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Climate Change 2016
October 27-29, 2016
Volume 7, Issue 9(Suppl) J Earth Sci Clim Change
ISSN: 2157-7617 JESCC, an open access journal
conferenceseries .com
October 24-26, 2016 Valencia, Spain
World Conference onClimate Change
Effects of various ecological factors on process-based ecological model behavior uncertainty
Yin Ren
Chinese Academy of Sciences, China
Ecological forecasting predicts the effects of environmental changes on ecosystemstate and activity. At present, technologically
comprehensive forecasting estimates are estimated using process-based ecological models. However, it is difficult to
isolate the ecological factors that cause models behavior uncertainty. To solve this problem, this study aimed to construct
an ecoinformatic diagnostic framework to explain uncertainty in model behavior with respect to both the mechanisms and
algorithms involved in ecological forecasting. We introduce a complicated ecological driving mechanism to the process-based
ecological model using analytical software and algorithms. The ecological forecasting study involved three components: (1)
model-observation validation, (2) diagnostic framework, and (3) temporal and spatial forecasting methods. Subsequently, as a
case study, we apply the diagnostic framework to detect Eucalyptus biomass forest patches at a regional scale (196,158 ha) using
the 3PG2 (Physiological Principles in Predicting Growth) model. Our results show that this technique improves the accuracy of
ecological simulation for ecological forecasting and prevents new uncertainties from being produced by adding a new driving
mechanism to the original model structure. We achieved the highest cost performance ratio between the accuracy requirement
of model simulation and the availability of observation data. This result was supported by our Eucalyptus biomass simulation
using the 3PG2 model, in which ecological factors caused 21.83% and 9.05% uncertainty in model behavior temporal and
spatial forecasting, respectively. In conclusion, the systematic ecoinformatic diagnostic framework developed here provides a
new method that could be applied to research requiring comprehensive ecological forecasting.
Biography
Yin Ren has completed his PhD at the age of 29 years from Nanjing Forestry University and Postdoctoral studies from Institute of Urban Environment, Chinese
Academy of Sciences. He is the Director of Urban Environmental Planning and Management. He has published more than 10 papers in reputed journals and has
been serving as an Editorial Board Member of repute.
Yin Ren, J Earth Sci Clim Change 2016, 7:9(Suppl)