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Climate change is expected to alter hourly and daily rainfall regimes and, in turn, the dynamics of ecosystem processes controlling greenhouse gas emissions that affect climate. Here, we investigate the effects of expected twenty-first century changes in hourly and daily rainfall on soil carbon and nitrogen emissions, soil organic matter (SOM) stocks, and leaching using a coupled mechanistic carbon and nitrogen soil biogeochemical model (BAMS2). The model represents various abiotic and biotic processes involving 11 SOM pools. These processes include fungal depolymerization, heterotrophic bacterial mineralization, nitrification, denitrification, microbial mortality, necromass decomposition, microbial response to water stress, protection, aqueous advection and diffusion, aqueous complexation, and gaseous dissolution. Multi-decadal modeling with varying rainfall patterns was conducted on nine Australian grasslands in tropical, temperate, and semi-arid regions. Our results show that annual CO 2 emissions in the semi-arid grasslands increase by more than 20% with a 20% increase in annual rainfall (with no changes in the rainfall timing), but the tropical grasslands have opposite trends. A 20% increase in annual rainfall also increases annual N 2O and NO emissions in the semi-arid grasslands by more than 10% but decreases emissions by at least 25% in the temperate grasslands. When subjected to low frequency and high magnitude daily rainfall events with unchanged annual totals, the semi-arid grasslands are the most sensitive, but changes in annual CO 2 emissions and SOM stocks are less than 5 %. Intensification of hourly rainfall did not significantly alter CO 2 emissions and SOM stocks but changed annual NH 3 emissions in the tropical grasslands by more than 300%.

Microbial decomposition of Soil Organic Matter (SOM) is largely controlled by environmental and edaphic factors such as temperature, pH, and moisture. However, microbial metabolism is controlled by catabolite repression, which leads microbes to grow on preferred nutrient and energy sources first. In particular, Catabolite Repression for Carbon (CR-C) defines the hierarchical preference of bacteria for particular C sources. This control depends on the presence of signal molecules conferring bacteria a memory for recent growth conditions on less preferred C sources. The combined effect of catabolite repression and microbial memory (called here Memory-Associated Catabolite Repression for Carbon, MACR-C) has not yet been investigated in detail. First, we use observations and a numerical model to test the hypothesis that MACR-C explains substrate preferential consumption in a simple, 2-C substrate system, whereas Michaelis-Menten-Monod kinetics of competitive substrate consumption, non-competitive inhibition, or their combination, do not. Next, we carry out numerical analyses to explore the sensitivity of (1) estimated parameters to experimental observations and (2) model structure to steady-state substrate concentration under pulse or continuous substrate application. Our results show that MACR-C substantially affected substrate consumption and microbial readiness to switch between C sources.

Accurate prediction of the temperature response of the velocity v of a biochemical reaction has wide applications in cell biology, reaction design, and biomass yield enhancement. Here, we introduce a simple but comprehensive mechanistic approach that uses thermodynamics and biochemical kinetics to describe and link the reaction rate and Michaelis–Menten constants (kT and K T) with the biomass yield and mortality rate (YT and δT) as explicit functions of T. The temperature control is exerted by catabolic enthalpy at low temperatures and catabolic entropy at high temperatures, whereas changes in cell and enzyme–substrate heat capacity shift the anabolic electron use efficiency eA and the maximum reaction velocity vmax. We show that cells have optimal growth when the catabolic (differential) free energy of activation decreases the cell free energy harvest required to duplicate their internal structures as long as electrons for anabolism are available. With the described approach, we accurately predicted observed glucose fermentation and ammonium nitrification dynamics across a wide temperature range with a minimal number of thermodynamics parameters, and we highlight how kinetic parameters are linked to each other using first principles.

A framework that introduces the concept of near-activation complexation and differential free energy of activation is presented to extend the capabilities of the classical transition-state theory in enzymatic reactions. In our approach, reaching a near-equilibrium energy level is assumed to be necessary for complexation near the activation point, whereas an additional differential energy level is required for the near-equilibrium complex to activate and release reaction products. Integration of these energy levels within the transition-state theory explains the thermodynamic nature of the Michaelis–Menten (affinity) constant and its relationship with the rate constant under the quasi-steady-state assumption. The concepts of near-activation complexation and differential free energy of activation were tested on 57 independent experiments of NH+4 and NO-3 uptake by various microalgae and bacteria at temperatures ranging between 1 and 45°C. Results showed that near-activation complexation was always favored, whereas the differential energy of activation led to an apparent energy barrier consistent with earlier observations. Temperature affected all energy levels within this framework but did not alter substantially their thermodynamic features. The approach (1) mutually links the thermodynamics and kinetics of Michaelis-Menten and rate constants with a mathematical expression; (2) describes the likelihood of formation of sub-, super-, and activated complexes; and (3) shows direction and thermodynamic likelihood of each reaction branch within the transition state.

The role of organo-mineral interactions, microbial dynamics, and vertical plant input profiles are hypothesized to be important in controlling soil organic matter (SOM) stocks and dynamics. To test this hypothesis, we enhanced and applied a model (Biotic and Abiotic Model of SOM – BAMS1) that represents microbial dynamics and organo-mineral interactions integrated with a multiphase reactive transport solver for variably saturated porous media. The model represents aqueous chemistry, aqueous advection and diffusion, gaseous diffusion, sorption processes, bacterial and fungal activity, and archetypal monomer- and polymer-carbon substrate groups, including dead cell wall material. This model structure is fundamentally different, and produces different SOM dynamics, than the pseudo-first order relationships that are used in most site- and global-scale terrestrial SOM models. We simulated two grasslands, a seven-site chronosequence (∼3.9–240 Ky) in Northern California, and a Russian Chernozem site where soils were sampled 100 years apart. We calibrated the model's vertically-resolved soil bulk specific surface area (SBSSA) using observed bulk SOM content, and then tested the model against observed Δ14C profiles. The modeled microbial processes, organo-mineral interactions, and vertical aqueous transport produced realistic vertically-resolved predictions of bulk SOM content, Δ14C values of SOM, lignin content, and fungi-to-aerobic bacteria biomass ratios. Using sensitivity analyses, we found that vertical carbon input profiles were important controls over the Δ14C depth distribution. Shallower carbon input profiles lead to older carbon at depth. In addition, the SBSSA was the dominant control over the magnitude and vertical distribution of SOM stocks. The findings of this study demonstrate the value of explicitly incorporating microbial activity, sorption, and vertical transport into land models to predict SOM dynamics.