Effects of Climate Change and Land Use on the Hydrologic Regime Using the Hydro-bid Tool: Andean Mountain Basin Case Study
Bibliographic record
Abstract
Abstract Changes on the land surface from human activities or natural events generate changes in land cover, which directly effect water availability and quality in watersheds. This article evaluates the effects on the hydrological regime Andean Mountain basin case study on the Coello river basin in Colombia due to changes in land use/land cover during the 2000–2019 period by the use of the Hydro-Bid tool. The physical analysis of the land surface included the processing of Landsat 7 ETM and Landsat 8 OLI satellite images for the years 2001, 2003, 2015 and 2019. Seven types of coverage were determined based on these data using the Mixed Gaussian Method that is part of the dzetsaka plugin in QGIS. The changes between each year were evaluated, after which the land use/land cover change for the year 2050 was predicted using a Markov chain in the TerrSet software package. The multitemporal analysis showed a decrease in forested areas during the studied period, while low vegetation significantly increased within the watershed. This trend was shown to continue in the future scenario for the year 2050, where the predicted losses in forest cover were estimated at 135 km2 with an increase in flow on the watershed of 59.6%. Additionally, the climate change scenarios were modeled with the changes in land use. The combined effects (climate change + land use) established a progressive decrease in the modal flow. The results from this study will allow authorities to improve decision-making in land use planning and climate change adaptation. However, uncertainties associated with data availability and modelling performance must be taken into account when applying the presented results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".