In managing vulnerability to natural disasters, with case studies of volcanic disasters on non-industrialized islands


PART I Concepts and Models for Technology in Managing Vulnerability to Natural Disasters

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PART I


Concepts and Models for Technology in Managing Vulnerability to Natural Disasters
3. Society’s Vulnerability to Natural Disasters

3.1 Introduction

Chapter 2 noted that one of the reactions or responses of society to its interaction with the environment is to use and misuse technology in managing the interaction. Society’s risk from natural disasters, a combination of natural hazards and vulnerability, is an interaction which society usually wishes to manage. Section 5.4 demonstrates that managing natural hazards is generally not as feasible or as desirable as managing vulnerability, and so society’s focus should be on managing vulnerability.

This chapter examines society’s vulnerability to natural disasters in order to build upon the concepts introduced in section 2.7 and to better understand the role of technology. The influences discussed in sections 3.2 through 3.5 are all characteristics of society and at times they intersect and influence each other. The separation suggested in this chapter is for improving the clarity of the discussion rather than for establishing distinct categories.

3.2 Demographic Influences

3.2.1 Individuals’ Characteristics

An individual’s physical and cultural characteristics influence the individual’s vulnerability to death or injury from natural hazards. Such characteristics include age, gender, linguistic ability and background, ethnicity, race, and state of physical and mental health. An individual’s state of health incorporates physical mobility, speed of reaction, intelligence, and medical history. The following illustrative examples of how these characteristics influence vulnerability have been collated and adapted predominantly from Brenner and Noji (1993), Carter et al. (1989), Ewald (1993), and Pearce (1994):

•Race: Individuals of black African heritage are more susceptible to sickle cell anaemia than those of other heritages, but having sickle cell anaemia greatly reduces an individual’s vulnerability to the biological hazard of malaria.

•Linguistic ability (oral and reading comprehension):

Individuals who do not understand warnings and safety instructions--due to educational background, youth, hearing impediments (state of health), intelligence, or a linguistic background different from the language of the community--are more vulnerable to rapid-onset hazards such as tornadoes and flash floods.

Individuals who speak a language with an absence of words describing certain hazards (e.g., in Spanish, “lahar” (a mudflow) is usually translated as “avalanche”) may be more vulnerable to that hazard due to improper communication and comprehension of the hazard.

•Gender and age:

Elderly females are more susceptible to osteoporosis than males and younger females and thus are more vulnerable to bone injuries during structural collapse caused by natural hazards such as tornadoes and earthquakes.

Elderly people of both genders are more vulnerable to some biological hazards than younger people such as the bacteria which cause tuberculosis and the viruses which cause influenza, and also have decreased mobility (state of health) which increases vulnerability to rapid-onset hazards.

•State of health:

As a person’s state of health declines, physical mobility is impaired, linguistic ability may regress, and ability to respond appropriately to warnings or situations may be compromised. Alcoholism, multiple sclerosis, and asthma are examples where vulnerability is increased in situations requiring rapid response due to decreased mobility.

Vulnerability to biological hazards is heavily influenced by state of health and medical history. Immunodeficient individuals, due to an illness such as AIDS (acquired immune deficiency syndrome), are highly vulnerable to other microbiological hazards, such as the bacteria which cause tuberculosis and the viruses which cause influenza. Hospitalized patients are also highly susceptible to microbiological hazards.



3.2.2 Populations’ Characteristics

Characteristics of a population influence vulnerability of that population to natural hazards. The predominant influences are that populations are increasing, urbanizing, and mobile. Encroachment into areas of higher vulnerability occurs when a larger population seeks new places to live. Soil in volcanic areas or along river banks can be especially tantalizing as good farmland, particularly to subsistence farmers, yet these areas are dangerous due to their respective hazards. Subsistence farmers in tropical areas are also becoming increasingly vulnerable to biological hazards. As populations increase and old farmland becomes barren, population pressures force farmers into destroying and settling previously unexplored areas of wilderness which not only brings them into contact with new microbial pathogens3 but also reduces the range, food supply, and patience of large mammals. Coastal areas are another highly vulnerable area experiencing rapid population increases (Burton et al., 1993).

Urbanization affects vulnerability in a complex fashion. Urbanization implies increasing population densities concentrated in relatively small areas compared to generally sparse but equal distributions over large rural districts. If there is an evenly distributed probability of a natural hazard occurring over a large area, the urban population in a smaller area will experience less frequent natural hazards than the rural population, but when a natural hazard does strike the urban area, more people will be impacted. Furthermore, because the natural hazard strikes urban areas less frequently than rural areas, the urban population has less experience in coping with the situation and so has augmented vulnerability (see section 3.3). On the other hand, urban areas tend to have much better resources for response and recovery, such as quick access to high-quality emergency services and medical care, than rural areas.

If the probability of a natural hazard occurring is not evenly distributed, then the location of an urban area determines the impact of urbanization on vulnerability. An expanding city which is more vulnerable to a natural hazard than the surrounding countryside will augment the population’s vulnerability because of the increased population exposed to, and the comparative lack of experience of coping with, the natural hazard. An expanding city which is less vulnerable to a natural hazard than the surrounding countryside will reduce a population’s vulnerability by drawing the rural population away from hazardous areas. The impact on vulnerability of urbanization is significant, but the factors determining the final result are complex.

As well, the expansion of cities upwards (i.e., apartment blocks and skyscrapers) affects vulnerability. Tornado vulnerability can be reduced (McCulloch, 1994) while earthquake vulnerability can be augmented (Bolt, 1993). Concentrated populations also assist the spread of microbiological hazards, a fact known throughout European history when people would depart cities for rural areas to escape plague outbreaks. Furthermore, urban areas promote socioeconomic disparity and socioeconomic class influences vulnerability (section 3.4, with some examples under “State of Mind” in section 3.3).

Urbanization is an example of large-scale, rapid population mobility, a relatively new phenomenon for the global population. Analogous, small-scale mobility occurs globally, primarily in richer populations: convenient, global transportation systems permit families and individuals to be highly mobile between communities. People frequently travel between cities or countries for reasons such as taking holidays, conducting business, and changing jobs. Being in an unfamiliar environment increases vulnerability to unfamiliar natural hazards, because without an awareness of potential natural hazards, one will not likely know how to watch out for or respond to a natural disaster. Linguistic ability and knowledge of community emergency procedures and safe locales will also be obstructed.



3.3 Attitude and Belief System Influences

Characteristics of individuals and populations help shape, and are shaped by, their psychological characteristics, so attitudes and belief systems influence vulnerability. Some influences are culturally focussed--through religion, language, and past collective experience--and some influences are individually focussed--through personality and available opportunities. Much of the discussion on how the psychological state of mind influences vulnerability is better explored in the context of psychological boundaries, and so it is deferred until section 6.4. Illustrative examples of how these psychological characteristics, and other attitudes and belief systems, influence vulnerability are:

•Lifestyle:

-Substance intake:

Diet and drug use (including nicotine, alcohol, banned substances, and prescriptions) impact one’s mental and physical health which influences vulnerability to natural disasters (section 3.2).

-Sexual practices:

Multiple sexual partners and non-use of barrier prophylactics increase vulnerability to sexually-transmitted microbiological hazards. The use of oral contraceptives increases a female’s vulnerability to gonorrhoea (Madigan et al., 1997).

-Outdoor activities:

Outdoor activities such as hiking, skiing, camping, and canoeing bring individuals into more frequent contact with storms, landslides, avalanches, insects, and large mammals thereby increasing vulnerability; however, they also tend to improve one’s level of fitness, health, and ability to cope with extreme situations, thereby decreasing vulnerability.

-Activities at historical sites:

Individuals in buildings preserved for historical reasons (such as churches, tourist pioneer villages, and forts) tend to be more vulnerable to atmospheric hazards because the buildings are unlikely to have been retrofitted to modern standards (Pearce, 1994). The same argument holds for vulnerability to seismic hazards.

-Occupation:

Health care workers tend to be frequently exposed to microbiological hazards, although frequent, low-level exposure to microbial pathogens does induce a level of immunity.

Farmers’ incomes are highly vulnerable to floods, droughts, and temperature extremes.

Volcanologists, seismologists, atmospheric scientists, microbiologists, biologists, and geologists will encounter their respective hazards more frequently than other occupations.

•State of Mind

-Religion:

One factor identified in the surprisingly high rate of tornado fatalities in the Bible Belt in the southern U.S.A. is fatalism, the doctrine that all events are inevitable and humanity should submit to fate without dispute (Brenner and Noji, 1993).

Keeping the Aeta (Filipino aboriginal people) outside the danger zone of the volcano Mount Pinatubo is difficult, because the Aeta believe that the mountain is their protector/saviour and that they are not permitted to live anywhere but on its slopes (England, 1993a & 1993b; Goertzen, 1991; Shimizu, 1989; see also section 11.3.2).

-Past experience:

Toronto continues functioning as a city through snowstorms which would bring Glasgow or Dublin to a standstill. Torontonians are used to, and are generally prepared for, dealing with heavy snowstorms while Glaswegians and Dubliners do not expect large snowfalls. Torontonians, however, would generally deal with a major earthquake poorly compared with San Franciscans, Los Angelenos, or Tokyoites.

-Factors which supersede vulnerability to natural disasters:

A geodesic dome or sphere section would likely be the safest building in a tornado, yet society continues to construct rectangular prisms which are dangerous. Aesthetics and familiarity of shape and construction practices supersede vulnerability to tornadoes.

Many of the fatalities during Chicago’s 1995 heat wave were attributed to a legitimate fear that opening windows would increase vulnerability to crime. Personal and property safety concerns superseded concerns about the heat’s health impacts.

Wealthier socioeconomic classes sometimes choose to live in areas vulnerable to natural hazards for prestige, isolation, climate, and lifestyle concerns. California--vulnerable to landslides, earthquakes, and brush fires--and Florida--vulnerable to hurricanes, tornadoes, and alligators--are popular locales in the U.S.A.

Many of the aforementioned examples can be placed in one link of a chain of factors connecting attitude and belief systems to vulnerability (adapted from accident avoidance factors described by Wilde (1994)):

•Society (or an individual) must be aware of the state of vulnerability by:

being conscious (awake);

 being attentive;

 possessing the necessary sensory capabilities (a state of health influence);

 and having an accurate perception of vulnerability.

•Then, society must:

be motivated to manage the vulnerability;

 possess the necessary decision-making skills for taking action;

 possess the necessary analytical skills for choosing appropriate action;

 and enact appropriate action before a natural disaster occurs.

If any of these links fail to connect, then society’s vulnerability will be increased.

3.4 Economic Influences

Even when the vulnerability chain described at the end of section 3.3 is intact, the desire to take appropriate action before a natural disaster occurs might be unfulfillable due to economic factors. Tackling vulnerability might not be affordable, or decision-makers might label the actions as being unaffordable. Technology inevitably has an economic cost, and measures designed to manage vulnerability to natural disasters could be deemed too expensive by the government, business, organization, or individual which must pay the immediate cost. For example, Harris et al. (1992) design schools and public buildings in tornado-prone areas in the U.S.A., yet emphasize that added costs for tornado protection must be virtually insignificant or else they will not be awarded contracts. Developing, implementing, and enforcing adequate standards or regulations to demand adequate protection are often deemed too expensive.

As will become evident throughout this thesis--particularly in Chapter 5 on preventive engineering, section 6.3 on temporal boundaries, and aspects of the case studies--the “immediate cost” tends to be emphasized as being too expensive, even though avoiding the immediate cost of preventive and mitigative measures often incurs future costs which are far greater than this initial, immediate cost. Nonetheless, the bias towards avoiding immediate costs, however misguided, is a dominant economic influence on vulnerability. At other times, especially for individuals, the immediate cost truly cannot be covered. For example, even if the need had been recognized in advance, many of the fatalities during Chicago’s 1995 heat wave could not have afforded the installation of either air conditioning or security systems which would have permitted window screens with crime prevention devices. Similarly, many deaths in urban homes from cold temperatures are attributed to “fuel poverty”, where residents cannot afford adequate insulation or heating.

These examples also illustrate how socioeconomic class influences vulnerability. Generally, poverty breeds vulnerability. Lower socioeconomic classes tend to have increased vulnerability because they:

•occupy more inadequately constructed and maintained dwellings;

•live in more dangerous locales, which others prefer to avoid;

•have poorer nutrition and less access to appropriate water supplies and sanitation;

•have a poorer state of health and less access to proper medical care;

•have fewer resources for solving these problems, through techniques such as acquiring technology, social activism, purchasing insurance, and legal proceedings.

Wealthier socioeconomic classes have more control over their vulnerability because lifestyle and residence preferences are more easily affordable. Other influences, such as urban problems (section 3.2.2) and attitude (section 3.3), can lead to preferences which increase vulnerability, but wealth permits these wishes to be fulfilled. For example, section 3.3 discussed how vulnerable areas of California (and Florida) are prestigious locales and hence are desirable for settling: in San Francisco’s Marina District, chic residences built on reclaimed land prone to liquefaction succumbed during the 1989 Loma Prieta earthquake, and expensive homes built near Californian bluffs proved disastrous during several El Niño induced landslides in the first few months of 1998. Similarly, lifestyle choices such as outdoors activities are available with wealth. A lower socioeconomic class forces vulnerability in many aspects of lifestyle, but a higher socioeconomic class provides choice, which includes the choice to be vulnerable.



3.5 Political Influences

One method for reducing the impetus of choosing vulnerable lifestyles is education, demonstrating how political influences can affect vulnerability. Communicating information about all aspects of natural disasters, including the importance of vulnerability, will educate society about the problems and potentially motivate society into developing solutions. These actions can be carried by any sector of society--government, academia, community groups, industry, and businesses--but may be hampered by economic restrictions. Engineers should use education to complement the technology they provide, in order to ensure proper development and application of their technology.

Another political influence on vulnerability is effective leadership and decision-making. A lack of these qualities can result in devastating natural disasters. In countries such as Canada, France, Ireland, Japan, and the U.S.A., incompetence ranging from wilful negligence to unfortunate ignorance have spread microbiological hazards amongst people who use products from publicly-donated blood for medical reasons, such as transfusions during operations and surviving with haemophilia. Westphal et al. (1990), for example, describe protozoal, bacterial, viral, and rickettsial infections which have been transmitted by blood transfusions. Corruption and mismanagement also led to the failure of dykes designed to protect surrounding communities from Mount Pinatubo lahars (mudflows) in the Philippines, following the 1991 eruption (“Like Pompeii”, 1996; Tiglao, 1996; see also section 11.3.4).

Even when the political will exists to promulgate procedures or regulations to deal with the influences on vulnerability discussed in this chapter, further political will is required to monitor and enforce the standards or statutes. In 1992, Dade County, Florida had one of the toughest building codes in the U.S.A., but much of the damage caused that year by Hurricane Andrew occurred because buildings were not designed in accordance with the code and because poor enforcement practices failed to uncover the problems (Coch, 1995). During various tornadoes in eastern Canada, “buildings in which well over 90% of the occupants were killed or seriously injured did not satisfy two key requirements of [Canada’s] National Building Code” (Allen, 1992, p. 361).

The chain of political influences on vulnerability is:

educate and inform society about the issues involved;

 enact appropriate decision-making and management; and

 monitor and enforce the decisions which are taken.

As with the chain described at the end of section 3.3, a difficulty at any stage in this process can have detrimental impacts on vulnerability.


3.6 Conclusions: Vulnerability and Technology

The examples in this chapter foreshadow the many challenges in managing vulnerability to natural disasters. Vulnerability arises from a combination of diverse factors and there are sequences of conditions which must be satisfied before society will have an appropriate grasp on all aspects of vulnerability. Technology plays an important role in these issues, particularly as it permits society to expand and explore: to settle new, and at times vulnerable, areas (Burton et al., 1993); to partake in new, and at times vulnerable, lifestyles; and to try out new, and perhaps vulnerable, policies and procedures. Technology provides a strong degree of protection to society, yet society accepts this protection with few questions. Eventually, however, technology will fail at some level, such as being overwhelmed by an inevitable extreme natural hazard event, and then society does question its vulnerability and the role which technology played in achieving that state of vulnerability. The role of technology in influencing and solving challenges of vulnerability are detailed in the following chapters.



4. Tool of Technology

4.1 Introduction

As discussed in Chapters 2 and 3, society is highly vulnerable to natural disasters and the vulnerability arises from numerous sources. As discussed in section 2.4, there are many tools which society uses, individually and in combination, for managing vulnerability to natural disasters and one of these tools is technology. Using the tool of technology lies in the realm of engineering. This chapter explores that realm by examining the role of engineers (section 4.2), the framework used by engineers for managing vulnerability to natural disasters (section 4.3), and the main challenges inherent in using the framework (section 4.4).


4.2 Role of Engineers

Technology refers to the systems, techniques, designs, and approaches created and used by engineers (section 2.4). Engineers play roles throughout the entire process of creating and using technology, including basic research, development, testing, implementation, operation and maintenance, monitoring, decommissioning, and analysis of these stages.



4.3 Framework Used by Engineers

Figure 4-1 illustrates the framework which engineers apply in the case of designing for natural disasters (Adams and Karney, 1989).


Figure 4-1: Framework which Engineers Apply in Designing for Natural Disasters

(Adams and Karney, 1989; see text for details)




Load

(a pressure on or input to the system)



System



Response

(the effect on or output from the system)



The elements of Figure 4-1 are described in sections 4.3.1 (system), 4.3.2 (load), and 4.3.3 (response); the system is described first for reasons of clarity. Table 4-1 lists examples of the framework.


4.3.1 System

The system can comprise any combination of the three following components:

•the environment, or a subset such as a waterway, a watershed, an ecological hierarchical level, or a forest;

•society, or a subset such as a country, a town, an ethnic group, a linguistic group, an age group, a socioeconomic class, or an occupational group; and

•technology, or a subset such as bridges, information systems, energy lifelines, high-rise buildings, or schools.


Table 4-1: Examples of Loads, Systems, and Responses for Natural Disasters


Possible Load

Possible System

Possible Response4

Horizontal and vertical shaking loads on structural supports due to an earthquake.

Technology: bridges.

Loss of the transportation lifeline

(i.e., collapse of the bridges).



Ice loads on above ground power lines or heat loads on underground power lines.

Technology: the power conduit.

Loss of the energy lifeline

(i.e., blackouts).



Wind loads on and air pressure gradients across walls and roofs due to a tornado.

Technology: houses, mobile homes, and recreational vehicles.

Loss of shelter

(i.e., destruction of the buildings).



A heavy rainfall.

Technology: dams.

Loss of the water supply or lifeline

(i.e., failure of the dam and flash flooding).



A newly emerging microbial pathogen transferred through contact with body fluids.

Society: people who have been given blood products or who practice unsafe sex or unsafe drug use.

Loss of human health and/or life.

(i.e., an epidemic of the pathogen’s disease).



Several days of cold air temperature.

Society: people who cannot afford adequate heating.

Loss of human health and/or life.

(i.e., hypothermia casualties).



A collision between a large asteroid and the Earth.

Environment: the biosphere.

Loss of ecosystem health and/or life

(i.e., a global, mass extinction).



Underwater volcanic eruption.

Environment: area surrounding the volcano.

Loss of stability in the ocean

(i.e., a tsunami).



Tsunami.

Society: people at the affected coast.

Technology: structures built on the affected coast.



Loss of human and ecosystem health and life, shelter, and property.

(i.e., flooding).



Engineers generally have responsibility for, and play a role in, systems of technology, or systems of the environment and society which are closely related to technology. Discussions relating to engineers assume that such systems are being considered.

4.3.2 Load

The load is a pressure on or input to the system, and can be of either internal or external source. The load can originate in the environment, in which case it is a natural hazard. The load can also originate in society, in which case it is vulnerability. If the load originates in technology, then the situation would normally be considered to yield “non-natural” responses, such as a non-natural disaster (Table 2-4). Even when the technological load is transferred to the environment or society, the disaster would be considered to be non-natural. This situation is discussed in section 2.9 with reference to Table 2-5; for example, a technological load was transferred to the environment to produce the London fog in December 1952. This thesis does not regard such events as natural disasters.

The same load can often be viewed as being generated from either within or outside of the system. Earthquakes are ostensibly external to the system of Tokyo and blizzards are ostensibly external to the system of Moscow, yet the inhabitants of those cities are aware of these potential loads. Engineers, along with other sectors of society, have put immense effort into helping these systems deal with the potential load. The system changes with these actions. The inhabitants have adapted their surroundings and lifestyles based, among other factors, on the perceived threat from the natural hazard.

Thus, an “external” load is not entirely external; most natural hazards are part of the system of the planet Earth and all natural hazards are part of the system of this universe, or all universes--a rather large system which is difficult to analyze. Therefore, labelling a load as “external” actually means that the load is perceived to be external to the system, especially for design purposes. This externality for design purposes is somewhat artificial because it is the subjective selection of the system which determines whether the load is internal or external. This subjective decision, though, enables the selection of a system which is of reasonable size for assessing, describing, and analyzing vulnerability and the impact of technology.



4.3.3 Response

The response is the output from the system or the system’s effect on the environment which arises due to the influence of the load.



4.3.4 Using the Framework

Engineers select objectives for design along with methodologies of selecting these objectives. The three-stage framework in Figure 4-1 is used as follows:

Load/Response: Engineers select objectives for what response is desired for a given load.

System: Engineers select a design which they believe fulfils the load/response objectives.

Engineers understand and can predict what occurs throughout the sequence in Figure 4-1 relatively well. Given a beginning (a load), the system can often be designed to achieve a desired response with a reasonable level of accuracy and precision. Therefore, engineers have generally adopted the approach of stating “Tell me the problem--define it for me--and I will solve it according to your definition”. Usually engineers do a good job of solving the problem according to the given definition, so that the results are accurate and appropriate, but only for the given problem.

Finding a “beginning”--i.e., a load--is the main challenge for the engineer, although this issue is seldom recognized. The engineer might not have the opportunity of assisting the definition of the problem because it is usually the engineer’s client who approaches the engineer after the client has identified a problem. The client explains to the engineer the problem, and hires the engineer to solve that problem. Doing otherwise is not always an option, because it is the client who pays the engineer. As part of the job, the engineer certainly should include a critical analysis of the problem as defined by client, and possibly make suggestions on improvements, but the client might not be willing to listen or might not be able to afford additional services.

The largest community of engineers who would have the opportunity to explore the nature of problem definition would be those in research careers or positions, particularly those in academia and government. As well, other sectors of society have a responsibility to convince engineers of the need to spend time ensuring that design loads are properly selected. Such action need not be altruistic; considering that the society’s safety is affected by the engineer’s behaviour, other sectors of society have a clear selfish need to ensure that design loads are determined appropriately. In the end, though, the engineer is ultimately (morally and legally) responsible for the work s/he undertakes. If society or the client is unaware of the problem or refuses to try to solve it, then the engineer should venture, as much as possible, to warn and to educate. The challenges of understanding the load are deep and require immense innovation to solve.


4.4 Challenges in Understanding the Load

The predominant challenges which engineers must overcome in order to properly understand the load are that:

•there are currently gaps in knowledge about natural disasters (section 4.4.1);

•past experience is normally used to design for future events, but it might not always be appropriate (section 4.4.2); and

•taking into account every potential scenario is a formidable task (section 4.4.3).

Many aspects of the above challenges, and some new ones (such as that society tends to plan for the short-term rather than the long-term), can be described as the difficulty of choosing boundaries and scales for the engineering problem (Chapter 6).


4.4.1 Understanding Natural Disasters

The state of knowledge about many properties of natural disasters contains large gaps, even with respect to the causes and origins of natural hazards. Thus, predictions of natural disaster behaviour often include surmises, and the subsequent load input during the natural disaster can be equally hypothetical.

For example, there is no accepted definition for a tornado and the formation mechanisms of tornadoes are not precisely understood. Discussions in Doswell III & Burgess (1993), and several other papers in Church et al. (1993), describe some of the ambiguities. The environment’s persistent habit of producing multitudinous, complex phenomena also makes interpretation of these phenomena difficult. In addition to tornadoes, related phenomena are termed waterspouts, landspouts, funnel clouds, fair weather vortices, gustnadoes, and dust devils, although dust devils have a different manner of formation than the preceding phenomena (Woodcock, 1991). Although the load produced by all of these phenomena can be similar, the engineer must be aware that any differences amongst the phenomena (rather than just amongst the terminology) could lead to real differences in the load.

These knowledge gaps lead to statements such as “we don’t design for tornadoes...because we don’t know what to design for” (by Alan Davenport, quoted by McCulloch, 1994). As a fundamental fact, the veracity of this statement is questionable, but it does provide an appropriate summary of the problems which engineers face when selecting loads for tornado design. Similarly, there is still a debate about how likely it is for a tornado to hit the centre of a large urban area, as the influences on tornado behaviour from urban heat islands and from surface roughness due to skyscrapers is not well-known (McCulloch, 1994).

A geological example of the challenges in understanding natural disasters is earthquakes. Generally, the model of tectonic plates covering the Earth yields good long-term predictive capabilities in terms of the probability of an earthquake with a certain magnitude striking a geographical area. The short-term predictive capabilities, though, are fairly poor, particularly for high-magnitude earthquakes. Therefore, for long-term planning, engineers have a reasonable idea of the geographical areas subjected to loadings caused by earthquakes, yet there are problems. Predictions for earthquake location can only be as good as the model of tectonic plates. Strong earthquakes strike far from known tectonic plate boundaries, in supposedly aseismic zones such as the Australian outback and northern Québec (Ungava). Either there are undiscovered faults or at least some earthquakes are being caused by another mechanism. Any weakness in seismologists’ understanding may become a weakness in engineering design.

The “undiscovered faults” hypothesis also applies to the issue that the load from earthquakes in known seismic areas can be surprising (Bolt, 1993). On January 17, 1994 near Los Angeles, a previously unknown fault caused an earthquake which killed 61 people and resulted in approximately US$20 billion (1994 dollars) of damage (FEMA, 1997; Klebs and Sylvies, 1996). The Northridge earthquake, as the event is known, was also surprising in that most of the damage resulted from vertical shaking, whereas most earthquake designs had assumed horizontal shaking loads (Coch, 1995). Exactly one year after the Northridge earthquake, on January 17, 1995, a region in Japan which was not thought to be too vulnerable to earthquakes was hit by one. The city of Kobe and the surrounding area were devastated by 5,426 dead, 26,804 injured, and approximately US$125 billion (1995 dollars) in damage (Kuribayashi et al., 1996; Lekkas et al., 1996). The damage was exacerbated by building codes written to protect structures from typhoons, which were assumed to be the main natural hazard of concern in that region (Smith, 1996).

The lesson from tornadoes is that the understanding of the causes of a natural hazard phenomenon is not always complete. The lesson from earthquakes is that a good understanding of (some aspects of) a natural hazard phenomenon does not necessarily yield a good predictive capability for that phenomenon. Analyzing the potential of predictive capability for a phenomenon is also not necessarily straightforward. For example, even if precise predictions are not feasible, it might be known (or believed) that a certain precision of prediction is achievable or that the limits of prediction could be well-defined. Predictions can apply to any spatiotemporal scale (such scales are discussed in sections 6.2 and 6.3) and indicating the limits and limitations of predictive capability for a phenomenon5 provides useful information, even if the phenomenon itself cannot be predicted. In fact, stating that a phenomenon is inherently unpredictable is a precise prediction of the phenomenon’s behaviour.

Furthermore, even if a natural hazard phenomenon is well-understood and can be accurately and precisely predicted, there is no guarantee that the ability or desire to engineer for managing vulnerability will exist. For example, the causes, physical behaviour, and consequences of tsunamis are well-known, but the most effective solution currently available is to monitor the formation of tsunamis, warn the population, evacuate, and rebuild. Otherwise, large-scale coastal engineering with severe environmental implications would be necessary to protect populations and property from tsunamis.

The challenge in understanding the load which this section discusses can be summarized as:

•knowledge gaps exist;

•knowledge does not imply predictability; and

•neither knowledge nor predictability imply successful engineering for managing vulnerability.



4.4.2 Using Past Experience to Define Problems

As occurred in the Kobe, Japan area where buildings were designed for typhoons leaving them vulnerable to earthquakes (section 4.4.1), design load problems are often defined based on past experience in a region. The historical record of design loads which would have been required to prevent a disaster form the basis for contemporary designs. The legal/political sector of society tends to implement regulations based on previous failures/disasters or near-failures/disasters, and thus engineers (are required to) design in this manner. The problem is defined for the engineer based on previous incidents. For example, Jamaica, Japan, and Los Angeles promulgated their first building codes for seismic protection after extensive damage from earthquakes in 1907, 1923, and 1933 respectively (Levy and Salvadori, 1995). In Manitoba, flood plain protection is based on the worst historical flood on record (IJC, 1997).

This approach is frequently reactive or after-the-fact engineering. Following a natural disaster, there is an impetus from society to ensure that a recurrence of that event producing similar damage cannot occur. In contrast, preventive engineering tends to be more sustainable, as described in Chapter 5.

The focus on using past events is related to the theory of uniformitarianism, which assumes that environmental processes of the past are similar to those of today and the future. This assumption is reasonable, since the current understanding of physical laws is that they do not change perceptibly on Earth over time, even though society’s understanding and interpretation may change. The application of uniformitarianism, however, can be flawed.

In contrast to Manitoba, the U.S.A. implements flood plain protection based on the 100-year event (IJC, 1997); i.e., the flood which has a probability of 0.01 of striking in any given year. The 100-year event is calculated using statistics of past events. The problem with this approach is that a natural hazard usually comprises a combination of different events each of which has its own return period, and each return period is only somewhat dependent on the other events’ return periods. For example, a flood can be simplistically described as a certain water level or a certain flow rate, and observations of these levels and flow rates over many years could be statistically analyzed to yield return periods for certain water levels and flow rates. There are, however, several methods of attaining a certain water level or flow rate and some methods have many steps. For example, heavy rainfall or a quick snow melt upstream could each produce the same water level or flow rate. Floods due to heavy rainfall are also influenced by soil saturation and runoff properties. Floods due to snow melt are influenced by air temperature, surface temperature, insolation, and snow depth (which is influenced by prior precipitation rate, air temperature, surface temperature, and insolation). Each meteorological, hydrological, or pedological variable has its own set of statistics and its own set of return periods with varying degrees of independence from each other.

There are correlations, but a 100-year snowfall does not necessarily imply a 100-year snow melt, and a 100-year snow melt or a 100-year rainfall does not necessarily imply a 100-year water level or a 100-year flow rate. Each event of this natural hazard chain has its own return period. Furthermore, the same maximum water level or flow rate does not necessarily produce the same design loads for engineering works. The rate of change of water level and flow rate, the amount of sediment in the water, the time period of maximum value, and the total water throughput all influence the ultimate design load experienced. Similarly, during an earthquake, a 100-year fault slippage distance or a 100-year moment magnitude does not necessarily imply 100-year vibration loads. Numerous variables and rates of change define a natural hazard, so determining a return period for a natural hazard is complex, requiring several inputs.

The problem is the separation between the initial natural hazard event and the design load. Designs tend to be based on the event at the end of the natural hazard chain, such as water level, flow rate, or vibration load. Designing for the event near the start of the natural hazard chain--such as rainfall, snow melt, or fault slippage--is not always practical. The transition from the statistics of these hydrometeorological and geological data to the statistics of design loads must be completed.

Even if statistics for natural hazard events could be accurately translated into statistics for design loads, plans based on the 100-year event would still incorporate an implicit assumption that the 100-year event of the past equals the 100-year event of the future; i.e., statistical properties of natural hazard events are unchanging (uniform) in time. This assumption is ostensibly reasonable, provides a useful rule-of-thumb, and might actually be correct. If the assumption happens to be inappropriate, there can be devastating consequences in the case of underestimating the expected flood, or society may have wasted resources in implementing protective solutions which are not needed, in the case of overestimating the probable flood.

There is a particular danger that natural disaster properties are changing relatively rapidly, due to either anthropogenic or natural processes. For example, changes in global climate, deforestation, and engineering waterways all affect the transport of water through a watershed, implying that future flood statistics could be radically altered from those of the past. The past is certainly a good model for the future, but judgement is required when deciding whether or not the past is a good enough model for the future. In selecting design loads, the engineer should be aware of these problems, and should be prepared to propose innovative and flexible solutions which could be appropriate for a wide range of, or even changing, design loads.

4.4.3 Designing for Every Potential Scenario

Furthermore, the arbitrary selection of a return period for design leaves society susceptible to any event which exceeds the return period. As alluded to previously, a N-year event has a probability of 1/N of occurring in any given year. Thus, although a 500-year event is unlikely to be witnessed for several generations--there is approximately a 2/3 probability that it will not occur in the next 200 years--it could occur tomorrow. Similarly, there is more than a 1/3 probability that a 100-year event will not occur within the next century. An additional problem is interpreting the meaning of a N-year event for data sets which span less than N years, since the validity of a statistical model beyond the realm of the statistics used to develop the model is questionable. There are particular concerns when outliers, particularly near the limits of the data set, are eliminated. Such outliers might represent non-linearities in nature6 rather than anomalies. Furthermore, a data set which spans less than N years would miss peaks or troughs in an environmental cycle with a period greater than N years, and the statistical analyses would be correspondingly flawed. Predicting which design loads will and will not be necessary on the basis of return periods involves a great deal of uncertainty.

Conjunctive, or simultaneous, events present another realm which involves a great deal of uncertainty. Conjunctive events tend to have long return periods, and the discussion in the previous paragraph also applies to designing for conjunctive events. Conjunctive events could be multiple natural disasters. For example, in 1991 in the Philippines, the climactic eruption of Mount Pinatubo occurred hours before a typhoon made landfall (Chapter 11). The combined load of volcanic ash, wind, and water would not necessarily be an anticipated design load. A hypothetical scenario of conjunctive natural disasters would be a major earthquake striking Vancouver in the middle of a blizzard or during heavy flooding from the Fraser River. Conjunctive events could also be a combination of natural and non-natural disasters such as terrorists attacking military relief operations during a flood or an earthquake in the midst of a fire at a tire dump.

Another multiple-event category is disasters which occur in linear sequence. Such events can also be challenging to predict, even though later events are directly caused by the caused by earlier ones. An earthquake leading to a flash flood from a dam failure and a jumbo jet crashing into a residential neighbourhood during a blizzard are two examples. Disease often follows natural disasters, particularly in the developing world where damage to the water treatment and distribution system often leads to outbreaks of cholera and typhoid. An example from California was the outbreak of coccidioidomycosis (the flu-like “valley fever”) following the Northridge earthquake of January 17, 1994; victims had inhaled the pathogenic fungal spores with dust which had been stirred up by the earthquake, the aftershocks, and the cleanup (Coch, 1995). Famine frequently follows floods and droughts, particularly in the developing world. An example of unrelated multiple disaster events in linear sequence occurred in northern Afghanistan: on May 30, 1998 a strong earthquake flattened dozens of villages, killing thousands of people, but in the aftermath, relief operations were hampered as torrential rains washed out roads and prevented helicopters from flying to the region.

There are a few instances of multiple events assisting the mitigation of natural disaster effects. For example, a volcanic eruption or an earthquake during a period of heavy rain would result in less damage from fire than normally would be expected. Most multiple-event scenarios, though, add significantly to the consequences of a disaster event, not only in the combination of hazards but also in their unexpectedness which can derail effective plans, introduce unanticipated challenges, and induce confusion.

Therefore, predicting rare events (conjunctive or otherwise) for design loads is extremely challenging. At some point in the design process, however, a scenario for the design load has to be selected in order to proceed with the design. Given the uncertainties, it might seem reasonable to err on the side of caution by, for example, designing for a 1000-year event or detailing many conceivable multiple-disaster scenarios. There would also likely be a substantial margin of safety even if the return period statistics were incorrect or if the problems with calculating return periods (as discussed earlier in this section and in section 4.4.2) were to manifest. Such an approach requires an immense amount of resources, potentially to the point where creating the proposed system is not affordable, in terms of time, money, labour, or environmental resources. As well, the design might provide reasonable protection from or during natural disasters, but might have detrimental impacts on other facets of society. For example, underground dwellings provide superb protection against tornadoes, but severely impact both the environment and society’s quality of life.

Moreover, the most exacting designs cannot provide absolute protection. Designs for a 1000-year event will be susceptible to a 1500-year event while underground, tornado-proof abodes could be highly vulnerable to flooding. Designing to eliminate vulnerability to every conceivable natural disaster event is not possible. The engineer must investigate and prominently indicate the limitations of their designs while attempting the immense challenge of designing systems which produce desired responses to a variety of loads individually and in combination.


4.5 Conclusions

Engineers are faced with design load specifications which are developed under extensive uncertainty. At some point in deciding which set of design loads to consider and which design loads to assume, arbitrary and subjective decisions must be made. During this process, the engineer must be aware, and explicitly acknowledge, that the predominant challenge in the framework which engineers use lies in the design load. The engineer must also perceive the limits of knowledge and professional capability: natural disasters are not all well-understood phenomena, past experience is not entirely appropriate for defining desired design loads, and the ability to predict and take into account all possible events is limited.

Avoiding the difficult and subjective decisions about design loads is not possible, but it is possible to design with the uncertainty in mind and to design so that deleterious impacts are reduced, if unexpected events occur. As well, the engineer should lucidly describe uncertainties and should indicate how society can deal with the uncertainties. The engineer should be active in educating society that engineers do not have all the answers.

Considering every potential scenario is impossible, and it is certainly not an effective use of resources to try. The engineer does not have to anticipate every possible scenario, but should aim for adaptable, flexible designs which are appropriate for a wide range of scenarios. By choosing appropriate design loads, the engineer will ensure that the creation of the system, and the system’s response to a wide range of design loads, assist in properly managing society’s vulnerability to natural disasters.




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