The increase in aridity, mainly by decreases in precipitation but also by higher temperatures, is likely the main threat to the diversity and survival of Mediterranean forests. Changes in land use, including the abandonment of extensive crop activities, mainly in mountains and remote areas, and the increases in human settlements and demand for more resources with the resulting fragmentation of the landscape, hinder the establishment of appropriate management tools to protect Mediterranean forests and their provision of services and biodiversity. Experiments and observations indicate that if changes in climate, land use and other components of global change, such as pollution and overexploitation of resources, continue, the resilience of many forests will likely be exceeded, altering their structure and function and changing, mostly decreasing, their capacity to continue to provide their current services. A consistent assessment of the impacts of the changes, however, remains elusive due to the difficulty of obtaining simultaneous and complete data for all scales of the impacts in the same forests, areas and regions. We review the impacts of climate change and other components of global change and their interactions on the terrestrial forests of Mediterranean regions, with special attention to their impacts on ecosystem services. Management tools for counteracting the negative effects of global change on Mediterranean ecosystem- services are finally discussed.

The European Centre for Medium‐Range Weather Forecasts land surface model has been extended to include a carbon dioxide module. This relates photosynthesis to radiation, atmospheric carbon dioxide (CO₂) concentration, soil moisture, and temperature. Furthermore, it has the option of deriving a canopy resistance from photosynthesis and providing it as a stomatal control to the transpiration formulation. Ecosystem respiration is based on empirical relations dependent on temperature, soil moisture, snow depth, and land use. The CO₂ model is designed for the numerical weather prediction (NWP) environment where it benefits from good quality meteorological input (i.e., radiation, temperature, and soil moisture). This paper describes the CO₂ model formulation and the way it is optimized making use of off‐line simulations for a full year of tower observations at 34 sites. The model is then evaluated against the same observations for a different year. A correlation coefficient of 0.65 is obtained between model simulations and observations based on 10 day averaged CO₂ fluxes. For sensible and latent heat fluxes there is a correlation coefficient of 0.80. To study the impact on atmospheric CO₂, coupled integrations are performed for the 2003 to 2008 period. The global atmospheric growth is well reproduced. The simulated interannual variability is shown to reproduce the observationally based estimates with a correlation coefficient of 0.70. The main conclusions are (i) the simple carbon dioxide model is highly suitable for the numerical weather prediction environment where environmental factors are controlled by data assimilation, (ii) the use of a carbon dioxide model for stomatal control has a positive impact on evapotranspiration, and (iii) even using a climatological leaf area index, the interannual variability of the global atmospheric CO₂ budget is well reproduced due to the interannual variability in the meteorological forcing (i.e., radiation, precipitation, temperature, humidity, and soil moisture) despite the simplified or missing processes. This highlights the importance of meteorological forcing but also cautions the use of such a simple model for process attribution.

Severe droughts strongly impact photosynthesis (GPP), and satellite imagery has yet to demonstrate its ability to detect drought effects. Especially changes in vegetation functioning when vegetation state remains unaltered (no browning or defoliation) pose a challenge to satellite-derived indicators. We evaluated the performance of different satellite indicators to detect strong drought effects on GPP in a beech forest in France (Hesse), where vegetation state remained largely unaffected while GPP decreased substantially. We compared the results with three additional sites: a Mediterranean holm oak forest (Puéchabon), a temperate beech forest (Hainich), and a semi-arid grassland (Bugacpuszta). In Hesse, a three-year reduction in GPP following drought was detected only by the Enhanced Vegetation Index (EVI). The Photochemical Reflectance Index (PRI) also detected this drought effect, but only after normalization for absorbed light. In Puéchabon normalized PRI outperformed the other indicators, while the short-term drought effect in Hainich was not detected by any tested indicator. In contrast, most indicators, but not PRI, captured the drought effects in Bugacpuszta. Hence, PRI improved detection of drought effects on GPP in forests and we propose that PRI normalized for absorbed light is considered in future algorithms to estimate GPP from space.

This article examines the possibility of exploiting ground reflectance in the near-infrared (NIR) for monitoring grassland phytomass on a temporal basis. Three new spectral vegetation indices (infrared slope index, ISI; normalized infrared difference index, NIDI; and normalized difference structural index, NDSI), which are based on the reflectance values in the H25 (863–881 nm) and the H18 (745–751 nm) Chris Proba (mode 5) bands, are proposed. Ground measurements of hyperspectral reflectance and phytomass were made at six grassland sites in the Italian and Austrian mountains using a hand-held spectroradiometer. At full canopy cover, strong saturation was observed for many traditional vegetation indices (normalized difference vegetation index (NDVI), modified simple ratio (MSR), enhanced vegetation index (EVI), enhanced vegetation index 2 (EVI 2), renormalized difference vegetation index (RDVI), wide dynamic range vegetation index (WDRVI)). Conversely, ISI and NDSI were linearly related to grassland phytomass with negligible inter-annual variability. The relationships between both ISI and NDSI and phytomass were however site specific. The WinSail model indicated that this was mostly due to grassland species composition and background reflectance. Further studies are needed to confirm the usefulness of these indices (e.g. using multispectral specific sensors) for monitoring vegetation structural biophysical variables in other ecosystem types and to test these relationships with aircraft and satellite sensors data. For grassland ecosystems, we conclude that ISI and NDSI hold great promise for non-destructively monitoring the temporal variability of grassland phytomass.