EVALUATING THE POTENTIAL OF TEMPERATE INLAND MINERAL SOIL WETLANDS AS NATURAL CLIMATE SOLUTIONS

Increasing global efforts aim to mitigate climate change by leveraging natural climate solutions (NCS), with temperate inland mineral soil wetlands emerging as key ecosystems due to their ability to sequester atmospheric carbon dioxide (CO₂) while providing numerous co-benefits. However, wetlands are considered substantial natural sources of methane (CH₄), a potent greenhouse gas (GHG) with a higher warming potential than CO₂. Therefore, even modest CH₄ emissions from wetlands could compromise their capacity as NCS. Further, anthropogenic climate change has been found to alter biotic and abiotic controls of wetland carbon fluxes, complicating our understanding of carbon cycling and associated flux patterns. Given the dual role wetlands play in the radiative forcing of climate and the sensitivity of wetland biogeochemical properties to anthropogenic climate change, their efficacy as NCS depends on three main factors: (1) the relative warming potential of wetland GHGs (i.e. CO₂ and CH₄) in the atmosphere; (2) the relative capacity of wetlands to sequester CO₂ versus emitting CH₄; and (3) the sensitivity of wetland carbon turnover processes to ongoing anthropogenic climate change. Comprehensive evaluations of the NCS capacity of wetlands requires (1) suitable CO₂-equivalent (e.q.) metrics to facilitate direct comparison between CO₂ sequestration and CH₄ emission; (2) biogeochemical models to predict long term wetland carbon fluxes under varying biotic and abiotic conditions; and (3) process-based models to simulate wetland climate feedback under diverse future climate scenarios. This thesis responds to these crucial needs by examining the impact of wetland state conversions on net wetland carbon budgets using three different CO₂-e.q. metrics. Further, using random forest models trained on environmental predictors, this thesis predicts long term wetland carbon fluxes for more than 200 wetlands in the Prairie Pothole Region. It also simulates wetland climate feedback loops under diverse future socioeconomic pathways using a process-based GHG perturbation model. It demonstrates that the use of dynamic GWP* CO₂-e.q. metric is essential for detecting climate benefits or detriments associated with wetland drainage and restoration. Dynamic GWP* outperforms static GWP and SGWP metrics in terms of aligning wetland net CO₂-e.q. budgets to their warming/cooling status. Based on GWP* and the GHG perturbation model, this research affirms that wetland conservation and restoration are viable strategies for promoting wetlands as NCS. Conservation of intact wetlands was shown to deliver immediate climate benefits consistent with IPCC timelines, while restoration offered longer-term benefits that depend heavily on CH₄ emissions from restored wetlands. This research also underscores that while wetlands act as long-term CO₂ sinks, their near-term effectiveness as NCS is increasingly compromised by warming-driven CH₄ intensification. The projected increase in CH₄ emissions may transform wetlands from climate regulators to amplifiers of global warming, especially under high-forcing future climate scenarios. These results highlight the urgency of pursuing sustainable development pathways that limit global temperature rise to below 1.5–2.0 °C, not only to preserve the NCS potential of wetlands but to prevent their transformation into reinforcing agents of climate change within the Earth’s climate system.

Name thesis:degree_name

Doctor of Philosophy (Ph.D.)

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Environment and Sustainability

Grantor

University of Saskatchewan

Year dc:date.issued

2025

Author dc:creator

• Ma, Shizhou

Advisor dc:contributor.advisor

• Creed, Irena

Committee members dc:contributor.committeemember

• Bradford, Lori
• Westbrook, Cherie
• Famiglietti, Jay
• Badiou, Pascal
• Strack, Maria
• Weegman, Mitch

• Climate Change
• Natural Climate Solutions
• Wetland Carbon Cycle
• Remote Sensing
• Geospatial Analysis

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10388/17208

OAI identifier oai:identifier

oai:harvest.usask.ca:10388/17208