The use of geographical information systems in ... - CiteSeerX

The use of geographical information systems in climatology and meteorology Lee Chapman and John E. Thornes Climate and Atmospheric Research Group, School of Geography and Environmental Science, University of Birmingham, Birmingham B15 2TT, UK

Abstract: The proliferation of 'commercial off the shelf' geographical information systems into the scientific community has resulted in the widespread use of spatial climate data in a variety of applications. This paper presents a review of the role of geographical information systems in climatology and meteorology by i) discussing methods used to derive and refine spatial climate data and ii) reviewing the bespoke application of GIS and spatial climate datasets in agriculture, ecology, forestry, health and disease, weather forecasting, hydrology, transport, urban environments, energy and climate change. Key Words: geographical information system, climatology, meteorology, weather forecasting, digital terrain model, bespoke system

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I

Introduction

The early origins of GIS (Geographical Information Systems) can be traced to the influx of micro-computers into North America in the early 1960s (Bernherdson, 1992). Early GIS such as CGIS (Canadian GIS) and McIDAS (the US equivalent) were used to provide a simplified view of the real world by displaying digital spatial information as dynamic electronic maps. As GIS has developed, the definition of GIS as a spatial visualisation facility is too vague as any spatial display of information such as a simple weather chart or raster satellite image could be thought of as a GIS. Nevertheless, GIS has now evolved into a powerful management tool used for capturing, modelling, analysing and displaying spatial data (Worboys, 1995) and represents an amalgamation of database technology with computer assisted cartography (Bernherdson, 1992). Analysis is achieved across data layers in an object orientated programming environment allowing spatial variables to be statistically compared and thus producing new spatial datasets beneficial to range of applications. Climatological and meteorological phenomena are naturally spatially variable and hence GIS represent a useful solution to the management of vast spatial climate datasets for a wide number of applications. For the purpose of this review the use of GIS in climatology and meteorology is conceptually classified into two categories of usage. A distinction is made between the derivation of spatial climate datasets from their subsequent bespoke application (Figure 1). This dual role of GIS is discussed individually in more detail in following sections. DATA IN

RASTER GEOGRAPHICAL DATA E.G. DIGITAL TERRAIN MODELS

POINT CLIMATOLOGICAL DATA E.G. WEATHER STATION OBSERVATIONS

GEOSTATISTICS

DATA EXTRACTION EXTRAPOLATION

DATA ENHANCEMENT INTERPOLATION

1. DERIVATION

DATA OUT / VISUALISATION

DATA IN

GIS

SPATIAL CLIMATE DATASET

layer 1

OTHER GEOGRAPHICAL DATA

layer 2

OTHER GEOGRAPHICAL DATA

layer 3

OTHER GEOGRAPHICAL DATA

layer n

MODELLING / ANALYSIS 2. CUSTOMISATION

DATA OUT / VISUALISATION

GIS

TERTIARY NON CLIMATOLOGICAL DATA

Figure 1 Conceptual model of the dual role of GIS in climate and meteorology. GIS is a useful tool to aid in the derivation of climate datasets which are then used in a variety of tertiary applications. 2

II

Derivation of spatial climate data

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Remote sensing

Distinctions between the two disciplines of GIS and remote sensing are difficult to determine as the two subjects are intimately related. Remote sensing enables the acquisition of large-scale comprehensive datasets where as GIS provides a means to display and analyse the data. For example, Digital Terrain Models (DTMs) can be manipulated in a GIS to provide a baseline climatological dataset. Traditionally these were derived using land-surveying techniques but are now remotely determined using Synthetic Aperture Radar (e.g. the UK LANDMAP project; Anon 2001). Comprehensive raster climate datasets can also be inferred from satellite imagery. For example, Schadlich et al (2001) produced land surface temperature maps by combining a DTM with brightness temperatures derived from METEOSATs thermal infrared channel. Similar approaches have been used by Verdebout (2000) and El Garounani

(2000) to generate surface ultra-violet

maps of

Europe and

evapotranspiration maps of Tunisia respectively. Distinctions need to be drawn between the two disciplines of GIS and remote sensing as often there is no need to use 'commercial off the shelf' (COTS) GIS for image analysis. Indeed, many 'pure' remote sensing applications utilising just image data require no more than a means of displaying the results obtained. A classic example of this is provided by satellite rainfall climatology (e.g. Levizzani et al, 2001). However, the need for synergy between the two subjects becomes evident for a wide number of other applications, particularly those utilising input data from a variety of sources. Here, GIS provides a standard means of overlaying and combining data for analysis. For example, Nichol (1995; 1994) combined geographical vector data with surface temperatures derived from Landsat TM thermal imagery to explore the spatial characteristics of forest canopy temperatures with elevation and landuse in Singapore. The study served as the preliminary investigation of a monitoring exercise in an attempt to conserve the remaining 5% coverage of rainforest on the island. Landsat TM data was also used by Suga et al (1995) where it was combined with NOAA/AVHRR data to monitor sea surface temperature change in the Sea of Japan via a GIS.

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Baseline climatologies

Climate data can be displayed in a GIS in a variety of ways; lightning strikes are point features, rain radar is raster (gridded) and isolines are vector. Climate data is typically point source in nature, meaning that one of the biggest challenges facing meteorology is the extrapolation of point climate data across a wide spatial domain. The extraction or extrapolation of climate data using DTMs has enabled good estimates of an area's baseline climatology without the need for extensive consultation of weather records. For example, high elevation mountainous environments suffer from a lack of frequent observations and hence, the development of techniques to infer climates from a sparse network of weather stations is highly advantageous. Over the past decade, DTMs have become increasingly available to the degree that high resolution models of many areas of the world, are available for free to the academic community. Raster DTMs are then simplified into TINs (Triangular Irregular Network) from which the microclimate of each triangle or facet is readily calculated via a variety of simple algorithms (Bernhardson, 1992). A good recent example of this approach is the PRISM (Parameter-elevation Regressions on Independent Slopes Model) project in the USA which has successfully been used to compile a series of high quality spatial datasets around the world (Daly et al, 2000; 1994).

PRISM is a knowledge based climate analysis system that

generates GIS compatible estimates of climate variables accrued from a variety of sources including point climate (station) data, DTMs and other spatial datasets. By using a co-ordinated set of rules, decisions and calculations, extensive gridded estimates of precipitation and temperature can be made with respect to the different climate regime (distance, elevation, atmospheric boundary layer, hillslope orientation and proximity to the coastline) of each DTM facet. However, PRISM is not just an empirical climate approximation tool, but is ultimately a two-layer model of the boundary layer and the free atmosphere. The depth of the boundary layer is variable to model the development of temperature inversions and the maritime influences on precipitation. Agnew & Palutikof (2000) developed a multiple regression model using a 1km resolution DTM to analyse the variation in geographical parameters (latitude, altitude, continentality, slope, aspect and ratio of land to sea). The model was less robust than PRISM as it needed to be initialised with 248 temperature and 285 rainfall sites to infer the variation in mean seasonal temperature and rainfall across the Mediterranean 4

basin. However, the results were highly accurate attaining a typical explanation of 87% (summer) and 97% (winter).

Comparable results were also attained in an

independent study by Ninyerola et al (2000) where a multiple regression analysis on topographical variables in Catalonia yielded coefficients of determination of the order 79-97% for temperature and precipitation. Polynomial regression was the approach utilised by Goodale et al (1998) who modelled monthly precipitation, temperature and solar radiation in Ireland.

Again the technique was highly accurate with mean

absolute errors of 5-15mm for precipitation, 0.2-0.5 degrees for temperture and 6-15 minutes for sunshine hours. The examples discussed so far have been large or macro-scale. However, the high resolution of modern DTMs allow the study of the impacts of terrain on climate at meso or micro-scales.

Examples include determination of solar radiation

topoclimatologies (Dubayah et al, 1994; Moore et al, 1993), modelling the sensitivity and response of mountainous terrain (delBarrio et al 1997) and development of baseline island climatologies. For example, de Azevado et al (1999) used advective and radiative submodels in a GIS to extrapolate climate data obtained at sea-level across the whole of small volcanic islands. 3

Climate interpolation

When dealing with more spatially comprehensive climate datasets, the issue is not the inference of a 'first approximation' baseline climatology, but instead the interpolation of point station data across the landscape by geostatistical techniques (e.g. Tveito et al, 2001).

Splining is a deterministic spatial regression technique which fits a

mathematical function or 'rubber sheet' across irregularly spaced data. Lennon & Turner (1995) used thin plate splines determined from a DTM to model the climatic distribution of temperature in the UK. 16 independent geographical variables were used in the splines and were shown to be more accurate than basic interpolation techniques such as multiple regression. They concluded that just 30 temperature recording stations would be sufficient to model temperature variation in the UK. Thin plate smoothing splines were also used by Fleming et al (2000) in the derivation of an Alaskan baseline climatology from a sparse met station record. Kriging is another common interpolation technique used in spatial climate studies. Unlike splining, the technique is stochastic and requires some user input, but has the same aim of fitting a surface to irregular spaced data. This is achieved by using 5

variograms to analyse the tail structure of variables in given directions to build up a map of spatial variation from a small sample of datapoints. Kriging was used by Hudson & Wackernagel (1994) to map mean January temperatures in Scotland and by Jeffrey et al (2001) to interpolate daily and monthly rainfall between 4600 weather stations in Australia. However for interpolation of other climate variables a thin plate spline was used. The accuracy of the two techniques was tested in a study by Jarvis & Stuart (2001) who interpolated minimum and maximum temperatures for 1976 at a 1km resolution over England and Wales. They discovered that thin plate splines were more accurate than kriging, with RMS errors of the order of 0.8°C for minimum temperatures and 1.14°C for maximum temperatures. Finally, A relatively new method of interpolation is the application of neural networks.

Antonic et al (2001) derived an empirical model for seven climatic

variables via a neural network. The model typically explained 98% of the variation in climatic parameters, which was improved further by kriging of the residuals for model correction. A feed-forward back propagation neural network was also used by Rigol et al (2001) which considers both trend and spatial associations of climatic variables. Performance of the network was comparable to that achieved with kriging, but has the advantage that guiding variables (such as terrain) do not need to be linearly related to the interpolation data. III

Applications of spatial climate data

Several examples of methods in which spatial climate datasets can be derived have been discussed.

The advantage of spatial climate datasets is that they can be

compared in a GIS with dissimilar data accrued from many sources. Hence, GIS has enabled the environmental impact of the variation of climate to be studied for many applications at a variety of scales. This section outlines some of the many tertiary applications of spatial climate datasets.

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1

Agriculture

In much the same way as spatial climate datasets are derived, GIS has massive potential for agroclimatic modelling (Jurisic et al, 1999). From a GIS, maps can be produced and combined of soils, nutrients, climate, water stress, fertility and predicted yield. An early example of this capability is provided by Soderstrom & Magnusson (1995) who produced an agroclimatic assessment of an area of south-western Sweden. Radiation mosaics were calculated using a DTM and cold air drainage modelled via a network analysis tool. This information was then combined in a GIS with kriged data from mobile temperature surveys to produce the final map. McKenny et al (2001) used thin plate splines to model climatic gradients in Canada to determine plant hardiness zones. By using a trivariate position of latitude, longitude and elevation, maps of temperature and rainfall enabled the mapping of each variable required for plant hardiness formulae at a 1km resolution. By incorporating temperature and aridity thresholds, agroclimatic models can be logically adapted to be species specific. For example, Menkir et al (2000) identified four potential agroecological zones for the growing of Maize in West and Central Africa, where as Panigrahy & Chakraborty (1998) used temporal remote sensing data along with spatial soil, rainfall and temperature data to derive a potato growing index for West Benghal. They discovered that currently 37% of the agricultural area is used for potato crop cultivation but concluded that a further 48% of agricultural area was suitable for potato crop intensification via a crop rotation system. GIS agroclimatic modelling is not just limited to agricultural zoning. Hill et al (1996) use SPOT and Landsat TM satellite imagery, climate, edaphic and topographic data along with a simple bioclimatic model to analyse the pastoral limit of cattle grazing in New South Wales, Australia. The variables used to locate species can also be used to provide estimates of yields via crop simulation models. For example, Priya & Shibaski (2001) use interpolated climate data to drive their model where as Kravchenko et al (2000) inferred climatic impacts from a DTM. Physiological models are particularly useful to predict yields when crops have been subjected to prolonged stress, for example, drought (e.g. Lourens & deJager, 1997), cool summers (e.g. Yajima, 1996) and disease (e.g. Hijmans et al, 2000). The success of such models can then be tested by using remote sensing techniques (e.g. Carbone et al, 1996)

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GIS is also used in agriculture to monitor biogenic emissions and agricultural pollution / water stress. Andronopoulos et al (2000) modelled the transportation of biogenic volatile organic compound emissions with respect to the sea breeze for the east coast of Spain. Benjamin et al (1997) estimated biogenic emissions in California by combining a biomass inventory with emission rates corrected by light intensity, canopy shading and temperature.

A more specific example is provided by the

measurement of biogenic emission from rice fields where GIS crop simulation models are coupled with daily weather data to measure methane (Knox et al, 2000; Matthews et al; 2000).

Finally, rainfall data can be used to estimate leaching effects of

agricultural fertilisers into water supplies (e.g. Udouj & Scott, 1999; van Wesenbeeck & Havens, 1999; Wu & Babbcock, 1999) 2

Ecology

In much the same way as potential crop distributions can be modelled using GIS based agroclimatic models, ecological biodiversity can be modelled with respect to spatial climate datasets. For example, Jones et al (1997) used latitude, longitude and altitude data coupled with long term monthly means of rainfall and temperature to model 'bean-favouring' climates. The GIS approach to modelling biodiversity has been successfully used in many other studies; Birnie et al (2000) modelled bracken spread in Scotland, Guisan & Theurillat (2000) used a DTM coupled with satellite data to model alpine plant distributions, Kadmon & Danin (1999) studied the distribution of plant species in Israel with respect to rainfall and Franklin (1998) predicted the distribution of shrub species in southern California with respect to bioclimatic attributes derived from terrain.

Although these examples essentially

concentrate on flora distributions, the same ideas can be applied to fauna. Examples include the Portuguese dung beetle (Hortal et al, 2001), the New Zealand flatworm (Boag et al, 1998), land-snails (Kadmon & Heller, 1998), threatened butterflies (Weiss & Weiss, 1998) the effect of different wind speeds and directions on albatrosses (Reinke et al, 1998) and even the impact of sea surface temperatures on fish distributions (Waluda et al, 2001; Zheng et al, 2001).

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3

Forestry

As in agriculture and ecology, GIS can be used to produce climate zones to select site suitability for afforestation (e.g. Ellis et al, 2000) or used to predict yields (e.g. Valdes et al, 1994). However, GIS is used in forest science for many other applications. For example, DTMs are used to analyse and highlight forested areas potentially exposed to wind-snap. Lekes & Dandul (2000) used a GIS containing soil and terrain data with an airflow model to evaluate the wind exposure by producing a wind damage risk classification.

Analysis of more extreme events has been accomplished by

Pleshikov et al (1998) who modelled the impact of a hurricane on pine stands in Central Siberia and Foster & Boose (1992) who used GIS to analyse the spatial distribution of wind damage and rank the susceptibility of particular forest types. Frost prediction is a further example of the use of GIS in topoclimatic forestry studies which is important with respect to seedling mortality (Blennow & Lindqvist, 2000). By modelling the stagnation of cold air via a DTM coupled with sky-view factor data relating to the forest canopy, Blennow (1998) explained 89% of the spatial variation of air temperature. At the opposite end of the spectrum from frost is the major hazard of fire. GIS is used to associate climate data with remote sensing imagery where it is used to model and monitor the spread of forest fires (e.g. Pew & Larsen, 2001; Sunar & Ozkan, 2001; Vazquez & Moreno, 2001; Zhu et al, 2000). After fire, Belda & Melia (2000) use a GIS integrated with remote sensing techniques to model forest recovery. By analysing the influence of climatic parameters in the regeneration of forest areas, spatial variations in the amount of vegetation could be predicted. 4

Health and disease

Vector-borne infections are geographically restricted by climate and topography and can be modelled effectively using remotely sensed climate datasets with GIS and global positioning system technology (Bergquist, 2001).

Typical examples of

application are in the developing world and include malaria (Srivastava et al, 2001; Manquin & Boussinesq, 1999), lymphatic filariasis (Lindsay & Thomas, 2000) and schistosomiasis (Bavia et al, 2001; Malone et al, 2001).

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Weather forecasting

Conceptually, visual weather forecasts combine layers of weather data in what is effectively a GIS environment. GIS has become a key management component in weather processing systems allowing instantaneous plotting, interpolation and animation of weather data across any isobaric level of the atmosphere. The synoptic situation across different levels is then gauged by a forecaster, from which the GIS is used to rapidly calculate the speed of progression of weather systems. An extreme example of this is the relational positioning and monitoring of tornado's and tropical cyclones, where GIS is used to issue warnings to precise locations using remote sensing signatures (Kumar et al, 1998). An alternative use of GIS is the combination of different layers of weather information in expert classification systems.

For

example, specific humidity is often compared with wind flow to identify areas of fog, cloud and precipitation in relation to orographic and coastal influences. Similarly, the spatial offset of rawinsonde data (normally plotted at the location where the balloons have been released), can be calculated by superimposing layers of upper wind data. As advances are made away from conventional methods of synoptic forecasting, interpolated climate datasets are used to set the boundary conditions for numerical weather prediction such as mesoscale forecast models and general circulation models. For example, Cheng & Shang (1998) use a GIS to manipulate topographic and roughness data to model wind fields using a numerical kinematic flow model. However, difficulties exist with numerical models in the assimilation of interactions between the land surface and the upper atmosphere. To some extent, GIS has greater facilitated the incorporation of numerical weather model output into weather processing systems, onto which satellite imagery and topography can be superimposed; an approach which greatly aids the skill of the weather forecaster. However, GIS is not just used as an end-point in weather forecasting, Gabella & Perona (1998) use a DTM coupled with geometric optics to assess the siting of weather radar where as Pfister & Fischer (1995) studied the influence of roughness length calculated from terrain on the vertical sounding of temperature profiles. Overall, GIS partially automates forecasting by facilitating speed and throughput of weather data in real-time as well as providing support for traditional weather processing tasks such as contouring and superposition.

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Hydrology

Hydrometeorological modelling provides a good example of how COTS GIS products can be used with meteorological and other detailed datasets to develop bespoke systems with the specific aim of the end user in mind. The spatial measurement of precipitation is an obvious starting point for many hydrometeorological models. Again, DTMs and climate interpolation techniques provide a useful means of producing rainfall datasets from point data obtained from raingauges techniques (e.g. Tsanis & Gad, 2001; Prudhomme, 1999). However, the use of interpolated gauge data is largely restricted to validation studies such as agricultural water management (e.g. Sousa & Pereira, 1999) or for studying the hydrological impact of various climatic scenarios. For example, spatial rainfall patterns derived from point data have been used to model the hydrological response to ENSO in Australia (Wooldridge et al, 2001) and the seasonal variability of the Indian monsoon (Wilk & Andersson, 2001). For hydrological forecasting purposes, the alternative and preferred methodology of data acquisition is the use of satellite data. Bell & Moore (1998) used raster radar rainfall estimates combined with diffusion models across isochrone pathways derived from a DTM for use in real time flood forecasting. Similarly, Carpenter et al (1999) used weather radar coupled with a DTM to model threshold flash flood runoff. This is achieved by using a GIS to model the contributing area to stream segments categorised using the similar hydrological response unit concept (Gorokhovich et al, 2000). Radar provides real-time estimates of precipitation, but to achieve forecasts at an increased timescale, mesoscale weather forecast models need to be incorporated into the system. For example, Yarnal et al (2000) simulated the hydrological response of the Susquehanna river basin, US to atmospheric forcing. This was achieved by using a linked system of a mesoscale meteorological model with grid increments of 4km to drive the hydrological modelling system which contained information layers regarding soils, terrain and landuse. A comparison of these different techniques was conducted by Taschner et al (2001). The study in the Ammer catchment in Germany, showed that mesoscale model data and rain radar overestimated flood volume by 15 36% where as interpolated raingauge data underestimated runoff volume by 15%. Depending on the region of study, models may require additional input data to adequately model local hydrological regimes. So far, this review has concentrated on flood analysis, but the opposite extreme is that of drought. Ghosh (1997) used a GIS 11

to investigate the distribution of drought in India by comparing albedo and vegetation derived from satellite data with isoheytal maps based on 70 years of rainfall data. In such (semi-) arid environments, an important parameter to model is potential evapotranspiration. This is estimated by overlaying temperature and DTM-derived slope, aspect and elevation data (Shevenell, 1999).

The resulting maps tend to

inversely mimic topography. Topography is also important in snowy environments where there is a need to incorporate the influence of snowmelt into hydrometeorological models. The first step in estimating snowmelt is to assess variations in the distribution of snow caused as a consequence of strong winds and terrain (Bruland et al, 2001; Tappeiner et al, 2001). Lapen & Martz (1996) used a 10m resolution DTM to show that snow depth is more closely related to relative topographic position as opposed to local morphology. Modelled results can then be validated with satellite imagery such as Landsat TM (e.g. Fily et al, 1999). Once snow distributions are clearly delineated, values of snow water equivalence are calculated. As snowmelt is dependent on available energy controlled by the elevation, aspect and shading of site, DTMs coupled with a temperature threshold can be effectively used to provide estimates (Cazorzi & DallaFontana, 1996). Examples of this approach have been undertaken by Bell & Moore (1999) and Cline et al (1998) who developed models for upland Britain and the Sierra Nevada, California respectively. Overall, hydrometeorological models provide an example of how COTS GIS can be successfully adapted into commercially viable bespoke products.

However, the

success of such products is extremely dependent on the ability to operate in real time, a void recently filled by the internet which has facilitated efficient data transfer. Indeed, internet based GIS software products now exist which allow spatial outputs to be viewed by an end user who could be totally inexperienced in GIS. 7

Transport

Bespoke systems are also being developed to aid decision-making and to set budgets for winter road maintenance.

For example, Gustavsson et al (1998) present a

technique to assess potential winter road maintenance costs to be taken into consideration when planning new road stretches. Similarly, Cornford & Thornes (1996) developed a spatial winter index by using kriging with altitude as external drift to predict spatial expenditure on winter road maintenance in Scotland. The decision 12

whether to salt a road or not often has to be made short notice, and hence continuous updates of how road conditions are varying around a road network are required by maintenance personnel.

Hence, research is ongoing into the incorporation of

mesoscale forecast models into GIS to extrapolate road conditions across a region. For example, Chapman et al (2001) numerically model road surface temperature using weather data and a geographical parameter database consisting of latitude, skyview factor, screening, altitude, topography, road construction, surface roughness and traffic (anthropogenic heat). These parameters are combined in a numerical model which predicts up to 72% of the variation in road surface temperature across a study route to within 1.06°C RMS.

Bradley et al (2002) used a spatial analysis of

topography and classified Landsat imagery to model the impact of the urban heat island on road surface temperatures in the West Midlands, UK. Winter road maintenance provides one example of how GIS can be combined with weather data to solve a logistical problem. Li & Eglese (1996) used a GIS to devise an heuristic algorithm to optimise winter salting routes with respect to minimising the distance travelled and treated by gritters within the temporal framework of the time roads need to be treated. Similar ideas are utilised by Moore et al (1995) and Patel & Horowitz (1994) who use GIS to develop optimal and specific routing for radioactive and hazardous materials.

This is achieved by combining spatial information of

meteorology, demography and dispersion (i.e. wind-speed, toxicity and size of spill) with vector road data. 8

Urban environments

Pollution is a major problem in urban environments and the largest contributing factor is traffic. Hao et al (2001) produced a GIS based emission inventory for Beijing, China. By using a Gaussian dispersion air quality model, it was shown that vehicle sources contributed 76.5% and 68.4% of the total CO and NOX emission totals. Emissions are modelled using traffic counts and empirical equations, for example, Mensink et al (2000) modelled CO, NOx, VOC, Phl, SO2, and Pb emissions in Antwerp as a function of ambient air temperature with six road and vehicle parameters. New bespoke technologies are constantly being developed to improve the accuracy of incoming information. Global positioning systems interfaced in a GIS can now be used to monitor traffic (Taylor et al, 2000) and high performance 3D GIS

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models of air pollution and traffic simulation are being developed (Schmidt & Schafer, 1998; McHugh et al, 1997; Moreselli et al, 1997). For example, Zakarin & Mikarimova (2000) numerically model urban air pollution using GIS as an interface; a common approach of displaying emissions inventory data produced using dispersion models (e.g Fedra & Haurie, 1999; Prabha & Mursch-Radlgrubber, 1999). Other than traffic, further sources of urban pollution exist as a result of human activity. Chang et al (1999) used a 3D diffusion model displayed in a GIS for use in risk assessment studies in industrial areas of Taiwan, where as Romero et al (1999) studied the impacts of rapid urban growth and surrounding topography impacted upon air pollution in Santiago, Chile. Other than pollution, climate modelling studies involving GIS in urban environments are mostly all directed at studying the urban heat island phenomenon. The common approach is to integrate remote sensing data with GIS to produce an appraisal of how temperature is spatially controlled by landuse (Lo et al, 1997). GIS can then be used further to monitor the impacts of urban growth. For example, Weng (2001) found that urban development in the Zhujiang Delta, China could account for increasing surface radiant temperatures by 13°C.

GIS has also facilitated increased study into the

vertical structure of the heat island phenomenon (Nichol, 1998) which is then used for planning applications such as climate control in high rise buildings in tropical cities. The development of a planning tool was also the aim of Scherer et al (1999), who used GIS to produce a series of climate maps documenting the influence of surface properties on temperature, wind fields and ventilation for Basel, Switzerland. 9

Energy

Temperature and humidity are the primary factors controlling energy demand; the hotter it is the less energy is required, hence transmission operators monitor the weather to efficiently manage production. Estimates of demand can be made by considering degree days (Hargy, 1997), although other environmental indicators are more commonly used for the energy planning of urban areas (Balocco & Grazzini, 2000). GIS is used to monitor the entire infrastructure of energy provision. As well as being an invaluable tool to match demand with supply (Sorenson & Meibom, 1999), real-time lightning and storm data is also incorporated into decision support systems to track potential problems along transmission lines.

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As fossil fuel supplies diminish, research is ongoing into the increased application of renewable energy. GIS has been used to aid the locations of wind farms by modelling wind energy potential whilst considering planning limitations (e.g. Baban & Parry, 2001; Hillring & Krieg, 1998). By linking satellite data with a GIS, optimisation of other renewable resources can be achieved. For example, this technique is used to identify and monitor biomass energy resources (Phillips et al, 1992) and to estimate solar resources such as potential downward radiation, cloud regimes and albedo. Solar information is then matched with population data in a GIS to model supply and demand (Sorenson, 2001), before being used to site thermal power plants (Broesamle et al, 2000; Vandenbergh et al, 1999). Overall, the role of GIS is seen as critical into increasing exploitation of renewable energy sources, particularly biomass, which is seen as an essential component in reducing global carbon emissions from the energy sector (Schneider et al, 2001). 10

Climate change

Many if not all of the research discussed in this review is potentially subject to the impact of climate change. GIS has become a visualisation tool for the output of climate models such as general circulation models used to predict the global impacts of hypothesised climate change scenarios.

Many articles exist in the scientific

literature which are far too numerous to list here, however good examples of potential effects modelled with GIS include changes to agricultural and ecological distributions (e.g. Davies et al, 1998; Eatherall, 1997), varying public health implications (e.g. Patz & Balbus, 1996), increases in landscape sensitivity (e.g. Collison et al, 2000; Thumerer et al; 2000) and varying pressures on hydrological resources (e.g. Strzepek & Yates, 1997). Hence, the assessment and monitoring of the effects of climate change is truly a multidisciplinary exercise of which GIS provides a pivotal unifying role (Kozoderov, 1995; Din, 1992).

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Although, many of the predicted impacts of climate change are ultimately hypothesised as future events, GIS has already been used to present evidence of environmental change. For example, Chen (2001) showed that tree diversity was changing in north-east China and Jorgenson et al (2001) demonstrated evidence of widespread permafrost degradation in Alaska. However, few monitoring studies are currently evident in the scientific literature, which perhaps indicates the alarmist tendencies of projected climate change scenarios. Continued monitoring with GIS using data from satellite earth observation will ultimately confirm or disprove current thinking. Either way, future studies are heavily dependent on data intensive GISbased spatial analysis. IV

Conclusions

Over the last decade, research has greatly increased into the use of GIS in a variety of applications involving the processing of climatological and meteorological data. GIS can be used for deriving and enhancing point weather data by the use of DTMs, or alternatively used as a spatial input dataset to provide boundary conditions for a wide number of tertiary applications. Reasons for the upsurge in use of GIS is largely related to the fall in price of COTS GIS products coupled with large advances in computer processing ability. Add to this the proliferation of the internet and the result is a fast real-time bespoke solution for many end-users. Commercial interest in such products is massive, be it for risk assessment (e.g. storm tracking) or for mitigation purposes (e.g. insurance claims from flooding and fire). As computer systems become increasingly capable of handling the high resolution datasets provided by 21st century earth observation techniques, the future of GIS in climatology and meteorology research is virtually assured. The advent of COTS products has facilitated the development of standard formats for spatial weather data with GIS providing the management tool.

The manipulation of spatial data by

meteorologists and climatologists has never been easier. Acknowledgements This work has been funded by the EU COST 719 directive to increase the use of GIS in climatological and meteorological research.

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