Effects of catchment characteristics and climatic conditions on reservoir water capacity in a drought prone area
Bibliographic record
Abstract
Crop production in semi-arid sub-Saharan Africa (SSA) is limited by over-reliance on erratic and inadequate rainfall, which often results in yield reduction or total crop failure. The effects of frequent droughts and dry spells need to be circumvented by water conservation. Where rainwater is harvested, research recommendations are based on direct use of the water without relating it to catchment characteristics, climatic conditions and long term storage. A study aimed at predicting sizes of seasonal open surface reservoir based on rainfall and runoff rainwater was conducted from 2012 to 2013 at Ukwe Area, Malawi. The work premised on assessment of land and hydrological factors as they impinge on runoff water storage. Rainfall-runoff relative analysis showed runoff trend following the magnitude of rainfall. Findings showed that runoff water harvested, under the Ukwe area landscape conditions, is linearly related to seasonal rainfall amount with coefficient of correlation of greater than 0.75, demonstrating vitality of rain and timing of rain harvesting for reservoir sustenance. Runoff amount was almost four times that of infiltrated amount, highlighting the fact that drought prone areas can be flood prone as well. Results further demonstrate that weekly reservoir balance using crop, livestock and domestic consumption, and losses through evaporation and seepage, as dry season progresses are critical for reservoir sizing during dam construction or crop field sizing at the onset of dry season. Key words: Semi-arid, rainwater, reservoir, Malawi, runoff, coefficient.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".