1006: Ideas in Geography Environmental Modelling: I

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1006 Ideas in GeographyEnvironmental Modelling
I

• Dr. Mathias (Mat) Disney
• UCL Geography
• Office 113 Pearson Building
• Tel [phone]
• Email mdisney_at_ucl.geog.ac.uk
• www.geog.ucl.ac.uk/mdisney/currentteaching.html

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• A hypothesis or theory model is clear,
  decisive, and positive, but it is believed by no
  one but the man who created it. Experimental
  findings observations, on the other hand, are
  messy, inexact things, which are believed by
  everyone except the man who did that work
• Harlow Shapley (1885-1972), eminent American
  astronomer, from his autobiography Through
  Rugged Ways to the Stars (1969)

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Models in Geography?

• The advantage of a mathematical statement is
  that it is so definite that it might be
  definitely wrong..Some verbal statements have
  not this merit they are so vague that they could
  hardly be wrong, and are correspondingly
  useless."
• Lewis Fry Richardson (1881-1953), Mathematician,
  Quaker, pacifist first to apply mathematical
  methods to numerical weather prediction

Key modellers need to know strengths AND
weaknesses of their models All models are wrong
but some are useful George Box The purpose of
models is not to fit the data but to sharpen the
questions Samuel Karlin No one trusts a model
except the person who wrote it. Everyone trusts
an observation except the person who made it.
Anon.
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Models in Geography How and why?

• Empirical
• Based purely on observation e.g. rainfall v
  latitude, popn. density v energy consumption.
• Physical
• Simplified representation of physical processes
  e.g. climate, hydrology, remote sensing,
  geomorphology etc. etc.
• Semi-empirical (semi-physical?)
• Based partly on observations, partly on physical
  principles e.g. population dynamics, biodiversity
  etc. etc.

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Models in Geography How and why?

• Black/grey box / process models
• Stocks (how much stuff?) and fluxes (how does
  stuff move?) e.g. simple hydrological and glacier
  mass balance..
• No physics in boxes based on conservation of
  mass, energy momentum etc. i.e. stuff in stuff
  out
• Describe key processes only e.g. terrestrial
  and/or oceanic carbon cycle
• Conceptual?
• Use broad concepts to explain systems e.g.
  evolution, plate tectonics. Daisy World?
• Ideally lead to more powerful models

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Cover today

• Examples
• Conceptual Evolution Gaia hypothesis - Daisy
  World
• Empirical Latitude v. T or Energy v. pop.
  density
• Physical 1 Hydrological
• Physical 2 Remote Sensing models

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Daisy World and Gaia Hypothesis

• Gaia - Greek goddess who drew the living world
  forth from Chaos
• Dr. James Lovelock
• British atmospheric chemist - invented detector
  for measuring trace elements in atmosphere -
  measure impact of CFCs
• Late 1970s, revolutionary idea - Gaia Hypothesis
  
• The biosphere (plants and animals) can regulate
  climate and hence conditions for growth
• i.e. Earth as a self-regulating system (Gaia)

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Evolution Charles Darwin, Alfred Russel Wallace

• How and why do species change?
• Two men independently arrived at same idea at
  almost same time (1855-1858)
• Evolution through natural selection

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Evolution Charles Darwin, Alfred Russel Wallace

• Both mens ideas arose through observation of
  related species
• Darwins famously of Galapagos finches on his
  voyage aboard the Beagle
• Darwins ideas based around competition for
  survival between individuals within a species
• Wallace emphasis on adaptation due to ecological
  pressure
• Concept of evolution (initially) allowed no
  predictions, understanding of mechanism
• BUT forced us to look more closely at evidence

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Daisy World

• V. simple hypothetical (conceptual) model
• Earth-like planet, orbiting Sun which has grown
  progressively brighter through time, radiating
  more and more heat (like ours)
• YET surface T constant because biosphere
  consists only of dark (black) and light (white)
  coloured daisies
• Daisies act to moderate temperature through their
  albedo or reflectivity
• dark daisies absorb most of the Sun's heat
• light daisies reflect much of it back to space.
• Can we use idea to understand/predict
  homeostasis?
• ability of an organism or cell to maintain
  internal equilibrium by adjusting its
  physiological processes

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Daisy World
White daisies
Black daisies
Available fertile land
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Assumptions?

• Rate of population change depends on the death
  rate and potential birth rate and amount of
  fertile land available for growth
• Birth rate for both species of daisy depends on
  temperature, Tlocal
• Tlocal depends on ?planet - ?local and on Tglobal
• Tglobal depends on luminosity of Sun and ?planet
• ?planet is sum of local albedo components i.e.
• ?planet areablack?black areawhite?white
  (areaplanet - areablack - areawhite)?bare soil
• Available fertile land depends on the total
  amount of fertile land (fixed) and the current
  coverage of the two species of daisy

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Daisy World results

• What happens to planet if sun goes on getting
  hotter?
• More white daisies grow at expense of black
  (reducing ?planet)
• Eeventually gets too hot even for white daisies
  (4 Gyr) and Tplanet ?
• Allows us to ask real-world questions re
  planetary albedo and climate feedbacks
• Deforestation ? reduced albedo ? increase T?
• Increase T ? reduce snow cover ? reduce albedo ?
  increase T? ve feedback??
• Increase CO2 ? increase vegetation ? increase low
  clouds ? reduce T? -ve feedback??

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So what?

• Simple approach can lead to improved
  understanding and asking new questions e.g.
• CLAW hypothesis
• Charlson, Lovelock, Andreae and Warren (1987)
  Oceanic phytoplankton, atmospheric sulphur, cloud
  albedo and climate. Nature, 326, 655-661.
• Increasing temperature (e.g. global warming)
  causes phytoplankton to emit more dimethyl
  sulphide (DMS), causing increased cloudiness and
  hence reducing solar radiation
• Regulate temperature via negative feedback!
• Has biosystem evolved to regulate climate for own
  benefit??
• Conceptual models can be very powerful What
  if? tools

http//www.atmosphere.mpg.de/enid/1w1.html
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Empirical latitude v temperature

• Observations may indicate a relationship
• E.g. simple best-fit line
• Allows us to interpolate (between observations)

• BUT extrapolation dangerous
• NEVER infer causality!
• To find a reason we need some physical
  description (physical model?)

From http//www.uwsp.edu/geo/faculty/ritter/geog1
01/textbook/essentials/stats.htmlFigure20EG.22
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Empirical 2 pop. density v per capita energy use

• Not simple linear relationship?
• Negative exponential?
• Function of e-pop
• Implies sparse urban areas use more energy
• Travel further to work?
• BUT no causal relationship
• Maybe use other observations??

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City lights from remote sensing

• Bright Lights, Big City http//earthobservatory.n
  asa.gov/Study/Lights/
• Develop some empirical relationship between light
  intensity, popn. density and energy usage

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Process-type catchment models

• River catchment/basins
• Function of precipitation, evapotranspiration,
  infiltration
• soil moisture conditions (saturation, interflow,
  groundwater flow, throughflow, overland flow,
  runoff etc.)
• From conservation of stuff - water balance
  equation
• dS/dt R - E - Q
• i.e. rate of change of storage of moisture in the
  catchment system, S, with time t, is equal to
  inflow (rainfall, R), minus outflow (runoff, Q
  plus evapotranspiration, E)
• E.g. STORFLO model (in Kirkby et al.)

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More complex?

• Consider basin morphometry (shape) on runoff
• Slope, area, shape, density of drainage networks
• Consider 2D/3D elements, soil types and hydraulic
  properties
• How to divide catchment area?
• Lumped models
• Consider all flow at once... Over whole area
• Semi-distributed
• isochrone division, sub-basin division
• Distributed models
• finite difference grid mesh, finite element
  (regular, irregular)
• Use GIS to represent - vector overlay of network?

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Time / space issues?

• How accurate is space/time representation
  required, mm, m, km etc.?
• More accurate spatial/temporal representation
  means bigger memory/processing requirement
• Limits of temporal representation
• discrete time jumps (e.g. month by month - may
  miss/cause discontinuities)
• Limitations of (spatial) grid-based methods
• problem of flows between grid units
• size/shape of grid units

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Very complex MIKE-SHE

• Mike-SHE (System Hydrological European)
• Combination of physical, empirical and black-box
• Can simulate all major processes in land phase
  of hydrological cycle !!

From http//www.dhisoftware.com/mikeshe/Key_featu
res/
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MIKE-SHE catchment soil water content
From http//www.geog.ucl.ac.uk/jthompso/shyloc_m
odelling.stm
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Physical models for remote sensing

• Highly detailed 3D models
• Simulate canopy reflectance behaviour
• Compare with remote sensing observations
• Allow us to understand what we see from space
• Make predictions e.g. about carbon cycle

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Physical models for remote sensing
 
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Remember!

• Empirical (black box) models are simple
• BUT only valid for observations/system they are
  based on, so useful for explaining but NOT
  predicting (limited power)
• Physical models much more complex
• BUT have physically meaningful parameters, used
  for estimating parameter values /or predictions
  (most powerful)
• Conceptual models explore concepts
• Not necessarily detailed, allow us to conduct
  thought experiments and explore ideas, refine
  and develop new more detailed models.

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Reading

• Basic texts
• Barnsley, M. J., 2007, Environmental Modelling A
  Practical Introduction, (Routledge). Excellent,
  practical introduction with many examples, and
  code using freely-available software.
• Kirkby, M.J., Naden, P.S., Burt, T.P. and
  Butcher, D.P. 1993 Computer Simulation in
  Physical Geography, (Chichester John Wiley and
  Sons). Good introduction with simple computer
  programs of environmental models.
• Computerised Environmental Modelling A practical
  introduction using Excel, J. Hardisty et al.,
  1993, John Wiley and Sons.
• Casti, John L., 1997 Would-be Worlds (New York
  Wiley and Sons). A nice easy-to-read introduction
  to the concepts of modelling the natural world.
  Excellent examples, and well-written. A good
  investment.
• Advanced texts
• Gershenfeld, N. , 2002, The Nature of
  Mathematical Modelling,, CUP.
• Boeker, E. and van Grondelle, R., Environmental
  Science, Physical Principles and Applications,
  Wiley.
• Monteith, J. L. and Unsworth, M. H., Principles
  of Environmental Physics, Edward Arnold.

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Models in Geography?

• Believe nothing just because a so-called wise
  person said it. Believe nothing just because a
  belief is generally held. Believe nothing just
  because it is said in ancient books. Believe
  nothing just because it is said to be of divine
  origin. Believe nothing just because someone else
  believes it. Believe only what you yourself test
  and judge to be true.
• Siddartha Gautama (Buddha) c. 500BC

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Hydrological (catchment) models

• How much water comes out of catchment in a given
  time
• Response to rainfall event? How much water left
  in soil?
• Flood prediction, resource management etc.
• Simplest models not dependent on space i.e. 1D
  lumped model
• Catchment as simple bucket
• Stuff out stuff in
• Time-area hydrograph some consideration of area
• predicts discharge, Q (m3s-1), based on rainfall
  intensity, i (mm hr-1), and catchment area, A
  (m2)
• i.e. Q ciA (c is (empirical) runoff coefficient
  i.e. fraction of rainfall which becomes runoff,
  )
• more than one area? Divide drainage basins into
  isochrones (lines of equal travel time along
  channel), and add up.
• Qt c1A1i(t-1) c2A2i(t-2) .. cnAni(t-n)

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Physical models for remote sensing

• Change detection

Can we derive relationship between reflectance
(colour) and forest cover?
http//earth.jsc.nasa.gov/lores.cgi?PHOTOSTS046-0
78-026 http//www.yale.edu/ceo/DataArchive/brazil.
html