EnerGEO biomass pilot

Kurzfassung

In the framework of the EU FP7 project EnerGEO (Earth Observation for Monitoring and Assessment of the Environmental Impact of Energy Use) sustainable energy potentials for forest and agricultural areas were estimated by applying three different model approaches. Firstly, the Biosphere Energy Transfer Hydrology (BETHY/DLR) model was applied to assess agricultural and forest biomass increases on a regional scale with the extension to grassland. Secondly, the EPIC (Environmental Policy Integrated Climate) – a cropping systems simulation model – was used to estimate grain yields on a global scale and thirdly the Global Forest Model (G4M) was used to estimate global woody biomass harvests and stock. The general objective of the biomass pilot is to implement the observational capacity for using biomass as an important current and future energy resource. The scope of this work was to generate biomass energy potentials for locations on the globe and to validate these data. Therefore, the biomass pilot was focused to use historical and actual remote sensing data as input data for the models. For validation purposes, forest biomass maps for 1987 and 2002 for Germany (Bundeswaldinventur (BWI-2)) and 2001 and 2008 for Austria (Austrian Forest Inventory (AFI)) were prepared as reference. The output of BETHY/DLR, EPIC and G4M was used as input for the energy scenario-models REMIX (Renewable Energy Mix for Sustainable Electricity Supply in Europe, developed and operated by DLR-TT) , TASES (Time And Space resloved Energy Simulation, developed and operated by Research-Studio, Salzburg) and BeWhere (a techno-economic model developed by IIASA and Lud university and operated by IIASA). The EPIC modelling results for agricultural areas are input to TASES and REMIX. G4M also provided input data for TASES on a global scale starting with the year 2000 and ending in 2050 with 10 years steps. The main conclusions from the Biomass Pilot are: 1) It is possible to calculate biomass energy potentials for wood and agricultural crops by applying BETHY/DLR, EPIC or G4M models for Europe (1x1 km2) and the globe (0.5° x 0.5°). 2) The outcomes of biomass energy models are sensitive to input data by 40% or more. This is a consequence of biological sensitiveness to factors that determine growth such as weather, soil, species and cultivation. Collecting more and better input data is therefore essential. 3) Intensive effort was put on validation activities for all three models as well as a model intercomparison. For agricultural and forested areas all models showed significant linear relationship with reference data (R2 up to 0.95). 4) Remote sensing data can be used for generating some input data for biomass potential modelling such as weather and land use data 5) Remote sensing data have to be further developed before a differentiation can be made between different species and crops or biomass stacks can be modelled.