Data assimilation with ParFlow-CLM
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.
This research project focuses on assimilating measurement data with ParFlow-CLM. ParFlow-CLM is a better land surface model than CLM. The ParFlow component calculates the subsurface water and energy fluxes and includes lateral exchange fluxes between grid cells and a realistic representation of groundwater. ParFlow also simulates subsurface flow and overland flow in a fully coupled manner.
We expect that data assimilation with ParFlow-CLM yields potentially better model predictions than CLM alone, as the model structure error is smaller. This should allow that data are more effective in correcting states and parameters of the simulation model, which is of especial relevance for a highly parameterized model like ParFlow. In order to test this and other questions ParFlow-CLM will be applied for the Rollesbroich site in the Rur catchment (TERENO), where exhaustive datasets are available for calibration and verification.
Forschungszentrum Jülich GmbH
Tel. +49 02461 9038
Alternative contact person:
Prof. Dr. Harrie-Jan Hendricks Franssen
Forschungszentrum Jülich GmbH
Tel. +49 02461 4462