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Record W6926679287 · doi:10.25394/pgs.8029211

QUANTIFYING PEATLAND CARBON DYNAMICS USING MECHANISTICALLY-BASED BIOGEOCHEMISTRY MODELS

2019· dissertation· en· W6926679287 on OpenAlexaboutno aff

Bibliographic record

VenuePurdue · 2019
Typedissertation
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPeatCarbon sinkSoil carbonBiogeochemistryCarbon cycleSink (geography)Soil organic matterPermafrostGlobal warmingTotal organic carbon

Abstract

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Peatlands are the most efficient natural carbon sink on the planet. They are the most carbon-intensive storages than any other vegetation types. However, recent studies indicate that global peatlands can potentially release 6% of the global soil carbon into the atmosphere when they are drained or deforested. They cover only about 3% of the total global land area, but sequester over 30% of the Earth’s soil organic carbon. Peatlands in northern mid-to-high latitudes (45°-90°N) occupy ~90% of the global peatland area and account for ~80% of the total global peat organic carbon stock. Those peatlands are mainly located in Canada, Russia, and the USA. Peatlands in tropical regions cover ~10% of the global peatlands area and store 15-19% of the global peat organic carbon. They are mainly distributed in Southeast Asia and South and Central America. The temperature at the global scale has been rising since the middle of the last century and has accelerated during the last 40 years and the warming will continue in this century. The large storage of soil organic carbon within the peatlands can significantly respond to the changing climate by varying the roles between their carbon sink (from atmosphere to soil) and source (from soil to atmosphere) activities. This dissertation focuses on quantifying the soil organic carbon dynamics in North America and South America using mechanistically-based biogeochemistry models. Peatlands in Alaska occupy 40 million hectares and account for ~10% of the total peatland area in northern mid-to-high latitudes. The regional soil organic carbon dynamics and its response to climate are still with large uncertainty. Most of the studies on peatlands to date are based on short-term site-level observation. This dissertation first used an integrated modeling framework that coupled the dynamics of hydrology, soil thermal regime, and ecosystem carbon and nitrogen to quantify the long-term peat carbon accumulation in Alaska during the Holocene. Modeled hydrology, soil thermal regime, carbon pools and fluxes and methane emissions were evaluated using long-term observation data at several peatland sites in Minnesota, Alaska, and Canada. The model was then applied for a 10,000-year (15 ka to 5 ka; 1 ka = 1000 cal yr before present) simulation at four peatland sites. The model simulations matched the observed carbon accumulation rates at fen sites during the Holocene (R^2= 0.88, 0.87, 0.38 and -0.05 for four sites respectively using comparisons in 500-year bins from 15 ka to 5 ka). The simulated (2.04 m) and observed peat depths (on average 1.98 m) also compared well (R^2 = 0.91). The early Holocene carbon accumulation rates, especially during the Holocene thermal maximum (HTM) (35.9 g 〖C m〗^(-2) yr^(-1)), were estimated up to 6-times higher than the rest of the Holocene (6.5 g 〖C m〗^(-2) yr^(-1)). It suggested that high summer temperature and the lengthened growing season resulted from the elevated insolation seasonality, along with wetter-than-before conditions might be major factors causing the rapid carbon accumulation in Alaska during the HTM. The sensitivity tests indicated that, apart from climate, initial water-table depth and vegetation canopy were major drivers to the estimated peat carbon accumulation. To further quantify the regional long-term soil organic carbon accumulation rates and the current carbon stocks in Alaska, the second part of my research focused on quantifying the soil organic carbon accumulation in multiple Alaskan terrestrial ecosystems over the last 15,000 years for both peatland and non-peatland ecosystems. Comparable with the previous estimates of 25-70 Pg carbon (C) in peatlands and 13-22 Pg C in non-peatland soils within 1-m depth in Alaska using peat core data, our model estimated a total SOC of 36-63 Pg C at present, including 27-48 Pg C in peatland soils and 9-15 Pg C in non-peatland soils. Current living vegetation stored 2.5-3.7 Pg C in Alaska with 0.3-0.6 Pg C in peatlands and 2.2-3.1 Pg C in non-peatlands. The simulated average rate of peat soil C accumulation was 2.3 Tg C yr^(-1) with a peak value of 5.1 Tg C yr^(-1) during the Holocene Thermal Maximum (HTM) in the early Holocene, four folds higher than the average rate of 1.4 Tg C yr^(-1) over the rest of the Holocene. The accumulation slowed down, or even ceased, during the neo-glacial climate cooling after the mid-Holocene, but increased again in the 20th century. The model-estimated peat depths ranged from 1.1 to 2.7 m, similar to the field-based estimate of 2.29 m for the region. The changes in vegetation and their distributions were the main factors to determine the spatial variations of SOC accumulation during different time periods. Warmer summer temperature and stronger radiation seasonality, along with higher precipitation in the HTM and the 20th century might have resulted in the extensive peatland expansion and carbon accumulation. Most studies on the role of tropical peatlands have focused on Indonesian peatlands. Few have focused on the Amazon basin, where peatlands remain intact and have been a long-term carbon sink. To address the problem, my third study quantified the carbon accumulation for peatland and non-peatland ecosystems in the Pastaza-Marañon foreland basin (PMFB), the most extensive peatland complex in the Amazon basin from 12,000 years before present to 2100 AD. Model simulations indicated that warming accelerated peat carbon loss while increasing precipitation accelerated peat carbon accumulation at millennial time scales. The uncertain parameters and spatial variation of climate were significant sources of uncertainty to modeled peat carbon accumulation. Under warmer and presumably wetter conditions over the 21st century, the warming effect on increasing peat carbon loss might overwhelm the wetter effect on increasing peat carbon accumulation. Peat soil carbon accumulation rate in the PMFB slowed down to 7.9 (4.3~12.2) g C m^(-2) yr^(-1) from the current rate of 16.1 (9.1~23.7) g C m^(-2) yr^(-1) and the region might turn into a carbon source to the atmosphere at -53.3 (-66.8~-41.2) g C m^(-2) yr^(-1) (negative indicates source), depending on the level of warming. Peatland ecosystems showed a higher vulnerability than non-peatland ecosystems as indicated by the ratio of their soil carbon density changes (change of soil carbon/existing soil carbon stock) ranging from 3.9 to 5.8). This was primarily due to larger peatlands carbon stocks and more dramatic responses of their aerobic and anaerobic decompositions in comparison with non-peatland ecosystems under future climate conditions. Peatland and non-peatland soils in the PMFB might lose up to 0.4 (0.32~0.52) Pg C by 2100 AD with the largest loss from palm swamp. The carbon-dense Amazonian peatland might switch from a current carbon sink into a source in the 21st century.Peatlands are important sources and sinks for greenhouse gases (carbon dioxide and methane). Their carbon (C) balance between soil and atmosphere remains unquantified due to the large data gaps and uncertainties in regional peat carbon estimation. My final study was to quantify the C accumulation rates and C stocks within North America peatlands over the last 12,000 years. I find that 85-174 Pg C have been accumulated in North American peatlands over these years including 0.37-0.76 Pg C in subtropical peatlands in this region. During the 10- 8 ka period, the warmer and wetter conditions might have played an important role in stimulating peat C accumulation by enhancing plant photosynthesis. The enhanced peat decomposition due to warming through the Holocene slows down carbon accumulation in the region.<br><br>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.253
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
Admission routes1
Has abstractyes

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