Dietze, Michael: Boston University (Project Lead)
Andrews, Arlyn: NOAA Earth System Research Laboratory (Co-Investigator)
Serbin, Shawn: NASA Goddard Space Flight Center (Co-Investigator)
Kennedy, Robert: Oregon State University (Collaborator)
Zarada, Katie: Boston University (Participant)
Morrison, Bailey: University of California, Merced (Post-Doc)
Project Funding:
2017 - 2020
NRA: 2016 NASA: Carbon Monitoring System
Funded by NASA
Abstract:
NASA in general, and CMS in particular, have devoted considerable resources to developing remote sensing data products aimed at quantifying and understanding the terrestrial carbon (C) cycle. Similar efforts have been taken throughout the research
community, generating bottom-up estimates based on inventory data, eddy covariance, process-based models, etc. While these efforts collectively span a wide range of observations (optical, lidar, radar, field-measurements) and response variables (cover, pools, fluxes, disturbances), each data product typically only leverages one or two data sources. However, what is fundamentally needed to improve monitoring, reporting and verification (MRV) isn’t numerous alternative C estimates but a synthetic view of the whole. Furthermore, any approach to synthesis needs to be flexible and extensible, so that it can deal with different data sources with different spatial and temporal resolutions, extents, and uncertainties, as well as new sensors and products as they are brought online. Finally, it needs to inform top-down atmospheric inversions, which currently cannot ingest these bottom-up C estimates an a constraint.
We propose to develop a prototype synthesis, focused initially on the continental US (CONUS), by employing a formal Bayesian model-data assimilation between process- based ecosystem models and multiple data sources to estimate key C pools and fluxes. Models are at the center of our novel system, but rather than providing a prognostic forward-simulation they serve as a scaffold in a fundamentally data-driven process by allowing different data sources to be merged together. Essentially, while data on different scales and processes are difficult to merge directly, all of these data can be used to inform the state variables (i.e. pools not parameters) in the models. In addition to a ‘best estimate’ of the terrestrial C cycle, a key outcome of such a synthesis would be a robust and transparent accounting of uncertainties. This approach is also exceedingly extensible to new data products, or to changes in the availability of data in space and time, as assimilation only requires the construction of simple data models (e.g. Likelihoods) that link model states to observations. The proposed bottom-up model-data assimilation will also provide informative prior means and uncertainties for the CarbonTracker-Lagrange (CT-L) inverse modeling framework. This assimilation of a robust, data-driven bottom- up prior will provide, for the first time, a formal synthesis between top-down and bottom- up C estimates.
While new to the CMS team, PIs Dietze and Serbin have extensive experience with remote sensing, field measurements, process-based modeling, and model-data fusion. The proposed work explicitly builds upon their PEcAn model-data informatics system and directly leverages numerous data products CMS has already invested in over the CONUS region. The prototype system will build on existing PEcAn data assimilation case studies focused on inventory data, phenology, and hyperspectral remote sensing. The proposed project leverages three parallel and interlocking lines of research. First, we will extend our existing system to iteratively ingest a range of CMS data products (airborne lidar, GLAS satellite lidar, radar, hyperspatial forest cover, disturbance products, etc.). Second, to address the challenges in assimilating disturbance and land use, we will incorporate the well-established Ecosystem Demography scaling approach into the data assimilation system itself. Third, we will coordinate with Co-PI Andrews' CMS inversion team to prototype informative land priors for use in top-down inversions as a proof-of-concept on top-down/bottom-up integration. Finally, our proposed prototype project has an obvious extension to global-scale bottom-up international MRV and REDD activities as well as a
range of top-down inversions. Overall, this proposal has the potential to strengthen the entire CMS portfolio.
Publications:
Dokoohaki, H., Morrison, B. D., Raiho, A., Serbin, S. P., Dietze, M. A novel model-data fusion approach to terrestrial carbon cycle reanalysis across the contiguous U.S using SIPNET and PEcAn state data assimilation system v. 1.7.2 DOI: 10.5194/gmd-2021-236
Fer, I., Kelly, R., Moorcroft, P. R., Richardson, A. D., Cowdery, E. M., Dietze, M. C. 2018. Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation. Biogeosciences. 15(19), 5801-5830. DOI: 10.5194/bg-15-5801-2018
Hurtt, G. C., Andrews, A., Bowman, K., Brown, M. E., Chatterjee, A., Escobar, V., Fatoyinbo, L., Griffith, P., Guy, M., Healey, S. P., Jacob, D. J., Kennedy, R., Lohrenz, S., McGroddy, M. E., Morales, V., Nehrkorn, T., Ott, L., Saatchi, S., Sepulveda Carlo, E., Serbin, S. P., Tian, H. 2022. The NASA Carbon Monitoring System Phase 2 synthesis: scope, findings, gaps and recommended next steps. Environmental Research Letters. 17(6), 063010. DOI: 10.1088/1748-9326/ac7407
More details may be found in the following project profile(s):
