A new validation approach to assess the quality of modeled agricultural biomass potentials using BETHY/DLR

Abstract

A new validation approach is presented to assess the quality of modeled agricultural biomass potentials with statistical data on high resolution. First investigations in Germany and Austria show coefficients of determination (r²) of up to 0.79 on district level. Our modeled net primary productivity is computed with the dynamic biomass model BETHY/DLR. Primarily the photosynthetic rate of vegetation types is computed with time steps of one hour and currently with a spatial resolution of about 1km x 1km. Included models compute the water balance and radiative energy transfer between atmosphere, vegetation and soil. The model is driven by meteorological data provided by the European Center for Medium Range Weather Forecast (ECMWF), remote sensing data derived through SPOTVEGETATION and soil type information by the Food and Agriculture Organisation (FAO). The model output (gross primary productivity (GPP)) is calculated daily. Net primary productivity (NPP) is determined by subtracting the cumulative plant maintenance respiration from GPP. In order to validate the modeled NPP, data of crop yield estimations derived from national statistics are used to calculate above ground biomass by using conversion factors about corn to straw relations. Furthermore conversion factors about shoot to root relations are used to determine total biomass. Finally the carbon content of dry matter is estimated. With this method coefficients of determination (r²) of up to 0.67 combined with a slope of 0.83 are found for Germany. For Austrian NUTS-3 units slightly higher coefficients of determination are found (0.74) combined with a slope of 1.08. The results show that modelling NPP using the process model BETHY/DLR and remote sensing data and meteorological data as input delivers reliable estimates of above ground biomass when common agricultural conversion factors are taking into account.