A Framework to Determine Present and Future Effects of Rain‐on‐Snow on Spring Hydrology and Nutrient Loading in the Lake Erie Basin
Bibliographic record
Abstract
ABSTRACT Rain‐on‐snow (ROS) events occur when temperatures allow for liquid precipitation to fall onto an existing snowpack. Although ROS is a fundamental facet of winter and spring hydrology in the Great Lakes Basin with the potential to result in severe flooding and influence water quality issues, its role in this region is understudied compared with alpine regions. Many watershed models do not comprehensively characterise the ROS process and thus may misrepresent hydrological and water quality outputs. Here, we elucidate the importance of ROS on spring water balance and nutrient loading in the Big Creek watershed, part of the Lake Erie Basin (LEB), through an ensemble of statistical, hydrological, and climate modelling tools. We found that spring flow events with enhanced ROS melt are conducive to excessive loading export from both agricultural and natural land uses. The incorporation of a novel ROS routine into the Soil and Water Assessment Tool (SWAT) model demonstrated that the modified version improved performance in 76% of 504 random streamflow simulations. The ROS characterisation can more accurately recreate the magnitude of extreme flow events in spring, which is a commonly reported shortcoming of the SWAT model. The ROS submodel simulated earlier shifts in snowmelt, water yield and evapotranspiration by 1 month compared with the original model. In examining a climate scenario associated with modest greenhouse gas emission changes, we found that monthly average streamflow over the 21st century is projected to remain relatively stable, but the occurrence of extreme flow conditions will increase. ROS event frequency is projected to increase in February and March and decrease in April in urban and natural land uses, but agricultural areas will only experience a slight decline, suggesting that the landscape attributes play an important role in localised shifts in ROS event frequency. We contend that ongoing watershed modelling work must include the ROS process to improve representation of critical facets of hydrology and water quality that could be extrapolated to other more complex watersheds within the LEB and elsewhere.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".