Redefinition and global estimation of basal ecosystem respiration rate

Yuan, Wenping; Luo, Yiqi; Li, Xianglan; Liu, Shuguang; Yu, Guirui; Zhou, Tao; Bahn, Michael; Black, Andy; Desai, Ankur R.; Cescatti, Alessandro; Marcolla, Barbara; Jacobs, C.M.J.; Chen, Jiquan; Aurela, Mika; Bernhofer, Christian; Gielen, Bert; Bohrer, Gil; Cook, David; Dragoni, D.; Dunn, Allison L.; Gianelle, Damiano; Grünwald, Thomas; Ibrom, Andreas; Leclerc, Monique Y.; Lindroth, Anders; Li, Heping; Marchesini, Luca Belelli; Montagnani, Leonardo; Pita, Gabriel; Rodeghiero, Mirco; Rodrigues, Abel; Starr, Gregory; Stoy, Paul C.

doi:10.1029/2011gb004150

Cited by 46 publications

(54 citation statements)

References 123 publications

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“…6b). Previous studies also indicated that reference respiration depends strongly on mean annual GPP (e.g., Yuan et al, 2011), and can be estimated by LAI max (e.g., Migliavacca et al, 2011) or mean annual springtime EVI (e.g., Jägermeyr et al, 2014).…”

Section: Characteristics Of the Model Parametersmentioning

confidence: 99%

A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau

Gao

et al. 2015

Ecological Modelling

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a b s t r a c tEcosystem respiration (R e ) is rarely quantified from remote sensing data because satellite technique is incapable of observing the key processes associated with soil respiration. In this study, we develop a Remote Sensing Model for R e (ReRSM) by assuming that one part of R e is derived from current photosynthate with the respiratory rate coupling closely with gross primary production (GPP), and the other part of R e is derived from reserved ecosystem organic matter (including plant biomass, plant residues and soil organic matter) with the respiratory rate responding strongly to temperature change. The ReRSM is solely driven by the Enhanced Vegetation Index (EVI), the Land Surface Water Index (LSWI) and the Land Surface Temperature (LST) from MODIS data. Multi-year eddy CO 2 flux data of five vegetation types in Northern China and the Tibetan Plateau (including temperate mixed forest, temperate steppe, alpine shrubland, alpine marsh and alpine meadow-steppe) were used for model parameterization and validation. In most cases, the simulated R e agreed well with the observed R e in terms of seasonal and interannual variation irrespective of vegetation types. The ReRSM could explain approximately 93% of the variation in the observed R e across five vegetation types, with the root mean square error (RMSE) of 0.04 mol C m −2 d −1 and the modeling efficiency (EF) of 0.93. Model comparison showed that the performance of the ReRSM was comparable with that of the RECO in the studied five vegetation types, while the former had much fewer parameters than the latter. The ReRSM parameters showed good linear relationships with the mean annual satellite indices. With these linear functions, the ReRSM could explain approximately 90% of the variation in the observed R e across five vegetation types, with the RMSE of 0.05 mol C m −2 d −1 and the EF of 0.89. These analyses indicated that the ReRSM is a simple and alternative approach in R e estimation and has the potential of estimating spatial R e . However, the performance of ReRSM in other vegetation types or regions still needs a further study.

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Section: Characteristics Of the Model Parametersmentioning

confidence: 99%

A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau

Gao

et al. 2015

Ecological Modelling

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“…For the spatial variability of RE (Reref ), even though it has been gradually confirmed to vary systematically with the spatial variation of productivity (GPP or LAI) and temperature [8,[20][21][22]27,50], to our knowledge, this process has not yet been linked to moisture change in satellite-based RE studies at large scales and with a high resolution. Our findings reveal that Reref has a significant linear relation with LSWI mean in both the Tibetan and Inner Mongolian grasslands (Figure 3, p < 0.01), which is also supported by another recent study in northern China [24].…”

Section: Model Evaluationmentioning

confidence: 99%

“…It is used to describe the difference in basal respiration between sites and years, thereby reflecting the spatial variability of RE [8]. Numerous studies have indicated that a spatially heterogeneous Reref results in a better estimation of RE at different sites [20][21][22]27].…”

Section: Representation Of Spatial Variability Of Rementioning

confidence: 99%

“…Ecosystem respiration (RE) is an important outflow component of ecosystem carbon cycle and represents the second largest carbon exchanges between terrestrial ecosystems and the atmosphere [8]. However, as the result of the complex interactions among physical, chemical, and biological processes, RE varies greatly at different temporal and spatial scales [9].…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, the basal respiration rate, an important parameter in RE Remote Sens. 2018, 10, 149 3 of 20 models to describe the location-specific respiration rate, is usually set as a constant parameter, which introduces considerable error in the simulated spatial variability of RE [8,20,21]. Therefore, it is critical to identify the controlling mechanism involved in the spatial variations of RE with more comprehensive data, not just in the temporal variations, thus serving as a valuable basis to better understand patterns of RE at regional scale and to scale-up from specific sites to vegetation biomes.…”

Section: Introductionmentioning

confidence: 99%

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A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands

Ren

et al. 2018

Remote Sensing

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Abstract:It is important to accurately evaluate ecosystem respiration (RE) in the alpine grasslands of the Tibetan Plateau and the temperate grasslands of the Inner Mongolian Plateau, as it serves as a sensitivity indicator of regional and global carbon cycles. Here, we combined flux measurements taken between 2003 and 2013 from 16 grassland sites across northern China and the corresponding MODIS land surface temperature (LST), enhanced vegetation index (EVI), and land surface water index (LSWI) to build a satellite-based model to estimate RE at a regional scale. First, the dependencies of both spatial and temporal variations of RE on these biotic and climatic factors were examined explicitly. We found that plant productivity and moisture, but not temperature, can best explain the spatial pattern of RE in northern China's grasslands; while temperature plays a major role in regulating the temporal variability of RE in the alpine grasslands, and moisture is equally as important as temperature in the temperate grasslands. However, the moisture effect on RE and the explicit representation of spatial variation process are often lacking in most of the existing satellite-based RE models. On this basis, we developed a model by comprehensively considering moisture, temperature, and productivity effects on both temporal and spatial processes of RE, and then, we evaluated the model performance. Our results showed that the model well explained the observed RE in both the alpine (R 2 = 0.79, RMSE = 0.77 g C m −2 day −1 ) and temperate grasslands (R 2 = 0.75, RMSE = 0.60 g C m −2 day −1 ). The inclusion of the LSWI as the water-limiting factor substantially improved the model performance in arid and semi-arid ecosystems, and the spatialized basal respiration rate as an indicator for spatial variation largely determined the regional pattern of RE. Finally, the model accurately reproduced the seasonal and inter-annual variations and spatial variability of RE, and it avoided overestimating RE in water-limited regions compared to the popular process-based model. These findings provide a better understanding of the biotic and climatic controls over spatiotemporal patterns of RE for two typical grasslands and a new alternative up-scaling method for large-scale RE evaluation in grassland ecosystems.

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The effect of water table fluctuation on soil respiration in a lower coastal plain forested wetland in the southeastern U.S.

et al. 2013

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[1] Anthropogenic and environmental pressures on wetland hydrology may trigger changes in carbon (C) cycling, potentially exposing vast amounts of soil C to rapid decomposition. We measured soil CO 2 efflux (R s ) continuously from 2009 to 2010 in a lower coastal plain forested wetland in North Carolina, U.S., to characterize its main environmental drivers. To understand and quantify the spatial variation due to microtopography and associated differences in hydrology, measurements were conducted at three microsites along a microtopographic gradient. The seasonal hysteresis in R s differed by microtopographic location and was caused by the transitions between flooded and nonflooded conditions. Because flooded R s was small, we reported R s dynamics mainly during nonflooded periods. A nested model, modified from conventional Q 10 (temperature sensitivity) model with dynamic parameters, provided a significantly better simulation on the observed variation of R s . The model performed better with daily data, indicating that soil temperature (T s ) and water table depth (WTD) were the primary drivers for seasonal variation. The diel variation of R s was high and independent of T s and WTD, which both had small diel variations, suggesting the likely association with plant activity. Overall, the site-average soil CO 2 efflux was approximately 960-1103 g C m À2 yr À1 in 2010, of which 93% was released during nonflooded periods. Our study indicates that R s is highly linked to hydroperiod and microtopography in forested wetlands and droughts in wetlands will accelerate soil C loss.

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Redefinition and global estimation of basal ecosystem respiration rate

Cited by 46 publications

References 123 publications

A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau

A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau

A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands

The effect of water table fluctuation on soil respiration in a lower coastal plain forested wetland in the southeastern U.S.

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