Lateral groundwater flow and pond interactions during dry and wet years
Bibliographic record
Abstract
Groundwater and surface water are tightly coupled elements of the hydrologic cycle that have often been treated as separate entities. Future climate change modelling has predicted that hydrologic cycle changes, namely increasing drought frequency and flood-type events, are likely to occur. These events may directly impact the quality and quantity of both groundwater and surface water. Future water management policies must therefore be based on an understanding of how interactions between groundwater and surface water will change with a warming climate. The aim of this study was to model and analyze the lateral flow of groundwater and its interactions with a nearby pond in a shallow, unconsolidated, unconfined aquifer. Data were collected as part of a larger and ongoing study during the year 2012, a comparatively dry year, and 2013, a comparatively wet year. We first used ArcGIS and Visual MODFLOW Flex to create a conceptual model of the system, its soil layers, monitoring wells, and potential flow patterns. We then analyzed hydraulic head data, and calculated groundwater flow volumes using the Dupuit equation. We found that the groundwater flow direction reversed in the summer of 2012 and continued until the spring of 2013. Additonally, flow rate was greater in 2013 than 2012. The flow reversal was likely caused by higher evaporative demand during the summer months of 2012, drawing substantially more water from the pond than from the soil. The two-year timeframe was not long enough to determine whether this was a typical, yearly pattern, or was primarily due to the fact that 2012 was a particularly dry year.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".