A Mechanistic Analysis of Wetland Biogeochemistry in Response to Temperature, Vegetation, and Nutrient Input Changes

Abstract

Wetlands represent the most significant natural greenhouse gas (GHG) source and their annual emissions tightly depend on climatic and anthropogenic factors. Biogeochemical processes occurring in wetlands are still poorly described by mechanistic models and hence their dynamic response to environmental changes are weakly predicted. We investigated wetland GHG emissions, relevant electron acceptors and donors concentrations, and microbial composition resulting from changes in temperature, CH 4 plant uptake efficiency, and SO 2− 4 deposition using a mechanistic biogeochemical model (here called BAMS3) that integrates the carbon (C), nitrogen (N), and sulfur (S) cycles. Parameters constraining the coupled C-N-S cycles were retrieved from controlled experiments and were validated against independent field data of CH 4 emissions, and CH 4 (aq) and SO 2− 4 concentration profiles in a wetland in southern Michigan, USA (Shannon & White, 1994, http://hdl.handle.net/102.100.100/236252? index=1). We found that +1.75 • C increase in temperature leads to 22% and 30% increment in CH 4 and N 2 O emissions, respectively. A decrease in the CH 4 plant uptake efficiency causes the prevalent CH 4 emission pathway to become diffusion mediated and resulted in 50% increase in the daily average CH 4 emissions. Finally, a decreasing SO 2− 4 deposition rate can increase CH 4 emissions up to 5%. We conclude that the increasing GHG emissions from wetlands is a result of both environmental and anthropogenic causes rather than global warming alone. An increase in model complexity does not necessary improve the estimation of GHG emissions but it aids interpretation of intermediate processes to a greater detail. Plain Language SummaryWetlands are the largest natural source of greenhouse gasses; hence, climate change and human development have become a major concern for the conservation of these ecosystems. In this study, we explore the effect of rising temperature, plants community, and changes in nutrient input rate on the emission rate and quality in a wetland. The assessment was conducted using a mechanistic model that accounts for carbon, nitrogen, and sulfur cycles on test scenarios. The model was initially tested on field data of a wetland in southern Michigan, and then used for scenarios predictions. Results suggest that increasing the average soil temperature leads to a substantial increase in greenhouse gas emissions; in particular, methane emissions increase by 22%. Methane emissions are also affected by the plant composition, which controls the main emission pathway; small composition changes can induce high emissions variations. Finally, we showed how a change in atmospheric sulfate deposition to wetlands can control the methane emissions. We conclude that modeling coupled chemical, biological, and physical processes helped to describe wetland nutrients dynamics under both climate change and anthropogenic factors.

Spatiotemporal Assessment of GHG Emissions and Nutrient Sequestration Linked to Agronutrient Runoff in Global Wetlands

“…The BAMS4 reaction network is a simplification of the SOM cycle described in Pasut et al. (2020) and elaborated from Riley et al. (2014), Tang et al.…”

“…RX identifies the reactions, which are expanded in Tables S1–S6 and are characterized by parameters earlier estimated and validated in Pasut et al. (2020). BAMS4, Biotic and Abiotic Model for SOM‐version 4.…”

“…Tables S1–S6 summarize all the reactions and corresponding parameters for both biotic and abiotic processes used in BAMS4, which were derived from earlier estimation and validation (Maggi et al., 2008; Pasut et al., 2020; Riley et al., 2014) and are not expanded here.…”

“…The BAMS4 reaction network is a simplification of the SOM cycle described in Pasut et al (2020) and elaborated from Riley et al (2014), Tang et al (2019), andCeriotti et al (2020). It consists of two organic polymer pools of PolyC (lignin and cellulose) and PolyCN (peptidoglycan), three organic monomer pools of MonoC, MonoCN, and MonoCS, representing the organic carbon, nitrogen, and sulfur, respectively, and two inorganic carbon molecules (CH 4 and CO 2 ).…”

“…Each C, N, and S pool can be present in aqueous, protected (e.g., on the mineral surface binding), or gaseous phase. Each reaction includes specific microbial functional groups (ammonia-oxidizing bacteria B AOB , nitrite-oxidizing bacteria B NOB , denitrifying bacteria B DEN , sulfur reducing bacteria B SrRB , thiosulfate-and sulfide-reducing bacteria B ThSRB , sulfate-reducing bacteria B SRB , thiosulfate and sulfide disproportioning bacteria B SDB , and photolithoautotroph oxidizing bacteria B SOB ) described in detail in Pasut et al (2020) and represented in Figure 1. The first two functional groups are aerobic, while the others are anaerobic.…”