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by David Herring|
September 10, 1999
What if we could simultaneously observe the production of green vegetation by land plants all over the world? We can't, but if we could, what would we learn about how land plants respond as meteorological conditions change every day? And what would the response of land plants teach us about where, when, and how much carbon dioxide is being exchanged with the terrestrial ecosystem?
Because we cannot directly observe these things, forest ecologist Steven Running and his research team at the University of Montana constructed a computer model of the Earth's terrestrial biosphere that he expects will teach us a great deal about the dynamic interactions between land plants and the lower atmosphere. The model enables him to watch as changes in the biosphere literally ripple across continents in response to meteorological changes (Ford et al. 1994).
"We can watch daily photosynthetic activity as plants react to changes in weather patterns and storm tracks going across continents," Running says. "Suppose it is in the middle of the growing season. A storm comes through that drops temperatures from the 70°F to the 50°F range, and the skies go from sunny to cloudy. The biosphere will react by dropping its rate of photosynthesis. We can see this response sweep across the landscape over a period of hours."
What is a computer model?
According to Levine, scientists don't have enough information about the Earth's processes to understand how they function as a whole, integrated system over time. We know that ecosystems vary greatly from local to global scales. Yet, in order to characterize how these ecosystems function, scientists need to generalize across each region (Weishampel et al. 1999). Scientists put the information they know about a system--as either numerical measurements, or theoretical estimates--into a model and in turn the model tells them two things: (1) how the inputs respond to each other, and (2) where there are gaps in their knowledge that need to be filled.
Levine works with two types of models: (1) simulation models and (2)
data-driven models. Simulation models use equations that are written into a
program that acts as a copy of a real system. Inputs that make the model run
are things like site conditions (soil properties, land cover, slope, aspect,
latitude, longitude, etc.), temperature, and precipitation values. The types of
simulation models she works with are at the "process" level, which
means they look at the system at the local level that gives details of how
specific mechanisms work. The results of these models can then be generalized
to larger, coarser scales (Knox et al. 1997). When the model is run, the
equations work together to make a prediction about a certain part of the
ecosystem that is being simulated (such as the amount of moisture in the soil at
a certain depth and time, the amount of photosynthesis occurring at a certain
The second type of model uses observed measurements that scientists have collected across the globe to generalize real processes and properties seen in nature. Levine uses Geographic Information System (GIS) software tools to combine maps and other types of data from satellite sensors (such as land cover, soil types, and climate data). Then, she applies models that use techniques such as statistics or "neural networks" to interpret the real relationships between each of the different layers of data. [A neural network is a system of multiple computer processors connected together so that they share information via a common program that enables them to "learn" by trial and error in a manner similar to the way the human brain works.]
For example, Levine uses satellite images of land surface overlain on soil data and, together with the GIS software and a neural network, relates soil properties with the spectral signatures from the satellite image. These signatures are colors of light that are reflected (both visible and infrared light) by the vegetation and other objects on the ground. Using the relationships that are derived from this information, Levines model can make predictions about soil carbon and other properties for areas where there are little or no data (Levine and Kimes 1997).
When the model predicts something interesting or unexpected, Levine says, then it is time to go make more measurements.