Abstract
A ten-year data-set descriptive of Italian forest gross primary production (GPP) has been recently constructed by the application of Modified C-Fix, a parametric model driven by remote sensing and ancillary data. That data-set is currently being used to develop multivariate regression models which link the inter-year GPP variations of five forest types (white fir, beech, chestnut, deciduous and evergreen oaks) to seasonal values of temperature and precipitation. The five models obtained, which explain from 52% to 88% of the inter-year GPP variability, are then applied to predict the effects of expected environmental changes (+2 °C and increased CO2 concentration). The results show a variable response of forest GPP to the simulated climate change, depending on the main ecosystem features. In contrast, the effects of increasing CO2 concentration are always positive and similar to those given by a combination of the two environmental factors. These findings are analyzed with reference to previous studies on the subject, particularly concerning Mediterranean environments. The analysis confirms the plausibility of the scenarios obtained, which can cast light on the important issue of forest carbon pool variations under expected global changes.
Highlights
The increasing level of atmospheric CO2 and consequent global climate change are enhancing the need for assessing the amount of carbon stored by terrestrial ecosystems
Climate changes that are already visible and that are expected to increase in the decades may play a fundamental role in the capacity of carbon sequestration of forest ecosystems located in vulnerable areas like the Mediterranean basin [3]
The performance of the statistical methodology applied to simulate future gross primary production (GPP) patterns is dependent on the quality of the data-set analyzed and on its representativeness for the inter-year meteorological variability which can affect forest production
Summary
The increasing level of atmospheric CO2 and consequent global climate change are enhancing the need for assessing the amount of carbon stored by terrestrial ecosystems. Climate changes that are already visible and that are expected to increase in the decades may play a fundamental role in the capacity of carbon sequestration of forest ecosystems located in vulnerable areas like the Mediterranean basin [3]. A more comprehensive understanding of change impact in highly heterogeneous Mediterranean areas would require the consideration of both climate and morphological spatial variability This has stimulated the use of remotely sensed images, which offer the fundamental advantage of being directly applicable to estimate forest production over wide areas for multiyear periods. C-Fix, a parametric model driven by remote sensing and ancillary data This data-set is statistically analyzed in order to develop multivariate regression models which link the inter-year GPP variations of five forest types to seasonal values of temperature and rainfall. The likely consequences of each scenario are discussed together with the main sources of uncertainty introduced
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