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A 1 km 2 resolution digital elevation model (DEM) of Ireland was constructed and used as the basis for generating digital maps of the climate parameters required to run a model of ecosystem carbon and water cycling. The DEM had mean absolute errors of 30 m or less for most of Ireland. The ecosystem model requires inputs of monthly precipitation, monthly averaged maximum and minimum daily temperature, and monthly averaged daily solar radiation. Long-term (1951Long-term ( to 1980) ) averaged monthly data were obtained from sites measuring precipitation (618 sites), temperature (62 sites), and the number of hours of bright sunshine per day ('sunshine hours') (61 sites). Polynomial regression was used to derive a simple model for each monthly climate variable to relate climate to position and elevation on the DEM. Accuracy assessments with subsets of each climate data set determined that polynomial regression can predict average monthly climate in Ireland with mean absolute errors of 5 to 15 mm for monthly precipitation, 0.2 to 0.5C for monthly averaged maximum and minimum temperature, and 6 to 15 min for monthly averaged sunshine hours. The polynomial regression estimates of climate were compared with estimates from a modified inverse-distance-squared interpolation. Prediction accuracy did not differ between the 2 methods, but the polynomial regression models demanded less time to generate and less computer storage space, greatly decreasing the time required for regional modeling runs.
Goodale et al. (Thu,) studied this question.