Estimating deep soil water depletion and availability under planted forest on the Loess Plateau, China
Bibliographic record
Abstract
Deep soil water (DSW) plays a pivotal role in tree growth, susceptibility to drought-induced mortality, and belowground carbon and nutrient cycling. Assessing DSW depletion is essential for evaluating the resilience and sustainability of planted forests. But, due to the poor accessibility of deep soil layers, little is known about large scale DSW depletion. In this study, we leverage the concept that “plants are reliable indicators of deep soil water” to estimate DSW depletion in planted forests within the arid and semi-arid regions of the Chinese Loess Plateau (CLP). Our approach involves establishing a model that correlates forest age with DSW depletion. We then employ this model to estimate DSW depletion across the region, utilizing readily available data on the distribution of forest age and utilize the boundary models to consider the variability of DSW depletion estimated with forest age. Our results indicate that the model effectively estimates DSW depletion in planted CLP forests, demonstrating a strong fit with an R2 of 0.71 and a low root mean square error (RMSE) of 332 mm. Notably, a substantial portion of the planted forest areas on the CLP has experienced DSW depletion from 800 mm to 1600 mm, and totaling 2.41 × 1010 m3 DSW depletion from 1995 to 2020 based on the general model. However, the available DSW in the existing planted forests on the CLP is estimated at only 1.73 × 1010 m3 by 2038. This suggests that there is potential risks and unsustainability for further afforestation efforts and carbon sequestration on the CLP under the current continuous afforestation measures. Our study holds significant implications for sustainable regional ecological management and quantifying water resources for carbon trading through afforestation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".