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The effect of drought on carbon storage capacity in a tropical rainforest of French Guiana
Maricar Aguilos, Benoît Burban, Jocelyn Cazal, Jean-Yves Goret, Bruno Herault, Damien Bonal
To cite this version:
Maricar Aguilos, Benoît Burban, Jocelyn Cazal, Jean-Yves Goret, Bruno Herault, et al.. The effect of drought on carbon storage capacity in a tropical rainforest of French Guiana. AGU Fall Meeting 2015, Dec 2015, San Francisco, United States. 2015, AGU. Fall Meeting. San Francisco: 14-18 December 2015. Transformative science for a sustainable future. �hal-01269439�
The effect of drought on carbon storage capacity
in a tropical rainforest of French Guiana
Paper number:
B33C0671
Maricar Aguilos1, Benoit Burban1, Jocelyn Cazal1, Jean-Yves Goret1, Bruno Hérault2 and Damien Bonal3
1INRA Institut National de la Recherche Agronomique, UMR Ecologie des Forêts de Guyane, BP 316 Avenue de France Kourou Cedex, French Guiana 2CIRAD, UMR Ecologie des Forets de Guyane, BP 316 Avenue de France, Kourou Cedex, French Guiana and 3INRA, UMR EEF 1137, Champenoux, France
I. Introduction
II. Materials and Methods
French Guiana • Amazon basin plays an important role in the global carbon budget. • It accounts for 15% of global photosynthesis and a major contributor to terrestrial carbon sink. • Unfortunately, this biome is facing threats from drought. • Amazon region had suffered two exceptional droughts in 2005 and 2010 (Xu et al., 2011 and Lewis et al., 2011) • Increasing occurrences of drought urge us to study their impacts on terrestrial carbon budget. Objectives • Present the interannual and seasonal variations in carbon fluxes (GPP, RE, NEE) and carbon balance at a tropical rainforest site in French Guiana and understand which climatic factors influenced these fluxes; and • Determine the effect of severe drought events (20052010) on the CO2 fluxes using the perfectdeficit approach. Hypothesis • Drought will cause significant impact to the carbon fluxes; and • Differences in solar radiation, air temperature, soil water content (rain) may explain differences in GPP, RE and NEE among years or among seasons.
Study site
• French Guiana, South America – a tropical wet forest • 10 – 40 m above sea level; soils are nutrientpoor acrisol • Ave. rainfall is 3041 mm; ave. air temperature is 25.7°C • Guyaflux experimental unit covers >400 ha of undisturbed forest • 620 tree ha1 tree density (DBH >0.1 m); Tree species richness is 140 species ha1 ; Mean tree height is 35 m; emergent trees>40mThe CO2 flux monitoring tower
• 55 m high • 20 m higher than the overall canopy height • Atmospheric pressure • soil temperature • volumetric soil water content • Vapor Pressure Deficit data were collected and compiled as 30 min averages or sums and processed following the standard flux data processing, gapfilling and analysis procedures. PCPC curve (perfect canopy photosynthetic capacity curve) perfect annual CPC curve CPC deficitsThe Perfect Deficit Approach
(daily maximum ecosystem productivity in a given year)
(maximum ecosystem productivity during the entire record period e.g 10
years)
Amazon forest − a very important carbon sequestering ecosystem did not escape from the wrath of droughts, two of which occurred in 2005 and
2010. Thus, this study was conducted to elucidate how these dry spells alter the interannual and seasonal variations in carbon fluxes and verify the
commonlyinvoked notion that drought will create significant impact to the ecosystem’s productivity. Using an 11year (2004–2014) eddy covariance
flux and meteorological data, the relationships between potential productivity and droughts occurring in a tropical rainforest of French Guiana, South
America were examined. A new method in determining disturbance effect called ‘perfectdeficit’ approach was also explored. Surprisingly, the effect
of drought in ecosystem productivity was observed to be subtle. The ecosystem even remained a net sink all throughout the study period in an
annual basis. Despite being the driest year, 2010 has the highest gross primary production (GPP). However, there was large
III. Results
Meteorological and flux data
• Air temperature and humidity • bulk rainfall • wind direction and speed • global and infrared incident and reflected radiations • incident and reflected photosynthetic photon flux densityAcknowledgment:
Jena platform, Labex CEBA,
Observatoire du Carbone, CPER-Guyaflux.
Maricar Aguilos is supported by an INRA grant.
IV. Conclusions
• CPC chart shows a subtle effect of drought on GPP. • Despite being the driest year, 2010 has the highest GPP making it a remarkable record of GPP for the decade. • GPP is higher than RE all throughout the recorded timeframe, thus the ecosystem remains a carbon sink. • Dry periods have higher ecosystem productivity than wet periods. • Solar radiation remain the key factors influencing the variations in ecosystem productivity. • Reduced soil water content during dry periods only moderately reduce GPP• Caution must be taken in modeling drought effects because forests, even located at the
same region, may respond differently with drought. • How this rainforest will cope with the climate extremes in the long run is a worthy question that needs to be answered, hence, must be closely monitored.
Interannual variation in GPP, RE and NEE
Seasonal variation in GPP, RE and NEE
Drivers of ecosystem productivity and respiration
wet
wet
dry
dry
dry
dry
Strong significant effect No significant effect
No significant effect Moderate effect With significant effect
Moderate effect
interannual variation in net ecosystem exchange (NEE) from 0.66 ± 0.50 tC ha1 y1 in 2004 to 5.72 ± 0.50 tC ha1 y1 in 2009, with an average annual sink
of around 3.21 ± 0.50 tC ha1 y1. Seasonally, there were higher GPP and ecosystem respiration (RE) during dry than in wet seasons. Highest GPP occurred
in 2010 which is the driest period from 20042014 across Amazonia. Global radiation (Rg) caused significant effect to GPP and RE during wet seasons but not
during dry seasons. Weaker relationships between GPP or RE and soil water content (SWC) were observed when dry seasons were moderate but the effect
was stronger during extreme dry seasons, thus requiring for a deeper understanding during these extreme drought years. Modeling studies should therefore
consider sitespecific drought responses since it did not seem to negatively alter carbon fluxes in this Amazonian forest. Longterm observation is needed to
examine closely how this ecosystem will respond to future drought incidents as these events are occurring more frequent and becoming severe.
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 CPC 20.9 23.6 27.3 26.9 26.4 25.6 18.5 18.6 21.5 26.4 35.3 Air temp 3.4 2.9 4.6 4.5 4.3 4.5 3.0 4.3 4.4 4.9 5.0 RH 1.3 3.2 4.0 4.9 4.9 5.3 4.6 4.4 3.8 3.6 3.5 Rg 14.4 16.7 18.6 17.2 18.7 17.9 18.2 16.8 14.9 17.3 17.1 Rain 76.8 75.7 72.3 72.3 77.2 76.7 71.6 73.3 74.6 73.6 78.1 VPD 23.2 21.5 24.6 23.0 19.4 22.2 21.6 23.9 23.6 29.0 29.9 PET 15.0 17.0 20.2 17.3 18.3 17.4 18.0 17.1 18.5 20.7 18.2CPC and climatic parameters percent reduction from perfect values (%)
The CPC Chart
P C P C , C P C (g C m -2 d -1)Day of the year
Abstra
ct
• The critical time or extreme period in a year can be clearly identified through the perfectdeficit graphs.
• Any increase or decrease in CPC deficits would reflect the impact of extreme climatic events on the potential
productivity of the forest
dry wet dry wet dry wet dry wet dry wet dry wet
2004 26.3 25.3 78.9 84.2 250.4 189.0 260.4 2495.6 0.8 0.5 0.5 0.6 2005 26.4 25.6 78.0 82.2 252.1 187.2 70.0 3002.2 0.8 0.6 0.5 0.6 2006 26.2 25.2 78.0 82.1 247.5 175.9 214.8 3294.8 0.8 0.6 0.5 0.6 2007 26.2 25.3 77.5 81.0 248.8 178.6 312.7 3237.8 0.8 0.6 0.6 0.7 2008 26.4 25.1 76.8 80.1 252.6 178.9 92.4 2745.6 0.9 0.7 0.5 0.6 2009 26.2 25.4 77.4 80.4 252.5 185.8 183.2 2935.2 0.8 0.7 0.5 0.6 2010 26.6 25.7 77.4 81.0 248.7 183.4 93.8 3127.4 0.8 0.7 0.5 0.6 2011 26.2 25.4 78.6 80.7 244.5 197.8 107.8 3040.0 0.8 0.7 0.5 0.6 2012 26.2 25.0 77.9 82.2 254.3 187.8 80.2 3220.6 0.8 0.6 0.5 0.7 2013 26.2 25.4 79.1 81.8 252.9 193.3 69.6 2652.2 0.8 0.6 0.6 0.6 2014 26.3 25.4 79.9 82.0 256.9 188.9 69.2 2665.2 0.7 0.6 0.5 0.6 Rg (Wm-2) Rain (mm) VPD (kPa) SWC (m3m-3) Year Air Temp (°C) RH (%)
