How efficient are pre-dams as reservoir guardians? A long-term study on nutrient retention
Bibliographic record
Abstract
Assessing nutrient loading and processing is crucial for water quality management in lakes and reservoirs. Quantifying and reducing external nutrient inputs in these systems remains a significant challenge. The difficulty arises from low monitoring frequencies of the highly dynamic external inputs and the limited availability of measures to reduce diffuse source loading. One option for the latter is the use of pre-dams, i.e. small impoundments at the inflow points into reservoirs, designed to retain nutrients by algal uptake and sedimentation. This study analyzes long-term (ranging from 8 to 22 years) nutrient and discharge time series for nine German pre-dams to assess their retention capacity. For that, we (i) quantified nutrient loading using four different mathematical methods, (ii) derived their retention efficiencies, and (iii) identified environmental factors determining the retention of nitrogen (N), phosphorus (P), and silica (Si). We show that retention of soluble reactive phosphorus (SRP) (43.6 %) and total phosphorus (TP) (39.9 %) is far higher than for nitrate (NO 3 ) (15.3 %) and Si (15.9 %). The retention efficiency for SRP and TP was higher during the warm seasons because of higher algal nutrient uptake and thus higher nutrient sedimentation. Mixed effects models documented a significant positive effect of the pre-dams’ hydraulic residence time (HRT) on retention efficiency. Pre-dams provide substantial service in retaining nutrients and help to protect downstream waterbodies from nutrient inputs. They provide effective measures for trapping nutrients including those originating from non-point sources.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".