Remote Sensing and Hydrodynamic Modeling to Assess Riparian Vegetation and Geomorphological Responses to Flow Regulation, Rio Chama, New Mexico, USA.
Bibliographic record
Abstract
The alteration of the hydrological regime caused by damming remains a critical challenge for river systems. The Rio Chama, a highly regulated river, has undergone severe alterations in its hydrological regime due to a series of dams that impact sediment transport mechanisms and riparian vegetation dynamics. This study employs remote sensing to assess reach-scale changes in riparian vegetation and geomorphology and field-informed 2D hydrodynamic modeling to determine sediment transport processes. We used a random forest classifier within Google Earth Engine and high-resolution NAIP imagery (2011 to 2022) to assess changes in riparian vegetation and channel planform. We evaluated the impact of different flows on sediment transport mechanisms, along with field-data-informed sediment distribution at the sub-reach scale. Following a 2009 high flow release, channel width remained relatively stable, although planform changes were observed, including shifts in the channel centerline and localized bank erosion, especially in the sinuous sections. Vegetation expanded over time and encroached along bars, linked to reduced overbank flooding and sustained baseflow year-round. Furthermore, results indicated that even smaller flows can lead to fine sediment displacement, while higher flows are necessary to mobilize coarse sediments. This study offers valuable insights for ecological flow recommendations, particularly for the Rio Chama, and supports improved restoration strategies and long-term river management in other dam-regulated systems across arid and semi-arid regions.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".