Multicriteria analysis on rock moisture and streamflow in a rainfall‐runoff model improves accuracy of model results
Bibliographic record
Abstract
Abstract Although shallow (1.5 m) soil water storage has been extensively studied, the significance of deeper unsaturated zone water storage to flow generation is poorly documented. However, a limited but growing body of empirical work shows that the weathered bedrock vadose zone, not soil, stores the majority of plant available water in many seasonally dry and semi‐arid landscapes. Moreover, this storage dynamic mediates recharge to hillslope groundwater systems that generate stream discharge and support ecologically significant baseflows. Explicit representations of bedrock vadose zone processes are rarely incorporated into runoff models, due in part to a paucity of observations that can constrain simulations. Here, we develop a simple representation of the weathered bedrock vadose zone that is guided by in situ field observations. We incorporate this representation into a rainfall‐runoff model, and calibrate it on streamflow alone, on rock moisture (i.e., weathered bedrock vadose zone moisture) alone, and on both using the concept of Pareto optimality. We find that the model is capable of accurately simultaneously simulating dynamics in rock moisture and streamflow, in terms of Kling‐Gupta Efficiency, when using Pareto optimal parameter sets. Calibration on streamflow alone, however, is insufficient to accurately simulate rock moisture dynamics. We further find that the posterior distributions of some model parameters are sensitive to choice of calibration scenario. The posterior distribution of high‐performing model parameters resulting from the streamflow only calibration scenario include physically unrealistic values that are not yielded by the rock moisture only or Pareto calibration strategies. These results suggest that the accuracy of some model results can be increased and parameter uncertainty decreased via incorporation of rock moisture data in calibration, without sacrificing streamflow simulation quality. Emerging recognition of the global significance of weathered bedrock water storage in seasonally dry and semi‐arid regions motivates more observations of weathered bedrock moisture and integration of this variable into earth system 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".