River Flow Analysis using HEC-HMS and Assessing the Impact of Climate Change on Hydropower Generation by MODSIM in the Koka Reservoir of Ethiopia
Bibliographic record
Abstract
Climate change results in precipitation variation in rivers and other water bodies, resulting in a decline in reservoir inflow and hydropower generation. This study is aimed at the assessment of climate change under different environments for hydropower generation and optimal reservoir operation using updated representative pathways (RCPs) in the Koka reservoir of Ethiopia. The power transformation function and variance scaling techniques of numerous meteorological data were adopted for bias correction. The simulation of flow was performed using HEC-HMS and the generation of hydropower from the reservoir was estimated using MODSIM 8.1 under different climatic scenarios. NSE and R2 (90.72 and 0.70) were calculated based on the model performance features for calibration and validation, respectively. There was a remarkable anomaly in the pattern of precipitation and temperature based on the projection of future climate scenarios. This change ultimately affects the power generation with an apprehension of reduction of 0.54% and 0.72% in the near term (2021–2050) and long term (2051–2080) under RCP4.5. Similarly, the mean generation of energy will reduce by 1.04% in the short term and 1.32% in the long-term for RCP8.5. This shows that the reduction is more prevalent in RCP8.5 in comparison to RCP4.5. Simply, it can be concluded that if strict measures are not timely initiated, there will be an acute power shortage. The research findings warn the concerned bodies to take timely measures to reduce recurring sediment deposits and ensure future hydropower output.
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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 | 0.001 | 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 | 0.001 |
| 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".