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Gross Primary Productivity (GPP) and Net Primary Productivity (NPP)


The Gross Primary Productivity (GPP) and Net Primary Productivity (NPP) products for Europe and North Africa is derived from simulations with the Biosphere Energy Transfer (BETHY/DLR) model (Knorr, 1997, Wiβkirchen, 2005). It is a fixed grid map in rectangular projection annotated with latitude, longitude, and WGS84 date with a spatial resolution of 1km2. The total size covering Europe and North Africa is 8016 columns by 5010 lines. Monthly and annual NPP is calculated for 11 different land cover classes as well as for an overall NPP. This product is currently in a preoperational status. Currently for Europe both products are available for the years 2000 to 2007 and 2010.

Product Example

Annual NPP of Europe for 2010 in Tg*a-1*km-2. Sum over all vegetation types.


The Biosphere Energy Transfer Hydrology (BETHY/DLR) model (Knorr, 1997, Knorr and Heimann 2001, Wiβkirchen, 2005) belongs to the family soil-vegetation-atmosphere-transfer (SVAT) models and is used to simulate NPP over Europe. Input data are the "GLC2000" landcover classification and 10-day-composites of LAI which are both based on SPOT/VEGETATION. Meteorological data (temperature, radiation, precipitation and wind speed) are also required and are provided by the European Center for Medium Range Weather Forecast (ECMWF). Furthermore static information as the Harmonized World Soil Database (HWSD) from the International Institute for Applied Systems Analysis (IIASA) and an elevation model (SRTM) are used.

The model is used for simulations of European NPP with a resolution of 1km2, which is the resolution of the used land cover classification and LAI. Currently BETHY/DLR is capable to simulate NPP of 33 inherent vegetation types, including major crop and tree species (Tum and Günther, 2011). The internal parameterisation of BETHY/DLR allows a given vegetation class to be represented as a fraction of two BETHY/DLR vegetation types. The model was validated for agricultural areas in Germany and Austria (Tum and Günther, 2011) and Germany′s forests (Tum et al., 2011).

Vegetation type taxonomy

Number Vegetation type
1 Tropical broadleaved evergreen trees
2 Tropical broadleaved deciduous trees
3 Temperate broadleaved evergreen trees
4 Temperate broadleaved deciduous trees
5 Evergreen coniferous trees
6 Deciduous coniferous trees
7 Evergreen shrubs
8 Deciduous shrubs
9 C3 short grasses
10 C3 long grasses
11 C4 short grasses
12 C4 long grasses
13 Tundra vegetation
14 Swamp vegetation
15 Arable crops
16 Irrigated crops
17 Tropical tree crops
18 Citrus crops
19 Temperate deciduous tree crops

Estimated fraction of cover

With BETHY/DLR it is possible to estimate NPP for each grid cell two inherent vegetation types plus a fraction of bare soil. The estimated fractions follow the LCCS system and are given in values ranging from 0 to 1.

Vegetation type 1 = 5 (Evergreen coniferous trees); estimated fraction of cover: 0.8
Vegetation type 2 = 9 (C3 short grasses); estimated fraction of cover: 0.1

In this case it is assumed that 80% of the grid cell is covered with evergreen coniferous trees, 10% by C3 short grasses and 10% by bare soil.

Produced datasets


Knorr W., 1997. Satellite Remote Sensing and Modelling of the Global CO2 Exchange of Land Vegetation: A Synthesis Study [PhD Thesis]. Max-Planck-Institut für Meteorologie, Hamburg.

Knorr W. and Heimann M., 2001. Uncertainties in global terrestrial biosphere modelling 1. A comprehensive sensitivity analysis with a new photosynthesis and energy balance scheme. Global Biogeochemical Cycles, 15, 207-225.

Wisskirchen K., 2005. Modellierung der regionalen CO2-Aufnahme durch Vegetation [PhD Thesis]. Meteorologisches Institut der Rhein. Friedrich Wilhelm Universität, Bonn.

Tum M. and Günther K.P., 2011. Validating Modelled NPP Using Statistical Yield Data. Biomass and Bioenergy, 35, 4665-4674.

Tum M., Buchhorn M., Günther K.P., Haller B.C., 2011. Validation of Modelled Forest Biomass in Germany using BETHY/DLR. Geoscientific Model Development, 4, 1019-1034.