The impact of climatic variability and change in the hydroclimatology of Lake Winnipeg watershed
Bibliographic record
Abstract
Abstract The hydroclimatology of prairie‐dominated portions of the Lake Winnipeg watershed was investigated to determine the possible presence of trends and shifts in variables that may influence the streamflow regimes and water quality of Lake Winnipeg. The total annual streamflow, precipitation, runoff ratio and daily maximum streamflow in the two major tributaries of the Assiniboine River and Red River were analysed for a range of nonstationary behaviours. Each of these rivers has been gauged for more than 90 years. The methods used included a nonparametric Mann–Kendall test modified to account for diverse memory properties (i.e. short term versus long term) and a Bayesian change point detection model to identify possible segments of time series with inconsistent nonstationary behaviour. Although there is no evidence of statistically significant trends in precipitation and streamflow in the Assiniboine River watershed, a shift‐type nonstationarity in annual runoff and runoff ratio was observed in this area, which is manifested in the form of a sequence of wet and dry spells during the last century. Precipitation and runoff metrics in the American portion of the study area (i.e. Red River watershed) were characterised with both gradual and abrupt changes with an extremely increasing rate of streamflow beyond that of intensified precipitation. The nonproportional watershed runoff response is attributed to the dynamic nature of contributing areas that, together with the semiarid climate, leads to sudden changes of streamflow due to major or even some times minor changes in climate inputs. It is evident that streamflow in the depression‐dominated landscapes of the semiarid glaciated plains of North America is particularly sensitive and vulnerable to minor climate variability and change. This study provides valuable insights into the highly complex precipitation–runoff relationship in depression‐dominated landscapes and could have important implications for water management in this part of North America and comparable regions. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| 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".