Simulation of hydrological and land surface variables over Iran – a new 24/7 operational system based on the coupled WRF-SEBAL model
Bibliographic record
Abstract
<p>Water scarcity is regarded as one of the most important issues in many dry climates, with serious impacts on the food security and national development. Since agriculture irrigation accounts for the most of water use worldwide, providing a water management system is of paramount importance to cope with water stress and its challenges. Agricultural water management is an interdisciplinary task including meteorological and environmental factors. In this work, we have established a 24/7 system to simulate those land surface variables, associated with evapotranspiration, biomass growth, and water deficit, using the Surface Energy Balance Algorithm for Land (SEBAL). SEBAL simulates the energy balance, using satellite data for shortwave and thermal bands, as well as soil and meteorological data (such as wind speed and humidity).</p><p>This system consists of 3 interconnected units: WRF model, Python implementation of the SEBAL model (pySEBAL), and a web-based management panel for the visualization, reanalysis, and publishing the results. The WRF model is run in a daily basis for 36 hours, starting from 12:00 UTC, to provide the meteorological data for the next day. At the next stage, the simulated WRF data after some required processing (converting formats and units of files and variables, etc.)  will be incorporated as input data into the SEBAL model. The key data for the SEBAL model is the “Visible Infrared Imaging Radiometer Suite” (VIIRS) real-time data over the Suomi satellite, which is received automatically after tracking the satellite and picking the appropriate data files for download. SEBAL outputs include some of the variables with key role in agricultural water management, such as actual and potential evapotranspiration, biomass production and deficit, albedo, NDVI, etc, with a resolution of 375m over Iran.</p><p>The third part of operational system is a web-based panel, consisting of an open-source server to share and edit the SEBAL outputs. An open-source database management system for the client-based analysis of the SEBAL outputs, and an open-source JavaScript library for displaying the maps of the SEBAL outputs in web browsers.</p>
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 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".