Spatial distribution of precipitation and evapotranspiration estimates from Worldclim and Chelsa datasets: Improving long-term water balance at the watershed-scale in the Urabá region of Colombia
Bibliographic record
Abstract
In this paper, we have evaluated high-resolution spatial gridded climate data from two long-term global datasets, WorldClim V.2.0 and Chelsa V.1.2, in representing variables like precipitation and temperature for the urabá region of Colombia.additionally, climate variables from these datasets have been used to estimate evapotranspiration using traditional methods such as the Turc, hargreaves and Thornthwaite equations.finally, the results of long-term spatial climate characterization are used to apply the water balance equation in the surface at the watershed scale, to obtain the long-term average streamflow of the main streams of the urabá region; these streamflows are compared with the observations of hydrological stations.We find that the WorldClim and Chelsa rainfall estimates show average differences between 20% and 23% compared to the average annual rainfall in the area from in situ measurements.both datasets are able to reproduce the rainfall average annual cycle, although Chelsa shows a slightly better performance.regarding near surface air temperature we find that WorldClim shows a good performance, while Chelsa significantly underestimates the average temperature.finally, we found that the hargreaves and Thornthwaite methods lead to the best performance in estimating streamflow from the water balance, probably because details of the seasonal behavior of variables like temperature and radiation are explicitly included in these methods.On the other hand, the Turc method yields larger estimates of evapotranspiration and therefore the corresponding derived streamflows are lower than those observed.The good performance of the WorldClim and Chelsa datasets in representing variables like precipitation, temperature, and the derived watershed-scale streamflow, suggest that these long-term global climate datasets can be used to study the spatial distribution of important hydrological variables in the urabá region of Colombia, and consequently the estimation of average streamflows through the method of the long-term water balance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".