Regressions on Monthly Stream Discharge to Predict Sediment Inflow to a Reservoir in Algeria
Bibliographic record
Abstract
An understanding of the quantity of sediment deposited in a reservoir is necessary for effective reservoir andbasin management. Sedimentation affects the useful life of a reservoir as flood control and water supply. Thepresent study has been carried out to assess sediment load in the wadi Bouhamdane in northeast Algeria to beable to estimate sedimentation in a reservoir located upstream of the gauging station. Measurement data ofsuspended sediment and water discharge were available during 13-year period (1975/76-1987/88) in the MedjezAmar gauging station. The data were provided by the National Agency of Hydraulic Resources, which carriedout the sampling and initial calculations of concentration. Several data sets, derived from daily mean waterdischarges and daily mean suspended sediment discharges were used to develop sediment rating curves for smallperiods of time when water sampling was insufficient. Relationships between monthly mean water discharge andmonthly sediment load were used according to single and seasonal ratings. Additionally, a technique was devisedto correct for log-transform bias on the sediment rating curves. Once the water discharge-sediment rating curveswere defined, they were used with water discharge data to predict monthly and annual sediment inflow to thereservoir. The sediment rating estimates were judged adequate to predict the Bouhamdane reservoir fill. The totalsuspended sediment load carried by the wadi Bouhamdane from 1975/76 to 1987/88 was calculated to 3.90 x 106tonnes and the mean annual sediment yield was 271 T km-2 yr-1. During the reservoir period 1988/89 to2006/2007 the water discharge-sediment rating curves gave 5.38 x 106 tonnes. In terms of sediment entrapmentin the reservoir by damming, the sedimentation in the reservoir during the 19-year period was predicted to be6.29x106 m3. This study has estimated a loss of storage in the reservoir due to sedimentation of 0.17% yr-1.
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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".