Modeling at the Arctic LTER
Soil-Plant-Atmosphere model (SPA) is a detailed sub-diurnal model of canopy-atmosphere interactions, linking a radiative transfer scheme, a biochemical model of photosynthesis, CO2 diffusion, and a hydraulic model of stomatal opening The Aggregated Canopy Model (ACM) is directly derived from SPA, but operates on a daily, rather than 30 minute, time-step and at the whole canopy, rather than leaf, level. ACM has been used to access the patterns of gross primary production in the Kuparuk River basin.
Below is a comparison of the soil-plant-atmosphere (SPA) model to data collected from eddy flux towers at several location on the North Slope of Alaska (Williams et al. 2000).
That same model was used to explain the relationship between leaf area and canopy nitrogen across the North Slope (Williams and Rastetter 1999). The GPP isopleths on the graph were generated with the SPA model. The clustering of data suggests a strong optimization of leaf display and N allocation to maximize productivity. Productivity cannot increase substantially by increasing either leaf are or canopy nitrogen alone. To increase GPP, both must increase in tandem. The data are from 7 distinct vegetation types, suggesting that all 7 use a similar optimization strategy. The gray line is the relationship expected if N is distributed exponentially down through the canopy.
Williams et al. (2001) Used an aggregated for of the SPA model (the Aggregated Canopy Model) to predict the spatial distribution of photosynthesis for the Kuparuk River Basin.
Shaver et al (in press) developed a simple model of Arctic Net Ecosystem Exchange (ANEE) of CO2 based of several hundred chamber-based measurements made for seven vegetation types (Betula, Heath, Mesic, Salix, Snowbed, Tussock, Wet Sedge) in Alaska and Sweden:
where R0 (Ámol m-2 leaf s-1) and Rx (Ámol m-2 ground s-1) are respiration parameters, L is the leaf area index (m2 leaf m-2 ground), β is a temperature-response parameter (║C-1), T is air temperature (║C), Pm is the leaf-level, light-saturated photosynthetic rate per unit leaf area (Ámol m-2 leaf s-1), k is the Beer’s extinction coefficient (m2 ground m-2 leaf), E0 is the initial slope of the leaf-level light response curve (Ámol C Ámol-1 photons), and I0 is the photosynthetically active photon flux density (PPFD) above the canopy (μmol m-2 s-1).
The model has two remarkable characteristics:
Indeed, the parameters estimated for one type of vegetation work to simulate NEE in the other six vegetation types and parameters estimated for Sweden are indistinguishable from those estimated for Alaska (Fig. 1). We have since tested the model with data from Western Greenland and from Svalbard and again the same three measurements and the same parameter values work well to predict NEE for these new sites.
The Marine Biological Laboratory General Ecosystem Model (MBL-GEM) is a linked-process model describing the interactions between carbon and nitrogen in terrestrial ecosystems. The model is intended to be generally applicable to most terrestrial ecosystems and, in its original form, has been used to analyze the responses of temperate deciduous forests, tropical evergreen forests, and arctic tundra to changes in CO2 concentration, temperature, N inputs, irradiance, and soil moisture.
In the arctic MBL-GEM has been used to synthesize and analyze long-term experiments on tundra responses to fertilizer and warming. McKane et al. (1997a) calibrated of the General Ecosystem Model (GEM) to plot scale experiments of tussock tundra and the calibrated model was used in various analyses of long-term responses to changes in climate (McKane et al. 1997b, Rastetter et al 1997, 2004).
Rastetter et al. (2004) linked several GEM models along a transect to examine the effects of down-slope movement of water and nutrients on the responses of tussock tundra to changes in climate. The simulations indicate that over the next 100 years, the movement of nutrient down hill will result in a 30% higher net carbon storage at the base of the 100 m transect than at the top.
Stieglitz et al. (2003) analyzed how the hydrological connectivity changes as arctic hill slope wet up and dry out. Connectivity does not simply expand upward from the valley floor and then contract back down slope. Patterns of connectivity also depend on the topography, depth of thaw, and soil characteristics. It is this connectivity that determines the down-slope transport of nutrients on hill slopes.
We are currently trying to link the hill-slope hydrology models with our plot scale biogeochemical models to predict the complex biogeochemical interactions on hill slopes and ultimately the delivery of nutrients and organic matter to streams and lakes at the base of these hill slopes.
McKane, R. B., E. B. Rastetter, G. R. Shaver, K. J. Nadelhoffer, A. E. Giblin, J. A. Laundre, and F. S. Chapin, III. 1997. Reconstruction and analysis of historical changes in carbon storage in arctic tundra. Ecology 78:1188-1198.
McKane, R. B., E. B. Rastetter, G. R. Shaver, K. J. Nadelhoffer, A. E. Giblin, J. A. Laundre, and F. S. Chapin, III. 1997. Climatic effects on tundra carbon storage inferred from experimental data and a model. Ecology 78:1170-1187.
Rastetter, E. B., G. I. Aagren, and G. R. Shaver. 1997. Responses of N-limited ecosystems to increased CO2: A balanced-nutrition, coupled-element-cycles model. Ecological Applications [Ecol. Appl.] 7:444-460.
Rastetter, E. B., B. L. Kwiatkowski, S. Le Dizes, and J. E. Hobbie. 2004. The role of down-slope water and nutrient fluxes in the response of arctic hill slopes to climate change. Biogeochemistry 69:37-62.
Stieglitz, M., J. Shaman, J. McNamara, G. W. Kling, V. Engel, and J. Shanley. 2003. An Approach to Understanding Hydrologic Connectivity on the Hillslope and the Implications for Nutrient Transport. Global Biogeochemical Cycles 17:1105, doi:1110.1029/2003GB002041
Williams, M., W. Eugster, E. B. Rastetter, J. P. McFadden, and F. S. Chapin, III. 2000. The controls on net ecosystem productivity along an Arctic transect: a model comparison with flux measurements. Global Change Biology 6:116-126.
Williams, M., and E. B. Rastetter. 1999. Vegetation characteristics and primary productivity along an arctic transect: implications for scaling-up. Journal of Ecology [J. Ecol.] 87:885-898.
Williams, M., E. B. Rastetter, E. Carpino, J. E. Hobbie, G. R. Shaver, and B. L. Kwiatkowski. 2001. Primary production of an arctic watershed: An uncertainty analysis. Ecological Applications 11:1800-1816.
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