Watershed Classification Predicts Streamflow Regime and Organic Carbon Dynamics in the Northeast Pacific Coastal Temperate Rainforest
Bibliographic record
Abstract
Abstract Watershed classification has long been a key tool in the hydrological sciences, but few studies have been extended to biogeochemistry. We developed a combined hydro‐biogeochemical classification for watersheds draining to the coastal margin of the Northeast Pacific coastal temperate rainforest (1,443,062 km 2 ), including 2,695 small coastal rivers (SCR) and 10 large continental watersheds. We used cluster analysis to group SCR watersheds into 12 types, based on watershed properties. The most important variables for distinguishing SCR watershed types were evapotranspiration, slope, snowfall, and total precipitation. We used both streamflow and dissolved organic carbon (DOC) measurements from rivers ( n = 104 and 90 watersheds respectively) to validate the classification. Watershed types corresponded with broad differences in streamflow regime, mean annual runoff, DOC seasonality, and mean DOC concentration. These links between watershed type and river conditions enabled the first region‐wide empirical characterization of river hydro‐biogeochemistry at the land‐sea margin, spanning extensive ungauged and unsampled areas. We found very high annual runoff (mean > 3,000 mm, n = 10) in three watershed types totaling 59,024 km 2 and ranging from heavily glacierized mountain watersheds with high flow in summer to a rain‐fed mountain watershed type with high flow in fall‐winter. DOC hotspots (mean > 4 mg L −1 , n = 14) were found in three other watershed types (48,557 km 2 ) with perhumid rainforest climates and less‐mountainous topography. We described four patterns of DOC seasonality linked to watershed hydrology, with fall‐flushing being widespread. Hydro‐biogeochemical watershed classification may be useful for other complex regions with sparse observation networks.
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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 | 0.000 | 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) | 0.000 | 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 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".