Linking the Surface and Subsurface in River Deltas—Part 1: Relating Surface and Subsurface Geometries
Bibliographic record
Abstract
Abstract River deltas are densely populated regions of the world with vulnerable groundwater reserves. Contamination of these groundwater aquifers via saline water intrusion and pollutant transport is a growing threat due to both anthropogenic and climate changes. The arrangement and composition of subsurface sediment is known to have a significant impact on aquifer contamination; however, developing accurate depictions of the subsurface is challenging. In this work, we explore the relationship between surface and subsurface properties and identify the metrics most sensitive to different forcing conditions. To do so, we simulate river delta evolution with the rule‐based numerical model, DeltaRCM, and test the influence of input sand fraction and steady sea level rise (SLR) on delta evolution. From the model outputs, we measure a variety of surface and subsurface metrics chosen based on their applicability to imagery and modeling results. The Kullback‐Leibler (KL) divergence is then used to quantitatively gauge which metrics are most indicative of the imposed forcings. Both qualitative observations and the KL divergence analysis suggest that estimates of subsurface connectivity can be constrained using surface information. In particular, more variable shoreline roughness values and higher surface wetted fraction values correspond to increased subsurface connectivity. These findings complement traditional methods of estimating subsurface structure in river‐dominated delta systems and represent a step toward the identification of a direct link between surface observations and subsurface form.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".