The Drying of the Arkavathy River: Understanding Hydrological Change in a Human-Dominated Watershed
Bibliographic record
Abstract
Human interventions in the hydrologic cycle have intensified to the extent that water resources cannot be managed and understood in isolation from anthropogenic influences. New approaches are needed to understand the effects of humans on hydrology, especially in regions of the world with limited hydrologic records. This dissertation focuses on a case study of the Arkavathy watershed adjacent to Bangalore, India, which has been transformed by rapid urbanization, intensification of agriculture, and over-exploitation of water resources over the last 50 years. During this time, the disappearance of streamflow in the watershed was largely overlooked as Bangalore shifted from Arkavathy-sourced water supply to imported water and farmers from surface water to groundwater irrigation. With Bangalore continuing to expand its water footprint and local groundwater resources drying up, moving towards sustainable water resources management in the Arkavathy requires overcoming the general absence of local hydrological records to develop an understanding of the changing hydrology of the watershed. To this end, a multifaceted research approach is developed and applied to the Arkavathy watershed to identify the dominant hydrologic dynamics within the watershed and understand the conditions under which hydrologic change occurred. This research reveals a number of important findings. First, humans are the primary drivers of change in this watershed, as neither precipitation variability nor increases in temperature can explain the observed changes in hydrology. Second, hydrologic change within the watershed is spatially heterogeneous, with drying occurring in the northern part of the watershed and increased surface water availability downstream of Bangalore. Third, streamflow decline in the northern Arkavathy has most likely been caused by extensive groundwater depletion driven by groundwater irrigated agriculture. And finally, management strategies designed to reverse groundwater depletion by constructing check dams within the surface water network are unlikely to succeed on the scales pertinent to watershed management. In addition to understanding water resources within the Arkavathy, this work serves as a foundation for understanding the trajectory of water resources in the region. This research also presents an approach for investigating historical hydrologic change in a poorly monitored watershed, understanding human-water interactions, and supporting long-term predictions for sustainable water management.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".