Predicting the Impact of Global Climatic Change on Land Use Patterns in Europe

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Predicting the Impact of Global Climatic Change
on Land Use Patterns in Europe

• Andy Turner
• Centre for Computational Geography
• University of Leeds, Leeds, UK
• andyt_at_geog.leeds.ac.uk

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Thanks are due to

• Stan Openshaw
• Ian Turton
• Tim Perree

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Contents

• Why model agricultural land use change for 75
  years hence?
• Existing models
• A neurocomputing approach
• Assembling the data
• Running the models
• Results
• What next?

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Why?

• It is an important subject
• It potentially affects many millions
• It emphasizes what little we know
• It provides a first attempt that others will have
  to beat later
• Someone had to try and do it!
• It provides a good example of how GIS can be used
  to model environmental systems showing both the
  strengths and weaknesses

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Some Background

• This research was part of the EU Medalus III
  project
• Medalus Mediterranean Desertification and Land
  Use
• Wide range of environmental research topics
  mainly concerned with modelling hill slope
  erosion, hydrological systems, water management,
  ecosystems, and climatic change in semi-arid
  Mediterranean climate zones
• Study Area The Mediterranean climate region of
  the EU

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Main objective was to incorporate a
socio-economic systems modelling component into
physical environmental models of LAND DEGRADATION
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The research challenge!

• To identify ways of predicting the likely impacts
  of climatic change on agricultural land use
  patterns for around 25 to 50 years time
• In order to
• raise awareness of land degradation problems
• inform political and public debate
• contribute to a pro active framework for action

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The hardness of this challenge should not be
under-estimated!
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Modelling Challenges

• model contemporary agricultural land use patterns
  based on a range of climatic, physical and
  socio-economic variables
• obtain and forecast these variables in order to
  predict future agricultural land use
• translate the land use changes into a land
  degradation risk indicator
• combine various land degradation risk indicators
  to produce a synoptic forecast of land degradation

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Previous Research

• Very little research on long-term land use
  prediction
• Most of what little exists is non-spatial or at a
  very coarse level of geography
• Some micro-studies exist at the level of
  individual farms BUT these cannot yet be scaled
  up to the EU level or used to make long term
  forecasts easily

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The CLUE modelling framework

• The CLUE (Conversion of Land Use and its Effects)
  model of Veldkamp and Fresco (1997) is probably
  the best and most relevant of existing models
• A multi-scale stepwise regression model
• Its relates land use change to socio-economic and
  biophysical factors
• Operates at 7.5 km2 for Costa Rica and 32 km2
  scale for China

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CLUE Model

• linear
• It is run recursively
• It produces nice computer movies
• Its runs at too coarse a scale to be useful
• It will probably have dreadful error propagation
  properties

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Modelling Design Checklist

• Highest possible level of spatial resolution
• Consistency in coverage and application
• Make forecasts for 75 years hence
• Link with other Medalus III Projects and models
• Incorporate the principal driving factors and
  processes
• Produce outputs that can be instantly understood
  by Joe Public
• Provide a framework that can be refined later

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Building a Synoptic Prediction System (SPS)

• Objective was to build a GIS based computer
  modelling system able to link changes in the
  climate with associated physical and
  socio-economic changes in order to make synoptic
  land degradation forecasts for the entire
  Mediterranean climate region of the EU
• It was to function in a manner similar to a
  long-term weather forecast

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SPS Modelling

• A model was required to link
• climate (temperature and rainfall)
• soil characteristics (permeability, texture,
  fertility, parent material)
• biomass
• elevation
• population densities
• to predict current and future patterns of
  agricultural land use

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Synoptic Prediction System
biomass
height
Physical
climate
soil
F U T U R E
N O W
IMPACT
Classification
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SPS is limited by the following

• available data from other Medalus teams and
  elsewhere
• almost complete absence of space-time data series
• lack of knowledge of all the principal mechanisms
  thought to be at work
• the need to incorporate a broad range of inputs
  to ensure plausibility
• the necessity of working at a fine level of
  spatial detail

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Other Problems

• Relationship between environment and land use is
  mediated by
• technology
• market forces
• historical traditions
• inertia
• culture
• various economic factors
• behavioural aspects

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but
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but
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there is little that can be done about any of
this!
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The problem was HOW to OPERATIONALISE this
schematic model in the best possible way
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In essence it is a kind of non-linear regression
model

• The inputs can be converted into outputs via
  either
• mathematical equations
• statistical equations
• fuzzy rules
• neural networks

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SPS is based on a neurocomputing approach
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Do not PANIC!

• the basic idea is very simple

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Its just an artificial neural network!

• they are now quite common
• not much to them
• they are not black magic
• its just a black box that performs a function
  similar to regression
• they cannot bite!

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A representation of an Artificial Neuron
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A representation of a 6x4x1 simple network
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Neural Networks Offer several advantages

• they are universal approximators
• they are equation free
• they are highly non-linear
• they are robust and noise resistant
• probably offer the best levels of performance
• they can model hard problems
• widely applicable modellers

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Neural Headaches

• They are essentially black box models
• Training can be problematic
• over-training
• length runs
• Choice of Architecture is subjective with an
  element of black art or luck or intuition
• Often a presumption of prejudice against because
  of the lack of process understanding

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Some Key Assumptions

• the training data were representative
• the predictor variables were appropriate
• the effects of missing variables were implicit in
  the available variables
• the neural net architectures were reasonable
• that there is a systematic relationship between
  environmental variables and land use that is
  modellable

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Building a SPS

• Step 1. Assemble the data for a common EU wide
  geography for
• present day
• Step 2. Obtain or make forecasts for these data
  for
• 75 years time

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Building a SPS (Part 2)

• Step 3. Construct Neural Nets to model the
  relationships between climate-soil-biomass-elevati
  on-population density in order to predict present
  day land use
• Step 4. Compute estimates for 75 years time using
  neural nets trained for the present

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Building a SPS (Part 3)

• Step 5. Create maps of changes
• Step 6. Consider modifying the predictions and
  forecasts to reflect knowledge using fuzzy logic
• Step 7. Repeat everything to test different
  change scenarios
• Step 8. Make estimates of uncertainties using
  Monte Carlo simulation

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Step1 Assemble data for a common EU wide
geography

• Not easy!
• A major reason for the lack of models linking
  environmental and socio-economic variables is the
  lack of a common data geography
• Environmental data tends to be grid-square based
  for small areas whilst socio-economic data tend
  to be for far larger and irregular polygons

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Data required to predict agricultural land use

• Soil Type
• Soil Quality
• Biomass
• Temperature seasonal
• Precipitation seasonal
• digital elevation model
• population density

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Why these variables?

• They are clearly related in some way to
  agricultural land use patterns
• They reflect the research by other Medalus teams
• They were available in some form
• They were available or could be estimated

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1 Decimal Minute EU database

• Decided to use grid-squares
• Best scale was about 1km2 or 1 decimal minute of
  resolution
• Most environmental data can be manipulated into a
  1 DM cell format using GIS
• BUT.. socio-economic data need to be interpolated
  from a coarser to a finer geography

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EVERY data set caused problems and required its
own set of GIS operations in order to create the
data base
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Estimating and Interpolating population data

• First task was to develop a means of creating
  population (and other socio-economic) data for 1
  DM cells for the EU when the best available data
  was at NUTS 3 level of geography
• For example, in UK there are 64 NUTS-3 regions
  and 150,000 1 DM cells
• The task was to interpolate from 64 to 150,000
  cells!

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The Interpolation Problem
Nuts3
1DM
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Methodology

• Use available GB census data as target data
• Nothing as good available for anywhere else in
  the EU
• Test out different methods of estimating these
  data from EU wide predictor variables
• Apply the best method to rest of EU

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Review of Existing methods

• There are SOME existing methods that can be used
• A very old simple method
• uniform area shares
• Various surface interpolation methods
• Toblers pycnophylactic surface
• RIVMs Goodchild et al (1993) method

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RIVM population density surface
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RIVM Smart Interpolation

• Weighting factors were used to create a
  population potential surface
• auxiliary data sources were used to modify the
  weights Sea, Roads, Rivers
• An estimate of population was made using the
  weights
• Best of the existing methods
• Errors are large

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Maybe it is possible to do better using a neural
net to perform the interpolation

• Extend the RIVM approach to use a broader set of
  digital variables
• Train a neural net on UK data
• Apply to rest of EU
• Modify to meet accounting constraints based on
  NUTS-3 control totals

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What Spatial Data for the EU is available that
can help?

• Data available for all of EU are
• Bartholomews 11000 000 digital map data with
  various layers ( DCW)
• Other spatial data (DTM, slope, land cover)
• NUTS 3 coverages
• RegioMap and Eurostat Statistical Data forecasts
  at NUTS3 level
• Satellite data (eg. Night-time lights data)

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Population Predictor Variables

• distance to
• built up areas
• airport
• parks
• river and canals
• towns by size
• location of
• built up areas
• place names

• density of
• communication networks
• various roads
• railways
• height above sea level
• night-time lights
• RIVMs population

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Communications network density
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Motorway and dual carriageway density
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Main and minor road network density
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Railway network density
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Night-time lights frequency
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Distance from extra large towns
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Distance from large towns
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Distance from medium sized towns
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Distance from small towns
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Distance from internatonal airports
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NUTS3 population from Eurostat
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Although the errors are still large, the
Population interpolation maps look good!
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23x20x20x1 prediction
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23x20x20x1 prediction close up of Italy
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(continue..) Step1 Assemble data for a common
EU wide geography

• Climatic Data based on global climatic change
  models
• results for a network of 50 weather stations
• linear interpolation to 0.5 DM grid
• imported into ArcInfo and aggregated to 1 DM
  cells
• Spatial interpolation errors probably less than
  forecast errors!!!!!!

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Seasonal Temperature Data
N O W
F U T U R E
Forecast
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Seasonal Precipitation Data
N O W
F U T U R E
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Climatic Biomass Potential
Height above Sea level
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Step 2. Obtain or make forecasts for these data
for 75 years time

• We used other Medalus III project partners
  forecasts for climate and climatic biomass
  potential
• Population forecasts made by changing the
  accounting constraints to reflect Eurostat
  forecasts
• Note the convoy effect in that the various data
  only need be a similar degree if inaccuracy!

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Step 3. Construct Neural Nets to model the
relationships between climate-soil-biomass-elevati
on-population in order to predict present day
land use

• used a feed forward multi-layer perceptron
• training data based on 20,000 randomly selected
  cells
• trained using a hybrid approach
• genetic optimiser to start training
• fine tuning using a conjugate gradient method

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SPS Neural Net

• Aim was to model current land use
• Various architectures investigated
• Best had 18 inputs, a single hidden layer with 50
  neurons, and 1 output neuron
• Net trained on present and then given the same
  inputs for the forecast years
• Results appear promising!

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Results
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Dominant Arable Landuse
Observed
Predicted
Forecast
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Dominant Tree Landuse
Observed
Predicted
Forecast
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Dominant Waste Landuse
Observed
Predicted
Forecast
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Deficiencies!

• many social, economic, and political processes
  are only implicitly present
• neural net modelling would be better if the data
  inputs were better
• uncertainty levels remain unidentified
• mixture of data sources with very different error
  and uncertainty levels

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(yet more grave) Deficiencies!

• a major assumption that global climatic change is
  equivalent to a shift in the boundaries of
  agricultural capability
• there is an assumption that technology and
  behavioral influences remain constant as implicit
  in the training data
• land use categorization is very crude

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Good Points?

• a first attempt at socio-environmental modelling
• a common methodology for the EU
• brave (maybe foolish) attempt at broad-brush
  forecasts for 50 years ahead
• framework can be used to yield improved results

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Good Points (more??)

• results can be readily updated as improved data
  become available
• sets a benchmark that subsequent models will have
  to beat
• difficult to see how else the research objectives
  can be achieved
• offers a context for discussion and debate
• results are understandable

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Step 5. Create maps of changesStep 6. Consider
modifying the predictions and forecasts to
reflect knowledge expressed as fuzzy rules
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Fuzzy Interpretation of Impacts

• Important to handle the uncertainty in the
  predictions and data
• Fuzzy logic is a good way to achieve this
• Also allows incorporation of intelligent rules of
  thumb to add realism to computer model results
• Can be further extended as required

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Schematic of Fuzzy Land Degradation Interpreter
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16 Fuzzy Rules

• If landuse_now is arable and landuse_future is
• arable then land degradation is possible
• trees then land degradation is unlikely
• waste then land degredation is serious
• other landuse then land degradation is probable

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• If landuse_now is trees and landuse_future is
• arable then land degradation is possible
• trees then land degradation is possible
• waste then land degredation is serious
• other landuse then land degradation is probable

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• If landuse_now is waste and landuse_future is
• arable then land degradation is possible
• trees then land degradation is possible
• waste then land degredation is extensive
• other landuse then land degradation is possible

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• If landuse_now is other and landuse_future is
• arable then land degradation is possible
• trees then land degradation is possible
• waste then land degredation is severe
• other landuse then land degradation is unlikely

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Fuzzification of landuse data
1.0
Non-arable fuzzy landuse class
degree of member- ship
Arable fuzzy landuse class
0.0
0.0
1.0
Arable Landuse
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Land degradation membership function
fuzzy classes
possible
probable
unlikely
extensive
severe
serious
degree of member- ship
land degradation scale
1.0
0.0
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A Map of Land Degradation
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Step 7. Repeat everything to test different
change scenariosStep 8. Make estimates of
uncertainties using Monte Carlo simulation
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Conclusions

• 50 YEARS is a long time BUT the topic is SO
  IMPORTANT that it is important attempts are made
  to make these types of predictions
• Predicting affects of not yet visible global
  climatic change on land use in 50 years time has
  one outstanding advantage...
• when the true results are known I will not be
  around to see them!!

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The results presented today are

• Preliminary
• Subject to change
• They need to be improved upon
• They are almost certainly WRONG
• They are probably very WRONG
• Its even conceivable they could be COMPLETELY
  WRONG

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andyt_at_geog.leeds.ac.uk

• http//www.geog.leeds.ac.uk/staff/a.turner
• http//www.medalus.leeds.ac.uk/SEM/home.htm