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Dr. Arturo Sanchez-Azofeifa Earth and Atmospheric Sciences Department
University of Alberta, Edmonton, Alberta, Canada
Carbon Fluxes in Tropical Dry Forests and Savannas: Human, Ecological and Biophysical
Dimensions
Outline • Basic background
• What do we know about tropical dry forest CO2 fluxes and their response to climate change in arid and semi-arid regions of the Americas?
• Continental response of tropical dry forests to climate
change (the last 25 years).
• The Chamela-Cuixmala, Mexico case study.
• What the future holds? …
• Conclusions
What do know? •Well… Very little to start with (although 47% of the tropics are tdfs).
•Arid and semi-arid regions lag on long term studies aimed to understand their response to climate change. In fact, dry forest research lags on a ratio of 1:300 scientific papers when compared with tropical rainforests.
•Ecological studies are systematic in two regions: Chamela-Cuixmala biosphere reserve and Santa Rosa National Park in Costa Rica.
•Tropical Forests are not considered part of any global networks aimed to link climate change observations and models to phenological response; nor are part of long term monitoring efforts.
• Tropical dry forests present well define phenological signals allowing for unique opportunities to evaluate their response to climate change and specially drought effects.
Assessments of global change impacts have identified phenology as a key indicator of ecosystem alteration
(IPCC 2007; Morisette et al. 2009)
Study Area •IAI – Tropi-Dry: Human and Biophysical Dimensions of Tropical Dry Forests in the Americas. Ecology, Remote sensing and social components.
•Chamela-Cuixmala Biosphere Reserve region located in the Pacific coast of Mexico.
•One of the most studied sites in the neotropics and part of the IAI Tropi-Dry network (Sites in Mexico, Costa Rica, Venezuela and Brazil).
Non Parametric Seasonal Kendall Test for Trend Precipitation: No statistically significant trends for precipitation (annual and growing season). Temperature: Statistically significant trends for positive on temperature (annual and growing season).
20
22
24
26
28
30
32
1970 1980 1990 2000 2010
Tem
pera
ture
(Cel
cius
Deg
)
Chamela-Cuixmala: Temperature
Mean Annual Mean Growing
Linear (Mean Annual) Linear (Mean Growing)
0
100
200
300
400
500
600
0
100
200
300
400
500
600
700
800
900
1000
1975 1980 1985 1990 1995 2000 2005 2010
Gro
win
g Se
ason
(mm
)
Annu
al (m
m)
Chamela-Cuixmala: Precipitation
Annual Growing Linear (Annual) Linear (Growing)
150
170
190
210
230
250
270
290
310
330
350
1988
-89
1989
-90
1990
-91
1991
-92
1992
-93
1993
-94
1994
-95
1995
-96
1996
-97
1997
-98
1998
-99
1999
-00
2000
-01
2001
-02
2002
-03
2003
-04
2004
-05
2005
-06
Length of Growing Season
40
60
80
100
120
140
160
180 19
89-9
0
1990
-91
1991
-92
1992
-93
1993
-94
1994
-95
1995
-96
1996
-97
1997
-98
1998
-99
1999
-00
2000
-01
2001
-02
2002
-03
2003
-04
2004
-05
Length of Dry Season
Non Parametric Seasonal Kendall Test for Trend Length of dry season: Statistically significant positive trend. Length of Growing Season: Statistically significant negative trend.
3
4
5
6
7
8
9
Ecosystem Productivity
Non Parametric Seasonal Kendall Test for Trend
Ecosystem Productivity: Statistically significant negative trend.
Tower Phenology
Enhanced Vegetative Index (EVI2) for Forest Canopy in Mexico 2008-2010
0,15
0,25
0,35
0,45
0,55
0,65
0,75
1 31 61 91 121 151 181 211 241 271 301 331 361
Mid
-Day
Can
opy
Tow
er E
VI2
Day of Year
2008 2009 2010
30 day difference in half-max green-up date
Cumulative distribution of winter (dry season) and summer (wet season) rainfall for 1977-2013 at Chamela, Jalisco, Mexico. Years of flux measurement are highlighted.
Effect of winter rains on structural (LAI) and optical properties (greenness) of the Tropical Dry Forest
0
50
100
150
200
250
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
22/0
3/20
08
21/0
5/20
08
20/0
7/20
08
18/0
9/20
08
17/1
1/20
08
16/0
1/20
09
17/0
3/20
09
16/0
5/20
09
15/0
7/20
09
13/0
9/20
09
12/1
1/20
09
11/0
1/20
10
12/0
3/20
10
11/0
5/20
10
10/0
7/20
10
08/0
9/20
10
07/1
1/20
10
06/0
1/20
11
07/0
3/20
11
06/0
5/20
11
05/0
7/20
11
03/0
9/20
11
02/1
1/20
11
01/0
1/20
12
01/0
3/20
12
30/0
4/20
12
29/0
6/20
12
28/0
8/20
12
27/1
0/20
12
26/1
2/20
12
24/0
2/20
13
Rai
nfal
l (m
m)
EVI2
mid
day
2008 2009 2010 2011 2012 2013
Summer rain Winter rain EVI2 midday
Daily course of energy and matter fluxes
Chamela, Mexico
Santa Rosa, Costa Rica
Mata Seca, Brazil
Phenology Towers: 1) NDVI 2) EVI 3) PAR Albedo 4) Digital Greenness
Meteorological Sensor Networks: Air & Soil Temperature and Humidity
Photosynthetically Active Radiation Total Solar Radiation
Precipitation
Serra do Cipo, Brazil
Chancani Forest, Argentina
A way forward: measuring phenology across the Americas
WSNs Applied to Ecology Huge potential for Earth Sciences with
numerous advantages over wired monitoring
Purelink.ca Altenergymag.com
WSN Hardware
Temperature & Relative Humidity
Photosynthetically Active Radiation
Soil Moisture Probe
ENV-LINK MINI
PHENO TOWER WSN PTH NODE
Phenology Sensors
* Denotes significance at the p = 0.05
Early Stage TDF
Intermediate Stage TDF
Late Stage TDF
© 2009 IBM Corporation
Current fact finding
Analyze data in motion – before it is stored
Low latency paradigm, push model
Data driven – bring the data to the query
Historical fact finding
Find and analyze information stored on disk
Batch paradigm, pull model
Query-driven: submits queries to static data
Traditional Computing Stream Computing
Query Data Results Data Query Results
Stream computing – Analyze data in motion
© 2009 IBM Corporation
Modify Filter / Sample
Classify
Fuse
Annotate
Big Data from WSN and CO2/H2O fluxes using Stream Analytics
Final Remarks Tropical dry forests and other arid/semi-arid environments
represent a true barometer to climate change, but their potential has been ignored.
Climate change effects will impact close to 60M people living in tropical dry forest environments across the Americas, and this pose serious political problems (e.g. migration).
Food security and migration as well as unknown impacts on ecosystem services are and will be present in the long run in 47% of all forests of the Americas.
We need to confront the need that a new shift on the way that we process and interpret environmental data is emerging (eScience). This new paradigm is changing the rate at which we process and
deliver the outcome of environmental data associated to climate change in dry forests tropical environments.
Thank You!