1 / 34

SIBERIA II Use of Remote Sensing to infer Ecological Processes for Carbon Fluxes Estimates

SIBERIA II Use of Remote Sensing to infer Ecological Processes for Carbon Fluxes Estimates. 5th Framework Program of the European Commission, Generic Activity 7.2: Development of generic Earth Observation Technologies ( EVG1-CT-2001-00048 ). Thuy Le Toan, Nicolas Debart, Manuela Grippa,

gwidon
Download Presentation

SIBERIA II Use of Remote Sensing to infer Ecological Processes for Carbon Fluxes Estimates

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. SIBERIA II Use of Remote Sensing to infer Ecological Processesfor Carbon Fluxes Estimates 5th Framework Program of the European Commission, Generic Activity 7.2: Development of generic Earth Observation Technologies (EVG1-CT-2001-00048)

  2. Thuy Le Toan, Nicolas Debart, Manuela Grippa, Sergo Vicente-Serrano and Laurent Kergoat CESBIO, Toulouse, France Nelly Mognard LEGOS, Toulouse, France Richard Kidd, Wolfgang Wagner IPF, Vienna, Austria Ghislain Picard, Shaun Quegan CTCD, UK Philippe Peylin, Philippe Ciais LSCE, France

  3. Applicability of simple spring phenology model?

  4. Remote Sensing 1987 Model

  5. Regions of similar temporal variations byPrincipal Component Analysis Trend 82-04 D/decade -2.20 -4.04 -1.12 -5.72 -4.06 -0.52 -0.03 -1.62 -2.22 -1.04 Strongest advance in Siberia central (components 1 and 4)

  6. Remote Sensing Model

  7. Trend from remote sensing 42 -42 42 -42 Trend from model

  8. Comparison Remote Sensing- Model Other models needed

  9. How do phenological events (thaw start, snow melt, phenology dates) coincide with start of carbon efflux and carbon uptake? .Correspondence with in situ flux measurements ? Correspondence with atmospheric inversion data?

  10. Materials and methods • Development of EO methods to determine: spring phenology dates (CESBIO) snowmelt datesand snow water equivalent (CESBIO) thaw freeze dates (IPF) • Analysis of correspondence with seasonal variations of CO2 measurements (TCOS-Siberia) Inferring vegetation processes and questions

  11. Snow products in SIBERIA • Snow melt dates using SSM& SSM/I (1978-2001) • Snow depth (snow water equivalent): • SIBERIA-2: Development of new method using • SSM/I (1987-2003) - M. Grippa, N. M. Mognard, T. Le Toan and E. G. Josberger (2004) “Siberia snow depth climatology derived from SSM/I data using a combined dynamic and static algorithm.”Rem. Sens. Environ. 93 (2004) 30-41 2 - M.Grippa, N.M. Mognard, T. Le Toan (2005) “Comparison between the interannual variability of snow parameters derived from SSM/I and the Ob river discharge” Rem. Sens. Environ. Data base of Snow products from 1987 to 2003 ~25 km, Siberia and global

  12. SIBERIA end of snow melt dates (1988-2002)

  13. Freeze thaw dates in SIBERIA SIBERIA-2: Development of a new method using Quikscat in Siberia Data base of Freeze Thaw dates from 2000 to 2003 25 km Siberia and Eurasia

  14. Freeze/Thaw Indicator by Quikscat SIBERIA II Start of Thaw 2000

  15. End Snowmelt Beginning 60°45 ’N, 89°23 ’E , Shibistova et al. 2002 In situ soil CO2 efflux and EO beginning and end snow melt Soil respiration starts at end of snow melt

  16. NEE of bog, in 1998, 1999, 2000, at at 60°45 ’N, 89°23 ’E (Schulze et al., 2002)

  17. 1999 1999 Thaw startSnowmelt Budburst day 128 day 150 60°45 ’N, 89°23 ’E (Schulze et al., 2002)

  18. NEE at 60°45 ’N, 89°23 ’E of bog (Schulze et al., 2002) 2000 2000 Thaw startSnowmelt Budburst day 100 day 133 day 146 (Schulze et al., 2002)

  19. 1998 1998 Thaw startSnowmelt Budburst day 148 day 153 • Bog: • CO2 efflux starts at thaw dates, • turns from source to sink at greening date

  20. NEE at forest stands of Betula, Abies and mixed stands at 61°01 ’N, 89°34 ’E (Roser et al., 2002) EO data Thaw start: day 100 (10April) Snowmelt: 143 (17 May) Budburst: 146 (20 May) Forests: beginning of carbon release at thaw start Deciduous forest:carbon source to sink after greening Evergreen: already carbon sink at greening

  21. Snow melt Thaw 2000 Greening Pinus Sylvestris Almut Arneth Evergreen forest: Carbon sink at EO greening

  22. Phase in CO2 measurements and EO dates 1. There is an indication that thaw start - beginning of snow melt - coincides with onset of respiration for all cover types 2. For bogs and deciduous forests greening up occurs in transition from C02 efflux to CO2 take up 3. Evergreen forest is already a sink at EO greening up dates Determination of growing season according to temperature? according to EO vegetation indices?

  23. Preliminary comparison with atmospheric inversion data

  24. Atmospheric Inversion data (from P.Peylin, LSCE) Snow melt dates Occur at the first decrease

  25. Atmospheric Inversion data (from P.Peylin, LSCE) Greening up dates Occur at 0 crossing, but data are monthly!

  26. Correspondence snow melt, greening up dates

  27. Interannual variations of the negative (sinks) and positive (sources) of CO2 Annual CO2<0 Annual CO2>0 Annual CO2 Data from P. Peylin, LSCE Interannual variations dominated by variations of the positive part in spring, autumn and winter Interannual variability caused by respiration?

  28. Impacts of change in snow melt dates and snow depth on ecosystem functioning in Siberia? Insitu studies: deeper snow pack increases - summer vegetation productivity - summer leaf nitrogen content - soil nitrogen mineralization (Schimel et al., 2004, Dorrepal et al., 2004, Grogan and Jonasson, 2003) -carbon uptake by vegetation 6 months later (Welker et al., 2000) Large scale observations? M. Grippa, L. Kergoat, T. Le Toan, N. Delbart, J. L’Hermitte and S. Vicente-Serrano (2005) “On the relationship between vegetation and snow indicators derived from remote sensing over central siberia” Geophys. Res. Lett.

  29. Correlations between the interannual variations variations of NDVI and the snowmelt dates. NDVI cumulative - snowmelt dates Values>0.4 are significant above 90% Negative correlation Late snowmelt Less cumulative NDVI • lenght of the growing season • correspondence between snowmelt and positive temperatures • effects of the snowmelt on the reflectance used to calculate the NDVI

  30. Correlations between the interannual variations variations of NDVI and the snowmelt dates. NDVI summer - snowmelt dates Positive correlation Late snowmelt more NDVI in summer • water availabilityafter snowmelt(steppe) • late snowmelt: proctection from the snow layer against freeze events (fine root mortality)

  31. Correlations between the interannual variations variations of NDVI and the winter snow depth. Cumulative NDVI - snow depth Summer NDVI - snow depth Positive correlation more snow depth in winter more NDVI • more water available after snowmelt ? • soil insulation provided by a thicker winter snowpack (enhancement of microbial activity during winter->enhance leaf nitrogen content, soil nitrogen mineralization?

  32. Highlights from EO Siberia: advance in spring phenology and snow melt dates in the last 23 years which may result in opposite effects on NPP

More Related