Simulating streamflow and dissolved organic matter export from a forested watershed
Bibliographic record
Abstract
Stream water concentrations of dissolved organic matter (DOM) exhibit large temporal variations during precipitation on forested, headwater catchments. We present a modeling framework appropriate for describing streamflow and event‐driven export of DOM from small, forested watersheds. Our model links parametrically simple formulations for rainfall‐runoff generation and soil water carbon dynamics. The rainfall‐runoff formulation is developed by modifying the catchment model of Kirchner (2009) to account for hysteresis in the relationship between stream discharge and catchment water storage. Time series computations of catchment water storage are used by the soil carbon model to approximate the effects of leaching, adsorption, and mineralization on soil water DOM concentrations and the export of DOM from the terrestrial reservoir to the stream. Our findings show that this model is capable of reproducing hourly variations of stream discharge (ranging from 0.01 to 0.38 mm h−1) and stream water DOM concentrations (ranging from 1.8 to 14 mg C L−1) measured in a forested headwater stream in north central Massachusetts. Our analysis highlights the strong linkage between soil carbon dynamics and hydrological processes that govern catchment water storage and flow paths connecting the terrestrial system to the stream.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".