SINDBAD: A modular framework for model data integration of carbon-water processes across scales

Terrestrial carbon and water cycles are intricately related across spatial (leaf to global) and temporal (instantaneous to multi-annual) scales through multitude of coupled biogeochemical processes that govern the land water and carbon states and fluxes and their feedback to climate. Yet, there are clear discrepancies in modeling key carbon-water processes that lead to large uncertainties in model simulations that often divert away the observations. In fact, the terrestrial biogeochemical models used to represent vegetation-water-carbon interactions vary in complexity and parameterization that are often underconstrained. The current era of rapid growth of satellite Earth Observation, observational networks, and well as observation-based estimate, therefore, provides unprecedented opportunities to improve the models. Unfortunately, most terrestrial biogeochemical models often contain too rigid model structures, and are too demanding to carry out model-data-fusion experiments that leverage the strengths of observational data constraints.

In this study, we present a newly developed terrestrial/ecosystem model-data-integration (MDI) framework, the SINDBAD, that allows for seamless integration of diverse observational data to constrain terrestrial models of varying complexity. The SINDBAD provides a modular framework to create different combinations of terrestrial processes to realize a terrestrial model structure, which can be driven by observed data of climate and/or land characteristic and optimized against provided observation constraints using different cost metrics and parameter optimization methods. To demonstrate the capabilities, we present three MDI experiments of SINDBAD: setup E1 - a global scale model focused on vegetation's role on water cycle; setup E2 - a regional scale model with physiological coupling of water and carbon cycle focused on role of interannual variability of vegetation fraction; and setup E3 - an ecosystem scale model with a prognostic carbon cycle that is used to evaluate the values of using data for ecosystem carbon states.

In the simplest E1 setup, where vegetation only has structural influence on the water cycle, we find that the spatial information of vegetation using satellite-based vegetation index as model input shows clear improvement in the simulation of monthly runoff, as well as interannual variability of terrestrial water storage in arid regions. In setup E2, use of vegetation fraction data from geostationary satellite to drive a physiologically coupled model of water-carbon relations shows a clear improvement in the simulations of interannual variability of gross primary productivity only when the data includes the year-to-year variability of vegetation fraction. In fact, we find that the using mean seasonal cycle of vegetation fraction is able to reproduce the monthly variation but not the interannual variability. Lastly, in setup E3, which includes fully coupled water-carbon model with prognostic evolution of carbon pools with dynamic allocation scheme (with competition for light and water) reveals that the remote sensing observation of carbon states provides better constraints for the carbon cycle compared to the experiment where only eddy covariance measurements are used. The results also indicate that even coarser remote sensing data have a potential to complement ecosystem scale measurements of water and carbon fluxes to improve the prediction of carbon-water coupling at the ecosystem scale.