Evaluating streamflow potential, demand and allocation of the upper Genale River basin under current and future development plan, Ethiopia
Bibliographic record
Abstract
The management of water in river basins depends on accurate assessment and efficient distribution of limited water resources. This study investigates the water resource potential, demand, and allocation in the upper Genale River basin under various future scenarios. The Soil and Water Assessment Tool was used to generate streamflow data, and the Water Evaluation And Planning model was used to allocate water demands optimally for the period 2020–2050. Scenarios considered population growth, increased water consumption, irrigation expansion, and climate change (RCP4.5 and RCP8.5). Results indicate the livestock sector as the current primary water user, followed by domestic and commercial sectors, with irrigation having the lowest consumption. The reference scenario projects a significant increase in total water demand (79.99 MCM in 2020 to 330.91 MCM in 2050) due to population and livestock growth. Scenarios with high population growth and increased consumption or irrigation development showed substantial demand increases, highlighting the pressure these factors exert. Additionally, the study highlights a significant risk of water scarcity , especially in scenarios with a combination of high population growth, increased irrigation development, and climate change. Furthermore, the study highlights the need for diversifying energy sources beyond hydropower due to potential water limitations. The need for proactive water management policies to tackle these challenges is emphasized and such policies should be based on improved local-scale monitoring and accurate projection of freshwater to promote sustainable water allocation for future generations. For the upper Genale River basin where we have significantly improve understanding of water availability, demand, and allocation, a robust regulatory framework on water supply is crucial for water resources management under rising human population.
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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".