A stochastic model of nitrate transport and cycling at basin scale

[1] A stochastic framework for modelling catchment-scale hydrologic and nitrate responses ( as a byproduct of transport processes and of a biogeochemical model of nitrogen cycling and transformations in heterogeneous soils) is proposed and applied to a 53 km(2) basin in northeastern Italy, where observational data and complex land-use distribution and geomorphology demand suitable descriptions. The model is based on a geomorphological scheme of the hydrologic response coupled with suitable Lagrangian transport models (mass-response functions) applied in a Montecarlo framework which explicitly addresses the random character of the processes controlling nitrate generation to the hydrologic cycle, and its transformations and transport. This is obtained by coupling the stochastic generation of climatic and rainfall series with the hydrologic and biogeochemical models. Special attention is devoted to the spatial and temporal variability of nitrogen sources of agricultural origin and to the effects of the relative timing and intensity of the forcing rainfall fields on the ensuing nitrate leaching. The influence of random climatic variables on biogeochemical processes affecting the nitrogen cycle in the soil-water system ( e. g., plant uptake, nitrification and denitrification, mineralization) is also considered. Besides its conceptual interest, the relevance of the model stems from the capabilities of estimating the return period of nitrate loads to the receiving water body and the probability distribution of the variables computed. We found that the modes of nitrogen injection through fertilization significantly affect the form of probability distribution of nitrate contained in soil moisture even when the total amount is fixed. As a result, the return period of the water volumes discharged and of the nitrate loads released ( in this case into the Venice lagoon) can be linked directly to the ongoing climatic and agricultural regimes, with implications for sustainable management practices.