Modeling the impact of agricultural management practices on riverine N export in the transboundary Nooksack River watershed, Washington
Bibliographic record
Abstract
The eutrophication of fresh and coastal waters is a growing global concern. Agricultural best management practices (BMPs) mitigate nutrient pollution, but their effectiveness at the watershed scale is often untested, creating uncertainty around which practices should be prioritized. In the Salish Sea, on the Pacific coast of Washington State and British Columbia, seasonal hypoxia threatens food webs, already-impacted salmon populations, and tribal fishing rights. To what extent can different BMPs alleviate N export in watersheds with heavy agricultural influence? We used the InVEST® Nutrient Delivery Ratio (NDR) model to estimate reductions in N export under BMP scenarios in the transboundary (US and Canada) Nooksack River watershed. In the process, we evaluated several aspects of NDR relevant to assessing watershed nutrient retention, including the addition of a groundwater component. We then calibrated and validated NDR against measured annual fluxes of total N from the whole Nooksack River watershed and multiple subwatersheds. We used the validated model to explore reductions in N export possible from factorial combinations of BMPs: riparian restoration on all waterways, 20% lower agricultural N input, and improving crop nitrogen use efficiency (NUE) to 75%. NDR estimated that lowering agricultural N input was the most effective BMP, followed by riparian restoration and improved NUE, with reductions in anthropogenic export by 22%, 17%, and 14%, respectively. Combining all BMPs yielded a 44% reduction in anthropogenic export from 2,458 to 1,725 (Mg N yr-1), though total N export was still over 2-fold greater than pre-industrial fluxes. Our results suggest that combining management practices is needed to effectively reduce N export from watersheds with heavy agricultural influence. When using default parameters, NDR chronically underestimated measured export, and we found that including subsurface N flux was essential for model estimates to match measured values of export. NDR did not allow detailed analysis of N retention in different land use or vegetation types. After extensive calibration, however, NDR was reliable in estimating the reduction of N export at the watershed level achievable from multiple BMPs. A 20% reduction in N inputs is estimated to be more effective than restoration of 100% of riparian buffers on waterways to reduce anthropogenic N export from the Nooksack River watershed. Future efforts should examine the cost effectiveness of these different practices and incentives available to implement such reductions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".