Dr. Arturo Sanchez-Azofeifa Earth and Atmospheric Sciences Department

University of Alberta, Edmonton, Alberta, Canada

arturo.sanchez@ualberta.ca

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).

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22

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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

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600

0

100

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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

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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

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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

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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

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3/20

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16/0

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15/0

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12/1

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07/0

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02/1

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01/0

3/20

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30/0

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26/1

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2/20

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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!