Simulating measurable ecosystem carbon and nitrogen dynamics with the mechanistically-defined MEMS 2.0 model

Abstract

Abstract. For decades, predominant soil biogeochemical models have used conceptual soil organic matter (SOM) pools and only simulated them to a shallow depth in soil. Efforts to overcome these limitations have prompted the development of new generation SOM models, including MEMS 1.0, which represents measurable biophysical SOM fractions, over the entire root zone, and embodies recent understanding of the processes that govern SOM dynamics. Here we present the result of continued development of the MEMS model, version 2.0. MEMS 2.0 is a full ecosystem model with modules simulating plant growth with above and below-ground inputs, soil water, and temperature by layer, decomposition of plant inputs and SOM, and mineralization and immobilization of nitrogen (N). The model simulates two commonly measured SOM pools – particulate and mineral-associated organic matter (POM and MAOM), respectively. We present results of calibration and validation of the model with several grassland sites in the U.S. MEMS 2.0 generally captured the soil carbon (C) stocks (R2 of 0.89 and 0.6 for calibration and validation, respectively) and their distributions between POM and MAOM throughout the entire soil profile. The simulated soil N matches measurements but with lower accuracy (R2 of 0.73 and 0.31 for calibration and validation of total N in SOM, respectively) than for soil C. Simulated soil water and temperature were compared with measurements and the accuracy is comparable to the other commonly used models. The seasonal variation in gross primary production (GPP; R2 = 0.83), ecosystem respiration (ER; R2 = 0.89), net ecosystem exchange (NEE; R2 = 0.67), and evapotranspiration (ET; R2 = 0.71) were well captured by the model. We will further develop the model to represent forest and agricultural systems and improve it to incorporate new understanding of SOM decomposition.

In‐N‐Out: A hierarchical framework to understand and predict soil carbon storage and nitrogen recycling

“…A confluence of recent developments is leading us toward a breakthrough in our ability to model and predict soil C and N processes at the ecosystem scale. First, our mechanistic understanding of soil organic matter (SOM) dynamics has been greatly advanced (Basile‐Doelsch et al, 2020) enabling a new generation of SOM models based on measurable pools and fluxes (Zhang et al, 2021 and references therein). However, these advances have been largely C‐centric with less attention focused on understanding the shifts in soil N dynamics across ecosystems in response to climate and interactions with C availability.…”

“…Plant versus microbial activity limitation is a key determinant of soil C storage and N recycling, driving the dynamic coupling/decoupling of C and N cycles, within ranges imposed by the relatively constrained C:N stoichiometry of microbial and faunal biomass, and organic matter pools in soil (Cleveland & Liptzin, 2007). Our framework represents multiple hypotheses that can be empirically tested using quantifiable proxies (Figure 1b) and represented in ecosystem models (e.g., Zhang et al, 2021).…”

“…We also hypothesize (Figure 1a) that subsurface SOM dynamics are inherently C input‐limited and more strongly affected by soil traits (e.g., texture and mineralogy) than climate (Mathieu et al, 2015). Physicochemical and biological properties differ markedly between subsoils and topsoils and, while the exact delineation between topsoil and subsoil can vary across soil types, there is an increasing evidence that models of soil C and N storage and cycling should consider topsoils and subsoils separately (Zhang et al, 2021). Subsoils typically have a lower organic matter content and are characterized by 14 C ages thousands of years greater than the surface, lower C/N ratio, and a higher natural abundance of the heavy C and N isotopes than topsoils (Rumpel & Kogel‐Knabner, 2011).…”

Comment on bg-2020-493

“…The model can also directly use daily NPP as an input driving variable. Estimation of plant respiration uses the method from Yin and Laar (2005).…”

“…The partitioning of aboveground dry matter to leaves, stems, and seeds adopts the method in Zhang et al (2018). Crop phenology is calculated based on heat accumulation and photoperiod (Soltani and Sinclair, 2012;Yin and Laar, 2005). Root distribution is modelled using the simple curve from MEMS 1.0 (Robertson et al, 2019).…”