Effects of Sedimentation on Small Reservoirs in the Mushibemba Catchment, Mkushi Farm Block, Central Zambia
Bibliographic record
Abstract
Sedimentation is one of the problems that affects the storage capacity of most small reservoirs, and if not addressed on time it may lead to the dams being filled up with sediment. The aim of this study was to assess the effects of sedimentation on the storage capacity losses of Moffat dam and GRZ Weir located in the Mushibemba Catchment of Mkushi Farm Block, Central Zambia. The method used to understand reservoir storage capacity loss was bathymetry survey using the hydrographic survey boat (RC-S2) which consists of an echo sounder and a Differential GPS. Information on the initial storage capacity of the two reservoirs was obtained from the Water Resources Management Authority (WARMA). The analysis of the data revealed that the measured volumes of Moffat dam and GRZ Weir were 1,180,462 m3 and 197,218 m3, respectively. The estimated storage capacity losses for Moffat reservoir was found to be 223,789 m³ and 53,312 m3 for the GRZ Weir.  The estimated rates of sedimentation loss were found to be 13,986.81 m3 yr-1 (Moffat reservoir), with a lifespan of 84 years, and 1,480.89 m3 yr-1 (GRZ Weir) with a lifespan of 133 years. The source of the accumulated sediment was mainly attributed to originate from the cleared commercial agricultural land which predominantly consists of clayey to loamy soils. This also accounted for the high turbidity of the reservoir water. This situation therefore calls for periodic dredging of the deposited sediment to increase reservoir storage capacity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".