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Sensitivity analyses of the nitrogen simulation model, DRAINMOD-N II

Wang, X., Youssef, M.A., Skaggs, R.W., Atwood, J.D., Frankenberger, J.R.
Transactions of the ASAE 2005 v.48 no.6 pp. 2205-2212
simulation models, agricultural soils, nitrogen, nitrate nitrogen, model validation, losses from soil, agricultural management models, drainage, drainage water, sandy loam soils, North Carolina
A two-step global sensitivity analysis was conducted for the nitrogen simulation model DRAINMOD-N II to assess the sensitivity of model predictions of NO3-N losses with drainage water to various model inputs. Factors screening using the LH-OAT (Latin hypercube sampling - one at a time) sensitivity analysis method was performed as a first step considering 48 model parameters; then a variance-based sensitivity analysis was conducted for 20 model parameters, which were the parameters ranked 1 to 14 by the LH-OAT method, five organic carbon (OC) decomposition parameters, and the empirical shape factor of the temperature response function for the nitrification process. DRAINMOD-N II simulated a continuous corn production on a subsurface drained sandy loam soil using a 40-year (1951-1990) eastern North Carolina climatological record. Results from the first 20-year period of the simulations were used to initialize the soil organic matter pools, and results from the last 20-year period of the simulations were considered for the sensitivity analyses. Both yearly and 20-year average model predictions of NO3-N losses through drainage flow were used in the analyses. Both sensitivity analysis methods indicated that DRAINMOD-N II is most sensitive to denitrification parameters, especially those controlling temperature effect on process rate. Results also indicated that the model is mildly sensitive to the parameters controlling OC decomposition and associated N mineralization/immobilization. The use of different sensitivity analysis methods with dissimilar theoretical foundations increases the confidence in key parameters identification. More efforts should be focused on quantifying key parameters for more accurate model predictions.