Visualising the potential impacts of climate change on rural landscapes

Computers, Environment and Urban Systems

29 (2005) 297–320

www.elsevier.com/locate/compenvurbsys

Visualising the potential impacts ofclimate change on rural landscapes

Trudie Dockerty a,*, Andrew Lovett a, Gilla S€unnenberg a,Katy Appleton a, Martin Parry b

a School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UKb IPCC, Hadley Centre, UK Meteorological Office, Fitzroy Road, Exeter EX1 3PB, UK

Abstract

Climate change is an issue that will increasingly require policy consideration, but for which

knowledge and information at the local or landscape scale is either lacking or largely inac-

cessible. This paper explores the possibility of reinterpreting climate impacts information and

presenting it through GIS-based visualisations in a manner that might assist decision-making

at the local level. A GIS database was constructed for an agricultural landscape in Norfolk.

Future land-use changes under climate change scenario for the 2020s, provided by a land use

allocation model at 1 km grid-square resolution, were downscaled to the field-level database

using a series of decision rules. The predicted land use changes were then visualised using

photorealistic image rendering software. As a technical exercise this work illustrates the extent

of recent advances in GIS-based visualisation, but it is also recognised that there needs to be

further work on a range of topics (including impact assessment methodologies, the repre-

sentation of uncertainty and design guidelines) if such images are to be widely used as a

information provision and decision support tool in relation to climate change.

� 2004 Elsevier Ltd. All rights reserved.

Keywords: Climate change; Land use; Scenarios; Landscape visualisation; GIS

1. Introduction

There is currently a huge international effort to understand the science of climate

change and its impacts, and a growing awareness of the need to adapt policy and

behaviours, and to develop possible mitigation strategies. At the local level, however,

* Corresponding author. Tel.: +44-1603-591359/592583; fax: +44-1603-591327/593896.

E-mail address: [email protected] (T. Dockerty).

0198-9715/$ - see front matter � 2004 Elsevier Ltd. All rights reserved.

doi:10.1016/j.compenvurbsys.2004.05.004

mailto:[email protected]

298 T. Dockerty et al. / Comput., Environ. and Urban Systems 29 (2005) 297–320

in the policy arena which influences the everyday environments in which people live

and work, our experience has been that climate change is often regarded as:

• too abstract: it’s hard to understand the local implications of pronouncements

such as ‘‘the globally averaged surface temperature is projected to increase by

1.4–5.8 �C over the period 1990–2100’’ (Intergovernmental Panel on Climate

Change, 2001, p. 13),

• not relevant to us/our organisation: limited appreciation of possible local impacts,

• too far in the future: there are more urgent things to deal with right now,

• having too much uncertainty to merit adapting policy.

The political reality is also one of relatively short policy lifetimes, typically in the

region of 5–10 years. It is therefore easy to see how it is possible to put off consid-

ering climate change to the next policy round (for further discussion of public

attitudes regarding climate change see Nicholson-Cole, 2004).

In the UK, rural landscapes are fashioned not only by planning law, and the

policy guidance of various government and other agencies with an interest in

landscape, but also, fundamentally, by farming practice. Agricultural land use is a

major determinant of the visual realisation of rural landscapes. Future developmentsin agriculture brought about both by social and economic pressures, and also

increasingly by climate change (acting both directly and indirectly), will be a key

influence on the visual appearance of rural landscapes (DEFRA, 2002; see also

Swetnam, Wilson, Whittingham, & Grice, 2004).

There are many schemes in place to guide desirable landscape developments

(relating to good farming practice, biodiversity conservation, water resource man-

agement etc., e.g. see MacFarlane, 2000; Townshend, Stace, & Radley, 2004).

However, the statutory framework relating to landscape protection in Britain re-mains influenced by two controlling factors––private ownership of land and the

‘voluntary principle’. 1 Success is dependent on engaging the interest of individual

landowners who are largely able to choose whether or not to participate in particular

schemes. Farm enterprise and forestry activities, which have given rise to some of the

greatest landscape changes in recent decades (and are likely to do so in future), are

exempt from most planning controls. Forces behind change in the rural landscape

therefore, are largely beyond the jurisdiction of law; plans put together by agencies

for landscape management may amount to little more than ‘wish lists’ (Dockerty,Lovett, Appleton, & S€unnenberg, 2001). O’Riordan, Wood, and Shadrake (1993, p.

125) point out that

1 Th

manag

imposi

‘‘the landscape in its totality is not envisioned, it simply materialises’’.

at is, where the legislation provides for negotiated agreements between landowners and

ement authorities, with incentives to reward beneficial types of land management, rather than

ng prohibitions.

T. Dockerty et al. / Comput., Environ. and Urban Systems 29 (2005) 297–320 299

Within this complex system, it will become increasingly important to engage the

public and a wide variety of stakeholder organisations in understanding the impact-

implications of climate change, and to enhance decision-making processes relating tolandscape protection.

2. Approach

How can we further understanding of climate change at the local level and help

ensure policies currently being formulated will adequately manage the potential

consequences of climate change? The UK Climate Impacts Programme (UKCIP) has

been addressing the need to evaluate impacts at a more local scale through a series of

regional ‘scoping studies’ that assess vulnerability to climate change, and discuss

options for appropriate responses. It has also published the results of a more detailed

project, REGIS (Regional Climate Change and Response Studies in East Anglia andNorth West England) (Holman et al., 2002), which sought to model the integrated

impacts of climate change on these two regions across a number of sectors––

agriculture, water, biodiversity, coastal and river flood risk. The outputs of that study

were conventionally presented in the form of maps and tables in written reports.

Our work focuses on expressing climate change impacts information in another

way, namely by visualising ‘futurescapes’ i.e. impressions of potential future land-

scapes based on a synthesis of current policy and scientific research. Much of the

existing climate change impacts research has taken place at the international tonational scale (e.g. Harrison & Butterfield, 1996; Parry et al., 1999). Two key

objectives of our study were therefore to assess how far it is possible:

• to ‘downscale’ or re-interpret existing impacts information at a scale more relevant

to the local policy arena,

• to make such information more accessible by using visualisation techniques.

The appeal of the visual format is that information presented in this way isprocessed and absorbed by the human brain much more efficiently than textual,

numerical or even diagrammatic data (Tufte, 1992). Many different visualisation

techniques are available. Examples of pre-computer based representations of rural

landscapes in Britain include Benefield and Bunce’s (1982) visualisation of the

Institute of Terrestrial Ecology’s Land Classification system, which through a series

of drawings portrayed generalised views incorporating features found in each land

class. O’Riordan et al.’s (1993) ‘Landscapes for Tomorrow’ project used a series of

water colour paintings as a means of engaging the public, landowners, farmers andpolicy managers in considering possible changes within the Yorkshire Dales Na-

tional Park.

In recent years there has been a move to digital technologies. These include 3D

object modelling, rendering and animation, developed from computer-aided design

(CAD) software, plus image manipulation programs, which have provided a means

for achieving increased realism in landscape images (Ervin & Hasbrouck, 2001;


Lange, 2002). Geographical Information Systems (GIS) have also been used for

landscape planning purposes (e.g. Herrmann & Osinski, 1999; SDRI, 1999) and

there is an increasing ability to link GIS databases with more sophisticated visu-alisation tools (e.g. Appleton, Lovett, S€unnenberg, & Dockerty, 2002).

The pre-cursor to the work described in this paper was a GIS-based study re-

ported in O’Riordan, Lovett, Dolman, Cobb, and S€unnenberg (2000), Dolman,

Lovett, O’Riordan, and Cobb (2001) and Lovett et al. (2002). In that work, future

scenarios were devised for an Oxfordshire landscape, based on current policy doc-

uments, a programme of interviews with farmers, and discussions with a number of

stakeholder organisations, that reflected plans for agricultural changes, improved

visual amenity value and biodiversity conservation. The scenarios were visualised inthe form of large format (A0) maps plus Virtual Reality Modelling Language

(VRML) representations of the landscapes that could be viewed and navigated via a

web browser.

Fig. 1 shows two photomontages from the same area. These images were created

as part of initial work on visualising the potential landscape impacts of climate

change. They were based on the results of research into the impacts of climate change

on agriculture (Parry et al., 1999), SPRU (1999) socio-economic scenarios and in-

sights from experimental work being carried out by the Institute of Arable CropResearch, Rothamsted on possible future UK crops. Fig. 1(a) depicts the current

situation and Fig. 1(b) illustrates the kind of landscape that a changing climate might

bring. As the climate warms it will become increasingly possible to grow crops in

England currently seen further south in Europe (e.g. sunflowers). Some agricultural

land may no longer be required for crops presently grown, allowing the land to be

used for other purposes. One example would be the growth of biofuel crops, such as

the tall perennials grasses illustrated, which could be harvested and burned in farm-

Fig. 1. Digital photomontage of an Oxfordshire Landscape (a) current situation, (b) potential landscape

under climate change.


scale CHP units (centre-left of Fig. 1(b)), supplying the renewable energy market and

providing alternative farm income. Other forms of energy generation could also lead

to landscape change, such as an expansion in wind farms (far left of Fig. 1(b)).Photomontages such as those in Fig. 1 have the advantage of providing detailed

and immediately recognisable landscape views, but it has long been recognised that

there are limits on the types of changes that can be shown and that considerable time

and skill is often required to prepare such images (Sheppard, 1989). The provision of

greater flexibility (e.g. in terms of the ability to alter viewpoints, terrain or the

positions of buildings and vegetation) has been an important factor in the recent use

of GIS-based techniques to visualise landscape changes (e.g. Jude, Jones, Bateman,

& Andrews, 2003; Stock & Bishop, 2002) and for similar reasons such an approach isadopted in the research described in the remainder of this paper.

3. Development of a GIS database for the Norfolk Arable LMI

Part of the region covered by the Norfolk Arable Land Management Initiative

(NALMI) was selected as a case study area to assess the issues involved in visualising

potential climate change impacts. The Land Management Initiative is a CountrysideAgency programme, working with farmers and rural communities in several parts of

Britain to identify means of facilitating more sustainable land management practices

(for further details see http://www.countryside.gov.uk/livinglandscapes/sustain-

able_land_management/LMIs.asp). Previous research for NALMI had included

some assessment of potential climate change impacts in the locality (e.g. Lorenzoni,

Jordan, Favis-Mortlock, Viner, & Hall, 2001) and the area was also considered a

good exemplar of the types of agriculture and environmental conditions present in

much of south-eastern England.The NALMI project area covers just over 17,000 ha and is situated between the

towns of Dereham, Swaffham and Watton (Fig. 2). It is an area of muted relief, with

rivers and brooks draining out to the east and west, forming a number of small

valleys. The landscape of the NALMI area is one of intensive arable agriculture, in

which cereals dominate. High use of pesticides and loss of hedgerow and marginal

habitat through field enlargement have impoverished biodiversity. Mixed farming

has given way to arable, with a decline in the amount of pasture land.

3.1. Database construction

Elevation and topographic data for the NALMI area were obtained from two

Ordnance Survey (OS) products. These were the Panorama� 1:50,000 scale digitalelevation model and the Meridian� mid-scale vector maps (Ordnance Survey, 2004).

Data were downloaded from the Digimap project web site at the University of

Edinburgh and then imported into the Arc/Info GIS software. The NALMI area

consists of 13 parishes and digital boundaries for these administrative units were

constructed using 1991 Census databases available via the MIMAS service at the

University of Manchester.

http://www.countryside.gov.uk/livinglandscapes/sustainable_land_management/LMIs.asp

http://www.countryside.gov.uk/livinglandscapes/sustainable_land_management/LMIs.asp

Fig. 2. The location of the NALMI project and study area.


OS Land-Line� vector data were used as a framework for more detailed land use

mapping in a 9 km · 5 km sub-region around the upper River Wissey (outlined in

Fig. 2), which essentially constitutes a small river valley with slightly higher eleva-

tions on the northern, eastern and southern sides. The River Wissey flows west from

its source north of Shipham out of the NALMI parishes and joins the Great Ouse

Fig.3.Landuse

intheWisseystudyarea,summer

2001.



which drains into The Wash at King’s Lynn. This area was selected for particular

attention as there are several current NALMI projects concerned with future land

management or habitat restoration in the locality.Land-Line� data consist of point, line and annotation features. Polygons have to

be explicitly created within a GIS and this process tends to require an element of

manual editing for some feature types (e.g. fields). There are also limitations in the

attribute coding for many types of landscape features (see Lovett et al., 2002 for

details). Considerable work was therefore required to convert the raw Land-Line�

data into more relevant and structured formats. Manipulation and editing of the

data using the Arc/Info software generated a map layer for the 9 km · 5 km sub-

region that contained over 10,000 polygons. Attributes for features such as buildings,roads and water bodies could be assigned from the Land-Line� information, but to

distinguish categories of land use a field survey was carried out in the area during the

summer of 2001. Over 30 land use classes were distinguished in the survey, some 20

of which referred to different crops or agricultural activities. These survey results

were subsequently used to assign land use codes to polygons in the GIS database. A

simplified version of the survey data (with a number of land use categories com-

bined) is shown in Fig. 3, highlighting the importance of cereal production in the

area.

4. Creating landscape scenarios from climate change information

Making projections of climate change requires knowledge of past and current

emissions of greenhouse gases and an estimation of likely future emissions. The level

of future emissions depends on how society develops, a question that is subject to

rather greater uncertainty than those which remain in understanding the science of

the climate system (UKCIP, 2000). To provide a framework for climate change

projections the IPCC have therefore produced a set of scenarios representing pos-

sible directions for human development over the next 100 years, encompassing dif-

ferent systems of governance and societal structures (Nakicenovic & Swart, 2000).Four equi-plausible scenarios have been formulated that together cover a sig-

nificant proportion of the uncertainties in future demographic, economic and tech-

nological change. These ‘storylines’ are differentiated on two dimensions (see Fig. 4),

with the horizontal axis representing an emphasis on economic development at one

end and sustainability at the other. The vertical axis differentiates systems of gov-

ernance, with the lower extreme denoting a convergence in the policies of individual

governments and globalised markets, while the upper one constitutes a future where

national or regional policy differences are maintained. Combining the two dimen-sions defines a range of societal futures where levels of warming are likely to vary, as

shown in Fig. 4 by the predicted temperature changes from the latest UK projections

prepared by the Tyndall Centre for Climate Change Research (Hulme et al., 2002).

The approach taken in this research in constructing scenarios of potential future

landscape change builds on the method used by O’Riordan et al. (2000). This study

likewise utilises prescriptions from policy guidance documents as the basis for sce-

Fig. 4. Characteristics of future world development pathways (Sources: Hulme et al., 2002; Nakicenovic &

Swart, 2000; SPRU, 1999).


nario development, but also includes land use changes based on the output of a land

use allocation model (CLUAM) that evaluates the impacts of climate change on the

viability of different farming enterprises (Parry et al., 1999). This inclusion is animportant extension because the economic return available from the land is likely to

be as fundamental an influence on landscape development in the future as it has been

in the past (see also MacFarlane, Stagg, Turner, & Lievesley, 2004).

The process used to derive landscape-scale scenarios is illustrated in Fig. 5. We

have attempted to include information at a hierarchy of scales from national through

regional to local. In the case of policy guidance, the quantity and detail of infor-

mation increases with the move from national to local scale. With climate change

impacts the situation is reversed with most information available at the nationalscale, and virtually nothing of any detail at local scale. All of the information

gathered has been checked for consistency with UKCIP02 climate change scenarios

(see Fig. 4) and UKCIP socio-economic futures (SPRU, 1999), and has been allo-

cated to sit within the most appropriate of the four storylines (i.e. World Markets,

National Enterprise, Global Sustainability, Local Stewardship).

In order to isolate the influence of climate change from other environmental and

socio-economic factors that will shape the future, it is necessary to create futures

scenarios that exclude climate change effects alongside scenarios that include climatechange effects (Parry & Carter, 1998). This feature has been included in our full

range of scenarios (see Fig. 5). In addition it is desirable to adopt scenarios that

reflect possible conditions across the range of uncertainty, as exemplified by the four

storylines mentioned above. Ideally, our impact assessment would present landscape

futures across all four, however at the time of this research CLUAM output was

available for only two of these futures––National Enterprise (A2/Medium–High)

Fig.5.Theprocedure

usedto

derivelandscape-scale

scenarios.



and Local Stewardship (B2/Medium–Low). These are not the optimum scenarios of

choice, SPRU (1999) recommends that if utilising a sub-set of the four scenarios, a

combination of World Markets (A1/High) +Local Stewardship (B2/Medium–Low)is most appropriate to obtain maximum contrast. Nevertheless, there are sufficient

differences between the two available scenarios, with National Enterprise emphas-

ising economic development and Local Stewardship strong environmental policies,

to provide a sense of the possible future landscapes.

Insights into potential trends at the regional and local scale have been gleaned

from many documents, including an environmental strategy for the NALMI

(Hawkins & MacMullen, 2001) and a study of possible climate change impacts

(Lorenzoni et al., 2001; Viner, Jordan, Lorenzoni, & Favis-Mortlock, 2000). Thelatter included increased irrigation requirements, risks of soil erosion, changes in

agricultural yields, and importantly, alterations in the distribution of particular

agricultural activities.

5. Land use change modelling

To take account of possible future changes in agricultural activities, particularly

alterations in crop distributions, output from CLUAM (a climate and land use

allocation model) was used. CLUAM incorporates the effects of global climate

change on agriculture in England and Wales through links with a model of world

food trade (the Basic Linked System or BLS) (Hossell et al., 1996; Parry et al., 1999).The BLS is run for a particular socio-economic and climate change scenario and the

outputs (yields, prices and demands for the modelled commodities––crops and grass-

based livestock production) are subsequently input to CLUAM. Base data within

CLUAM are structured around the ITE Land Classification System (Bunce & Heal,

1984) and total areas for each agricultural activity are calibrated to accord with

MAFF Census statistics (Clark, 1982), so providing a baseline against which changes

suggested by individual scenarios can be compared. The current baseline is centred

on the mid-1990s. CLUAM essentially treats England and Wales as a single farmand, for a given scenario and associated constraints, employs linear programming

techniques to allocate 1 km squares to uses in a manner that optimises the gross

margin achievable from all the possible agricultural activities on the different parcels

of land. CLUAM output indicates the percentage land use per 1 km square for each

of the following; wheat, barley, oats, oilseeds, peas/beans, sugar beet, potatoes,

maize, sunflowers, ley grassland, permanent grassland, and rough grassland.

5.1. Comparing CLUAM mid-1990s output with the 2001 land use survey

In order to evaluate modelled projections of future land use it is helpful to have an

idea of how well CLUAM output reflects the current ‘on-the-ground’ experience.

The ideal situation would be to have an actual land use survey for the area in the mid

1990s (i.e. the same baseline as used by CLUAM). Unfortunately such data were not


available, but there are only a few years between the model baseline and our 2001

survey.

CLUAM baseline data (percentage land use per 1 km square) were extracted forthe 9 km · 5 km Wissey sub-region and compared with the 2001 survey results. All of

the study area was coded as ITE Land Class 3 in CLUAM so, not surprisingly, the

survey recorded more land uses than were present in the modelled data. In order to

allow a more appropriate comparison, those land uses not represented in the

modelled data (e.g. urban, water, woodland, soft fruits) were excluded from tabu-

lated survey data, reducing the total area from 4500 to 3858.5 ha. The surveyed land

uses were then aggregated to match the CLUAM categories and percentages cal-

culated (see Table 1).Due to the wet conditions in autumn 2000 and spring 2001, which meant that it was

not possible to get machinery onto the land for the sowing of crops, quite a high

proportion of land was under set aside in 2001. Taking this situation into account,

along with the possibility that more oil seed rape and linseed might have been grown if

conditions had been more favourable, there is reasonable agreement between our

Table 1

Comparison of recorded and modelled land use data

2001 Land use survey CLUAMmid 1990s baseline

Categories Hectares Total hectares % Categories %

Wheat 1393.1 36 Wheat 29

Barley 529.6 14 Barley 21

Free range pigs 2.2

Ploughed/bare 79.2

Set aside 350.1

431.5 11 Unallocated 3

Maize 63.6 2 Maize 0

Sunflowers 1.5 0 Sunflowers 0

Oats 2.8 0 Oats 0

Rape seed 240.0

Linseed 20.6

260.6 7 Oilseeds 22

Beans 145.8

Peas 128.8

274.6 7 Beans/peas 0

Sugar beet 322.9 8 Sugar beet 0

Potatoes 57.4 1 Potatoes 0

Total 23 Other crops 22

Pasture/grass 359.4 9 Ley 24

Bracken 6.6

Heath 35.9

Paddock 10.6

53.1 1 Permanent grass 1

Rough grazing 108.4 3 Rough grazing 0

Total 13 Grasslands 25

Total 3858.5 100 100

Table 2

Rules for reallocating land use codes

Priority order Action

First Allocate land to Ley: Prescriptions from the Environmental Strategy and other

reports were examined to determine where grassland reversion should be located (e.g.

in river valleys, along water courses, where hedgerows exist––indicating previously

existing livestock land etc.). Some smaller fields in these locations of other land uses

were also reallocated to make contiguous areas of grassland, up to the total area

required

Second Cereal crops in the current land use map are mostly associated with the largest open

fields and brown earth soils away from the river valley. It was assumed that the largest

of these fields would remain the most practical and profitable for cereal growth.

Therefore the current land use coding for these was preserved, except for allowing

transfer from one cereal type to another (e.g. wheat to barley) to adjust to the scenario

proportions of each crop

Third The smaller of the cereal fields and larger of other surplus crop fields were transferred

to maize and other crops to match the scenario proportions


survey and the CLUAM data for the mid 1990s with respect to cereal and oilseed

crops. The main discrepancy in Table 1 is in the amount of land down to ley grass-

land. We recorded relatively little grassland in the area, and in fact reasonable pro-

portions of other crops such as sugar beet or peas/beans that were not predicted by theCLUAM data. However, the survey area is a very small part of Land Class 3 which

actually covers a broad expanse of south-east England (over 1.5 million ha, around

10% of the land area of England and Wales). It therefore needs to be recognised that

the survey data were most unlikely to have an exact correspondence with model

predictions based on land class averages, and for our purposes the degree of agree-

ment was deemed sufficient to proceed to the downscaling stage of the research.

5.2. Downscaling and applying predicted land use changes to the GIS database

A set of rules was developed to apply the percentages for each 1 km grid square in

the CLUAM scenario output for the 2020s to the field polygons in the GIS database.

This exercise sought to create new polygon coverages for each of the two scenarios

that achieved the percentages of land use in each category suggested by CLUAM

and also resulted in a plausible crop pattern given the nature of the terrain, soils andland use history in the area. Table 2 outlines the approach adopted, with the initial

emphasis being on the allocation of land to ley grass, followed by changes in the

distribution of crops. All the polygon attribute assignments were made using rese-

lection or other editing commands within the Arc/Info GIS, and it proved possible to

approximate the required hectare targets without splitting fields.

6. Visualising landscape change

A raster DEM and shapefiles of the land use distributions were imported into the

Visual Nature Studio (VNS) software (3DNature, 2004) to produce landscape


visualisations. This software simulates landscape scenes by generating photorealistic

images of the view from camera positions defined by the user (Appleton et al., 2002).

Within VNS, visual properties such as colour, texture and the presence of vegetationcan be assigned to areas of the terrain surface as defined by polygon or grid cell

attributes. Buildings and other structures can be linked to specified points, and other

features such as water, sky and lighting can also be controlled. Externally produced

3D objects can be imported to represent buildings or other structures, and may also

be used for vegetation, although two-dimensional images are more commonly em-

ployed (as in this research) due to high processing loads when they are as numerous

as trees and plants in a rural landscape. For processing reasons, it was also decided

to represent most buildings by simple extrusions of their footprint polygon.In this study, three sets of land use information were involved (for 2001 and the

two climate change scenarios) and search queries in VNS were used to dynamically

relate feature attributes (e.g. as defined in a shapefile) to the relevant visual prop-

erties. The software’s own ‘scenario’ capability was also used to link together and

activate/deactivate different sets of visual properties depending on the landscape to

be simulated. For example, in the National Enterprise scenario grass, trees and

bushes were altered to appear more yellow/brown, additional gaps were added to

hedges, dead trees were introduced, and the river level was reduced to reflect theeffects of greater warming and poor water management.

Table 3 summarises the overall pattern of land use in the 9 km · 5 km study area

as recorded in the 2001 survey and predicted for the A2 National Enterprise and B2

Local Stewardship scenarios in the 2020s. These results suggest that there could be

considerable alterations in agricultural activities by the 2020s and also that the

changes could vary substantially between scenarios. For instance, in the National

Enterprise scenario wheat production remains important, but the drier conditions

and competition for water supplies result in other crops requiring irrigationbecoming less common. Some of this land transfers to new crops (such as sun-

flowers), but there is also predicted to be a shift into animal production systems, as

indicated by an increase in ley grassland. Fig. 6 maps the distribution of land use

Table 3

Changes in land use between 2001 and the two climate change scenarios

Land use 2001 Survey A2 Scenario B2 Scenario

Hectares % Hectares % Hectares %

Wheat 1393.1 31.0 1790.5 39.8 192.8 4.3

Barley 526.6 11.7 348.1 7.7 1747.7 38.8

Maize 63.6 1.4 0.0 0.0 615.6 13.7

Other crops 1536.3 34.1 665.0 14.8 239.6 5.3

Grassland 514.3 11.5 1235.0 27.4 1242.9 27.6

Woodland 171.6 3.8 170.5 3.8 170.5 3.8

Urban/roads 294.5 6.5 290.9 6.5 290.9 6.5

Total 4500.0 100.0 4500.0 100.0 4500.0 100.0

Fig.6.Potentiallanduse

in2020(A

2NationalEnterprise

scenario).


Fig.7.Potentiallanduse

in2020(B2LocalStewardship

scenario).



under this scenario and indicates a particular increase in grassland around the river,

with cereal production mainly situated along the surrounding slopes.

In the Local Stewardship scenario there is more emphasis on environmentalprotection, with less intensive agricultural systems and better water conservation

practices. As with the National Enterprise results, the CLUAM output predicts a

Fig. 8. Landscape visualisations from a bird’s-eye perspective (viewpoint V1) (a) land use in summer 2001,

(b) potential land use in 2020 (A2 scenario), (c) potential land use in 2020 (B2 scenario).


shift into animal production and increased grassland cover (see Fig. 7). However,

there is also a marked change in crop composition, with wheat production moving to

more favourable climatic conditions further north in England and being replaced bybarley and maize.

Fig. 9. Landscape visualisations from a ground level perspective (viewpoint V2) (a) land use in summer

2001, (b) potential land use in 2020 (A2 scenario), (c) potential land use in 2020 (B2 scenario).


3D landscape scenes were rendered using the VNS software to illustrate these land

use changes, as well as other alterations in vegetation and the built environment that

would be anticipated under each scenario. Two viewpoints were selected (see loca-tions marked on Fig. 3), the first providing a bird’s-eye perspective looking south

east across the Wissey valley and the second a ground-level view towards the river.

These viewpoints were also chosen to be complementary, one supplying an overview

of the landscape and the other a typical scene that would be observed while walking

along a roadside path.

Fig. 8(a) shows the bird’s-eye view of the 2001 landscape. The River Wissey can

be seen in the foreground, with rough grassland to the left, sugar beet to the right,

and cereal fields in the right middle distance. In Fig. 6(b), the National Enterprisescenario, the sugar beet is replaced by sunflowers as the climate becomes less

favourable for the former and more so for the latter. Changing profit margins also

lead to the introduction of livestock farming, as indicated by the cows on the ley

grassland to the left of the river. The underlying water-stress is indicated by a re-

duced river level and a decline in vigour of hedgerow trees and shrubs, with gaps

appearing where a lower water table leads to die-back. Fossil fuels are still widely

used and the only evidence of adaptation in the built environment is the white-

washing of walls to reflect the heat.In Fig. 8(c) the climate is also warmer than today, but the landscape reflects a

different ethos at work, with greater emphasis on mitigation and adaptation meaning

that water stresses are not so severe. Hedgerows are maintained and the move away

from the use of fossil fuels is indicated by the wind generator and solar panels on the

house in the right foreground and the windfarm on the horizon. Agricultural changes

include maize on the field to the right of the river and a lower density of cattle on the

grassland to the left.

Fig. 9 shows the changes discussed above from a ground-level viewpoint. Therepresentation of different types of vegetation is particularly striking and illustrates

the rapid advances being made in this area of landscape visualisation (Muhar,

2001).

7. Discussion

Viewed in sequence, the images in Figs. 8 and 9 certainly provide more vivid and

immediate illustrations of the potential local impacts of climate change than are

possible with conventional maps. The variation between scenarios also conveys the

point that the future is not fixed and that much will depend on how societies respond

to the challenges posed by climate change. Disseminating such a message isimportant from a policy perspective and, overall, the results demonstrate how

information on the potential impacts of climate change might be made more

accessible using GIS-based landscape visualisation techniques.

Nevertheless, a number of caveats are necessary. In particular, it is important

to recognise that the visualisations are no more than ‘plausible representations’

of possible futures and should not be interpreted too literally. A number of


assumptions, and no little creative interpretation, were involved in downscaling and

visualising the CLUAM output. Similar types of decision are inherent in the pro-

duction of almost any landscape visualisation (MacFarlane et al., 2004), but suchsubjectivity does mean that it is important for the data sources and production

process to be as transparent as possible if the products are to be regarded as credible

by their intended audiences (Sheppard, 2001). In this paper we have tried to be

thorough and clear regarding the approach taken, but we would be the first to

recognise that there are limitations in what we have done. Crucially, there need to be

improvements in the frameworks and methods for assessing climate change impacts

at the local scale, and there are encouraging signs of progress in this respect (e.g. see

Tyndall Centre, 2004; UKCIP, 2004).In terms of the development and use of landscape visualisations, there are several

ways in which the research described in this paper could be extended. One important

issue is the representation of uncertainty. This occurs in several different forms, one

aspect being the visualisation of boundary zones. Images such as Fig. 8 tend to show

quite abrupt and clearly defined changes in land use, whereas in the real world there

is frequently a visible degree of transition or fuzziness at a boundary (Fisher, 1999).

For instance, in the lowland regions of the UK it is increasingly common to have

uncropped areas at field margins due either to the requirements of agri-environ-mental schemes or simply machinery limitations and the impact of hedgerow wildlife

on peripheral seeds and young crops. Transitions are also important in less heavily

managed landscapes, where there may be a need to show different types of vegetation

merging gradually into one another (e.g. from open heath into varying densities of

scrub, and then into more mature woodland). Facilities do exist in some visualisation

packages (such as Terraffectors in VNS) to define symmetrical variations in land

cover either side of a linear feature (e.g. to represent roadside verges or river banks),

but creating asymmetrical transitions across polygon boundaries does not appearstraightforward at present. Overcoming this limitation will probably require a more

complete integration of GIS analysis tools (e.g. fuzzy membership functions as

discussed by Zhang & Goodchild, 2002) and visualisation software than has been

achieved to date.

Visualising uncertainty is also a key issue when the probability of particular

features being present in a landscape is likely to vary. Sets of images, such as those in

Figs. 8 and 9, can illustrate a range of possible outcomes, but in each view all the

landscape elements appear equally definite. In reality, some components of the scene(e.g. buildings and field boundaries) are more likely to be present in the 2020s than

others (e.g. particular crops in fields). Refining this aspect of the visualisations

obviously requires better understanding and prediction of climate change impacts,

but assuming such scientific knowledge does become available there is still the

question of how to best convey these differences in likelihood. Many different

techniques have been used to represent uncertainty in spatial data (Ehlschlaeger,

Shortridge, & Goodchild, 1997; Hunter & Goodchild, 1996; Kardos, Moore, &

Benwell, 2003), but some of these (e.g. blinking pixels or contrasts in colour satu-ration) do not appear especially suitable in the context of 3D landscape visualisation

because they would impair the realism with which features could be shown. The use


of paired images, with one depicting a landscape view and the other the likelihood of

different features being present, could be one way forward, and the scope for

employing animations (where duration in a time sequence is related to probability ofoccurrence) also merits investigation. At present, however, it seems likely that

considerable research will be necessary before operational solutions become

available.

Another way in which the research could be extended is through the provision of

more interactive landscape displays. Interactivity can be defined in many ways, but

essentially refers to the situation where a visual display changes in response to user

input (Crampton, 2002). Navigating through a virtual landscape is therefore one

example of interactivity and while this is not possible with the conventional VNSoutput used in this research, a newly released extension, SceneExpress, allows vi-

sualisations to be output to various real-time display formats including Virtual

Reality Modelling Language (VRML). Several other research projects have con-

verted GIS databases to VRML as a means of generating navigable virtual envi-

ronments (see examples in Fisher & Unwin, 2002) and software products such as

TerraVista (Terrex, 2004) now provide more sophisticated means of creating large

terrain databases optimised for interactive landscape display. Researchers are also

beginning to address interactivity in the sense of allowing users to move or alterlandscape elements in real-time (e.g. Stock & Bishop, 2002) and this enhanced

ability to pose ‘what if’ questions is important in facilitating greater stakeholder

engagement in decision-making processes. There is still some tradeoff between

providing interactivity and the degree of realism with which features are repre-

sented, but as the costs of high-end image generation and projection systems

continue to fall the use of real-time landscape display methods seems likely to

become much more widespread.

A final issue concerns the way in which landscape visualisations are used. It isevident that the techniques for generating 3D visualisations have advanced enor-

mously in the past few years (Hudson-Smith & Evans, 2003), but it is also increas-

ingly recognised that there is still much to learn about how best to apply such tools

for environmental management and planning purposes (MacFarlane et al., 2004;

Orland, Budthimedhee, & Uusitalo, 2001). Research is starting to provide insights as

to how the public perception of virtual landscapes is influenced by the realism with

which different features are represented (e.g. Appleton & Lovett, 2003; Lange, 2001)

and this is important in terms of developing guidelines for the production of suchvisualisations (Sheppard, 2001). With respect to climate change, there is also a need

to better evaluate how people respond to images of potential impacts (Nicholson-

Cole, 2004). For instance, to what extent does viewing such images actually motivate

individuals to change aspects of their personal behaviour and adopt decarbonisation

measures? Overall, therefore, it can be concluded that at present the development

and use of landscape visualisations representing the possible local impacts of climate

change is not without its problems, but the scope clearly exists for such images to

contribute to information communication, debate and policy formulation in thefuture.


Acknowledgements

The initial phase of this research was primarily funded by the Jackson Founda-tion, with additional resources provided by the NALMI project. More recent work

has been supported by the Economic and Social Research Council as part of the

Programme on Environmental Decision Making based at the Centre for Social and

Economic Research on the Global Environment (CSERGE) at the University of

East Anglia. We would like to thank John Terry (NALMI Project Officer) for advice

and two referees for some thought provoking comments. All maps are derived from

Ordnance Survey digital data, � Crown copyright Ordnance Survey. The provision

of these data is an EDINA Digimap/JISC supplied service.

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