Do dams and levees impact nitrogen cycling? Simulating the effects of flood alterations on floodplain denitrification
Bibliographic record
Abstract
Abstract A fundamental challenge in understanding the global nitrogen cycle is the quantification of denitrification on large heterogeneous landscapes. Because floodplains are important sites for denitrification and nitrogen retention, we developed a generalized floodplain biogeochemical model to determine whether dams and flood‐control levees affect floodplain denitrification by altering floodplain inundation. We combined a statistical model of floodplain topography with a model of hydrology and nitrogen biogeochemistry to simulate floods of different magnitude. The model predicted substantial decreases in NO 3 ‐N processing on floodplains whose overbank floods have been altered by levees and upstream dams. Our simulations suggest that dams may reduce nitrate processing more than setback levees. Levees increased areal floodplain denitrification rates, but this effect was offset by a reduction in the area inundated. Scenarios that involved a levee also resulted in more variability in N processing among replicate floodplains. Nitrate loss occurred rapidly and completely in our model floodplains. As a consequence, total flood volume and the initial mass of nitrate reaching a floodplain may provide reasonable estimates of total N processing on floodplains during floods. This finding suggests that quantifying the impact of dams and levees on floodplain denitrification may be possible using recent advances in remote sensing of floodplain topography and flood stage. Furthermore, when considering flooding over the long‐term, the cumulative N processed by frequent smaller floods was estimated to be quite large relative to that processed by larger, less frequent floods. Our results suggest that floodplain denitrification may be greatly influenced by the pervasive anthropogenic flood‐control measures that currently exist on most majors river floodplains throughout the world, and may have the potential to be impacted by future changes in flood probabilities that will likely occur as a result of climate shifts.
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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".