Towards understanding nitrogen legacies in European catchments
Bibliographic record
Abstract
Reducing nitrogen (N) levels in European water bodies is a pressing issue, as evidenced by the recent fines imposed by the European Count Justice on countries such as France, Germany and Greece for exceeding the regulatory limits for nitrate (World Bank report on “Quality Unknown: The Invisible Water Crisis” by Damania et al., 2019). N levels can depend not only on current N inputs to the landscape, but also on the past N inputs that have accumulated through time in the soil root zone and the groundwater in so-called ‘legacy stores’. Effective N management strategies should therefore account for these N legacies. This study aims to gain a better understanding of the impact of N legacies on in-stream nitrate concentration and loading at annual time scale in European catchments. To this end, we apply a parsimonious nitrate model, called ELEMENT (Van Meter et al., 2017, Global Biogeochem Cycles), given the limited amount of information available to constrain and test the model simulations. We construct a nitrogen input dataset (N-surplus) to force the model from the early 19th Century, thus ensuring the build-up of the model soil and groundwater legacy stores. We estimate the model parameters based on the application of ‘soft rules’, to account for the uncertainty in the model inputs and the output measurements, and we examine the model controlling processes using sensitivity analysis. We present here the results for the case of the Weser catchment, a large catchment in northern Germany that discharges into the North Sea. In particular, our results show that the model reproduces well nitrate stream loading. Despite the parsimonious structure of the ELEMENT model, we identify the presence of parameter equifinality, when the model is constrained using in-stream concentration and loading only. We discuss the possibility of using additional information (such as soil organic N content) to improve parameter identifiability and the overall simulation results.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".