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Calibration of the INCA-N Model in the Pyhäjoki and Yläneenjoki Catchments in Finland

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  2010 Pittsburgh, Pennsylvania, June 20 - June 23, 2010  1008920.(doi:10.13031/2013.29757)
Authors:   J Randall Etheridge, Ahti Lepistö, Kirsti Granlund, Katri Rankinen, Michael R Burchell II, François Birgand
Keywords:   Nitrogen, INCA-N, Finland, Catchment, Water Quality

Models serve as a method of gaining an understanding of nutrient processes occurring in natural systems and testing scenarios on how to cost effectively reduce nutrient loading. The Integrated Catchment model for Nitrogen (INCA-N) was applied to the catchments of the River Ylneenjoki (233 km2) and the River Pyhjoki (78 km2) to improve the estimations of nitrogen load reaching Lake Pyhjrvi in south-western Finland. The model was calibrated for flow, nitrate concentrations, and ammonium concentrations for each catchment for the years 2003-2008. The simulated hydrograph was similar to the observed hydrograph in each catchment with the major difference being the flow peaks were lower in the simulated results (R2=0.71-0.72). The dynamics of nitrate concentrations were reasonably represented in each catchment, but the extreme observed values in summer and winter were not reproduced in the simulations (R2=0.37-0.46). The simulated ammonium concentrations followed the seasonal trends of the observed data based on visual inspection, but statistically were not as good as the flow or nitrate calibrations (R2=0.20-0.25). In the Ylneenjoki catchment, the simulated ammonium concentrations are much lower than the observed concentrations in the summer. For the Pyhjoki catchment, the simulated spring and winter peaks in ammonium were higher than the observed data in all years except 2003. The results support the need for accurate inputs to the model, especially fertilizer application rates. The calibrations also showed that continuous nutrient monitoring data is beneficial for producing accurate watershed models.

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