The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Anthony S. Kiem - One of the best experts on this subject based on the ideXlab platform.
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how and to what extent does precipitation on multi Temporal Scales and soil moisture at different depths determine carbon flux responses in a water limited grassland ecosystem
Science of The Total Environment, 2018Co-Authors: Qingqing Fang, Anthony S. KiemAbstract:Abstract In water-limited ecosystems, hydrological processes significantly affect the carbon flux. The semi-arid grassland ecosystem is particularly sensitive to variations in precipitation (PRE) and soil moisture content (SMC), but to what extent is not fully understood. In this study, we estimated and analyzed how hydrological variables, especially PRE at multi-Temporal Scales (diurnal, monthly, phenological-related, and seasonal) and SMC at different soil depths (0–20 cm, 20–40 cm, 40–60 cm, 60–80 cm) affect the carbon flux. For these aims, eddy covariance data were combined with a Vegetation Photosynthesis and Respiration Model (VPRM) to simulate the regional gross primary productivity (GPP), ecosystem respiration (Reco), and net ecosystem exchange of CO2 (NEE). Interestingly, carbon flux showed no relationship with diurnal PRE or phenological-related PRE (precipitation in the growing season and non-growing season). However, carbon flux was significantly related to monthly PRE and to seasonal PRE (spring + summer, autumn). The GPP, Reco, and NEE increased in spring and summer but decreased in autumn with increasing precipitation due to the combined effect of salinization in autumn. The GPP, Reco, and NEE were more responsive to SMC at 0–20 cm depth than at deeper depths due to the shorter roots of herbaceous vegetation. The NEE increased with increasing monthly PRE because soil microbes responded more quickly than plants. The NEE significantly decreased with increasing SMC in shallow surface due to a hysteresis effect on water transport. The results of our study highlight the complex processes that determine how and to what extent PRE at multi-Temporal scale and SMC at different depths affect the carbon flux response in a water-limited grassland.
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how and to what extent does precipitation on multi Temporal Scales and soil moisture at different depths determine carbon flux responses in a water limited grassland ecosystem
Science of The Total Environment, 2018Co-Authors: Qingqing Fang, Guoqiang Wang, Anthony S. KiemAbstract:Abstract In water-limited ecosystems, hydrological processes significantly affect the carbon flux. The semi-arid grassland ecosystem is particularly sensitive to variations in precipitation (PRE) and soil moisture content (SMC), but to what extent is not fully understood. In this study, we estimated and analyzed how hydrological variables, especially PRE at multi-Temporal Scales (diurnal, monthly, phenological-related, and seasonal) and SMC at different soil depths (0–20 cm, 20–40 cm, 40–60 cm, 60–80 cm) affect the carbon flux. For these aims, eddy covariance data were combined with a Vegetation Photosynthesis and Respiration Model (VPRM) to simulate the regional gross primary productivity (GPP), ecosystem respiration (Reco), and net ecosystem exchange of CO2 (NEE). Interestingly, carbon flux showed no relationship with diurnal PRE or phenological-related PRE (precipitation in the growing season and non-growing season). However, carbon flux was significantly related to monthly PRE and to seasonal PRE (spring + summer, autumn). The GPP, Reco, and NEE increased in spring and summer but decreased in autumn with increasing precipitation due to the combined effect of salinization in autumn. The GPP, Reco, and NEE were more responsive to SMC at 0–20 cm depth than at deeper depths due to the shorter roots of herbaceous vegetation. The NEE increased with increasing monthly PRE because soil microbes responded more quickly than plants. The NEE significantly decreased with increasing SMC in shallow surface due to a hysteresis effect on water transport. The results of our study highlight the complex processes that determine how and to what extent PRE at multi-Temporal scale and SMC at different depths affect the carbon flux response in a water-limited grassland.
Qingqing Fang - One of the best experts on this subject based on the ideXlab platform.
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how and to what extent does precipitation on multi Temporal Scales and soil moisture at different depths determine carbon flux responses in a water limited grassland ecosystem
Science of The Total Environment, 2018Co-Authors: Qingqing Fang, Anthony S. KiemAbstract:Abstract In water-limited ecosystems, hydrological processes significantly affect the carbon flux. The semi-arid grassland ecosystem is particularly sensitive to variations in precipitation (PRE) and soil moisture content (SMC), but to what extent is not fully understood. In this study, we estimated and analyzed how hydrological variables, especially PRE at multi-Temporal Scales (diurnal, monthly, phenological-related, and seasonal) and SMC at different soil depths (0–20 cm, 20–40 cm, 40–60 cm, 60–80 cm) affect the carbon flux. For these aims, eddy covariance data were combined with a Vegetation Photosynthesis and Respiration Model (VPRM) to simulate the regional gross primary productivity (GPP), ecosystem respiration (Reco), and net ecosystem exchange of CO2 (NEE). Interestingly, carbon flux showed no relationship with diurnal PRE or phenological-related PRE (precipitation in the growing season and non-growing season). However, carbon flux was significantly related to monthly PRE and to seasonal PRE (spring + summer, autumn). The GPP, Reco, and NEE increased in spring and summer but decreased in autumn with increasing precipitation due to the combined effect of salinization in autumn. The GPP, Reco, and NEE were more responsive to SMC at 0–20 cm depth than at deeper depths due to the shorter roots of herbaceous vegetation. The NEE increased with increasing monthly PRE because soil microbes responded more quickly than plants. The NEE significantly decreased with increasing SMC in shallow surface due to a hysteresis effect on water transport. The results of our study highlight the complex processes that determine how and to what extent PRE at multi-Temporal scale and SMC at different depths affect the carbon flux response in a water-limited grassland.
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how and to what extent does precipitation on multi Temporal Scales and soil moisture at different depths determine carbon flux responses in a water limited grassland ecosystem
Science of The Total Environment, 2018Co-Authors: Qingqing Fang, Guoqiang Wang, Anthony S. KiemAbstract:Abstract In water-limited ecosystems, hydrological processes significantly affect the carbon flux. The semi-arid grassland ecosystem is particularly sensitive to variations in precipitation (PRE) and soil moisture content (SMC), but to what extent is not fully understood. In this study, we estimated and analyzed how hydrological variables, especially PRE at multi-Temporal Scales (diurnal, monthly, phenological-related, and seasonal) and SMC at different soil depths (0–20 cm, 20–40 cm, 40–60 cm, 60–80 cm) affect the carbon flux. For these aims, eddy covariance data were combined with a Vegetation Photosynthesis and Respiration Model (VPRM) to simulate the regional gross primary productivity (GPP), ecosystem respiration (Reco), and net ecosystem exchange of CO2 (NEE). Interestingly, carbon flux showed no relationship with diurnal PRE or phenological-related PRE (precipitation in the growing season and non-growing season). However, carbon flux was significantly related to monthly PRE and to seasonal PRE (spring + summer, autumn). The GPP, Reco, and NEE increased in spring and summer but decreased in autumn with increasing precipitation due to the combined effect of salinization in autumn. The GPP, Reco, and NEE were more responsive to SMC at 0–20 cm depth than at deeper depths due to the shorter roots of herbaceous vegetation. The NEE increased with increasing monthly PRE because soil microbes responded more quickly than plants. The NEE significantly decreased with increasing SMC in shallow surface due to a hysteresis effect on water transport. The results of our study highlight the complex processes that determine how and to what extent PRE at multi-Temporal scale and SMC at different depths affect the carbon flux response in a water-limited grassland.
Douglas D Garrett - One of the best experts on this subject based on the ideXlab platform.
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standard multiscale entropy reflects neural dynamics at mismatched Temporal Scales what s signal irregularity got to do with it
PLOS Computational Biology, 2020Co-Authors: Julian Q Kosciessa, Niels A Kloosterman, Douglas D GarrettAbstract:Multiscale Entropy (MSE) is used to characterize the Temporal irregularity of neural time series patterns. Due to its’ presumed sensitivity to non-linear signal characteristics, MSE is typically considered a complementary measure of brain dynamics to signal variance and spectral power. However, the divergence between these measures is often unclear in application. Furthermore, it is commonly assumed (yet sparingly verified) that entropy estimated at specific time Scales reflects signal irregularity at those precise time Scales of brain function. We argue that such assumptions are not tenable. Using simulated and empirical electroencephalogram (EEG) data from 47 younger and 52 older adults, we indicate strong and previously underappreciated associations between MSE and spectral power, and highlight how these links preclude traditional interpretations of MSE time Scales. Specifically, we show that the typical definition of Temporal patterns via “similarity bounds” biases coarse MSE Scales–that are thought to reflect slow dynamics–by high-frequency dynamics. Moreover, we demonstrate that entropy at fine time Scales–presumed to indicate fast dynamics–is highly sensitive to broadband spectral power, a measure dominated by low-frequency contributions. Jointly, these issues produce counterintuitive reflections of frequency-specific content on MSE time Scales. We emphasize the resulting inferential problems in a conceptual replication of cross-sectional age differences at rest, in which scale-specific entropy age effects could be explained by spectral power differences at mismatched Temporal Scales. Furthermore, we demonstrate how such problems may be alleviated, resulting in the indication of scale-specific age differences in rhythmic irregularity. By controlling for narrowband contributions, we indicate that spontaneous alpha rhythms during eyes open rest transiently reduce broadband signal irregularity. Finally, we recommend best practices that may better permit a valid estimation and interpretation of neural signal irregularity at time Scales of interest.
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standard multiscale entropy reflects spectral power at mismatched Temporal Scales what s signal irregularity got to do with it
bioRxiv, 2020Co-Authors: Julian Q Kosciessa, Niels A Kloosterman, Douglas D GarrettAbstract:Multiscale Entropy (MSE) is used to characterize the Temporal irregularity of neural time series patterns. Due to its9 presumed sensitivity to non-linear signal characteristics, MSE is typically considered a complementary measure of brain dynamics to signal variance and spectral power. However, the divergence between these measures is often unclear in application. Furthermore, it is commonly assumed (yet sparingly verified) that entropy estimated at specific time Scales reflects signal irregularity at those precise time Scales of brain function. We argue that such assumptions are not tenable. Using simulated and empirical electroencephalogram (EEG) data from 47 younger and 52 older adults, we indicate strong and previously underappreciated associations between MSE and spectral power, and highlight how these links preclude traditional interpretations of MSE time Scales. Specifically, we show that the typical definition of Temporal patterns via "similarity bounds" biases coarse MSE Scales - that are thought to reflect slow dynamics - by high-frequency dynamics. Moreover, we demonstrate that entropy at fine time Scales - presumed to indicate fast dynamics - is highly sensitive to broadband spectral power, a measure dominated by low-frequency contributions. Jointly, these issues produce counterintuitive reflections of frequency-specific content on MSE time Scales. We emphasize the resulting inferential problems in a conceptual replication of cross-sectional age differences at rest, in which scale-specific entropy age effects could be explained by spectral power differences at mismatched Temporal Scales. Furthermore, we demonstrate how such problems may be alleviated, resulting in the indication of scale-specific age differences in rhythmic irregularity. By controlling for narrowband contributions, we indicate that spontaneous alpha rhythms during eyes open rest transiently reduce broadband signal irregularity. Finally, we recommend best practices that may better permit a valid estimation and interpretation of neural signal irregularity at time Scales of interest.
Julian Q Kosciessa - One of the best experts on this subject based on the ideXlab platform.
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standard multiscale entropy reflects neural dynamics at mismatched Temporal Scales what s signal irregularity got to do with it
PLOS Computational Biology, 2020Co-Authors: Julian Q Kosciessa, Niels A Kloosterman, Douglas D GarrettAbstract:Multiscale Entropy (MSE) is used to characterize the Temporal irregularity of neural time series patterns. Due to its’ presumed sensitivity to non-linear signal characteristics, MSE is typically considered a complementary measure of brain dynamics to signal variance and spectral power. However, the divergence between these measures is often unclear in application. Furthermore, it is commonly assumed (yet sparingly verified) that entropy estimated at specific time Scales reflects signal irregularity at those precise time Scales of brain function. We argue that such assumptions are not tenable. Using simulated and empirical electroencephalogram (EEG) data from 47 younger and 52 older adults, we indicate strong and previously underappreciated associations between MSE and spectral power, and highlight how these links preclude traditional interpretations of MSE time Scales. Specifically, we show that the typical definition of Temporal patterns via “similarity bounds” biases coarse MSE Scales–that are thought to reflect slow dynamics–by high-frequency dynamics. Moreover, we demonstrate that entropy at fine time Scales–presumed to indicate fast dynamics–is highly sensitive to broadband spectral power, a measure dominated by low-frequency contributions. Jointly, these issues produce counterintuitive reflections of frequency-specific content on MSE time Scales. We emphasize the resulting inferential problems in a conceptual replication of cross-sectional age differences at rest, in which scale-specific entropy age effects could be explained by spectral power differences at mismatched Temporal Scales. Furthermore, we demonstrate how such problems may be alleviated, resulting in the indication of scale-specific age differences in rhythmic irregularity. By controlling for narrowband contributions, we indicate that spontaneous alpha rhythms during eyes open rest transiently reduce broadband signal irregularity. Finally, we recommend best practices that may better permit a valid estimation and interpretation of neural signal irregularity at time Scales of interest.
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standard multiscale entropy reflects spectral power at mismatched Temporal Scales what s signal irregularity got to do with it
bioRxiv, 2020Co-Authors: Julian Q Kosciessa, Niels A Kloosterman, Douglas D GarrettAbstract:Multiscale Entropy (MSE) is used to characterize the Temporal irregularity of neural time series patterns. Due to its9 presumed sensitivity to non-linear signal characteristics, MSE is typically considered a complementary measure of brain dynamics to signal variance and spectral power. However, the divergence between these measures is often unclear in application. Furthermore, it is commonly assumed (yet sparingly verified) that entropy estimated at specific time Scales reflects signal irregularity at those precise time Scales of brain function. We argue that such assumptions are not tenable. Using simulated and empirical electroencephalogram (EEG) data from 47 younger and 52 older adults, we indicate strong and previously underappreciated associations between MSE and spectral power, and highlight how these links preclude traditional interpretations of MSE time Scales. Specifically, we show that the typical definition of Temporal patterns via "similarity bounds" biases coarse MSE Scales - that are thought to reflect slow dynamics - by high-frequency dynamics. Moreover, we demonstrate that entropy at fine time Scales - presumed to indicate fast dynamics - is highly sensitive to broadband spectral power, a measure dominated by low-frequency contributions. Jointly, these issues produce counterintuitive reflections of frequency-specific content on MSE time Scales. We emphasize the resulting inferential problems in a conceptual replication of cross-sectional age differences at rest, in which scale-specific entropy age effects could be explained by spectral power differences at mismatched Temporal Scales. Furthermore, we demonstrate how such problems may be alleviated, resulting in the indication of scale-specific age differences in rhythmic irregularity. By controlling for narrowband contributions, we indicate that spontaneous alpha rhythms during eyes open rest transiently reduce broadband signal irregularity. Finally, we recommend best practices that may better permit a valid estimation and interpretation of neural signal irregularity at time Scales of interest.
Mei Po Kwan - One of the best experts on this subject based on the ideXlab platform.
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advancing analytical methods for urban metabolism studies
Resources Conservation and Recycling, 2017Co-Authors: Mei Po KwanAbstract:Abstract This article reviews conventional methods applied in current urban metabolism studies. Based on the limitations of these conventional methods, it highlights two urgent methodological needs for urban metabolism research: the need for using different spatial and Temporal Scales and the need for addressing issues of sustainable development. In order to meet these urgent needs, we propose a research framework based on 3D geovisualization. The article argues that GIS and visualization can play an important role in enhancing the transparency and comprehensibility of the results of urban metabolism studies. Furthermore, it is also an effective platform for investigating urban metabolism at various spatial and Temporal Scales. Specifically, introducing the various speeds of flows and incorporating the differences in the rhythm of these flows will be helpful. GIS and visualization can help to translate analysis results into urban policy suggestions.
