Location: Integrated Cropping Systems Research
Project Number: 3080-12620-006-005-R
Project Type: Reimbursable Cooperative Agreement
Start Date: Oct 1, 2021
End Date: Sep 29, 2026
Objective:
This project will explore how wetland restoration through the Conservation Reserve Program (CRP) in the Agricultural Midwest (~81% of wetland CRP) contributes to climate mitigation by measuring and modeling carbon stocks and greenhouse gas (GHG) fluxes in restored wetlands and comparing those stocks and fluxes with drained wetlands as well as intact natural wetlands to establish changes in net radiative forcing associated with land use change. We will explore how drivers like climate, surrounding land-use, soils and hydrology impact wetland carbon cycling and use these relationships to model and scale estimates of changes in carbon and GHG fluxes across a time series of land-use change (wetland drainage for agriculture and restoration) and across spatial gradients of geomorphology, soil, surrounding land use, and climate.
Approach:
Task 1a: Informed by the needs of both DayCent and additional modeling tools, the team will collect data on wetland soil and biomass organic carbon stocks, organic carbon accumulation rates, soil texture and site descriptors (vegetation community, conductivity, water depth, land use, %N) for ~150 previously sampled sites and 150 new wetlands across a 15 state region. We will also augment our sampling to include natural and drained wetlands to supplement existing data. Task 1b: For a subset of sampling sites ( ~50 new and ~50 previously sampled sites), in addition to the variables described in 1a, we will also sample greenhouse gas flux rates and belowground biomass.
Task 2: Informed by the needs of the modeling tools above, we will extract climate and remote sensing data to generate a comprehensive record of land use, water level, biomass and nutrient loading data for each of the 300 sample sites that covers a time period concomitant with the wetland restoration. For a subset of sites, we will also explore implementing a remote-sensing based approach for estimating belowground biomass and other vegetation parameters which are critical for predicting carbon cycling.
Task 3: Leveraging previous projects that attempted to leverage DayCent for depressional wetlands, we will utilize data collected on wetland sites to calibrate and validate the DayCent model for depressional wetlands, concentrating on the newly collected soil and gas data and representation of flooding and vegetation, as well as exploring and documenting any additional data collection or model adaptation needs. Simultaneously, we will also calibrate and validate existing GHG and carbon models developed by project collaborators and develop a new hybrid model both as a contingency for DayCent and because these tools are more appropriate for scaling outcomes across space and time and comparing multiple landscape scenarios. We will model scenarios that account for the changes in soil carbon and GHG emissions that take place when wetlands are drained and when wetlands are restored as well as scenarios that examine how surrounding land-use, in particular perennial cover versus row-crop agriculture, impacts the soil carbon and GHG emissions. Finally, we will implement a novel methodology that assesses the change in albedo, converted to CO2 equivalents due to land use change.
Task 4: We will model the potential for water quality improvement, flood storage, and groundwater recharge by CRP sites accounting for nutrient loading from surrounding land use and the degree of wetland connectivity to local surface and groundwater. We will also model migratory bird habitat co-benefits of CRP wetland programs using vegetation, hydroperiod, and surrounding land-use data developed as part of Task 3 to model breeding and pair habitat suitability in the breeding geographies of our study area and the bioenergetics and water availability in the wintering and migration geographies of our study area using existing models. These data can then be overlaid with climate mitigation data in a geospatial framework that will allow the team to assess various tradeoffs between multiple outcomes.
