Spatial characterization of long-term hydrological change in the Arkavathy watershed adjacent to Bangalore, India
Bibliographic record
Abstract
Abstract. The complexity and heterogeneity of human water use over large spatial areas and decadal timescales can impede the understanding of hydrologic change, particularly in regions with sparse monitoring of the water cycle. In the Arkavathy watershed in south India, surface water inflows to major reservoirs decreased over a 40 year period during which urbanization, groundwater depletion, modification of the river network, and changes in agricultural practices also occurred. These multiple, co-varying drivers along with limited hydrological monitoring make attribution of the causes of water scarcity in the basin challenging, and limit the effectiveness of policy responses. We develop a novel, spatially distributed dataset to understand hydrologic change by characterizing trends in surface water area in nearly 1700 rainwater harvesting and irrigation structures known as tanks. Using an automated classification approach with subpixel unmixing, we classified water surface area in tanks in Landsat images from 1973 to 2010. The classification results compared well with a reference dataset of water surface area of tanks (R2 = 0.95). We modeled water surface area of 42 clusters of tanks in a multiple regression on simple hydrological covariates and time, and found distinguishable trends in water surface area in different regions of the watershed. Agricultural areas with considerable groundwater irrigation exhibited the strongest drying. Urban land use was associated with intra-urban drying, likely due to tank encroachment, and downstream periurban wetting, likely due to increased urban effluents. Disaggregating the watershed-scale hydrological response via remote sensing of surface water bodies over multiple decades yielded a spatially resolved characterization of hydrological change in an otherwise poorly monitored watershed. This approach presents an opportunity for understanding hydrological change in heavily managed watersheds where surface water bodies integrate upstream runoff and can be delineated using satellite imagery.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".