Modelling multiseasonal preferential transport of dissolved organic carbon in a shallow forest soil: Equilibrium versus kinetic sorption

Abstract

Parameterization of transformation and transport processes of dissolved organic carbon (DOC) in soils is challenging especially under variable hydrological conditions. In this study, DOC concentrations in stormflow were analysed with a physically based modelling approach. A one‐dimensional dual‐continuum vertical flow and transport model was applied to simulate subsurface processes in a macroporous forest hillslope soil over a period of 4.5 years. Microbially mediated transformations of DOC were assumed to depend primarily on soil moisture and soil temperature. Two conceptually different descriptions of the sorption of DOC to soil were examined with equilibrium and kinetic approaches. In order to quantify the uncertainties associated with the model parameterization, Monte Carlo analyses in conjunction with Latin hypercube sampling was performed. Despite the complexity of microbial transformations, the simulated temporal patterns of DOC concentrations in stormflow showed similar behaviour to those reflected in the observed DOC fluxes. Due to preferential flow, the hillslope DOC export (5.0 ± 0.5 g C · m−2 · year−1) was higher than the amounts usually reported in the literature. Overall DOC transport in hillslope scenarios could be described appropriately using the equilibrium sorption assumption. The performed analyses showed that the inclusion of the kinetic description of DOC sorption only slightly improved the predictions of the DOC hillslope export. Moreover, influences of seasonal hydro‐climatological conditions on hillslope export of DOC could be observed. Reduced DOC transport during an extreme warm and dry summer was described with lower accuracy, thus indicating the difficulties in the representation of DOC transformations under dry conditions.

Modeling Reactive Solute Transport in Permafrost‐Affected Groundwater Systems

“…Volumetric latent heat of fusion for water, L i J m −3 Ice density × mass-based latent heat = 3.03 × 10 8 (Bense et al, 2009) Residual saturation, S Wres -0.05 W K 0.5 ("clean" water) to 1.8 ("dirty" water) where λ a ref is the reference degradation rate for the reference temperature, T ref [°C], and Q 10 is the factor for increase in metabolic rate per 10 °C increase in temperature. Sorption, S C (Table 2) is modeled assuming a linear equilibrium sorption approach (Dusek et al, 2019). The implementation of Equation 4 in FlexPDE was benchmarked against the classic Ogata and Banks (1961) analytical solution, and Equations 1-3 have been extensively benchmarked in Grenier et al (2018).…”

“…Specified concentrations applied to the lagoon boundary conditions (Table 2) were based on water samples taken from the lagoon. The parameters for contaminant degradation and sorption were estimated based on available literature for similar processes and/or systems, and the temperature dependency in terms described in Section 2 (Dusek et al., 2019; Yurova et al., 2008). Values for all parameters are listed in Tables 1 and 2.…”

Revealing subsurface processes in the Uhlířská catchment through combined modelling of unsaturated and saturated flow

“…Besides process‐oriented research, one of the key research themes in the Uhlířská catchment has been the development and application of the S1D model approach (Vogel, Březina, et al, 2010) to simulate flow and transport in the dual continuum domains of the vadose zone. Applications of S1D at Uhlířská included transport of oxygen isotopes (Dohnal et al, 2012; Dušek & Vogel, 2018; Dušek & Vogel, 2019; Dušek et al, 2012; Dušek, Vogel, & Šanda, 2012; Vogel, Šanda, et al, 2010) and solutes (Dušek et al, 2017; Dušek et al, 2019) through the vadose hillslope zone and provided a background for the vadose zone modelling in our study.…”

Is a simple model based on two mixing reservoirs able to reproduce the intra-annual dynamics of DOC and NO3 stream concentrations in an agricultural headwater catchment?

“…Such exports can alter the stoichiometry and lead to degradation of the quality of water bodies (Lee et al, 2000;Borah et al, 2002;Fuß et al, 2017). Reducing nutrient transfer from land to stream requires knowledge about their sources, and transport and transformation processes (Pettersson et al, 2001;Ford et al, 2018;Dusek et al, 2019).…”

“…Models can be used to test hypotheses about physical and biogeochemical processes that govern the transfer and transformation of water and solutes (Pettersson et al, 2001;Birkel et al, 2017;Dusek et al, 2019;Trevisan et al, 2019). Because internal states of a catchment cannot be observed directly and hydro-chemical properties cannot be measured everywhere, at least some model parameters need to be calibrated using metrics that compare model outputs to observed time series (Hrachowitz and Clark, 2017).…”