A Method to Efficiently Apply a Biogeochemical Model to a Landscape |
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Authors: | Robert E Kennedy David P Turner Warren B Cohen Michael Guzy |
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Institution: | (1) Present address: Department of Forest Science, Oregon State University, 321 Richardson Hall, 97331 Corvallis, OR, USA;(2) USDA Forest Service, PNW Research Station, 3200 SW Jefferson Way, 97331 Corvallis, OR, USA;(3) USDA Forest Service, PNW Research Station, 3200 SW Jefferson Way, Corvallis, OR 97331, USA;(4) Present address: Department of Bioresource Engineering, Oregon State University, 116 Gilmore Hall, 97331 Corvallis, OR, USA |
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Abstract: | Biogeochemical models offer an important means of understanding carbon dynamics, but the computational complexity of many
models means that modeling all grid cells on a large landscape is computationally burdensome. Because most biogeochemical
models ignore adjacency effects between cells, however, a more efficient approach is possible. Recognizing that spatial variation
in model outputs is solely a function of spatial variation in input driver variables such as climate, we developed a method
to sample the model outputs in input variable space rather than geographic space, and to then use simple interpolation in
input variable space to estimate values for the remainder of the landscape. We tested the method in a 100 km×260 km area of
western Oregon, U.S.A. , comparing interpolated maps of net primary production (NPP) and net ecosystem production (NEP) with
maps from an exhaustive, wall-to-wall run of the model. The interpolation method can match spatial patterns of model behavior
well (correlations>0.8) using samples of only 5 t o 15% of the landscape. Compression of temporal variation in input drivers
is a key step in the process, with choice of input variables for compression largely determining the upper bounds on the degree
of match between interpolated and original maps. The method is applicable to any model that does not consider adjacency effects,
and could free up computational expense for a variety of other computational burdens, including spatial sensitivity analyses,
alternative scenario testing, or finer grain-size mapping. |
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Keywords: | Biome-BGC Carbon modeling Interpolation Mapping Net ecosystem production Net primary production Oregon |
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