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Record W4220732662 · doi:10.5194/egusphere-egu22-10378

Reforestation drastically reduces CO2 release from sulfide oxidation and its climatic sensitivity – Insight from a paired catchment approach at the Draix-Bleone Observatory

2022· preprint· en· W4220732662 on OpenAlexaboutno aff
Robert Hilton, Mateja Ogrič, Mathieu Dellinger, Guillaume Soulet, Sebastian Klotz, Jordon Hemingway, Alexandra V. Turchyn, Caroline Le Bouteiller, Christian Schiffer

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryAdenylate kinaseBiochemistry

Abstract

fetched live from OpenAlex

Deforestation of steep mountain forests can result in a large increase in physical erosion rates. A growing body of work has highlighted that erosion may set the pace of oxidative weathering of sulfide minerals, which results in the production of sulfuric acid and can release CO2 from carbonate minerals in rocks to the atmosphere. However, the role of land use change on this CO2 release pathway has not been assessed, primarily due to the lack of measurements to isolate this driver over other potential environmental controls (e.g. temperature, hydrology). Here we study the stream water chemistry of two marl-dominated catchments, the Laval (0.86 km2) and the Brusquet (1.07 km2), in the Draix-Bléone Critical Zone Observatory, Provence, France. The Laval has very high rates of physical erosion (sediment yields of 8,700 t km-2yr-1 and 15,800 t km-2yr-1 in 2016-2017 and 2017-2018, respectively) that result from the combination of deforestation, bare rock surfaces, weak rocks and the hydroclimatic setting. In contrast, the Brusquet catchment was reforested at the end of the 19th Century and has much lower present day sediment yields (45 t km-2 yr-1 and 492 t km-2 yr-1 in 2016-2017 and 2017-2018, respectively). We collected samples from every storm event and during flow over two water years in each catchment. We measure the major ions and the sulfur and oxygen isotopic composition of dissolved sulfate (SO4). In both catchments cation partitioning shows a dominance of carbonate (>70%) over silicate weathering. The stable sulfur isotopic signature suggests sulfide oxidation is the dominant source of sulfate in these catchments. Examination of dissolved ion rations (HCO3/∑Cat+, SO4//∑Cat+) shows that sulfuric acid governs mineral dissolution, rather than carbonic acid, accounting for 90±6% and 63±9% in the Laval and Brusquet, respectively. In the highly erosive Laval catchment, the estimated CO2 release from sulfide oxidation coupled to carbonate weathering was very high, at 22.1±7.1 tC km-2 yr-1. We also find evidence for seasonal changes in sulfate flux which suggest that the rates are moderated by changes in air temperature, with elevated sulfide oxidation rates in summer. These observations support independent measurements in the shallow weathering zone of the Laval catchment, that shows an increase in CO2 release from sulfide oxidation with temperature. In marked contrast, the CO2 release estimated in the reforested Brusquet catchment is 4 to 5 x lower (at 4.6±0.8 tC km-2 yr-1) and the fluxes are not seasonally moderated (i.e. not temperature controlled). We suggest this relates to changes in the supply of mineral surfaces to the shallow, oxygenated weathering zone. Reforestation could result in a marked decrease in the release of carbon from rock to the atmosphere in areas where sulfide and carbonate minerals outrcop, and make the resultant fluxes less sensitive to changing climate.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.222
Teacher spread0.199 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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