Simulated surface and shallow groundwater resources in the Abaya-Chamo Lake basin, Ethiopia using a spatially-distributed water balance model
Bibliographic record
Abstract
Study region The volcano-tectonic lakes basin of Abaya-Chamo is part of the Main Ethiopian Rift system and exhibits large variations in geomorphology, physiography and climate between the rift floor and the plateau. Study focus Despite the importance of streamflow for water resources management and planning in the basin, many of the rivers there are ungauged. To make quantitative estimates of streamflow for spatially resolved water availability in such a highly heterogeneous environment, therefore, requires numerical modeling. This study is the first to quantify the surface and shallow groundwater resources in Abaya-Chamo, and to validate the physically fully distributed hydrologic model WetSpass under highly data-limited conditions, in a complex two-lake environment. New hydrological insights Simulated total river flow and estimated baseflow were verified at 15 gauging stations, with a good agreement. The WetSpass model is shown to be suitable for such a complex setting with a correlation coefficient of 0.95 and 0.97 for total flow and baseflow respectively at a statistically significant level (p-value < 0.05). The simulated annual water budget reveals that 74.6% of the 22.1 billion lit/yr in total precipitation in the basin is lost through evapotranspiration, 15.7% through surface runoff, and only 9.7% recharges the groundwater system. The simulations also revealed the surface runoff and groundwater recharge are the most sensitive to soil textural class, while evapotranspiration depends more strongly on land use.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".