Snow cover estimation using blended MODIS and AMSR‐E data for improved watershed‐scale spring streamflow simulation in Quebec, Canada
Bibliographic record
Abstract
Abstract Estimation of the amount of water stored in snow is a principal source of error for spring streamflow simulations in snow‐dominant regions. Measuring this variable throughout large and often remote areas using snow surveys is an expensive task since they are practically point measurements. Remote sensing is an alternative method, which can cover much larger areas in little time, but further research is required to reduce uncertainties on snow water equivalent (SWE) estimations, especially during the melting period. However, optical‐near infrared (NIR) and passive microwave remote sensing can detect snow cover area (SCA) with greater certainty, which can be used as a proxy for SWE. The two datasets work in complementary ways considering their spatial resolutions and cloud cover limitations. This study developed an SCA product from blended passive microwave (Advanced Microwave Scanning Radiometer ‐ Earth Observing System: AMSR‐E) and optical‐NIR (Moderate Resolution Imaging Spectroradiometer: MODIS) remote sensing data to improve estimates of streamflow caused by snowmelt during the spring period. The blended product was assimilated in a snowmelt model (SPH‐AV) coupled with the MOHYSE hydrological model through a modified direct insertion method. SCA estimated from AMSR‐E data was first compared with in situ snow‐depth measurements and SCA estimated with MODIS. Results showed an agreement of over 95% between AMSR‐E‐derived and cloud‐free MODIS‐derived SCA products in the spring. Comparison with ground stations confirmed the underestimation of snow cover by AMSR‐E. Assimilation of the blended snow product in SPH‐AV coupled with MOHYSE yielded an overall improvement of the Nash–Sutcliffe coefficient comparable with simulations with no updates, which is comparable to results driven by biweekly snow surveys. Assimilation of remotely sensed passive microwave data was also found to have little positive impact on streamflow simulation due to the difficulty of differentiating melting snow from snow‐free surfaces. © 2013 The Authors. Hydrological Processes published by John Wiley & Sons Ltd.
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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 | 0.000 | 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".