A comparative study of snowmelt runoff modelling at Rheraya watershed in the Moroccan High Atlas Mountains 
Bibliographic record
Abstract
In the Atlas Mountains range, streamflow is largely generated from meltwater supplied by the snowpack during spring and early summer. In this manner, snow is considered an important factor which determining water availability in semi-arid and arid mountains. This substantial part of freshwater stored in the form of snow contributes significantly to mountainous runoff. However, the contribution of snow and rain to the annual and multi-annual water balance remain largely unknown. Hydrological modeling is needed to support water resource assessment and management in the Atlas range. As meteorological data is often scarce, the models must be able to simulate the spatiotemporal heterogeneity of forcing variables while maintaining a low data input requirement. In this study, the performance of the snowmelt runoff model (SRM) is assessed to simulate and forecast daily runoff essentially from snowmelt and rainfall at the Rheraya watershed in the Moroccan High Atlas range over the 2010 - 2016 period. The SRM runoff simulation is tested under two forcing inputs: (i) four snowmelt rates previously estimated by a classical temperature-index model (TI) and three enhanced temperature index models that respectively include the potential clear-sky direct radiation (HTI), the incoming solar radiation (ETI-A), and net solar radiation (ETI-B); (ii) calculated snowmelt from the snow cover area (SCA) products of Moderate-Resolution Imaging Spectroradiometer (MODIS). All SRM simulated runoff were subjected to calibration and validation through the measured runoff in the Tahanaout weather station. The sensibility of recession coefficients was also evaluated. The SRM simulations results over the validation period show an acceptable performance. Keywords: Runoff, SRM, snowmelt, SCA, temperature index model; enhanced degree-day models, MODIS, semi-arid climate, Rheraya, High Atlas, Morocco.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".