Assessment of the impact of meteorological network density on the estimation of basin precipitation and runoff: a case study
Bibliographic record
Abstract
Abstract In recent years in North America, a number of government agencies and industries have begun to reinvest in meteorological networks. This investment must be based on sound scientific advice. Increased meteorological station network density can be beneficial for a number of purposes, including flood forecasting. This study aimed at investigating the impact of network density at two temporal scales, i.e. for the estimation of total annual precipitation and for the estimation of daily precipitation during specific rain events. This was done using kriging as a means to estimate the spatial distribution and variance of rainfall. Kriged precipitation from two network scenarios (sparse and dense) were used as input into the HSAMI hydrological model and simulations were compared on five drainage basins in the Mauricie area (Québec, Canada). A comparison of the distribution of total annual precipitation interpolated from the two network scenarios showed that adding stations changed the distribution and magnitude of rainfall in the study area. High precipitation cells were better defined with the denser network, and decreases in the relative spatial variance were observed. Similarly, kriged daily precipitation provided a more defined spatial distribution of rainfall during important rain events of 1999, and variance was also reduced when the denser network was used. Finally, simulations performed with the HSAMI model were generally improved when the precipitation inputs were estimated using a denser station network for most drainage basins studied, as expressed by increased Nash coefficients and a decreased root‐mean‐square error. Peak flows during important summer flood events were generally better simulated when a denser network was used to calculate the mean daily precipitation used as input. Total cumulated volume estimations during the rain events were also generally improved with a denser network. This study showed that the estimation of variance remains an important tool for rain gauge network design. Moreover, network density was shown to have an important impact on the quality of flow simulations, even when a lumped model is used. Copyright © 2003 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".