How Important Are Those Fracture Zones? Scale Dependent Characteristics Revealed Through Field Studies and an Integrated Hydrological Model of a Mountain Headwater Catchment
Bibliographic record
Abstract
Modeling groundwater flow in bedrock can be particularly challenging due to heterogeneities associated with fracture zones. However, fracture zones can be difficult to map, particularly in forested areas where tree cover obscures land surface features. This study presents the evidence of fracture zones in a small, snowmelt-dominated mountain headwater catchment and explores the significance of these fracture zones on groundwater flow in the catchment. A newly acquired bare earth image acquired using LiDAR identifies a previously undetected linear erosion zone that passes near a deep bedrock well at low elevation in the catchment. Borehole geophysical logs indicate more intense fracturing in this well compared to two wells at higher elevation. The well also exhibited a linear flow response during a pumping test, which is interpreted to reflect the influence of a nearby vertical fracture zone. The major ion chemistry and stable isotope composition reveal a slightly different chemical composition and a more depleted isotopic signature for this well compared to other groundwaters and surface waters sampled throughout the catchment. With this evidence of fracturing at the well scale, an integrated land surface – subsurface hydrologic model is used to explore four different model structures at the catchment scale. The model is refined in steps, beginning with a single homogeneous bedrock layer, and progressively adding 1) a network of large-scale fracture zones within the bedrock, 2) a weathered bedrock zone, and 3) an updated LiDAR-derived digital elevation model, to gain insight into how increasing subsurface geological complexity and land surface topography influence model fit to observed data and the various water balance components. Ultimately, all of the models are considered plausible, with similar overall fit to observed data (snow, streamflow, pressure heads in piezometers, and groundwater levels) and water balance results. However, the models with fracture zones and a weathered zone had better fits for the low elevation well. These models contributed slightly more baseflow (~14% of streamflow) compared to models without a weathered zone (~1%). Thus, in the watershed scale model, including a weathered bedrock zone appears to more strongly influence the hydrology than only including fracture zones.
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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) | 0.000 | 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".