• Nenhum resultado encontrado

Identifying environmental controls on vegetation greenness phenology through model-data integration

N/A
N/A
Protected

Academic year: 2017

Share "Identifying environmental controls on vegetation greenness phenology through model-data integration"

Copied!
109
0
0

Texto

Loading

Imagem

Table 1. Overview of optimization experiments with information sources for prior and posterior parameter sets
Table D1. Description of LPJmL model parameters that were addressed in this study.
Table D2. Prior parameter values of LPJmL-OP (OP.prior). The values in brackets are ranges of uniform parameter distributions that were used during optimization
Table D3. Posterior parameter values for LPJmL-OP based on grid cell-level optimization ex- ex-periments (OP.gc)
+7

Referências

Documentos relacionados

Kim., Grunwald [ 9 ] used nine different spectral vegetation indices derived from three different satellite images and environmental ancillary data, and developed prediction models

Tendo em vista que os educadores apresentam dificuldades ao abordarem a orientação sexual, mostrando ser um limite a ser vencido pelos mesmos, vemos a possibilidade de

At that time the primary sources of information on livestock dis- ease and production were personal im- pressions and data derived from a subjec- tive

O uso das novas tecnologias na concepção dos trabalhos escolares envolve o comprometimento de todos os agentes envolvidos (alunos, professores, supervisão e

L’idée de consacrer une séance à la mise en scène et à la mise en exposition de la chasse dans les cultures extra-européennes est née d’un double constat : d’une part, les

The analyses will be based on time series of vegetation Indices, ENSO and climatic elements for nine years (2000 – 2008) and also aims to predict vegetation condition

the cross-correlation coe ffi cient between the time series of the proxy variable and the variable that is being reconstructed on the basis of the available simultaneous observa-

Time-series analysis is characterized, as a data mining tool which facilitates understanding nature of manufacturing processes and permits prediction of future values of the