The Influence of Meteorology and Hydrology on Phytoplankton Blooms in Lake Winnipeg Over the Last 37 Years (1984–2020)
Bibliographic record
Abstract
Eutrophication poses a significant threat to the health of aquatic ecosystems worldwide. Wetlandscapes—networks of hydrologically connected wetlands—can potentially mitigate eutrophication. However, climate change and human activities have altered wetlandscapes properties, reducing their effectiveness in controlling nutrient transfer to downstream lakes. In this study, we focus on the Lake Winnipeg Watershed (LWW) in North America to examine how climate-driven changes in wetlandscape characteristics affect nutrient loading and subsequent phytoplankton blooms in Lake Winnipeg (LW) in the face of global warming. First, we developed a method for mapping wetlands and extracting wetlandscape properties using a fusion of two Landsat-derived inundation products, Global Surface Water Extent (GSWE) and Dynamic Surface Water Extent (DSWE), finding that this fusion reduced omission errors from 17% for GSWE and 18% for DSWE to 8% overall. We then mapped the properties of the LWW's wetlandscapes (i.e., number, size, and wetland-to-wetland and wetland-to-river connectivity) from 1984 to 2020. During this period, we observed a trend toward more extensive wetlands that are better interconnected and have increased hydrological connectivity to LW (p ≤ 0.1). We then created the longest time series of chlorophyll-a (Chl-a), a proxy for phytoplankton biomass, for LW (1984–2023) using Landsat data and LW basins-specific Chl-a prediction models, demonstrating that accounting for the variance in optical complexity within lake’s basins can improve Chl-a predictions (p ≤ 0.1). The Chl-a time series showed significant increases in the frequency, magnitude, and extent of LW phytoplankton blooms concomitant with the observed changes in the wetlandscape properties. The two major basins of LW responded differently to climate-driven changes. In the north basin, climate-driven wetter conditions with larger, connected wetlands elevated nutrient loading and intensified phytoplankton blooms (p ≤ 0.1). This phenomenon was not observed in the south basin (p > 0.1). Within the watershed, those sub-watersheds with shorter flow distances to LW exhibited stronger correlations with blooms (p ≤ 0.1), while those with longer flow paths showed weak or no correlations, likely due to nutrient retention and transformation. Incorporating runoff travel distance into wetlandscape assessments improved correlations with nutrient inputs and bloom dynamics. A second climate factor, rising temperature, further exacerbated bloom extent and magnitude (p ≤ 0.1). As climate change amplifies nutrient loading and warming in LW, prioritizing wetlandscape management in high- impact sub-watersheds is essential for sustainable water quality. Using globally available Landsat products, the methodologies developed here provide scalable tools for assessing wetlandscape impacts on water quality worldwide.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".