Hourly and daily rainfall intensification causes opposing effects on C and N emissions, storage, and leaching in dry and wet grasslands
Summary (4 min read)
- Climate change is predicted to increase rainfall temporal variability, with a consensus of a shift towards a higher frequency of droughts and heavier rainfall events (Easterling et al. 2000; Zhang et al. 2013) .
- Studies based on single and multiple cycles of drying-rewetting experiments have arrived at very different conclusions regarding the carbon sources and mechanisms contributing to the observed CO 2 pulses (Schimel 2018) .
- The available inorganic nitrogen produced by increased SOM mineralization after a rainfall pulse may be immobilized into microbial biomass (Dijkstra et al. 2012) , taken up by plants (LLü et al. 2014) , leached (Neilen et al. 2017) , nitrified (Bateman and Baggs 2005; Stark and Firestone 1995) , or lost as nitrogen gases through denitrification (Li et al.
- To this end, the authors aim to quantify the long-term impacts of hourly and daily rainfall variations on carbon and nitrogen emissions, leaching, and storage in grasslands with different seasonal rainfall regimes using a mechanistic model.
BAMS2 reaction network
- To account for the control of nitrogen availability on SOM dynamics, the BAMS1 carbon model described in Riley et al. (2014) was coupled to the nitrogen cycle model developed in Maggi et al. (2008) .
- All microbial functional groups assimilate both carbon and nitrogen for growth, with fungi and bacteria having a C:N ratio of 8 and 5, respectively (Mouginot et al. 2014 ).
- The original stoichiometric parameters of SOM decomposition reactions in BAMS1 (Riley et al. 2014) were recalculated to account for the nitrogen immobilization into microbial biomass (Supplementary Information Table S .1).
- Plants uptake both and (R20-R21) and produce aboveground (R28-R29, leaf and wood litter with C:N ratio of 35, Moretto et al. 2001; Thomas and Asakawa 1993) and belowground (R27, root exudates with C:N ratio of 12, Grayston et al.
- SOM polymers are considered to be non-soluble (in solid phases) organic carbon and do not undergo protection processes.
Biogeochemical and transport solver
- The BAMS2 reaction network (Fig. 1 ) was solved in the general-purpose multi-phase and multi-component bioreactive transport simulator BRTSim-v3.1a (Maggi 2019) .
- Equations used to model the transport of fluids and compounds in aqueous, gaseous, and biological phases are described in detail in Maggi (2019) .
- Chemical (R30-R39) is described using Langmuir kinetics to account for the protective capacity of soil, such that (Atkins and De Paula 2005) , where [X(p)] and [X(aq)] are the concentrations of chemical X in protected (p) and aqueous (aq) phases, respectively; k a and k d are the forward and reverse (un-) rate constants, respectively; and Q max is the maximum soil protective capacity.
- The function f(S L ) in Eq. 5 describes the reduction of microbial activity as a result of changes in water saturation to account for processes not explicitly modeled, such as physiological stress and substrate diffusion within a soil layer; note that chemical transport across soil layers is explicitly modeled as described above.
- Descriptions of mathematical equations, numerical methods, and solution convergence criteria used in BRTSim-v3.1a are detailed in Maggi (2019) .
- The BAMS2 reaction network was applied in nine Australian grasslands in tropical, temperate, and semi-arid regions that have distinct seasonal rainfall regimes.
- Site locations were determined based on the Dynamic Land Cover Dataset (Lymburner et al. 2011 ) and the modified KOppen climate classification of the Bureau of Meteorology, Australia (Stern and Dahni 2013) (Table 1 ).
- In contrast, the wet season in the temperate region starts from May to September with lower annual rainfall but a higher number of wet days than the tropical region.
- Historical daily rainfall and temperature data (from 1979 to 2017) at each site were obtained from the CPC US Unified Precipitation data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA (Xie et al. 2010) , and the Global Historical Climatology Network-Daily dataset (Menne et al. 2012) , respectively.
- Plant water uptake , plant nitrogen uptake (R20-R21), and root exudation (R27) were allocated over the soil depth according to the root distribution.
- Numerical experiments were conducted with three rainfall scenarios.
- The weather generator in Chen et al. ( 2010) was modified to generate rainfall time series with varying statistical properties specific for each scenario, whereas no modification was applied to the evapotranspiration time series.
- The authors discuss the possible implication of this simplification below.
- Change in annual cumulative rainfall amount, also known as Scenario 1.
- Rainfall time series were modified so that the annual cumulative rainfall amount (P cum ) ranged within +/-20% of the historical value, while the annual number of wet days (D wet ) remained constant.
Analyses and benchmarking
- Prior to the numerical experiments, baseline simulations (using historical rainfalls) were initialized with SOM concentrations close to the organic carbon content reported in the SoilGrids database (Hengl T et al. 2017 ) and the microbial biomass close to zero.
- The simulations were run for 2000 years for biochemical reactions in the root zone to reach a steady state and to develop a steady microbial biomass profile.
- The outputs of the 2000-year simulations were then used as initial conditions in the numerical experiments.
- Because BAMS2 includes only microbial heterotrophic respiration, CO 2 emissions in the baseline simulations were compared against heterotrophic soil respiration flux (R H ) of 353 natural and unmanaged grasslands reported in the Soil Respiration Database Version 4.0 (SRDB-V4 Bond-Lamberty and Thomson 2018).
- The lag time between two time series was quantified using cross-correlation analysis (function xcorr in Matlab2017a).
Benchmarking of baseline simulations
- In baseline simulations, the semi-arid grasslands, which received the lowest amount and least frequent rainfall, had the lowest CO 2 emissions and SOM inputs (Fig. 2 ).
- CO 2 emissions in these sites were slightly lower than those in the temperate grasslands.
- Other studies argued that a wetter soil would have higher anaerobicity, and therefore should have higher N 2 O emissions (Skiba and Smith 2000) .
- In BAMS2, is the only source of inorganic nitrogen to the soil, mainly coming from N 2 fixation (R19) and mineralization of N-containing monomers (R9-R11).
- The authors note however that, in wet soils that have low concentrations, the nitrifiers may have adapted to a K M value lower than that applied in BAMS2, which was calibrated against temperate soils (Maggi et al. 2008) .
Controls of soil moisture dynamics on C and N emissions
- In all grasslands, the correlation R(S, P) was relatively weak with slightly higher values observed in the tropical grasslands in the wet season.
- Impacts of annual rainfall amount Contrary to the general expectation that increasing annual rainfall (P cum ) would have a larger impact on drier lands, their simulations suggested that both dry and wet grasslands are very sensitive to changes in P cum , and they have distinctive responses (Fig. 4 ), also known as Scenario 1.
- Together with increased water advection at high P cum , the increased biological activity also led to a substantial increase in DOC and DIC leaching to soils below the root zone (Supplementary Fig. S.7a, b) .
- Nitrification and denitrification rates in the temperate grasslands decreased substantially with increasing P cum , leading to the reduction in N 2 O and NO emissions (Fig. 4b, c ).
- Increased water content also decreased the volatilization of ammonia (Fig. 4d ).
Scenario 2: impacts of daily rainfall amount and frequency
- The authors investigated the response of C and N dynamics to variations in daily rainfall amount and frequency by changing the number of wet days D wet in a year while keeping the total annual rainfall constant; that is, a time-series with a smaller D wet value has fewer but larger rainfall events.
- The balance between increased SOM inputs and decomposition caused a slight increase in SOM stocks (<2%, Fig. 5e ) and a substantial increase in DOC and DIC leaching to below the root zone (Supplementary Fig. S.8a, b) .
- The effects of increased rainfall intensity and reduced frequency on nitrogen emissions in the semi-arid grasslands matched relatively well with the numerical-experiments tested in Gu and Riley (2010) .
- Gu and Riley (2010) also found that, when applied with a low total rainfall amount, high intensity and low frequency rainfall events reduced N 2 O emissions in sandy loams soils, but increased NO emissions.
- Big pulses of water diluted and transported inorganic nitrogen out of the root zones, and hence decreased the nitrification and denitrification rates.
- The BAMS2 model represents the highly complex interplay between many biotic and abiotic mechanisms hypothesized to be important for carbon and nitrogen cycles, including depolymerization, SOM mineralization, microbial mortality, necromass decomposition, N 2 fixation, nitrification, denitrification, protection, advection, and diffusion.
- These mechanisms have different responses to soil water content, and therefore a detailed description of their interactions is pivotal to this study that explicitly aims at assessing the impact of rainfall variability on soil carbon and nitrogen dynamics.
- The authors note however that the determination of model parameter values can be difficult for a model with high complexity, and this can introduce additional uncertainties.
- The authors note that the parameter sensitivity may change after coupling the two models, and therefore a global sensitivity analysis of BAMS2 is needed, and it is the target of their next work.
- Hence, field studies that spanned across time-scales of months may capture only the transient effects.
- The authors present a C-N coupled mechanistic SOM model (BAMS2) to investigate the effects of hourly and daily rainfall variations on soil carbon and nitrogen emissions, stocks, and leaching in grasslands with different seasonal rainfall regimes.
- BAMS2 captured relatively well the Birch effect and the carbon and nitrogen dynamics observed in grasslands, with model outputs falling within the range of field observations compiled in various published databases.
- Dry and wet grasslands responded differently to variations in rainfall patterns and rainfall variability had a different impact on carbon and nitrogen emissions.
- The balance between SOM inputs and decomposition, however, always resulted in increasing SOM stocks with increasing annual rainfall in all grasslands.
- High rainfall amounts can dilute concentrations to below optimal values for nitrification, thus reducing N 2 O and NO emissions in the temperate grasslands.
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Cites background or methods from "Hourly and daily rainfall intensifi..."
...BAMS also represented a reduced number of SOM molecular structures (Riley et al. 2014; Dwivedi et al. 2017; Tang et al. 2019)....
...…1998, 2010; Jenkinson et al. 2008) to more complex models such as the Biotic and Abiotic Model of SOM (BAMS; Riley et al. 2014; Dwivedi et al. 2017; Tang et al. 2019) and the COntinuous representation of SOC in the organic layer and the mineral soil, Microbial Interactions and Sorptive…...
...…Gu et al. 2009), PFLOTRAN (Hammond et al. 2014), CRUNCH (Steefel et al. 2015), ecosys (Grant 2013), BAMS (Riley et al. 2014; Dwivedi et al. 2017a; Tang et al. 2019), and BeTR (Tang et al. 2013; Tang and Riley 2018)—that are available to describe and can represent the interaction of various…...
...Overall, we argue that there is a need to represent SOM compounds using their molecular structures in reactive transport models along with physical protection mechanisms (e.g., MAOM) and different functional groups of microbes (e.g., Riley et al. 2014; Dwivedi et al. 2017; Tang et al. 2019.)...
...2015), ecosys (Grant 2013), BAMS (Riley et al. 2014; Dwivedi et al. 2017a; Tang et al. 2019), and BeTR (Tang et al....
Cites background from "Hourly and daily rainfall intensifi..."
...One of the consistent predictions climate modelers have made over the last decade is that climate change will result in less frequent but more intense rainfall events in many parts of the world (e.g., Trenberth et al., 2003; Sun et al., 2007; Min et al., 2011; Ipcc, 2013; Intergovernmental Panel on Climate Change, 2014; Kendon et al., 2014; Berghuijs et al., 2017; Tang et al., 2019; Hess et al., 2020; Morán-Ordóñez et al., 2020; O’Donnell and Thorne, 2020)....
"Hourly and daily rainfall intensifi..." refers methods in this paper
...evapotranspiration ET0 estimated using the FAO ETO calculator (Allen et al. 1998)....
...Plant actual evapotranspiration (ET) is calculated as ET=kc×ET0 with the plant coefficient kc=0.8 (Allen et al. 2005) and the potential evapotranspiration ET0 estimated using the FAO ETO calculator (Allen et al. 1998)....
"Hourly and daily rainfall intensifi..." refers methods in this paper
...The water flow along a one-dimensional variably saturated soil column is modeled using the Richards equation (Richards 1931) in conjunction with the empirical relative permeability-potential-saturation relationship of the Brooks-Corey model (Brooks and Corey 1964)....
"Hourly and daily rainfall intensifi..." refers background in this paper
...…Soil organic carbon, Carbon cycle, Nitrogen cycle, SOM model, Precipitation Introduction Climate change is predicted to increase rainfall temporal variability, with a consensus of a shift towards a higher frequency of droughts and heavier rainfall events (Easterling et al. 2000; Zhang et al. 2013)....
"Hourly and daily rainfall intensifi..." refers methods in this paper
...Microbial dynamics is described using Monod kinetics (Monod 1949), where δ is the microbial mortality rate constant....
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