Nonstationarity in Snowmelt Partitioning: A Mechanistic Modeling Approach to Explore the Role of Catchment Structure and Antecedent Climate
Bibliographic record
Abstract
Understanding how snowmelt is partitioned into different hydrologic flowpaths/storages—and how such partitioning varies over time—is essential for predicting water availability/quality under climate variability. In this study, we examine the (non)stationarity of snowmelt partitioning patterns (SPP), in response to interannual variations in antecedent (Fall) rainfall prior to snowmelt seasons, across two snow-dominated catchments with contrasting geologic and topographic features: one in Canada and the other in Sweden. Using integrated subsurface–surface flow and transport modeling (ParFlow-CLM-EcoSLIM), combined with observational data, we simulate monthly to annual partitioning of snowmelt into shallow flowpaths, deep flowpaths, evapotranspiration, and long-term storage. Further, to generalize our findings beyond the two case studies, we design a suite of virtual experiments that systematically vary catchment slope, conductivity’s vertical contrast, and conductivity’s lateral heterogeneity. Results show that lateral heterogeneity in conductivity strongly mediates the sensitivity of snowmelt partitioning to antecedent rainfall: catchments with heterogeneous lateral structure store significantly more snowmelt and reduce shallow flow contributions during wetter years, while laterally homogeneous catchments display minimal sensitivity to wet or dry Fall rainfall condition. In contrast, slope and vertical conductivity architecture govern SPP but play a limited role in mediating SPP’s temporal sensitivity to antecedent rainfall variability. These findings reveal that subsurface structure—particularly lateral heterogeneity—mediates the extent to which climate variability alters snowmelt partitioning. This has implications for predicting streamflow responses, groundwater recharge, and solute transport under changing climate regimes, and highlights the importance of representing time-variable hydrologic behavior (or functions) in hydrologic models
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".