MAPPING MONTHLY PRECIPITATION, TEMPERATURE, AND SOLAR-RADIATION FOR IRELAND WITH POLYNOMIAL REGRESSION AND A DIGITAL ELEVATION MODEL

A 1 km(2) resolution digital elevation model (DEM) of Ireland was cons tructed and used as the basis for generating digital maps of the clima te parameters required to run a model of ecosystem carbon and water cy cling. The DEM had mean absolute errors of 30 m or less for most of Ir eland. The ecosystem model requires inputs of monthly precipitation, m onthly averaged maximum and minimum daily temperature, and monthly ave raged daily solar radiation. Long-term (1951 to 1980) averaged monthly data were obtained from sites measuring precipitation (618 sites), te mperature (62 sites), and the number of hours of bright sunshine per d ay ('sunshine hours') (61 sites). Polynomial regression was used to de rive a simple model for each monthly climate variable to relate climat e to position and elevation on the DEM. Accuracy assessments with subs ets 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 0.5 degrees C for mont hly averaged maximum and minimum temperature, and 6 to 15 min for mont hly averaged sunshine hours. The polynomial regression estimates of cl imate were compared with estimates from a modified inverse-distance-sq uared interpolation. Prediction accuracy did not differ between the 2 methods, but the polynomial regression models demanded less time to ge nerate and less computer storage space, greatly decreasing the time re quired for regional modeling runs.