is out there for us to study and quantify
  • Observed: Risk we can measure given our particular view of the world (and given it is real and objective)
  • Subjective: Risk is about mental states of individuals who are only human and so plagued by fear, worry, uncertainty and doubt
  • Perceived: subjective estimate of risk by individual or group

  I would argue that all risk is perceived and that risk tends to be defined by the judgements of people, singularly and in groups, based on their application of some knowledge or information about the uncertainty involved, where this knowledge or information is objective, observed or subjective. When we believe or precieve the risk to be generated by some real, physical phenomenon then we can meausre it and calculate risk. This does not mean others will share our view of the world as objective nor our view of risk as soemthing objective.

  What this means is that the perception and belief of risk varies from individual to individual, from group to group, from place to place and even from event to event. Trying to model or generalize about the actions of individuals in the face of risk is difficult but in future blogs I hope to present some models and general ideas about how people have tackled this complicated problem of understanding how people perceive and react to risk.