Modelling study of soil C, N and pH response to air pollution and climate change using European LTER site observations
Bibliographic record
Abstract
Current climate warming is expected to continue in coming decades, whereas high N deposition may stabilize, in contrast to the clear decrease in S deposition. These pressures have distinctive regional patterns and their resulting impact on soil conditions is modified by local site characteristics. We have applied the VSD+ soil dynamic model to study impacts of deposition and climate change on soil properties, using MetHyd and GrowUp as pre-processors to provide input to VSD+. The single-layer soil model VSD+ accounts for processes of organic C and N turnover, as well as charge and mass balances of elements, cation exchange and base cation weathering. We calibrated VSD+ at 26 ecosystem study sites throughout Europe using observed conditions, and simulated key soil properties: soil solution pH (pH), soil base saturation (BS) and soil organic carbon and nitrogen ratio (C:N) under projected deposition of N and S, and climate warming until 2100. The sites are forested, located in the Mediterranean, forested alpine, Atlantic, continental and boreal regions. They represent the long-term ecological research (LTER) Europe network, including sites of the ICP Forests and ICP Integrated Monitoring (IM) programmes under the UNECE Convention on Long-range Transboundary Air Pollution (LRTAP), providing high quality long-term data on ecosystem response. Simulated future soil conditions improved under projected decrease in deposition and current climate conditions: higher pH, BS and C:N at 21, 16 and 12 of the sites, respectively. When climate change was included in the scenario analysis, the variability of the results increased. Climate warming resulted in higher simulated pH in most cases, and higher BS and C:N in roughly half of the cases. Especially the increase in C:N was more marked with climate warming. The study illustrates the value of LTER sites for applying models to predict soil responses to multiple environmental changes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".