An application of artificial neural networks to carbon, nitrogen and phosphorus concentrations in three boreal streams and impacts of climate change

Projected changes in monthly temperature and precipitation under a changing climate were used to generate daily temperature and precipitation series for the 2050s. Daily runoff values for this hypothetical year were produced with an operational runoff model. Carbon and nitrogen loads were calculated for the 2050s using the neural network models of TOC, Ntot and Ptot concentrations. The low change scenario resulted in annual fluxes close to present fluxes, while the high change scenario gave an increase of approximately 26% of annual TOC, Ntot and Ptot fluxes.