Tritium and trees: A bomb peak perspective on soil water dynamics in semi-arid apple orchards
Bibliographic record
Abstract
Understanding the relationship between forest age and soil water dynamics is crucial for effective land and water resources management. However, the complexities of these dynamics – such as soil water recharge and depletion – hamper in-depth understanding, particularly in water-scarce, semi-arid regions. In this study, we examine soil water recharge and depletion in relation to stand age of apple trees over four years in a semi-arid region at China’s Loess Plateau (CLP). We collected soil cores extending to over 20 meters deep from four different apple orchards with variable afforestation ages (established in 2008, 2005, 1998, and 1994). For comparison, we selected an adjacent cropland devoid of afforestation for comparison. We measured soil water content and tritium distributions to model soil water dynamics and estimate water ages across different soil profiles. Our results show that diverse patterns of recharge and depletion in both shallow and deep soils are influenced by the age of afforestation. The tritium peak depths, which indicate the 1963 bomb peak depth, show a significant disparity between afforested between afforested and non-afforested plots. In particular, afforestation reduced the velocity of soil water over a span of 20 years. Furthermore, our model suggests that the transpired deep soil water age exceeded 200 years in the oldest orchard. Our findings underline a complex interaction between newly infiltrated water and existing water, possibly explained by varying soil pore size distributions. These results contribute valuable insights into the ecohydrological impacts of afforestation on the CLP and similar climatic regions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".