Environmental Engineering Reference
In-Depth Information
3. In the third analysis, the inter-annual variability has been analyzed defining for
each site with more than 3 years of data, the phenological period that mostly
contribute to annual NEE variability. Following Marcolla et al. (
2011
), for each
site-year the time series of cumulative monthly NEE and its standard deviation
have been computed. According to Marcolla et al. (
2011
), variations of standard
deviation from one month to the preceeding one (DStDev) quantifies the contri-
bution of each month to the observed interannual variability (IAV). The analysis
has been conducted also at weekly time scale with comparable results (data not
shown). Resulting DStDev positive values means that the specific period con-
tributes to the increase of the between year flux variability. Positive peaks of
DStDev mean that climatic conditions in the period in which the peak occurs
largely influence the inter annual variability. Periods with negative values of
DStDev tend instead to mitigate the inter annual variability of NEE. Once the
critical period for each PFT has been identified, the stepwise AIC regression
between annual NEE and different predictors has been re-computed. Not only
have the average annual values of climate predictors been tested, but also sub-
annual variable aggregation (i.e. average temperature in spring and summer;
cumulative precipitation in summer etc.). For each PFT the main driver control-
ling spatial and year-to-year variation in NEE has then been identified.
The results of the pair wise correlation analysis between annual NEE and climate
variables conducted for each PFT are reported in Table
2.1
. The results show that
average annual climatic variables cannot explain the variability of NEE except for
the EBF, for which a statistically significant correlation (
p
< 0.05) with Ta, Ts and
Rg have been found and for ENF, for which a correlation between NEE and Soil
Water Content (SWC) is observed.
In Table
2.2
the results of the stepwise AIC regression between cumulative
NEE and annual climatic predictors are reported. Even including multiple climate
predictors, a statistical significant correlation with NEE is observed only for the
evergreen PFTs (EBF and ENF).
By including as predictors the CUP and LAI, an important improvement
of the R
2
adj has been observed, highlighting the importance of structural site
Table 2.1
Pairwise correlation (r) between annual NEE cumulated and annual average of mete-
orological variables
Rg
Ta
Ts
VPD
Precip
SWC
0.01
0.00
0.00
0.00
0.00
0.44
ENF
DBF
0.02
0.00
0.01
0.01
0.03
0.02
0.49
0.70
0.58
0.01
0.08
0.04
EBF
0.03
0.00
0.03
0.09
0.04
0.02
CRO
GRA
0.26
0.00
0.02
0.01
0.00
0.21
Bold numbers
represent statistical significant correlations (
p
< 0.01).
Rg
Shortwave
Incoming Radiation,
Ta
Air Temperature,
Ts
Soil Temperature,
VPD
Vapur Pressure Deficit,
Precip
Precipitation,
SWC
Soil Water Content
