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Abstract Background Precipitation plays an important role in crop production and soil greenhouse gas emissions. However, how crop yield and soil nitrous oxide (N 2 O) emission respond to precipitation change, particularly with different background precipitations (dry, normal, and wet years), has not been well investigated. In this study, we examined the impacts of precipitation changes on corn yield and soil N 2 O emission using a long-term (1981–2020, 40 years) climate dataset as well as seven manipulated precipitation treatments with different background precipitations using the DeNitrification-DeComposition (DNDC) model. Results Results showed large variations of corn yield and precipitation but small variation of soil N 2 O emission among 40 years. Both corn yield and soil N 2 O emission showed near linear relationships with precipitation based on the long-term precipitation data, but with different response patters of corn yield and soil N 2 O emission to precipitation manipulations. Corn yield showed a positive linear response to precipitation manipulations in the dry year, but no response to increases in precipitation in the normal year, and a trend of decrease in the wet year. The extreme drought treatments reduced corn yield sharply in both normal and wet years. In contrast, soil N 2 O emission mostly responded linearly to precipitation manipulations. Decreases in precipitation in the dry year reduced more soil N 2 O emission than those in the normal and wet years, while increases in precipitation increased more soil N 2 O emission in the normal and wet years than in the dry year. Conclusions This study revealed different response patterns of corn yield and soil N 2 O emission to precipitation and highlights that mitigation strategy for soil N 2 O emission reduction should consider different background climate conditions. 

We applied a microbial-explicit model – the CLM-Microbe – to investigate the dynamics of C in vegetation, litter, soil, and microbes during 1901-2016. The CLM-Microbe model was able to reproduce global averages and latitudinal trends of gross (GPP) and net (NPP) primary productivity, heterotrophic (HR) and soil (SR) respiration, biomass C in fungi (FBC) and bacteria (BBC) in the top 30 cm and 1 m, dissolved (DOC) and soil organic C (SOC) in the top 30 cm and 1 m. In addition, the CLM-Microbe model captured the grid-level variation in GPP (R2=0.78), NPP (R2=0.63), SR (R2=0.26), HR (R2=0.23), DOC in 0-30 cm (R2=0.2) and 0-1 m (R2=0.22), SOC in 0-30 cm (R2=0.36) and 0-1 m (R2=0.26), FBC (R2=0.22) and BBC (R2=0.32) in 0-30 cm, and MBC in 0-1 m (R2=0.21). From the 1900s to 2007-2016, simulated C variables increased by approximately 30 PgC yr-1 for GPP, 15 PgC yr-1 for NPP, 12 PgC yr-1 for HR, 25 PgC yr-1 for SR, 1.0 PgC for FBC and 0.4 PgC for BBC in 0-30 cm, 1.5 PgC for FBC, 0.8 PgC for BBC, 2.5 PgC for DOC, 40 PgC for SOC, and 5 PgC for litter C in 0-1 m, and 40 PgC for vegetation C. The relative increases in C fluxes and pools varied across the globe. Increases in vegetation C were closely related to warming and increased precipitation, while C accumulation in microbes and soils was jointly governed by vegetation C input and soil temperature and moisture. 

Incorporating microbial processes into soil biogeochemical models has received growing interest. However, determining the parameters that govern microbially driven biogeochemical processes typically requires case‐specific model calibration in various soil and ecosystem types. Here each case refers to an independent and individual experimental unit subjected to repeated measurements. Using the Microbial‐ENzyme Decomposition model, this study aimed to test whether a common set of microbially‐relevant parameters (i.e., generalized parameters) could be obtained across multiple cases based on a two‐year incubation experiment in which soil samples of four distinct soil series (i.e., Coland, Kesswick, Westmoreland, and Etowah) collected from forest and grassland were subjected to cellulose or no cellulose amendment. Results showed that a common set of parameters controlling microbial growth and maintenance as well as extracellular enzyme production and turnover could be generalized at the soil series level but not land cover type. This indicates that microbial model developments need to prioritize soil series type over plant functional types when implemented across various sites. This study also suggests that, in addition to heterotrophic respiration and microbial biomass data, extracellular enzyme data sets are needed to achieve reliable microbial‐relevant parameters for large‐scale soil model projections.

 

Soil nitrous oxide (N 2 O) emissions are an important driver of climate change and are a major mechanism of labile nitrogen (N) loss from terrestrial ecosystems. Evidence increasingly suggests that locations on the landscape that experience biogeochemical fluxes disproportionate to the surrounding matrix (hot spots) and time periods that show disproportionately high fluxes relative to the background (hot moments) strongly influence landscape-scale soil N 2 O emissions. However, substantial uncertainties remain regarding how to measure and model where and when these extreme soil N 2 O fluxes occur. High-frequency datasets of soil N 2 O fluxes are newly possible due to advancements in field-ready instrumentation that uses cavity ring-down spectroscopy (CRDS). Here, we outline the opportunities and challenges that are provided by the deployment of this field-based instrumentation and the collection of high-frequency soil N 2 O flux datasets. While there are substantial challenges associated with automated CRDS systems, there are also opportunities to utilize these near-continuous data to constrain our understanding of dynamics of the terrestrial N cycle across space and time. Finally, we propose future research directions exploring the influence of hot moments of N 2 O emissions on the N cycle, particularly considering the gaps surrounding how global change forces are likely to alter N dynamics in the future. 

Global soil organic carbon (SOC) stocks may decline with a warmer climate. However, model projections of changes in SOC due to climate warming depend on microbially-driven processes that are usually parameterized based on laboratory incubations. To assess how lab-scale incubation datasets inform model projections over decades, we optimized five microbially-relevant parameters in the Microbial-ENzyme Decomposition (MEND) model using 16 short-term glucose (6-day), 16 short-term cellulose (30-day) and 16 long-term cellulose (729-day) incubation datasets with soils from forests and grasslands across contrasting soil types. Our analysis identified consistently higher parameter estimates given the short-term versus long-term datasets. Implementing the short-term and long-term parameters, respectively, resulted in SOC loss (–8.2 ± 5.1% or –3.9 ± 2.8%), and minor SOC gain (1.8 ± 1.0%) in response to 5 °C warming, while only the latter is consistent with a meta-analysis of 149 field warming observations (1.6 ± 4.0%). Comparing multiple subsets of cellulose incubations (i.e., 6, 30, 90, 180, 360, 480 and 729-day) revealed comparable projections to the observed long-term SOC changes under warming only on 480- and 729-day. Integrating multi-year datasets of soil incubations (e.g., > 1.5 years) with microbial models can thus achieve more reasonable parameterization of key microbial processes and subsequently boost the accuracy and confidence of long-term SOC projections.

 

Abstract. Tropical ecosystems contribute significantly to global emissionsof methane (CH4), and landscape topography influences the rate ofCH4 emissions from wet tropical forest soils. However, extreme eventssuch as drought can alter normal topographic patterns of emissions. Here weexplain the dynamics of CH4 emissions during normal and droughtconditions across a catena in the Luquillo Experimental Forest, Puerto Rico.Valley soils served as the major source of CH4 emissions in a normalprecipitation year (2016), but drought recovery in 2015 resulted in dramaticpulses in CH4 emissions from all topographic positions. Geochemicalparameters including (i) dissolved organic carbon (C), acetate, and soil pH and (ii) hydrological parameters like soil moisture and oxygen (O2)concentrations varied across the catena. During the drought, soil moisturedecreased in the slope and ridge, and O2 concentrations increased in thevalley. We simulated the dynamics of CH4 emissions with theMicrobial Model for Methane Dynamics-Dual Arrhenius and Michaelis–Menten (M3D-DAMM), which couples a microbialfunctional group CH4 model with a diffusivity module for solute and gastransport within soil microsites. Contrasting patterns of soil moisture,O2, acetate, and associated changes in soil pH with topographyregulated simulated CH4 emissions, but emissions were also altered byrate-limited diffusion in soil microsites. Changes in simulated availablesubstrate for CH4 production (acetate, CO2, and H2) andoxidation (O2 and CH4) increased the predicted biomass ofmethanotrophs during the drought event and methanogens during droughtrecovery, which in turn affected net emissions of CH4. A variance-basedsensitivity analysis suggested that parameters related to aceticlasticmethanogenesis and methanotrophy were most critical to simulate net CH4emissions. This study enhanced the predictive capability for CH4emissions associated with complex topography and drought in wet tropicalforest soils.