Added Value of Alternative Information in Interpolated Precipitation Datasets for Hydrology
Bibliographic record
Abstract
Abstract Gridded climate datasets are produced in many parts of the world by applying various interpolation methods to weather observations, to which are sometimes added secondary information (in addition to geographic location) such as topography and radar or atmospheric model outputs. For a region of interest, the choice of a dataset for a given study can be a significant challenge given the lack of information on the similarities and differences that exist between datasets, or about the benefits that one dataset may present relative to another. This study aims to provide information on the spatial and temporal differences between gridded precipitation datasets and their implication for hydrological modeling. Three gridded datasets for the province of Quebec are considered: the Natural Resources Canada (NRCan) dataset, the Canadian Precipitation Analysis (CaPA) dataset, and the dataset from the Ministère du Développement Durable, de l’Environnement et de la Lutte contre les Changements Climatiques du Québec (MDDELCC). Using statistical metrics and diagrams, these precipitation datasets are compared with each other. Hydrological responses of 181 Quebec watersheds with respect to each gridded precipitation dataset are also analyzed using the hydrological model HSAMI. The results indicate strong similarities in the southern parts and disparities in the central and northern parts of the province of Quebec. Analysis of hydrological simulations indicates that the CaPA dataset offers the best results, particularly for watersheds located in the central and northern parts of the province. MDDELCC shows the best performance in watersheds located on the south shore of the St. Lawrence River and comes out as the overall second-best option.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".