Upscaling of biogeochemical fluxes to characterize regional carbon balance
This is one of several projects concerned with improving land surface model predictions through data assimilation approaches. Land surface models try to predict exchanges of water, energy and trace gases between the land surface and the lower atmosphere, as well as storage and transport of water and energy on the land surface. Land surface models are used for modeling the lower boundary condition of regional and global circulation models and play therefore an essential role both for weather forecasting and climate predictions. Predictions with land surface models are affected by several sources of uncertainty:
- Uncertainty with respect to model forcings. Especially the uncertainty with regard to the precipitation amounts at a certain location plays an important role. Precipitation shows a strong spatio-temporal variability which is not captured well by rain gauge networks. The combination of rainfall radar and rain gauge data can improve the characterization of spatio-temporal variability, but uncertainty remains significant.
- Model parameter uncertainty. The uncertainty with respect to soil hydraulic parameters like porosity or saturated hydraulic conductivity and vegetation parameters like stomata resistance or rooting depth contributes significantly to the overall model prediction uncertainty. Given the fact that grid scales of land surface models are very large, it is very difficult to derive meaningful values for these parameters.
- Model structure uncertainty. Land surface models simplify the complex processes at the land surface: For example in CLM, the soil respiration is described with an empirical approach. Another example is neglecting lateral exchange fluxes of water and energy between grid cells. A third example is the fact that vegetation development is described with a static approach, neglecting the role of water stress or early/late phenology due to warm/cold conditions in spring.
- Uncertainty of initial conditions. The initial state of the system is not exactly known and subject to uncertainty as well.
These uncertainties can be reduced with help of measurement data. In an operational setting, measurement data can be assimilated with data assimilation methods to update states (for example soil moisture contents, soil temperature, leaf area index) and also model parameters (especially soil hydraulic and vegetation parameters). The updating of model parameters also has an impact for long-term climate predictions. An improved model parameterization should also improve the quality of the prediction of long-term exchange fluxes of water, energy and greenhouse gases between the land and the atmosphere.
The objective of this study is to upscale biogeochemical fluxes from point to catchment scale, focusing on Net Ecosystem Exchange of CO2 (NEE). We want to derive a reliable carbon balance at this scale, taking into account different land use types. The Community Land Model (CLM) is used as land surface model. It is a sophisticated modeling approach for investigating and predicting large-scale ecosystem responses to environmental change. To account for the interaction of hydrological and biogeochemical processes, CLM represents the environment as a coupled system, including the cycling of energy, water, carbon and nitrogen. So far, CLM was primarily used on continental and global scales. Data assimilation methods (e.g. the Ensemble Kalman Filter) will be used to provide more accurate model predictions, considering model and measurement uncertainty. Carbon dioxide flux measurements from six Eddy Covariance towers (including one mobile station) and remote sensing information (LAI, photosynthetic activity) will be considered as conditioning information in the data assimilation procedure. At first, CLM-CN (4.0) will be applied and evaluated for single Eddy Covariance towers within the Rur-catchment (North Rhine-Westphalia, Germany), situated on grassland, forest and agricultural test sites of the TERENO network. Later, upscaling will be conducted to evaluate how far CLM-CN is capable to accurately determine biogeochemical fluxes for the entire Rur-catchment.
Weblink to project (start of our project March 2012):
Other relevant link:
Forschungszentrum Jülich GmbH
Tel. +49 02461 6783
Alternative contact person:
Prof. Dr. Harrie-Jan Hendricks Franssen
Forschungszentrum Jülich GmbH
Tel. +49 02461 4462