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Linking soil respiration and water table depth in tropical peatlands with remotely sensed changes in water storage from the gravity recovery and climate experiment

  • Erin Swails
  • X. Yang
  • S. Asefi
  • K. Hergoualc’h
  • L. Verchot
  • R. E. McRoberts
  • D. Lawrence
Original Article

Abstract

Carbon dioxide (CO2) emissions from Southeast Asia peatlands are contributing substantially to global anthropogenic emissions to the atmosphere. Peatland emissions associated with land-use change, and fires are closely related to changes in the water table level. Remote sensing is a powerful tool that is potentially useful for estimating peat CO2 emissions over large spatial and temporal scales. We related ground measurements of total soil respiration and water table depth collected over 19 months in an Indonesian peatland to remotely sensed gravity recovery and climate experiment (GRACE) terrestrial water storage anomoly (TWSA) data. GRACE TWSA can be used to predict changes in water storage on land. We combined ground observations from undrained forest and drained smallholder oil palm plantations on peat in Central Kalimantan to produce a representation of the peatland landscape in one 0.5° × 0.5° GRACE grid cell. In both ecosystem types, total soil respiration increased with increasing water table depth. Across the landscape grid, monthly changes in water table depth were significantly related to fluctuations in GRACE TWSA. GRACE TWSA explained 76% of variation in water table depth and 75% of variation in total soil respiration measured on the ground. By facilitating regular sampling across broad spatial scales that captures essential variation in a major driver of soil respiration and peat fires, our approach could improve information available to decision makers to monitor changes in water table depth and peat CO2 emissions. This would enable measures better targeted in space and time to more effectively mitigate CO2 emissions from tropical peat drainage and fires. Testing over larger regions is needed to operationalize this exploratory approach.

Keywords

Indonesia Land use Oil palm Greenhouse gas emissions Climate change 

References

  1. Bloom A, Palmer P, Fraser A et al (2010) Large-scale controls of methanogenesis inferred from methane and gravity spaceborne data. Science 327:322–325CrossRefGoogle Scholar
  2. Bloom A, Palmer P, Fraser A et al (2012) Seasonal variability of tropical wetland CH4 emissions: the role of the methanogen-available carbon pool. Biogeosciences 9:2821–2830CrossRefGoogle Scholar
  3. Cai W, Bolace S, Lengaigne M et al (2014) Increasing frequency of extreme El Niño events due to greenhouse warming. Nat Clim Chang 4:111–116CrossRefGoogle Scholar
  4. Chen D, Huang F, Jackson T (2005) Vegetation water content estimation for corn and soybeans using spectral indices from MODIS near- and short-wave infrared bands. Remote Sens Environ 98:225–236CrossRefGoogle Scholar
  5. Comeau LP, Hergoualc'h K, Hartill J, Smith J, Verchot LV, Peak D, Salim AM (2016) How do the heterotrophic and the total soil respiration of an oil palm plantation on peat respond to nitrogen fertilizer application? Geoderma 268:41–51CrossRefGoogle Scholar
  6. Famiglietti JS, Lo M, Ho SL, Bethune J, Anderson KJ, Syed TH, Swenson SC, de Linage CR, Rodell M (2011) Satellites measure recent rates of groundwater depletion in California’s Central Valley. Geophys Res Lett 38: L03403.  https://doi.org/10.1029/2010GL04644
  7. Field R, van der Werf G, Fanin T et al (2016) Indonesian fire activity and smoke pollution in 2015 show persistent nonlinear sensitivity to El Niño-induced drought. Proc Natl Acad Sci U S A 113:9204–9209CrossRefGoogle Scholar
  8. Fletchner F, Morton P, Watkins M et al (2014) Status of the GRACE follow-on mission. Gravity, Geoid, and Height Systems 114:117–121Google Scholar
  9. Gaveau DLA, Salim MA, Hergoualc'H K, Locatelli B, Sloan S, Wooster M, Marlier ME, Molidena E, Yaen H, DeFries R, Verchot L, Murdiyarso D, Nasi R, Holmgren P, Sheil Douglas (2015) Major atmospheric emissions from peat fires in Southeast Asia during non-drought years: evidence from the 2013 Sumatran fires. Sci Rep 4(1):6112Google Scholar
  10. Harris N, Brown S, Hagen S et al (2012) Baseline map of carbon emissions from deforestation in tropical regions. Science 336:1573–1576CrossRefGoogle Scholar
  11. Hergoualc’h K, Verchot LV (2014) Greenhouse gas emission factors for land use and land-use change in Southeast Asian peatlands. Mitig Adapt Strateg Glob Chang 19:789–807CrossRefGoogle Scholar
  12. Hergoualc’h K, Hendry DT, Murdiyarso D, Verchot LV (2017) Total and heterotrophic soil respiration in a swamp forest and oil palm plantations on peat in Central Kalimantan, Indonesia. Biogeochemistry 135:203–220CrossRefGoogle Scholar
  13. Hirano T, Segah H, Harada T et al (2007) Carbon dioxide balance of a tropical peat swamp forest in Kalimantan, Indonesia. Global Change Biol 13:412–425CrossRefGoogle Scholar
  14. Hirano T, Jauhiainen J, Inoue T, Takahashi H (2009) Controls on the carbon balance of tropical peatlands. Ecosystems 12:873–887CrossRefGoogle Scholar
  15. Jauhiainen J, Limin S, Silvennoinen H, Vasander H (2008) Carbon dioxide and methane fluxes in drained tropical peat before and after hydrological restoration. Ecology 89:3503–3514CrossRefGoogle Scholar
  16. Jones L, Kimball J, Madani N et al (2016) The SMAP level 4 carbon product for monitoring terrestrial ecosystem-atmosphere CO2 exchange. In: Geoscience and Remote Sensing Symposium (IGARSS), 2016, IEEE International. IEEE, pp 139–142Google Scholar
  17. Khalid H, Zin ZZ, Anderson JM (1999) Quantification of oil palm biomass and nutrient value in a mature oil palm plantation: belowground biomass. J Oil Palm Res 11:63–71Google Scholar
  18. Marwanto S, Agus F (2014) Is CO2 flux from oil palm plantations on peatland controlled by soil moisture and/or soil and air temperatures? Mitig Adapt Strateg Glob Chang 19:809–819CrossRefGoogle Scholar
  19. Miettinen J, Shi S, Liew SC (2016) Land cover distribution in the peatlands of Peninsular Malaysia, Sumatra, and Borneo in 2015 with changes since 1990. Glob Ecol Conserv 6:67–78CrossRefGoogle Scholar
  20. Miettinen J, Hooijer A, Vernimmen R, Liew SC, Page SE (2017) From carbon sink to carbon source: extensive peat oxidation in insular Southeast Asia since 1990. Environ Res Lett 12:024014CrossRefGoogle Scholar
  21. Nelson PN, Banabas M, Scotter DR, Webb MJ (2006) Using soil water depletion to measure spatial distribution of root activity in oil palm (Elaeis guineensis Jacq.) plantations. Plant Soil 286:109–121CrossRefGoogle Scholar
  22. Novita N (2016) Carbon stocks and soil greenhouse gas emissions associated with forest conversion to oil palm plantations in Tanjung Puting tropical peatlands, Indonesia Dissertation, Oregon State UniversityGoogle Scholar
  23. Painter T, Rittger K, McKenzie C (2009) Retrieval of subpixel snow covered area, grain size, and albedo from MODIS. Remote Sens Environ 113:868–879CrossRefGoogle Scholar
  24. Piepmeier J, Focardi P, Horgan K et al (2017) SMAP L-band microwave radiometer: instrument design and first year on orbit. IEEE Trans Geosci Remote Sens 55:1954–1966CrossRefGoogle Scholar
  25. Pumpanen J, Longdoz B, Kutsch WL (2009) Field measurements of soil respiration: principles and constraints, potentials and limitations of different methods. In: Soil carbon dynamics—an integrated methodology. Cambridge Univ. Press, pp 16–33Google Scholar
  26. Rodell M, Velicogna I, Famiglietti JS (2009) Satellite-based estimates of groundwater depletion in India. Nature 460:999–1002.  https://doi.org/10.1038/nature08238 CrossRefGoogle Scholar
  27. Spruce J, Sader S, Ryan R et al (2011) Assessment of MODIS NDVI time series products for detecting forest defoliation by gypsy moth outbreaks. Remote Sens Environ 115:427–437CrossRefGoogle Scholar
  28. Swails E, Jaye D, Verchot L, Hergoualc’h K, Schirrmann M, Borchard N, Wahyuni N, Lawrence D (2017) Will CO2 emissions from drained tropical peatlands decline over time? Links between soil organic matter quality, nutrients, and C mineralization rates. Ecosystems.  https://doi.org/10.1007/s10021-017-0190-4
  29. Vernimmen R, Hooijer A, Aldrian E et al (2012) Evaluation and bias correction of satellite rainfall data for drought monitoring in Indonesia. Hydrol Earth Syst Sci 16:133–146CrossRefGoogle Scholar
  30. Vina A, Bearer S, Zhang H et al (2008) Evaluating MODIS data for mapping wildlife habitat distribution. Remote Sens Environ 112:2160–2169CrossRefGoogle Scholar
  31. Voss KA, Famigliett JS, Lo MH et al (2013) Groundwater depletion in the Middle East from GRACE with implications for transboundary water management in the Tigris-Euphrates-Western Iran region. Water Resour Res 49:904–914.  https://doi.org/10.1002/wrcr.20078 CrossRefGoogle Scholar
  32. Watkins M, Wiese D, Yuan D et al (2015) Improved methods for observing Earth’s time variable mass distribution with GRACE using spherical cap mascons. J Geophys Res Solid Earth 120:2648–2671.  https://doi.org/10.1002/2014JB011547 CrossRefGoogle Scholar
  33. Wiese D (2015) GRACE monthly global water mass grids NETCDF RELEASE 5.0 Ver. 5.0. PO.DAAC, CA, USA. Dataset accessed 2016–10-01 at doi: https://doi.org/10.5067/TEMSC-OCL05
  34. Wiese D, Landerer F, Watkins M (2016) Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution. Water Resour Res 52:7490–7502CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Department of Environmental SciencesUniversity of VirginiaCharlottesvilleUSA
  2. 2.Center for International Forestry Research (CIFOR)LimaPeru
  3. 3.International Center for Tropical AgricultureCaliColombia
  4. 4.United States Forest ServiceNorthern Research StationSaint PaulUSA

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