Linking the Surface and Subsurface in River Deltas—Part 2: Relating Subsurface Geometry to Groundwater Flow Behavior
Bibliographic record
Abstract
Abstract Understanding subsurface structure and groundwater flow in deltaic aquifers is essential for evaluating the vulnerability of groundwater resources in delta systems. Deltaic aquifers contain coarse‐grained paleochannels that preserve a record of former surface river channels as well as fine‐grained floodplain deposits. The distribution of these deposits and how they are interconnected control groundwater flow and contaminant transport. In this work, we link depositional environments of deltaic aquifers to stratigraphic (static) and flow and transport (dynamic) connectivity metrics. Numerical models of deltaic stratigraphy were generated using a reduced‐complexity numerical model (DeltaRCM) with different input sand fractions (ISF) and rates of sea‐level rise (SLR). The groundwater flow and advective transport behavior of these deltas were simulated using MODFLOW and MODPATH. By comparing the static and dynamic metrics calculated from these numerical models, we show that groundwater behavior can be predicted by particular aspects of the subsurface architecture, and that horizontal and vertical connectivity display different characteristics. We also evaluate relationships between connectivity metrics and two environmental controls on delta evolution: ISF and SLR rate. The results show that geologic setting strongly influences both static and dynamic connectivity in different directions. These results provide insights into quantitatively differentiated subsurface hydraulic behavior between deltas formed under different external forcing (ISF and SLR rate) and they are a potential link in using information from delta surface networks and depositional history to predict vulnerability to aquifer contamination.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".