Responses of net primary productivity to phenological dynamics in the Tibetan Plateau, China

Wang, Siyuan; Zhang, Bing; Yang, Qichun; Chen, Guangsheng; Yang, Bojuan; Lu, Linlin; Ming, Shen; Peng, Yaoyao

doi:10.1016/j.agrformet.2016.08.020

Cited by 135 publications

(73 citation statements)

References 65 publications

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“…The spatially heterogeneous temporal trends in temperature and precipitation appeared on the Tibetan Plateau both over the past decades (Figure ) (Kuang, Liu, Dong, Chi, & Zhang, ; Yang et al., ), which might induce complex responses in vegetation dynamics (Cong et al., ; Zhang, Yang, et al., ). Based on long‐term records of vegetation indices from satellite constellations, several studies show that warming would advance the green‐up date, delay the withering period, and lengthen the growing season on the Tibetan Plateau (Ding et al., ), which is also related to increase in NPP of alpine grassland (Wang et al., ). However, the response of alpine grassland to climate warming varies with different moisture gradients and grassland types.…”

Section: Attribution Resultsmentioning

confidence: 99%

Current challenges in distinguishing climatic and anthropogenic contributions to alpine grassland variation on the Tibetan Plateau

Zhang

Liu

et al. 2018

Ecology and Evolution

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Quantifying the impact of climate change and human activities on grassland dynamics is an essential step for developing sustainable grassland ecosystem management strategies. However, the direction and magnitude of climate change and human activities in driving alpine grassland dynamic over the Tibetan Plateau remain under debates. Here, we systematically reviewed the relevant studies on the methods, main conclusions, and causes for the inconsistency in distinguishing the respective contribution of climatic and anthropogenic forces to alpine grassland dynamic. Both manipulative experiments and traditional statistical analysis show that climate warming increase biomass in alpine meadows and decrease in alpine steppes, while both alpine steppes and meadows benefit from an increase in precipitation or soil moisture. Overgrazing is a major factor for the degradation of alpine grassland in local areas with high level of human activity intensity. However, across the entire Tibetan Plateau and its subregions, four views characterize the remaining controversies: alpine grassland changes are primarily due to (1) climatic force, (2) nonclimatic force, (3) combination of anthropogenic and climatic force, or (4) alternation of anthropogenic and climatic force. Furthermore, these views also show spatial inconsistencies. Differences on the source and quality of remote sensing products, the structure and parameter of models, and overlooking the spatiotemporal heterogeneity of human activity intensity contribute to current disagreements. In this review, we highlight the necessity for taking the spatiotemporal heterogeneity of human activity intensity into account in the models of attribution assessment, and the importance for accurate validation of climatic and anthropogenic contribution to alpine grassland variation at multiple scales for future studies.

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Section: Attribution Resultsmentioning

confidence: 99%

Current challenges in distinguishing climatic and anthropogenic contributions to alpine grassland variation on the Tibetan Plateau

Zhang

Liu

et al. 2018

Ecology and Evolution

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“…When westerlies meet the TP, the anticyclonically curved flow to the north side forms the dynamic high pressure ridge, whereas the other branch flow to the south cyclonically shapes the dynamic low pressure trough, which is named as the southern branch trough or the India-Burma trough [1]. Qin et al [2] indicated that the India-Burma trough, also known as the subtropical westerly trough, is closely related to the upstream teleconnection and the Arabian Sea trough.…”

Section: Introductionmentioning

confidence: 99%

A New Dynamical Index for India-Burma Trough

Liu

Zha

Yang

et al. 2018

Advances in Meteorology

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Based on the vertical velocity field of reanalysis datasets, this study defines a new dynamical index for the India-Burma trough and supports this index's advantages by analyzing reanalysis and observational datasets. For a convenient understanding, the vertical velocities of 5 levels ranging from 700 hPa to 500 hPa within the area of 15.625 ∘ N-24.375 ∘ N and 90.625 ∘ E-100.625 ∘ E are multiplied by −1 and summed up into a time series involving each year from 1979 to 2012. The standardized value of the time series is defined as the index of India-Burma trough (IIBT). IIBT can reflect the characteristics of the annual strength and the interdecadal variation of the India-Burma trough. IIBT can also well reveal the relationship between the India-Burma trough and its upstream teleconnection. What is more, through a correlation analysis on the grid point precipitation field, respectively, with the IIBT and the India-Burma trough indices defined with vorticity and geopotential height, over southern Asia the correlation pattern between the IIBT and the precipitation field is found to nearly be the sum of the correlation patterns of the latter 2 indices with the precipitation. To the south of the TP, the correlation field between the IIBT and the grid point precipitation shows dipolar distribution, which is consistent with the correlation patterns of the IIBT with the vertical velocity, specific humidity, and the mid-level geopotential height in the same spatial location. IIBT is beneficial for more accurate study of the impact of the India-Burma trough on the associated weather and climate.

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“…The LWC was retrieved from the normalized difference spectral indices (NDSI 1370 ), 54 difference spectral indices (DSI 1100 and DSI 1940 ), 54 and modified normalized difference water index (NDWI * ), which is obtained by multiplying the enhanced vegetation index (EVI) and normalized difference water index (NDWI 2500 ). [55][56][57] Specific definitions of the vegetation indices and evaluation indices used are as follows:…”

Section: Evaluation Criteriamentioning

confidence: 99%

Denoising vegetation spectra by combining mathematical-morphology and wavelet-transform-based filters

Zhang

et al. 2019

J. Appl. Rem. Sens.

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Denoising vegetation spectra by combining mathematical-morphology and wavelet-transform-based filters,"Abstract. Spectral noise causes distorted spectra, shifting the central wavelength and thus reducing the accuracy of surface parameter retrieval. A hybrid method combining mathematical-morphology and wavelet-transform (WB)-based filters was used to remove spectral noise. First, a generalized mathematical-morphology (GM) filter was used to remove large-amplitude noise, and then the processed spectra were smoothed using the WT-based filter to remove smallamplitude noise. The simulated noise spectrum and 76 measured canopy spectra for winter wheat were denoised with three filters: the combination filter (CF), GM, and WT. In the simulated experiments, five evaluation indices were calculated to evaluate the denoising effects. For measured spectra, qualitative analyses were performed based on spectral characteristics. Quantitative evaluations were conducted by deriving various vegetation indices from denoised spectra to retrieve wheat's biophysical and biochemical parameters. The results indicated that the CF removed both large-and small-amplitude noise efficiently, improving signal-to-noise ratio and peak signal-to-noise ratio of simulated noise spectrum and retrieval accuracy of leaf water content (LWC) significantly. Meanwhile, it better maintained the waveform and smoothness of spectrum, improving the retrieval accuracies of leaf area index and chlorophyll data slightly. The coefficient of determination (R 2 ) of developed model between the modified normalized difference water index and LWC was improved from 0.428 to 0.622 using the CF, 0.555 using the GM, and 0.549 using the WT. The R 2 and root mean square error between the measured and retrieval LWC were improved from 0.364 and 0.027 to 0.611 and 0.018 using the CF, whereas the corresponding values were 0.504 and 0.022 for the GM, and 0.478 and 0.023 for the WT. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Responses of net primary productivity to phenological dynamics in the Tibetan Plateau, China

Cited by 135 publications

References 65 publications

Current challenges in distinguishing climatic and anthropogenic contributions to alpine grassland variation on the Tibetan Plateau

Current challenges in distinguishing climatic and anthropogenic contributions to alpine grassland variation on the Tibetan Plateau

A New Dynamical Index for India-Burma Trough

Denoising vegetation spectra by combining mathematical-morphology and wavelet-transform-based filters

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