Spatiotemporal changes of nitrate retention at the interface between surface water and groundwater: Insight from watershed scale in an elevated nitrate region
Bibliographic record
Abstract
Abstract Understanding the spatiotemporal nitrate retention in streambed is essential for developing management practices in reducing nitrate enrichment. However，the process of nitrate change in the profile of streambed at an elevated nitrate across a watershed scale is still not sufficiently investigated. In this study, we used a combination of hydraulic and hydro‐geochemistry methods to quantify total nitrate retention in streambeds of an agriculture‐intensive watershed in Central China. To conduct surface and groundwater measurements, we collected 1440 water samples for nitrate analysis from 40 shallow drilled wells during the dry and wet seasons from 2018 to 2020. The results showed a clear spatiotemporal variation of nitrate retention in streambed in the watershed. Spatially, nitrate retention in the midstream and downstream reaches was higher than that of the upstream reach. The lowest point of nitrate retention in downstream both in dry and wet seasons was at the depth of 0.75 m. While the lowest nitrate retention was found in midstream and upstream reaches, both in the dry and wet seasons at the depth between 1.5 and 2.5 m. Temporally, nitrate retention was higher in the wet season (1.56 μg N m−2d−1) than in dry season (1.41 μg N m−2d−1). DO min at 3 mg/L was found to the nitrate retention zero threshold in up and midstream. Water change fluxes and nitrate retention both have positive and negative relationship at watershed scale. Nitrate retention at the watershed scale was strongly affected by streambed lithology, precipitation, surface water ‐ groundwater exchange, and human activities. Those findings can provide reference for nitrate removal in international important agricultural areas.
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) | 0.000 | 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".