Temporally dependent effects of rainfall characteristics on inter- and intra-event branch-scale stemflow variability in two xerophytic shrubs
Bibliographic record
Abstract
Stemflow is important for recharging root-zone soil moisture in arid regions. Previous studies have generally focused on stemflow volume, efficiency and influential factors but have failed to depict stemflow processes and quantify their relations with rainfall characteristics within events, particularly for xerophytic shrubs. Here, we measured the stemflow volume, intensity, funneling ratio and time lags to rain at two dominant shrub species ( Caragana korshinskii and Salix psammophila ) and rainfall characteristics during 54 events at the semiarid Liudaogou catchment of the Loess Plateau, China, during the 2014–2015 rainy seasons. The funneling ratio was calculated as the ratio between stemflow and rainfall intensities at the inter- and intra-event scales. Our results indicated that the stemflow of C. korshinskii and S. psammophila , on average, started at 66.2 and 54.8 min, maximized 109.4 and 120.5 min after rain began, and ended 20.0 and 13.5 min after rain ceased. The two shrubs had shorter stemflow duration (3.8 and 3.4 h) and significantly larger stemflow intensities (517.5 and 367.3 mm h −1 ) than those of rain (4.7 h and 4.5 mm h −1 ). As branch size increased, both species shared the decreasing funneling ratios (97.7–163.7 and 44.2–212.0) and stemflow intensities (333.8–716.2 and 197.2–738.7 mm h −1 ). Tested by the multiple correspondence analysis and stepwise regression, rainfall amount and duration controlled stemflow volume and duration, respectively, at the event scale by linear relations ( p < 0.01). Rainfall intensity and raindrop momentum controlled stemflow intensity and time lags to rain for both species within the event by linear or power relationships ( p < 0.01). Rainfall intensity was the key factor affecting stemflow process of C. korshinskii , whereas raindrop momentum had the greatest influence on stemflow process of S. psammophila . Therefore, rainfall characteristics had temporally dependent influences on corresponding stemflow variables, and the influence also depended on specific species.
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 | 0.000 |
| 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".