The availability of daily meteorological data extended over wide areas is a common requirement for modeling vegetation processes on regional scales. The present paper investigates the applicability of a pan-European data set of daily minimum and maximum temperatures and precipitation, E-OBS, to drive models of ecosystem processes over Italy. Daily meteorological data from a 10 yr period (2000 to 2009) were first downscaled to 1 km spatial resolution by applying locally calibrated regressions to a digital elevation model. The original and downscaled E-OBS maps were compared with meteorological data collected at 10 ground stations representative of different eco-climatic conditions. Additional tests were performed for the same sites to evaluate the effects of driving a model of vegetation processes, BIOME-BGC, with measured and estimated weather data. The tests were carried out using 10 BIOME-BGC versions characteristic for local vegetation types (Holm oak, other oaks, chestnut, beech, plain/hilly conifers, mountain conifers, Mediterranean macchia, olive trees, and C3 and C4 grasses). The experimental results indicate that the applied downscaling performs best for maximum temperatures, which is the most decisive factor for driving BIOME-BGC simulation of vegetation production. The downscaled data set is particularly suitable for the modeling of forest ecosystem processes, which could be further improved by the use of information obtained from remote sensing imagery.

This paper aims to provide general considerations, in the form of a scientific review, with reference to selected experiences of ALS applications under alpine, temperate and Mediterranean environments in Italy as case studies. In Italy, the use of ALS data have been mainly focused on the stratification of forest stands and the estimation of their timber volume and biomass at local scale. Potential for ALS data exploitation concerns their integration in forest inventories on large territories, their usage for silvicultural systems detection and their use for the estimation of fuel load in forest and pre-forest stands. Multitemporal ALS may even be suitable to support the assessment of current annual volume increment and the harvesting rates.

Several studies have demonstrated that Monteith's approach can efficiently predict forest gross primary production (GPP), while the modeling of net ecosystem production (NEP) is more critical, requiring the additional simulation of forest respirations. The NEP of different forest ecosystems in Italy was currently simulated by the use of a remote sensing driven parametric model (modified C-Fix) and a biogeochemical model (BIOME-BGC). The outputs of the two models, which simulate forests in quasi-equilibrium conditions, are combined to estimate the carbon fluxes of actual conditions using information regarding the existing woody biomass. The estimates derived from the methodology have been tested against daily reference GPP and NEP data collected through the eddy correlation technique at five study sites in Italy. The first test concerned the theoretical validity of the simulation approach at both annual and daily time scales and was performed using optimal model drivers (i.e., collected or calibrated over the site measurements). Next, the test was repeated to assess the operational applicability of the methodology, which was driven by spatially extended data sets (i.e., data derived from existing wall-to-wall digital maps). A good estimation accuracy was generally obtained for GPP and NEP when using optimal model drivers. The use of spatially extended data sets worsens the accuracy to a varying degree, which is properly characterized. The model drivers with the most influence on the flux modeling strategy are, in increasing order of importance, forest type, soil features, meteorology, and forest woody biomass (growing stock volume).

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