Predicting the impact of global climatic change on land use patterns in Europe

Stan Openshaw and Andy Turner

Centre for Computational Geography
School of Geography
University of Leeds


The paper describes the development of a Synoptic Prediction System designed to estimate possible impacts of global climatic change on land use patterns across the European Union (EU).  Designing such a system is a challenging task because many of the theoretically desirable data sets are either not available or do not exist whilst significant uncertainties exist in the data that do exist. Additionally, process knowledge is woefully deficient as virtually all the principal mechanisms for linking the dynamics of the physical environment and climate with the associated socio-economic systems are poorly understood. Our belief is that currently no computer models exist which can appropriately address this task and that presently a synoptic GIS style of approach is the best available option if you wish to make broad brush geographical predictions of the possible impacts of global climatic change on land use for around 50 years hence. The paper describes the construction and application of a Synoptic Prediction System that employs a mix of GIS, neurocomputing, and fuzzy logic technologies to attempt this almost impossible but potentially extremely important task.

Keywords: GIS, neurocomputing, fuzzy logic, climatic change, land use forecasts


1. Background and Context

1.1 GIS and the environmental modelling challenge

2. Design of a Synoptic Prediction System (SPS)

2.1 Basic design objectives
2.2 Basic System
2.3 Problems
2.4 Stages in Operationalising a SPS

3. Assembling, aggregating, and estimating the data

3.1 The spatial interpolation problem
3.2 Creating a common spatial database
3.3 Estimating the Population distribution
3.4 Climatic Data
3.5 Other Environmental Data Sets

4 Results of Modelling Land-use change.

4.1 Now Land-use Modelling
4.2 Future Land-use Modelling
4.3 Assessing the impact of change

5. Conclusions

1. Background and Context

This paper is based on ongoing research undertaken by the authors on a major European Union (EU) funded project, MEDALUS III, concerned with Mediterranean Desertification and Land Use.  MEDALUS III consists of a wide range of topics aiming to analyse and model various aspects of environmental, climatic, and land use change at different spatial scales in the Mediterranean region, see McMahon (1998).  The overall objective in our part of this project is to add a socio-economic dimension to the MEDALUS III physical models.  This task can be tackled in various ways.  The strategy used here involves developing a modelling methodology able to link estimates of socio-economic data with available physical-climatic data in order to express forecasts of climatic, environmental, and socio-economic change in terms of agricultural land use impacts.  The final goal is to package the various components into an integrated Spatial Decision Support System which could be used both by EU politicians both to raise awareness of possible impacts and help distribute funds to combat desertification, and by land use planners to evaluate and devise mitigation strategies to alleviate land degradation.

1.1 GIS and the environmental modelling challenge

There is a growing likelihood that global climatic change will soon start to have a visible and increasingly fundamental environmental and socio-economic impact within the next 25 to 50 years on many parts of the world.  In some regions the effects may well be unnoticed or are irrelevant or are well within the capacity of existing ecosystems to cope; however, in other regions the environment is far more fragile.  Here it is possible that even small changes in climate may be sufficient to cause a major impact on the environment and socio-economic systems that relate to it.  The research challenge is to identify a plausible way of making reasonable predictions of climatic change on land-use for 25 to 50 years hence as a possible basis for raising awareness and creating a framework for action.  The hardness of this challenge should not be underestimated but equally there is an increasing urgency to know something of what may be happening to our world in the medium term future so that thinking and strategic planning may be proactive rather than purely reactive.

From a methodological perspective there has been very little research performed on predicting future land use patterns on a local let alone national or EU scale. Most computer models that exist are generally concerned with only very limited aspects of the problem; for instance, regional employment change or population dynamics. An exception is the work by the CLUE Group (see de Koning et al (1997), Veldkamp and Fresco (1996, 1997), Verburg et al (1997)).  The CLUE modelling framework (The Conversion of Land Use and its Effects) is based on a multi-scale stepwise regression approach that attempts to analyse and model land-use and land-use change as a function of socio-economic and biophysical factors at an aggregate spatial scale for China, Ecuador, and Costa Rica. The model is a linear continuous time simulation at a fairly coarse spatial scale; ranging from 7.5 km2 for Coasta Rica to 32 km2 for China.  For the EU something far more sophisticated is needed.  Here the basic requirements are: that the level of spatial resolution is sufficiently detailed to be useful, the key driving factors and process mechanisms that link human and physical environments are explicitly incorporated, and that the entire system is driven by climatic and environmental change.  Additionally, the model should seek to make good use of the research and model results produced by other teams involved in the Medalus Project.

The principal justification for a modelling approach is that the more traditional mapping of indicators will fail to incorporate many of the most important process variables.  Also such maps are static, they can only be produced for case study regions preventing a global overview, and the data and methodologies employed have not been standardised so that like is not being compared with like.  This is not a criticism that mapping land degradation and of environmentally sensitive areas for this or that part of the EU state is not useful, only that this may be the wrong technology to employ if the objective is to provide a synoptic overview across the whole of the southern Mediterranean of global climatic change.

Additionally, if scientific research on land degradation processes, like MEDALUS III, is to have a major political and public impact commensurate with the academic and theoretical importance of the subject then a means needs to be found, however imperfect, to translate the scientific research into a form non-technical decision-makers, politicians, and a non-technically sophisticated "Joe public" can understand and appreciate.  GIS provides a good map based communications medium but what is also needed are results that are mappable across the EU, that are consistently defined, based on the best science at the present moment in time, and which make predictions of what it all means in terms of agricultural land-use impacts.

Section 2 outlines the structure of a Synoptic Prediction System (SPS) which is designed to meet this challenge.  Section 3 describes how the various data components were assembled.  Section 4 discusses the operation of the system to make both nowcasts and forecasts of future land-use patterns.  Section 5 presents some more general conclusions.

2. Design of a Synoptic Prediction System (SPS)

2.1 Basic design objectives

The challenge is to devise GIS based computer modelling systems that are able to link changes in climatic, physical environment, and socio-economics to agricultural land-use in a land degradation context focusing on the effects of global climate change.  The objective is to model the relationship between climate (temperature and rainfall), soil characteristics (permeability, texture, fertility, parent material), biomass, elevation, population densities, and other socio-economic variables to predict contemporary land-use, then forecast future land-use patterns under various different climatic change scenarios.  What is needed, in our view, is some kind of Synoptic Prediction System (SPS) which is intended to function rather like a long term weather forecast.  Regrettably and unavoidably the geographical details will be error prone and maybe even "wrong" but the expectation is that the more general synoptic or broad brush forecasts will be reasonable once they have been aggregated and generalised to a sufficiently coarse level of meta scale detail.  The term "synoptic" derives from the desire to include a broad range of relevant general indicator variables whose interrelationships can be modelled to justify the synoptic designation.

2.2 Basic System

The basic system is outlined in Figure 1.  The simplest view is that of a series of key input variables that are related via a computer model to some outputs.  It can be considered to be some kind of complex regression model except that the mapping of the inputs on to the outputs employs a neurocomputing approach as the non-linear relationships are little understood and too ill defined for more conventional statistical or mathematical modelling specifications to work well.  A fuzzy inference style of approach would probably work better, but initially it is easier to use artificial neural networks - especially as training data are not in short supply.  Table 1 briefly outlines some of the strengths and weaknesses of models based on artificial neural networks; see also Openshaw and Openshaw (1997).

In operationalising Figure 1 the choice of input variables is restricted to those available for MEDALUS III research which reflect "obvious" processes.  Figure 2 outlines the SPS in greater detail.  The available variables are not ideal, but then probably no one knows what would be ideal in this context.  Nevertheless Figure 2 shows that we are using the most obvious variables.  Table 2 gives the full list of 18 predictors used in the modelling which is described in Section 3.

2.3 Problems

There are various problems associated with the modelling structure displayed in Figure 1 and Figure 2.  The principal one is that the relationship between climate and environment and land use is not conditioned by the laws of physics or chemistry or biology but involves many ill defined human factors.  In particular, the relationship between the input physical variables and land use is mediated and affected by at least the following: available technology, market mechanisms, historical tradition, inertia, culture, and various economic factors such as subsidies.  All these aspects are currently invisible to the model and are not directly present in any of the available data; however their integrated effects are present in current patterns of land-use that the neural net model is being asked to represent.  It would of course be nice to have a model into which the price of crops, EU agricultural subsidies, irrigation practices, and farmer behaviour could be input and then operationalise this model at a fine spatial scale for the entire EU.  However, such a model is probably beyond technological feasibility and current data availability, although doubtlessly such models could be built one day by discovering how to model individual people in artificial world laboratories; a bottom-up approach.  However, this dream is probably 10 or more years off in the future and to perform the research now we are forced by scientific circumstances, data restrictions and ignorance to adopt an aggregate top down approach.  It is hoped that the missing variables are invisibly present in the data that are used and are thus taken into account, somehow, by the neural nets that are applied.  This is probably a most optimistic view particularly when the forecasts made for 2048 assume a continuation, at the same level as today of these invisible influences; if indeed they have any direct impact at all.  Unfortunately, we have no choice in this matter given the urgency of the task and the necessity to do the best we can with current technology, knowledge, and data.  However, in defence we would note that there is not a necessary nor a direct relationship between overall model performance and the accuracy of the individual model subcomponents or different data layers.  Indeed one of the most compelling justifications for a fuzzy approach is the belief that there can come a point in conventional models where improved precision and more detail in systems of equations can result in a deterioration of performance; see Kosko (1994).

A further word of caution.  The results will critically depend on the quality of the inputs, and although this has not yet been quantified, we believe the current SPS uses the best available data even though it is deficient.   The aim is to provide broad brush forecasts which are not necessarily accurate but which offer a synoptic view of the likely impacts.  The SPS simultaneously demonstrates what is needed to model the process of land degradation as well as indicating the likely effects of global climate change on land use.  If the results are what is required then doubtlessly their accuracy can be improved.  If critics do not like this approach, then let them demonstrate that they can do better given the same objectives, the same data restrictions and research resource constraints that apply here.  What is proposed here captures the very essence of a GIS based approach to modelling environmental systems from a geographical perspective.

2.4 Stages in Operationalising a SPS

The system outlined in Figure 2 involves the following steps:
Step 1: Assemble the data for a common EU wide geography for the present day (circa 1991).

Step 2: Obtain forecasts for these variables for 25 years hence (notionally 2023) and 50 years hence (designated 2048) for the same geography.

Step 3: Construct neural nets to model the relationship between climate (temperature and rainfall), soil characteristics (permeability, texture, fertility, parent material), biomass, elevation, population densities, and other socio-economic variables to predict contemporary land-use patterns.

Step 4: Compute estimates of land-use for 2023 and 2048 by using forecast values of the inputs to the neural nets and also investigate different climatic change scenarios.

Step 5: Create maps of the impact by comparing these forecasts with predictions for the present day.

Step 6: Consider modifying the forecasts and the predictions to reflect knowledge expressed as fuzzy rules and repeat Step 5.

Section 3 describes Steps 1 and 2.  Section 4 covers 3, 4 and 5 whilst step 6 is yet to be completed.

3. Assembling, aggregating, and estimating the data

3.1 The spatial interpolation problem

One major reason that much of the environmental change research has ignored the socio-economics is the lack of socio-economic data with an appropriate level of spatial and temporal detail so that they can be directly linked to the outputs from the environmental models; see Clarke and Rhind (1991).  Quite simply major differences in quality, scale, and aggregation between existing physical environmental-climatic data and socio-economic data present serious obstacles to the straightforward integration of the models.  Physical models of land degradation generally operate at more detailed spatial and temporal scales compared to existing socio-economic models.  Socio-economic data generally relates to irregularly shaped zones that are historically unstable and subject to continuous change, whereas physical environmental-climatic models tend to use and produce data in regular gridded structures albeit at a range of different scales.

Using GIS most available environmental data for the EU can probably be manipulated into a regular grid orientated at a spatial resolution of approximately 1 km2.  A grid was selected as the framework in which to store, manipulate, link and map the data since it offers the greatest flexibility in aggregating upwards and can yet still provide a realistic representation of regional or local variation provided the grid cells are sufficiently small.  A geographical latitude-longitude projection was chosen as a compromise given the traditional problems in map projections regarding the representation of distance, direction and area distortion of the data caused by the curvature of the earth.  A 1-decimal-minute (1 DM) resolution (which is roughly equivalent to a 1 km2 for most of the EU) was selected as providing the most appropriate and probably the best possible level of spatial resolution that was practicable for this research.

A common spatial framework for all the available climatic, environmental, and socio-economic data is an essential pre-requisite before any modelling can be attempted.  This is not a trivial task as typically the climatic data are produced at a coarser scale than the socio-economic which is itself far coarser than that at which many physical models have been applied.  Aggregation upwards is fairly trivial but interpolation from coarse to finer levels of spatial resolution is far more problematic and error prone yet this is an unavoidably essential activity that needs to be mastered before much progress can be made. Virtually every data source involved in the SPS and described in Figure 2 (see also Tables 2 and 3) had a unique set of problems associated with it and necessitated various GIS operations and sometimes modelling applications to create a common scale data base.

3.2 Creating a common spatial database

The task of creating a common spatial database for the EU for a 1 DM set of cells for those variables described in Tables 2 and 3 was not straightforward and involved the following somewhat convoluted procedures.  Initially, attention was concentrated on processing those variables need to create the population data; see Table 2; using the procedure described later in Section 3.3.

The first data set to be processed was the Global Land 1 km Base Elevation source data (GLOBE).  This provides a grid of 0.5 DM resolution cells in a geographical projection based on the World Geodetic System 84 datum.  It was imported into ArcInfo.  The grid was aggregated to a resolution of 1 DM where each 1 DM cell was assigned the mean value of the four 0.5 DM cells from which it was composed.  Since the average height above sea level values of this height variable are normalised by the neural net program the sum or some other composite combination of these values could have been used.  There are several reason for selecting to use the mean; compared with the mode and median, it is easier to compute since there are four values being summarised, the loss of data precision is less than if just a random selection of one of the four values was assigned, and finally the resulting 1 DM values are still standard distance units of height above sea level whereas they would not be if the sum had been used.  The grid was clipped to a size of 2205 rows and 2568 columns which covered the whole of the EU and most of the rest of Europe.  The GLOBE data itself did not need to be projected, but all the other source data based on different projections had to be transformed.  There are various ways of doing this, they all suffer from problems and there is a trade off depending on the nature of both the non-nesting aggregations between the projections and the nature of the spatial variable (whether it is density, distance or direction related).

Gridded night-time lights source data was imported into ArcInfo, converted into a polygon coverage and projected from its original Goode Homosline projection into the required geographical projection using the projection capabilities of the software.  The projected polygons were intersected with a polygon coverage which coincides with the grid in the chosen 1 DM spatial framework.  A value was calculated and attached to each small intersected polygon by dividing the night-time lights intensity value by the area of this small polygon.  The intersected polygon values within each 1 DM grid polygon were then summed and the resulting coverage was converted into a grid.

Similarly, the Surpop 0.2 km source data was imported into ArcInfo, converted into a polygon coverage, projection, then intersected with a polygon coverage which coincides with the 1 DM chosen spatial framework, and again the intersected polygons were assigned proportions of the population depending on the area of the intersections.  As before the intersected polygon values within each 1 DM grid polygon were summed and the resulting coverage was converted into a grid.  The total population in the source data was compared with the total population in the transformed data to ensure that they were not significantly different.  The values in the transformed data were generally not integer values, although this was not a problem for the neural net, and an integerised version was created for mapping purposes.

The Bartholomew digital map data was manipulated into various grids in the 1 DM spatial framework reflecting either; the location of, distance from, or density of geographical features.  To begin all the various map layers were imported into ArcInfo and mapped using ArcView.  Geographical features which appeared to be consistently defined across the EU and whose location, proximity or density were believed to be correlated with population density were manipulated into location, cost-distance and density layers respectively.  Cell values in location layers were either 0 or 1 depending on whether the cell lay mainly inside or outside the location of a selected geographical feature.  For the distance layers the spatial analyst module of ArcView was used to assign the proximity of each cell to selected geographical features.  Creating the density layers employed a point-in-polygon routine and the grid module in ArcInfo.  The density of a selected spatial variable or geographical feature was calculate at various spatial scales and some results from certain resolutions were aggregated to coarser spatial scales where their values appeared to spatially correlate with population density.  The coarser resolution grids were then disaggregated in a simple fashion where each 1 DM cell was assigned the value of the larger cell in which it was contained.  Using a weighted linear function all the density grids relating to a particular theme were combined at the 1DM resolution, these combined grids were again mapped to examine the correlation with population density.

Finally population related variables were included even though they occur at coarser levels of geography; for instance, population densities at NUTS 3 scale and RIVM's estimates at 10 km2 scale data.  The values of the 1DM cells were simply assigned the closest value of these coarser data units.  The purpose was to provide a multi-scale contextual element.

3.3 Estimating the Population distribution

The principal problem here is that socio-economic data are only readily available for the EU at NUTS 3 level (equivalent to UK county scale) whereas the required resolution is far smaller.  In the UK there are 64 counties (all with some pop but there are about 150,000 1 DM cells of which approximately 75,000 are inhabited) and it is these data that are required for the whole of the EU.  This is a massive spatial interpolation problem.  Goodchild et al (1993) reviews a range of spatial interpolation methods which are relevant to creating surfaces of socio-economic data.  All the methods reported therein suffer drawbacks many of which may only be overcome by using a "smarter" approach.  Deichmann (1996) describes an intelligent spatial interpolation procedure which makes estimates based on potential surface accessibility relationships with population related spatial variables.  This basic idea of using surrogate information to make a smart guess at the distribution of population (and for that matter any other socio-economic data).  This approach is developed further here by broadening the range of input variables to reduce subjectivity, then using a neural net to model the non-linear relationship between the surrogate variables and population density.  The neural net is trained on known UK data and than applied to the rest of the EU.

The aim, therefore, is to use widely available digital map derived summary variables that are probably related in some non-linear surrogate fashion with population density; for instance, road network density, distance to the nearest train station, the location and size of settlements, height above sea level, etc.  The full list is given in Table 2.  There is also some external knowledge that can be imposed on the results; in particular, areas known to be uninhabited (e.g. sea or lakes) can be set to zero whilst known population counts in NUTS 3 regions can be used to constrain the predicted values.

The basic idea, therefore, was to use the 1991 Surpop census population surface to build a neural net based spatial interpolator to relate population density to a selected set of predictor variables.  For each 1 DM cell the values of the variable chosen to model the population densities were concatenated into a large file of vectors from which a randomly selected small training data set of 10,000 1 DM cells was created, together with the associated Surpop counts.  The small number of training cases reflected a desire not to produce nets that only worked well in the UK.  Ideally, the training data should have been based on data for multiple countries in the southern EU but no small area census data were available to us from which Surpop-like estimates might be provided.  As a result there is a risk that population digital map surrogate relationships are different in the southern EU (i.e. different lifestyle) but there was little that could be done to reduce this source of uncertainty due to an absence of data.  The EU really does need to organise its basic data resources to a far better degree than is current.  It is really most unsatisfactory that even NUTS 5 (equivalent to UK ward level) resolution data are not available throughout the EU and that data copyright and ownership prevents access to high resolution data even for those applications where the results are of potential public benefit.

A variety of simple feed forward perceptron networks were applied.  Tests indicated that those nets with a single hidden layer of 25, 50, 75, or 100 neurons were out performed by a net with two hidden layers each with 20 neurons in them.  The neural net training used a hybrid approach: first an evolutionary optimiser was used to find a good solution and then this was fine tuned using a conjugate non-linear optimisation method.  The trained network weights were then applied for the rest of the data across the EU.  The NUTS 3 population totals from Eurostat were used to constrain the predictions of the 1 DM cells in each area. Errors were analysed using the Surpop data in the UK at the 1 DM scale but also at the NUTS 5 scale in Britain and Italy (the only two countries for which we had these data). Tests were also made of the likely improvement if NUTS 5 data had been available across the EU.

The results appear to be remarkably good; see Figure 3.  The predicted surfaces correctly pick up the main features of the population distribution of the EU even if there is a slight loss of peakiness.  It was surprising how well these surfaces matched reality given the nature of the input data and with further post-processing to add lumpiness could further improve the estimates.  Use of accounting constraints based on NUTS 5 data appeared to make little difference to the results.  Finally, forecasts for 2023 and 2048 were produced by using available EU forecasts for 2023 in NUTS 3 areas, and our own for 2048 (as no official ones existed) to constrain the estimates.

3.4 Climatic Data

This was supplied by the MEDALUS III team from the Climatology Research Unit (CRU) at the University of East Anglia.  The data was produced by interpolating measurements from a network of about 50 weather stations across the Northern Mediterranean to produce 0.5 DM resolution seasonal average temperature and precipitation totals, see Palutikof and Agnew (1997) for an explanation of the statistical down scaling procedure.  This data was imported into ArcInfo and the nested cells were simply aggregated to produce the desired 1 DM resolution data.  For temperature data the mean of the smaller grid values were used and for the precipitation data the sum of the smaller grid values were used.  Based on global climate change models forecasts of future seasonal average temperature and precipitation totals were also produced by the CRU, maps of temperature and precipitation for both now and around 2048.  The levels of spatial uncertainty in these data are matched by equal or greater amounts of climatic scenario forecast uncertainty.  Figure 4 and Figure 5 show these climatic change estimates for temperature and precipitation.

3.5 Other Environmental Data Sets

Six different classes of soil were selected with the help of a soil expert based on general similarities between a classification of 26 types provided in the soil source data.  These data are locations on a grid whose value was 1 if the land belong to the soil class and 0 if it didn't.  A measure of soil quality was also developed to make use of the data on the characteristics of soils in the source data.  The fundamental physical properties of soil profiles including; the rooting depth, soil texture, water regime, slope, and existence of impermeable layers were combined by coding the expert knowledge of the soil scientist into a set of fuzzy rules and employing MATLAB (a mathematical software package with fuzzy inference capabilities).  The soil quality layer was developed in this way to make use of the data on the characteristics of soils in the soil database without having to add each as a separate input into the Synoptic Prediction System (SPS).  It was designed in a similar way to a general land capability classification, but in the end it is simpler as it does not take into account all the physical interactions between the soil and climate.

Estimates of potential biomass were provided by MEDALUS III colleagues researching at the University of Leeds.  This is the output of a model which translates temperature and rainfall data into measurements of expected or potential Biomass.  The present potential biomass model is fairly primitive as it does not take into account factors like the height above sea level or soil type and the output used thus far is at a relatively coarse level of resolution as it has been built from 30 DM resolution monthly temperature and rainfall data.  Nevertheless, the 30 DM resolution potential biomass data was imported into ArcInfo and interpolated into the desired 1 DM resolution using the interpolation capability provided in the spatial analyst extension of ArcView.  It is used here mainly to provide a contextual variable.

The other inputs concern a set of broad land-use categories.  There were two land use source variables attached with the soils source data which relate to dominant and secondary land use classes.  These classes are derived from a satellite imagery, there is considerable uncertainty regarding the class of secondary land use but the dominant land use classification is believed to be relatively accurate.  The dominant land use variable was thus selected as the target land use for which the neural network trained to classify.  Prior to this dominant land-use was aggregated into four broad categories; arable, trees and orchards, wasteland and others and each in turn was used as a dependent target variable to train the contemporary land use classifier.  Each cell was assigned a value 0 or 1 depending on whether it belonged or not to the dominant land use class which was being modelled.  The classification could be greatly improved by assigning values for each cell, based on the original satellite data, which give the probability of each cell belonging to a particular land use class; see Moody et al (1996) and Carpenter et al (1997).

4 Results of Modelling Land-use change.

4.1 Now Land-use Modelling

The first task was to build another neural network to recognise agricultural land use based on patterns between: soil type; soil quality; potential biomass; average air temperature in spring, summer, autumn and winter; average monthly precipitation in spring, summer, autumn and winter; height above sea level; and population.  The full list of 18 predictors are shown in Table 3.  Three independent neural nets were trained to classify; arable-land (assumed to be the highest quality), wasteland (the lowest quality land), and trees and orchards.  All these nets had one hidden layer with 50 neurons in it.  These classifications were then combined to create maps of predicted agricultural land-use.  The fit is remarkably good; see for example, Figure 6 showing predicted arable land distributions and the observed.

4.2 Future Land-use Modelling

Predicting the future land use classification involves applying the trained contemporary neural networks using forecasts of the variables used in the now land-use classification.  For some variables forecasts were unavailable and for others there was assumed to be no change.  The difference between the two classifications can be mapped to visualise the effects of global climatic change on land use.  Figure 6, Figure 7, and Figure 8 show these predictions for dominant arable, trees, and waste land use categories.  Currently, we would prefer not to interpret these maps until further computer simulations have been produced.  There is also nothing to compare these maps with right now.

4.3 Assessing the impact of change

The broad brush maps of predicted land degradation are believable and understandable so can be used as decision making aids for allocating funds to combat land degradation.  The maps are essentially decision making tools and the SPS a specific kind of Spatial Decision Support System (SDSS) relating to land degradation.  This will enable informed debate and provide a means for politicians to justify fund allocation at national and regional scales without the need for a deep understanding of the science involved.

5. Conclusions

The paper has outlined how to construct a SPS capable of providing broad brush land use forecasts for 2023 and 2048 that reflect data and modelling results provided by several of the Medalus project teams.  It attempts to embody the essence of what might be styled a GIS approach.  Almost by definition GIS is a visual technology that uses maps which are generalised to varying degrees to present results and communicate findings.  There is nothing wrong in creating visual presentations, indeed, it is a very useful communications device even if the uncertainties and fuzzyness tends to be hidden in the crisp map displays.

However, it is important not to overlook the deficiencies.  To be frank the results are broad brush and can be criticised on the following grounds:

  • the market mechanisms and agricultural subsidy levels which may well link (somehow) environmental change to the socio-economics to produce a land-use response are implicit rather than explicit and assume a continuation of the present;
  • the neural net model results could be improved if better quality climatic and environmental data were available;
  • the uncertainty in the outputs has not yet been made explicit;
  • there is a mixture of inputs with very different levels of data uncertainty and forecast reliability;
  • global climatic effects are equivalent to a shift in the boundaries of agricultural capability;
  • it assumes technology remains more or less the same; and,
  • the land-use categorisation is very crude.
  • On the other hand, the SPS does have some good points, in particular:
  • it is the first attempt to predict 2023 and 2048 land-use changes linked to forecast climatic change;
  • there is a linkage of physical - environmental and socio-economic aspects;
  • the same methodology has been consistently applied across the southern EU;
  • it is a brave attempt to make broad brush land-use impact predictions for 50 years ahead;
  • it offers a different but useful approach to assessing the possible impacts of climatic change on land-use by linking all the various components in a novel and interesting way;
  • it has produced broad brush results relatively quickly which can be updated as new and improved outputs from socio-economic and other environmental models become available;
  • it is difficult to see how the challenge could be done in any better way at present; and,
  • the results are understandable and should help focus the political debate about how to handle these desertification problems by putting them into a pan-EU context.
  • Of course the forecast predictions will be wrong!  The hope is that when aggregated to an appropriate level of geography they will not be so wrong as to be useless.  The aim is to raise awareness and to communicate the possible impacts on land-use 50 years into the future. It is a straw man!  Let those who dislike these results demonstrate how with existing science they can do better.  It is also a challenge for those who like the results, the onus being to improve them by reducing the uncertainties in the inputs and enhancing the modelling that was used.  There is nothing in this paper that could not be improved either by the availability of better data, improved forecasts, and more key variables; or by the input of more effort to enhance the modelling. However, given current data, current knowledge and current science it is difficult to see how we could do much better.  We wanted to create a need for land-use forecasting models that incorporate climatic, environmental, and socio-economic variables.  We have chosen to meet this goal by outlining a practical system, however imperfect. If the results outlined here are at all useful, then maybe the resources needed to improve them will be forthcoming.  Meanwhile we would argue that our results are unique in that they are all that exists right now so the principle of caveat emptor should be applied.  The results are the first of their kind and really only serve as a benchmark and a preliminary test of methodology.  All in all, the SPS appears to provide a useful framework for assessing the possible impacts of climatic change on land-use by linking all the various components in a novel and interesting way.

    Further details of the research can be found on the www at

    Table 1. Advantages and disadvantages of neural networks
    Advantages Disadvantages
    universal approximators computationally intensive
    equation free may require long training times
    highly non-linear choice of architecture is subjective
    promise of good performance depends on training data
    handle hard to model problems black box technology
    automated conveys little knowledge

    Table 2. Variables used to create European population surfaces
    Digital Elevation Model 2
    Night time lights intensity at 1 km scale 3
    Distance from nearest built up areas 1
    Distance from nearest canal 1
    Distance from nearest international airport 1
    Distance from nearest national park 1
    Distance from nearest river 1
    Communications network density 1
    Motorway and dual carriageway road network density 1
    Main and minor road network density 1
    Railway network density 1
    Distance from extra large towns 1
    Distance from large towns 1
    Distance from medium sized towns 1
    Distance from small towns 1
    Location of built-up areas containing extra large town centres 1
    Location of built-up areas containing large town centres 1
    Location of built-up areas containing medium sized town centres 1
    Location of built-up areas containing small town centres 1
    Location of named settlements and built-up areas 1
    Regiomap population density at NUTS-3 4
    Tobler's pycnophylactic population density based on NUTS-3 5
    RIVM's population density at 10 km scale 6
    Surpop Great Britain Census target population density 7

    Table 3. Variables used for predicting and forecasting agricultural land-use
    Variable Label and Data Source Description
    Location of soil type 1 11 This includes the following soil classes; cambisol, chernozem, luvisol, vertisol, plaggensols.
    Location of soil type 2 11 This includes the following soil classes; rendzina, gleysol, phaeozem, fluvisol, kastanozem, histozol, andosol.
    Location of soil type 3 11 This includes the following soil classes; arensol, ferralsol, ranker, planosol.
    Location of soil type 4 11 This includes the following soil classes; acrisol, podzoluvisol, greyzem, podzol, solonchak.
    Location of soil type 5 11 This includes the following soil classes; solonetz, xerosol.
    Location of soil type 6 11 This includes the following soil classes; lithosol, regosol, rock outcrops
    Soil quality 11 Physical properties of the soil were indexed in terms of their limitations or restrictions for agricultural capability and combined to produce a crude measure of soil quality.
    Potential biomass 10 Estimated potential biomass model output at 30DM resolution.
    Average temperature in Spring 9 Average monthly air temperature in March, April and May. 
    Average temperature in Summer 9 Average monthly air temperature in June, July and August.
    Average temperature in Autumn 9 Average monthly air temperature in September, October and November.
    Average temperature in Winter 9 Average monthly air temperature in December, January and February.
    Average monthly precipitation in Spring 9 Average monthly precipitation in March, April and May.
    Average monthly precipitation in Summer9 Average monthly precipitation in June, July and August.
    Average monthly precipitation in Autumn9 Average monthly precipitation in September, October and November.
    Average monthly precipitation in Winter 9 Average monthly precipitation in December, January and February.
    Digital Elevation Model 12 Height above sea level.
    Population 1x10x10x1 Neural Network output.
    Dominant agricultural land-use 11 The dominant agricultural land use categorised into the following groups; arable, olive groves and orchards, wasteland, and others.

    Appendix. Data sources

    1. Bartholomews European 1DM dataset.
    2. Digital Chart of the World.
    3. The Defence Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) Night- time lights intensity dataset.
    4. RegioMap (Eurostat) CD-ROM data.
    5. Tobler's pycnophylactic (mass preserving) smooth interpolated population density surface.
    6. RIVM's raw population count for geographical regions.
    7. UK Census data: Surpop 200 meter total population and population seeking work surfaces of Great Britain; SAS Small Area Statistics.
    8. Italian National Statistical Institute Registration total population count point data.
    9. CRU temperature and precipitation data.
    10. Biomass estimations from Medalus at Leeds.
    11. Soils geographical database of Europe at scale 1:1,000,000 version 3.2.
    12. GLOBE: Global Land One-KM Base Elevation Data version 0.1.


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    Figure 1. Basic outline of the Synoptic Preiction System


    Figure 2. Structure of the Synoptic Prediction System


    Figure 3. A map of EU Population at a 1DM resolution


    Figure 4. Temperature maps

    Present day

    Forecast for 2048




    Figure 5. Precipitation maps

    Present day


    Forecast for 2048




    Figure 6. Observed, predicted and forecast dominant arable landuse







    Figure 7. Observed, predicted and forecast dominant tree landuse







    Figure 8. Observed, predicted and forecast dominant wasteland