Applicability of using ArcMap to spatially calculate and display monthly evapotranspiration rates : An investigation using government climate data in British Columbia, Canada
Bibliographic record
Abstract
Evapotranspiration (ET) is the sum of the evaporation of water from the Earth’s surface and the total transpiration from plants. Spatially calculating ET is necessary because it is a major component in quantifying a water budget, and maps provide the spatial ability to display the distribution. Geographic information systems (GIS) are a powerful and capable tool which can spatially process and integrate equations in order to quantify ET rates. Probable ET equation types that best fit with ArcMap software were investigated, and the methodology of España et al was evaluated in terms of usefulness and ease of replication, while beneficial areas for future expansion were also commented on. Interpolation of some weather and other variables, as well as the use of the raster calculator in ArcMap was the basis of the project methodology. Temperature based ET equations were selected as the best equation category, and then specifically the Blaney-Criddle, Thornthwaite, and Hargreaves equations were used to calculate potential evapotranspiration (PET) rates in British Columbia (BC), Canada. The methodology of España et al provided a relatively easy way to spatially display algebraic evapotranspiration equations. The results were compared to values of sixteen reference stations, which had been computed by the Penman-Monteith equation. PET values that were interpolated were not as accurate as hoped, however the Hargreaves and Blaney-Criddle methods produced better results than the Thornthwaite method, which resulted in underestimates. Nonetheless, the PET distribution pattern was displayed, and of use to show the areas of highest and lowest rates of PET. In order to produce more accurate values, regional or crop coefficients could be applied to calculate actual evapotranspiration (AET), but time constraints placed on the project restricted the trial of this.
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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".