Sensitivity of Subsurface Permeability in Coastal Deltas to Their Morphodynamic and Geomorphic Characteristics
Bibliographic record
Abstract
Abstract The amount of fresh water moving through coastal deltas worldwide is controlled by the complex subsurface structures within a delta. Morphodynamic influences produced by the feeding river, waves, and tides, in addition to sea level transgressions and regressions, have resulted in deltaic aquifers that are highly heterogeneous. We use 171 unique two‐dimensional morphodynamic models to explore the range of subsurface permeability, hydraulic gradient, and groundwater flux within three end‐member delta types (fluvial, wave, and tidal). We quantify the connectiveness of the subsurface permeability and estimate the horizontal heterogeneity and anisotropy of the permeability. A distance‐based generalized sensitivity analysis is used to investigate the impact morphodynamic influences (fluvial, wave, and tidal), basin conditions (sediment concentration and bathymetric gradient), and geomorphic characteristics (number of channels, shape of the delta plain, and shoreline rugosity) have on the subsurface permeability, hydraulic gradient, and connectivity. We find that the median permeability in deltaic landforms is 4.0 × 10 −12 m 2 (relating to a hydraulic conductivity of 2.1 × 10 −5 m/s), the average hydraulic gradient is 3.9 × 10 −4 , and the mean specific discharge is 1.3 × 10 −8 m/s. High permeability bodies are highly connected and are associated with channelization. Subsurface permeability, hydraulic gradient, and the connectiveness of high permeability areas are sensitive to morphodynamic influences (fluvial, wave, and tidal) and the geomorphic characteristics (number of channels and shoreline rugosity) within a delta. Since morphodynamic influences and geomorphic characteristics are easily identified by looking at the surface of the delta, we suggest that the deltaic subsurface can be characterized by identifying features on the delta surface.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".