Exploring the pathways of precipitation, snowmelt and glacier melt through the subsurface in high resolution, coupled, data-driven modeling experiment of the Langshisha catchment in the Himalaya
Bibliographic record
Abstract
Groundwater can be an important water source for mountain streams. To gain insights into the sources of groundwater recharge and their pathways to the downstream environments, the interactions between surface water and groundwater are investigated for the Langshisha catchment, in the Langtang basin, Nepal Himalaya. The 0.81 km2 study area ranges in elevation from 4130 to 4450 m. a.s.l., with a landscape of coarse debris, pocket meadows and moraine sediments. It is bordered on three sides by steep mountain cliffs, the Langshisha glacier outlet creek, and the Langtang river. To simulate the hydrological behaviour of the area, we couple the glacio-hydrological model Spatial Processes in Hydrology (SPHY), a spatially distributed water balance model and the groundwater flow model MODFLOW6. We analyze three approaches to simulate the subsurface hydrology of the area: (1) using the glacio-hydrological model alone, (2) a one-way coupling of the glacio-hydrological model with a groundwater numerical model, where the groundwater recharge from the glacio-hydrological model is used as input to the groundwater model, and (3) a two-way coupled surface water and groundwater model. The model is evaluated with in-situ field data of soil moisture, shallow groundwater levels and streamflow measurements collected intermittently over the 2013-2022 period as well as isotopic and geochemistry water sample data collected in November 2022. Preliminary results suggest that despite the additional computational demands and time required to develop and apply a fully coupled approach, it provides essential knowledge regarding the cryosphere-surface water-groundwater interactions. Our preliminary results showcase the importance of field observations to constrain modelling efforts and will serve to guide further model applications to assess the importance of representing cryosphere-surface water-groundwater interactions in mountain landscapes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".