Development of a countrywide spatially predictive hydrological model for Panama using the Soil and Water Assessment Tool
Bibliographic record
Abstract
Water availability and extremes in river discharge associated with floods and droughts are critical determinants of human welfare and ecological function. Modeling the effects of climate scenarios and other social and environmental changes on waterways is thus a key component of effective planning and risk mitigation. Yet, the calibration of multiple-basin models, such as for a national planning framework, can be difficult due to limitations on quality and spatial coverage of available hydrological observations. In this manuscript, we build a process-based whole-country hydrological model for Panama using the Soil and Water Assessment Tool (SWAT). We also extend SWAT by deriving a precipitation interpolation model that incorporates regional climatic variability and spatial autocorrelation of precipitation, and we validate the model using data from 35 hydrological stations. Without calibration, the default application of SWAT reasonably predicted spatiotemporal variability in mean monthly discharge (NSE=0.70), but largely failed to predict variability (NSE=0.26) and maxima (NSE=0.22). However, with our relatively simply precipitation interpolation sub-model, we were able to strengthen predictions of discharge (NSE=0.87), but also able to more than double predictive ability for variance (NSE=0.62) and maxima (NSE=0.53). This moderate modification may allow process-based hydrological models such as SWAT to be much more broadly applied; crucially, even across regions with scarce hydrological data. The resulting precipitation and hydrology layers provide important baseline information for Panama.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".