Chris Kollas

Analysis and classification of data sets for calibration and validation of agro-ecosystem models

Experimental field data are used at different levels of complexity to calibrate, validate and improve agro-ecosystem models to enhance their reliability for regional impact assessment. A methodological framework and software are presented to evaluate and classify data sets into four classes regarding their suitability for different modelling purposes. Weighting of inputs and variables for testing was set from the aspect of crop modelling. The software allows users to adjust weights according to their specific requirements. Background information is given for the variables with respect to their relevance for modelling and possible uncertainties. Examples are given for data sets of the different classes. The framework helps to assemble high quality data bases, to select data from data bases according to modellers requirements and gives guidelines to experimentalists for experimental design and decide on the most effective measurements to improve the usefulness of their data for modelling, statistical analysis and data assimilation. A software is presented to classify and label data suitability for modelling.Data requirements for modelling are specific and vary with model purpose.Quantitative classification of data sets facilitates their use for modelling.Test of model and data consistency improves data usability.Guidelines for experimentalist to improve data suitability for modelling.

Mistletoe-induced growth reductions at the forest stand scale

The hemiparasite European mistletoe (Viscum album L.) adversely affects growth and reproduction of the host Scots pine (Pinus sylvestris L.) and in consequence may lead to tree death. Here, we aimed to estimate mistletoe-induced losses in timber yield applying the process-based forest growth model 4C. The parasite was implemented into the eco-physiological forest growth model 4C using (literature-derived) established impacts of the parasite on the trees water and carbon cycle. The amended model was validated simulating a sample forest stand in the Berlin area (Germany) comprising trees with and without mistletoe infection. At the same forest stand, tree core measurements were taken to evaluate simulated and observed growth. A subsample of trees were harvested to quantify biomass compartments of the tree canopy and to derive a growth function of the mistletoe population. The process-based simulations of the forest stand revealed 27% reduction in basal area increment (BAI) during the last 9 years of heavy infection, which was confirmed by the measurements (29% mean growth reduction). The long-term simulations of the forest stand before and during the parasite infection showed that the amended forest growth model 4C depicts well the BAI growth pattern during >100 years and also quantifies well the mistletoe-induced growth reductions in Scots pine stands.

Combining multiple statistical methods to evaluate the performance of process-based vegetation models across three forest stands

Abstract Process-based vegetation models are crucial tools to better understand biosphere-atmosphere exchanges and ecophysiological responses to climate change. In this contribution the performance of two global dynamic vegetation models, i.e. CARAIB and ISBACC, and one stand-scale forest model, i.e. 4C, was compared to long-term observed net ecosystem carbon exchange (NEE) time series from eddy covariance monitoring stations at three old-grown European beech (Fagus sylvatica L.) forest stands. Residual analysis, wavelet analysis and singular spectrum analysis were used beside conventional scalar statistical measures to assess model performance with the aim of defining future targets for model improvement. We found that the most important errors for all three models occurred at the edges of the observed NEE distribution and the model errors were correlated with environmental variables on a daily scale. These observations point to possible projection issues under more extreme future climate conditions. Recurrent patterns in the residuals over the course of the year were linked to the approach to simulate phenology and physiological evolution during leaf development and senescence. Substantial model errors occurred on the multi-annual time scale, possibly caused by the lack of inclusion of management actions and disturbances. Other crucial processes defined were the forest structure and the vertical light partitioning through the canopy. Further, model errors were shown not to be transmitted from one time scale to another. We proved that models should be evaluated across multiple sites, preferably using multiple evaluation methods, to identify processes that request reconsideration.