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In this paper, a method is presented to estimate excess nitrogen on largescales considering single field processes. The approach was implemented byusing the physically based model J2000-S to simulate the nitrogen balance aswell as the hydrological dynamics within meso-scale test catchments. Themodel input data, the parameterization, the results and a detailed systemunderstanding were used to generate the regression tree models with GUIDE(Loh, 2002). For each landscape type in the federal state of Thuringia aregression tree was calibrated and validated using the model data andresults of excess nitrogen from the test catchments. Hydrological parameterssuch as precipitation and evapotranspiration were also used to predictexcess nitrogen by the regression tree model. Hence they had to becalculated and regionalized as well for the state of Thuringia. Here themodel J2000g was used to simulate the water balance on the macro scale. Withthe regression trees the excess nitrogen was regionalized for each landscapetype of Thuringia. The approach allows calculating the potential nitrogeninput into the streams of the drainage area. The results show that theapplied methodology was able to transfer the detailed model results of themeso-scale catchments to the entire state of Thuringia by low computing timewithout losing the detailed knowledge from the nitrogen transport modeling.This was validated with modeling results from Fink (2004) in a catchmentlying in the regionalization area. The regionalized and modeled excessnitrogen correspond with 94%.  The study was conducted within the framework of a project in collaborationwith the Thuringian Environmental Ministry, whose overall aim was to assessthe effect of agro-environmental measures regarding load reduction in thewater bodies of Thuringia to fulfill the requirements of the European WaterFramework Directive (Bäse et al., 2007; Fink, 2006; Fink et al., 2007)

Regionalization of meso-scale physically based nitrogen modeling outputs to the macro-scale by the use of regression trees