Modelling Approaches for Subsurface Drainage Water Quality Management

Contamination of surface waters by agricultural activities is a serious problem. Two different modelling approaches to simulate nutrient and pesticide transport in subsurface drained soils were investigated in this study. First, artificial neural network (ANN) models, a trainable fast back-propagation (FBP) network and a self-organizing radial basis function (RBF) network, were developed for simulation of NO3--N concentration in tile effluent. Second, a hydrologic model, DRAINMOD, was linked with a chemical transport model, GLEAMS, to simulate chemical transport of atrazine through the soil into subsurface drain outflow. The ANN models and linked DRAINMOD-GLEAMS model were calibrated and validated against experimental data collected at the Greenbelt Research Farm of Agriculture Canada during the years 1988, 1989 and from 1991 to 1994. Several statistical parameters were calculated to evaluate model performance. A comparison of results indicated that the RBF neural network model was superior to the FBP model in predicting drain outflow and NO3--N concentration. Results obtained from the linked DRAINMOD-GLEAMS model demonstrate that atrazine simulations were underpredicted in subsurface drain outflows for spring and fall seasons. Both modelling approaches provide a useful tool for management of fertilizer/manure and pesticides transport through soil and crop root zones into surface water.

Validation of the GLEAMS simulation model for estimating net nitrogen mineralisation and nitrate leaching under cropping in Canterbury, New Zealand

Simulation models require testing and calibration prior to their application to regions beyond those involved in their development. This paper reports on the calibration and testing of the groundwater loading effects of agricultural management systems (GLEAMS) model for the simulation of nitrate leaching under cropping in Canterbury. The GLEAMS model was first calibrated using crop and nitrogen leaching data collected from 4 consecutive years (1991–94) of spring-sown cereals following the ploughing of a temporary grass/clover pasture. Nitrate leaching losses were calculated from a combination of measured soil-solution nitrate concentration at 0.6 m depth, estimated drainage, and mineral N from soil cores. These calculated leached-N values were then used to calibrate the GLEAMS model. Parameters controlling denitrification and mineralisation rate in the model needed modification to provide sufficient mineral N for plant growth and nitrate leaching. The calibrated model was then tested against 3 independent validation data sets that were collected over 3 years from an adjacent experimental site, under the same management practices. Predictions from the calibrated GLEAMS model provided close agreement with measured values of mineralisation and leached N for the validation data sets. The amount of leached N averaged 43 kg N/ha.year and varied from 14 to 104 kg N/ha.year. The annual amount of drainage accounted for 97% of the variance in leached N, but the period in arable cropping was poorly correlated with leached N.