Neural networks modelling of nitrogen export: model development and application to unmonitored boreal forest...

In remotely located boreal forest watersheds, monitoring nitrogen (N) export in stream discharge often is not feasible because of high costs and site inaccessibility. Therefore, modelling tools that can predict N export in unmonitored watersheds are urgently needed to support management decisions for these watersheds. The hydrological and biogeochemical processes that regulate N export in streams draining watersheds are complex and not fully understood, which makes artificial neural network (ANN) modelling suitable for such an application. This study developed ANN models to predict N export from watersheds relying only on easily accessible climate data and remote sensing (RS) data from the public domain. The models were able to predict the daily N export (g/km2/d) in five watersheds ranging in size from 5-130 km2 with reasonable accuracy. Similarity indices were developed between any two studied watersheds to quantify watershed similarity and guide the transferability of models from monitored watersheds to unmonitored ones. To demonstrate the applicability of the ANN models to unmonitored watersheds, the calibrated ANN models were used to predict N export in different watersheds (unmonitored watersheds in this perspective) without further calibration. The similarity index based upon a rainfall index, a peatland index and a RS normalized difference water index showed the best correlation with the transferability of the models. This study represents an important first step towards transferring ANN models developed for one watershed to unmonitored watersheds using similarity indices that rely on freely available climate and RS data.