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Background and Purpose: Determination of an extreme year from the aspect of the vegetation activity using only meteorological data might be ambiguous and not adequate. Furthermore, in some ecosystems, e.g. forests, the response is not instantly visible, but the effects of the meteorological anomaly can be seen in the following year. The aim of the present paper is to select and characterize typical and anomalous years using satellite-based remote sensing data and meteorological observations during the recent years of 2000-2014 for Central Europe, based on the response of the vegetation. Materials and Methods: In the present study vegetation characteristics were described using remotely sensed official products of the MODerate resolution Imaging Spectroradiometer (MODIS), namely NDVI, EVI, FPAR, LAI, GPP, and NPP, with 8-day temporal and 500 meter spatial resolution for the period of 2000-2014. The corresponding mean temperature and precipitation data (on the same grid) were derived from the Open Database for Climate Change Related Impact Studies in Central Europe (FORESEE) daily meteorological dataset. Land cover specific anomalies of the meteorological and vegetation characteristics were created and averaged on a country-scale, where the distinction between the main land cover types was based on the synergetic use of MODIS land cover and Coordination of Information on the Environment (CORINE) Land Cover 2012 datasets. Results: It has been demonstrated that the anomaly detection based solely on basic meteorological variables is ambiguous since the strength of the anomaly depends on the selected integration time period. In contrast, the effect-based approach exploiting the available, state-of-the-art remote sensing based vegetation indices is a promising tool for the characterization of the anomalous behaviour of the different land cover types. The selection of extreme years was performed in an explicit way using percentile analysis on pixel level. Conclusions: Plant status in terms of both positive and negative anomalies shows strong land cover dependency in Central Europe. This is most likely due to the differences in heat and drought resistance of the vegetation, and species composition. The selection of country-specific extreme years can serve as a basis for forthcoming research.

Background and Purpose: Biogeochemical process‑based models use a mathematical representation of physical processes with the aim of simulating and predicting past or future state of ecosystems (e.g. forests). Such models, usually executed as computer programs, rely on environmental variables as drivers, hence they can be used in studies of expected changes in environmental conditions. Process‑based models are continuously developed and improved with new scientific findings and newly available datasets. In the case of forests, long-term tree chronologies, either from monitoring or from tree-ring data, offer valuable means for testing modelling results. Information from different tree cores can cover a wide range of ecological and meteorological conditions and as such provide satisfactory temporal and spatial resolution to be used for model testing and improvement. Materials and Methods: In our research, we used tree-ring data as a ground truth to test the performance of Biome-BGCMuSo (BBGCMuSo) model in two distinct pedunculate oak forest areas, Kupa River Basin (called Pokupsko Basin) and Spačva River Basin, corresponding to a wetter and a drier site, respectively. Comparison of growth estimates from two different data sources was performed by estimating the dynamics of standardized basal area increment (BAI) from tree-ring data and standardized net primary productivity of stem wood (NPPw) from BBGCMuSo model. The estimated growth dynamics during 2000-2014 were discussed regarding the site-specific conditions and the observed meteorology. Results: The results showed similar growth dynamic obtained from the model at both investigated locations, although growth estimates from tree-ring data revealed differences between wetter and drier environment. This indicates higher model sensitivity to meteorology (positive temperature anomalies and negative precipitation anomalies during vegetation period) than to site-specific conditions (groundwater, soil type). At both locations, Pokupsko and Spačva, BBGCMuSo showed poor predictive power in capturing the dynamics obtained from tree‑ring data. Conclusions: BBGCMuSo model, similar to other process-based models, is primarily driven by meteorology, although site-specific conditions are an important factor affecting lowland oak forests’ growth dynamics. When possible, groundwater information should be included in the modelling of lowland oak forests in order to obtain better predictions. The observed discrepancies between measured and modelled data indicate that fixed carbon allocation, currently implemented in the model, fails in predicting growth dynamics of NPP. Dynamic carbon allocation routine should be implemented in the model to better capture tree stress response and growth dynamics.