3.5.2. Scaling Up from Stand to Landscape

This section addresses the implications of the definitional scenarios introduced in Section 3.2 for reported changes in carbon stocks in the ARD land at the landscape level. We use a model that simulates the carbon dynamics in a hypothetical landscape. We simulate nine "cases" of human activities. For each case, we compare the "actual" change in carbon stocks of the landscape with that reported using the definitional scenarios.
The hypothetical landscape has an area of 150,000 ha in three land types: high- and low-productivity forest and agricultural land, each containing 50,000 ha. We assume that this landscape has been managed for some time. One percent of the forest area (i.e., 1,000 ha) has been harvested and planted annually for the past 100 years, so the forest age-class distribution of the landscape is stable. Each of 100 age-classes contains 1 percent of the forest area. We also assume that the carbon pools in agricultural land are constant.

To demonstrate the differences between the definitional scenarios, we simulate nine cases of various human activities in the landscape (Table 3-10). In all cases, we assume that the area harvested and planted prior to 1990 was as in case A. Starting in 1990, the areas affected by human activities are as described in Table 3-10 and are held constant throughout the simulation.

Case A represents a managed forest in which the rate of harvest is equal to the rate of forest growth. Thus, the standing wood volume in the landscape is in steady state. Case B is similar to A, except that 40 percent of the area harvested is allowed to reforest through natural regeneration that we assume (for the sake of illustration) does not involve DHI activity. In Case C, the rate of harvest is greater than the rate of forest growth, thus reducing wood volume. Case D is the opposite case of C: The harvest rate is less than the growth rate, allowing wood volume to increase. In both cases, the change in the harvest rate will result in a change in the age-class distribution of the forest. Cases E and F include human activities that result in degrading and aggrading forests, respectively, as a result of a change in the potential carbon stock at maturity. Activities in Case E convert high-productivity forest to low-productivity forest; in case F, low-productivity forest is converted to high-productivity forest. For these examples, the high- and low-productivity forest are in the same land-use category. In cases G and H, 100 ha yr^-1 of each productive and degraded forest are converted to (G) or from (H) agricultural land in addition to the annual harvest of 500 ha yr^-1 of each forest type. These changes are associated with a change in land use. Case I combines G and H: Every year 100 ha of forest land each of high- and low-productivity forest is converted to agricultural land, and 200 ha of agricultural land is converted to forest. Thus, the total forest area is constant, but it shifts in space. In cases G and I, we assume that deforestation is a random process that affects stands of all age-classes in the landscape; we represent this effect in the model by deforesting stands of the average biomass. Timber harvesting in the model affects the oldest stands with the highest biomass.

Table 3-11 summarizes, for the seven definitional scenarios, the activities that create ARD land. For this purpose, the definitional scenarios are divided into three broad groups: scenarios that consider forest change only, scenarios in which the harvest/regeneration cycle creates ARD land, and scenarios in which forest degradation or aggradation create ARD land.

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