Field Evaluations of a Forestry Version of Drainmod-nii Model

This study evaluated the performance of the newly developed forestry version of DRAINMOD-NII model using a long term (21-year) data set collected from an artificially drained loblolly pine (Pinus taeda L.) plantation in eastern North Carolina, U.S.A. The model simulates the main hydrological and biogeochemical processes in drained forested lands. The model was calibrated using observed data during 1988-1997 and validated during 1998-2008. Predicted subsurface drainage, water table fluctuation, annual net primary production, leaf area index, and nitrate export were compared with measured values. Goodness-of-fit statistics include Nash-Sutcliffe coefficient (NSE), degree of agreement (d) and mean absolute error (MAE). Both annual and monthly drainage predictions were in very good agreement with measured values (NSE = 0.95, d = 0.95, and MAE = 53 mm yr -1 for yearly predictions and NSE = 0.91, d = 0.96 and MAE = 8.8 mm mo -1 for monthly predictions). Predicted daily water table depths closely followed observed values with goodness-of-fit statistics: NSE = 0.90, d = 0.96 and MAE = 0.10m. Predicted mean annual NPP was 18.7 t DM ha -1 , which was very close to estimated value of 18.6 t DM ha -1 . The goodness-of-fit statistics of the annual NPP predictions were: NSE =0.66, d = 0.78, and MAE =1.46 t ha -1 yr -1 . The model well predicted both the magnitude and dynamics of LAI. Predicted mean annual nitrate loss was 2.59±1.64kg ha -1 , which was very close to observed value of 2.64±1.50kg ha -1 . The goodness-of-fit statistics for predicted annual nitrate loss were: NSE = 0.88, MAE = 0.46kg ha -1 yr -1 and d = 0.93. The goodness-of-fit statistics for monthly nitrate export were: NSE = 0.76, MAE = 0.09 kg ha -1 mo -1 and d = 0.81, all of which indicated a good performance of the model in predicting monthly nitrate export. These overall accurate predictions clearly demonstrate the capabilities of the forestry version of DRAINMOD-N II as a model for simulating the hydrology, biogeochemistry, and forest growth for drained forested lands.