Impact of the spatial density of weather stations on the performance of distributed and lumped hydrological models
Bibliographic record
Abstract
This study aimed to quantify the ability of distributed and lumped hydrological models to use high-resolution precipitation and temperature data to improve streamflow simulation at watershed outlets. To that end, a 40-year, high-resolution, spatially distributed, meteorological dataset was extracted from a 15-km resolution regional climate model simulation (from the Canadian Regional Climate Model – CRCM v.4.2.4 driven by ERA40 reanalysis). This dataset was used to feed one distributed and four lumped hydrological models. The five models were calibrated on 192 watersheds located in the province of Quebec (Canada) using five different meteorological network densities of pseudo-stations. These densities ranged from one single station (located at the centre of gravity of the watershed) up to the maximum grid density of 1 station per 225 km2 (15 km × 15 km which corresponds to the CRCM spatial resolution). No significant decrease in validation performance for both types of hydrological models was observed when using any of the tested station densities. Similar results were also obtained when investigating the subsets of 54 smaller (≤2,500 km2) and 84 medium-sized (2,500 < area <10,000 km2) watersheds. However, for the 54 larger watersheds (≥10,000 km2), the decrease in performance was statistically significant for the distributed model when using one single station. While all lumped models showed a noticeable drop in performance only when using a single station, the distributed model was the only model to show a gradual decrease in performance as the network density decreased. These results indicate that when dealing with large watersheds, distributed models could benefit up to some extent from a larger meteorological network density. These conclusions are likely to be relevant to Canadian watersheds with similar physiographic characteristics and hydroclimatic conditions as the ones included in the Quebec database that was studied.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".