Quantifying the role of reservoirs in altering phosphorus dynamics using a combination of data analysis and process modeling
Bibliographic record
Abstract
Excess phosphorus (P) from agricultural watersheds promotes eutrophication in downstream aquatic systems. Reservoirs retain P generated from farm fields and protect downstream waters. Reservoirs also act as hotspots for P transformation, as anoxic conditions can facilitate the release of stored P from the lake sediments. The role of inland reservoirs in P speciation at the watershed scale is relatively unexplored. This problem is growing in importance as approximately half of the global river volume is at least moderately impacted by damming, and is projected to reach 93% with all the planned or proposed dams (Grill et al. 2015). Here we use a decade of soluble reactive P (SRP) and total P (TP) concentration data at the inlet and outlet of two reservoirs, Belwood Reservoir and Conestogo Reservoir, in the Grand River Watershed, Canada. The annual SRP and TP percent retention varied at both reservoirs, showing that the reservoirs acted as a sink in some years and as a source in other years. The percent TP retention in Belwood Reservoir varies from -40% to 32%, while percent TP retention in Conestogo Reservoir is generally lower, between -72% to 25%. The SRP retention in Belwood Reservoir varied between -68% and 43%, while SRP retention in Conestogo Reservoir varied between -71% and 28%. Interestingly, the source-sink behaviour is visible for both SRP and TP and they are similar between years. That is, in years that Belwood Reservoir acts as a source of TP, the reservoir often acts as a source of SRP too. At the seasonal scale, we found that both reservoirs increase the proportion of bioavailable P (SRP:TP ratio) from inlet to outlet between April and October. We then built a process-based model to examine the P cycling and sediment-water interactions controlling this speciation of P in the Belwood Reservoir. The model was able to capture downstream SRP export with NSESRP = 0.57-0.86 and TP export with NSETP = 0.60-0.91. The model had difficulty capturing the SRP:TP magnification from inlet to outlet and sediment P accumulation, especially for the first few years of model simulation (2007 - 2012). Model results highlight the role of internal loading during the summer months. As dam construction is on the rise globally, it is critical to understand the impact of reservoirs on the relative reactivity of P in order to mitigate nuisance and potentially harmful algal blooms.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".