Understanding nitrogen transfer dynamics in a small agricultural catchment: Comparison of a distributed (TNT2) and a semi distributed (SWAT) modeling approaches

摘要

The coupling of an hydrological and a crop model is an efficient approach to study the impact of the interactionsudbetween agricultural practices and catchment physical characteristics on stream water quality.udWe analyzed the consequences of using different modeling approaches of the processes controlling theudnitrogen (N) dynamics in a small agricultural catchment monitored for 15 years. Two agro-hydrologicaludmodels were applied: the fully distributed model TNT2 and the semi-distributed SWAT model. Using theudsame input dataset, the calibration process aimed at reproducing the same annual water and N balance inudboth models, to compare the spatial and temporal variability of the main N processes. The models simulateduddifferent seasonal cycles for soil N. The main processes involved were N mineralization and denitrification.udTNT2 simulated marked seasonal variations with a net increase of mineralization in autumn,udafter a transient immobilization phase due to the burying of the straw with low C:N ratio. SWAT predicteduda steady humus mineralization with an increase when straws are buried and a decrease afterwards.udDenitrification was mainly occuring in autumn in TNT2 because of the dynamics of Nudavailability in soil and of the climatic and hydrological conditions. SWAT predicts denitrification in winter,udwhen mineral N is available in soil layers. The spatial distribution of these two processes was differentudas well: less denitrification in bottom land and close to ditches in TNT2, as a result of N transferuddynamics. Both models simulate correctly global trend and inter-annual variability of N losses in smalludagricultural catchment when a sufficient amount data is available for calibration. However, N processesudand their spatial interactions are simulated very differently, in particular soil mineralization and denitrification.udThe use of such tools for prediction must be considered with care, unless a proper calibration andudvalidation of the different N processes is carried out.