Climate change and ecosystems
-studies in Tibetan Plateau
Xiaolin Zhu
zhu.381@osu.edu
11/05/2012
Geog 8901 class presentation
Outline
 Introduction
study area, phenology and detection by remote sensing
 Has the phenology changed during the past decades?
Paper I (Piao et al., 2011)
 Did these changes relate to the climate change?
Paper II (Shen et al., 2011)
 Conclusion and discussion
1
A story and a lesson for publication
 2011. Piao et al.,Altitude and temperature dependence of change in the spring
vegetation green-up date from 1982 to 2006 in the Qinghai-Xizang Plateau.
Agricultural and Forest Meteorology, 151, 1599-1608.
 2007. Zhu, X. Study of the Altitude Dependence of Phenological Variation in
Tibet-Qinghai Plateau, undergraduate thesis
 2011. Shen, M., Tang, Y., Chen, J., Zhu, X. and Zheng, Y. Influences of
temperature and precipitation before the growing season on spring phenology
in grasslands of the central and eastern Qinghai-Tibetan Plateau. Agriculture
and Forest Meteorology, 151, 1711-1722.
Be quick!!!
Introduction
Study area: Tibetan Plateau
Introduction
Study area: Tibetan Plateau
1) well known as “roof of the world” with an average
of altitude over 4,000 m
2) annual mean temperature ranges from -9 to10 ℃
with an average of 3.9 ℃
3) precipitation ranges from 100 to 1000 mm with an
average of 483 mm and spatially descending from
southeast to northwest
4) alpine meadow and alpine grassland cover more
than 60% area
Introduction
Introduction
Introduction
Introduction
Introduction
1.background
Phenology: ‘‘the study of the timing of biological phases’’.
1.background
Phenological changes：
Historical observations, remote sensing, and ecosystem
models agree on the fact that over the past several
decades, a significant extension of vegetation growing
season has been occurring in the northern regions,
marked by both an earlier beginning and a later
termination (Penuelas et al., 2002; Zhou et al., 2001;
Piao et al., 2007)
1.background
Phenology and climate
warming lengthens growing
season (short time)
Climate
Phenology
extension of the growing season
increases carbon sink (long
time)
3
1.background
Remote sensing in phenological studies
1) Global and Regional Scale
2) inter-annual and seasonal
3) AVHRR and MODIS VI product
4) Correlation to field observation
1.background
Basic idea: Vegetation index from remote sensing shows
the growing stages
Figure is from Zhang et al.(2003)
4 key phenological phases of vegetation dynamics: (1)
greenup; (2) maturity; (3) senescence; (4) dormancy;
(5) growing-season length (GSL)
Paper I: : Phenological Variation
 Piao et al., 2011. Altitude and temperature dependence
of change in the spring vegetation green-up date from
1982 to 2006 in the Qinghai-Xizang Plateau.
Agricultural and Forest Meteorology
 Xiaolin Zhu. 2007. Study of the Altitude Dependence
of Phenological Variation in Tibet-Qinghai Plateau,
undergraduate thesis
Paper I: Phenological Variation
1. Introduction
1) In most temperate ecosystems where temperature is
a key factor controlling phenology
2) global warming might show an altitude dependency
3) it is natural to ask the question whether the change
of high altitude ecosystem function also exhibits an
altitude dependency
Paper I: Phenological Variation
1. Introduction
Questions:
(1) What are the spatio-temporal patterns of spring vegetation green-up?
How does the spring vegetation green-up change over the QinghaiXizang Plateau?
(2) How does the spring vegetation green-up date and its temporal trends
vary across an increasing altitude gradient?
(3) How does the spring green-up date correlate with temperature?
(4) How does the critical temperature threshold of the vegetation green-up
onset respond to temperature changes?
Paper I: Phenological Variation
2. Methods and datasets
Datasets:
 8 km × 8 km and 15-day interval were derived from
NOAA/AVHRR series satellites from 1982 to 2006;
 Daily mean temperature data recorded from 50
meteorological stations across the plateau for the
period of 1982–2006
Paper I: Phenological Variation
Meteorological stations
Paper I: Phenological Variation
2. Methods and datasets
Analyses:
 a linear regression using ordinary least squares.
 In order to analyze changes in phenology at different
altitudes, we calculated the average and standard
deviation (SD) of vegetation green-up onset dates
along the altitude gradient for every 100 m bin.
 then analyzed the changes in the temporal trend of
vegetation green-up date along the altitude gradient.
Paper I: Phenological Variation
3.Results and discussion
3.1. Spatial patterns of vegetation green-up
The green-up date
increases from west to
east, reflecting spatial
differences in climate
and topography.
Paper I: Phenological Variation
3.Results and discussion
3.1. Spatial patterns of vegetation green-up
the green-up date
increases by 0.8 day
per 100 m increase
in elevation. Such
delayed green-up in
higher elevation tends
to be more obvious in
regions above 3600 m
(1.1 days per 100 m).
Paper I: Phenological Variation
3.Results and discussion
3.2. Temperature thresholds for vegetation green-up
vegetation in colder
environments requires a lower
threshold temperature to green
up than in warmer areas.
Paper I: Phenological Variation
3.Results and discussion
3.3. Change in vegetation green-up date in the whole area
Paper I: Phenological Variation
3.Results and discussion
3.3.2. Spatial patterns of trends in vegetation green-up
date
(A) Trend in vegetation green-up from 1982 to 1999, (C) trend in spring temperature from
1982 to 1999
Paper I: Phenological Variation
3.Results and discussion
3.3.2. Spatial patterns of trends in vegetation green-up
date
(B) Trend in vegetation green-up from 1999 to 2006, (D) trend in spring temperature from
1999 to 2006
Paper I: Phenological Variation
3.Results and discussion
 3.3.3. Green-up trends in relation to elevation: in the whole Plateau
(A) Trend in onset dates of vegetation green-up from
1982 to 1999
(B) trend in onset dates of vegetation green-up from
1999 to 2006
Paper I: Phenological Variation
3.Results and discussion
3.3.3. Green-up trends in relation to elevation: for each climate station
(A) Trend in onset dates of vegetation green-up from
1982 to 1999
(B) trend in onset dates of vegetation green-up from
1999 to 2006
Paper I: Phenological Variation
3.Results and discussion
3.3.3. Green-up trends in relation to elevation: for each climate station
(C) trend in spring temperature from 1982 to 1999
(D) Trend in spring temperature from 1999 to 2006
This result implies that these 50 climate stations are insufficient to well
represent the altitudinal gradient of change in vegetation green-up date as
well as temperature over the entire Plateau.
Paper I: Phenological Variation
4. Conclusion
 both the spatial pattern and temporal change of spring vegetation green-up
closely depends on altitude
 Change in temperature likely function as the major controller for the spatio-
temporal patterns of the vegetation green-up dates in the Qinghai-Xizang
Plateau
 Positive correlation between temperature critical threshold of vegetation green-
up and MAT implies potential vegetation adaptation in warmer climate
 these 50 climate stations are insufficient to well represent the spatio-temporal
change of vegetation green-up date as well as temperature over the entire
Qinghai-Xizang plateau
Paper II: Influences of Climate
2011. Shen, M., Tang, Y., Chen, J., Zhu, X. and Zheng, Y. Influences of
temperature and precipitation before the growing season on spring
phenology in grasslands of the central and eastern Qinghai-Tibetan
Plateau. Agriculture and Forest Meteorology, 151, 1711-1722.
What is the difference from the paper I:
1)
Consider both temperature and precipitation;
2)
Dig more information from the climate data
Paper II: Influences of Climate
1. Introduction
 the timing of spring phenological events under the
current warming climate differed in magnitude and
even in direction (i.e., advance vs. delay in the date)
among species and areas
 the underlying mechanisms responsible for these shifts
have been scarcely explored.
Paper II: Influences of Climate
1. Introduction
 In temperate zones, vegetation does not begin to grow
again until a certain cumulative temperature above a
threshold
 In addition, water availability is one of the critical
environmental factors that regulate vegetation
activities in many areas such as arid and semiarid
grasslands
Paper II: Influences of Climate
1. Introduction
Objective:
aims to untangle how changes in spring temperature and
precipitation influence the spring phenology of
grasslands on the plateau
Paper II: Influences of Climate
2. Materials and methods
2.1. Study area
Paper II: Influences of Climate
2. Materials and methods
2.2. Detection of green-up onset from satellite images
Data:
8 km AVHRR NDVI from 1982 to 2006 (same as paper I)
Method:
logistic function method (Zhang et al., 2003)
Paper II: Influences of Climate
2. Materials and methods
2.3. Climatological variables
daily surface air temperature and precipitation data from 50
meteorological stations
Paper II: Influences of Climate
2. Materials and methods
2.3.1. Holdridge aridity index
Holdridge aridity index (HAI) is an index of the potential
evapotranspiration rate, and is defined as the ratio of the biotemperature to the annual precipitation
Paper II: Influences of Climate
2. Materials and methods
2.3.2. Thermal spring onset date
thermal spring onset date (TSO), which is based on the cumulative
temperature above a threshold temperature that defines when green-up
can potentially begin.
How to decide time span : maximizing the correlation between GUD and
TSO
GUD
time span
Paper II: Influences of Climate
2. Materials and methods
2.3.3. Preseason precipitation
the cumulative preseason precipitation (PPT), which was found to be
negatively correlated with GUD
How to decide time span: minimizing the correlation between GUD and
PPT
GUD
time span
Paper II: Influences of Climate
3. Results
3.1. Correlation between GUD and preseason temperature
The grass in warmer and lower area needs more
energy to green-up.
Paper II: Influences of Climate
3. Results
3.1. Correlation between GUD and preseason temperature
GUD for about 80% of the stations tended to be affected by
temperatures within 5 weeks before the onset of green-up.
Paper II: Influences of Climate
3. Results
3.1. Correlation between GUD and preseason temperature
GUD was more strongly related to
TSO in areas with a lower HAI.
GUD in humid area more related to
temperature!
(C) Coefficients of correlations between GUD and
TSO (r(GUD, TSO)) along a spatial gradient of the
Holdridge aridity index (HAI)
Paper II: Influences of Climate
3. Results
3.1. Correlation between GUD and preseason precipitation
GUD at 32 out of the 50 stations was negatively correlated with
PPT within the 30 days
Paper II: Influences of Climate
3. Results
3.1. Correlation between GUD and preseason precipitation
There was a weak but statistically
significant (P < 0.01) decreasing
trend in the correlation coefficient as a
function of HAI
GUD in humid area less related to
precipitation!
(C) Coefficients of correlations between
GUD and PPT (r(GUD, PPT))
as a function of the Holdridge aridity
index (HAI)
Paper II: Influences of Climate
3. Results
3.2. Inter-annual variations in green-up onset and TSO days at a regional
scale
an advancing trend since
1982, despite a sharp delay
in 2000 and 2001.
advanced by 9.9 days from
1982 to 1999
Interannual variations in green-up onset date (GUD) and thermal spring onset
date (TSO) averaged for the meteorological stations. The positive r values
represent Pearson’s coefficient of correlation between GUD and TSO, and the
negative r values in bold are Pearson’s coefficient of correlation between GUD
and preseason precipitation (PPT).
Paper II: Influences of Climate
3. Results
3.3. Spatial patterns of the shifts in TSO and GU
On average, the
grasslands experienced a
delay of 3.3 days in GUD
from 1982 to 2006, but
with an advance of 9.6
days from 1982 to 1999
and a delay of 7.2 days
from 1999 to 2006.
Spatial patterns in the trends (day/year) of green-up onset
date (GUD) and thermal spring onset date (TSO) from 1982
to 2006
Paper II: Influences of Climate
3. Results
3.3. Spatial patterns of the shifts in TSO and GUD
Relationships between the trends for green-up onset
date (GUD) and thermal spring onset date (TSO) from
1982 to 2006
Paper II: Influences of Climate
4. Discussion
4.1. the effects of preseason temperature and precipitation on the onset of
green-up
 spring green-up of the vegetation in warm areas requires a higher
cumulative temperature than in cold areas
 Vegetation adaption? Additional research will be required to determine
whether this result indicates that the onset of green-up at a given
location will require an increased cumulative temperature as the climate
grows warmer.
Paper II: Influences of Climate
4. Discussion
4.1. the effects of preseason temperature and precipitation on the onset of
green-up
To show the effects of preseason temperature and precipitation on the
onset of green-up, we regressed GUD on TSO and PPT
Paper II: Influences of Climate
4. Discussion
4.2. Other factors that may have influenced spring phenology
1) permafrost: as warming, the available water might be partly provided
by thawing of the soil in spring and summer rather than by
precipitation
2) Human activities may also have influenced the onset of green-up by
changing components of the vegetation community.
Conclusion and discussion
Conclusion:
 Phenology in Tibet did change during the past 3
decades
 The change shows altitude dependence
 The change may be caused by the change of preseason
temperature and precipitation, but the influence is
different in different regions
Conclusion and discussion
Limitation:
 Satellite-derived vegetation phenology contain large
uncertainties and thus need careful validation.
 50 meteorological stations are very limited
Further work:
 Need more ground-based community-level phenological
studies
 Study the spatial scaling-up of phenology and climate
relationships (area and point problem)
Thank you!
Any questions?
My question:
is it a balance between climate and vegetation at a long time
scale? If so, do we still need worry about global warming?
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