| dc.description.abstract | The INTEGRATOR model is a GIS-based, multi-component tool developed to assess the impacts of European-scale changes in land-use, land-management and climate on Nitrogen (N) fluxes and Green House Gases (GHG). The inputs used in INTEGRATOR are detailed GIS data describing environmental factors related to N and GHG such as land cover, climate, soil type and soil properties and also farming and agricultural applications.
The objective of this research was to analyse how uncertainties in model inputs propagate to NH3, N2O, NOx, CH4 emissions, and N leaching into surface- and groundwater estimated by INTEGRATOR. The research was limited to agricultural areas in Europe.
In total 56 Agricultural Parameters (APs) were considered as model inputs and they were divided into four groups according to the four spatial-scale levels of INTEGRATOR in order of increasing size: NCU (local areas), NUTS2/3 (regional areas), CNTRY (EU member states) and EUROPE. The uncertainty in the APs was expressed by defining their probability distribution functions (pdfs) and taking into account their spatial- and cross-correlations. Additionally, three uncertainty scenarios (Optimistic, Reference, and Pessimistic) were incorporated in the research to investigate the robustness of the uncertainty analysis. The outputs were produced at Country and European level for the year 2000, and the propagated uncertainty in them was quantified by applying the Monte Carlo simulation to the model.
Results of this research indicate that: (i) when using the Reference scenario, the output uncertainty, expressed as Coefficient of Variation (CV), varies from 10-34% for outputs at European level and from 11-92% for outputs at Country level, (ii) the uncertainty increased going from CH4 and NH3 emissions to N2O emissions, to NOx emissions, to N leaching into surface- and groundwater, (iii) the maximum value of CV for the Pessimistic scenario was 55% whereas it was only 12 % for the Optimistic scenario, and (iv) the APs at NCU level had the largest contribution to the output uncertainty. | |
