The Role of Managed Forest Ecosystems: A Modeling Based Approach - The Greenhouse Gas Balance of Italy - page 72

parameter settings for all main biome types (White et al. 2000 ). These settings
were modified for six forest types to adapt to Mediterranean environments, which
show eco-climatic features markedly different from those the model was originally
developed for (see Chiesi et al. 2007 for details).
The application of BIOME-BGC in the Italian context required the transforma-
tion of the quasi-climax GPP, respiration and allocation estimates into estimates
of real forest ecosystems, which are generally far from climax due to the occurred
disturbances. The modeling strategy of Maselli et al. ( 2009b ) considers the ratio
between actual and potential forest standing volume as an indicator of ecosystem
proximity to climax. This ratio can therefore be used to correct the photosynthesis
and respiration estimates obtained by the model simulations. Accordingly, actual
forest NPP (NPP A , g C m − 2 year − 1 ) can be approximated as:

NPP A = GPP ∗ FC A − Rgr ∗ FC A − Rmn ∗ NV A

(5.2)

where GPP , Rgr and Rmn correspond to the GPP, growth and maintenance res-
piration estimated by BIOME-BGC (g C m − 2 year − 1 ), and the two terms FC A
(actual forest cover) and NV A (actual normalized standing volume), both dimen-
sionless, are derived from the ratio between actual and potential tree volume.
Due to the previously described functional equivalence of C-Fix and BIOME-
BGC GPP estimates, the outputs of the two models can be integrated by multi-
plying BIOME-BGC photosynthesis and respiration estimates for a ratio between
C-Fix and BIOME-BGC GPP. In the current case, BIOME-BGC was applied only
to the Tuscany territory, due to the lack of daily meteorological data for the rest of
Italy. This required the application of an approximation methodology based on the
use of two further assumptions. First, respiration simulated by BIOME-BGC was
assumed to vary linearly following photosynthesis, which allowed the calculation
of growth and maintenance respiration as constant fractions of GPP for each forest
type. Second, a similar assumption was applied to simulate spatial variations of
maximum standing volume and LAI, which were needed to compute FC A and NV A
(Maselli et al. 2009a ). Both these assumptions are in reasonable accordance with
BIOME-BGC logic, which simulates ecosystems whose all main properties and
functions are descriptive of a quasi-climax equilibrium.
The reference values of GPP, respirations, stem carbon and LAI were recov-
ered for each forest type from a BIOME-BGC simulation performed in Tuscany
over a 12-year time period (Chiesi et al. 2011 ). Stem carbon was converted into
maximum standing volume using the coefficients given by Federici et al. ( 2008 ).
BIOME-BGC estimates were then rescaled for each forest type following relevant
Modified C-Fix GPP outputs. The regional values of actual forest standing volume
needed to compute FC A and NV A were extracted for each forest type and Region
from the map of Gallaun et al. ( 2010 ). All these data were combined within
Eq. 5.2 to compute NPP A for each forest type and Region. CAI values (m 3 ha − 1
year − 1 ) were then computed through Eq. 5.3 :

CAI = NPP A ∗ SCA / BEF / BWD ∗ 2 ∗ 100

(5.3)