Analisi del ruolo delle variabili climatiche nei flussi di CO2 tra ecosistemi e atmosfera tramite modelli empirici e serie storiche di misure eddy covariance
Author(s)
Cavalli, Daniela
Date Issued
June 16, 2016
Type
Doctoral Thesis
Abstract
Increasing interest for increase of carbon dioxide in the atmosphere do human activities is directed in the last
years. The eddy covariance measurements are a fundamental of CO2 fluxes monitoring tool. At the present,
700 sites Such measures are mesured continuously on approximately 700 sites worldwide in different
ecosystems. In this study different empirical methods have been applied to release the importance of climatic
variables in driving GPP using the time series of eddy covariance measurements that covered at least six
years of data. Through the use of the SSA, signals of long and short term of GPP and driver variable have
been identified, to investigate which variable influence photosynthesis in different time frequencies. The
methods used are based on Neural Networks and Random Forest. These models show good performance,
confirming the effectiveness of these models to explain, in most cases, complex ecological phenomena. The
methodologies that have proven more robust in attributing importance to ecological drivers are three:
Forward stepwise method, destruction order method and random forest method (FM, DM and RM).
Generally in this study found, both in the short and long term, a greater dependence of photosynthesis to the
availability of water, rather than the type of vegetation and the temperatures. While, in the absence of
limiting factors, the radiation is the ecological driver that has the most influence on the GPP in the short term
and for temperate coniferous forest sites even over the long term.
Additional information
Dottorato di ricerca in Ecologia forestale
File(s)![Thumbnail Image]()
![Thumbnail Image]()
Name
Appendice_B_dcavalli_tesid.pdf
Description
Appendice B
Size
980.28 KB
Format
Adobe PDF
Checksum (MD5)
e7ebccfb5729e47bbd36313b5c3b550b
Name
dcavalli_tesid.pdf
Description
Tesi di dottorato
Size
2.26 MB
Format
Adobe PDF
Checksum (MD5)
88673f47a2875c3ba006d990f475f49d