Bibliographic record
Abstract
Climate variability, climate change and human activities have impacted various components of the hydrologic cycle in many regions across the world. In particular, changes to the global climate system can affect the magnitude and frequency of extreme hydrological events, thus altering the risk to critical infrastructure. Therefore, under the possible impact of climate change, the stationary assumption of the conventional frequency analysis is generally invalid. Assessing the validity of the stationarity assumption for Canadian hydroclimatic variables is important to justify the practice of conventional hydrologic frequency analysis in a changing climate. On the other hand, understanding how non-stationarity have affected the magnitude and frequency of hydrologic events in Canada, and developing new statistical techniques (or extensions of existing techniques) are important to address non-stationarity and to reduce uncertainties associated with frequency analysis. Alternatively, it will be beneficial to reliably predict future Canadian precipitation and streamflow under a changing climate and the impact of anthropogenic influences. Most past studies of Canadian hydroclimate change detection have focused on trends or slow varying changes in the mean precipitation and streamflow of Canada even though extreme precipitation and streamflow could cause more severe damage to human beings. The objectives of this dissertation are: 1) to understand the impact of changes in climatic and human factors to hydroclimatic processes over Canada, 2) to examine the nonstationary characteristics of the precipitation and streamflow under a changing climate, and 3) to assess the impacts of global changes in extreme climate on human and ecosystems. Chapter 1 describes an analysis of the nonstationary behavior of extreme streamflow over Canada by identifying abrupt changes, monotonic temporal trends, non-stationary probability distributions and long-term persistence of Canadian annual maximum streamflow. The results show that nonstationary frequency analysis should be employed in the future, because of widespread non-stationarities of streamflow resulting from both climate change and human impacts have been detected across Canada. Chapter 2 relates changes in streamflow over Canadian watersheds to climate change and human impacts. From elasticities of streamflow for each watershed are analytically derived using the Budyko Framework, it is shown that climate change caused an increase in the mean annual streamflow (MAS), while human impacts a decrease in MAS and such impact tends to become more severe with time. Chapter 3 relates non-stationarities of heavy precipitation over Canada in terms of frequency and intensity to large-scale climate patterns, such as El Niño Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Pacific decadal oscillation (PDO) and North Pacific Oscillation. This analysis shows that large-scale climate anomalies have affected Canadian heavy precipitation which may trigger large floods. Chapter 4 used variants of the Mann-Kendall (MK) test to analyze effects of short-term persistence (STP), long-term persistence (LTP) and large-scale climate anomalies on detected trends of seasonal (SMP) and annual extreme precipitation (AMP). The presence of LTP would increase the chances of detecting trends in AMP and SMPs, and similarly STP would also influence the detection of trends, while large-scale climate anomalies mainly contributed to trends detected for winter SMPs. Chapter 5 investigates the multifractality of Canadian daily precipitation and streamflow based on the universal multifractal model and the modified multiplicative cascade model. The differences in the multifractality of streamflow before and post dam operations are also analyzed to show the effects of human influences on the characteristics of streamflow of Canada. Chapter 6 shows significant interannual and interdecadal oscillations of Canadian extreme precipitation and their teleconnections to large-scale climate anomalies from applying variants of wavelet analysis methods and the composite analysis to 131 stations of climate data of Canada. Results show that both ENSO and PDO modulated the interannual variability, and PDO modulated the interdecadal variability, of extreme precipitation over Canada. Chapter 7 identifies projected time of emergence (ToE) in global extreme climate, when the signal of extreme climate change will exceed the natural climate variability. This timing for terrestrial and marine ecoregions is identified for hot-spot regions across the world where the ecosystem could be severely impacted by changes in the magnitude, frequency and severity of extreme temperature and precipitation that exceed the tolerance limit of respective ecosystems. The impacts of extreme climate change on human society are also examined. Conclusions and future work are provided in Chapter 8.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".