Understanding and Modeling Current and Future "Hot Moments" in Coastal Wetlands
| Active Dates | 9/1/2023-8/31/2026 |
|---|---|
| Program Area | Environmental Systems Science |
Project Description
The coastal terrestrial-aquatic interface (TAI) is a highly dynamic component of the Earth system that plays a critical role in biogeochemical cycling. Due to its dynamic nature, rates of biogeochemical cycling can be highly variable in response to episodic changes in
inundation
(flooding),
salinity,
temperature, or nutrient availability. This can lead to “hot moments” where emissions of greenhouse gases such as nitrous oxide (N2O) and
methane
(CH4) are unusually high. Even though these hot moments can have a disproportionate effect on annual-scale TAI greenhouse gas emissions, we have a limited understanding of both the underlying biogeochemical mechanisms and the relative frequency, magnitude, and duration of the events. This constrains our ability to represent GHG hot moments in biogeochemical and Earth systems models.
Our overall objective is to gain a predictive understanding of the mechanisms controlling hot moments of greenhouse gas emissions at the coastal TAI, such that these processes and their causal factors can be incorporated into process-oriented biogeochemical models. We have identified the following current knowledge gaps as particularly limiting: (1) magnitude and frequency of hot moments; (2) biogeochemical mechanisms underlying hot moments; (3) effects of simultaneous episodic drivers on mechanisms; and (4) multi-faceted datasets.
To address these knowledge gaps, we have designed a ModEx approach in which we will couple observations and experiments to generate data for ongoing modeling efforts. Observationally, we will use a field-based autochamber system to understand the frequency and magnitude of naturally-occurring hot moments and their effect on ecosystem-scale greenhouse gas emissions. Experimentally, we will build a mesocosm system to determine the mechanisms driving hot moments in response to pulses of warming, flooding events, or nitrogen addition. Our proposed efforts will result in the collection of continuous chamber-level CH4 and N2O flux measurements, across natural and single-, double-, and triple- crossed episodic events as well as corresponding measurements of porewater chemistry, soil carbon quality, plant biomass, and redox reaction rates, filling key gaps in the datasets currently available for model parameterization and configuration. We will then integrate the data from these empirical components into ongoing modeling efforts in two ways. We will update our PFLOTRAN reaction network to include nitrogen cycling and run simulations of hot moments resulting from pulses of inundation, salinity changes, warming, and nitrogen availability. We will also supply data products to two current DOE-funded modeling projects with modeling objectives that will be greatly facilitated with our high-temporal-resolution measurements of GHG emissions, simultaneous measurements of nitrogen and carbon cycling rates, porewater depth profiles, and contextual sediment characterization. Overall, our proposed project will generate new data leading to a better mechanistic understanding of hot moments of greenhouse gas emissions in the coastal TAI and improve representation of these events in biogeochemical and Earth systems models.
Our overall objective is to gain a predictive understanding of the mechanisms controlling hot moments of greenhouse gas emissions at the coastal TAI, such that these processes and their causal factors can be incorporated into process-oriented biogeochemical models. We have identified the following current knowledge gaps as particularly limiting: (1) magnitude and frequency of hot moments; (2) biogeochemical mechanisms underlying hot moments; (3) effects of simultaneous episodic drivers on mechanisms; and (4) multi-faceted datasets.
To address these knowledge gaps, we have designed a ModEx approach in which we will couple observations and experiments to generate data for ongoing modeling efforts. Observationally, we will use a field-based autochamber system to understand the frequency and magnitude of naturally-occurring hot moments and their effect on ecosystem-scale greenhouse gas emissions. Experimentally, we will build a mesocosm system to determine the mechanisms driving hot moments in response to pulses of warming, flooding events, or nitrogen addition. Our proposed efforts will result in the collection of continuous chamber-level CH4 and N2O flux measurements, across natural and single-, double-, and triple- crossed episodic events as well as corresponding measurements of porewater chemistry, soil carbon quality, plant biomass, and redox reaction rates, filling key gaps in the datasets currently available for model parameterization and configuration. We will then integrate the data from these empirical components into ongoing modeling efforts in two ways. We will update our PFLOTRAN reaction network to include nitrogen cycling and run simulations of hot moments resulting from pulses of inundation, salinity changes, warming, and nitrogen availability. We will also supply data products to two current DOE-funded modeling projects with modeling objectives that will be greatly facilitated with our high-temporal-resolution measurements of GHG emissions, simultaneous measurements of nitrogen and carbon cycling rates, porewater depth profiles, and contextual sediment characterization. Overall, our proposed project will generate new data leading to a better mechanistic understanding of hot moments of greenhouse gas emissions in the coastal TAI and improve representation of these events in biogeochemical and Earth systems models.
Award Recipient(s)
- Smithsonian Institution (PI: Noyce, Genevieve)