Evaluation of the WAVE model for predicting nitrate leaching for two contrasted soil and climate conditions
2003
Duwig, C. | Normand, B. | Vauclin, M. | Vachaud, G. | Green, S.R. | Becquer, T.
The integrated soil-crop-atmosphere model Water and Agrochemicals in the Vadose Environment (WAVE) (Vanclooster et al., 1995) was evaluated for two contrasted sets of data. One was from the tropical climate and ferrallitic soil conditions that exist in Mare Island, in New Caledonia. The other was from a glacial terrace under the continental climate of La Cote Saint-Andre (Isere) in France. Water and NO3 concentrations and fluxes were monitored during three consecutive years at instrumented sites with different surface covers (maize [Zea mays L.] or bare soil) or amount of applied fertilizer. The comprehensive set of measurements allowed us to evaluate the prediction capabilities of the WAVE model. Several parameters were determined independently, while others were adjusted on the basis of simulations for the wettest year at Mare and an average hydrological year at La Cote Saint-Andre. A stepwise approach was used to calibrate these parameters by sequentially integrating each individual model component. A screening sensitivity analysis was performed to address the most critical parameters. The predictive ability of the model was evaluated by comparing simulated and measured states variables and water and NO3 fluxes using two different years of data obtained at the same sites. For both sites, the model gave the best results for wet conditions, which actually posed the most critical problems in terms of groundwater pollution under our specific conditions. However, the model was used beyond its capacity as both soils had specificities for which the model was not designed. Overall, WAVE gave quite good predictions, but further studies are needed to fully evaluate WAVE with its crop growth model, SUCROS.
Show more [+] Less [-]AGROVOC Keywords
Bibliographic information
This bibliographic record has been provided by National Agricultural Library