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

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

Paper presented at International Conference on Geographic Modelling and Environmental Systems with GIS, Hong Kong 23-25 June 1998

Some Background

Results reported today reflect a 5 person-year sub-project concerned with adding a socio-economic systems modelling component to the physical environmental models

The research challenge!

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

The Model Design Brief

The mainproblem is ..

itsIMPOSSIBLEat a micro level of detail!

Yet.. there is an increasing urgency to know what is happening to our world!

Previous Research has been very deficient

The CLUE modelling framework

Mapping Environmentally Sensitive Areas is a non-model alternative

Modelling Design Checklist

Building a Synoptic Prediction System (SPS)

SPS is limited by the following:

there is little that can be done about any of this!

The question is HOW to OPERATIONALISE this schematic model in the best way that is possible right now?

In essence it is a kind of non-linear regression model

SPS is based on a neurocomputing approach

Its just an artificial neural network!

Some Key Assumptions

Artificial Neural Networks are used for all the modelling in this presentation

Building a SPS (Part 2)

Building a SPS (Part 3)

Step1: Assemble data for a common EU wide geography

Data required for Land use Predictor Variables

Why these variables?

1 Decimal Minute EU database

EVERY data set caused problems and required its own set of GIS operations in order to create the 1 DM grid data base

Estimating and Interpolating population data

Maybe BUT that is the degree of difficulty associated with this environmental-socioeconomic modelling task!

RIVM method seemed best

The Errors are too LARGE

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

Population Predictor Variables

The Errors are still LARGEbut far smaller

Population interpolation maps look good!

Lets have a closer look at the UK and Italy

(continue..) Step1: Assemble data for a common EU wide geography

Other Data (continued)

Basically EU data and all aspects relating to it are in

Step 2. Obtain or make forecasts for these data for 25 and 50 years time

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

(yet more grave) Deficiencies!

Good Points (more??)

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

Fuzzy Interpretation of Impacts

Step 7. Repeat everything to test different change scenariosStep 8. Make estimates of uncertainties using Monte Carlo simulation

The results presented today are:

No need to be too pessimistic!

its very important to the future of the world that we try!

Authors: Stan Openshaw and Andy Turner, University of Leeds

Email:
stan@geog.leeds.ac.uk
a.turner@geog.leeds.ac.uk

Home Page:
http://www.geog.leeds.ac.uk/staff/s.openshaw/
http://www.geog.leeds.ac.uk/staff/a.turner/