Properly integrating vertical land motion with sea-level change – towards robust projections of relative sea-level rise 
Bibliographic record
Abstract
Coastal lowlands in the world increasingly face accelerating rates of relative sea-level rise, as global sea level rises and coastal land subsidence increases. Originating from both natural and anthropogenic processes, land subsidence (i.e. downward vertical land motion) is particularly prominent in densely populated coastal-deltaic settings where human activities can accelerate subsidence rates to several centimetres or even decimetres per year, thereby dominating local, contemporary relative sea-level rise. Proper inclusion of vertical land motion dynamics into sea-level change projects, combined with high-accurate and correctly referenced coastal elevation data, is crucial to accurately project relative sea-level change in these critical, densely populated coastal areas.Recent advancements in satellite-based InSAR data acquisition and processing capacity provide insights into contemporary vertical land motion dynamics at unprecedented spatial scale, complementary to traditional measurements of vertical land motion by e.g. tide gauges and GNSS stations. However, it requires a robust InSAR-data processing framework that ensures internal consistency of SAR data and rigorously assesses output accuracy. In addition, correct interpretation of InSAR results is important as observations provide reflector movements which may not align with land surface movements, particularly in urban areas. This poses the risks of oversimplification and misinterpretation when linking InSAR results to sea-level change.In addition, coastal subsidence is the result of various subsurface processes at different depths and can be highly non-linear over time, unlike sea-level change, resulting in complex spatio-temporal patterns and dynamics. This makes projection of non-linear vertical land motions and relative coastal elevation change not straightforward and robust strategies have yet to be developed. We advocate the development of standardized InSAR (post-)processing workflows and interdisciplinary collaboration to improve the observation and proper interpretation of vertical land movement, particularly in coastal cities and river deltas. We also discuss how to move from contemporary observations of coastal vertical land motion towards disentangling drivers and processes, move to process-based projections of coastal subsidence and integrate them in robust projections of future relative sea-level changes and coastal exposure assessments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".