Global warming impact to River Basin of Blue Nile and the optimum operation of its multi-reservoir system for hydropower production and irrigation
Bibliographic record
Abstract
The water resource of the Blue Nile River basin (BNRB) has been under pressure due to growing demands from many users, and the climate change impact. Potential impact of climate change for the maximum, median and minimum projected changes in the simulated streamflow of BNRB by a hydrologic model, VIC, driven by Representative Concentration Pathways climate scenarios, RCP4.5 and RCP8.5, of 4 GCMs (global climate models) downscaled dynamically by a regional climate model, WRF (Weather Research Forecasting) using a one-domain framework that covers the entire NRB for 2041-2070 and 2071-2100. These projected changes in streamflow were used to assess its future water allocations using a stochastic Dual Dynamic Programming (SDDP) algorithm and a hydro-economic model to optimize hydropower production and irrigated agriculture. Overall, it seems the Grand Ethiopian Renaissance Dam (GERD) reservoir will likely not operate at full storage level because the streamflow of BNRB is assumed to be regulated by three upstream reservoirs. The outflow from the reservoir of GERD or BNRB's annual flow at Khartoum is projected to increase under maximum, but is expected to decrease under minimum and median projected changes in streamflow for 2041-2070 and 2071-2100, respectively. Given the annual net benefit obtained from hydropower production and irrigated agriculture of the reservoir is projected to increase (decrease) under the maximum (median and minimum) projected changes in streamflow, the potential climate change impact should be considered in designing and developing the future water resources of BNRB.
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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 | 0.000 |
| 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 | 0.001 | 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".