Scale issues and uncertainty analysis |
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In applications of the PESERA-DESMICE modeling framework two complications were frequently encountered:
The effects of these bottlenecks are explored in two case studies in the subsections below. Effect of spatial variability of investment costs Taking as an example the application of bench terraces with loess soil walls in the Yanhe River basin in the Loess Plateau of China, spatial variability of investment costs was defined as follows:
INVs=US$1,823 *S/30 [1]
Calculating the average investment cost per hectare across the area where the technology is applicable (3,732 km²) with Equation 1 gives US$1,591 ± 717. To assess the effect of different levels of variation of investment costs with slope gradient, the mean was subtracted from the INVs data layer and the resulting raster multiplied with factors 0.75, 0.5, 0.25 and 0 before adding the mean investment cost again. This approach resulted in a number of rasters with the same average investment cost but different standard deviation and ranges (Table 1), which were subsequently used to assess the financial viability of the technology following the steps of the PESERA-DESMICE framework. Table 1: Levels of spatial cost variability and resulting range of investment costs for bench terraces in Yan River Basin, China.
Effect of timing of biophysical effects
Gully control with atriplex in Sehoul, Morocco is not very sensitive to small changes around the assumed 20 years it takes to reach maximum productivity (Figure 11B). However, this is a rough assumption, so we should look further than the short range between 18 and 25 years where the viability of the technology is not affected. When approximating a TTM of 15 years, the viability of atriplex planting rapidly reaches 100% of the applicable area, up from 82% on the stable area from 18-25 year. Even more dramatic is the drop between a TTM of 25 and 30 years, when the technology seizes to viable in more than 60% of the applicable area. The negative slope of the relation flattens of after 30 years, but gully control with atriplex by then remains profitable in only 13% of the area. From this example, it is clear that one would need to be confident of the interval 18-25 years it would take vegetation to reach maximum productivity, outside of which the system becomes very sensitive to the issue of timing.
Discussion of scale issues
The timing of biophysical effects has potentially significant influence on viability of technologies. The point version of PESERA allows simulation in time series mode after equilibrium conditions have been established. The grid version of the model, which was used here, lacks this facility. Still, model validation, specifically of timing of effects, is difficult due to interactions and the paucity of long-term field trials which are intensively monitored. Although the illustrative case study had a long term restoration goal, the cumulative effects of annually repeated SLM technologies may also be significant (see e.g. Hobbs et al., 2008). The importance of the temporal dimension in evaluating technologies is clear from the inclusion of a discount factor in CBA. This can work two ways: in the case of technology application, it is important for land users to start reaping benefits as early as possible; but in the without case, ongoing degradation can further affect yield levels (Lal, 1995).
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Acknowledgement
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The DESIRE project was
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Contract no: 037046 GOCE
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