The importance of snowmelt in the water balance of the Toconao sub-basin, Salar de Atacama
Bibliographic record
Abstract
The Salar de Atacama basin is one of the best-studied saline endorheic basins in the world due to the delicate balance between extraction of lithium-rich brine from its core, tourism, and the unique ecosystems of its surrounding lagoons. However, no study to date has quantified the contribution of snowmelt compared to rainfall in supporting groundwater recharge in the basin. In this work, satellite information (Moderate Resolution Imaging Spectroradiometer, MODIS) is used to characterize the spatial and temporal dynamics of snow coverage. However, snow equivalent water is not available from remote sensing, so the Cold Regions Hydrological Model (CRHM) was used to simulate snow water equivalent, runoff, infiltration and other hydrological processes governing the water balance and groundwater recharge. CRHM makes it possible to link physical processes to hydrological processes using hydrological response units (HRU) as control volumes for water balances and as a means of discretizing the basin. HRU were defined in the Toconao sub-basin, in the eastern part of the Salar de Atacama watershed and CRHM was parameterized from regional hydrological knowledge and run for several years, forced by reanalysis data. Special attention was paid to better understand the energy balance of snow, including sublimation and wind transport ablation losses, soil infiltration processes, and the role of snowmelt in surface runoff generation and direct and indirect groundwater recharge.Satellite observations of snow cover recorded from 2000 to 2020 showed frequent snowfalls both in summer and winter. The greatest extent of snow cover occurred during winter, accounting for 60% of the annual snow-cover extent. Snow cover is generally located above 4500 m asl in summer, while in winter the snow cover is more extensive, covering a large part of the basin. The CRHM simulations show that the greatest amount of precipitation of the year falls as rain in the summer months with the drier winter dominated by snowfall. The intense summer rains produce the greatest annual fluxes of runoff and infiltration. In winter, snowmelt infiltration is approximately twice that from rainfall. Snow losses by wind transport and sublimation had little impact on the overall water balance despite the dry environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".