Geoscience Reference
In-Depth Information
Representing such data on a map poses problems. Theoretically, the only valid
method of representing this punctual data on a map involves superimposing the
results of the measurements onto the precise area from where the measurement was
taken (Figures 2.1 and 2.5). However, this method of cartography does not provide
any further information about the distribution and quantity of rainfall in France.
This type of cartographic representation does not show any information on what
happens in the areas between the measurement stations.
2.1.2.
Geographic information systems (GIS) and spatial analysis
Spatial analysis, which is carried out by GIS, makes it possible to overcome the
problems described above. The principle here is that the information that is available
(the information type in question is the first point that needs to be examined)
contains both random variables and regularities. The term random is used here to
describe everything resulting from the inaccuracy of a measurement, from errors
that occur during the recording of the information and from the transmission of this
information. This random part of the information cannot be modeled. Conversely,
the term regularity can be translated by gradients and these can be modeled, if the
tools that are required to model them are available. The tools required to model the
gradients are the second point that needs to be examined. Variation in precipitation
linked to altitude is a trend that exists almost everywhere. Other trends also exist
and they need to be identified, and this is what the technique known as interpolation
aims to do. Interpolation makes it possible to recreate continuous spatial fields from
punctual information.
Spatial climatology requires that a piece of information be as complete as it
possibly can, and that the information be gathered and organized in such a way that
it can be used by specific tools for analysis. The results of any analysis could,
therefore, be used in agronomic models or represented on a map. The interpolation
of the climatological data belong to the GIS field (raster GIS to be more precise). A
raster GIS is the most suitable system that can be used because it can deal with the
problems that are associated with spatial analysis. The aim of this chapter is not to
provide an analysis of the interpolation technique, as there is a lot of material
already available that deals with this subject. The aim of this chapter is to provide an
understanding of the problems that arise when geographic information is used
during the interpolation of climate data. Other issues that this chapter will address
include: what spatial resolution is the most suitable for providing the best results?
How does one choose the best independent variable or variables? What method of
interpolation should be used?
- For example, is it best to have a piece of information rasterized at a rough
resolution (250 m, 500 m, or even 1 km), which is available on a large scale at a low
cost, rather than having a piece of information with a smaller resolution (50 m),
which is more expensive and difficult to manage if the area to be investigated is
large? What impact would this spatial information have on the results of climatic
interpolations? It is quite tempting to believe that the quality of the results is
