Mapping Surface Water Presence and Hyporheic Flow Properties of Headwater Stream Networks With Multispectral Satellite Imagery
Bibliographic record
Abstract
Abstract Growth and contraction of headwater stream networks determine habitat extent, and open a window to the hyporheic zone. A fundamental challenge is observation of this process: wetted channel extent is dynamic in space and time, with wetted channel length varying by orders of magnitude over the course of a single storm event in headwater catchments. To date, observational data sets are produced from boots‐on‐the‐ground campaigns, drone imaging, or flow presence sensors, which are often laborious and limited in their spatial and temporal extents. Here, we evaluate satellite imagery as a means to detect wetted channel extent via machine learning methods trained on local surveys of wetted channel extent. Even where channel features are smaller than the imagery's spatial resolution, the presence of surface water may be imprinted upon the spectral signature of an individual pixel. For two catchments in northern California with minimal riparian canopy cover and highly dynamic wetted channel extent, we train a random forest model on RapidEye imagery captured contemporaneously with the existing surveys to predict wetted channel extent (accuracy >91%). The model is used to produce length‐discharge (L‐Q) relations and to calculate spatially distributed estimates of channel hyporheic flow capacity and exchange. A sharp break in hyporheic flow capacity occurs from main stem channels to lower order tributaries, resulting in a stepped L‐Q relationship that cannot be captured by traditionally used power law models. Remotely sensed imagery is a powerful tool for mapping wetted channels at high spatial resolution.
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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 | 0.000 |
| 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) | 0.000 | 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".