Bibliographic record
Abstract
Scale issues represent an unsolved problem in hydrological sciences. Distributed hydrological models are capable of accounting for catchment heterogeneity, but it remains unclear to what extent the variations in the representation of spatio-temporal resolution in these models leads to uncertainty in the simulations. Moreover, the added value of more refned spatio-temporal discretization is also unclear. The present thesis addresses these topics in the context of streamfow simulation, food projection and regionalization of model parameters . \n \nAll catchments studied in this thesis are located in Southern Quebec, Canada . This research uses two process-based distributed hydrological models with diferent degrees of complexity (Hydrotel and WaSiM). For the food simulation and projection, the models are calibrated with four diferent levels of spatial discretization of physiographic data and in models’ parameters. The climate extreme project (ClimEx) dataset is bias corrected for 3- and 24-hour time-steps using the n-dimensional multivariate bias correction method (MBCn), and used as inputs to the hydrological models to project streamfow over the 1991-2100 period. \n \nThe results show that the variation of temporal resolution has only minor impacts on the uncertainty of historical simulations, and the impact depends on the choice of the model. The more sophisticated model (WaSim) has a larger uncertainty. As for varying the spatial discretization, it can cause uncertainties for catchments with low slopes or uneven areas. \n \nRegarding food projection, by refning the temporal scale, the results show that both the frequency and amplitude of extreme summer-fall fow increases in the future. Moreover, the choice of hydrological model for food projection is more important for larger catchments. Finally, no distinct pattern exists regarding the uncertainty related to the spatial resolution and catchment size. However, this afects the direction and signifcance of the trends observed for extreme fow in the simulations. \n \nThis thesis also proposes and tests a regionalization method based on random forests (RF). It is applied to the parameters of Hydrotel at diferent spatio-temporal resolutions. The results show that the proposed regionalization technique performs better for shorter time-steps. Moreover, the regionalized parameters are spatially consistent. In the end, using catchment descriptors that have a better spatial representativity results in an improvement (more than 10%) in the simulations using a 24h time-step.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".