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

1. 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@geog.leeds.ac.uk

2. Thanks are due to: • Stan Openshaw • Ian Turton • Tim Perree

3. 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?

4. 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

5. 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

6. Main objective was to incorporate a socio-economic systems modelling component into physical environmental models of LAND DEGRADATION

7. 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

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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. biomass height Physical soil climate Landuse Landuse Socio-economic Socio-economic population population Synoptic Prediction System biomass height Physical soil climate F U T U R E N O W IMPACT Classification

17. 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

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

19. but

20. but

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

22. The problem was HOWto OPERATIONALISE this schematic model in the best possible way

23. 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

24. SPS is based on a neurocomputing approach

25. Do not PANIC! the basic idea is very simple

26. 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!

27. A representation of an Artificial Neuron

28. A representation of a 6x4x1 simple network

29. 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

30. 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

31. 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

32. 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

33. Building a SPS (Part 2) • Step 3. Construct Neural Nets to model the relationships between climate-soil-biomass-elevation-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

34. 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

35. 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

36. Data required to predict agricultural land use • Soil Type • Soil Quality • Biomass • Temperature: seasonal • Precipitation: seasonal • digital elevation model • population density

37. 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

38. 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

39. EVERY data set caused problems and required its own set of GIS operations in order to create the data base

40. 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!

41. The Interpolation Problem 1DM Nuts3

42. 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

43. 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 • Tobler’s pycnophylactic surface • RIVM’s Goodchild et al (1993) method

44. RIVM population density surface

45. 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

46. 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

47. What Spatial Data for the EU is available that can help? • Data available for all of EU are: • Bartholomew’s 1:1000 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)

48. 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 RIVM’s population Population Predictor Variables

49. Communications network density

50. Motorway and dual carriageway density