A parsimonious model of longitudinal stream DOC patterns based on groundwater inputs and in-stream uptake
Bibliographic record
Abstract
Abstract. The supply of terrestrial dissolved organic carbon (DOC) to aquatic ecosystems affects local in-stream processes and downstream transport of DOC in the fluvial network. However, we have an incomplete understanding on how terrestrial DOC inputs alter longitudinal variations of DOC concentration along headwater stream reaches because groundwater discharge, groundwater DOC concentration and in-stream DOC uptake vary at relatively short spatial and temporal scales. In the riparian zone, the convergence of subsurface flow paths can facilitate the inflow of terrestrial DOC from large upslope contributing areas to narrow sections of the stream. We refer to these areas of flow path convergence as discrete riparian inflow points (DRIPs). In this study, we ask how longitudinal patterns of stream DOC concentrations are affected by DRIPs, as they are major inputs of terrestrial DOC and important locations for in-stream processes. We used a mixing model to simulate stream DOC concentrations along a 1.5 km headwater reach for fifteen sampling campaigns with flow conditions ranging from droughts to floods. Four sets of model scenarios were used to compare different representations of hydrology (distributed inputs of DRIPs vs diffuse groundwater inflow), and in-stream processes (passive transport vs in-stream biological uptake). Results showed that under medium (10–50 l/s) and high flow conditions (> 50 l/s), accounting for lateral groundwater inputs from DRIPs improved simulations of stream DOC concentrations along the reach. Moreover, in-stream biological uptake improved simulations across low to medium flow conditions (< 50 l/s). Only during an experimental drought, longitudinal patterns of stream DOC concentration were simulated best using diffuse groundwater inflow and passive transport scenarios. These results show that the role of hydrology and in-stream processes on modulating downstream DOC exports varies over time. Importantly, we demonstrate that accounting for preferential groundwater inputs to the stream is needed to capture longitudinal dynamics in mobilization and in-stream uptake of terrestrial DOC. The dominant role of DRIPs in these transport and reaction mechanisms suggests that consideration of DRIPs can improve stream biogeochemistry frameworks and help inform management of near-stream areas that exert a large influence on stream conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".