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Previous studies have suggested that a major part of the observed variability in vegetation state might be associated with variability in climatic drivers during relatively short periods within the year. Identification of such critical climate periods, when a particular climate variable most likely has a pronounced influence on the vegetation state of a particular ecosystem, becomes increasingly important in the light of climate change. In this study, we present a method to identify critical climate periods for eight different semi-natural ecosystem categories in Hungary, in Central Europe. The analysis was based on the moving-window correlation between MODIS NDVI/LAI and six climate variables with different time lags during the period 2000–2020. Distinct differences between the important climate variables, critical period lengths, and direction (positive or negative correlations) have been found for different ecosystem categories. Multiple linear models for NDVI and LAI were constructed to quantify the multivariate influence of the environmental conditions on the vegetation state during the late summer. For grasslands, the best models for NDVI explained 65–87% variance, while for broad-leaved forests, the highest explained variance for LAI was up to 50%. The proposed method can be easily implemented in other geographical locations and can provide essential insight into the functioning of different ecosystem types. [ABSTRACT FROM AUTHOR]

• Spring green-up duration (GUD) of broadleaf forests was assessed for Central Europe. • There is considerable interannual variability in the GUD. • Elevation dependent relationship was found between start of season date and the GUD. • Multiple linear regression models were created to estimate the GUD. The present study focuses on the leaf unfolding dynamics of deciduous broadleaf forests in Central Europe. MODerate resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) was used to quantify green-up duration (GUD) for the wider Carpathian Basin located in Central Europe, covering the time period 2000–2019. GUD was calculated for ~170 000 pixels with deciduous broadleaf forest cover at 500 m spatial resolution. The GUD exhibited large interannual and elevation-dependent variability where the latter likely indicates the distribution of the different species. The longest mean GUD occurred in 2017 (32.7 days), while the shortest (14.5 days) was associated with 2018. The relationship between the start of leaf unfolding (SOS) and the GUD (R = -0.62) reveals that the timing of the bud break plays an important role in the leaf unfolding process. Multiple linear regression models were constructed to explain and forecast the GUD based on the date of SOS, the elevation and the meteorological variables. The main explanatory variable was the SOS date, explaining 38.3% of the GUD variability (RMSE =8 days), while the addition of the elevation and its square to the model increased the explained variance to 47.8% (RMSE =7.34 days). Further addition of meteorological variables covering periods prior to and after the SOS increased the explained variance (the best R 2 was 0.65). The results indicate the complexity of processes that drive the leaf unfolding. We propose that future studies should consider SOS, elevation and meteorological variables together to interpret GUD dynamics. Earlier SOS implies longer GUD, while delayed SOS is associated with short GUD, which means that the benefits of climate change possibly realized as a longer growing season could be smaller than anticipated. Accurate estimation of the SOS is a prerequisite for the successful modeling of the GUD. Image, graphical abstract [ABSTRACT FROM AUTHOR]

Water vapor and carbon dioxide are the main atmospheric constituents which are controlling the Earth's climate. The rapid increase in the atmospheric content of carbon dioxide since the beginning of the industrial revolution is considered as one of the main drivers of the recent climate changes on Earth. Only about 40% of the total anthropogenic emissions of CO2 remain in the atmosphere, while the rest of the emitted CO2 is stored in oceans and land. The terrestrial sinks of carbon are global soils and forests. Forests sequester CO2 from the atmosphere and assimilate carbon into above- and belowground biomass and by that partially offset anthropogenic emission of CO2 and participate in a regulation of climate. Because of these findings, monitoring of CO2 exchange between atmosphere and underlying forest ecosystems has gained significant importance. Micrometeorological eddy covariance technique has shown as the most accurate way for direct flux measurement of trace gases, and today it is a standard tool for estimating net ecosystem exchange (NEE) of trace gases between the atmosphere and the underlying surface. Within this research 10-year eddy covariance experiment (2008S2017) was carried out in young pedunculate oak (Quercus robur L.) stands (35S44 years old) which are part of the forest complex of the Kupa River basin, about 35 km SW from Zagreb, Croatia. Over the entire study period, Jastrebarsko forest acted as a carbon sink, with an average annual NEE of -319 ± 30 g C m-2yr-1. Estimated NEE was partitioned into gross primary production (GPP) and ecosystem respiration (RECO). Furthermore, RECO was partitioned into heterotrophic (Rh) and autotrophic respiration (Ra). Most important carbon flux in forest ecosystems, net primary production (NPP), was estimated by subtracting NEE from heterotrophic respiration. In this study the causes of interannual variability of carbon NEE were investigated. Also, the impact of extreme weather events (droughts and floods) on carbon fluxes was investigated. For validation of EC measurements, a biometric estimate of the net primary productivity (NPPBM), which was built on periodic measurement and simple modelling, was compared with NPPEC. Comparison of NPPEC and NPPBM showed good agreement (R2=0.46). [ABSTRACT FROM AUTHOR]