Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET

• Published in: Geoscientific Model Development Vol. 15; no. 8; pp. 3233 - 3252
• Main Authors: Dokoohaki, Hamze, Morrison, Bailey D, Raiho, Ann, Serbin, Shawn P, Zarada, Katie, Dramko, Luke, Dietze, Michael
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 20-04-2022; Copernicus Publications
• Subjects: Bayesian analysis; Bayesian theory; Biological assimilation; Biomass; Biosphere; Calibration; Carbon; Carbon content; Carbon cycle; Carbon cycle (Biogeochemistry); Constraints; Data assimilation; Data collection; Datasets; Eddy flux; ENVIRONMENTAL SCIENCES; Fluxes; Hardwoods; Heterogeneity; Informatics; Kalman filters; Landsat; Leaf area; Leaf area index; Leaves; Localization; Mathematical models; Modelling; MODIS; Pools; Probability theory; Regional development; Remote sensing; Root-mean-square errors; Sampling methods; Satellite observation; Soil; Soils; Stratified sampling; Terrestrial environments; Towers; Uncertainty; Workflow; United States > US
• ISSN: 1991-9603; 1991-959X; 1991-962X; 1991-9603; 1991-962X
• DOI: 10.5194/gmd-15-3233-2022

The ability to monitor, understand, and predict the dynamics of the terrestrial carbon cycle requires the capacity to robustly and coherently synthesize multiple streams of information that each provide partial information about different pools and fluxes. In this study, we introduce a new terrestrial carbon cycle data assimilation system, built on the PEcAn model–data eco-informatics system, and its application for the development of a proof-of-concept carbon “reanalysis” product that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986–2019. We first calibrated this system against plant trait and flux tower net ecosystem exchange (NEE) using a novel emulated hierarchical Bayesian approach. Next, we extended the Tobit–Wishart ensemble filter (TWEnF) state data assimilation (SDA) framework, a generalization of the common ensemble Kalman filter which accounts for censored data and provides a fully Bayesian estimate of model process error, to a regional-scale system with a calibrated localization. Combined with additional workflows for propagating parameter, initial condition, and driver uncertainty, this represents the most complete and robust uncertainty accounting available for terrestrial carbon models. Our initial reanalysis was run on an irregular grid of ∼ 500 points selected using a stratified sampling method to efficiently capture environmental heterogeneity. Remotely sensed observations of aboveground biomass (Landsat LandTrendr) and leaf area index (LAI) (MODIS MOD15) were sequentially assimilated into the SIPNET model. Reanalysis soil carbon, which was indirectly constrained based on modeled covariances, showed general agreement with SoilGrids, an independent soil carbon data product. Reanalysis NEE, which was constrained based on posterior ensemble weights, also showed good agreement with eddy flux tower NEE and reduced root mean square error (RMSE) compared to the calibrated forecast. Ultimately, PEcAn's new open-source regional data assimilation framework provides a scalable workflow for harmonizing multiple data constraints and providing a uniform synthetic platform for carbon monitoring, reporting, and verification (MRV) as well as accelerating terrestrial carbon cycle research.