Does hillslope trenching enhance groundwater recharge and baseflow in the <scp>Peruvian Andes</scp>?
Bibliographic record
Abstract
Abstract As Andean glaciers rapidly retreat due to climate change, the balance of groundwater and glacial meltwater contributions to stream discharge in tropical, proglacial watersheds will change, potentially increasing vulnerability of water resources. The Shullcas River Watershed , near Huancayo, Peru, is fed only partly by the rapidly receding Huaytapallana glaciers (<20% of dry season flow). To potentially increase recharge and therefore increase groundwater derived baseflow, the government and not‐for‐profit organizations have installed trenches along large swaths of hillslope in the Shullcas Watershed . Our study focuses on a nonglacierized subcatchment of the Shullcas River Watershed and has 2 objectives: (a) create a model of the Shullcas groundwater system and assess the controls on stream discharge and (b) investigate the impact of the infiltration trenches on recharge and baseflow. We first collected hydrologic data from the field including a year‐long hydrograph (2015–2016), meteorological data (2011–2016), and infiltration measurements. We use a recharge model to evaluate the impact of trenched hillslopes on infiltration and runoff processes. Finally, we use a 3‐dimensional groundwater model, calibrated to the measured dry season baseflow, to determine the impact of trenching on the catchment. Simulations show that trenched hillslopes receive approximately 3.5% more recharge, relative to precipitation, compared with unaltered hillslopes. The groundwater model indicates that because the groundwater flow system is fast and shallow, incorporating trenched hillslopes (~2% of study subcatchment area) only slightly increases baseflow in the dry season. Furthermore, the location of trenching is an important consideration: Trenching higher in the catchment (further from the river) and in flatter terrain provides more baseflow during the dry season. The results of this study may have important implications for Andean landscape management and water resources.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".