Deep Soil Water Reservoirs Modulate Land Use and Drought Effects on the Water Budget of Amazon Headwaters
Bibliographic record
Abstract
Abstract The southeastern Amazon has been transformed by widespread land use and climate changes, altering the hydrologic cycle. In this seasonally dry tropical forest, the soil water reservoir plays a strong role in mediating the water balance by buffering forests during dry seasons and moderate droughts, as well as modulating runoff to streams. Few studies have examined the response of soil water reservoirs to large‐scale forest loss in headwater regions, much less how these changes may influence catchment water balances during droughts. This study compares the water balance of forested and cropland watersheds in the headwaters of the Xingu River basin (Mato Grosso, Brazil). We combined measurements of stream discharge and soil moisture (from 0.3 to 8 m depth) from the 2014–2018 water years, along with remotely sensed precipitation and evapotranspiration (ET) data, comparing normal precipitation years to an extreme drought year. Streams within agricultural catchments had four times higher discharge (29% of total precipitation) than in forested catchments (8%). During normal years, this difference was balanced by differences in ET. We found that groundwater outflow—water that bypasses a catchment without reaching the stream—is a significant water export term under both landcovers (19% in forest; 26% in croplands). However, during drought years, this outflow disappears in forested catchments and decreases in agricultural catchments, suggesting enhanced groundwater uptake by vegetation that diminishes contributions to rivers downstream. Multiyear droughts projected under future climate changes could threaten the soil water reservoir, leaving forests without a critical resource and downstream communities vulnerable to streamflow loss.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".