Enhancing flood and drought forecasting tools in the South Nation River Watershed
Bibliographic record
Abstract
With climate change, managing the variability in changing watershed conditions, from floods to droughts, is a challenge. Having the necessary information, in a timely manner, is critical for proper planning and implementing mitigating measures. South Nation Conservation's (SNC) recent project with IBM and Aquanty, the development of a data-assimilation and hydrological simulation platform, aims to improve short term (1 - 14 d) forecasting of flooding and droughts. The project will construct a real-time hydrologic model for South Nation Watershed. This fully integrated 3-dimensional groundwater - surface water model is dynamically coupled to state-of-the-art high resolution (4000 m) short-term weather forecasts (provided by IBM's Weather Company) and real-time field sensors throughout the Watershed. The model will enhance SNC's Flood Forecasting and Warning and Low Water Response programs with more accurate forecasts; utilizing over 200 local weather data points (compared to the previous 3 Environment Canada stations) to capture the diversity of weather patterns across the Watershed. The new real-time sensors to collect precipitation (rain and snow) and soil moisture will allow the model to better predict how forecasted weather events will impact watershed conditions. For example, is a significant rainfall event more likely to runoff into nearby watercourses or soak into the ground. Beyond its direct application to the South Nation Watershed, this project will provide a proof-of-concept demonstration of how the latest in weather forecasting and hydrologic modelling technologies can support water resources management under increasingly variable climate/weather conditions. It is anticipated that this project will provide far-reaching benefit to SNC, local producers, municipalities, and rural landowners throughout the region This project was made possible with funding support from Agriculture and Agri-Food Canada.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".