__________________ ____________________  

Measuring the Abilities and Predicting the Future of


Computer Modeling As Applied To Gcms
 When trying to predict the activity of a forest system
there are many variables to be considered. Not only must
stimuli be monitored and predicted on a wide range of
structures, i.e. from the organelle to stand levels, but
the sheer number of the stimuli involved is dizzying. This
incredible number of stimuli make it impossible for any
model to ever weigh all of their effects. Thus, a model's
creator must pick and choose between the stimuli trying to
choose those that have the most impact on the population
When considering soils one must take in to account levels
of various nutrients, hydrogen ion levels, poison levels,
the type of soil, water content, the soils susceptibility
to change, its structure, and the varying structures at
different depths. Weather variables that must be considered
include the changing of seasons, changes in sun activity,
precipitation levels, acid levels in precipitation, wind
patterns, and temperature variance. Animal impact must be
considered also. The effects of insects and animal
destruction (such as snowshoe hare feeding on willow
seedlings in the winter) can have a lasting impact on a
population. Not only do animals have an impact on their
environment but they also act as a reliable indicator of
biological change. The effects that a change in the
environment have on a species can be reliably measured and
a new field of study called zooindication has recently
formed. Through zooindication the impacts of industrial
pollution and climate change can be monitored.(Krivolutzky,
1985) Human intervention can have the greatest impact on a
population but it is very difficult to consider trends such
as human caused forest fires, forest harvesting, and urban
and rural development. However, trends of some of the
byproducts of industrialization can be monitored in the
form of pollutants in the airways and waterways. Worse yet
is the huge impact that the various stimuli can have on
each other. Cooling trends can lead to increased snow
levels. Increased snow levels themselves cause increased
mortality rates and deprive animals of underbrush to eat.
They in turn must take to eating the bark of trees to
survive and thus increased tree mortality results. The
spread of a city can result in the direst removal of
forests while the construction of buildings calls for more
trees to be harvested for building materials. The increase
in factories and city wastes can pollute the air streams
and waterways and effect forests downstream and downwind.
This is not all, however, as weather patterns and
stream/river conditions decide how far these impacts are
felt and to what extent. Among the many techniques used to
monitor and predict climate change general circulation
models (most often referred to as GCMs) are the most
sophisticated as well as most popular. GCMs are global
vegetation dynamics models and predict the kinds and rates
of changes in global vegetation communities in response to
climate change.(Dale, 1994) GCMs are the only type of
climate models that include the physical and geographical
detail required for long-term analyses of climate impact on
a regional level. The results from a GCM are used to
evaluate the effect of a given climatic change on resources
such as agriculture, forests, and water resources.(Cushman,
1988) There are however major limitations to the GCMs in
use to day. These limitations show up on environmental,
organizational, and technological levels. It is the
shortcomings in the definition of GCMs presented in the
previous paragraph, however, that show the environmental
limits of models past and present. These shortcomings are
the shortage of information on the interplay between the
weather and the effected systems. Not only are agriculture,
forests, and water resources sensitive to climate, but
climate is sensitive to the conditions of agriculture,
forests, and water resources. Taking this a logical one
step further water resources are dependent on agriculture
and forests and so on and so on. The problem with most of
these models is that they have been "stand-alone" in
nature, which makes it difficult to introduce
inter-dependencies amongst them in a reliable
manner.(Bindingdale, 1995) In other words it is the
feedbacks that are so hard to calculate and cause many
problems. In the area of global warming, for example,
warmer temperatures result in the atmosphere holding more
water vapor. Water vapor is a powerful greenhouse gas and
can further increase global warming. On the other hand,
increased cloud formations can decrease temperatures. Add
into this the observation that increased CO2 could result
in increased plant growth, and thus decreased temperatures,
and you have a very complicated set of interactions.(PUC,
1994) Taking the step to implement biological factors with
those of weather and geography is not an easy step; nor is
it the final solution. Other environmental shortcomings of
present systems that must be addressed are the impact of
many of the stimuli mentioned earlier; most notably those
of human origin. Biophysical models estimate potential, not
expected, vegetation levels. These estimates do not take in
to account the human drains on land and water resources to
produce food, forest products, habitation, transportation,
and industrial products. It is obvious that this human
impact has greatly changed the shape and look of the land
over the last few hundred years. We as humans have
consciously manipulated the landscape and its ecosystems
for human use. These changes result in the introduction of
non-native species, the destroying of others, the
deposition of airborne and waterborne pollutants and
nutrients, and the exhaustion of natural resources.
Pollutants in particular, especially the combined effe s of
various pollutants as well as the combined effects of
pollutants and existing conditions and nutrients, are an
area that is less studied than the limits of the
problem.(Kozuharov, 1985) The resulting lack of realism in
the models is not necessarily a result of the failure of
the biophysical model but it is a clear indicator that
nonbiophysical factors must also be taken into
account.(Frederick, 1994) The next set of problems with
current modeling methods is coordinational in nature and
involves the assimilation of the widely varying types and
source of data. The interfacing of geophysical and
ecological data is the coordination and combination of data
from widely varying sources for the purpose of modeling at
various scales. The data being interfaced can be products
of a single integrated study or can be taken from several
studies performed at various times and places using various
methods. It is not necessary that the data in question was
collected with integration in mind, however the process of
integration is very much complicated when considering
varying observation techniques, purposes, levels of detail,
and degrees of accuracy. The data available for analysis is
often inconsistent and adds its own margin of error to the
equation. As a result the ways in which the data is
integrated area often ill-defined and constantly
changing.(NRC, 1995) At its most basic level interfacing
involves the identification, reading, and combination of
data. However, in practice these simple concepts are
technically complex, stretching the limits of existing
knowledge to its limits. In addition, the very act of
interfacing frequently requires crossing disciplinary,
administrative, and international boundaries, thereby
adding another level of complexity to the process.
Interfacing efforts can be confounded by a variety of
obstacles. The challenges facing global change research are
extreme because of the massive volume of data, the
geographic scale, the scope of modeling efforts, the number
of organizations involved, and the ever changing nature of
the research itself. To solve these problems, or at least
cope with them, there are a number of steps that can be
taken. The problem of the amount of data is twofold: first,
data from past years is either unavailable or goes
unprocessed and second, the sheer quantity of new data
poses significant challenges for nearly every type of data
storage. In order to process existing data there must be a
steady effort made to carefully and gradually collect,
interpret, and input the countless and priceless records
kept by the Forest Service and other government agencies.
The impact, weight, and format of this data must constantly
be judged and monitored. To cope with the large influx of
information, especially the more and more detailed
information being collected daily, data management and
interfacing methods must be weighed carefully in terms of
their ability to deal with large volumes of data. Better,
more efficient storage methods must constantly be sought.
(NRC, 1995) In order to solve the problem of inadequate
data there are obvious steps that must be taken. One,
process the information lying in filing cabinets and small
computer systems across the country and around the world.
This would involve a large scale effort to find all of this
information and make it compatible with existing systems.
Two, increase, on both a national and international level,
the gathering of pertinent information. The step necessary
to facilitate all of these problems is a worldwide
cooperation between governments on a level such as the
United Nations to establish a wide reaching agreement on
resource sharing, data research, and data compatibility. If
organizational and governmental cooperations continue to
stay at today's levels there is definitely a finite limit
to the degree of effectiveness any GCM will have. The
scientific limitations of predicting global change are very
frustrating. However, we can be reassured by the fact that
daily advances in computing power and observational
techniques are happening almost faster than they can be
understood and implemented. Computers especially are
advancing at such a rate that we can be safe in guessing
that the GCMs of only five years from now will make current
models look like a magic eight ball. (Are global
temperatures rising? Outlook is promising. Or... Will ocean
levels rise? Concentrate and ask again.) New methods such
as microwave modeling, and synthetic aperture radar (SAR)
are being used to investigate the characteristics of forest
stands. For example, a mixed coniferous forest stand has
been modeled at SAR frequencies. The extensive measurements
of ground truth and canopy geometry parameters were
performed in a 200 m-square hemlock-dominated plot inside a
forest. Hemlock trees in the forest are modeled by
characterizing tree trunks, branches, and needles. (IEEE,
p. 630) The lack of computing power is a very limiting
factor. Reliable computation of the dynamic behavior of
complex systems under future and novel conditions is
required for environmental assessments. This requires
modeling and simulation. Descriptive models which merely
represent historical time series observations by regression
functions are ill-suited for reliable dynamic assessments
of possible development paths. This requires models which
model relevant interactions and processes and use them to
simulate real systems behavior.(IT+TI, p. 20) From a
processing standpoint there is always a shortage of
computing power available to process the job at hand, much
less any increase in data. This lack of power leads to
perhaps the largest area of concern: the scale of models.
The size of the plots a model makes its calculations on can
greatly affect the accuracy. Current models vary in the
size of the plots they calculate. The GISS model has
dimensions of 7.83( X 10( while the NCAR model has gridcell
dimensions of 4.44( X 7.5(. These are areas of 650,000 km2
and 330,000 km2, respectively. The most accurate model now
in use is UKMO model with a gridcell size of 3( X 330 km,
or 110,000 km2. The UKMO, however, has only been used for
weather forecasting. Advances in the next five to ten years
should produce gridcell dimensions in the range of 1.2( X
1.2( (15,000 km2) to an optimistic 0.5( X 0.5( (2,000
km2).(Cushman, 1988) In software design advances looming
ever closer are mostly in the category of "smart" programs.
These are programs which are, to a degree, able to learn
from the information they are presented. They can be
considered the practical side of artificial intelligence.
One example of such advancements is the neural network.
Neural networks are a new, capable technology that is
as-of-yet still poorly understood by programmers. Neural
networks learn how to recognize patterns and solve problems
that befuddle other types of computer programs. Coded once,
a good neural network program (simulation) can be trained
to solve a variety of different problems.(Phillips, 1996)
The application to GCMs that arises here is obvious. A
program which is given a general algorithm for a simulation
of a climate process, such as a pattern of floods, can take
its predicted results and use the discrepancies between the
predicted results and observed results to form a more
accurate algorithm. As time goes on the program is able o
develop more and more accurate algorithms and even
algorithms to predict the interactions between its various
algorithmic parts.
To store and manage the data being collected it is
necessary to relate it all geographically. Current efforts
focus on the global positioning system(GPS) and the
geographic information system(GIS). The functional
limitations of GPS unfortunately are only down to within 76
meters. This currently excludes any possibility for small
scale predictions within a GCM. Although methods such as
Differential GPS(DGPS) and Survey GPS allow for precise
measurements of events down to the scale of continental
drift, the hundreds of thousands of dollars and time needed
for such calculations make such measurements impossible for
forest and ecosystem modeling.( Rozmiarek, 1995) Thus, GIS
is ideal for the tracking and interpretation of information
on a stand to forest level. The drawback to this system is
that there is still a large amount of ambiguity present in
a system as digital as this. We cannot get away from the
fact that many of the stimuli affect forest on a individual
level.(Hall, 1977) There is, however, presently little
effort to form a GPS mapping system capable of keeping
track of trees on an individual levels. This is perhaps the
most frustrating aspect of global modeling because almost
every problem associated with the field hinders any quick
strides to a individual by individual system. It is at this
point that we can draw the conclusion that extremely
accurate models are dependent upon the development of high
powered, accurate satellite systems capable of monitoring
human expansion, the slightest change in stands of trees,
the patterns of the weather, and the doings of ocean
currents; and an international database to monitor this
information; as well as the self correcting software
algorithms just on the horizon required to quickly
compensate for even the slightest variations. This leads to
the conclusion that, in the long run, GCMs are most
dependent upon technological advances. Problems associated
with the gathering, the coordination, and compatibility of
data will be a thing of the past. Although it will always
be necessary to suggest new algorithms and relationships
between natural systems to the programs much of the dirty
work will fall out of the hands of humans. Perhaps Isaac
Asimov was right when he wrote about . Perhaps one day, and
one day soon, we will have a computer capable of answering
our questions; of learning, of thinking, problem solving;
of adapting to the future; and we will sit back and ask:
How does it do that? And we will answer: We don't know.
Bindingnavle-U. Knox-R. Kalb-V. Edited by: Roberts-C-A.
Beumariage-T. Herring-C. Wallace-J. An Object-Oriented
Environment for Re-Use of Ecosystem Models. SCS. San Diego,
CA. 1995.
Bossel-H. "Modeling and simulation for environmental
applications". IT+TI Informationstechnik und Technische
Informatik. Vol. 36, No. 4-5. pp. 20-5. Aug. 1994.
Chauhan-N-S. Lang-R-H. Ranson-K-J. "Radar modeling of a
boreal forest". IEEE Transactions on Geoscience and Remote
Sensing. Vol. 29, No. 4. pp. 627-38. July 1991.
Cushman, R., Farrell, M., Koomanoff, F. "Climate and
Regional Analysis: The Effect of Scale on Resource
Homogeneity". Climatic Change. Vol. 13, No. 2. October 1988.
Dale, V., Rauscher, H. "Assessing Impacts of Climate Change
on Forests: The State of Biological Modeling". Climatic
Change. Vol. 28, No. 1-2. October 1994.
Frederick, K., Rosenberg, N. "Conclusions, Remaining
Issues, and Next Steps". Climatic Change. Vol. 28, No. 1-2.
October 1994.
Gold, H. J. Mathematical Modeling of Biological Systems -
An Introductory Guidebook. 

Wiley-Interscience. New York. 1977.
Hall, C.A.S., Day, J.W. Ecosystem Modeling in Theory and
Practice: An Introduction with Case Histories.
Wiley-Interscience. New York. 1977.
Kozuharov, S.I. "Plants as Bioindicators". Biological
Monitoring of the State of the Environment: Bioindicators.
IRL Press. Oxford, UK. 1985.
Krivolutzky, D.A. "Animals as Bioindicators". Biological
Monitoring of the State of the Environment: Bioindicators.
IRL Press. Oxford, UK. 1985.
National Research Council(NRC). Finding the Forest in the
Trees. National Academy Press.
Washington, DC 1995.
Our Changing Planet: The FY 1996 U.S. Global Change
Research Program. 1996.
Phillips, D. "The Backpropagation Neural Network". C/C++
Users Journal. Vol. 14, No. 1. 

January 1996.
Preparing for an Uncertain Climate. 1994
Rozmiarek, A. "Global Positioning System: The New North
Star". WIRED. Vol. 3, No. 10. 

October 1995.
Thomas. W., Goldstein, G., Wilcox, W. Biological Indicators
of Environmental Quality. Ann Arbor Science. Ann Arbor, MI.



Quotes: Search by Author