Review of the hydrological effects of the extreme event Hurricane Eta in the Chiriquí Viejo River basin, November 2020
Bibliographic record
Abstract
This study analyzes the effects of Hurricane Eta on the Chiriquí Viejo River basin, revealing the significant impact of extreme weather events on the hydrological dynamics of the region. The maximum rainfall recorded on November 4, 2020, reached 223.8 mm, while the flow in Paso Canoa reached 638.03 m³/s, demonstrating the magnitude of the event and the inability of the basin to handle such high volumes of water. Through a detailed analysis, it was observed that soil saturation resulted in direct runoff of up to 70.0 mm that same day, which shows that the infiltration capacity of the soil was quickly exceeded. Despite the damage observed, there are currently no advanced hydrological studies on extreme events in critical basins such as the Chiriquí Viejo River. This lack of research reflects a serious lack of planning and assessment of the risks associated with phenomena of this magnitude. One of the most critical problems found is the lack of specialized hydrology professionals, who are essential to carry out detailed studies and ensure sustainable management of water resources. In a context where climate change increases the frequency and intensity of extreme events, the absence of hydrologists in the region puts the resilience of the basin to future disasters at risk. The basin's hydraulic system demonstrated its inability to handle high flows, underscoring the need to improve flood control and water retention infrastructure. In addition, the lack of effective hydrological planning and coordination in the management of hydraulic infrastructures compromises both the safety of downstream communities and the sustainability of hydroelectric reservoirs, vital for the region.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".