IMPROVED FLOOD QUANTILE ESTIMATION THROUGH INTEGRATED HYDROLOGIC MODELING AND STATISTICAL ANALYSIS
Bibliographic record
Abstract
Floods are one of the most destructive natural disasters. The annual flood damages in Canada are estimated in billions of dollars, which drain the Canadian economy and affect lives. A safe and cost-effective design of infrastructures, such as dams, bridges, and culverts, is essential to alleviate flood losses and requires accurate estimates of the magnitude and frequency of floods. This thesis comprehensively examines the methodologies of flood quantile estimation within the context of Canadian hydrologic characteristics, with a special focus on the Canadian Prairies. The conducted research aims to (1) evaluate existing flood frequency analysis distributions and suggest better alternatives, (2) characterize the complex generation mechanisms of peak spring streamflow, and (3) introduce an innovative methodology for enhanced regional flood frequency assessments. The methods are carefully set to comprehensively investigate each objective. First, a thorough analysis of 1088 streamflow records across Canada challenges the prevailing Generalized Extreme Value distribution (GEV) in flood frequency analysis. Alternative models are proposed (i.e., Burr type III and Burr type XII) and compared to the GEV model. The results show comparable performance between the models while highlighting the advantage of using Burr distributions. The assessment also raises questions about the reliability of the GEV in predicting extreme flows. Second, different flood generation mechanisms associated with historical floods at 109 Canadian Prairie basins are characterized using seven novel descriptors that account for key hydro-climatic basin conditions, which could trigger peak spring flows. The descriptors are set to be comprehensive enough to describe the complicated hydrologic processes of the Canadian Prairies. The descriptors are estimated using daily historical precipitation and temperature records to implicitly account for winter snowpack, overall basin wetness throughout the winter, rain-on-snow, soil wetness before soil freezing, basin connectivity, snowmelt rate, and losses in winter snowpack before the occurrence of peak spring flows. Additionally, a multivariate copula-based approach is proposed to describe the dependence structure between the proposed basin descriptors and historical peak spring flows using the t-copula. The characterization results in identifying nine different flood generation mechanisms and contributes to advancing the understanding of peak spring streamflow generation. The spatial and temporal distribution of the different flood generation mechanisms across the Canadian Prairies is found heterogeneous which challenges the association of mechanisms to sub-regions or sub-seasons across the Canadian Prairies. Furthermore, the t-copula is found feasible to probabilistically assess the interaction between the basin descriptors and peak spring flows, by describing the response of a basin according to its concurrent basin conditions, based on the knowledge obtained from historical basin responses. Third, a novel site similarity measure is introduced for regional flood frequency analysis to address land depressions. The new measure is based on hydrologic simulations and relates to land depressions. A case study that involves 109 sites across the Canadian Prairies is used to evaluate the pooling (cluster analysis) of the 109 sites using 30 combinations of site similarity measures. The index flood method is applied to regionally quantify flood quantities using several probability distributions. The results prove the need to consider the state of land depressions for pooling hydrologically similar sites and estimating accurate regional quantiles in prairie regions. It demonstrates the role of the proposed site similarity measure in enhancing groups' homogeneity and underscores the susceptibility of regionally estimated quantiles to the chosen site similarity measures. In summary, this research significantly contributes to enhancing the methodology for estimating flood quantiles, understanding flood generation mechanisms, and refining regional flood frequency analysis techniques, in the context of the Canadian Prairies.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".