Tank Sedimentation, Soil Erosion Simulations and Conservation Interventions of the Sub-catchments in Palugaswewa Tank Cascade System, Sri Lanka
Bibliographic record
Abstract
Tank Cascade Systems (TCS) in the dry zone of Sri Lanka is threatened by soil erosion and high levels of sedimentation. Despite these challenges, the nation lacks studies exploring spatial soil loss variations within TCS contexts. Consequently, this research aimed to assess the sedimentation levels of five tanks and to analyze the spatial distribution of potential soil erosion rates across six selected sub-catchments within the Palugaswewa TCS. By utilizing sediment depth contour maps, the current sedimentation volume for each tank was computed. The study employed the revised universal soil loss equation (RUSLE) and geographic information system techniques to evaluate the potential average annual soil erosion rate, considering both existing land use scenarios and conservation interventions. The potential annual sediment yield was calculated using the sediment delivery ratio and potential average annual soil erosion rate. At present, 40 to 50 % of the tank storage capacity has been filled with sediments under existing land use. The potential average annual erosion rates of the sub-catchments of Palugaswewa TCS ranged from 19 t/ha/yr to 44 t/ha/yr. Notably, Sri Lanka's acceptable erosion rate stands below 12 t/ha/yr, rendering the erosion rates within Palugaswewa TCS unsuitable and destructive to sustained land productivity. The sediment delivery ratio varied from 0.18 to 0.9. This study suggests that adapting appropriate conservation measures such as cover cropping and soil contour bunding reduces the potential average annual erosion rate by 8.9 t/ha/yr to 14.5 t/ha/yr in the Palugaswewa sub-catchments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".