An application of the GLUE methodology for estimating the parameters of the INCA-N model.
The conceptual and parameter uncertainty of the semi-distributed INCA-N (Integrated Nutrients in Catchments-Nitrogen) model was studied using the GLUE (Generalized Likelihood Uncertainty Estimation) methodology combined with quantitative experimental knowledge, the concept known as 'soft data'. Cumulative inorganic N leaching, annual plant N uptake and annual mineralization proved to be useful soft data to constrain the parameter space. The INCA-N model was able to simulate the seasonal and inter-annual variations in the stream-water nitrate concentrations, although the lowest concentrations during the growing season were not reproduced. This suggested that there were some retention processes or losses either in peatland/wetland areas or in the river which were not included in the INCA-N model. The results of the study suggested that soft data was a way to reduce parameter equifinality, and that the calibration and testing of distributed hydrological and nutrient leaching models should be based both on runoff and/or nutrient concentration data and the qualitative knowledge of experimentalist.
Rankinen
Katri
K
Finnish Environment Institute, P.O.Box 140, FIN-00251 Helsinki, Finland. Katri.Rankinen@ymparisto.fi
Karvonen
Tuomo
T
Butterfield
Dan
D
D000383:Agriculture; D003198:Computer Simulation; D004784:Environmental Monitoring; D005308:Fertilizers; D008954:Models, Biological; D009566:Nitrates / Q000032:analysis* / Q000378:metabolism; D009584:Nitrogen / Q000032:analysis* / Q000378:metabolism; D010758:Phosphorus / Q000032:analysis* / Q000378:metabolism; D012989:Soil Pollutants / Q000032:analysis*; D012995:Solubility; D035501:Uncertainty; D014873:Water Pollutants / Q000032:analysis*; D014881:Water Supply
D005308:Fertilizers; D009566:Nitrates; D012989:Soil Pollutants; D014873:Water Pollutants; D010758:Phosphorus; D009584:Nitrogen