Adapting remotely sensed snow data for daily flow modeling on the Upper Humber River, Newfoundland and Labrador
Bibliographic record
Abstract
This thesis investigated the use of remotely sensed snow information to help improve flood forecasting in western Newfoundland's Humber River Basin. Flood forecasting on the Humber River is important because of the large population settlements within the Humber Valley. In this research, two types of remotely sensed snow data were considered for analysis: (1) snow cover (or snow extent) and (2) snow water equivalent (SWE). The majority of this thesis focuses on the remotely sensed snow cover data. Moderate Resolution Imaging Spectroradiometer (MODIS) Terra snow cover images were acquired over the Humber Valley watershed throughout the snowmelt period, from March to June, for the years 2000 to 2009. MODIS is an optical sensor on NASA's (National Aeronautics and Space Administration) Earth Observing System (EOS) Terra and Aqua satellites. Its daily temporal data are advantageous and the data are free and easily accessible. Daily snow cover data were extracted from the National Snow and Ice Data Center (NSIDC) daily snow product, specifically MOD10A1: a product derived from MODIS data, using a custom EASI script run in PCI Geomatica. PCI Geomatica is a robust remote sensing and image processing software. One major obstacle, regarding the acquisition of MODIS imagery over the Humber Valley watershed, is the presence of over 50% cloud cover for 80% of the days on average from March to June every year. This was a concern for data collection: affecting the sample size of acquired data and the accuracy of the snow cover data. When cloud cover is high there is a greater chance that it may be misclassified as snow and/or snow is misclassified as cloud cover. For this reason, a cloud-cover threshold was determined. The Rango-Martinec snowmelt runoff model, a widely used degree-day model which incorporates snow cover data as a direct input, was evaluated. It was found that the next day's flow is highly dependent on the previous day's flow and less dependent on the meteorological data: rainfall, snow cover, and temperature. The results from the snowmelt runoff model using the snow cover data provided very good final Nash-Sutcliffe coefficients of 0.85 for the calibration stage and 0.81 for the validation stage, but a consistent one-day lag of the modeled flow values was also observed. Although these results were not superior to currently employed flood forecasting models for the Upper Humber (because of a one-day lag in the modeled flows), the methodology developed herein may be useful for other river basins in NL where the flows are dominated by snowmelt during the spring such as the Exploits River Basin located in central NL. Remotely sensed snow water equivalent (SWE) data obtained from an advanced microwave scanning radiometer (AMSR-E), aboard the Aqua satellite, was also investigated for daily flow modeling applications. SWE often provide a better estimate of snowmelt than snow cover but this data had several disadvantages in the Humber River Basin. The major obstacles included large spatial resolution (25 km), data inaccuracy for wet snow, boreal forest, mountainous regions, and time step irregularities. Extremely large variances in the SWE data rendered the information inaccurate and ineffective for streamflow forecasting on Newfoundland and Labrador's Humber River. This research makes significant contributions to the field of hydrology providing a valuable methodology in adapting remotely sensed snow data to daily flow simulation and will be helpful to local authorities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".