and productivity (Prince et al. 1995). Some studies have also used NDVI data to
assess regional phenology (the so-called green wave) (Moulinetal 1997) and to
develop phenology models at a regional scale. Other indices are also used based
on the objective of research such as the enhanced vegetation index (EVI), Green-Red
Vegetation Index (GRVI), Leaf Area Index, Canopy Colour Index, and Leaf Strategy Index.
Other technologies, such as web cameras attached to drones (Fisher et al. 2007)
and eddy covariance measurements (Gu et al. 2003; Baldocchi et al. 2005), are
providing automated, continuous, and areally averaged measures of phenology
(Noormets 2009). Web cameras allow continuous monitoring of a particular area
of study.
Phenology modelling has emerged as a new branch of modelling to establish
relationships between the climate and phenological patterns and to examine the
reactions of plant phenology towards climate change: these models now have an
eminent role in regional ecosystem simulation models and biosphere/atmosphere
general circulation models (Chuine et al. 2000; Liu et al. 2019). A different global
climatic model (GCM) is used to study the dynamics of climate and to provide
different future projections of climate for a particular region. In the decade of the
1980s, a number of phenological models evolved (Chuine et al. 1999; Piao et al.
2019). The main attribute of these models is that they are result oriented: their
prediction is based on previous experimental results, which have established the
responses of plant phenology towards different climatic factors. These models,
which are based on the data of a small confined area, might work for large spatial
and temporal scale predictions. Hence, they can be used in making assumptions
about changes in plant phenology caused by future climate change at a different scale
(Chuine et al. 2000).
Hence, plant phenology, which has easily noticeable elements, is now widely
used as an indicator of climate change that will have a primary role in the identification and assessment of climate change. In the fourth report of the IPCC in 2007,
phenology was reported as a large part of the corroboration on climate change
impacts (Rosenzweig et al. 2008).
The literature for this chapter was perused using the Web of Science website. The
search words “climate change,” “forest,” and “phenology” were used in the first step
to shortlist the literature. The search yielded 1961 documents in the past two decades
starting from year 2000. The number of publications has steadily risen from about
20 articles in 2006 to more than 240 in the year 2018 (Fig. 8.1). Although the number
of articles has increased, a multivariate and inclusive approach is needed. In the
second step, articles were selected based on their abstract and general content. The
rest of the chapter describes the climatic factors responsible for plant phenological
changes, the impact of climate change on tree phenology, the consequences of a shift
in the phenological cycle, and a few mitigation strategies.
188
P. Tiwari et al.
assess regional phenology (the so-called green wave) (Moulinetal 1997) and to
develop phenology models at a regional scale. Other indices are also used based
on the objective of research such as the enhanced vegetation index (EVI), Green-Red
Vegetation Index (GRVI), Leaf Area Index, Canopy Colour Index, and Leaf Strategy Index.
Other technologies, such as web cameras attached to drones (Fisher et al. 2007)
and eddy covariance measurements (Gu et al. 2003; Baldocchi et al. 2005), are
providing automated, continuous, and areally averaged measures of phenology
(Noormets 2009). Web cameras allow continuous monitoring of a particular area
of study.
Phenology modelling has emerged as a new branch of modelling to establish
relationships between the climate and phenological patterns and to examine the
reactions of plant phenology towards climate change: these models now have an
eminent role in regional ecosystem simulation models and biosphere/atmosphere
general circulation models (Chuine et al. 2000; Liu et al. 2019). A different global
climatic model (GCM) is used to study the dynamics of climate and to provide
different future projections of climate for a particular region. In the decade of the
1980s, a number of phenological models evolved (Chuine et al. 1999; Piao et al.
2019). The main attribute of these models is that they are result oriented: their
prediction is based on previous experimental results, which have established the
responses of plant phenology towards different climatic factors. These models,
which are based on the data of a small confined area, might work for large spatial
and temporal scale predictions. Hence, they can be used in making assumptions
about changes in plant phenology caused by future climate change at a different scale
(Chuine et al. 2000).
Hence, plant phenology, which has easily noticeable elements, is now widely
used as an indicator of climate change that will have a primary role in the identification and assessment of climate change. In the fourth report of the IPCC in 2007,
phenology was reported as a large part of the corroboration on climate change
impacts (Rosenzweig et al. 2008).
The literature for this chapter was perused using the Web of Science website. The
search words “climate change,” “forest,” and “phenology” were used in the first step
to shortlist the literature. The search yielded 1961 documents in the past two decades
starting from year 2000. The number of publications has steadily risen from about
20 articles in 2006 to more than 240 in the year 2018 (Fig. 8.1). Although the number
of articles has increased, a multivariate and inclusive approach is needed. In the
second step, articles were selected based on their abstract and general content. The
rest of the chapter describes the climatic factors responsible for plant phenological
changes, the impact of climate change on tree phenology, the consequences of a shift
in the phenological cycle, and a few mitigation strategies.
188
P. Tiwari et al.