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1006: Ideas in Geography Environmental Modelling: I


... Modelling: I. Dr. Mathias (Mat) Disney. UCL Geography ... Divide drainage basins into isochrones (lines of equal travel time along channel), and add up... – PowerPoint PPT presentation

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Title: 1006: Ideas in Geography Environmental Modelling: I

1006 Ideas in GeographyEnvironmental Modelling
  • Dr. Mathias (Mat) Disney
  • UCL Geography
  • Office 113 Pearson Building
  • Tel 7670 0592
  • Email mdisney_at_ucl.geog.ac.uk
  • www.geog.ucl.ac.uk/mdisney/currentteaching.html

  • 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)

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
  • 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.
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.

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
  • 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

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

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)

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

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
  • Concept of evolution (initially) allowed no
    predictions, understanding of mechanism
  • BUT forced us to look more closely at evidence

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
  • ability of an organism or cell to maintain
    internal equilibrium by adjusting its
    physiological processes

Daisy World
White daisies
Black daisies
Available fertile land
  • 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

Daisy World results
  • What happens to planet if sun goes on getting
  • 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??

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
  • Conceptual models can be very powerful What
    if? tools

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
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??

City lights from remote sensing
  • Bright Lights, Big City http//earthobservatory.n
  • Develop some empirical relationship between light
    intensity, popn. density and energy usage

Process-type catchment models
  • River catchment/basins
  • Function of precipitation, evapotranspiration,
  • soil moisture conditions (saturation, interflow,
    groundwater flow, throughflow, overland flow,
    runoff etc.)
  • From conservation of stuff - water balance
  • 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.)

More complex?
  • Consider basin morphometry (shape) on runoff
  • Slope, area, shape, density of drainage networks
  • Consider 2D/3D elements, soil types and hydraulic
  • 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?

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

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
MIKE-SHE catchment soil water content
From http//www.geog.ucl.ac.uk/jthompso/shyloc_m
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

Physical models for remote sensing
  • 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.

  • 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
  • Advanced texts
  • Gershenfeld, N. , 2002, The Nature of
    Mathematical Modelling,, CUP.
  • Boeker, E. and van Grondelle, R., Environmental
    Science, Physical Principles and Applications,
  • Monteith, J. L. and Unsworth, M. H., Principles
    of Environmental Physics, Edward Arnold.

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

Hydrological (catchment) models
  • How much water comes out of catchment in a given
  • 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
  • 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)

(No Transcript)
Physical models for remote sensing
  • Change detection

Can we derive relationship between reflectance
(colour) and forest cover?
78-026 http//www.yale.edu/ceo/DataArchive/brazil.
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