Assessment of hydrologic connectivity in an ungauged wetland with InSAR observations
Bibliographic record
Abstract
The Ciénaga Grande de Santa Marta (CGSM) is one of the world's most productive tropical wetlands and one that has witnessed some of the greatest recorded dieback of mangroves. Human-driven loss of hydrologic connectivity by roads, artificial channels and water flow regulation appears to be the reason behind mangrove mortality in this ungauged wetland. In this study, we determined the CGSM's current state of hydrologic connectivity by combining a remote sensing technique, termed as Wetland Interferometric Synthetic Aperture Radar (InSAR), with a hydrologic study of river water discharge. For this research, we processed 29 ALOS-PALSAR acquisitions taken during the period 2007–2011 and generated 66 interferograms that provide information on relative surface water level changes. We found that change in water discharge upstream on the main tributary of the CGSM could explain at most 17% of the variance of the change in water level in the CGSM. Fresh water inputs into the wetland were identified only when the mean daily water discharge in the river exceeded 700 m 3 s −1 , which corresponds to only 30% of the days during the period. The interferogram analysis also revealed that artificial channels within the wetland serve as barriers to water flow and contribute to the overall loss in hydrologic connectivity. We recommend increasing fresh water inputs from the Magdalena River by reducing water regulation of fresh water from the river and improving connectivity on either side of the artificial channels crossing the CGSM. This study emphasizes the potential of the application of wetland InSAR to determine hydrologic connectivity in wetlands that are completely or poorly ungauged and to define the necessary guidelines for wetland hydrologic restoration.
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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 | 0.001 | 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 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".