Impact of Afforestation on Atmospheric Recharge to Groundwater in a Semiarid Area
Bibliographic record
Abstract
Abstract While the global forest area is generally decreasing, various afforestation projects have been implemented, including the large‐scale Three‐North Afforestation Program (TNAP) and the Conversion of Cropland to Forest Program in China, under which 0.63 million square kilometers of trees have been planted. The large‐scale land use and cover change (LUCC) would affect the redistribution of precipitation and change the water cycle, especially groundwater recharge. The chloride mass balance (CMB) between precipitation and soil water/groundwater is the most widely used technique to estimate the recharge in arid and semiarid areas. However, in the case of decreased recharge following LUCC, a new equilibrium of water and chloride flux is not easily reached and the identification of steady state is the premise to use CMB. This study provided a method to assess the steady state by comparing the history of LUCC and chloride cumulative age at sampling depth and by checking the breaks in the slope of the line for cumulative chloride and soil water. The case study in the Mu Us Sandy Land, affected by the TNAP, shows that soil profiles beneath sparse grassland, shrubland, and woodland have reached steady state. However, new equilibrium has not been reached in the soil profiles beneath the dense shrubland. The estimated recharge rates beneath the plantations represent reductions from 33% to >90% relative to the surrounding bare sandy land (50–54 mm/year). The results highlight the unfavorable effects of some afforestation and ecological rehabilitation approaches in arid and semiarid areas on regional groundwater resources.
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 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) | 0.001 | 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".