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 km 2 for Costa Rica and 32 km 2 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 4 Highest possible level of spatial resolution 4 Consistency in coverage and application 4 Make forecasts for 75 years hence 4 Link with other Medalus III Projects and models 4 Incorporate the principal driving factors and processes 4 Produce outputs that can be instantly understood by “Joe Public” 4 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. Synoptic Prediction System NOWNOW FUTUREFUTURE IMPACT Landuse population Socio-economic Physical climate height biomass soil Physical climate height biomass soil population Socio-economic Classification

17. SPS is limited by the following: 6 available data from other Medalus teams and elsewhere 6 almost complete absence of space-time data series 6 lack of knowledge of all the principal mechanisms thought to be at work 6 the need to incorporate a broad range of inputs to ensure plausibility 6 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

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

22. The problem was HOW to 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 6 mathematical equations ? statistical equations 3fuzzy rules 3neural 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!they cannot bite!

27. A representation of an Artificial Neuron

28. A representation of a 6x4x1 simple network

29. Neural Networks Offer several advantages 4 they are universal approximators 4 they are equation free 4 they are highly non-linear 4 they are robust and noise resistant 4 probably offer the best levels of performance 4 they can model hard problems 4 widely applicable modellers

30. Neural Headaches 6 They are essentially black box models 6 Training can be problematic èover-training èlength runs 6 Choice of Architecture is subjective with an element of black art or luck or intuition 6 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 1km 2 or 1 decimal minute of resolution Most environmental data can be manipulated into a 1 DM cell format using GIS BUT..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 Nuts31DM

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 n There are SOME existing methods that can be used n A very old simple method uniform area shares n Various surface interpolation methods Tobler’s pycnophylactic surface RIVM’s Goodchild et al (1993) method

44. RIVM population density surface

45. RIVM Smart Interpolation n Weighting factors were used to create a population potential surface n auxiliary data sources were used to modify the weights: Sea, Roads, Rivers n An estimate of population was made using the weights n Best of the existing methods n 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? n 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. 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 RIVM’s population

49. Communications network density

50. Motorway and dual carriageway density

51. Main and minor road network density

52. Railway network density

53. Night-time lights frequency

54. Distance from extra large towns

55. Distance from large towns

56. Distance from medium sized towns

57. Distance from small towns

58. Distance from internatonal airports

59. NUTS3 population from Eurostat

60. Although the errors are still large, the Population interpolation maps look good!

61. 23x20x20x1 prediction

62. 23x20x20x1 prediction close up of Italy

63. (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!!!!!!

64. spring summer autumn winter Forecast Seasonal Temperature Data NOWNOW FUTUREFUTURE spring summer autumn winter

65. spring summer autumn winter FUTUREFUTURE spring summer autumn winter Seasonal Precipitation Data NOWNOW

66. Height above Sea level Climatic Biomass Potential

67. 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!

68. Step 3. Construct Neural Nets to model the relationships between climate-soil- biomass-elevation-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

69. 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!Results “appear” promising!

70. Results

71. Dominant Arable Landuse Observed Predicted Forecast

72. Dominant Tree Landuse Observed Predicted Forecast

73. Dominant Waste Landuse Observed Predicted Forecast

74. 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

75. (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

76. 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

77. 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

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

79. 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

80. Schematic of Fuzzy Land Degradation Interpreter

81. 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

82. 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

83. 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

84. 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

85. 0.01.0 0.0 1.0Non-arable fuzzy landuse class Arable fuzzy landuse class Fuzzification of landuse data Arable Landuse degree of member- ship

86. Land degradation membership function possibleprobableunlikelyextensivesevereserious fuzzy classes land degradation scale degree of member- ship 0.0 1.0

87. A Map of Land Degradation

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

89. Conclusions è50 YEARS is a long time BUT the topic is SO IMPORTANT that it is important attempts are made to make these types of predictions 4Predicting 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!!

90. 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

91. andyt@geog.leeds.ac.uk http://www.geog.leeds.ac.uk/staff/a.turner http://www.medalus.leeds.ac.uk/SEM/home.htm