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Approaches to scaling up physiologically based soil-plant models in space and time
R. J. Luxmoore1, A. W. King, M. L. Tharp
1Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA.
Tree Physiology
|July 1, 1991
Summary
This study explores extended-range modeling (ERM) and phenomena-added modeling (PAM) to scale soil-plant models for broad environmental issues. These methods, though data-intensive, enable analysis of large-scale phenomena not captured by small-scale studies.
Area of Science:
- Environmental Science
- Ecology
- Computational Biology
Background:
- Broad-scale environmental phenomena are often studied by extrapolating from small-scale event data.
- Physiologically based soil-plant models offer a detailed approach but require scaling for large-scale applications.
Purpose of the Study:
- To evaluate methods for using soil-plant models to address broad-scale environmental issues.
- To introduce and demonstrate extended-range modeling (ERM) and phenomena-added modeling (PAM) for scaling.
- To assess the utility of these modeling approaches for environmental research.
Main Methods:
- Extended-range modeling (ERM) involves increasing the spatial and temporal domains of soil-plant simulators.
- Phenomena-added modeling (PAM) incorporates phenomena not represented at the small scale.
- Latin hypercube sampling was used as a Monte Carlo procedure for ERM, estimating forest photosynthate production under varying CO(2) levels.
Main Results:
- ERM and PAM were illustrated through spatial scaling (plot to watershed) and temporal scaling (hourly to annual time steps).
- Integrating small-scale processes into large-scale models can reduce computation for faster processes.
- Information transfer between models, facilitated by Latin hypercube sampling, allows for large-scale frequency distribution analysis.
Conclusions:
- ERM and PAM are data and computer-intensive but essential for addressing large-scale environmental issues.
- These methods provide frequency distributions for statistical comparisons, aiding in confidence interval determination.
- The evaluated approaches offer valuable tools for environmental research where traditional scaling methods are insufficient.
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