Geography Reference
In-Depth Information
Fig. 1 Screen shot of temperature time series data in Google Earth displaying changes in mean
annual average temperatures between the year 2000 and 2050
of spatiotemporal changes in temperature and rainfall across Victoria. End-users
could use the Google Earth slider bar to select a year of interest and examine the
spatial variations of the selected variable as illustrated. In order to improve the
readability of the displayed spatiotemporal data, a graduated colour legend ramp
was used. This was produced in ArcGIS and was added as a KML screen overlay.
The
spatiotemporal
temperature
change
data
visualisation
across
Victoria
is
illustrated in Fig.
1
.
A Python script was written to automate raster data calculation and export
routines, allowing the process to be easily repeated with alternate climate datasets.
This mode of communication of complex scientific modeling outputs presents
advantages over conventional data communication methods such as static maps or
two-dimensional animations. The KML output viewable via Google Earth
provided the capacity to investigate different locations and zoom in and out of
regional areas during the animation or for a specific time step, thereby increasing
the relevance of model outputs at the regional and local scales. However, the value
of time series image overlays in isolation is limited as it only allows users to view
the data without the possibility of querying the underlying dataset (for example,
to extract the exact temperature/rainfall values at specific locations). The user also
needs to be aware that as one zooms into the farm or paddock level the uncertainty
of the forecast climate data increases. Unless such data is calibrated with a specific
weather station or other finer-scale data its relevance is predominantly for regional
decision making.
