Functional model of water balance variability at the catchment scale: 1. Evidence of hydrologic similarity and space‐time symmetry
Bibliographic record
Abstract
This paper presents analysis of annual water balance variability, (1) regional (between‐catchment) variability and (2) between‐year (interannual) variability and the symmetry between the two. This involved analysis of the annual water balance in terms of a two‐stage partitioning, first, of annual precipitation into quick flow and soil wetting and, subsequently, of the resulting soil wetting into slow flow and vaporization. The nature of this water balance partitioning is explored by completing the above analysis in 377 Model Parameter Estimation Experiment (MOPEX) catchments located across the continental United States. We fitted analytical functional relationships to the partitioning at each stage, producing expressions for the three components of quick flow, slow flow, and vaporization. They indicate that the heterogeneity of water balance partitioning among the MOPEX catchments is underlain by a universal relationship that is transferable regionally. Key nondimensional similarity parameters are identified that serve to connect this invariant regional relationship to site‐specific response characteristics. These nondimensional formulations are extended to derive analytical expressions for several common metrics of annual water balance. The ability of the functional theory to predict regional patterns of mean annual water balance and interannual variability in individual catchments is assessed. Our analyses show a close symmetry between spatial (regional) variability of mean annual water balances and general trends of temporal (interannual) variability. The suggested functional theory can thus be the basis for data‐based assessments of hydrologic similarity and used to assist with predictions of the effects of long‐term climate variability and change, through providing a theoretical framework for “space for time” substitutions.
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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 | 0.003 |
| 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 | 0.000 |
| 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".