Land use change and water loss in the Upper Paraguay River Basin: Trends, future scenarios, and implications for the Pantanal
Bibliographic record
Abstract
The Upper Paraguay River Basin (UPRB), encompassing the Planalto highlands and the Pantanal, the world's largest tropical wetland, has undergone intense Land Use and Land Cover (LULC) transformations and recurrent droughts that threaten regional water security. This study analyzed LULC dynamics from 1985 to 2020 and projected scenarios for 2030, 2040, and 2050 using a hybrid modeling framework that combines Markov Chains, Cellular Automata, and a Multi-Layer Perceptron, based on annual satellite-derived LULC maps. Results show a drastic decline in water bodies (93.5 % reduction) and wetlands (71.2 % reduction) in the Pantanal, mainly converted to grasslands, and a sharp expansion of pasture (139.5 % increase) and agriculture in the Planalto. By 2050, pasture is projected to cover 34.7 % of the basin, grassland 17.8 % (683.0 % increase since 1985), and agriculture 9.0 % (202.0 % increase), while native vegetation will shrink from 76.4 % in 1985 to 53.9 %. Elevation, precipitation, and proximity to soybean crops and pasture were the most influential predictors in the model. These changes, driven by the combined pressures of climate variability, land-use intensification, and economic demand for agricultural commodities, threaten hydrological connectivity, biodiversity, and ecosystem services. Urgent, integrated, and spatially differentiated management strategies are essential to mitigate these impacts, including targeted conservation of wetlands and riparian zones, sustainable agricultural practices, and improved hydrological governance. While the CA-Markov-MLP approach provides spatially explicit projections useful for policy formulation and basin-wide planning, uncertainties remain, particularly for wetlands, due to class-specific misclassification and interannual variability in reference data. Therefore, projected magnitudes should be interpreted as indicative trends rather than precise forecasts.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".