Uncertainty in predicting precipitation volume at the Hubbard Brook Experimental Forest: A model comparison

Quantifying the influx of water and elements to ecosystems via atmospheric deposition is uncertain mainly because of spatial variability; interpolation between precipitation stations is a major source of uncertainty at the ecosystem scale. Various methods of interpolation are used in precipitation and atmospheric deposition studies, but the uncertainty in the interpolation is rarely reported. Temporal dynamics generally contribute less uncertainty to estimates of deposition, because precipitation amounts are measured at short intervals (15 minute steps or shorter) or are cumulative, giving good estimates of rainfall amounts at a point. 

Dr. John Campbell (USDA Forest Service) used five models of precipitation volume in ARCGIS to demonstrate the difference in predictions of annual precipitation volume for five watersheds at the Hubbard Brook Experimental Forest. The models used were Thiessen polygons, Spline, Inverse Distance Weighting, Kriging, and regression using longitude and elevation as predictor variables. He found that model differences varied by watershed. The coefficient of variation between models was small, ranging from 0.24 - 0.83% of average annual precipitation.