Sociohydrological Impacts of Water Conservation Under Anthropogenic Drought in Austin, TX (USA)
Bibliographic record
Abstract
Abstract Municipal water providers increasingly respond to drought by implementing outdoor water use restrictions to reduce urban water withdrawals and maintain water availability. However, restricting urban outdoor water use to support watershed‐scale drought resilience may generate unanticipated cross‐scale interactions, for example, by altering drought response and recovery in urban vegetation or urban streamflow. Despite this, urban water conservation is rarely conceptualized or modeled as endogenous to the water cycle. Here we investigate cross‐scale interactions among urban water conservation and water availability, water use, and sociohydrological response in Austin, TX (USA) during a recent anthropogenic (human‐influenced) drought. Multiscalar statistical analyses demonstrated that outdoor water conservation for reservoir management at the municipal scale produced responses that can cascade both “upward” from the city to the watershed (e.g., decoupling streamflow patterns upstream and downstream of Austin at the watershed scale) and “downward” to exert heterogeneous effects within the city (e.g., redistributing water along a socioeconomic gradient at submunicipal scales, with effects on terrestrial and aquatic ecosystems). We suggest that adapting to anthropogenic drought through irrigation curtailment requires sustained engagement between hydrology and social sciences to integrate socioeconomic status and political feedbacks within and among irrigator groups into the water cycle. Findings from this cross‐disciplinary study highlight the importance of a multiscalar and spatially explicit perspectives in urban sociohydrology research to uncover how water conservation as adaptation to anthropogenic drought links hydrological processes with issues of socioeconomic inequality and spatiotemporal scale in the Anthropocene.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".