Tracking a blue wave of ephemeral water across arid southern Africa
Bibliographic record
Abstract
Abstract Surface water in arid regions is essential to many organisms including large mammals of conservation concern. For many regions little is known about the extent, ecology and hydrology of ephemeral waters, because they are challenging to map given their ephemeral nature and small sizes. Our goal was to advance surface water knowledge by mapping and monitoring ephemeral water from the wet to dry seasons across the Kavango–Zambezi Transfrontier Conservation Area of southern Africa (300 000 km2). We mapped individual waterholes for six time points each year from mid-2017 to mid-2020, and described their presence, extent, duration, variability, and recurrence. We further analyzed a wide range of physical and landscape aspects of waterhole locations, including soils, geology, and topography, to climate and soil moisture. We identified 2.1 million previously unmapped ephemeral waterholes (85%–89% accuracy) that seasonally extend across 23.5% of the study area. We confirmed a distinct ‘blue wave’ with ephemeral water across the region peaking at the end of the rainy season. We observed a wide range of waterhole types and sizes, with large variances in seasonal and interannual hydrology. We found that ephemeral surface water was associated with loam soils in the study area. From the wettest time period to the driest, there was a ∼44 000 km2 (62%) decrease in ephemeral water extent across the region—these dramatic seasonal fluctuations have implications for wildlife movement. A warmer and drier climate, expected human population growth, and associated agricultural expansion and development may threaten these sensitive and highly variable water resources and the wildlife that depend on them.
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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 | 0.000 | 0.000 |
| 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".