clearly expressed as in an advectively dominated system, because solutes are also re-distributed along

concentration gradients (diffusion) and transversally and longitudinally along the advective flow directions (dispersion). The biogeochemical simulations were performed using 5-day time steps, which was necessary because of computational constraints during the flow modeling (e.g. memory overflow, storage limitations). However, it is known that hydrological events at time scales of hours (e.g. single rainstorm events) can influence the biogeochemical processes within wetlands, as e.g.

demonstrated for pulses of N₂O emission [Goldberg et al., 2010] or high instantaneous CO₂ production [Deppe et al., 2010] after wetting. Dynamics at these time scales, however, were not the main focus of this work and at this point cannot be fully accounted for in the present modeling approach because of computational limitations. Further it is known that organic carbon in wetlands typically consists of a fraction of labile components that can be easily utilized by micro-organisms (mostly within shallow layers) and more recalcitrant components (more abundant in deeper layers) [Yavitt and Lang, 1990; Reiche et al., 2010; Moore et al., 2007]. Labile organic carbon is not uniformly available as is assumed in our approach. However, there are two main reasons why we think that our assumption of unlimited carbon supply is nonetheless reasonable. Firstly, labile organic carbon availability is higher in shallow peat layers, in which most of the modeled processes occur, mostly due to inputs from the vegetation and high fermentation activity in the rhizosphere [Knorr et al., 2008; Wachinger et al., 2000; Reiche et al., 2010]. Secondly, we did not include methanogenesis, for which the supply of electron donors will be the key control, as the ubiquitous CO2 may serve as electron acceptor [Achtnich et al., 1995]. Field observations suggested that if alternative electron acceptors were present, the respective process proceeded, while under methanogenic conditions, respiratory activity slowed down and partly ceased [Beer and Blodau, 2007; Knorr et al., 2009].

Nevertheless, the process rate, constant in this case, depends on the quality of organic matter used and is not universal but substrate specific. The application of the Redfield ratio to simulate release of organic bound nitrogen due to decomposition of organic material in terrestrial ecosystems was probably a weak model assumption. Recent literature reported that C:N:P ratios in terrestrial ecosystems vary depending on vegetation types, but on the global scale average at about 186:13:1 for soil biomass and 60:7:1 for soil microbial biomass [Cleveland and Liptzin, 2007]. In our biogeochemical model we assumed that the majority of organic carbon available to microbes originates from vegetation and fermented plant material processed by microorganisms. The Redfield ratio is, however, narrower than the global average observed for soil biomass (106:16:1 compared to 186:13:1) and nitrogen release would be overestimated by our model. That means that the concentrations of ammonia, rates of nitrification and thus also nitrate pools available for denitrification may also be overestimated. Nevertheless, this should translate into slightly longer phases of nitrification or subsequent denitrification only, thus not fundamentally altering spatial patterns of the model output.

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4.2 Comparison with field observations

Despite these simplifications, the presented model is capable of reproducing spatial variations in pore water concentrations of redox-sensitive solutes in the field (Figure 10). Vertical concentration profiles were measured in pore water from six different locations at the Lehstenbach field site, for an area, which is comparable in size to the spatial domain of the flow model (10m x 20m) [Goldberg et al., 2010; Knorr et al., 2009]. Simulated maxima in nitrate concentrations are found at a depth of ~0.1m and not directly at the surface, which agrees with measured data. The observed shift of nitrate concentration maxima has been explained as a result of plant uptake from the upper layers, as plant cover often leads to rapid depletion of nitrate concentrations [Silvan et al., 2005]. However our biogeochemical simulations suggest an additional explanation for the increased nitrate concentrations at shallow depth: As shown for the cross sections (Figure 6 C) high nitrification rates are limited to a relatively thin layer where turnover of ammonium to nitrate is highest. This layer of higher reactivity is the result of the vertical transport of water, which is being enriched with ammonium as it passes the unsaturated zone. Because nitrification rates under aerobic conditions depend on the local availability of ammonium, higher ammonium concentrations result in higher nitrification rates, which can be found directly above the de-nitrification zone where anaerobic conditions trigger rapid nitrate reduction. Similar findings were reported for different field studies [Regina et al., 1999; Goldberg et al., 2010]. Measured depth profiles as shown in Figure 10 are often used to calculate biogeochemical turnover rates based on a simplified approach treating wetlands as diffusion limited systems where the resupply of dissolved electron acceptors/donors is solely controlled by diffusion [Beer and Blodau, 2007; Clymo and Bryant, 2008]. However, model results show that advective transport can be an important component especially for slightly sloping wetlands with micro-topography and can significantly affect the spatial availability and re-distribution of electron acceptors and donors within the subsurface. Vertical concentration profiles simulated in this study suggest that depth variations in the concentrations of redox-sensitive solutes observed in the field are probably the result of a complex interplay between three-dimensional advective transport processes and biogeochemical reactions, which are in turn controlled by micro-topography moderated interactions between surface and subsurface flow processes and do not arise from pure diffusion and reactions alone.

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Figure 10: Observed and simulated variations of depth profiles for redox-sensitive species (nitrate, iron(II) and sulfate). Grey areas represent envelopes for predicted depth profiles and the black lines (mean +/- standard deviation) actual field observations taken simultaneously at six different locations for an area which is comparable to the model 20 m x 10 m domain at the field site in the Lehstenbach catchment.

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