Spatial and temporal variations in global soil respiration and their relationships with climate and land cover

Huang, Ni; Wang, Li; Song, Xiao-Peng; Black, T. Andrew; Jassal, Rachhpal S.; Myneni, Ranga B.; Wu, Chaoyang; Wang, Lei; Song, Wanjuan; Ji, Dabin; Yu, Shanshan; Niu, Zheng

doi:10.1126/sciadv.abb8508

Cited by 133 publications

(94 citation statements)

References 64 publications

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“…In this study, we do not consider the effect of land use/cover change (cf. N. Huang et al., 2020), and we use a static land cover map in 2001 as input for the estimation model.…”

Section: Methodsmentioning

confidence: 99%

A Data‐Driven Global Soil Heterotrophic Respiration Dataset and the Drivers of Its Inter‐Annual Variability

Yao

Ciais

Viovy

et al. 2021

Global Biogeochemical Cycles

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Soil heterotrophic respiration (SHR), the CO 2 flux produced by free-living microbial heterotrophs and soil fauna feeding on soil organic matter (Carbone et al., 2016;Hanson et al., 2000), constitutes a key ecosystem-to-atmosphere carbon flux that affects soil carbon storage and carbon-climate feedbacks. Since the magnitude of SHR is roughly four times of global annual anthropogenic fossil fuel emission (Le Quéré et al., 2018) and SHR can regulate the net ecosystem carbon exchange variability in some regions (Liu, Ballantyne, et al., 2018), even small changes in this flux can cause carbon redistribution between soil and atmosphere, and modify the carbon sink. Enhanced microbial dynamics in soil organic matter decomposition have been detected as the dominant factor in an increasing imbalance between higher CO 2 loss rate and CO 2 uptake by plants . Therefore, a detailed understanding of the SHR spatial and temporal dynamics under changing climate conditions is pivotal to improve projections of the carbon-climate feedback (Ballantyne et al., 2017;Bradford et al., 2019).However, unlike other components of the terrestrial carbon cycle like gross primary productivity (GPP) that can be measured through eddy covariance flux tower at plot scale, SHR observations mainly come from small-scale chambers, combined with intrusive methods (trenching, root exclusion, root extraction), or non-intrusive methods of isotope labeling with uncertainty in 14 C measurements (Bond-Lamberty et al., 2004;Hanson et al., 2000) to partition the heterotrophic and autotrophic soil fluxes. Due to the considerable uncertainty underlying these measurements, SHR is the most poorly constrained ecosystem and global carbon flux (Ciais et al., 2020;Konings et al., 2019).

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“…In this study, we do not consider the effect of land use/cover change (cf. N. Huang et al., 2020), and we use a static land cover map in 2001 as input for the estimation model.…”

Section: Methodsmentioning

confidence: 99%

A Data‐Driven Global Soil Heterotrophic Respiration Dataset and the Drivers of Its Inter‐Annual Variability

Yao

Ciais

Viovy

et al. 2021

Global Biogeochemical Cycles

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“…Outcomes such as mesic‐becomes‐wetter and subhumid‐becomes‐drier can continue into the near future (Dore, 2005; Wang et al, 2012). Climate change factors, especially temperature and precipitation, mediate the changes in NPP and decomposition at relevant temporal (Doetterl et al, 2015) and spatial scales (Chen et al, 2013; Huang et al, 2020; Piao, Wang, Wang, et al, 2020). Since precipitation can potentially alter the effects of temperature on C‐fluxes (Reinsch et al, 2017), precipitation‐driven changes might become more important under future warmer conditions (Hursh et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Greening of the earth does not compensate for rising soil heterotrophic respiration under climate change

Naidu

Bagchi

2021

Global Change Biology

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Stability of the soil carbon (C) pool under decadal scale variability in temperature and precipitation is an important source of uncertainty in our understanding of land–atmosphere climate feedbacks. This depends on how two opposing C‐fluxes—influx from net primary production (NPP) and efflux from heterotrophic soil respiration (Rh)—respond to covariation in temperature and precipitation. There is scant evidence to judge whether field experiments which manipulate both temperature and precipitation align with Earth System Models, or not. As a result, even though the world is generally greening, whether the resultant gains in NPP can offset climate change impacts on Rh, where, and by how much, remains uncertain. Here, we use decadal‐scale global time‐series datasets on NPP, Rh, temperature, and precipitation to estimate the two opposing C‐fluxes and address whether one can outpace the other. We implement machine‐learning tools on recent (2001–2019) and near‐future climate scenarios (2020–2040) to assess the response of both C‐fluxes to temperature and precipitation variation. We find that changes in C‐influx may not compensate for C‐efflux, particularly in wetter and warmer conditions. Soil‐C loss can occur in both tropics and at high latitudes since C‐influx from NPP can fall behind C‐efflux from Rh. Precipitation emerges as the key determinant of soil‐C vulnerability in a warmer world, implying that hotspots for soil‐C loss/gain can shift rapidly and highlighting that soil‐C is vulnerable to climate change despite widespread greening of the world. The direction of covariation between change in temperature and precipitation, rather than their magnitude, can help conceptualize highly variable patterns in C‐fluxes to guide soil‐C stewardship.

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“…3 Derived from the FLUXNET2015 dataset. 4 Derived from a study by Huang et al [28]. 5 The "Type of variable" refers to if and when the values of the respective variable change for a given pixel.…”

Section: Discussionmentioning

confidence: 99%

“…To better represent the ecosystem respiration process, the global annual soil respiration (Rs) dataset was obtained from a study by Huang et al [28]. The Rs dataset, with a spatial resolution of 1 km, is constructed based on biome-specific statistical models and satellite remote sensing data.…”

Section: B Global Datasets For Candidate Predictor Variables 1) Remote Sensing Productsmentioning

confidence: 99%

Estimating the Net Ecosystem Exchange at Global FLUXNET Sites Using a Random Forest Model

Huang

Wang

Zhang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Despite considerable progress in scaling carbon fluxes from eddy covariance sites to globe, significant uncertainties still exist when estimating the global net ecosystem exchange (NEE). In this study, the site-level NEE was estimated from FLUXNET, a global network of eddy covariance towers, using a random forest (RF) model based on remote sensing products and precipitation data. The plant function type (PFT) had the highest relative explanatory power in predicting the global site-level NEE. However, within PFTs, water-related variables (i.e., the total precipitation, remotely sensed evapotranspiration, land surface water index, and the difference between daytime and nighttime land surface temperature) and soil respiration (Rs) were strong predictors of NEE variability. Cross-validation analyses revealed the good performance of RF in predicting the spatiotemporal variability of monthly NEE at 168 global FLUXNET sites, with an R 2 of 0.72 and an RMSE of 0.96 g C m -2 day -1 . The performance was also good when predicting across-site (R 2 =0.75) and seasonal patterns (R 2 =0.92) over the 58 sites with available data being longer than 2 years and the 12-month value being present for each year. The RF-estimated NEE showed better relationships with the tower-measured NEE than a global NEE product from FLUXCOM across all PFTs. The difference between the RFestimated NEE and FLUXCOM NEE was likely linked to the different predictor sets, such as those with more water-related variables and Rs. This study indicates the importance of considering the influence of water-related variables and Rs in the estimation of NEE at the global scale.

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Spatial and temporal variations in global soil respiration and their relationships with climate and land cover

Cited by 133 publications

References 64 publications

A Data‐Driven Global Soil Heterotrophic Respiration Dataset and the Drivers of Its Inter‐Annual Variability

A Data‐Driven Global Soil Heterotrophic Respiration Dataset and the Drivers of Its Inter‐Annual Variability

Greening of the earth does not compensate for rising soil heterotrophic respiration under climate change

Estimating the Net Ecosystem Exchange at Global FLUXNET Sites Using a Random Forest Model

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