Sedimentary records from human-made talavs reveal climate risks in semi-arid watersheds of India
Bibliographic record
Abstract
Assessing climate impacts in semi-arid watersheds, which are home to populous semi-arid regions of South Asia, are becoming increasingly critical as these regions emerge as climate hotspots. Century-scale records of climate impacts, preserved in terrestrial sedimentary archives, are some of only kinds of investigations that can provide the necessary insights into how local climate variations impact these watersheds. Here, we investigate sedimentary records preserved in a unique type of human-made water bodies, which are commonly present in arid and semi-arid regions of south Asia. Known as ‘ talavs ’, human-made water bodies are ubiquitous in south Asia and have been historically constructed by damming seasonal rain-fed distributaries to conserve rainwater for the purposes of sustenance and agriculture in water-stressed regions. Integrating a multidisciplinary approach comprising remote sensing, lake geophysics, lithostratigraphic (sedimentological, mineralogical & geochemical measurements), and radiometric dating, we reconstruct century-scale records of landscape erosion & resultant run-off and in water-stressed catchments in one of the most climatologically threatened watersheds of western India, namely the Bhima watershed. Our reconstructions show that land erosion and subsequent sediment deposition in talavs are tied to the regional expressions of the Indian summer monsoon (ISM). We also find that while the landscape evolution is sensitive to divisional expressions of hydroclimate variability (associated with the ISM), the intensity of run-off and erosion is not a simple function of rainfall intensity; in fact, we find that land-surface erodibility is impacted by land-use patterns and incidence of prior climate events (e.g. flooding) and that these effects are more prominent in drier catchments (which also experience more extreme climate events) than in wetter parts of the watersheds. Based on our investigation, we conclude that drier catchments of watersheds in semi-arid regions are at an elevated risk of direct climate impacts than the wetter catchments in the same watershed.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".