Evaluating the sustainability of groundwater abstraction in small watersheds using time series analysis
Bibliographic record
Abstract
Groundwater is crucial in meeting the water needs of communities, industries, and ecosystems. The effective management of this resource is essential for maintaining the long-term stability of both the environmental and socio-economic conditions. This work focuses on assessing the sustainability of groundwater abstraction in Charlottetown, Prince Edward Island, specifically from well fields situated within the small-scale watersheds of Winter River and North River. The advanced time series analysis techniques, including Vector Error Correction Models (VECM), Impulse Response Functions (IRFs), and Forecast Error Variance Decomposition (FEVD) are employed to investigate the relationships among precipitation, temperature, groundwater abstraction, and stream flows. The analysis of IRFs reveals dynamic responses of streams to various shocks, including the variation of temperature, precipitation and well discharges, which showcase related immediate impacts, short-term responses, and long-term relationships. Temperature fluctuations exhibit complex responses, with short-term response decreases followed by sustained increases. Precipitation emerges as a dominant factor, showing sustained positive impacts on streamflow. Well operations significantly influence stream ecosystems, emphasizing the importance of optimized well operation strategies. The FEVD revealed that the first forecast horizon for all stream flows is primarily influenced by past shocks in precipitation with 16–55% in addition to other factors. The walk forward cross-validated forecast values for the next 24 months align with seasonal trends, reflecting declining discharge in summer, variable but generally decreasing discharge in fall, and increased discharge in winter and spring. The study findings provide recommendations for sustainable groundwater abstraction practices, including optimizing well operation strategies, community and stakeholder engagement, and ecosystem preservation. • Time Series Analysis effectively evaluates groundwater sustainability in small-scale watersheds. • Impulse Response Functions (IRFs) highlighted hydrological connections in the watersheds. • Forecast Error Variance Decomposition revealed influence of past-shocks in forecasts. • Performed rolling window walk-forward cross-validation for forecasts. • Key recommendations for sustainable groundwater abstraction practices are provided.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".