Establishing a Hydrographic Framework for Watershed Management across Northern Chile
Bibliographic record
Abstract
This article demonstrates how currently available digital elevation (NASA SRTM; 30 m resolution) and hourly global precipitation data (rain, snow; 5 and 10 km resolution) can be used to hydrographically quantify the regional watershed management context across northern Chile. This is done through comprehensive derivations of flow direction, flow accumulation, flow channels, floodplain extent, depressions, and upslope watershed outlines. In turn, these derivations allow for estimating potential precipitation accumulations within any watershed, and turn these into subsequent storm-averaged discharge estimates at, e.g., at any road—flow-channel crossing points. This article elaborates on this by modelling and mapping hydrological conditions and subsequent storm damage at the regional scale and at select locations in terms of recent flood events on March 2015, May 2017, and June 2017. It was found that modelled flood extent and storm-estimated discharge volumes and rates generally conform to reported values including storm-caused damages within communities along the Huasco, Elqui, Limari, Copiapó and Salado rivers. This included the storm response assessment of six water reservoirs as these varied, as quantified, from normal (Puclaro, La Laguna, Cogoti), at capacity (La Paloma, Cogoli), and overflowing (Recoleta). The details of the local to regional assessments are presented in the form hydrologically explicit maps, figures and tables. Together, these attest to the general validity of the framework as introduced. Hydrometrically based stream-discharge calibrations would assist in further refining the approach, especially in terms of estimating local to regional run-off coefficients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".