Class Notes (839,626)
Canada (511,431)
Geography (177)
GEOG 312 (4)

geog 312 unit 1.docx

21 Pages

Course Code
GEOG 312
Maggie Squires

This preview shows pages 1,2,3,4. Sign up to view the full 21 pages of the document.
What are Natural Hazards? -natural hazards are events associated w/ normal biogeophysical phenomena that may cause harm to humans, such as death, injury, or loss of home, property or income -natural hazards are limited to inhabited areas, that is, to vulnerable human settlements & societies -reasons that humans may be the path of hazards include: -our need for resources provided by hazards such as fertile soils in vicinity of volcanoes, water supply (large rivers) in areas prone to flooding -our preference for scenic lanscapes such as cliffs overlooking ocean (which may be susceptible to landslides) & sandy beaches (which may be susceptible to flooding & erosion) -most natural hazards are initiated or mediated by weather: -initiated—hurricanes, floods, tornadoes -mediated—ash dispersal, landslides, slab avalanches -BC hazards of sufficient size, depth, or displacement to cause damage or disruption include fire, earthquake, tsunami, flooding, landslide, avalanche, & severe weather, such as wind & snow storms -of 179 documented natural hazard events in the Vancouver area most were: -floods caused by rain storms (24 events) -landslides (22 events) -windstorms (11 events) -forest fires (8 events) -snowstorms (7 events) -hazardous events resulting in most deaths include: -Spanish flu, 1918 -heat wave, 1936 -polio outbreak, 1950s -in BC, most events causing numerous casualties took place in early 1900 & involved avalanches & landslides triggered by development activities -in last 20 years a number of storms, landslides, & avalanches have each resulted in >5 deaths Location Date Approx Deaths Type Egypt & Syria 1201 1.1 million EQs Europe & beyond 1347-1350 25 million Bubonic Plague Western 16 -18 century XX million American Indians Hemisphere due to disease carried from Europe India 1769 10 million Famine/drought 1876-1879 9 million Famine/drought 1935 3.4 million Floods World wide 1918-19 50+ million influenza 1958-61 20 million Famine/drought Africa 1981-84 X million Famine/drought North Korea 1995-98 3 million Famine/floods Prediction -probability analysis—predicting where, how large, & when an event may occur based on historic/geologic data, combined w/ computer simulations to fill in data gaps -forecasting—new monitoring technologies, including satellite-based sensors, provide real-time data on current conditions, which may indicate an event (flooding, earthquake, volcanic eruption) is imminent -for most natural hazards, large magnitude events are infrequent & small magnitude events are frequent: M = Fe -x M=magnitude, F=frequency, e=natural logarithm, x=constant -return interval vs flood (discharge) magnitude for the Red River exemplifies that small magnitude events occur as frequently as annually (recurrence interval is one year) & that large magnitude events occur every ten to one hundred years -the terms used to describe the magnitude-frequency relation are recurrence interval (or return period), this is the time btwn events of a particular magnitude, eg, a 100 year flood is a large magnitude event that occurs for a give data set on average every 100 years -however, it’s possible to have two or more (or no) floods of this magnitude in any 100 year period -some simple math: Recurrence interval Probability of occurrence % chance of occurrence 100 1 in 100 1 50 1 in 50 2 25 1 in 25 4 10 1 in 10 10 5 1 in 5 20 2 1 in 2 50 -forecasting is based on real time monitoring -some examples are: -foreshocks may forecast an earthquake -deformation may forecast a volcanic eruption -snowmelt & rain may forecast a flood -heavy precipitation may forecast a landslide -seafloor earthquakes may forecast a tsunami -predicting—probability of occurrence for some period of time (1 to 1000 years) based on knowledge of the past (eg, discharge return intervals) coupled w/ current understanding of causes & precursors -forecasting—based on real time detection of precursor signals; vastly improved w/ GPS satellite (hours, days, months in advance) -warning—based on real time monitoring of an unfolding event; for ex, tsunami modeling, based on quick retrieval & analysis of ground shaking data, which may be only mins in the vicinity of an earthquake (nearfield) to hours at more distant locations (farfield) Vulnerability -vulnerability is the expected deg of impact of a hazard of particular magnitude & reflects capacity of individuals & groups to anticipate, cope w/, resist, & recover from impact of a geophysical event -the impact level of an event is more often related to human vulnerability than to event magnitude -impact may be expressed in terms of: -# of fatalities -cost to repair damage to buildings, facilities & services -revenue loss due to reduction in economic activity -longer term effects, such as disease, political unrest, &/or psychological distress Deaths from natural hazards (1991 -2005) Eruptions Floods Earthquakes and tsunamis Storms Droughts Slides -some ex of EQ & tsunami fatalities: -2003—EQ, Bam, Iran, 43,000 fatalities -2004—EQ, tsunami, Indian Ocean, 250,000 fatalities -2005—EQ, Kashmir, 80,000 fatalities -2008—EQ, China, 88,000 fatalities -2010—EQ, Haiti, 220,000 fatalities -vulnerability of a place is a func of potential exposure to a hazardous event & in turn is linked w/ socio-politico-economic factors, & cultural, land-use, population density, & ecological attributes -natural disasters are usually products of socio-politico-economic regimes, rather than natural hazard, eg, volcanic ash hazard in European air space is a func of dependence on air travel, & famine hazard in the African Horn is a func of geo- political circumstance -conditions that shape vulnerability or the potential for impacts: -personal—individual age/ gender/ education/ cultural-socio-ecological conditions, perception, experience may influence individual risk -economic—economic conditions (wealth) may dictate options -structural—building quality (material, codes) may affect impact -political—political conditions may limit -institutional—local, state, or national gov’ts affect outcomes through policy & enforcement -global economic interconnectedness & interdependence lead to global impacts of local events, eg, disruption of world oil supply after Hurricane Katrina -hazardous events may take individuals by surprise—not only for want of instrumental warnings & education but also b/c a human life & written & instrumental records of past few centuries span too little time to provide experience & perspective on full potential for disaster -large populous countries are disproportionately poor—high vulnerability due to high population density & poverty -affluent countries have disproportionately few ppl—low vulnerability due to low population density & better mitigation & preparation -on other hand, wealth can increase vulnerability, due to technological dependence in an interdependent & interconnected world (eg, complex transportation & communication networks) -increasing wealth may also correspond w/ change in perception of risk, that is, how benefits are weighed against potential losses, eg, low insurance rates may entice movement into hazardous areas -wealth=greater preparedness, mitigation & more capacity to get out of harm’s way -Hurricane Charley, Florida, 2004, caused relatively few casualties -poverty=more attention to provision of basic food & shelter than to mitigation, & less capacity to get out of harm’s way -Hurricane Katrina, Louisana—1 of the 5 deadliest hurricanes in US history -in 1999 earthquake in Marmara Sea area, Turkey, where a culture of corruption increased impact, saw the town of Golcuk (right) experience: -17,000 dead -15% of buildings collapsed -widespread power outages -due to lack of: -building code enforcement -infrastructure quality -zoning enforcement Risk -concept of risk involves weighing benefits vs potential loss: risk= func of hazard & vulnerability -hazard=the likelihood or probability that a natural hazard will occur at sufficient magnitude to impact a particular location -vulnerability=potential exposure of ppl, assets, goods, social & economic activities etc to harm -as an ex, the insurance industry calculates risk & potential losses & sets pricing by: -forecasting the likelihood of catastrophic events for 10,000 years, using data & statistical models for a particular location -generating annual exceedance probabilities (the inverse of recurrence intervals) of potentially damaging events -computing cost of repairs/rebuilding for a particular catastrophic event, using vulnerability & financial models -creating lsos probability distributions for a particular location -statistical models are used to fill in data gaps & extend record -geophysicists use same statistical methods as insurance risk analysts to assess hazard probability -Geist & Parsons (below) are interested in probability of occurrence of large magnitude tsunami at particular locations along eastern edge of Pacific Ocean -for this cumulative probability distribution for an earthquake data set, probability of magnitude (M) 7 event is 1 or one/y, probability of M 9 event is 0.001 or 1/1000y Calculating Risk A1 A2 B1 B2 -here, cumulative probability for tsunami run-up, ranging from 0.1m to 10m shows: -probability of 0.1m (A1) is about 0.1 (A2) (1/10y) -probability of 10m (B1) is about 0.01 (B2) (1/100y) -probability of 0.1 may be referred to as 10% annual exceedance probability event, & probability of 0.01 as a 1% exceedance probability, & so on -that is, an event w/ 0.1 probability has a 10% chance of being equaled or exceeded every 10 years, or an event w/ 0.01 probability has 1% chance of being equaled or exceeded every 100 years Hazard Mapping - probabilities for a # of coastal locations can be used to create hazard maps, such as the one at the right, this shows magnitude of tsunami run-up (size of circle) that could take place up & down the coast over a 150 year period, for earthquakes occurring in diff locations (colour of circle) -this hazard map shows annual probability of a = >1m run up, based on analysis of real data for an entire coas
More Less
Unlock Document

Only pages 1,2,3,4 are available for preview. Some parts have been intentionally blurred.

Unlock Document
You're Reading a Preview

Unlock to view full version

Unlock Document

Log In


Join OneClass

Access over 10 million pages of study
documents for 1.3 million courses.

Sign up

Join to view


By registering, I agree to the Terms and Privacy Policies
Already have an account?
Just a few more details

So we can recommend you notes for your school.

Reset Password

Please enter below the email address you registered with and we will send you a link to reset your password.

Add your courses

Get notes from the top students in your class.