Assessing the capability of the SWAT model to simulate snow, snow melt and streamflow dynamics over an alpine watershed
Bibliographic record
Abstract
Snow is an important hydrological reservoir within the water cycle, particularly when the watershed includes a mountainous area. Modellers often overlook water stocked in snow pack and its influence on water distribution, especially when only some portions of the watershed is snow dominated. Snow is usually considered to improve hydrological modelling statistics, but without any regard for the realism of its representation or its influence on the hydrological cycle. This is all the more true when semi-distributed models are used, often considered inadequate for spatially representing such phenomena. On the other hand, semi-distributed models are being increasingly used to realise water budget assessment at a regional scale and such studies should not be realised without a good representation of the snow pack. Lack of field measurements is also a frequent justification for avoiding validating simulated snow packs. In this study, remote sensing data provided by MODIS is combined with in situ data, enabling the validation of the snow pack simulated by the Soil and Water Assessment Tool (SWAT), a semi-distributed, physically-based model, implemented over a partly snow-dominated watershed. Snow simulation was performed without complex algorithms or calibration procedures, using the elevation bands option included in the model and related snow parameters. Representation of snow cover and hydrological simulation were achieved by a standard automatic calibration of the model, over the 2000–2010 period, performed by SWAT-Cup/SUFI2, using six hydrological gauging stations along the fluvial continuum downstream of the snow-dominated area. Results highlight three important points: (i) Set-up of elevation bands over mountainous headwater improved hydrological simulation performance, even well downstream of the snow-dominated area. (ii) SWAT produced a good spatial and temporal representation of the snow cover, using MODIS data, despite a slight overestimation at the end of the snow season on the highest elevation bands. A comparison of the model estimate of snowpack water content with in situ data revealed an underestimation in water content in the lower part of the watershed and a slight overestimation in its upper part. Those errors are linked and originate from difficulties of the model to incorporate very local spatial and temporal variations of the precipitation lapse rate. (iii) Elevation bands brought consistent changes in water distribution within the hydrological cycle of implemented watersheds, which are more in line with expected flow paths.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".