1 Spatial Modelling of the Terrestrial Environment - Georeferencial

266 Spatial Modelling of the Terrestrial Environment
of snow depth observed from space and point measurements on the ground are compared.
While there are similarities evident in the computed variograms, we are still uncertain of
the spatial variation characteristics of snow depth beyond those that can be measured with
a sparse network of points on the ground or from coarse resolution satellite observations.
As Burke et al. point out, downscaling methods using statistical techniques or land surface
modelling techniques can address this scaling issue but they are not straightforward.
An example of a science community attempting to improve our understanding of the
spatial variability of environmental parameters is the Cold Lands Processes Experiment
(Cline et al., 2001). This was a two-year field experiment in Colorado, USA, conducted
during the winters of 2002 and 2003 and designed to improve our fundamental understanding
of the hydrology, meteorology, and ecology of the terrestrial cryosphere. It is a
process-oriented experiment that seeks to address several key questions relating to mass
and energy dynamics in the cryosphere and the spatial variability of these dynamics. The
ultimate goal of the project is to provide a comprehensive benchmark data set that can
help refine models of cryospheric processes and define the nature and scale of observations
required from remote sensing instruments that could be used effectively to quantify key
cryospheric processes (snow cover, frozen ground, etc.). The emphasis is firmly on spatial
variability of processes and how processes scale up or down. This information should then
be used to inform modellers and technologists alike to which spatial scales their models
and instruments should be calibrated.
13.3 Outlook
The future of spatial modelling of the terrestrial environment, therefore, appears to look
very promising. The spatial resolution issue is related to our understanding of the scaling
of processes and different research communities have shown great coherence in addressing
this issue (e.g. the cryosphere, soil moisture, rainfall and vegetation monitoring communities).
The production of increasingly accurate DEMs (both in the vertical and horizontal
dimensions) is ongoing and the global characterization of vegetation from enhanced satellite
instruments is constantly being improved. These advances are the result of coherence
of the research communities at the international research community level and will help
pave the way for the important enhancement of improved land surface models, which will
enable us to better parameterize and constrain land surface conditions for global environmental
models. If we are to be able to predict future environmental trends in our world with
increased accuracy, spatial models of land surface processes (in both natural and human
environments) are a vital part of that effort.
References
Casti, J.L., 1997, Would-be Worlds: How Computer Simulation Is Changing the Frontiers of Science
(Chichester: John Wiley and Sons).
Cline, D., Armstrong, R., Davis, R.E., Elder, K. and Liston, G., 2001, NASA Cold Land Processes
Field Experiment plan 2002–2004, NASA Earth Science Enterprise: Land Surface Hydrology
Program, NASA: Washington, DC.