Quantifying weathering on variable rocks, an extension of geochemical mass balance: Critical zone and landscape evolution

Abstract

We present a statistical model of soil and rock weathering in deep profiles to expand the capacity to assess weathering to heterogeneous bedrock types, which are common at the Earth's surface. We developed the Weathering Trends (WT) model by extending the fractional mass change calculation (tau) of the geochemical mass balance model in two important ways. First, WT log transforms the elemental ratio data, to discern the log‐linear patterns that naturally develop from thermodynamic and kinetic laws of chemistry. Second, WT statistically fits log‐transformed element concentration ratio data – log(cj/ci), the only depth‐varying term in tau – as a function of depth to determine characteristic depths of transitions in weathering processes, along with confidence intervals. With no prior assumptions, WT estimates average parent material composition, average composition of the upper weathered zone and mean fractional mass change of each element over the entire weathering profile. WT displays the mean shape of weathering profiles of log‐transformed geochemical data bounded by calculated confidence intervals. We share the WT model code as an open‐source R package (https://github.com/fisherba/WeatheringTrends). The WT model was designed to interpret two 21 m cores from the Laurels Schist bedrock in the Christina River Basin Critical Zone Observatory in the Pennsylvania Piedmont, where our morphological and elemental data provided inconclusive estimates of bedrock depth. The WT model differentiated between rock variability and weathering to delineate the maximum extent of weathering at 12.3 m (CI 95% [9.2, 21.3]) in Ridge Well 1 and 7.2 m (CI 95% [4.3, 13.0]) in Interfluve Well 2. The water table was 5–8 m below fresh rock at Ridge Well 1, but at the same depth as fresh rock at the lower elevation interfluve. We assess statistical approaches to identify the best immobile element for use in WT and tau calculations. Copyright © 2017 John Wiley & Sons, Ltd.

The pedogenic Walker and Syers model under high atmospheric P deposition rates

“…7 Leaf Ca, Mg, Na and K concentrations in needles of P. canariensis along the soil chronosequence between the two chronosequences. Thus, plant uptake in the rhizosphere may have transferred essential nutrients-through plant litter recycling, throughfall and stemflow-from deeper horizons to the first centimeters of the soils (Hinsinger 2001;Fisher et al 2017). However, when deeper horizons are considered, it is likely that the overall cation content could show a net decrease over time.…”

How Slow Rock Weathering Balances Nutrient Loss During Fast Forest Floor Turnover in Montane, Temperate Forest Ecosystems

“…X-axis scale in panel a (not shown) is the same as in panel b. Given that some outliers were excluded from this figure, Figure 3a and from flux estimates that are based on τ X Xi , we show the depth distribution of element concentrations in Supplementary Figure 2, and τ X Xi in Supplementary Figure 3 and Supplementary Figure 4 [according to Fisher et al (2017) log10 (C X /C j )] including the outliers.…”

Do degree and rate of silicate weathering depend on plant productivity?

“…The amplitude of recycling varies from nutrient to nutrient and site to site. In the arid Pan de Azùcar, nutrients are primarily recycled via photodegradation of shrubs (e.g., Gallo et al, 2006;Day et al, 2015). In the remaining sites Rec X occurs through all organic-bearing soil horizons and increases from Santa Gracia to Nahuelbuta and is highest for Ca (increasing from 1 to 6), K (increasing from 6 to 15), and P (increasing from 5 to 30; Table 5).…”