Catchment export of base cations: Improved mineral dissolution kinetics influence the role of water transit time
Bibliographic record
Abstract
Abstract. Soil mineral weathering is one of the major sources of base cations (BC), which play a dual role for a forest ecosystem; they function both as plant nutrients, and for buffering against acidification of catchment runoff. On a long-term basis, the soil weathering rates will determine the highest sustainable forest productivity without causing acidification. It is believed that the hydrologic residence time plays a key role in determining weathering rates on a landscape scale. The weathering model PROFILE has been used for almost 30 years to calculate weathering rates in the rooting zone of forest soils. However, the mineral dissolution equations in PROFILE are not adapted for the unsaturated zone, and employing these equations on a catchment scale results in a significant over-prediction of base cation release rates to surface waters. In this study we use a revised set of PROFILE equations which, among other features, include retardation from silica concentrations. Relationships between the water transit time (WTT) and soil water concentrations were derived for each base cation, by simulating the soil water chemistry along a one-dimensional flowpath, using the mineralogy from a glacial till soil. We show how the revised PROFILE equations are able to reproduce patterns in BC- and Si-concentrations, as well as BC-ratios (Ca2+ / BC, Mg2++ / BC and Na+ / BC), observed in soil water profiles and catchment runoff. As opposed to the original set of PROFILE equations, the revised set of equations could reproduce how increasing WTT led to decreasing Na+ / BC, as well as increasing Ca2+ / BC and Mg2+ / BC. Furthermore, the total release of base cations from a hillslope was calculated using a mixing model, where water of different WTT was mixed according to an externally modelled WTT-distribution. The revised set of equations gave a 50 % lower base cation release (0.23 eq m−2 yr−1) than the original PROFILE equations, and are in better agreement with mass balance calculations of weathering rates. The results from this study thus demonstrate that the revised mineral dissolution equations for PROFILE are a major step forward in modelling weathering rates on a catchment scale.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".