Choice of Spatial Scale Effects in up Scaling Net Primary Productivity and Greenhouse Gas Emissions: A Model Simulation Study.

In practice, the choice of scale/resolution in upscaling the ecosystem models is often determined by availability of data. It has been long debated that model structure should change with spatial resolution/scale as different processes are likely to dominate in large and smaller resolutions. If the results of the low resolution agree closely with those of the high resolution, then the low resolution/scale are preferable, since they typically require lower computational resources and lesser input data. It is important to investigate how model efficiency change with Scale and how much error propagate due to the choice of scale in upscaling a model. As a first step towards this goal, we investigated the impact of climate data aggregation at various scales/resolutions on NPP and GHG emission simulation by applying eleven different models in Western Germany. To isolate effects of climate, soil data and management were assumed to be constant over the entire study area and over the entire study period of 29 years. Two crops, winter wheat and silage maize, were tested as monocultures. We have tested the models at five scales (1 km2, 10 km2, 25 km2, 50 km2 and 100 km2) in rage of 1-100 Km grid cells. Simulations of NPP averages over larger areas (e.g. regional scale) and longer time periods (29 years) are relatively insensitive to climate data aggregation. However, the scale of climate data is more relevant for impacts on annual average of NPP or if the period is strongly affected or dominated by drought stress. Impact of climate data aggregation on GHG emission by using DayCent model will be presented in this study.