Hydrological modelling using proxies for gauged precipitation and temperature
Bibliographic record
Abstract
Abstract Precipitation and temperature time series suffer from many problems, such as short time, inadequate spatial coverage, missing data, and biases from various causes, which are particularly critical in remote areas such as Northern Canada. The development of alternative datasets for using as proxies for inadequate/missing weather data represents a key research area. In this paper, the performance of 6 alternative datasets is evaluated for hydrological modelling over 12 watersheds located across Canada and the contiguous United States. The datasets can be classified into 3 distinct categories: (a) interpolated gridded data, (b) reanalysis data, and (c) climate model outputs. Hydrological simulations were carried out using a lumped conceptual hydrological model calibrated using standard weather data and compared against results using a calibration specific to each alternative dataset. Prior to the hydrological simulations, the alternative datasets were all evaluated with respect to their ability to reproduce gridded daily precipitation and temperature characteristics over North America. The results show that both the reanalysis data and climate model data adequately represent the spatial pattern of daily precipitation and temperature over North America. The North American Regional Reanalysis (NARR) dataset consistently shows the best performance. With respect to hydrological modelling, the observed discharges are accurately represented by both the gridded and NARR datasets, and more so for the NARR data. The National Centers for Environmental Prediction dataset consistently performs worst as it is unable to even capture the seasonal pattern of observed streamflow for 3 out of the 12 watersheds. These results indicate that the NARR dataset could be used as a proxy for gauged precipitation and temperature for hydrological modelling over watersheds where observational datasets are deficient. The results also illustrate the ability of climate model data to be used for performing hydrological modelling when driven by reanalysis data at their boundaries, and especially so for high‐resolution models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".