Quantifying groundwater–surface water interactions in a proglacial valley, Cordillera Blanca, Peru
Bibliographic record
Abstract
Abstract A myriad of downstream communities and industries rely on streams fed by both groundwater discharge and glacier meltwater draining the Cordillera Blanca, Northern Peruvian Andes, which contains the highest density of glaciers in the tropics. During the dry season, approximately half the discharge in the region's proglacial streams comes from groundwater. However, because of the remote and difficult access to the region, there are few field methods that are effective at the reach scale to identify the spatial distribution of groundwater discharge. An energy balance model, Rhodamine WT dye tracing, and high‐definition kite‐borne imagery were used to determine gross and net groundwater inputs to a 4‐km reach of the Quilcay River in Huascaran National Park, Peru. The HFLUX computer programme ( http://hydrology.syr.edu/hflux.html ) was used to simulate the Quilcay River's energy balance using stream temperature observations, meteorological measurements, and kite‐borne areal photography. Inference from the model indicates 29% of stream discharge at the reach outlet was contributed by groundwater discharge over the study section. Rhodamine WT dye tracing results, coupled with the energy balance, show that approximately 49% of stream water is exchanged (no net gain) with the subsurface as gross gains and losses. The results suggest that gross gains from groundwater are largest in a moraine subreach but because of large gross losses, net gains are larger in the meadow subreaches. These insights into pathways of groundwater–surface water interaction can be applied to improve hydrological modelling in proglacial catchments throughout South America. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".