Large‐scale estimation and uncertainty analysis of gross primary production in Tibetan alpine grasslands

He, Honglin; Liu, Min; Xiao, Xiangming; Ren, Xiaoyong; Zhang, Li; Sun, Xiaomin; Yang, Yuanhe; Li, Yingnian; Zhao, Liang; Shi, Peili; Du, Mingyuan; Ma, Yaoming; Ma, Ming; Yu, Zhongbo; Yu, Guirui

doi:10.1002/2013jg002449

Cited by 54 publications

(31 citation statements)

References 68 publications

(106 reference statements)

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“…It has been reported that 94% of globally retrieved EVI values fall within the theoretical MODIS one‐sigma error bar [± (0.02 + 0.02 × value)], indicating that the error in a given index value is 0.02 plus 2% of the index value (Vermote & Kotchenova, ). Similar to other studies (Lin et al ., ; He et al ., ), we thus used 0.02 + 0.02 × value as standard error for EVI data.…”

Section: Methodsmentioning

confidence: 97%

The permafrost carbon inventory on the Tibetan Plateau: a new evaluation using deep sediment cores

Ding

Yang

et al. 2016

Global Change Biology

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The permafrost organic carbon (OC) stock is of global significance because of its large pool size and the potential positive feedback to climate warming. However, due to the lack of systematic field observations and appropriate upscaling methodologies, substantial uncertainties exist in the permafrost OC budget, which limits our understanding of the fate of frozen carbon in a warming world. In particular, the lack of comprehensive estimates of OC stocks across alpine permafrost means that current knowledge on this issue remains incomplete. Here, we evaluated the pool size and spatial variations of permafrost OC stock to 3 m depth on the Tibetan Plateau by combining systematic measurements from a substantial number of pedons (i.e. 342 three-metre-deep cores and 177 50-cm-deep pits) with a machine learning technique (i.e. support vector machine, SVM). We also quantified uncertainties in permafrost carbon budget by conducting Monte Carlo simulations. Our results revealed that the combination of systematic measurements with the SVM model allowed spatially explicit estimates to be made. The OC density (OC amount per unit area, OCD) exhibited a decreasing trend from the south-eastern to the north-western plateau, with the exception that OCD in the swamp meadow was substantially higher than that in surrounding regions. Our results also demonstrated that Tibetan permafrost stored a large amount of OC in the top 3 m, with the median OC pool size being 15.31 Pg C (interquartile range: 13.03-17.77 Pg C). 44% of OC occurred in deep layers (i.e. 100-300 cm), close to the proportion observed across the northern circumpolar permafrost region. The large carbon pool size together with significant permafrost thawing suggests a risk of carbon emissions and positive climate feedback across the Tibetan alpine permafrost region.

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Section: Methodsmentioning

confidence: 97%

The permafrost carbon inventory on the Tibetan Plateau: a new evaluation using deep sediment cores

Ding

Yang

et al. 2016

Global Change Biology

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207

217

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“…The amount of variability of the three NDVI dataset used in the calculation was more important than the specific mathematical operations, which was also illustrated in Lauenroth et al (2006). If the model is very sensitive to an input with very small uncertainty, then the uncertainty contribution of this input might be small, and vice versa (He et al, 2014). For example, the larger uncertainty in regions with lower biomass could be attributed to the greater variability among three NDVI datasets.…”

Section: Uncertainty Partitioning In Grassland Biomass Estimationsmentioning

confidence: 99%

Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches

Jia

Liu

Yang

et al. 2016

Ecological Indicators

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a b s t r a c tAccurate estimation of grassland biomass and its dynamics are crucial not only for the biogeochemical dynamics of terrestrial ecosystems, but also for the sustainable use of grassland resources. However, estimations of grassland biomass on large spatial scale usually suffer from large variability and mostly lack quantitative uncertainty analyses. In this study, the spatial grassland biomass estimation and its uncertainty were assessed based on 265 field measurements and remote sensing data across Northern China during [2001][2002][2003][2004][2005]. Potential sources of uncertainty, including remote sensing data sources (DATsrc), model forms (MODfrm) and model parameters (biomass allocation, BMallo, e.g. root:shoot ratio), were determined and their relative contribution was quantified. The results showed that the annual grassland biomass in Northern China was 1268.37 ± 180.84 Tg (i.e., 532.02 ± 99.71 g/m 2 ) during 2001-2005, increasing from western to eastern area, with a mean relative uncertainty of 19.8%. There were distinguishable differences among the uncertainty contributions of three sources (BMallo > DATsrc > MODfrm), which contributed 52%, 27% and 13%, respectively. This study highlighted the need to concern the uncertainty in grassland biomass estimation, especially for the uncertainty related to BMallo.

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“…Mean annual temperature (MAT, ºC) and mean annual precipitation (MAP, mm) for each study site were derived from the meteorological database produced by CERN (He et al, 2014). At each plot, soil samples were taken at depths of 0-10 cm using a 6 cm diameter auger and were sieved to remove roots and visible organic debris.…”

Section: Environmental Datamentioning

confidence: 99%

Latitudinal variation of leaf morphological traits from species to communities along a forest transect in eastern China

Wang

et al. 2016

J. Geogr. Sci.

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Abstract:Comprehensive information on geographic patterns of leaf morphological traits in Chinese forests is still scarce. To explore the spatial patterns of leaf traits, we investigated leaf area (LA), leaf thickness (LT), specific leaf area (SLA), and leaf dry matter content (LDMC) across 847 species from nine typical forests along the North-South Transect of Eastern China (NSTEC) between July and August 2013, and also calculated the community weighted means (CWM) of leaf traits by determining the relative dominance of each species. Our results showed that, for all species, the means (± SE) of LA, LT, SLA, and LDMC were 2860.01 ± 135.37 mm 2 , 0.17 ± 0.003 mm, 20.15 ± 0.43 m 2 kg -1 , and 316.73 ± 3.81 mg g -1 , respectively. Furthermore, latitudinal variation in leaf traits differed at the species and community levels. Generally, at the species level, SLA increased and LDMC decreased as latitude increased, whereas no clear latitudinal trends among LA or LT were found, which could be the result of shifts in plant functional types. When scaling up to the community level, more significant spatial patterns of leaf traits were observed (R 2 = 0.46-0.71), driven by climate and soil N content. These results provided synthetic data compilation and analyses to better parameterize complex ecological models in the future, and emphasized the importance of scaling-up when studying the biogeographic patterns of plant traits.

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Large‐scale estimation and uncertainty analysis of gross primary production in Tibetan alpine grasslands

Cited by 54 publications

References 68 publications

The permafrost carbon inventory on the Tibetan Plateau: a new evaluation using deep sediment cores

The permafrost carbon inventory on the Tibetan Plateau: a new evaluation using deep sediment cores

Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches

Latitudinal variation of leaf morphological traits from species to communities along a forest transect in eastern China

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