Hot-Moments of Soil CO2 Efflux in a Water-Limited Grassland

Vargas, Rodrigo; P., Enrique Sánchez-Cañete; Serrano-Ortiz, Penélope; Yuste, Jorge Curiel; Domingo, Francisco; López‐Ballesteros, Ana; Oyonarte, Cecilio

doi:10.3390/soilsystems2030047

Cited by 51 publications

(41 citation statements)

References 67 publications

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“…The relatively small MAE of our model predictions may suggest generally well constrained Rs predictions at most pixels, while the relatively large RMSE may reflect the large prediction underestimations of extremely high observed Rs values (Figure a). Similar underestimation by machine learning approaches has been reported in models attempting to include hot moments of Rs in water limited ecosystems (Vargas et al, ) and for CO 2 and CH 4 fluxes in temperate forests (Warner et al, ).…”

Section: Discussionsupporting

confidence: 67%

Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale

Warner

Bond‐Lamberty

Jian

et al. 2019

Global Biogeochemical Cycles

108

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Soil respiration (Rs), the soil‐to‐atmosphere CO2 flux produced by microbes and plant roots, is a critical but uncertain component of the global carbon cycle. Our current understanding of the variability and dynamics is limited by the coarse spatial resolution of existing estimates. We predicted annual Rs and associated uncertainty across the world at 1‐km resolution using a quantile regression forest algorithm trained with observations from the global Soil Respiration Database spanning from 1961 to 2011. This model yielded a global annual Rs estimate of 87.9 Pg C/year with an associated global uncertainty of 18.6 (mean absolute error) and 40.4 (root mean square error) Pg C/year. The estimated annual heterotrophic respiration (Rh), derived from empirical relationships with Rs, was 49.7 Pg C/year over the same period. Predicted Rs rates and associated uncertainty varied widely across vegetation types, with the greatest predicted rates of Rs in evergreen broadleaf forests (accounting for 20.9% of global Rs). The greatest prediction uncertainties were in northern latitudes and arid to semiarid ecosystems, suggesting that these areas should be targeted in future measurement campaigns. This study provides predictions of Rs (and associated prediction uncertainty) at unprecedentedly high spatial resolution across the globe that could help constrain local‐to‐global process‐based models. Furthermore, it provides insights into the large variability of Rs and Rh across vegetation classes and identifies regions and vegetation types with poor model performance that should be prioritized for future data collection.

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Section: Discussionsupporting

confidence: 67%

Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale

Warner

Bond‐Lamberty

Jian

et al. 2019

Global Biogeochemical Cycles

108

View full text Add to dashboard Cite

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“…While supplementing a temperature-based model with either θ or GEP terms increased the amount of explained variation in F soil to a similar degree, the combination of T s , θ, and GEP was required to maximize model performance and predict seasonality in F soil . Models that accounted for θ captured the pulsed increase in metabolic activity at the monsoon onset characteristic of semiarid ecosystems [12,21,63], whereas GEP terms improved the prediction of F soil magnitude and seasonality by reflecting the stimulating effect of photosynthesis on basal F soil ([39]; Figure 6). We found that F soil increased rapidly in response to increased θ at the beginning of the monsoon, whereas GEP increased more gradually (Figure 2), which reflect differences in the timing of ecosystem responses to rainfall pulses [56].…”

Section: Moisture and Photosynthesis Terms Improve Modeled Carbon-watmentioning

confidence: 99%

Environmental and Vegetative Controls on Soil CO2 Efflux in Three Semiarid Ecosystems

et al. 2019

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Soil CO2 efflux (Fsoil) is a major component of the ecosystem carbon balance. Globally expansive semiarid ecosystems have been shown to influence the trend and interannual variability of the terrestrial carbon sink. Modeling Fsoil in water-limited ecosystems remains relatively difficult due to high spatial and temporal variability associated with dynamics in moisture availability and biological activity. Measurements of the processes underlying variability in Fsoil can help evaluate Fsoil models for water-limited ecosystems. Here we combine automated soil chamber and flux tower data with models to investigate how soil temperature (Ts), soil moisture (θ), and gross ecosystem photosynthesis (GEP) control Fsoil in semiarid ecosystems with similar climates and different vegetation types. Across grassland, shrubland, and savanna sites, θ regulated the relationship between Fsoil and Ts, and GEP influenced Fsoil magnitude. Thus, the combination of Ts, θ, and GEP controlled rates and patterns of Fsoil. In a root exclusion experiment at the grassland, we found that growing season autotrophic respiration accounted for 45% of Fsoil. Our modeling results indicate that a combination of Ts, θ, and GEP terms is required to model spatial and temporal dynamics in Fsoil, particularly in deeper-rooted shrublands and savannas where coupling between GEP and shallow θ is weaker than in grasslands. Together, these results highlight that including θ and GEP in Fsoil models can help reduce uncertainty in semiarid ecosystem carbon dynamics.

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“…A final aspect of temporal variability revolves around "hot spots" and "hot moments" in space and time (Leon et al, 2014). These localized and/or short bursts of CO 2 and other greenhouse gases (Kim et al, 2012) affect sampling priorities (Bond-Lamberty et al, 2016) but also provide insight into the degree to which these fluxes are driven by soil microbes or via plant roots (Jarvis et al, 2007;Smith et al, 2017;Vargas et al, 2018). Such dynamics have implications for how microbial processes might be appropriately represented in microbially explicit soil biogeochemical models (Bradford et al, 2019), as microbial activity depends on both the "hospitability" of the environment and the microbial community composition (Wieder et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Soil Respiration Variability and Correlation Across a Wide Range of Temporal Scales

et al. 2019

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The high temporal variability of the soil-to-atmosphere CO 2 flux (soil respiration, R S ) has been studied at hourly to multiannual time scales but remains less well understood than R S spatial variability. How R S fluxes vary and are autocorrelated at various time lags has practical implications for sampling and more fundamentally for our understanding of its abiotic and biotic underlying mechanisms. We examined the variability, correlation, and sampling requirements of R S over a wide range of temporal scales in a temperate deciduous forest in eastern Maryland, USA, using both automated (temporally continuous, N = 30,036 over 10 months) and survey (spatially diverse, temporally sparse, N = 1,912 over 17 months) data. Data from a global R S database were also used to examine interannual variability in comparable forests. The coefficient of variability of successive measurements generally varied from the minute (median coefficient of variation 16%) to hourly and daily (11-12%) time scales. Successive R S values measured at a given collar exhibited a strong hour-to-hour correlation (r = 0.931) and a moderate correlation at a 2-hr lag (0.289); day-to-day (i.e., 24 hr lag) hourly observations were uncorrelated. Daily R S means were well correlated at a 1-day lag (r = 0.856) but not at any further time lag. In a linear mixed-effects model predicting R S , soil temperature and moisture exerted consistently strong effects regardless of time scale, and model coefficient of variability was generally high (>80%). These results provide new opportunities to explore the drivers and variability of R S fluxes, quantify sampling requirements, and improve error propagation.Plain Language Summary Soils give off carbon dioxide, generated by microbes and plant roots, to the atmosphere. How this "soil respiration" (R S ) varies in time, as one measures at minute, hourly, daily, or longer time scales, is related to the processes driving it and has implications for how we estimate this flow of carbon across space and time. We measured R S in a coastal deciduous forest in Maryland, USA, and found that the variability of R S -how much it changed between successive measurementsvaried at the different time scales. R S values quickly became increasingly random (i.e., uncorrelated with each other) as one measured repeatedly over time. These results help us understand the factors driving this large flow of carbon to the atmosphere and improve our ability to estimate R S at times and places not directly measured.

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Hot-Moments of Soil CO2 Efflux in a Water-Limited Grassland

Cited by 51 publications

References 67 publications

Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale

Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale

Environmental and Vegetative Controls on Soil CO2 Efflux in Three Semiarid Ecosystems

Soil Respiration Variability and Correlation Across a Wide Range of Temporal Scales

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