Climate change and unsustainable land management practices have resulted in extensive soil degradation, including alteration of soil structure (i.e., aggregate and pore size distributions), loss of soil organic carbon, and reduction of water and nutrient holding capacities. Although soil structure, hydrologic processes, and biogeochemical fluxes are tightly linked, their interaction is often unaccounted for in current ecohydrological, hydrological and terrestrial biosphere models. For more holistic predictions of soil hydrological and biogeochemical cycles, models need to incorporate soil structure and macroporosity dynamics, whether in a natural or agricultural ecosystem. Here, we present a theoretical framework that couples soil hydrologic processes and soil microbial activity to soil organic carbon dynamics through the dynamics of soil structure. In particular, we link the Millennial model for soil carbon dynamics, which explicitly models the formation and breakdown of soil aggregates, to a recent parameterization of the soil water retention and hydraulic conductivity curves and to soil carbon substrate and O2 diffusivities to soil microsites based on soil macroporosity. To illustrate the significance of incorporating the dynamics of soil structure, we apply the framework to a case study in which soil and vegetation recover over time from agricultural practices. The new framework enables more holistic predictions of the effects of climate change and land management practices on coupled soil hydrological and biogeochemical cycles.

Rice fields are important contributors to the global methane budget. While rice production must increase to meet the demand from a growing population, there is a need to mitigate methane emissions through the employment of sustainable management strategies. A popular one consists of a short drainage that shortens the period of time spent in anaerobic conditions, but it is still unclear how drainage timing affects its overall effectiveness. Here we introduce a mechanistic model of methane emissions, coupled to the dynamics of redox potential and rice growth, to analyze the effect of a single drainage and the timing of its application on the temporal evolution of methane emissions. In particular, we identify the drainage timing that maximizes the mitigation efficiency of the drainage, defined as the reduction in methane emission relative to the emissions in continuous flooding. We also explore the role of organic amendment and show how it changes the optimal drainage timing. Application of this framework to a set of experiments demonstrates that emissions can be reduced by more than 60% if drainage is applied at optimal timing. The high efficiency and the limited negative effects on rice yields and N0 emissions place this water management among the most sustainable ones.

Seasonally dry tropical forests (SDTFs) are well adapted to seasonal and interannual rainfall variability and have mechanisms to control growth dynamics under water limitation. The forest starts flushing the leaves following the first rainfall events of the wet season, after which it grows fast and remains green until litterfall starts at the end of the wet season. Although ecosystem adaptation is known to buffer seasonal rainfall variability, yearly fluctuations in biomass productivity are still observed. In order to understand the origin of these productivity fluctuations, here we analyze the effects of rainfall variability (seasonal, interannual, and spatial) on the growth dynamics of an SDTF located in the semi-arid region of Brazil. We used a coupled soil water and vegetation model to simulate soil moisture and NDVI dynamics in three sites across a rainfall gradient (from 400 to 800 mm year-1) to compute specific assimilation (kA) and respiration (kR) rates for 19 hydrological years. Based on the time-series of the rainfall and kA, the analysis shows that in all three sites kA tends to increase following dry years and to decrease following wet ones, whereas kR is about ten times smaller and does not vary particularly over time. The observed patterns of kA and kR suggest an interannual mechanism to buffer long-term rainfall variability and mitigate water stress by tuning assimilation rates.

Soil carbon cycling and ecosystem functioning can strongly depend on how microbial communities regulate their metabolism and adapt to changing environmental conditions to improve their fitness. Investing in extracellular enzymes is an important strategy for the acquisition of resources, but the principle behind the trade-offs between enzyme production and growth is not entirely clear. Here we show that the enzyme production rate per unit biomass may be regulated in order to maximize the biomass specific growth rate. Based on this optimality hypothesis, we derive mathematical expressions for the biomass specific enzyme production rate and the microbial carbon use efficiency, and verify them with experimental observations. As a result of this analysis, we also find that the optimal enzyme production rate decays hyperbolically with the soil organic carbon content. We then show that integrating the optimal extracellular enzyme production into microbial models may change considerably soil carbon projections under global warming, underscoring the need to improve parameterization of microbial processes.