Disentangling the socio-natural dynamics of drought and water scarcity in Colombia's Tropical Andes
Bibliographic record
Abstract
The Guachal River Basin (GRB), a headwater of the Cauca River in Colombia's Tropical Andes. Droughts develop gradually, without clear time-space boundaries, affecting extensive geographical areas. When droughts occur, they exacerbate existing water scarcity, aggravating the negative impacts on vulnerable populations and ecosystems. The drivers of these phenomena are complex, ranging from natural to anthropogenic, and their impacts are cumulative and not structural. This research investigates the socio-natural dynamics of drought and water scarcity in the GRB. We explore these dynamics through secondary data review and stakeholder interviews, capturing perceptions and manifestations of droughts beyond official reported data. The Drivers-Response-Impacts framework is employed to unravel these complexities and offer insights to improve drought and water management. Our research reveals that water scarcity in the GRB primarily results from land use changes and water overconsumption by the local elite, who have transitioned to sugarcane farming. ENSO-driven droughts further exacerbate water shortages in the GRB. Policy responses to drought and water scarcity are often ineffective and reactive, addressing only immediate symptoms rather than long-term drivers, such as the role of the elite in perpetuating scarcity. We explore several strategies to enhance water management: exploring new drought indicators, creating comprehensive drought damage inventories, implementing adaptable demand control mechanisms targeting high-volume users, and enhancing stakeholder participation in decision-making processes. • DRI framework offers insights for drought management in Colombia's Tropical Andes. • Water scarcity is linked to El Niño and sugarcane irrigation. • Inefficient policies worsen scarcity, neglecting vulnerable groups and the environment. • Management needs monitoring, new drought indices, impact inventories, and stakeholder inclusion.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".