Runkle (CMS 2020): A national quantification of methane emissions from rice cultivation in the U.S.: integrating multi-source satellite data and process-based modeling
Benjamin Runkle, University of Arkansas, Dept of Biological & Agricultural Engineering, brrunkle@uark.edu (Presenter)
Rice is a significant food crop that is also responsible for 8% of global anthropogenic methane emissions, due to its cultivation in anaerobic soil environments. There is still considerable uncertainty to the methane emissions associated with rice cultivation in the United States, and a more consistent product is necessary to observe, plan, and reduce this greenhouse gas source. This project takes on the challenge of monitoring methane production in the US’s rice producing regions by integrating satellite data with a process-based model to produce a consistent national rice-CH4 product with quantified uncertainty estimates. The product will be validated against field flux observations taken by the eddy covariance method in the Lower Mississippi River Basin (LMRB) and California, two of the largest rice-producing states; the measured observations in those regions cover enough different agronomic and soil types to adequately extend into the Gulf Coast region.
The project overcomes three main factors inhibiting success in current rice-related CH4 inventories: (1) lack of gold-standard, temporally-continuous benchmark data for CH4 emissions, including measurements in the U.S.’s biggest rice production area (LMRB), (2) lack of inundation dynamics in the methane modeling and quantification, and (3) lack of observational constraints from plant growth and gross primary productivity (GPP) in CH4 quantification. In this project, we will overcome these challenges by assembling a database of 48 site-years of CH4 emissions over rice-cultivated fields to benchmark a new modeled product. An appropriate, tested, process-based model for rice-CH4 flux prediction will be used to generate daily flux estimates at the 500 m scale, for all the rice growing regions in the US. This product will be driven by a new daily, gap-free, cloud-free satellite-based map of both inundation and GPP.
The project will be arranged in three work packages delivering (1) new satellite products of leaf area index, GPP, and inundation at the daily scale, (2) methane emissions estimates using satellite observations and ground-truth benchmark eddy covariance flux observations to constrain process-based modeling, and (3) validation, verification, and uncertainty quantification. This last package includes uncertainty estimates from both satellite-derived variables and calibrated model parameters analyzed in a Monte Carlo sampling framework, a spatiotemporal trend analysis of rice CH4 dynamics, and a comparison to current benchmark products and their implications for inversion modeling.
Through this project, we aim to answer the following three major scientific questions:
(1) What is the spatial and temporal variability of rice-CH4 across the contiguous U.S. from 2008 to 2022, and is there a trend in CH4 emissions from rice over this 15-year period?
(2) What is the impact of explicit inclusion of inundation condition (derived from satellite) in rice-CH4 modeling and quantification?
(3) What are the benefits of explicitly including satellite constraints of carbon-budget components in rice-CH4 modeling and quantification?
This study follows stakeholder groups’ interests (including non-profit, business, and government) in methane emissions reduction, soil carbon storage, and sustainability for the agricultural landscape. The proposed outcomes meet several objectives of the NASA CMS program, including their advance in the “use of satellite remote sensing as an alternative or supplement to ground-based methods for quantifying net carbon emissions and/or storage.” The project also builds on several existing CMS products and enhances national reported carbon emissions inventories through its improved representation of crop methane emissions. The project products will be accessible to both research and practitioner (land management) communities.
Associated Project(s):
Poster Location ID: 39
Presentation Type: Poster
Session: Poster Session 1
Session Date: Wednesday (9/27) 1:15 PM