2023
Variable retention harvest (VRH) is a silvicultural approach that retains differing proportions and patterns of canopy trees across a harvested area to emulate natural disturbance effects on stand structure and enhance the resilience of the regenerating stand to abiotic and biotic stresses. Four VRH treatments were applied to an 83-year-old red pine (Pinus resinosa Ait.) plantation forest in the Mixedwood Plains Ecozone of Canada that included 55% aggregate retention (55A), 55% dispersed retention (55D), 33% aggregate retention (33A), 33% dispersed retention (33D) and an unharvested control (CN). In the sixth growing season after harvest, tree stem sap flow and eddy covariance flux measurements were used to examine the impacts of VRH on the dominant components of total stand evapotranspiration (ET), i.e., canopy transpiration (TC) and water flux from the understory vegetation and soil (ETU) as well as understory and canopy water use efficiency (WUE). A positive relationship was found between harvest intensity and the growth of understory vegetation and ETU. The contribution of ETU to ET was higher in the dispersed compared to the aggregate VRH treatments. Canopy transpiration contributed 83% of ET in the CN plot and 58%, 55%, 30% and 23% in the 55D, 55A, 33A and 33D treatments, respectively. Overall, VRH treatments resulted in increased canopy WUE but little comparative effect on understory WUE. Our results suggest that the dispersed retention pattern led to higher ET and productivity than the aggregate pattern of the same retention level. Where carbon sequestration and climate change mitigation is the primary management objective, higher retention levels such as 55D might be used to favour stand level carbon storage while accepting slower rates of understory development. Our findings on the effects of VRH on productivity and WUE of the canopy and understory will help forest managers to better employ VRH as an option to meet multiple objectives and adapt forests to a warmer, more variable climate.
Variable retention harvest (VRH) is a silvicultural approach that retains differing proportions and patterns of canopy trees across a harvested area to emulate natural disturbance effects on stand structure and enhance the resilience of the regenerating stand to abiotic and biotic stresses. Four VRH treatments were applied to an 83-year-old red pine (Pinus resinosa Ait.) plantation forest in the Mixedwood Plains Ecozone of Canada that included 55% aggregate retention (55A), 55% dispersed retention (55D), 33% aggregate retention (33A), 33% dispersed retention (33D) and an unharvested control (CN). In the sixth growing season after harvest, tree stem sap flow and eddy covariance flux measurements were used to examine the impacts of VRH on the dominant components of total stand evapotranspiration (ET), i.e., canopy transpiration (TC) and water flux from the understory vegetation and soil (ETU) as well as understory and canopy water use efficiency (WUE). A positive relationship was found between harvest intensity and the growth of understory vegetation and ETU. The contribution of ETU to ET was higher in the dispersed compared to the aggregate VRH treatments. Canopy transpiration contributed 83% of ET in the CN plot and 58%, 55%, 30% and 23% in the 55D, 55A, 33A and 33D treatments, respectively. Overall, VRH treatments resulted in increased canopy WUE but little comparative effect on understory WUE. Our results suggest that the dispersed retention pattern led to higher ET and productivity than the aggregate pattern of the same retention level. Where carbon sequestration and climate change mitigation is the primary management objective, higher retention levels such as 55D might be used to favour stand level carbon storage while accepting slower rates of understory development. Our findings on the effects of VRH on productivity and WUE of the canopy and understory will help forest managers to better employ VRH as an option to meet multiple objectives and adapt forests to a warmer, more variable climate.
The Hudson Bay basin is a large contributor of freshwater input in the Arctic Ocean and is also an area affected by destructive spring floods. In this study, the hydrological model MESH (Modelisation Environmentale Communautaire - Surface and hydrology) was set up for the Groundhog River watershed situated in the Hudson Bay basin, to simulate the future evolution of streamflow and annual maximum streamflow. MESH was forced by meteorological data from ERA5 reanalyses in the historical period (1979–2018) and 12 models of the Coupled model intercomparison Project Phase 5 (CMIP5) downscaled with the Canadian Regional Climate model version 5 (CRCM5) in historical (1979–2005) and scenario period (2006–2098). The projections consistently indicate an earlier spring flow and a reduction in the amount of annual maximum streamflow by the end of the 21st century. Under the RCP8.5 scenario, the annual maximum streamflow occurring in the spring is expected to be advanced by 2 weeks and reduced on average from 852 m3/s (±265) in the historical period (1979–2018) to 717m3/s (±250) by the end of the 21st century (2059–2098). Because the seasonal projection of streamflow was not investigated in previous studies, this work is an important first step to assess the seasonal change of streamflow in the Hudson Bay region under climate change.
The Hudson Bay basin is a large contributor of freshwater input in the Arctic Ocean and is also an area affected by destructive spring floods. In this study, the hydrological model MESH (Modelisation Environmentale Communautaire - Surface and hydrology) was set up for the Groundhog River watershed situated in the Hudson Bay basin, to simulate the future evolution of streamflow and annual maximum streamflow. MESH was forced by meteorological data from ERA5 reanalyses in the historical period (1979–2018) and 12 models of the Coupled model intercomparison Project Phase 5 (CMIP5) downscaled with the Canadian Regional Climate model version 5 (CRCM5) in historical (1979–2005) and scenario period (2006–2098). The projections consistently indicate an earlier spring flow and a reduction in the amount of annual maximum streamflow by the end of the 21st century. Under the RCP8.5 scenario, the annual maximum streamflow occurring in the spring is expected to be advanced by 2 weeks and reduced on average from 852 m3/s (±265) in the historical period (1979–2018) to 717m3/s (±250) by the end of the 21st century (2059–2098). Because the seasonal projection of streamflow was not investigated in previous studies, this work is an important first step to assess the seasonal change of streamflow in the Hudson Bay region under climate change.
2022
Most North American temperate forests are plantation or regrowth forests, which are actively managed. These forests are in different stages of their growth cycles and their ability to sequester atmospheric carbon is affected by extreme weather events. In this study, the impact of heat and drought events on carbon sequestration in an age-sequence (80, 45, and 17 years as of 2019) of eastern white pine (Pinus strobus L.) forests in southern Ontario, Canada was examined using eddy covariance flux measurements from 2003 to 2019.Over the 17-year study period, the mean annual values of net ecosystem productivity (NEP) were 180 ± 96, 538 ± 177 and 64 ± 165 g C m-2 yr-1 in the 80-, 45- and 17-year-old stands, respectively, with the highest annual carbon sequestration rate observed in the 45-year-old stand. We found that air temperature (Ta) was the dominant control on NEP in all three different-aged stands and drought, which was a limiting factor for both gross ecosystem productivity (GEP) and ecosystems respiration (RE), had a smaller impact on NEP. However, the simultaneous occurrence of heat and drought events during the early growing seasons or over the consecutive years had a significant negative impact on annual NEP in all three forests. We observed a similar trend of NEP decline in all three stands over three consecutive years that experienced extreme weather events, with 2016 being a hot and dry, 2017 being a dry, and 2018 being a hot year. The youngest stand became a net source of carbon for all three of these years and the oldest stand became a small source of carbon for the first time in 2018 since observations started in 2003. However, in 2019, all three stands reverted to annual net carbon sinks.Our study results indicate that the timing, frequency and concurrent or consecutive occurrence of extreme weather events may have significant implications for carbon sequestration in temperate conifer forests in Eastern North America. This study is one of few globally available to provide long-term observational data on carbon exchanges in different-aged temperate plantation forests. It highlights interannual variability in carbon fluxes and enhances our understanding of the responses of these forest ecosystems to extreme weather events. Study results will help in developing climate resilient and sustainable forestry practices to offset atmospheric greenhouse gas emissions and improving simulation of carbon exchange processes in terrestrial ecosystem models.
Abstract Background Variable Retention Harvesting (VRH) is a forest management practice applied to enhance forest growth, improve biodiversity, preserve ecosystem function and provide economic revenue from harvested timber. There are many different forms and compositions in which VRH is applied in forest ecosystems. In this study, the impacts of four different VRH treatments on transpiration were evaluated in an 83-year-old red pine (Pinus Pinus resinosa ) plantation forest in the Great Lakes region in Canada. These VRH treatments included 55% aggregated crown retention (55A), 55% dispersed crown retention (55D), 33% aggregated crown retention (33A), 33% dispersed crown retention (33D) and unharvested control (CN) plot. These VRH treatments were implemented in 1-ha plots in the winter of 2014, while sap flow measurements were conducted from 2018 to 2020. Results Study results showed that tree-level transpiration was highest among trees in the 55D treatment, followed by 33D, 55A, 33A and CN plots. We found that photosynthetically active radiation (PAR) and vapor pressure deficit (VPD) were major controls or drivers of transpiration in all VRH treatments. Our study suggests that dispersed or distributed retention of 55% basal area (55D) is the ideal forest management technique to enhance transpiration and forest growth. Conclusions This study will help researchers, forest managers and decision-makers to improve their understanding of water cycling in forest ecosystem and adopt the best forest management regimes to enhance forest growth, health and resiliency to climate change.
2021
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Interdecadal variability of streamflow in the Hudson Bay Lowlands watersheds driven by atmospheric circulation
Olivier Champagne,
M. Altaf Arain,
Shusen Wang,
Martin Leduc,
H A J Russell,
Olivier Champagne,
M. Altaf Arain,
Shusen Wang,
Martin Leduc,
H A J Russell
Journal of Hydrology: Regional Studies, Volume 36
• Streamflow was satisfactorily simulated by MESH model in Hudson Bay lowlands. • Higher precipitation and streamflow observed in the western watersheds in 1995–2008. • The wet period in 1995–2008 was due to a shift in regional atmospheric circulation. • PDO and EP-NP also influenced this wet period. • Dryer period but sustained streamflow in 2009–2019 due to permafrost thaw. Hudson Bay Lowlands watersheds, Ontario, Canada. The rivers in the Hudson Bay Lowlands are a major source of freshwater entering the Arctic Ocean and they also cause major floods. In recent decades, this region has been affected by major changes in hydroclimatic processes attributed to climate change and natural climate variability. In this study, we used ERA5 reanalysis data, hydrometric observations, and the hydrological model MESH, to investigate the impact of atmospheric circulation on the inter-decadal variability of streamflow between 1979 and 2018 in the Hudson Bay Lowlands. The natural climate variability was assessed using a weather regimes approach based on the discretization of daily geopotential height anomalies (Z500) from ERA5 reanalysis, as well as large scale oceanic and atmospheric variability modes. The results showed an anomalous convergence of atmospheric moisture flux between 1995–2008 that enhanced precipitation and increased streamflow in the western part of the region. This moisture convergence was likely driven by the combination of (i) low pressure anomalies in the East Coast of North America and (ii) low pressure anomalies in western regions of Canada, associated with the cold phase of the pacific decadal oscillation (PDO). Since 2009, streamflow remains high, likely due to more groundwater discharge associated with the degradation of permafrost.
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Interdecadal variability of streamflow in the Hudson Bay Lowlands watersheds driven by atmospheric circulation
Olivier Champagne,
M. Altaf Arain,
Shusen Wang,
Martin Leduc,
H A J Russell,
Olivier Champagne,
M. Altaf Arain,
Shusen Wang,
Martin Leduc,
H A J Russell
Journal of Hydrology: Regional Studies, Volume 36
• Streamflow was satisfactorily simulated by MESH model in Hudson Bay lowlands. • Higher precipitation and streamflow observed in the western watersheds in 1995–2008. • The wet period in 1995–2008 was due to a shift in regional atmospheric circulation. • PDO and EP-NP also influenced this wet period. • Dryer period but sustained streamflow in 2009–2019 due to permafrost thaw. Hudson Bay Lowlands watersheds, Ontario, Canada. The rivers in the Hudson Bay Lowlands are a major source of freshwater entering the Arctic Ocean and they also cause major floods. In recent decades, this region has been affected by major changes in hydroclimatic processes attributed to climate change and natural climate variability. In this study, we used ERA5 reanalysis data, hydrometric observations, and the hydrological model MESH, to investigate the impact of atmospheric circulation on the inter-decadal variability of streamflow between 1979 and 2018 in the Hudson Bay Lowlands. The natural climate variability was assessed using a weather regimes approach based on the discretization of daily geopotential height anomalies (Z500) from ERA5 reanalysis, as well as large scale oceanic and atmospheric variability modes. The results showed an anomalous convergence of atmospheric moisture flux between 1995–2008 that enhanced precipitation and increased streamflow in the western part of the region. This moisture convergence was likely driven by the combination of (i) low pressure anomalies in the East Coast of North America and (ii) low pressure anomalies in western regions of Canada, associated with the cold phase of the pacific decadal oscillation (PDO). Since 2009, streamflow remains high, likely due to more groundwater discharge associated with the degradation of permafrost.
Log-transforming the dependent variable of a regression model, though convenient and frequently used, is accompanied by an under-prediction problem. We found that this underprediction can reach up to 20%, which is significant in studies that aim to estimate annual budgets. The fundamental reason for this problem is simply that the log-function is concave, and it has nothing to do with whether the dependent variable has a log-normal distribution or not. Using field-observed data of soil CO2 emission, soil temperature and soil moisture in a saturated-specification of a regression model for predicting emissions, we revealed that the under-predictions of the log-transformed approach were pervasive and systematically biased. The key determinant of the problem's severity was the coefficient of variation in the dependent variable that differed among different combinations of the values of the explanatory factors. By applying a parsimonious (Gaussian-Gamma) specification of the regression model to data from four different ecosystems, we found that this under-prediction problem was serious to various extents, and that for a relatively weak explanatory factor, the log-transformed approach is prone to yield a physically nonsensical estimated coefficient. Finally, we showed and concluded that the problem can be avoided by switching to the nonlinear approach, which does not require the assumption of homoscedasticity for the error term in computing the standard errors of the estimated coefficients.
Log-transforming the dependent variable of a regression model, though convenient and frequently used, is accompanied by an under-prediction problem. We found that this underprediction can reach up to 20%, which is significant in studies that aim to estimate annual budgets. The fundamental reason for this problem is simply that the log-function is concave, and it has nothing to do with whether the dependent variable has a log-normal distribution or not. Using field-observed data of soil CO2 emission, soil temperature and soil moisture in a saturated-specification of a regression model for predicting emissions, we revealed that the under-predictions of the log-transformed approach were pervasive and systematically biased. The key determinant of the problem's severity was the coefficient of variation in the dependent variable that differed among different combinations of the values of the explanatory factors. By applying a parsimonious (Gaussian-Gamma) specification of the regression model to data from four different ecosystems, we found that this under-prediction problem was serious to various extents, and that for a relatively weak explanatory factor, the log-transformed approach is prone to yield a physically nonsensical estimated coefficient. Finally, we showed and concluded that the problem can be avoided by switching to the nonlinear approach, which does not require the assumption of homoscedasticity for the error term in computing the standard errors of the estimated coefficients.
Scaling sap flux measurements to whole-tree water use or stand-level transpiration is often done using measurements conducted at a single point in the sapwood of the tree and has the potential to cause significant errors. Previous studies have shown that much of this uncertainty is related to (i) measurement of sapwood area and (ii) variations in sap flow at different depths within the tree sapwood.This study measured sap flux density at three depth intervals in the sapwood of 88-year-old red pine (Pinus resinosa) trees to more accurately estimate water-use at the tree- and stand-level in a plantation forest near Lake Erie in Southern Ontario, Canada. Results showed that most of the water transport (65%) occurred in the outermost sapwood, while only 26% and 9% of water was transported in the middle and innermost depths of sapwood, respectively.These results suggest that failing to consider radial variations in sap flux density within trees can lead to an overestimation of transpiration by as much as 81%, which may cause large uncertainties in water budgets at the ecosystem and catchment scale. This study will help to improve our understanding of water use dynamics and reduce uncertainties in sap flow measurements in the temperate pine forest ecosystems in the Great Lakes region and help in protecting these forests in the face of climate change.
Scaling sap flux measurements to whole-tree water use or stand-level transpiration is often done using measurements conducted at a single point in the sapwood of the tree and has the potential to cause significant errors. Previous studies have shown that much of this uncertainty is related to (i) measurement of sapwood area and (ii) variations in sap flow at different depths within the tree sapwood.This study measured sap flux density at three depth intervals in the sapwood of 88-year-old red pine (Pinus resinosa) trees to more accurately estimate water-use at the tree- and stand-level in a plantation forest near Lake Erie in Southern Ontario, Canada. Results showed that most of the water transport (65%) occurred in the outermost sapwood, while only 26% and 9% of water was transported in the middle and innermost depths of sapwood, respectively.These results suggest that failing to consider radial variations in sap flux density within trees can lead to an overestimation of transpiration by as much as 81%, which may cause large uncertainties in water budgets at the ecosystem and catchment scale. This study will help to improve our understanding of water use dynamics and reduce uncertainties in sap flow measurements in the temperate pine forest ecosystems in the Great Lakes region and help in protecting these forests in the face of climate change.
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Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration
Bin Chen,
Xuehe Lu,
Shaoqiang Wang,
Jing M. Chen,
Yang Liu,
Hongliang Fang,
Zhenhai Liu,
Fei Jiang,
M. Altaf Arain,
Jinghua Chen,
Xiaobo Wang,
Bin Chen,
Xuehe Lu,
Shaoqiang Wang,
Jing M. Chen,
Yang Liu,
Hongliang Fang,
Zhenhai Liu,
Fei Jiang,
M. Altaf Arain,
Jinghua Chen,
Xiaobo Wang
Remote Sensing, Volume 13, Issue 20
In terrestrial ecosystems, leaves are aggregated into different spatial structures and their spatial distribution is non-random. Clumping index (CI) is a key canopy structural parameter, characterizing the extent to which leaf deviates from the random distribution. To assess leaf clumping effects on global terrestrial ET, we used a global leaf area index (LAI) map and the latest version of global CI product derived from MODIS BRDF data as well as the Boreal Ecosystem Productivity Simulator (BEPS) to estimate global terrestrial ET. The results show that global terrestrial ET in 2015 was 511.9 ± 70.1 mm yr−1 for Case I, where the true LAI and CI are used. Compared to this baseline case, (1) global terrestrial ET is overestimated by 4.7% for Case II where true LAI is used ignoring clumping; (2) global terrestrial ET is underestimated by 13.0% for Case III where effective LAI is used ignoring clumping. Among all plant functional types (PFTs), evergreen needleleaf forests were most affected by foliage clumping for ET estimation in Case II, because they are most clumped with the lowest CI. Deciduous broadleaf forests are affected by leaf clumping most in Case III because they have both high LAI and low CI compared to other PFTs. The leaf clumping effects on ET estimation in both Case II and Case III is robust to the errors in major input parameters. Thus, it is necessary to consider clumping effects in the simulation of global terrestrial ET, which has considerable implications for global water cycle research.
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Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration
Bin Chen,
Xuehe Lu,
Shaoqiang Wang,
Jing M. Chen,
Yang Liu,
Hongliang Fang,
Zhenhai Liu,
Fei Jiang,
M. Altaf Arain,
Jinghua Chen,
Xiaobo Wang,
Bin Chen,
Xuehe Lu,
Shaoqiang Wang,
Jing M. Chen,
Yang Liu,
Hongliang Fang,
Zhenhai Liu,
Fei Jiang,
M. Altaf Arain,
Jinghua Chen,
Xiaobo Wang
Remote Sensing, Volume 13, Issue 20
In terrestrial ecosystems, leaves are aggregated into different spatial structures and their spatial distribution is non-random. Clumping index (CI) is a key canopy structural parameter, characterizing the extent to which leaf deviates from the random distribution. To assess leaf clumping effects on global terrestrial ET, we used a global leaf area index (LAI) map and the latest version of global CI product derived from MODIS BRDF data as well as the Boreal Ecosystem Productivity Simulator (BEPS) to estimate global terrestrial ET. The results show that global terrestrial ET in 2015 was 511.9 ± 70.1 mm yr−1 for Case I, where the true LAI and CI are used. Compared to this baseline case, (1) global terrestrial ET is overestimated by 4.7% for Case II where true LAI is used ignoring clumping; (2) global terrestrial ET is underestimated by 13.0% for Case III where effective LAI is used ignoring clumping. Among all plant functional types (PFTs), evergreen needleleaf forests were most affected by foliage clumping for ET estimation in Case II, because they are most clumped with the lowest CI. Deciduous broadleaf forests are affected by leaf clumping most in Case III because they have both high LAI and low CI compared to other PFTs. The leaf clumping effects on ET estimation in both Case II and Case III is robust to the errors in major input parameters. Thus, it is necessary to consider clumping effects in the simulation of global terrestrial ET, which has considerable implications for global water cycle research.
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Species and stand-age driven differences in photochemical reflectance index and light use efficiency across four temperate forests
Shangrong Lin,
Nicholas C. Coops,
Riccardo Tortini,
Wen Jia,
Zoran Nesic,
Eric Beamesderfer,
M. Altaf Arain,
Jing Li,
Qinhuo Liu,
Shangrong Lin,
Nicholas C. Coops,
Riccardo Tortini,
Wen Jia,
Zoran Nesic,
Eric Beamesderfer,
M. Altaf Arain,
Jing Li,
Qinhuo Liu
International Journal of Applied Earth Observation and Geoinformation, Volume 98
• A new PRI based LUE estimation method was proposed. • This method separated the half hourly observed PRI into PRI0 and ΔPRI. • PRI0 indicated daily maximal light use efficiency (LUE max ). • The ΔPRI linked PRI to different diurnal meteorological stress conditions. • This new method significantly improves LUE accuracy (R 2 from 0.1 to 0.7). Photosynthetic light use efficiency (LUE) determines the ability of a plant to assimilate atmospheric carbon dioxide to biomass and is known to be controlled by environmental conditions, light regimes and forest age. The photochemical reflectance index (PRI), derived from leaf or canopy remotely sensed spectra, has been shown to be an effective and accurate estimator of LUE. In this study, we propose a new LUE estimation method that separates the PRI into daily maximal PRI (PRI0) for indicating daily maximal light use efficiency (LUE max ) and ΔPRI, defined as the difference between PRI0 and instantaneous PRI, for estimating the diurnal physiological stress ( fstress) . We develop and apply the method across three temperate pine stands and a deciduous stand of different ages, in Southern Ontario, Canada. Half hourly canopy level spectra were acquired from a tower-based spectro-radiometer system (AMSPEC-III) over the growing season at the four stands. Results show that the PRI0 predicted well LUE max (R 2 > 0.6, p < 0.05) in both coniferous and deciduous stands and was able to track seasonal changes in pigment pools sizes. The ΔPRI was sensitive to short-term meteorological conditions, specifically temperature, vapor pressure deficit (VPD), and light variations resulting in strong correlations ( p < 0.05 ) with fstress and half hourly LUE. This new method significantly improves the estimation accuracy (R 2 increases from 0.1 to around 0.7) for PRI-based LUE estimation across all four stands of varying age and species composition and suggests that PRI-based LUE estimation has the ability to inform on both the effects of seasonal and diurnal change in photosynthetic efficiency under different meteorological conditions.
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Species and stand-age driven differences in photochemical reflectance index and light use efficiency across four temperate forests
Shangrong Lin,
Nicholas C. Coops,
Riccardo Tortini,
Wen Jia,
Zoran Nesic,
Eric Beamesderfer,
M. Altaf Arain,
Jing Li,
Qinhuo Liu,
Shangrong Lin,
Nicholas C. Coops,
Riccardo Tortini,
Wen Jia,
Zoran Nesic,
Eric Beamesderfer,
M. Altaf Arain,
Jing Li,
Qinhuo Liu
International Journal of Applied Earth Observation and Geoinformation, Volume 98
• A new PRI based LUE estimation method was proposed. • This method separated the half hourly observed PRI into PRI0 and ΔPRI. • PRI0 indicated daily maximal light use efficiency (LUE max ). • The ΔPRI linked PRI to different diurnal meteorological stress conditions. • This new method significantly improves LUE accuracy (R 2 from 0.1 to 0.7). Photosynthetic light use efficiency (LUE) determines the ability of a plant to assimilate atmospheric carbon dioxide to biomass and is known to be controlled by environmental conditions, light regimes and forest age. The photochemical reflectance index (PRI), derived from leaf or canopy remotely sensed spectra, has been shown to be an effective and accurate estimator of LUE. In this study, we propose a new LUE estimation method that separates the PRI into daily maximal PRI (PRI0) for indicating daily maximal light use efficiency (LUE max ) and ΔPRI, defined as the difference between PRI0 and instantaneous PRI, for estimating the diurnal physiological stress ( fstress) . We develop and apply the method across three temperate pine stands and a deciduous stand of different ages, in Southern Ontario, Canada. Half hourly canopy level spectra were acquired from a tower-based spectro-radiometer system (AMSPEC-III) over the growing season at the four stands. Results show that the PRI0 predicted well LUE max (R 2 > 0.6, p < 0.05) in both coniferous and deciduous stands and was able to track seasonal changes in pigment pools sizes. The ΔPRI was sensitive to short-term meteorological conditions, specifically temperature, vapor pressure deficit (VPD), and light variations resulting in strong correlations ( p < 0.05 ) with fstress and half hourly LUE. This new method significantly improves the estimation accuracy (R 2 increases from 0.1 to around 0.7) for PRI-based LUE estimation across all four stands of varying age and species composition and suggests that PRI-based LUE estimation has the ability to inform on both the effects of seasonal and diurnal change in photosynthetic efficiency under different meteorological conditions.
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Interannual and spatial variability of net ecosystem production in forests explained by an integrated physiological indicator in summer
Ying Liu,
Chaoyang Wu,
Lin Liu,
Chengyan Gu,
T. Andrew Black,
Rachhpal S. Jassal,
Lukas Hörtnagl,
Leonardo Montagnani,
Fernando Moyano,
Andrej Varlagin,
M. Altaf Arain,
Ajit Govind,
Ying Liu,
Chaoyang Wu,
Lin Liu,
Chengyan Gu,
T. Andrew Black,
Rachhpal S. Jassal,
Lukas Hörtnagl,
Leonardo Montagnani,
Fernando Moyano,
Andrej Varlagin,
M. Altaf Arain,
Ajit Govind
Ecological Indicators, Volume 129
• 514 sites-years of flux data were used to analyze the potential of physiological and phenological metrics in explaining the variability of forest NEP; • Summer physiological metrics performed better than phenological metrics in explaining IAV of NEP; • Ecosystem respiration played an important role in controlling the variability of NEP in forest ecosystem; • MCUI exhibited a great potential in explaining both IAV and SV of NEP. Understanding the feedback of ecosystem carbon uptake on climate change at temporal and spatial scales is crucial for developing ecosystem models. Previous studies have focused on the role of spring and autumn phenology in regulating carbon sequestration in forest stands, but few on the impact of physiological status in summer. However, plant accumulated the most carbon in summer compared with spring and autumn, therefore, it is of great significance to explore the role of summer phenological metrics on the variability of carbon sequestration. Using 514 site-years of flux data obtained at 40 FLUXNET sites including three forest ecosystems (i.e. evergreen needleleaf forest (ENF), deciduous broadleaf forest (DBF) and mixed forest (MF)) in Europe and North America, we compared the potential of physiological and phenological metrics of Gross Primary Production (GPP) and Ecosystem Respiration (RECO) in explaining the interannual and spatial variability (IAV and SV) of forest net ecosystem production (NEP). In view of the better performance of physiological metrics, we developed the maximum carbon uptake index (MCUI), which integrated the physiology metrics of photosynthesis and respiration in summer, and further explored its ability in explaining the IAV and SV of NEP. The results suggest that the MCUI had a better ability than respiration-growth length ratio (RGR) in predicting NEP for all three forest types. The interpretation of MCUI based on meteorological variables illustrated that the controlling meteorological factors of MCUI differed substantially among ecosystems. The summer shortwave radiation had the greatest influence on MCUI at DBF sites, while the soil water content played an important but opposite role at ENF and DBF sites, and no significant meteorological driver was found at MF sites. The higher potential of MCUI in explaining IAV and SV of NEP highlights the importance of summer physiology in controlling the forest carbon sequestration, and further confirms the significant role of peak plant growth in regulating carbon cycle of forest ecosystems. Understanding the drivers of peak plant growth is therefore of a great significance for further improving the precious of ecosystem model in the future.
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Interannual and spatial variability of net ecosystem production in forests explained by an integrated physiological indicator in summer
Ying Liu,
Chaoyang Wu,
Lin Liu,
Chengyan Gu,
T. Andrew Black,
Rachhpal S. Jassal,
Lukas Hörtnagl,
Leonardo Montagnani,
Fernando Moyano,
Andrej Varlagin,
M. Altaf Arain,
Ajit Govind,
Ying Liu,
Chaoyang Wu,
Lin Liu,
Chengyan Gu,
T. Andrew Black,
Rachhpal S. Jassal,
Lukas Hörtnagl,
Leonardo Montagnani,
Fernando Moyano,
Andrej Varlagin,
M. Altaf Arain,
Ajit Govind
Ecological Indicators, Volume 129
• 514 sites-years of flux data were used to analyze the potential of physiological and phenological metrics in explaining the variability of forest NEP; • Summer physiological metrics performed better than phenological metrics in explaining IAV of NEP; • Ecosystem respiration played an important role in controlling the variability of NEP in forest ecosystem; • MCUI exhibited a great potential in explaining both IAV and SV of NEP. Understanding the feedback of ecosystem carbon uptake on climate change at temporal and spatial scales is crucial for developing ecosystem models. Previous studies have focused on the role of spring and autumn phenology in regulating carbon sequestration in forest stands, but few on the impact of physiological status in summer. However, plant accumulated the most carbon in summer compared with spring and autumn, therefore, it is of great significance to explore the role of summer phenological metrics on the variability of carbon sequestration. Using 514 site-years of flux data obtained at 40 FLUXNET sites including three forest ecosystems (i.e. evergreen needleleaf forest (ENF), deciduous broadleaf forest (DBF) and mixed forest (MF)) in Europe and North America, we compared the potential of physiological and phenological metrics of Gross Primary Production (GPP) and Ecosystem Respiration (RECO) in explaining the interannual and spatial variability (IAV and SV) of forest net ecosystem production (NEP). In view of the better performance of physiological metrics, we developed the maximum carbon uptake index (MCUI), which integrated the physiology metrics of photosynthesis and respiration in summer, and further explored its ability in explaining the IAV and SV of NEP. The results suggest that the MCUI had a better ability than respiration-growth length ratio (RGR) in predicting NEP for all three forest types. The interpretation of MCUI based on meteorological variables illustrated that the controlling meteorological factors of MCUI differed substantially among ecosystems. The summer shortwave radiation had the greatest influence on MCUI at DBF sites, while the soil water content played an important but opposite role at ENF and DBF sites, and no significant meteorological driver was found at MF sites. The higher potential of MCUI in explaining IAV and SV of NEP highlights the importance of summer physiology in controlling the forest carbon sequestration, and further confirms the significant role of peak plant growth in regulating carbon cycle of forest ecosystems. Understanding the drivers of peak plant growth is therefore of a great significance for further improving the precious of ecosystem model in the future.
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abs
Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
Gilberto Pastorello,
Carlo Trotta,
Eleonora Canfora,
Housen Chu,
Danielle Christianson,
You-Wei Cheah,
C. Poindexter,
Jiquan Chen,
Abdelrahman Elbashandy,
Marty Humphrey,
Peter Isaac,
Diego Polidori,
Markus Reichstein,
Alessio Ribeca,
Catharine van Ingen,
Nicolas Vuichard,
Leiming Zhang,
B. D. Amiro,
Christof Ammann,
M. Altaf Arain,
Jonas Ardö,
Timothy J. Arkebauer,
Stefan K. Arndt,
Nicola Arriga,
Marc Aubinet,
Mika Aurela,
Dennis Baldocchi,
Alan Barr,
Eric Beamesderfer,
Luca Belelli Marchesini,
Onil Bergeron,
Jason Beringer,
Christian Bernhofer,
Daniel Berveiller,
D. P. Billesbach,
T. Andrew Black,
Peter D. Blanken,
Gil Bohrer,
Julia Boike,
Paul V. Bolstad,
Damien Bonal,
Jean-Marc Bonnefond,
D. R. Bowling,
Rosvel Bracho,
Jason Brodeur,
Christian Brümmer,
Nina Buchmann,
Benoît Burban,
Sean P. Burns,
Pauline Buysse,
Peter Cale,
M. Cavagna,
Pierre Cellier,
Shiping Chen,
Isaac Chini,
Torben R. Christensen,
James Cleverly,
Alessio Collalti,
Claudia Consalvo,
Bruce D. Cook,
David Cook,
Carole Coursolle,
Edoardo Cremonese,
Peter S. Curtis,
Ettore D’Andrea,
Humberto da Rocha,
Xiaoqin Dai,
K. J. Davis,
Bruno De Cinti,
A. de Grandcourt,
Anne De Ligne,
Raimundo Cosme de Oliveira,
Nicolas Delpierre,
Ankur R. Desai,
Carlos Marcelo Di Bella,
Paul Di Tommasi,
A. J. Dolman,
Francisco Domingo,
Gang Dong,
Sabina Dore,
Pierpaolo Duce,
Éric Dufrêne,
Allison L. Dunn,
Jiří Dušek,
Derek Eamus,
Uwe Eichelmann,
Hatim Abdalla M. ElKhidir,
Werner Eugster,
Cäcilia Ewenz,
B. E. Ewers,
D. Famulari,
Silvano Fares,
Iris Feigenwinter,
Andrew Feitz,
Rasmus Fensholt,
Gianluca Filippa,
M. L. Fischer,
J. M. Frank,
Marta Galvagno,
Mana Gharun,
Damiano Gianelle,
Bert Gielen,
Beniamino Gioli,
Anatoly A. Gitelson,
Ignacio Goded,
Mathias Goeckede,
A. H. Goldstein,
Christopher M. Gough,
Michael L. Goulden,
Alexander Graf,
Anne Griebel,
Carsten Gruening,
Thomas Grünwald,
Albin Hammerle,
Shijie Han,
Xingguo Han,
Birger Ulf Hansen,
Chad Hanson,
Juha Hatakka,
Yongtao He,
Markus Hehn,
Bernard Heinesch,
Nina Hinko‐Najera,
Lukas Hörtnagl,
Lindsay B. Hutley,
Andreas Ibrom,
Hiroki Ikawa,
M. Jackowicz-Korczyński,
Dalibor Janouš,
W.W.P. Jans,
Rachhpal S. Jassal,
Shicheng Jiang,
Tomomichi Kato,
Myroslava Khomik,
Janina Klatt,
Alexander Knohl,
Sara Knox,
Hideki Kobayashi,
Georgia R. Koerber,
Olaf Kolle,
Yoshiko Kosugi,
Ayumi Kotani,
Andrew S. Kowalski,
Bart Kruijt,
Julia Kurbatova,
Werner L. Kutsch,
Hyojung Kwon,
Samuli Launiainen,
Tuomas Laurila,
B. E. Law,
R. Leuning,
Yingnian Li,
Michael J. Liddell,
Jean‐Marc Limousin,
Marryanna Lion,
Adam Liska,
Annalea Lohila,
Ana López‐Ballesteros,
Efrèn López‐Blanco,
Benjamin Loubet,
Denis Loustau,
Antje Lucas-Moffat,
Johannes Lüers,
Siyan Ma,
Craig Macfarlane,
Vincenzo Magliulo,
Regine Maier,
Ivan Mammarella,
Giovanni Manca,
Barbara Marcolla,
Hank A. Margolis,
Serena Marras,
W. J. Massman,
Mikhail Mastepanov,
Roser Matamala,
Jaclyn Hatala Matthes,
Francesco Mazzenga,
Harry McCaughey,
Ian McHugh,
Andrew M. S. McMillan,
Lutz Merbold,
Wayne S. Meyer,
Tilden P. Meyers,
S. D. Miller,
Stefano Minerbi,
Uta Moderow,
Russell K. Monson,
Leonardo Montagnani,
Caitlin E. Moore,
E.J. Moors,
Virginie Moreaux,
Christine Moureaux,
J. William Munger,
T. Nakai,
Johan Neirynck,
Zoran Nesic,
Giacomo Nicolini,
Asko Noormets,
Matthew Northwood,
Marcelo D. Nosetto,
Yann Nouvellon,
Kimberly A. Novick,
Walter C. Oechel,
Jørgen E. Olesen,
Jean‐Marc Ourcival,
S. A. Papuga,
Frans‐Jan W. Parmentier,
Eugénie Paul‐Limoges,
Marian Pavelka,
Matthias Peichl,
Elise Pendall,
Richard P. Phillips,
Kim Pilegaard,
Norbert Pirk,
Gabriela Posse,
Thomas L. Powell,
Heiko Prasse,
Suzanne M. Prober,
Serge Rambal,
Üllar Rannik,
Naama Raz‐Yaseef,
Corinna Rebmann,
David E. Reed,
Víctor Resco de Dios,
Natalia Restrepo‐Coupé,
Borja Ruiz Reverter,
Marilyn Roland,
Simone Sabbatini,
Torsten Sachs,
S. R. Saleska,
Enrique P. Sánchez‐Cañete,
Zulia Mayari Sánchez-Mejía,
Hans Peter Schmid,
Marius Schmidt,
Karl Schneider,
Frederik Schrader,
Ivan Schroder,
Russell L. Scott,
Pavel Sedlák,
Penélope Serrano-Ortíz,
Changliang Shao,
Peili Shi,
Ivan Shironya,
Lukas Siebicke,
Ladislav Šigut,
Richard Silberstein,
Costantino Sirca,
Donatella Spano,
R. Steinbrecher,
Robert M. Stevens,
Cove Sturtevant,
Andy Suyker,
Torbern Tagesson,
Satoru Takanashi,
Yanhong Tang,
Nigel Tapper,
Jonathan E. Thom,
Michele Tomassucci,
Juha‐Pekka Tuovinen,
S. P. Urbanski,
Riccardo Valentini,
M. K. van der Molen,
Eva van Gorsel,
J. van Huissteden,
Andrej Varlagin,
Joseph Verfaillie,
Timo Vesala,
Caroline Vincke,
Domenico Vitale,
N. N. Vygodskaya,
Jeffrey P. Walker,
Elizabeth A. Walter‐Shea,
Huimin Wang,
R. J. Weber,
Sebastian Westermann,
Christian Wille,
Steven C. Wofsy,
Georg Wohlfahrt,
Sebastian Wolf,
William Woodgate,
Yuelin Li,
Roberto Zampedri,
Yuanman Hu,
Guoyi Zhou,
Donatella Zona,
D. Agarwal,
Sébastien Biraud,
Margaret Torn,
Dario Papale,
Gilberto Pastorello,
Carlo Trotta,
Eleonora Canfora,
Housen Chu,
Danielle Christianson,
You-Wei Cheah,
C. Poindexter,
Jiquan Chen,
Abdelrahman Elbashandy,
Marty Humphrey,
Peter Isaac,
Diego Polidori,
Markus Reichstein,
Alessio Ribeca,
Catharine van Ingen,
Nicolas Vuichard,
Leiming Zhang,
B. D. Amiro,
Christof Ammann,
M. Altaf Arain,
Jonas Ardö,
Timothy J. Arkebauer,
Stefan K. Arndt,
Nicola Arriga,
Marc Aubinet,
Mika Aurela,
Dennis Baldocchi,
Alan Barr,
Eric Beamesderfer,
Luca Belelli Marchesini,
Onil Bergeron,
Jason Beringer,
Christian Bernhofer,
Daniel Berveiller,
D. P. Billesbach,
T. Andrew Black,
Peter D. Blanken,
Gil Bohrer,
Julia Boike,
Paul V. Bolstad,
Damien Bonal,
Jean-Marc Bonnefond,
D. R. Bowling,
Rosvel Bracho,
Jason Brodeur,
Christian Brümmer,
Nina Buchmann,
Benoît Burban,
Sean P. Burns,
Pauline Buysse,
Peter Cale,
M. Cavagna,
Pierre Cellier,
Shiping Chen,
Isaac Chini,
Torben R. Christensen,
James Cleverly,
Alessio Collalti,
Claudia Consalvo,
Bruce D. Cook,
David Cook,
Carole Coursolle,
Edoardo Cremonese,
Peter S. Curtis,
Ettore D’Andrea,
Humberto da Rocha,
Xiaoqin Dai,
K. J. Davis,
Bruno De Cinti,
A. de Grandcourt,
Anne De Ligne,
Raimundo Cosme de Oliveira,
Nicolas Delpierre,
Ankur R. Desai,
Carlos Marcelo Di Bella,
Paul Di Tommasi,
A. J. Dolman,
Francisco Domingo,
Gang Dong,
Sabina Dore,
Pierpaolo Duce,
Éric Dufrêne,
Allison L. Dunn,
Jiří Dušek,
Derek Eamus,
Uwe Eichelmann,
Hatim Abdalla M. ElKhidir,
Werner Eugster,
Cäcilia Ewenz,
B. E. Ewers,
D. Famulari,
Silvano Fares,
Iris Feigenwinter,
Andrew Feitz,
Rasmus Fensholt,
Gianluca Filippa,
M. L. Fischer,
J. M. Frank,
Marta Galvagno,
Mana Gharun,
Damiano Gianelle,
Bert Gielen,
Beniamino Gioli,
Anatoly A. Gitelson,
Ignacio Goded,
Mathias Goeckede,
A. H. Goldstein,
Christopher M. Gough,
Michael L. Goulden,
Alexander Graf,
Anne Griebel,
Carsten Gruening,
Thomas Grünwald,
Albin Hammerle,
Shijie Han,
Xingguo Han,
Birger Ulf Hansen,
Chad Hanson,
Juha Hatakka,
Yongtao He,
Markus Hehn,
Bernard Heinesch,
Nina Hinko‐Najera,
Lukas Hörtnagl,
Lindsay B. Hutley,
Andreas Ibrom,
Hiroki Ikawa,
M. Jackowicz-Korczyński,
Dalibor Janouš,
W.W.P. Jans,
Rachhpal S. Jassal,
Shicheng Jiang,
Tomomichi Kato,
Myroslava Khomik,
Janina Klatt,
Alexander Knohl,
Sara Knox,
Hideki Kobayashi,
Georgia R. Koerber,
Olaf Kolle,
Yoshiko Kosugi,
Ayumi Kotani,
Andrew S. Kowalski,
Bart Kruijt,
Julia Kurbatova,
Werner L. Kutsch,
Hyojung Kwon,
Samuli Launiainen,
Tuomas Laurila,
B. E. Law,
R. Leuning,
Yingnian Li,
Michael J. Liddell,
Jean‐Marc Limousin,
Marryanna Lion,
Adam Liska,
Annalea Lohila,
Ana López‐Ballesteros,
Efrèn López‐Blanco,
Benjamin Loubet,
Denis Loustau,
Antje Lucas-Moffat,
Johannes Lüers,
Siyan Ma,
Craig Macfarlane,
Vincenzo Magliulo,
Regine Maier,
Ivan Mammarella,
Giovanni Manca,
Barbara Marcolla,
Hank A. Margolis,
Serena Marras,
W. J. Massman,
Mikhail Mastepanov,
Roser Matamala,
Jaclyn Hatala Matthes,
Francesco Mazzenga,
Harry McCaughey,
Ian McHugh,
Andrew M. S. McMillan,
Lutz Merbold,
Wayne S. Meyer,
Tilden P. Meyers,
S. D. Miller,
Stefano Minerbi,
Uta Moderow,
Russell K. Monson,
Leonardo Montagnani,
Caitlin E. Moore,
E.J. Moors,
Virginie Moreaux,
Christine Moureaux,
J. William Munger,
T. Nakai,
Johan Neirynck,
Zoran Nesic,
Giacomo Nicolini,
Asko Noormets,
Matthew Northwood,
Marcelo D. Nosetto,
Yann Nouvellon,
Kimberly A. Novick,
Walter C. Oechel,
Jørgen E. Olesen,
Jean‐Marc Ourcival,
S. A. Papuga,
Frans‐Jan W. Parmentier,
Eugénie Paul‐Limoges,
Marian Pavelka,
Matthias Peichl,
Elise Pendall,
Richard P. Phillips,
Kim Pilegaard,
Norbert Pirk,
Gabriela Posse,
Thomas L. Powell,
Heiko Prasse,
Suzanne M. Prober,
Serge Rambal,
Üllar Rannik,
Naama Raz‐Yaseef,
Corinna Rebmann,
David E. Reed,
Víctor Resco de Dios,
Natalia Restrepo‐Coupé,
Borja Ruiz Reverter,
Marilyn Roland,
Simone Sabbatini,
Torsten Sachs,
S. R. Saleska,
Enrique P. Sánchez‐Cañete,
Zulia Mayari Sánchez-Mejía,
Hans Peter Schmid,
Marius Schmidt,
Karl Schneider,
Frederik Schrader,
Ivan Schroder,
Russell L. Scott,
Pavel Sedlák,
Penélope Serrano-Ortíz,
Changliang Shao,
Peili Shi,
Ivan Shironya,
Lukas Siebicke,
Ladislav Šigut,
Richard Silberstein,
Costantino Sirca,
Donatella Spano,
R. Steinbrecher,
Robert M. Stevens,
Cove Sturtevant,
Andy Suyker,
Torbern Tagesson,
Satoru Takanashi,
Yanhong Tang,
Nigel Tapper,
Jonathan E. Thom,
Michele Tomassucci,
Juha‐Pekka Tuovinen,
S. P. Urbanski,
Riccardo Valentini,
M. K. van der Molen,
Eva van Gorsel,
J. van Huissteden,
Andrej Varlagin,
Joseph Verfaillie,
Timo Vesala,
Caroline Vincke,
Domenico Vitale,
N. N. Vygodskaya,
Jeffrey P. Walker,
Elizabeth A. Walter‐Shea,
Huimin Wang,
R. J. Weber,
Sebastian Westermann,
Christian Wille,
Steven C. Wofsy,
Georg Wohlfahrt,
Sebastian Wolf,
William Woodgate,
Yuelin Li,
Roberto Zampedri,
Yuanman Hu,
Guoyi Zhou,
Donatella Zona,
D. Agarwal,
Sébastien Biraud,
Margaret Torn,
Dario Papale
Scientific Data, Volume 8, Issue 1
A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00851-9.
DOI
bib
abs
Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
Gilberto Pastorello,
Carlo Trotta,
Eleonora Canfora,
Housen Chu,
Danielle Christianson,
You-Wei Cheah,
C. Poindexter,
Jiquan Chen,
Abdelrahman Elbashandy,
Marty Humphrey,
Peter Isaac,
Diego Polidori,
Markus Reichstein,
Alessio Ribeca,
Catharine van Ingen,
Nicolas Vuichard,
Leiming Zhang,
B. D. Amiro,
Christof Ammann,
M. Altaf Arain,
Jonas Ardö,
Timothy J. Arkebauer,
Stefan K. Arndt,
Nicola Arriga,
Marc Aubinet,
Mika Aurela,
Dennis Baldocchi,
Alan Barr,
Eric Beamesderfer,
Luca Belelli Marchesini,
Onil Bergeron,
Jason Beringer,
Christian Bernhofer,
Daniel Berveiller,
D. P. Billesbach,
T. Andrew Black,
Peter D. Blanken,
Gil Bohrer,
Julia Boike,
Paul V. Bolstad,
Damien Bonal,
Jean-Marc Bonnefond,
D. R. Bowling,
Rosvel Bracho,
Jason Brodeur,
Christian Brümmer,
Nina Buchmann,
Benoît Burban,
Sean P. Burns,
Pauline Buysse,
Peter Cale,
M. Cavagna,
Pierre Cellier,
Shiping Chen,
Isaac Chini,
Torben R. Christensen,
James Cleverly,
Alessio Collalti,
Claudia Consalvo,
Bruce D. Cook,
David Cook,
Carole Coursolle,
Edoardo Cremonese,
Peter S. Curtis,
Ettore D’Andrea,
Humberto da Rocha,
Xiaoqin Dai,
K. J. Davis,
Bruno De Cinti,
A. de Grandcourt,
Anne De Ligne,
Raimundo Cosme de Oliveira,
Nicolas Delpierre,
Ankur R. Desai,
Carlos Marcelo Di Bella,
Paul Di Tommasi,
A. J. Dolman,
Francisco Domingo,
Gang Dong,
Sabina Dore,
Pierpaolo Duce,
Éric Dufrêne,
Allison L. Dunn,
Jiří Dušek,
Derek Eamus,
Uwe Eichelmann,
Hatim Abdalla M. ElKhidir,
Werner Eugster,
Cäcilia Ewenz,
B. E. Ewers,
D. Famulari,
Silvano Fares,
Iris Feigenwinter,
Andrew Feitz,
Rasmus Fensholt,
Gianluca Filippa,
M. L. Fischer,
J. M. Frank,
Marta Galvagno,
Mana Gharun,
Damiano Gianelle,
Bert Gielen,
Beniamino Gioli,
Anatoly A. Gitelson,
Ignacio Goded,
Mathias Goeckede,
A. H. Goldstein,
Christopher M. Gough,
Michael L. Goulden,
Alexander Graf,
Anne Griebel,
Carsten Gruening,
Thomas Grünwald,
Albin Hammerle,
Shijie Han,
Xingguo Han,
Birger Ulf Hansen,
Chad Hanson,
Juha Hatakka,
Yongtao He,
Markus Hehn,
Bernard Heinesch,
Nina Hinko‐Najera,
Lukas Hörtnagl,
Lindsay B. Hutley,
Andreas Ibrom,
Hiroki Ikawa,
M. Jackowicz-Korczyński,
Dalibor Janouš,
W.W.P. Jans,
Rachhpal S. Jassal,
Shicheng Jiang,
Tomomichi Kato,
Myroslava Khomik,
Janina Klatt,
Alexander Knohl,
Sara Knox,
Hideki Kobayashi,
Georgia R. Koerber,
Olaf Kolle,
Yoshiko Kosugi,
Ayumi Kotani,
Andrew S. Kowalski,
Bart Kruijt,
Julia Kurbatova,
Werner L. Kutsch,
Hyojung Kwon,
Samuli Launiainen,
Tuomas Laurila,
B. E. Law,
R. Leuning,
Yingnian Li,
Michael J. Liddell,
Jean‐Marc Limousin,
Marryanna Lion,
Adam Liska,
Annalea Lohila,
Ana López‐Ballesteros,
Efrèn López‐Blanco,
Benjamin Loubet,
Denis Loustau,
Antje Lucas-Moffat,
Johannes Lüers,
Siyan Ma,
Craig Macfarlane,
Vincenzo Magliulo,
Regine Maier,
Ivan Mammarella,
Giovanni Manca,
Barbara Marcolla,
Hank A. Margolis,
Serena Marras,
W. J. Massman,
Mikhail Mastepanov,
Roser Matamala,
Jaclyn Hatala Matthes,
Francesco Mazzenga,
Harry McCaughey,
Ian McHugh,
Andrew M. S. McMillan,
Lutz Merbold,
Wayne S. Meyer,
Tilden P. Meyers,
S. D. Miller,
Stefano Minerbi,
Uta Moderow,
Russell K. Monson,
Leonardo Montagnani,
Caitlin E. Moore,
E.J. Moors,
Virginie Moreaux,
Christine Moureaux,
J. William Munger,
T. Nakai,
Johan Neirynck,
Zoran Nesic,
Giacomo Nicolini,
Asko Noormets,
Matthew Northwood,
Marcelo D. Nosetto,
Yann Nouvellon,
Kimberly A. Novick,
Walter C. Oechel,
Jørgen E. Olesen,
Jean‐Marc Ourcival,
S. A. Papuga,
Frans‐Jan W. Parmentier,
Eugénie Paul‐Limoges,
Marian Pavelka,
Matthias Peichl,
Elise Pendall,
Richard P. Phillips,
Kim Pilegaard,
Norbert Pirk,
Gabriela Posse,
Thomas L. Powell,
Heiko Prasse,
Suzanne M. Prober,
Serge Rambal,
Üllar Rannik,
Naama Raz‐Yaseef,
Corinna Rebmann,
David E. Reed,
Víctor Resco de Dios,
Natalia Restrepo‐Coupé,
Borja Ruiz Reverter,
Marilyn Roland,
Simone Sabbatini,
Torsten Sachs,
S. R. Saleska,
Enrique P. Sánchez‐Cañete,
Zulia Mayari Sánchez-Mejía,
Hans Peter Schmid,
Marius Schmidt,
Karl Schneider,
Frederik Schrader,
Ivan Schroder,
Russell L. Scott,
Pavel Sedlák,
Penélope Serrano-Ortíz,
Changliang Shao,
Peili Shi,
Ivan Shironya,
Lukas Siebicke,
Ladislav Šigut,
Richard Silberstein,
Costantino Sirca,
Donatella Spano,
R. Steinbrecher,
Robert M. Stevens,
Cove Sturtevant,
Andy Suyker,
Torbern Tagesson,
Satoru Takanashi,
Yanhong Tang,
Nigel Tapper,
Jonathan E. Thom,
Michele Tomassucci,
Juha‐Pekka Tuovinen,
S. P. Urbanski,
Riccardo Valentini,
M. K. van der Molen,
Eva van Gorsel,
J. van Huissteden,
Andrej Varlagin,
Joseph Verfaillie,
Timo Vesala,
Caroline Vincke,
Domenico Vitale,
N. N. Vygodskaya,
Jeffrey P. Walker,
Elizabeth A. Walter‐Shea,
Huimin Wang,
R. J. Weber,
Sebastian Westermann,
Christian Wille,
Steven C. Wofsy,
Georg Wohlfahrt,
Sebastian Wolf,
William Woodgate,
Yuelin Li,
Roberto Zampedri,
Yuanman Hu,
Guoyi Zhou,
Donatella Zona,
D. Agarwal,
Sébastien Biraud,
Margaret Torn,
Dario Papale,
Gilberto Pastorello,
Carlo Trotta,
Eleonora Canfora,
Housen Chu,
Danielle Christianson,
You-Wei Cheah,
C. Poindexter,
Jiquan Chen,
Abdelrahman Elbashandy,
Marty Humphrey,
Peter Isaac,
Diego Polidori,
Markus Reichstein,
Alessio Ribeca,
Catharine van Ingen,
Nicolas Vuichard,
Leiming Zhang,
B. D. Amiro,
Christof Ammann,
M. Altaf Arain,
Jonas Ardö,
Timothy J. Arkebauer,
Stefan K. Arndt,
Nicola Arriga,
Marc Aubinet,
Mika Aurela,
Dennis Baldocchi,
Alan Barr,
Eric Beamesderfer,
Luca Belelli Marchesini,
Onil Bergeron,
Jason Beringer,
Christian Bernhofer,
Daniel Berveiller,
D. P. Billesbach,
T. Andrew Black,
Peter D. Blanken,
Gil Bohrer,
Julia Boike,
Paul V. Bolstad,
Damien Bonal,
Jean-Marc Bonnefond,
D. R. Bowling,
Rosvel Bracho,
Jason Brodeur,
Christian Brümmer,
Nina Buchmann,
Benoît Burban,
Sean P. Burns,
Pauline Buysse,
Peter Cale,
M. Cavagna,
Pierre Cellier,
Shiping Chen,
Isaac Chini,
Torben R. Christensen,
James Cleverly,
Alessio Collalti,
Claudia Consalvo,
Bruce D. Cook,
David Cook,
Carole Coursolle,
Edoardo Cremonese,
Peter S. Curtis,
Ettore D’Andrea,
Humberto da Rocha,
Xiaoqin Dai,
K. J. Davis,
Bruno De Cinti,
A. de Grandcourt,
Anne De Ligne,
Raimundo Cosme de Oliveira,
Nicolas Delpierre,
Ankur R. Desai,
Carlos Marcelo Di Bella,
Paul Di Tommasi,
A. J. Dolman,
Francisco Domingo,
Gang Dong,
Sabina Dore,
Pierpaolo Duce,
Éric Dufrêne,
Allison L. Dunn,
Jiří Dušek,
Derek Eamus,
Uwe Eichelmann,
Hatim Abdalla M. ElKhidir,
Werner Eugster,
Cäcilia Ewenz,
B. E. Ewers,
D. Famulari,
Silvano Fares,
Iris Feigenwinter,
Andrew Feitz,
Rasmus Fensholt,
Gianluca Filippa,
M. L. Fischer,
J. M. Frank,
Marta Galvagno,
Mana Gharun,
Damiano Gianelle,
Bert Gielen,
Beniamino Gioli,
Anatoly A. Gitelson,
Ignacio Goded,
Mathias Goeckede,
A. H. Goldstein,
Christopher M. Gough,
Michael L. Goulden,
Alexander Graf,
Anne Griebel,
Carsten Gruening,
Thomas Grünwald,
Albin Hammerle,
Shijie Han,
Xingguo Han,
Birger Ulf Hansen,
Chad Hanson,
Juha Hatakka,
Yongtao He,
Markus Hehn,
Bernard Heinesch,
Nina Hinko‐Najera,
Lukas Hörtnagl,
Lindsay B. Hutley,
Andreas Ibrom,
Hiroki Ikawa,
M. Jackowicz-Korczyński,
Dalibor Janouš,
W.W.P. Jans,
Rachhpal S. Jassal,
Shicheng Jiang,
Tomomichi Kato,
Myroslava Khomik,
Janina Klatt,
Alexander Knohl,
Sara Knox,
Hideki Kobayashi,
Georgia R. Koerber,
Olaf Kolle,
Yoshiko Kosugi,
Ayumi Kotani,
Andrew S. Kowalski,
Bart Kruijt,
Julia Kurbatova,
Werner L. Kutsch,
Hyojung Kwon,
Samuli Launiainen,
Tuomas Laurila,
B. E. Law,
R. Leuning,
Yingnian Li,
Michael J. Liddell,
Jean‐Marc Limousin,
Marryanna Lion,
Adam Liska,
Annalea Lohila,
Ana López‐Ballesteros,
Efrèn López‐Blanco,
Benjamin Loubet,
Denis Loustau,
Antje Lucas-Moffat,
Johannes Lüers,
Siyan Ma,
Craig Macfarlane,
Vincenzo Magliulo,
Regine Maier,
Ivan Mammarella,
Giovanni Manca,
Barbara Marcolla,
Hank A. Margolis,
Serena Marras,
W. J. Massman,
Mikhail Mastepanov,
Roser Matamala,
Jaclyn Hatala Matthes,
Francesco Mazzenga,
Harry McCaughey,
Ian McHugh,
Andrew M. S. McMillan,
Lutz Merbold,
Wayne S. Meyer,
Tilden P. Meyers,
S. D. Miller,
Stefano Minerbi,
Uta Moderow,
Russell K. Monson,
Leonardo Montagnani,
Caitlin E. Moore,
E.J. Moors,
Virginie Moreaux,
Christine Moureaux,
J. William Munger,
T. Nakai,
Johan Neirynck,
Zoran Nesic,
Giacomo Nicolini,
Asko Noormets,
Matthew Northwood,
Marcelo D. Nosetto,
Yann Nouvellon,
Kimberly A. Novick,
Walter C. Oechel,
Jørgen E. Olesen,
Jean‐Marc Ourcival,
S. A. Papuga,
Frans‐Jan W. Parmentier,
Eugénie Paul‐Limoges,
Marian Pavelka,
Matthias Peichl,
Elise Pendall,
Richard P. Phillips,
Kim Pilegaard,
Norbert Pirk,
Gabriela Posse,
Thomas L. Powell,
Heiko Prasse,
Suzanne M. Prober,
Serge Rambal,
Üllar Rannik,
Naama Raz‐Yaseef,
Corinna Rebmann,
David E. Reed,
Víctor Resco de Dios,
Natalia Restrepo‐Coupé,
Borja Ruiz Reverter,
Marilyn Roland,
Simone Sabbatini,
Torsten Sachs,
S. R. Saleska,
Enrique P. Sánchez‐Cañete,
Zulia Mayari Sánchez-Mejía,
Hans Peter Schmid,
Marius Schmidt,
Karl Schneider,
Frederik Schrader,
Ivan Schroder,
Russell L. Scott,
Pavel Sedlák,
Penélope Serrano-Ortíz,
Changliang Shao,
Peili Shi,
Ivan Shironya,
Lukas Siebicke,
Ladislav Šigut,
Richard Silberstein,
Costantino Sirca,
Donatella Spano,
R. Steinbrecher,
Robert M. Stevens,
Cove Sturtevant,
Andy Suyker,
Torbern Tagesson,
Satoru Takanashi,
Yanhong Tang,
Nigel Tapper,
Jonathan E. Thom,
Michele Tomassucci,
Juha‐Pekka Tuovinen,
S. P. Urbanski,
Riccardo Valentini,
M. K. van der Molen,
Eva van Gorsel,
J. van Huissteden,
Andrej Varlagin,
Joseph Verfaillie,
Timo Vesala,
Caroline Vincke,
Domenico Vitale,
N. N. Vygodskaya,
Jeffrey P. Walker,
Elizabeth A. Walter‐Shea,
Huimin Wang,
R. J. Weber,
Sebastian Westermann,
Christian Wille,
Steven C. Wofsy,
Georg Wohlfahrt,
Sebastian Wolf,
William Woodgate,
Yuelin Li,
Roberto Zampedri,
Yuanman Hu,
Guoyi Zhou,
Donatella Zona,
D. Agarwal,
Sébastien Biraud,
Margaret Torn,
Dario Papale
Scientific Data, Volume 8, Issue 1
A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00851-9.
DOI
bib
abs
Global transpiration data from sap flow measurements: the SAPFLUXNET database
Rafael Poyatos,
Víctor Granda,
Víctor Flo,
Mark A. Adams,
Balázs Adorján,
David Aguadé,
Marcos Pereira Marinho Aidar,
Scott T. Allen,
M. S. Alvarado-Barrientos,
Kristina J. Anderson‐Teixeira,
L. M. T. Aparecido,
M. Altaf Arain,
Ismael Aranda,
Heidi Asbjornsen,
Robert Baxter,
Eric Beamesderfer,
Z. Carter Berry,
Daniel Berveiller,
Bethany Blakely,
Johnny Boggs,
Gil Bohrer,
Paul V. Bolstad,
Damien Bonal,
Rosvel Bracho,
Patricia Brito,
Jason Brodeur,
Fernando Casanoves,
Jérôme Chave,
Hui Chen,
César Cisneros Vaca,
Kenneth L. Clark,
Edoardo Cremonese,
Hongzhong Dang,
Jorge S. David,
Teresa S. David,
Nicolas Delpierre,
Ankur R. Desai,
C. Frédéric,
Michal Dohnal,
Jean‐Christophe Domec,
Sebinasi Dzikiti,
Colin W. Edgar,
Rebekka Eichstaedt,
Tarek S. El‐Madany,
J.A. Elbers,
Cleiton B. Eller,
E. S. Euskirchen,
B. E. Ewers,
Patrick Fonti,
Alicia Forner,
David I. Forrester,
Helber C. Freitas,
Marta Galvagno,
Omar García-Tejera,
Chandra Prasad Ghimire,
Teresa E. Gimeno,
J. P. Grace,
André Granier,
Anne Griebel,
Yan Guangyu,
M.B. Gush,
Paul J. Hanson,
Niles J. Hasselquist,
Ingo Heinrich,
Virginia Hernández‐Santana,
Valentine Herrmann,
Teemu Hölttä,
F. Holwerda,
J. E. Irvine,
Supat Isarangkool Na Ayutthaya,
P. G. Jarvis,
Hubert Jochheim,
Carlos Alfredo Joly,
Julia Kaplick,
Hyun Seok Kim,
Leif Klemedtsson,
Heather Kropp,
Fredrik Lagergren,
Patrick N.J. Lane,
Petra Lang,
Andrei Lapenas,
Víctor Lechuga,
Minsu Lee,
Christoph Leuschner,
Jean‐Marc Limousin,
Juan Carlos Linares,
Maj‐Lena Linderson,
Anders Lindroth,
Pilar Llorens,
Álvaro López‐Bernal,
M. M. Loranty,
Dietmar Lüttschwager,
Cate Macinnis‐Ng,
Isabelle Maréchaux,
Timothy A. Martin,
Ashley M. Matheny,
Nate G. McDowell,
Sean M. McMahon,
Patrick Meir,
Ilona Mészáros,
Mirco Migliavacca,
Patrick J. Mitchell,
Meelis Mölder,
Leonardo Montagnani,
Georgianne W. Moore,
Ryogo Nakada,
Furong Niu,
Rachael H. Nolan,
Richard J. Norby,
Kimberly A. Novick,
Walter Oberhuber,
Nikolaus Obojes,
A. Christopher Oishi,
Rafael S. Oliveira,
Ram Oren,
Jean‐Marc Ourcival,
Teemu Paljakka,
Óscar Pérez‐Priego,
Pablo Luís Peri,
Richard L. Peters,
Sebastian Pfautsch,
William T. Pockman,
Yakir Preisler,
Katherine G. Rascher,
George R. Robinson,
Humberto Ribeiro da Rocha,
Alain Rocheteau,
Alexander Röll,
Bruno H. P. Rosado,
Lucy Rowland,
Alexey V. Rubtsov,
Santiago Sabaté,
Yann Salmon,
Roberto L. Salomón,
Elisenda Sánchez‐Costa,
Karina V. R. Schäfer,
Bernhard Schuldt,
Alexandr Shashkin,
Clément Stahl,
Marko Stojanović,
Juan Carlos Suárez,
Ge Sun,
Justyna Szatniewska,
Fyodor Tatarinov,
Miroslav Tesař,
Frank M. Thomas,
Pantana Tor‐ngern,
Josef Urban,
Fernando Valladares,
Christiaan van der Tol,
Ilja van Meerveld,
Andrej Varlagin,
Holm Voigt,
J. M. Warren,
Christiane Werner,
Willy Werner,
Gerhard Wieser,
Lisa Wingate,
Stan Wullschleger,
K. Yi,
Roman Zweifel,
Kathy Steppe,
Maurizio Mencuccini,
Jordi Martínez‐Vilalta,
Rafael Poyatos,
Víctor Granda,
Víctor Flo,
Mark A. Adams,
Balázs Adorján,
David Aguadé,
Marcos Pereira Marinho Aidar,
Scott T. Allen,
M. S. Alvarado-Barrientos,
Kristina J. Anderson‐Teixeira,
L. M. T. Aparecido,
M. Altaf Arain,
Ismael Aranda,
Heidi Asbjornsen,
Robert Baxter,
Eric Beamesderfer,
Z. Carter Berry,
Daniel Berveiller,
Bethany Blakely,
Johnny Boggs,
Gil Bohrer,
Paul V. Bolstad,
Damien Bonal,
Rosvel Bracho,
Patricia Brito,
Jason Brodeur,
Fernando Casanoves,
Jérôme Chave,
Hui Chen,
César Cisneros Vaca,
Kenneth L. Clark,
Edoardo Cremonese,
Hongzhong Dang,
Jorge S. David,
Teresa S. David,
Nicolas Delpierre,
Ankur R. Desai,
C. Frédéric,
Michal Dohnal,
Jean‐Christophe Domec,
Sebinasi Dzikiti,
Colin W. Edgar,
Rebekka Eichstaedt,
Tarek S. El‐Madany,
J.A. Elbers,
Cleiton B. Eller,
E. S. Euskirchen,
B. E. Ewers,
Patrick Fonti,
Alicia Forner,
David I. Forrester,
Helber C. Freitas,
Marta Galvagno,
Omar García-Tejera,
Chandra Prasad Ghimire,
Teresa E. Gimeno,
J. P. Grace,
André Granier,
Anne Griebel,
Yan Guangyu,
M.B. Gush,
Paul J. Hanson,
Niles J. Hasselquist,
Ingo Heinrich,
Virginia Hernández‐Santana,
Valentine Herrmann,
Teemu Hölttä,
F. Holwerda,
J. E. Irvine,
Supat Isarangkool Na Ayutthaya,
P. G. Jarvis,
Hubert Jochheim,
Carlos Alfredo Joly,
Julia Kaplick,
Hyun Seok Kim,
Leif Klemedtsson,
Heather Kropp,
Fredrik Lagergren,
Patrick N.J. Lane,
Petra Lang,
Andrei Lapenas,
Víctor Lechuga,
Minsu Lee,
Christoph Leuschner,
Jean‐Marc Limousin,
Juan Carlos Linares,
Maj‐Lena Linderson,
Anders Lindroth,
Pilar Llorens,
Álvaro López‐Bernal,
M. M. Loranty,
Dietmar Lüttschwager,
Cate Macinnis‐Ng,
Isabelle Maréchaux,
Timothy A. Martin,
Ashley M. Matheny,
Nate G. McDowell,
Sean M. McMahon,
Patrick Meir,
Ilona Mészáros,
Mirco Migliavacca,
Patrick J. Mitchell,
Meelis Mölder,
Leonardo Montagnani,
Georgianne W. Moore,
Ryogo Nakada,
Furong Niu,
Rachael H. Nolan,
Richard J. Norby,
Kimberly A. Novick,
Walter Oberhuber,
Nikolaus Obojes,
A. Christopher Oishi,
Rafael S. Oliveira,
Ram Oren,
Jean‐Marc Ourcival,
Teemu Paljakka,
Óscar Pérez‐Priego,
Pablo Luís Peri,
Richard L. Peters,
Sebastian Pfautsch,
William T. Pockman,
Yakir Preisler,
Katherine G. Rascher,
George R. Robinson,
Humberto Ribeiro da Rocha,
Alain Rocheteau,
Alexander Röll,
Bruno H. P. Rosado,
Lucy Rowland,
Alexey V. Rubtsov,
Santiago Sabaté,
Yann Salmon,
Roberto L. Salomón,
Elisenda Sánchez‐Costa,
Karina V. R. Schäfer,
Bernhard Schuldt,
Alexandr Shashkin,
Clément Stahl,
Marko Stojanović,
Juan Carlos Suárez,
Ge Sun,
Justyna Szatniewska,
Fyodor Tatarinov,
Miroslav Tesař,
Frank M. Thomas,
Pantana Tor‐ngern,
Josef Urban,
Fernando Valladares,
Christiaan van der Tol,
Ilja van Meerveld,
Andrej Varlagin,
Holm Voigt,
J. M. Warren,
Christiane Werner,
Willy Werner,
Gerhard Wieser,
Lisa Wingate,
Stan Wullschleger,
K. Yi,
Roman Zweifel,
Kathy Steppe,
Maurizio Mencuccini,
Jordi Martínez‐Vilalta
Earth System Science Data, Volume 13, Issue 6
Abstract. Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land–atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (https://doi.org/10.5281/zenodo.3971689; Poyatos et al., 2020a). The “sapfluxnetr” R package – designed to access, visualize, and process SAPFLUXNET data – is available from CRAN.
DOI
bib
abs
Global transpiration data from sap flow measurements: the SAPFLUXNET database
Rafael Poyatos,
Víctor Granda,
Víctor Flo,
Mark A. Adams,
Balázs Adorján,
David Aguadé,
Marcos Pereira Marinho Aidar,
Scott T. Allen,
M. S. Alvarado-Barrientos,
Kristina J. Anderson‐Teixeira,
L. M. T. Aparecido,
M. Altaf Arain,
Ismael Aranda,
Heidi Asbjornsen,
Robert Baxter,
Eric Beamesderfer,
Z. Carter Berry,
Daniel Berveiller,
Bethany Blakely,
Johnny Boggs,
Gil Bohrer,
Paul V. Bolstad,
Damien Bonal,
Rosvel Bracho,
Patricia Brito,
Jason Brodeur,
Fernando Casanoves,
Jérôme Chave,
Hui Chen,
César Cisneros Vaca,
Kenneth L. Clark,
Edoardo Cremonese,
Hongzhong Dang,
Jorge S. David,
Teresa S. David,
Nicolas Delpierre,
Ankur R. Desai,
C. Frédéric,
Michal Dohnal,
Jean‐Christophe Domec,
Sebinasi Dzikiti,
Colin W. Edgar,
Rebekka Eichstaedt,
Tarek S. El‐Madany,
J.A. Elbers,
Cleiton B. Eller,
E. S. Euskirchen,
B. E. Ewers,
Patrick Fonti,
Alicia Forner,
David I. Forrester,
Helber C. Freitas,
Marta Galvagno,
Omar García-Tejera,
Chandra Prasad Ghimire,
Teresa E. Gimeno,
J. P. Grace,
André Granier,
Anne Griebel,
Yan Guangyu,
M.B. Gush,
Paul J. Hanson,
Niles J. Hasselquist,
Ingo Heinrich,
Virginia Hernández‐Santana,
Valentine Herrmann,
Teemu Hölttä,
F. Holwerda,
J. E. Irvine,
Supat Isarangkool Na Ayutthaya,
P. G. Jarvis,
Hubert Jochheim,
Carlos Alfredo Joly,
Julia Kaplick,
Hyun Seok Kim,
Leif Klemedtsson,
Heather Kropp,
Fredrik Lagergren,
Patrick N.J. Lane,
Petra Lang,
Andrei Lapenas,
Víctor Lechuga,
Minsu Lee,
Christoph Leuschner,
Jean‐Marc Limousin,
Juan Carlos Linares,
Maj‐Lena Linderson,
Anders Lindroth,
Pilar Llorens,
Álvaro López‐Bernal,
M. M. Loranty,
Dietmar Lüttschwager,
Cate Macinnis‐Ng,
Isabelle Maréchaux,
Timothy A. Martin,
Ashley M. Matheny,
Nate G. McDowell,
Sean M. McMahon,
Patrick Meir,
Ilona Mészáros,
Mirco Migliavacca,
Patrick J. Mitchell,
Meelis Mölder,
Leonardo Montagnani,
Georgianne W. Moore,
Ryogo Nakada,
Furong Niu,
Rachael H. Nolan,
Richard J. Norby,
Kimberly A. Novick,
Walter Oberhuber,
Nikolaus Obojes,
A. Christopher Oishi,
Rafael S. Oliveira,
Ram Oren,
Jean‐Marc Ourcival,
Teemu Paljakka,
Óscar Pérez‐Priego,
Pablo Luís Peri,
Richard L. Peters,
Sebastian Pfautsch,
William T. Pockman,
Yakir Preisler,
Katherine G. Rascher,
George R. Robinson,
Humberto Ribeiro da Rocha,
Alain Rocheteau,
Alexander Röll,
Bruno H. P. Rosado,
Lucy Rowland,
Alexey V. Rubtsov,
Santiago Sabaté,
Yann Salmon,
Roberto L. Salomón,
Elisenda Sánchez‐Costa,
Karina V. R. Schäfer,
Bernhard Schuldt,
Alexandr Shashkin,
Clément Stahl,
Marko Stojanović,
Juan Carlos Suárez,
Ge Sun,
Justyna Szatniewska,
Fyodor Tatarinov,
Miroslav Tesař,
Frank M. Thomas,
Pantana Tor‐ngern,
Josef Urban,
Fernando Valladares,
Christiaan van der Tol,
Ilja van Meerveld,
Andrej Varlagin,
Holm Voigt,
J. M. Warren,
Christiane Werner,
Willy Werner,
Gerhard Wieser,
Lisa Wingate,
Stan Wullschleger,
K. Yi,
Roman Zweifel,
Kathy Steppe,
Maurizio Mencuccini,
Jordi Martínez‐Vilalta,
Rafael Poyatos,
Víctor Granda,
Víctor Flo,
Mark A. Adams,
Balázs Adorján,
David Aguadé,
Marcos Pereira Marinho Aidar,
Scott T. Allen,
M. S. Alvarado-Barrientos,
Kristina J. Anderson‐Teixeira,
L. M. T. Aparecido,
M. Altaf Arain,
Ismael Aranda,
Heidi Asbjornsen,
Robert Baxter,
Eric Beamesderfer,
Z. Carter Berry,
Daniel Berveiller,
Bethany Blakely,
Johnny Boggs,
Gil Bohrer,
Paul V. Bolstad,
Damien Bonal,
Rosvel Bracho,
Patricia Brito,
Jason Brodeur,
Fernando Casanoves,
Jérôme Chave,
Hui Chen,
César Cisneros Vaca,
Kenneth L. Clark,
Edoardo Cremonese,
Hongzhong Dang,
Jorge S. David,
Teresa S. David,
Nicolas Delpierre,
Ankur R. Desai,
C. Frédéric,
Michal Dohnal,
Jean‐Christophe Domec,
Sebinasi Dzikiti,
Colin W. Edgar,
Rebekka Eichstaedt,
Tarek S. El‐Madany,
J.A. Elbers,
Cleiton B. Eller,
E. S. Euskirchen,
B. E. Ewers,
Patrick Fonti,
Alicia Forner,
David I. Forrester,
Helber C. Freitas,
Marta Galvagno,
Omar García-Tejera,
Chandra Prasad Ghimire,
Teresa E. Gimeno,
J. P. Grace,
André Granier,
Anne Griebel,
Yan Guangyu,
M.B. Gush,
Paul J. Hanson,
Niles J. Hasselquist,
Ingo Heinrich,
Virginia Hernández‐Santana,
Valentine Herrmann,
Teemu Hölttä,
F. Holwerda,
J. E. Irvine,
Supat Isarangkool Na Ayutthaya,
P. G. Jarvis,
Hubert Jochheim,
Carlos Alfredo Joly,
Julia Kaplick,
Hyun Seok Kim,
Leif Klemedtsson,
Heather Kropp,
Fredrik Lagergren,
Patrick N.J. Lane,
Petra Lang,
Andrei Lapenas,
Víctor Lechuga,
Minsu Lee,
Christoph Leuschner,
Jean‐Marc Limousin,
Juan Carlos Linares,
Maj‐Lena Linderson,
Anders Lindroth,
Pilar Llorens,
Álvaro López‐Bernal,
M. M. Loranty,
Dietmar Lüttschwager,
Cate Macinnis‐Ng,
Isabelle Maréchaux,
Timothy A. Martin,
Ashley M. Matheny,
Nate G. McDowell,
Sean M. McMahon,
Patrick Meir,
Ilona Mészáros,
Mirco Migliavacca,
Patrick J. Mitchell,
Meelis Mölder,
Leonardo Montagnani,
Georgianne W. Moore,
Ryogo Nakada,
Furong Niu,
Rachael H. Nolan,
Richard J. Norby,
Kimberly A. Novick,
Walter Oberhuber,
Nikolaus Obojes,
A. Christopher Oishi,
Rafael S. Oliveira,
Ram Oren,
Jean‐Marc Ourcival,
Teemu Paljakka,
Óscar Pérez‐Priego,
Pablo Luís Peri,
Richard L. Peters,
Sebastian Pfautsch,
William T. Pockman,
Yakir Preisler,
Katherine G. Rascher,
George R. Robinson,
Humberto Ribeiro da Rocha,
Alain Rocheteau,
Alexander Röll,
Bruno H. P. Rosado,
Lucy Rowland,
Alexey V. Rubtsov,
Santiago Sabaté,
Yann Salmon,
Roberto L. Salomón,
Elisenda Sánchez‐Costa,
Karina V. R. Schäfer,
Bernhard Schuldt,
Alexandr Shashkin,
Clément Stahl,
Marko Stojanović,
Juan Carlos Suárez,
Ge Sun,
Justyna Szatniewska,
Fyodor Tatarinov,
Miroslav Tesař,
Frank M. Thomas,
Pantana Tor‐ngern,
Josef Urban,
Fernando Valladares,
Christiaan van der Tol,
Ilja van Meerveld,
Andrej Varlagin,
Holm Voigt,
J. M. Warren,
Christiane Werner,
Willy Werner,
Gerhard Wieser,
Lisa Wingate,
Stan Wullschleger,
K. Yi,
Roman Zweifel,
Kathy Steppe,
Maurizio Mencuccini,
Jordi Martínez‐Vilalta
Earth System Science Data, Volume 13, Issue 6
Abstract. Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land–atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (https://doi.org/10.5281/zenodo.3971689; Poyatos et al., 2020a). The “sapfluxnetr” R package – designed to access, visualize, and process SAPFLUXNET data – is available from CRAN.
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Sensitivity of vegetation dynamics to climate variability in a forest-steppe transition ecozone, north-eastern Inner Mongolia, China
Guangyong You,
Bo Liu,
Changxin Zou,
Haidong Li,
Shawn McKenzie,
Yaqian He,
Jixi Gao,
Xiru Jia,
M. Altaf Arain,
Shusen Wang,
Zhi Wang,
Xin Xia,
Wanggu Xu,
Guangyong You,
Bo Liu,
Changxin Zou,
Haidong Li,
Shawn McKenzie,
Yaqian He,
Jixi Gao,
Xiru Jia,
M. Altaf Arain,
Shusen Wang,
Zhi Wang,
Xin Xia,
Wanggu Xu
Ecological Indicators, Volume 120
Abstract Climate change and land use management were competing explanations for vegetation dynamics in cold and semi-arid region of north-eastern Inner Mongolia, China. In order to reveal the role of human disturbance and clarify the regional climate-vegetation relationship, long-term (1982–2013) datasets of climate variables and vegetation dynamics in a forest-steppe transition zone of north-eastern Inner Mongolia, China were collected. Partial correlation analyses, principal components regression (PCR), and residual analyses were conducted to reveal the vegetation sensitivities to different climate variables and the impact of anthropogenic activities on climate-vegetation relationship. The results showed that. (1) Annual mean air temperature (TMP) significantly increased at a linear slope of 0.08 °C per decade, annual precipitation (PRE) had an insignificantly linear slope of −16.42 mm per decade (p = 0.15). The average Normalized Difference Vegetation Index (NDVI) had a significantly negative trend over the past decades. A change point around the year 1998, coincided with the occurrence of an intense global El Nino event was also identified. (2) Regional climate change can be represented by changes in temperature, humidity and radiation. NDVI in the steppes display high sensitivity to moisture availability. Whereas, forests was influenced by the warmth index (WMI), accumulation of monthly temperature above a threshold of 5 °C. Partial correlation analyses showed that pixels of positive correlation with PRE (controlling TMP) overlap with the pixels of high partial correlation with minimum temperature (controlling maximum temperature), which suggests a hidden link between minimum temperature and PRE in this region. (3) The spatial distribution of significantly decreased NDVI overlap with cropland expansion, as well as the low residual square (R2) from PCR analysis. The NDVI decline in these expanded croplands suggests human disturbance on vegetation dynamics. Following climate warming, NDVI of forested land displayed positive trend. Whereas, most of steppe displayed negative trend, possibly resulting from combined effects of climate drying and human disturbance. We conclude that the regional climate change can be characterized as warming and drying. Steppe areas were sensitive to humidity changes while forested land was mostly influenced by growing season warmth. Overall, the regional NDVI displayed significantly negative trend over the past decades. Beyond climate drying, cropland expansion in the transition area between grassland and forested land is also an important driver for decreased NDVI. Further studies on the ecological and hydrological consequences of crop land expansion is necessary.
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Sensitivity of vegetation dynamics to climate variability in a forest-steppe transition ecozone, north-eastern Inner Mongolia, China
Guangyong You,
Bo Liu,
Changxin Zou,
Haidong Li,
Shawn McKenzie,
Yaqian He,
Jixi Gao,
Xiru Jia,
M. Altaf Arain,
Shusen Wang,
Zhi Wang,
Xin Xia,
Wanggu Xu,
Guangyong You,
Bo Liu,
Changxin Zou,
Haidong Li,
Shawn McKenzie,
Yaqian He,
Jixi Gao,
Xiru Jia,
M. Altaf Arain,
Shusen Wang,
Zhi Wang,
Xin Xia,
Wanggu Xu
Ecological Indicators, Volume 120
Abstract Climate change and land use management were competing explanations for vegetation dynamics in cold and semi-arid region of north-eastern Inner Mongolia, China. In order to reveal the role of human disturbance and clarify the regional climate-vegetation relationship, long-term (1982–2013) datasets of climate variables and vegetation dynamics in a forest-steppe transition zone of north-eastern Inner Mongolia, China were collected. Partial correlation analyses, principal components regression (PCR), and residual analyses were conducted to reveal the vegetation sensitivities to different climate variables and the impact of anthropogenic activities on climate-vegetation relationship. The results showed that. (1) Annual mean air temperature (TMP) significantly increased at a linear slope of 0.08 °C per decade, annual precipitation (PRE) had an insignificantly linear slope of −16.42 mm per decade (p = 0.15). The average Normalized Difference Vegetation Index (NDVI) had a significantly negative trend over the past decades. A change point around the year 1998, coincided with the occurrence of an intense global El Nino event was also identified. (2) Regional climate change can be represented by changes in temperature, humidity and radiation. NDVI in the steppes display high sensitivity to moisture availability. Whereas, forests was influenced by the warmth index (WMI), accumulation of monthly temperature above a threshold of 5 °C. Partial correlation analyses showed that pixels of positive correlation with PRE (controlling TMP) overlap with the pixels of high partial correlation with minimum temperature (controlling maximum temperature), which suggests a hidden link between minimum temperature and PRE in this region. (3) The spatial distribution of significantly decreased NDVI overlap with cropland expansion, as well as the low residual square (R2) from PCR analysis. The NDVI decline in these expanded croplands suggests human disturbance on vegetation dynamics. Following climate warming, NDVI of forested land displayed positive trend. Whereas, most of steppe displayed negative trend, possibly resulting from combined effects of climate drying and human disturbance. We conclude that the regional climate change can be characterized as warming and drying. Steppe areas were sensitive to humidity changes while forested land was mostly influenced by growing season warmth. Overall, the regional NDVI displayed significantly negative trend over the past decades. Beyond climate drying, cropland expansion in the transition area between grassland and forested land is also an important driver for decreased NDVI. Further studies on the ecological and hydrological consequences of crop land expansion is necessary.
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Seasonality in aerodynamic resistance across a range of North American ecosystems
Adam M. Young,
M. A. Friedl,
Bijan Seyednasrollah,
Eric Beamesderfer,
Carlos M. Carrillo,
Xiaolu Li,
Minkyu Moon,
M. Altaf Arain,
Dennis Baldocchi,
Peter D. Blanken,
Gil Bohrer,
Sean P. Burns,
Housen Chu,
Ankur R. Desai,
Timothy J. Griffis,
David Y. Hollinger,
M. E. Litvak,
Kim Novick,
Russell L. Scott,
Andrew E. Suyker,
Joseph Verfaillie,
Jeffrey D. Wood,
Andrew D. Richardson,
Adam M. Young,
M. A. Friedl,
Bijan Seyednasrollah,
Eric Beamesderfer,
Carlos M. Carrillo,
Xiaolu Li,
Minkyu Moon,
M. Altaf Arain,
Dennis Baldocchi,
Peter D. Blanken,
Gil Bohrer,
Sean P. Burns,
Housen Chu,
Ankur R. Desai,
Timothy J. Griffis,
David Y. Hollinger,
M. E. Litvak,
Kim Novick,
Russell L. Scott,
Andrew E. Suyker,
Joseph Verfaillie,
Jeffrey D. Wood,
Andrew D. Richardson
Agricultural and Forest Meteorology, Volume 310
• Phenological controls over aerodynamic resistance ( R ah ) were investigated. • R ah exhibits significant seasonal variability across a wide range of sites. • These shifts in R ah were caused by phenology in some ecosystems. • Accounting for variation in kB −1 is important for improving predictions of H . Surface roughness – a key control on land-atmosphere exchanges of heat and momentum – differs between dormant and growing seasons. However, how surface roughness shifts seasonally at fine time scales (e.g., days) in response to changing canopy conditions is not well understood. This study: (1) explores how aerodynamic resistance changes seasonally; (2) investigates what drives these seasonal shifts, including the role of vegetation phenology; and (3) quantifies the importance of including seasonal changes of aerodynamic resistance in “big leaf” models of sensible heat flux ( H ). We evaluated aerodynamic resistance and surface roughness lengths for momentum ( z 0m ) and heat ( z 0h ) using the kB −1 parameter (ln( z 0m / z 0h )). We used AmeriFlux data to obtain surface-roughness estimates, and PhenoCam greenness data for phenology. This analysis included 23 sites and ∼190 site years from deciduous broadleaf, evergreen needleleaf, woody savanna, cropland, grassland, and shrubland plant-functional types (PFTs). Results indicated clear seasonal patterns in aerodynamic resistance to sensible heat transfer ( R ah ). This seasonality tracked PhenoCam-derived start-of-season green-up transitions in PFTs displaying the most significant seasonal changes in canopy structure, with R ah decreasing near green-up transitions. Conversely, in woody savanna sites and evergreen needleleaf forests, patterns in R ah were not linked to green-up. Our findings highlight that decreases in kB −1 are an important control over R ah , explaining > 50% of seasonal variation in R ah across most sites. Decreases in kB −1 during green-up are likely caused by increasing z 0h in response to higher leaf area index. Accounting for seasonal variation in kB −1 is key for predicting H as well; assuming kB −1 to be constant resulted in significant biases that also exhibited strong seasonal patterns. Overall, we found that aerodynamic resistance can be sensitive to phenology in ecosystems having strong seasonality in leaf area, and this linkage is critical for understanding land-atmosphere interactions at seasonal time scales.
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Seasonality in aerodynamic resistance across a range of North American ecosystems
Adam M. Young,
M. A. Friedl,
Bijan Seyednasrollah,
Eric Beamesderfer,
Carlos M. Carrillo,
Xiaolu Li,
Minkyu Moon,
M. Altaf Arain,
Dennis Baldocchi,
Peter D. Blanken,
Gil Bohrer,
Sean P. Burns,
Housen Chu,
Ankur R. Desai,
Timothy J. Griffis,
David Y. Hollinger,
M. E. Litvak,
Kim Novick,
Russell L. Scott,
Andrew E. Suyker,
Joseph Verfaillie,
Jeffrey D. Wood,
Andrew D. Richardson,
Adam M. Young,
M. A. Friedl,
Bijan Seyednasrollah,
Eric Beamesderfer,
Carlos M. Carrillo,
Xiaolu Li,
Minkyu Moon,
M. Altaf Arain,
Dennis Baldocchi,
Peter D. Blanken,
Gil Bohrer,
Sean P. Burns,
Housen Chu,
Ankur R. Desai,
Timothy J. Griffis,
David Y. Hollinger,
M. E. Litvak,
Kim Novick,
Russell L. Scott,
Andrew E. Suyker,
Joseph Verfaillie,
Jeffrey D. Wood,
Andrew D. Richardson
Agricultural and Forest Meteorology, Volume 310
• Phenological controls over aerodynamic resistance ( R ah ) were investigated. • R ah exhibits significant seasonal variability across a wide range of sites. • These shifts in R ah were caused by phenology in some ecosystems. • Accounting for variation in kB −1 is important for improving predictions of H . Surface roughness – a key control on land-atmosphere exchanges of heat and momentum – differs between dormant and growing seasons. However, how surface roughness shifts seasonally at fine time scales (e.g., days) in response to changing canopy conditions is not well understood. This study: (1) explores how aerodynamic resistance changes seasonally; (2) investigates what drives these seasonal shifts, including the role of vegetation phenology; and (3) quantifies the importance of including seasonal changes of aerodynamic resistance in “big leaf” models of sensible heat flux ( H ). We evaluated aerodynamic resistance and surface roughness lengths for momentum ( z 0m ) and heat ( z 0h ) using the kB −1 parameter (ln( z 0m / z 0h )). We used AmeriFlux data to obtain surface-roughness estimates, and PhenoCam greenness data for phenology. This analysis included 23 sites and ∼190 site years from deciduous broadleaf, evergreen needleleaf, woody savanna, cropland, grassland, and shrubland plant-functional types (PFTs). Results indicated clear seasonal patterns in aerodynamic resistance to sensible heat transfer ( R ah ). This seasonality tracked PhenoCam-derived start-of-season green-up transitions in PFTs displaying the most significant seasonal changes in canopy structure, with R ah decreasing near green-up transitions. Conversely, in woody savanna sites and evergreen needleleaf forests, patterns in R ah were not linked to green-up. Our findings highlight that decreases in kB −1 are an important control over R ah , explaining > 50% of seasonal variation in R ah across most sites. Decreases in kB −1 during green-up are likely caused by increasing z 0h in response to higher leaf area index. Accounting for seasonal variation in kB −1 is key for predicting H as well; assuming kB −1 to be constant resulted in significant biases that also exhibited strong seasonal patterns. Overall, we found that aerodynamic resistance can be sensitive to phenology in ecosystems having strong seasonality in leaf area, and this linkage is critical for understanding land-atmosphere interactions at seasonal time scales.
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The Impact of Variable Retention Harvesting on Growth and Carbon Sequestration of a Red Pine (Pinus resinosa Ait.) Plantation Forest in Southern Ontario, Canada
Jessica Zugic,
Michael F. J. Pisaric,
Shawn McKenzie,
William C. Parker,
Ken A. Elliott,
M. Altaf Arain,
Jessica Zugic,
Michael F. J. Pisaric,
Shawn McKenzie,
William C. Parker,
Ken A. Elliott,
M. Altaf Arain
Frontiers in Forests and Global Change, Volume 4
As atmospheric carbon dioxide concentrations continue to rise and global temperatures increase, there is growing concern about the sustainability, health, and carbon sequestration potential of forest ecosystems. Variable retention harvesting (VRH) has been suggested to be a potential method to increase forest biodiversity, growth, and carbon (C) sequestration. A field trial was established in an 88-year-old red pine ( Pinus resinosa Ait.) plantation in southern Ontario, Canada, using a completely randomized design to examine the response of tree productivity and other forest values to five harvesting treatments: 33% aggregate retention (33A), 55% aggregate retention (55A), 33% dispersed retention (33D), and 55% dispersed retention (55D) in comparison to an unharvested control (CN). In this study, we explored the impacts of VRH on aboveground stem radial growth and annual C increment. Standard dendrochronological methods and allometric equations were used to quantify tree- and stand-level treatment effects during a five-year pre-harvest (2009–2013) and post-harvest (2014–2018) period. Tree-level growth and C increment were increased by the dispersed retention pattern regardless of retention level. At the stand level, the total C increment was highest at greater retention levels and did not vary with retention pattern. These results suggest that the choice of retention level and pattern can have a large influence on management objectives as they relate to timber production, climate change adaptation, and/or climate change mitigation.
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The Impact of Variable Retention Harvesting on Growth and Carbon Sequestration of a Red Pine (Pinus resinosa Ait.) Plantation Forest in Southern Ontario, Canada
Jessica Zugic,
Michael F. J. Pisaric,
Shawn McKenzie,
William C. Parker,
Ken A. Elliott,
M. Altaf Arain,
Jessica Zugic,
Michael F. J. Pisaric,
Shawn McKenzie,
William C. Parker,
Ken A. Elliott,
M. Altaf Arain
Frontiers in Forests and Global Change, Volume 4
As atmospheric carbon dioxide concentrations continue to rise and global temperatures increase, there is growing concern about the sustainability, health, and carbon sequestration potential of forest ecosystems. Variable retention harvesting (VRH) has been suggested to be a potential method to increase forest biodiversity, growth, and carbon (C) sequestration. A field trial was established in an 88-year-old red pine ( Pinus resinosa Ait.) plantation in southern Ontario, Canada, using a completely randomized design to examine the response of tree productivity and other forest values to five harvesting treatments: 33% aggregate retention (33A), 55% aggregate retention (55A), 33% dispersed retention (33D), and 55% dispersed retention (55D) in comparison to an unharvested control (CN). In this study, we explored the impacts of VRH on aboveground stem radial growth and annual C increment. Standard dendrochronological methods and allometric equations were used to quantify tree- and stand-level treatment effects during a five-year pre-harvest (2009–2013) and post-harvest (2014–2018) period. Tree-level growth and C increment were increased by the dispersed retention pattern regardless of retention level. At the stand level, the total C increment was highest at greater retention levels and did not vary with retention pattern. These results suggest that the choice of retention level and pattern can have a large influence on management objectives as they relate to timber production, climate change adaptation, and/or climate change mitigation.
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Evaluation of observed and projected extreme climate trends for decision making in Six Nations of the Grand River, Canada
Tariq A. Deen,
M. Altaf Arain,
Olivier Champagne,
Patricia Chow‐Fraser,
Nidhi Nagabhatla,
Dawn Martin-Hill,
Tariq A. Deen,
M. Altaf Arain,
Olivier Champagne,
Patricia Chow‐Fraser,
Nidhi Nagabhatla,
Dawn Martin-Hill
Climate Services, Volume 24
Hydrometeorological events have been the predominant type of natural hazards to affect communities across Canada. While climate change is a concern to all Canadians, Indigenous communities in Canada have been disproportionately more affected by these extreme climate events than non-Indigenous communities. As the impacts of climate change intensify, it becomes increasingly important that high-resolution climate services are made available to Indigenous decision makers for the development of climate change adaptation plans. This paper examined extreme climate trends in the Six Nations of the Grand River reserve, the most populated Indigenous community in Canada. A set of 12 indices were used to evaluate changes in extreme climate events from 1951 to 2013, and 2006 to 2099 under Representative Concentration Pathways (RCP) 4.5 and 8.5. Results indicated that from 1951 to 2013, Six Nations became warmer and wetter with an average temperature increase of 0.7 °C and precipitation increase of 42 mm. Over this period, the frequency and duration of extreme heat and extreme precipitation events also increased, while extreme cold events decreased. In the future (2006 to 2099), temperature is expected to increase by 3 to 6 °C, while seasonal precipitation is expected to increase in winter, early spring, and fall. Projected rate of increase of heatwaves is 0.4 to 1.5 days per year and extreme annual rainfall events is 0.2 to 0.5 mm per year under both RCP scenarios. The climate information and data provide by this study will help Six Nations’ decision makers in planning for climate change impacts.
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Evaluation of observed and projected extreme climate trends for decision making in Six Nations of the Grand River, Canada
Tariq A. Deen,
M. Altaf Arain,
Olivier Champagne,
Patricia Chow‐Fraser,
Nidhi Nagabhatla,
Dawn Martin-Hill,
Tariq A. Deen,
M. Altaf Arain,
Olivier Champagne,
Patricia Chow‐Fraser,
Nidhi Nagabhatla,
Dawn Martin-Hill
Climate Services, Volume 24
Hydrometeorological events have been the predominant type of natural hazards to affect communities across Canada. While climate change is a concern to all Canadians, Indigenous communities in Canada have been disproportionately more affected by these extreme climate events than non-Indigenous communities. As the impacts of climate change intensify, it becomes increasingly important that high-resolution climate services are made available to Indigenous decision makers for the development of climate change adaptation plans. This paper examined extreme climate trends in the Six Nations of the Grand River reserve, the most populated Indigenous community in Canada. A set of 12 indices were used to evaluate changes in extreme climate events from 1951 to 2013, and 2006 to 2099 under Representative Concentration Pathways (RCP) 4.5 and 8.5. Results indicated that from 1951 to 2013, Six Nations became warmer and wetter with an average temperature increase of 0.7 °C and precipitation increase of 42 mm. Over this period, the frequency and duration of extreme heat and extreme precipitation events also increased, while extreme cold events decreased. In the future (2006 to 2099), temperature is expected to increase by 3 to 6 °C, while seasonal precipitation is expected to increase in winter, early spring, and fall. Projected rate of increase of heatwaves is 0.4 to 1.5 days per year and extreme annual rainfall events is 0.2 to 0.5 mm per year under both RCP scenarios. The climate information and data provide by this study will help Six Nations’ decision makers in planning for climate change impacts.
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Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites
Housen Chu,
Xiangzhong Luo,
Zutao Ouyang,
Stephen Chan,
Sigrid Dengel,
Sébastien Biraud,
Margaret Torn,
Stefan Metzger,
Jitendra Kumar,
M. Altaf Arain,
T. J. Arkebauer,
Dennis Baldocchi,
Carl J. Bernacchi,
D. P. Billesbach,
T. Andrew Black,
Peter D. Blanken,
Gil Bohrer,
Rosvel Bracho,
S. M. Brown,
N. A. Brunsell,
Jiquan Chen,
Xingyuan Chen,
Kenneth L. Clark,
Ankur R. Desai,
Tomer Duman,
David Durden,
Silvano Fares,
Inke Forbrich,
John A. Gamon,
Christopher M. Gough,
Timothy J. Griffis,
Manuel Helbig,
David Y. Hollinger,
Elyn Humphreys,
Hiroki Ikawa,
Hiroki Iwata,
Yang Ju,
John F. Knowles,
Sara Knox,
Hideki Kobayashi,
Thomas E. Kolb,
B. E. Law,
Xuhui Lee,
M. E. Litvak,
Heping Liu,
J. William Munger,
Asko Noormets,
Kim Novick,
Steven F. Oberbauer,
Walter C. Oechel,
Patricia Y. Oikawa,
S. A. Papuga,
Elise Pendall,
Prajaya Prajapati,
John H. Prueger,
W. L. Quinton,
Andrew D. Richardson,
Eric S. Russell,
Russell L. Scott,
Gregory Starr,
R. M. Staebler,
Paul C. Stoy,
Ellen Stuart‐Haëntjens,
Oliver Sonnentag,
Ryan C. Sullivan,
Andy Suyker,
Masahito Ueyama,
Rodrigo Vargas,
Jeffrey D. Wood,
Donatella Zona,
Housen Chu,
Xiangzhong Luo,
Zutao Ouyang,
Stephen Chan,
Sigrid Dengel,
Sébastien Biraud,
Margaret Torn,
Stefan Metzger,
Jitendra Kumar,
M. Altaf Arain,
T. J. Arkebauer,
Dennis Baldocchi,
Carl J. Bernacchi,
D. P. Billesbach,
T. Andrew Black,
Peter D. Blanken,
Gil Bohrer,
Rosvel Bracho,
S. M. Brown,
N. A. Brunsell,
Jiquan Chen,
Xingyuan Chen,
Kenneth L. Clark,
Ankur R. Desai,
Tomer Duman,
David Durden,
Silvano Fares,
Inke Forbrich,
John A. Gamon,
Christopher M. Gough,
Timothy J. Griffis,
Manuel Helbig,
David Y. Hollinger,
Elyn Humphreys,
Hiroki Ikawa,
Hiroki Iwata,
Yang Ju,
John F. Knowles,
Sara Knox,
Hideki Kobayashi,
Thomas E. Kolb,
B. E. Law,
Xuhui Lee,
M. E. Litvak,
Heping Liu,
J. William Munger,
Asko Noormets,
Kim Novick,
Steven F. Oberbauer,
Walter C. Oechel,
Patricia Y. Oikawa,
S. A. Papuga,
Elise Pendall,
Prajaya Prajapati,
John H. Prueger,
W. L. Quinton,
Andrew D. Richardson,
Eric S. Russell,
Russell L. Scott,
Gregory Starr,
R. M. Staebler,
Paul C. Stoy,
Ellen Stuart‐Haëntjens,
Oliver Sonnentag,
Ryan C. Sullivan,
Andy Suyker,
Masahito Ueyama,
Rodrigo Vargas,
Jeffrey D. Wood,
Donatella Zona
Agricultural and Forest Meteorology, Volume 301-302
• Large-scale eddy-covariance flux datasets need to be used with footprint-awareness • Using a fixed-extent target area across sites can bias model-data integration • Most sites do not represent the dominant land-cover type at a larger spatial extent • A representativeness index provides general guidance for site selection and data use Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 10 3 to 10 7 m 2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applications and to provide general guidance for data use.
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Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites
Housen Chu,
Xiangzhong Luo,
Zutao Ouyang,
Stephen Chan,
Sigrid Dengel,
Sébastien Biraud,
Margaret Torn,
Stefan Metzger,
Jitendra Kumar,
M. Altaf Arain,
T. J. Arkebauer,
Dennis Baldocchi,
Carl J. Bernacchi,
D. P. Billesbach,
T. Andrew Black,
Peter D. Blanken,
Gil Bohrer,
Rosvel Bracho,
S. M. Brown,
N. A. Brunsell,
Jiquan Chen,
Xingyuan Chen,
Kenneth L. Clark,
Ankur R. Desai,
Tomer Duman,
David Durden,
Silvano Fares,
Inke Forbrich,
John A. Gamon,
Christopher M. Gough,
Timothy J. Griffis,
Manuel Helbig,
David Y. Hollinger,
Elyn Humphreys,
Hiroki Ikawa,
Hiroki Iwata,
Yang Ju,
John F. Knowles,
Sara Knox,
Hideki Kobayashi,
Thomas E. Kolb,
B. E. Law,
Xuhui Lee,
M. E. Litvak,
Heping Liu,
J. William Munger,
Asko Noormets,
Kim Novick,
Steven F. Oberbauer,
Walter C. Oechel,
Patricia Y. Oikawa,
S. A. Papuga,
Elise Pendall,
Prajaya Prajapati,
John H. Prueger,
W. L. Quinton,
Andrew D. Richardson,
Eric S. Russell,
Russell L. Scott,
Gregory Starr,
R. M. Staebler,
Paul C. Stoy,
Ellen Stuart‐Haëntjens,
Oliver Sonnentag,
Ryan C. Sullivan,
Andy Suyker,
Masahito Ueyama,
Rodrigo Vargas,
Jeffrey D. Wood,
Donatella Zona,
Housen Chu,
Xiangzhong Luo,
Zutao Ouyang,
Stephen Chan,
Sigrid Dengel,
Sébastien Biraud,
Margaret Torn,
Stefan Metzger,
Jitendra Kumar,
M. Altaf Arain,
T. J. Arkebauer,
Dennis Baldocchi,
Carl J. Bernacchi,
D. P. Billesbach,
T. Andrew Black,
Peter D. Blanken,
Gil Bohrer,
Rosvel Bracho,
S. M. Brown,
N. A. Brunsell,
Jiquan Chen,
Xingyuan Chen,
Kenneth L. Clark,
Ankur R. Desai,
Tomer Duman,
David Durden,
Silvano Fares,
Inke Forbrich,
John A. Gamon,
Christopher M. Gough,
Timothy J. Griffis,
Manuel Helbig,
David Y. Hollinger,
Elyn Humphreys,
Hiroki Ikawa,
Hiroki Iwata,
Yang Ju,
John F. Knowles,
Sara Knox,
Hideki Kobayashi,
Thomas E. Kolb,
B. E. Law,
Xuhui Lee,
M. E. Litvak,
Heping Liu,
J. William Munger,
Asko Noormets,
Kim Novick,
Steven F. Oberbauer,
Walter C. Oechel,
Patricia Y. Oikawa,
S. A. Papuga,
Elise Pendall,
Prajaya Prajapati,
John H. Prueger,
W. L. Quinton,
Andrew D. Richardson,
Eric S. Russell,
Russell L. Scott,
Gregory Starr,
R. M. Staebler,
Paul C. Stoy,
Ellen Stuart‐Haëntjens,
Oliver Sonnentag,
Ryan C. Sullivan,
Andy Suyker,
Masahito Ueyama,
Rodrigo Vargas,
Jeffrey D. Wood,
Donatella Zona
Agricultural and Forest Meteorology, Volume 301-302
• Large-scale eddy-covariance flux datasets need to be used with footprint-awareness • Using a fixed-extent target area across sites can bias model-data integration • Most sites do not represent the dominant land-cover type at a larger spatial extent • A representativeness index provides general guidance for site selection and data use Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 10 3 to 10 7 m 2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applications and to provide general guidance for data use.
2020
Forests play a major role in the global carbon cycle. Understanding the dynamics of the forest carbon cycle and its driving factors is challenging. This study utilized dendrochronology and long-term (2003–2017) eddy covariance (EC) carbon flux data to investigate the relationships between tree growth and gross and net ecosystem productivities (GEPEC and NEPEC) in different-age (15-, 42- and 78-year old) pine plantation forests in the Great Lakes region in eastern North America. Tree growth in these different-age pine forests was significantly (p < 0.05) correlated with observed annual GEPEC values, while coherence between tree growth and NEPEC was relatively poor. Current-year and 1-year lagged ring-width chronologies and climate variables, including spring (April–May) temperature (TSPR) and Standardized Potential Evapotranspiration Index (SPEISUM) over the summer months (June–August) were used to test ten different linear regression models to simulate tree-ring-based GEP (GEPTR) values at all three sites. This analysis showed that current-year growth was the best predictor of GEPTR at all three sites, when compared to observed GEPEC, except during drought years, when GEPTR was underestimated. Current-year tree growth models were then used to reconstruct GEPTR over the life span of each stand. These reconstructions showed low GEPTR values from 1978 to 1988 and from 2002 to 2007. Low GEPTR in late 1970s occurred in response to below average temperatures when there were no major drought periods, while low GEPTR in early 2000s occurred following drought-like conditions in 2002. However, in recent years relatively higher GEPTR was observed at all three different-age forest sites. This interdisciplinary study will help to improve our understanding of carbon exchanges and the key environmental controls and associated uncertainties on tree growth in these different-age plantation stands in eastern North America. It will also help to determine how these forests may respond to climate change.
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Seasonal variation in the canopy color of temperate evergreen conifer forests
Bijan Seyednasrollah,
D. R. Bowling,
Rui Cheng,
Barry A. Logan,
Troy S. Magney,
Christian Frankenberg,
Julia C. Yang,
Adam M. Young,
Koen Hufkens,
M. Altaf Arain,
T. Andrew Black,
Peter D. Blanken,
Rosvel Bracho,
Rachhpal S. Jassal,
David Y. Hollinger,
B. E. Law,
Zoran Nesic,
Andrew D. Richardson
New Phytologist, Volume 229, Issue 5
Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near-surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated. Here, we integrate on-the-ground phenological observations, leaf-level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower-based CO2 flux measurements, and a predictive model to simulate seasonal canopy color dynamics. We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter-dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy-level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature-based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color. These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color-based vegetation indices.
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Attribute parameter characterized the seasonal variation of gross primary productivity (αGPP): Spatiotemporal variation and influencing factors
Weikang Zhang,
Guirui Yu,
Zhi Chen,
Leiming Zhang,
Qiufeng Wang,
Yangjian Zhang,
Honglin He,
Lang Han,
Shiping Chen,
Shijie Han,
Yingnian Li,
Liqing Sha,
Peili Shi,
Huimin Wang,
Yanfen Wang,
Wenhua Xiang,
Junhua Yan,
Yiping Zhang,
Donatella Zona,
M. Altaf Arain,
Trofim C. Maximov,
Walter C. Oechel,
Yoshiko Kosugi
Agricultural and Forest Meteorology, Volume 280
Abstract The seasonal dynamic of gross primary productivity (GPP) has influences on the annual GPP (AGPP) of the terrestrial ecosystem. However, the spatiotemporal variation of the seasonal dynamic of GPP and its effects on spatial and temporal variations of AGPP are still poorly addressed. In this study, we developed a parameter, αGPP, defined as the ratio of mean daily GPP (GPPmean) to the maximum daily GPP (GPPmax) during the growing season, to analyze the seasonal dynamic of GPP based on Weibull function. The αGPP was a comprehensive parameter characterizing the shape, scale, and location of the seasonal dynamic curve of GPP. We calculated αGPP based on the data of GPP for 942 site-years from 115 flux sites in the Northern Hemisphere, and analyzed the spatiotemporal variation and influencing factors of the αGPP. We found that the αGPP of terrestrial ecosystems in the Northern Hemisphere ranged from 0.47 to 0.85, with an average of 0.62 ± 0.06. The αGPP varied significantly both among different climatic zones and different ecosystem types. The αGPP was stable on the interannual scale, while decreased as latitude increased, which was consistent across different ecosystem types. The spatial pattern of the seasonal dynamic of astronomical radiation was the dominating factor of the spatial pattern of αGPP, that was, the spatial pattern of the seasonal dynamic of astronomical radiation determined that of the seasonal dynamic of GPP by controlling that of seasonal dynamics of total radiation and temperature. In addition, we assessed the spatial variation of AGPP preliminarily based on αGPP and other seasonal dynamic parameters of GPP, indicating that the understanding of the spatiotemporal variation of αGPP could provide a new approach for studying the spatial and temporal variations of AGPP and estimating AGPP based on the seasonal dynamic of GPP.
In eastern North America, many deciduous forest ecosystems grow at the northernmost extent of their geographical ranges, where climate change could aid or impede their growth. This region experiences frequent extreme weather conditions, allowing us to study the response of these forests to environmental conditions, reflective of future climates. Here we determined the impact of seasonal and annual climate variations and extreme weather events on the carbon (C) uptake capacity of an oak-dominated forest in southern Ontario, Canada, from 2012 to 2016. We found that changes in meteorology during late May to mid-July were key in determining the C sink strength of the forest, impacting the seasonal and annual variability of net ecosystem productivity (NEP). Overall, higher temperatures and dry conditions reduced ecosystem respiration (RE) much more than gross ecosystem productivity (GEP), leading to higher NEP. Variability in NEP was primarily driven by changes in RE, rather than GEP. The mean annual GEP, RE, and NEP values at our site during the study were 1,343 ± 85, 1,171 ± 139, and 206 ± 92 g C m-2 yr-1, respectively. The forest was a C sink even in years that experienced heat and water stresses. Mean annual NEP at our site was within the range of NEP (69-459 g C m-2 yr-1) observed in similar North American forests from 2012 to 2016. The growth and C sequestration capabilities of our oak-dominated forest were not adversely impacted by changes in environmental conditions and extreme weather events experienced over the study period.
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The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
Gilberto Pastorello,
Carlo Trotta,
Eleonora Canfora,
Housen Chu,
Danielle Christianson,
You-Wei Cheah,
C. Poindexter,
Jiquan Chen,
Abdelrahman Elbashandy,
Marty Humphrey,
Peter Isaac,
Diego Polidori,
Markus Reichstein,
Alessio Ribeca,
Catharine van Ingen,
Nicolas Vuichard,
Leiming Zhang,
B. D. Amiro,
Christof Ammann,
M. Altaf Arain,
Jonas Ardö,
Timothy J. Arkebauer,
Stefan K. Arndt,
Nicola Arriga,
Marc Aubinet,
Mika Aurela,
Dennis Baldocchi,
Alan Barr,
Eric Beamesderfer,
Luca Belelli Marchesini,
Onil Bergeron,
Jason Beringer,
Christian Bernhofer,
Daniel Berveiller,
D. P. Billesbach,
T. Andrew Black,
Peter D. Blanken,
Gil Bohrer,
Julia Boike,
Paul V. Bolstad,
Damien Bonal,
Jean-Marc Bonnefond,
D. R. Bowling,
Rosvel Bracho,
Jason Brodeur,
Christian Brümmer,
Nina Buchmann,
Benoît Burban,
Sean P. Burns,
Pauline Buysse,
Peter Cale,
M. Cavagna,
Pierre Cellier,
Shiping Chen,
Isaac Chini,
Torben R. Christensen,
James Cleverly,
Alessio Collalti,
Claudia Consalvo,
Bruce D. Cook,
David Cook,
Carole Coursolle,
Edoardo Cremonese,
Peter S. Curtis,
Ettore D’Andrea,
Humberto da Rocha,
Xiaoqin Dai,
K. J. Davis,
Bruno De Cinti,
A. de Grandcourt,
Anne De Ligne,
Raimundo Cosme de Oliveira,
Nicolas Delpierre,
Ankur R. Desai,
Carlos Marcelo Di Bella,
Paul Di Tommasi,
A. J. Dolman,
Francisco Domingo,
Gang Dong,
Sabina Dore,
Pierpaolo Duce,
Éric Dufrêne,
Allison L. Dunn,
Jiří Dušek,
Derek Eamus,
Uwe Eichelmann,
Hatim Abdalla M. ElKhidir,
Werner Eugster,
Cäcilia Ewenz,
B. E. Ewers,
D. Famulari,
Silvano Fares,
Iris Feigenwinter,
Andrew Feitz,
Rasmus Fensholt,
Gianluca Filippa,
M. L. Fischer,
J. M. Frank,
Marta Galvagno,
Mana Gharun,
Damiano Gianelle,
Bert Gielen,
Beniamino Gioli,
Anatoly A. Gitelson,
Ignacio Goded,
Mathias Goeckede,
A. H. Goldstein,
Christopher M. Gough,
Michael L. Goulden,
Alexander Graf,
Anne Griebel,
Carsten Gruening,
Thomas Grünwald,
Albin Hammerle,
Shijie Han,
Xingguo Han,
Birger Ulf Hansen,
Chad Hanson,
Juha Hatakka,
Yongtao He,
Markus Hehn,
Bernard Heinesch,
Nina Hinko‐Najera,
Lukas Hörtnagl,
Lindsay B. Hutley,
Andreas Ibrom,
Hiroki Ikawa,
M. Jackowicz-Korczyński,
Dalibor Janouš,
W.W.P. Jans,
Rachhpal S. Jassal,
Shicheng Jiang,
Tomomichi Kato,
Myroslava Khomik,
Janina Klatt,
Alexander Knohl,
Sara Knox,
Hideki Kobayashi,
Georgia R. Koerber,
Olaf Kolle,
Yoshiko Kosugi,
Ayumi Kotani,
Andrew S. Kowalski,
Bart Kruijt,
Julia Kurbatova,
Werner L. Kutsch,
Hyojung Kwon,
Samuli Launiainen,
Tuomas Laurila,
B. E. Law,
R. Leuning,
Yingnian Li,
Michael J. Liddell,
Jean‐Marc Limousin,
Marryanna Lion,
Adam Liska,
Annalea Lohila,
Ana López‐Ballesteros,
Efrèn López‐Blanco,
Benjamin Loubet,
Denis Loustau,
Antje Lucas-Moffat,
Johannes Lüers,
Siyan Ma,
Craig Macfarlane,
Vincenzo Magliulo,
Regine Maier,
Ivan Mammarella,
Giovanni Manca,
Barbara Marcolla,
Hank A. Margolis,
Serena Marras,
W. J. Massman,
Mikhail Mastepanov,
Roser Matamala,
Jaclyn Hatala Matthes,
Francesco Mazzenga,
Harry McCaughey,
Ian McHugh,
Andrew M. S. McMillan,
Lutz Merbold,
Wayne S. Meyer,
Tilden P. Meyers,
S. D. Miller,
Stefano Minerbi,
Uta Moderow,
Russell K. Monson,
Leonardo Montagnani,
Caitlin E. Moore,
E.J. Moors,
Virginie Moreaux,
Christine Moureaux,
J. William Munger,
T. Nakai,
Johan Neirynck,
Zoran Nesic,
Giacomo Nicolini,
Asko Noormets,
Matthew Northwood,
Marcelo D. Nosetto,
Yann Nouvellon,
Kimberly A. Novick,
Walter C. Oechel,
Jørgen E. Olesen,
Jean‐Marc Ourcival,
S. A. Papuga,
Frans‐Jan W. Parmentier,
Eugénie Paul‐Limoges,
Marian Pavelka,
Matthias Peichl,
Elise Pendall,
Richard P. Phillips,
Kim Pilegaard,
Norbert Pirk,
Gabriela Posse,
Thomas L. Powell,
Heiko Prasse,
Suzanne M. Prober,
Serge Rambal,
Üllar Rannik,
Naama Raz‐Yaseef,
Corinna Rebmann,
David E. Reed,
Víctor Resco de Dios,
Natalia Restrepo‐Coupé,
Borja Ruiz Reverter,
Marilyn Roland,
Simone Sabbatini,
Torsten Sachs,
S. R. Saleska,
Enrique P. Sánchez‐Cañete,
Zulia Mayari Sánchez-Mejía,
Hans Peter Schmid,
Marius Schmidt,
Karl Schneider,
Frederik Schrader,
Ivan Schroder,
Russell L. Scott,
Pavel Sedlák,
Penélope Serrano-Ortíz,
Changliang Shao,
Peili Shi,
Ivan Shironya,
Lukas Siebicke,
Ladislav Šigut,
Richard Silberstein,
Costantino Sirca,
Donatella Spano,
R. Steinbrecher,
Robert M. Stevens,
Cove Sturtevant,
Andy Suyker,
Torbern Tagesson,
Satoru Takanashi,
Yanhong Tang,
Nigel Tapper,
Jonathan E. Thom,
Michele Tomassucci,
Juha‐Pekka Tuovinen,
S. P. Urbanski,
Riccardo Valentini,
M. K. van der Molen,
Eva van Gorsel,
J. van Huissteden,
Andrej Varlagin,
Joseph Verfaillie,
Timo Vesala,
Caroline Vincke,
Domenico Vitale,
N. N. Vygodskaya,
Jeffrey P. Walker,
Elizabeth A. Walter‐Shea,
Huimin Wang,
R. J. Weber,
Sebastian Westermann,
Christian Wille,
Steven C. Wofsy,
Georg Wohlfahrt,
Sebastian Wolf,
William Woodgate,
Yuelin Li,
Roberto Zampedri,
Yuanman Hu,
Guoyi Zhou,
Donatella Zona,
D. Agarwal,
Sébastien Biraud,
Margaret Torn,
Dario Papale
Scientific Data, Volume 7, Issue 1
Abstract The FLUXNET2015 dataset provides ecosystem-scale data on CO 2 , water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.
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Stomatal response to decreased relative humidity constrains the acceleration of terrestrial evapotranspiration
Mingzhong Xiao,
Zhongbo Yu,
Dongdong Kong,
Xihui Gu,
Ivan Mammarella,
Leonardo Montagnani,
M. Altaf Arain,
Lutz Merbold,
Vincenzo Magliulo,
Annalea Lohila,
Nina Buchmann,
Sebastian Wolf,
Mana Gharun,
Lukas Hörtnagl,
Jason Beringer,
Beniamino Gioli
Environmental Research Letters, Volume 15, Issue 9
Abstract Terrestrial evapotranspiration (ET) is thermodynamically expected to increase with increasing atmospheric temperature; however, the actual constraints on the intensification of ET remain uncertain due to a lack of direct observations. Based on the FLUXNET2015 Dataset, we found that relative humidity (RH) is a more important driver of ET than temperature. While actual ET decrease at reduced RH, potential ET increases, consistently with the complementary relationship (CR) framework stating that the fraction of energy not used for actual ET is dissipated as increased sensible heat flux that in turn increases potential ET. In this study, we proposed an improved CR formulation requiring no parameter calibration and assessed its reliability in estimating ET both at site-level with the FLUXNET2015 Dataset and at basin-level. Using the ERA-Interim meteorological dataset for 1979–2017 to calculate ET, we found that the global terrestrial ET showed an increasing trend until 1998, while the trend started to decline afterwards. Such decline was largely associated with a reduced RH, inducing water stress conditions that triggered stomatal closure to conserve water. For the first time, this study quantified the global-scale implications of changes in RH on terrestrial ET, indicating that the temperature-driven acceleration of the terrestrial water cycle will be likely constrained by terrestrial vegetation feedbacks.
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COSORE: A community database for continuous soil respiration and other soil‐atmosphere greenhouse gas flux data
Ben Bond‐Lamberty,
Danielle Christianson,
Avni Malhotra,
Stephanie Pennington,
Debjani Sihi,
Amir AghaKouchak,
Hassan Anjileli,
M. Altaf Arain,
Juan J. Armestó,
Samaneh Ashraf,
Mioko Ataka,
Dennis Baldocchi,
T. Andrew Black,
Nina Buchmann,
Mariah S. Carbone,
Shih‐Chieh Chang,
P. M. Crill,
Peter S. Curtis,
Eric A. Davidson,
Ankur R. Desai,
John E. Drake,
Tarek S. El‐Madany,
Michael Gavazzi,
Carolyn‐Monika Görres,
Christopher M. Gough,
Michael L. Goulden,
Jillian W. Gregg,
Omar Gutiérrez del Arroyo,
Jin He,
Takashi Hirano,
Anya M. Hopple,
Holly Hughes,
Järvi Järveoja,
Rachhpal S. Jassal,
Jinshi Jian,
Haiming Kan,
Jason P. Kaye,
Yuji Kominami,
Naishen Liang,
David A. Lipson,
Catriona A. Macdonald,
Kadmiel Maseyk,
Kayla Mathes,
Marguerite Mauritz,
Melanie A. Mayes,
Steven G. McNulty,
Guofang Miao,
Mirco Migliavacca,
S. D. Miller,
Chelcy Ford Miniat,
Jennifer Goedhart Nietz,
Mats B. Nilsson,
Asko Noormets,
H. Norouzi,
Christine S. O’Connell,
Bruce Osborne,
Cecilio Oyonarte,
Zhuo Pang,
Matthias Peichl,
Elise Pendall,
Jorge F. Pérez‐Quezada,
Claire L. Phillips,
Richard P. Phillips,
James W. Raich,
Alexandre A. Renchon,
Nadine K. Ruehr,
Enrique P. Sánchez‐Cañete,
Matthew Saunders,
K. E. Savage,
Marion Schrumpf,
Russell L. Scott,
Ulli Seibt,
Whendee L. Silver,
Wu Sun,
Daphne Szutu,
Kentaro Takagi,
Masahiro Takagi,
Munemasa Teramoto,
Mark G. Tjoelker,
Susan Trumbore,
Masahito Ueyama,
Rodrigo Vargas,
R. K. Varner,
Joseph Verfaillie,
Christoph S. Vogel,
Jinsong Wang,
G. Winston,
Tana E. Wood,
Zhenhua Wu,
Thomas Wutzler,
Jiye Zeng,
Tianshan Zha,
Quan Zhang,
Junliang Zou
Global Change Biology, Volume 26, Issue 12
Globally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil-to-atmosphere CO2 flux, commonly though imprecisely termed soil respiration (RS ), is one of the largest carbon fluxes in the Earth system. An increasing number of high-frequency RS measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open-source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long-term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured RS , the database design accommodates other soil-atmosphere measurements (e.g. ecosystem respiration, chamber-measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package.
Photosynthetic phenology is an important indicator of annual gross primary productivity (GPP). Assessing photosynthetic phenology remotely is difficult for evergreen conifers as they remain green year-round. Carotenoid-based vegetation indices such as the photochemical reflectance index (PRI) and chlorophyll/carotenoid index (CCI) are promising tools to remotely track the invisible phenology of photosynthesis by assessing carotenoid pigment dynamics. PRI, CCI and the near-infrared reflectance of vegetation (NIRV ) index may act as proxies of photosynthetic efficiency (ɛ), an important parameter in light-use efficiency models, or direct proxies of photosynthesis. To understand the physiological mechanisms reflected by PRI and CCI and the ability of vegetation indices to act as proxies of photosynthetic activity for estimating GPP, we measured leaf pigment composition, PRI, CCI, NIRV and photosynthetic activity at the leaf and canopy scales over 2 years in an evergreen and mixed deciduous forest. PRI and CCI captured the large seasonal carotenoid/chlorophyll ratio changes and good relationships were observed between PRI-ɛ and CCI-photosynthesis and NIRV -photosynthesis. PRI-, CCI- and NIRV -based models effectively tracked observed seasonal GPP. We propose that carotenoid-based and near-infrared reflectance vegetation indices may provide useful proxies of photosynthetic activity and can improve remote sensing-based models of GPP in evergreen and deciduous forests.
In forest ecosystems, soil CO2 efflux is an important component of ecosystem respiration (RE), which is generally driven by variability in soil temperature and soil moisture. Tree harvesting in forests can alter the soil variables and, consequently, impact soil CO2 efflux. This study investigated the response of total soil CO2 efflux, and its components, to a shelterwood harvesting event of a mature temperate white pine (Pinus strobus L.) forest located in Southern Ontario, Canada. The objective was to explore the response of soil CO2 effluxes to changes in the forest microclimate, such as soil temperature and soil moisture, after shelterwood harvesting removed approximately one-third of the overstory canopy. No significant differences were found in both soil temperature and soil moisture between the pre-harvesting (2008–2011) and post-harvesting (2012–2014) periods. Despite similar soil microclimates, total soil CO2 effluxes were significantly reduced by up to 37%. Soil CO2 effluxes from heterotrophic sources were significantly reduced post-harvesting by approximately 27%, while no significant difference in the mineral-soil horizon sources were measured. An analysis of RE, measured with an eddy covariance tower over the study area, showed an increase post-harvesting. However, the overall net ecosystem carbon exchange showed no significant difference between pre- and post-harvesting. This was due to an increase in the gross ecosystem productivity post-harvesting, compensating for the increased losses (i.e., increased RE). This study highlights the complexities of soil CO2 efflux after a disturbance, such as a harvest. The knowledge gained from this study adds to our understanding of how shelterwood harvesting may influence ecosystem carbon exchange and will be useful for forest managers focused on carbon sequestration and forest conservation.
Abstract. The annual carbon and water dynamics of two eastern North American temperate forests were compared over a 6-year period from 2012 to 2017. The geographic location, forest age, soil, and climate were similar between the two stands; however, stand composition varied in terms of tree leaf-retention and shape strategy: one stand was a deciduous broadleaf forest, while the other was an evergreen needleleaf forest. The 6-year mean annual net ecosystem productivity (NEP) of the coniferous forest was slightly higher and more variable (218±109 g C m−2 yr−1) compared to that of the deciduous forest NEP (200±83 g C m−2 yr−1). Similarly, the 6-year mean annual evapotranspiration (ET) of the coniferous forest was higher (442±33 mm yr−1) than that of the deciduous forest (388±34 mm yr−1), but with similar interannual variability. Summer meteorology greatly impacted the carbon and water fluxes in both stands; however, the degree of response varied among the two stands. In general, warm temperatures caused higher ecosystem respiration (RE), resulting in reduced annual NEP values – an impact that was more pronounced at the deciduous broadleaf forest compared to the evergreen needleleaf forest. However, during warm and dry years, the evergreen forest had largely reduced annual NEP values compared to the deciduous forest. Variability in annual ET at both forests was related most to the variability in annual air temperature (Ta), with the largest annual ET observed in the warmest years in the deciduous forest. Additionally, ET was sensitive to prolonged dry periods that reduced ET at both stands, although the reduction at the coniferous forest was relatively larger than that of the deciduous forest. If prolonged periods (weeks to months) of increased Ta and reduced precipitation are to be expected under future climates during summer months in the study region, our findings suggest that the deciduous broadleaf forest will likely remain an annual carbon sink, while the carbon sink–source status of the coniferous forest remains uncertain.
Abstract. Extreme events are widely studied across the world because of their major implications for many aspects of society and especially floods. These events are generally studied in terms of precipitation or temperature extreme indices that are often not adapted for regions affected by floods caused by snowmelt. The rain on snow index has been widely used, but it neglects rain-only events which are expected to be more frequent in the future. In this study, we identified a new winter compound index and assessed how large-scale atmospheric circulation controls the past and future evolution of these events in the Great Lakes region. The future evolution of this index was projected using temperature and precipitation from the Canadian Regional Climate Model large ensemble (CRCM5-LE). These climate data were used as input in Precipitation Runoff Modelling System (PRMS) hydrological model to simulate the future evolution of high flows in three watersheds in southern Ontario. We also used five recurrent large-scale atmospheric circulation patterns in north-eastern North America and identified how they control the past and future variability of the newly created index and high flows. The results show that daily precipitation higher than 10 mm and temperature higher than 5 ∘C were necessary historical conditions to produce high flows in these three watersheds. In the historical period, the occurrences of these heavy rain and warm events as well as high flows were associated with two main patterns characterized by high Z500 anomalies centred on eastern Great Lakes (HP regime) and the Atlantic Ocean (South regime). These hydrometeorological extreme events will still be associated with the same atmospheric patterns in the near future. The future evolution of the index will be modulated by the internal variability of the climate system, as higher Z500 on the east coast will amplify the increase in the number of events, especially the warm events. The relationship between the extreme weather index and high flows will be modified in the future as the snowpack reduces and rain becomes the main component of high-flow generation. This study shows the value of the CRCM5-LE dataset in simulating hydrometeorological extreme events in eastern Canada and better understanding the uncertainties associated with internal variability of climate.
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Modeling the impacts of diffuse light fraction on photosynthesis in ORCHIDEE (v5453) land surface model
Yuan Zhang,
Ana Bastos,
Fabienne Maignan,
Daniel S. Goll,
Oliviér Boucher,
Laurent Li,
Alessandro Cescatti,
Nicolas Vuichard,
Xiuzhi Chen,
Christof Ammann,
M. Altaf Arain,
T. Andrew Black,
Bogdan H. Chojnicki,
Tomomichi Kato,
Ivan Mammarella,
Leonardo Montagnani,
Olivier Roupsard,
María José Sanz,
Lukas Siebicke,
Marek Urbaniak,
Francesco Primo Vaccari,
Georg Wohlfahrt,
Will Woodgate,
Philippe Ciais
Geoscientific Model Development, Volume 13, Issue 11
Abstract. Aerosol- and cloud-induced changes in diffuse light have important impacts on the global land carbon cycle, as they alter light distribution and photosynthesis in vegetation canopies. However, this effect remains poorly represented or evaluated in current land surface models. Here, we add a light partitioning module and a new canopy light transmission module to the ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) land surface model (trunk version, v5453) and use the revised model, ORCHIDEE_DF, to estimate the fraction of diffuse light and its effect on gross primary production (GPP) in a multilayer canopy. We evaluate the new parameterizations using flux observations from 159 eddy covariance sites over the globe. Our results show that, compared with the original model, ORCHIDEE_DF improves the GPP simulation under sunny conditions and captures the observed higher photosynthesis under cloudier conditions in most plant functional types (PFTs). Our results also indicate that the larger GPP under cloudy conditions compared with sunny conditions is mainly driven by increased diffuse light in the morning and in the afternoon as well as by a decreased vapor pressure deficit (VPD) and decreased air temperature at midday. The observations show that the strongest positive effects of diffuse light on photosynthesis are found in the range from 5 to 20 ∘C and at a VPD < 1 kPa. This effect is found to decrease when the VPD becomes too large or the temperature falls outside of the abovementioned range, which is likely due to the increasing stomatal resistance to leaf CO2 uptake. ORCHIDEE_DF underestimates the diffuse light effect at low temperature in all PFTs and overestimates this effect at high temperature and at a high VPD in grasslands and croplands. The new model has the potential to better investigate the impact of large-scale aerosol changes and long-term changes in cloudiness on the terrestrial carbon budget, both in the historical period and in the context of future air quality policies and/or climate engineering.
Abstract. Fluvial systems in southern Ontario are regularly affected by widespread early-spring flood events primarily caused by rain-on-snow events. Recent studies have shown an increase in winter floods in this region due to increasing winter temperature and precipitation. Streamflow simulations are associated with uncertainties mainly due to the different scenarios of greenhouse gas emissions, global climate models (GCMs) or the choice of the hydrological model. The internal variability of climate, defined as the chaotic variability of atmospheric circulation due to natural internal processes within the climate system, is also a source of uncertainties to consider. Uncertainties of internal variability can be assessed using hydrological models fed by downscaled data of a global climate model large ensemble (GCM-LE), but GCM outputs have too coarse of a scale to be used in hydrological modeling. The Canadian Regional Climate Model Large Ensemble (CRCM5-LE), a 50-member ensemble downscaled from the Canadian Earth System Model version 2 Large Ensemble (CanESM2-LE), was developed to simulate local climate variability over northeastern North America under different future climate scenarios. In this study, CRCM5-LE temperature and precipitation projections under an RCP8.5 scenario were used as input in the Precipitation Runoff Modeling System (PRMS) to simulate streamflow at a near-future horizon (2026–2055) for four watersheds in southern Ontario. To investigate the role of the internal variability of climate in the modulation of streamflow, the 50 members were first grouped in classes of similar projected change in January–February streamflow and temperature and precipitation between 1961–1990 and 2026–2055. Then, the regional change in geopotential height (Z500) from CanESM2-LE was calculated for each class. Model simulations showed an average January–February increase in streamflow of 18 % (±8.7) in Big Creek, 30.5 % (±10.8) in Grand River, 29.8 % (±10.4) in Thames River and 31.2 % (±13.3) in Credit River. A total of 14 % of all ensemble members projected positive Z500 anomalies in North America's eastern coast enhancing rain, snowmelt and streamflow volume in January–February. For these members the increase of streamflow is expected to be as high as 31.6 % (±8.1) in Big Creek, 48.3 % (±11.1) in Grand River, 47 % (±9.6) in Thames River and 53.7 % (±15) in Credit River. Conversely, 14 % of the ensemble projected negative Z500 anomalies in North America's eastern coast and were associated with a much lower increase in streamflow: 8.3 % (±7.8) in Big Creek, 18.8 % (±5.8) in Grand River, 17.8 % (±6.4) in Thames River and 18.6 % (±6.5) in Credit River. These results provide important information to researchers, managers, policymakers and society about the expected ranges of increase in winter streamflow in a highly populated region of Canada, and they will help to explain how the internal variability of climate is expected to modulate the future streamflow in this region.
2019
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Cryptic phenology in plants: Case studies, implications, and recommendations
Loren P. Albert,
Natalia Restrepo‐Coupé,
Marielle N. Smith,
Jin Wu,
Cecilia Chavana‐Bryant,
Neill Prohaska,
Tyeen Taylor,
Giordane Martins,
Philippe Ciais,
Jiafu Mao,
M. Altaf Arain,
Wei Li,
Xiaoying Shi,
D. M. Ricciuto,
Travis E. Huxman,
Sean M. McMahon,
S. R. Saleska
Global Change Biology, Volume 25, Issue 11
Plant phenology—the timing of cyclic or recurrent biological events in plants—offers insight into the ecology, evolution, and seasonality of plant-mediated ecosystem processes. Traditionally studied phenologies are readily apparent, such as flowering events, germination timing, and season-initiating budbreak. However, a broad range of phenologies that are fundamental to the ecology and evolution of plants, and to global biogeochemical cycles and climate change predictions, have been neglected because they are “cryptic”—that is, hidden from view (e.g., root production) or difficult to distinguish and interpret based on common measurements at typical scales of examination (e.g., leaf turnover in evergreen forests). We illustrate how capturing cryptic phenology can advance scientific understanding with two case studies: wood phenology in a deciduous forest of the northeastern USA and leaf phenology in tropical evergreen forests of Amazonia. Drawing on these case studies and other literature, we argue that conceptualizing and characterizing cryptic plant phenology is needed for understanding and accurate prediction at many scales from organisms to ecosystems. We recommend avenues of empirical and modeling research to accelerate discovery of cryptic phenological patterns, to understand their causes and consequences, and to represent these processes in terrestrial biosphere models.
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Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests
Simon Besnard,
Nuno Carvalhais,
M. Altaf Arain,
T. Andrew Black,
Benjamin Brede,
Nina Buchmann,
Jiquan Chen,
J.G.P.W. Clevers,
L.P. Dutrieux,
Fabian Gans,
Martin Herold,
Martin Jung,
Yoshiko Kosugi,
Alexander Knohl,
B. E. Law,
Eugénie Paul‐Limoges,
Annalea Lohila,
Lutz Merbold,
Olivier Roupsard,
Riccardo Valentini,
Sebastian Wolf,
Xudong Zhang,
Markus Reichstein
PLOS ONE, Volume 14, Issue 2
Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate's temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE.
Abstract Flooding is a major concern for Canadian society as it is the costliest natural disaster type in Canada. Southern Ontario, which houses one-third of the Canadian population, is located in an area of high vulnerability for floods. The most significant floods in the region have historically occurred during the months of March and April due to snowmelt coupled with extreme rain events. However, during the last three decades, there has been a shift of flooding events to earlier months. The aim of this study was to understand the impacts of atmospheric circulation on the temporal shift of streamflow and high flow events observed in southern Ontario over 1957–2013 period. Predominant weather regimes over North America, corresponding to recurrent meteorological situations, were identified using a discretization of daily geopotential height at 500HpA level (Z500). A regime-normalized hypothetical temperature and precipitation dataset was constructed to quantify the contribution of atmospheric circulation on streamflow response. The hypothetical dataset was used as input in the Precipitation Runoff Modeling System (PRMS), a rainfall-runoff semi-distributed hydrological model, and applied to four watersheds in southern Ontario. The results showed an increase in the temporal frequency of the regime identified here as High Pressure (HP) close to eight occurrences per decade. Regime HP, characterized by a northern position of the polar vortex, is correlated with a positive phase of the NAO and is associated with warm and wet conditions over southern Ontario during winter. The temporal increase in HP contributed more than 40% of the increase in streamflow in winter and 30–45% decrease in streamflow in April. This atmospheric situation also contributed to increase the number of high flows by 25–50% in January. These results are important to improve the seasonal forecasting of high flows and to assess the uncertainty in the temporal evolution of streamflow in the Great Lakes region.
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Vegetation Functional Properties Determine Uncertainty of Simulated Ecosystem Productivity: A Traceability Analysis in the East Asian Monsoon Region
Erqian Cui,
Kun Huang,
M. Altaf Arain,
Joshua B. Fisher,
D. N. Huntzinger,
Akihiko Ito,
Yiqi Luo,
Atul K. Jain,
Jiafu Mao,
A. M. Michalak,
Shuli Niu,
Nicholas C. Parazoo,
Changhui Peng,
Shushi Peng,
Benjamin Poulter,
D. M. Ricciuto,
Kevin Schaefer,
Christopher R. Schwalm,
Xiaoying Shi,
Hanqin Tian,
Weile Wang,
Jinsong Wang,
Yaxing Wei,
En‐Rong Yan,
Liming Yan,
Ning Zeng,
Qiuan Zhu,
Jianyang Xia
Global Biogeochemical Cycles, Volume 33, Issue 6
Global and regional projections of climate change by Earth system models are limited by their uncertain estimates of terrestrial ecosystem productivity. At the middle to low latitudes, the East Asian monsoon region has higher productivity than forests in Europe‐Africa and North America, but its estimate by current generation of terrestrial biosphere models (TBMs) has seldom been systematically evaluated. Here, we developed a traceability framework to evaluate the simulated gross primary productivity (GPP) by 15 TBMs in the East Asian monsoon region. The framework links GPP to net primary productivity, biomass, leaf area and back to GPP via incorporating multiple vegetation functional properties of carbon‐use efficiency (CUE), vegetation C turnover time (τveg), leaf C fraction (Fleaf), specific leaf area (SLA), and leaf area index (LAI)‐level photosynthesis (PLAI), respectively. We then applied a relative importance algorithm to attribute intermodel variation at each node. The results showed that large intermodel variation in GPP over 1901–2010 were mainly propagated from their different representation of vegetation functional properties. For example, SLA explained 77% of the intermodel difference in leaf area, which contributed 90% to the simulated GPP differences. In addition, the models simulated higher CUE (18.1 ± 21.3%), τveg (18.2 ± 26.9%), and SLA (27.4±36.5%) than observations, leading to the overestimation of simulated GPP across the East Asian monsoon region. These results suggest the large uncertainty of current TBMs in simulating GPP is largely propagated from their poor representation of the vegetation functional properties and call for a better understanding of the covariations between plant functional properties in terrestrial ecosystems.
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Global vegetation biomass production efficiency constrained by models and observations
Yue He,
Shushi Peng,
Yongwen Liu,
Xiangyi Li,
Kai Wang,
Philippe Ciais,
M. Altaf Arain,
Yuanyuan Fang,
Joshua B. Fisher,
Daniel S. Goll,
Daniel J. Hayes,
D. N. Huntzinger,
Akihiko Ito,
Atul K. Jain,
Ivan A. Janssens,
Jiafu Mao,
Matteo Campioli,
A. M. Michalak,
Changhui Peng,
Josep Peñuelas,
Benjamin Poulter,
Dahe Qin,
D. M. Ricciuto,
Kevin Schaefer,
Christopher R. Schwalm,
Xiaoying Shi,
Hanqin Tian,
Sara Vicca,
Yaxing Wei,
Ning Zeng,
Qiuan Zhu
Global Change Biology, Volume 26, Issue 3
Plants use only a fraction of their photosynthetically derived carbon for biomass production (BP). The biomass production efficiency (BPE), defined as the ratio of BP to photosynthesis, and its variation across and within vegetation types is poorly understood, which hinders our capacity to accurately estimate carbon turnover times and carbon sinks. Here, we present a new global estimation of BPE obtained by combining field measurements from 113 sites with 14 carbon cycle models. Our best estimate of global BPE is 0.41 ± 0.05, excluding cropland. The largest BPE is found in boreal forests (0.48 ± 0.06) and the lowest in tropical forests (0.40 ± 0.04). Carbon cycle models overestimate BPE, although models with carbon-nitrogen interactions tend to be more realistic. Using observation-based estimates of global photosynthesis, we quantify the global BP of non-cropland ecosystems of 41 ± 6 Pg C/year. This flux is less than net primary production as it does not contain carbon allocated to symbionts, used for exudates or volatile carbon compound emissions to the atmosphere. Our study reveals a positive bias of 24 ± 11% in the model-estimated BP (10 of 14 models). When correcting models for this bias while leaving modeled carbon turnover times unchanged, we found that the global ecosystem carbon storage change during the last century is decreased by 67% (or 58 Pg C).
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Field-experiment constraints on the enhancement of the terrestrial carbon sink by CO2 fertilization
Yongwen Liu,
Shilong Piao,
Thomas Gasser,
Philippe Ciais,
Hui Yang,
Han Wang,
Trevor F. Keenan,
Mengtian Huang,
Shiqiang Wan,
Jian Song,
Kai Wang,
Ivan A. Janssens,
Josep Peñuelas,
Chris Huntingford,
Xuhui Wang,
M. Altaf Arain,
Yuanyuan Fang,
Joshua B. Fisher,
Maoyi Huang,
D. N. Huntzinger,
Akihiko Ito,
Atul K. Jain,
Jiafu Mao,
A. M. Michalak,
Changhui Peng,
Benjamin Poulter,
Christopher R. Schwalm,
Xiaoying Shi,
Hanqin Tian,
Yaxing Wei,
Ning Zeng,
Qiuan Zhu,
Tao Wang
Nature Geoscience, Volume 12, Issue 10
Clarifying how increased atmospheric CO2 concentration (eCO2) contributes to accelerated land carbon sequestration remains important since this process is the largest negative feedback in the coupled carbon–climate system. Here, we constrain the sensitivity of the terrestrial carbon sink to eCO2 over the temperate Northern Hemisphere for the past five decades, using 12 terrestrial ecosystem models and data from seven CO2 enrichment experiments. This constraint uses the heuristic finding that the northern temperate carbon sink sensitivity to eCO2 is linearly related to the site-scale sensitivity across the models. The emerging data-constrained eCO2 sensitivity is 0.64 ± 0.28 PgC yr−1 per hundred ppm of eCO2. Extrapolating worldwide, this northern temperate sensitivity projects the global terrestrial carbon sink to increase by 3.5 ± 1.9 PgC yr−1 for an increase in CO2 of 100 ppm. This value suggests that CO2 fertilization alone explains most of the observed increase in global land carbon sink since the 1960s. More CO2 enrichment experiments, particularly in boreal, arctic and tropical ecosystems, are required to explain further the responsible processes. The northern temperate carbon sink is estimated to increase by 0.64 PgC each year for each increase in atmospheric CO2 concentrations by 100 ppm, suggests an analysis of data from field experiments at 7 sites constraints.
Climate extremes such as heat waves and droughts are projected to occur more frequently with increasing temperature and an intensified hydrological cycle. It is important to understand and quantify how forest carbon fluxes respond to heat and drought stress. In this study, we developed a series of daily indices of sensitivity to heat and drought stress as indicated by air temperature (Ta ) and evaporative fraction (EF). Using normalized daily carbon fluxes from the FLUXNET Network for 34 forest sites in North America, the seasonal pattern of sensitivities of net ecosystem productivity (NEP), gross ecosystem productivity (GEP) and ecosystem respiration (RE) in response to Ta and EF anomalies were compared for different forest types. The results showed that warm temperatures in spring had a positive effect on NEP in conifer forests but a negative impact in deciduous forests. GEP in conifer forests increased with higher temperature anomalies in spring but decreased in summer. The drought-induced decrease in NEP, which mostly occurred in the deciduous forests, was mostly driven by the reduction in GEP. In conifer forests, drought had a similar dampening effect on both GEP and RE, therefore leading to a neutral NEP response. The NEP sensitivity to Ta anomalies increased with increasing mean annual temperature. Drier sites were less sensitive to drought stress in summer. Natural forests with older stand age tended to be more resilient to the climate stresses compared to managed younger forests. The results of the Classification and Regression Tree analysis showed that seasons and ecosystem productivity were the most powerful variables in explaining the variation of forest sensitivity to heat and drought stress. Our results implied that the magnitude and direction of carbon flux changes in response to climate extremes are highly dependent on the seasonal dynamics of forests and the timing of the climate extremes.
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No trends in spring and autumn phenology during the global warming hiatus
Xufeng Wang,
Jingfeng Xiao,
Xin Li,
Guodong Cheng,
Mingguo Ma,
Gaofeng Zhu,
M. Altaf Arain,
T. Andrew Black,
Rachhpal S. Jassal
Nature Communications, Volume 10, Issue 1
Phenology plays a fundamental role in regulating photosynthesis, evapotranspiration, and surface energy fluxes and is sensitive to climate change. The global mean surface air temperature data indicate a global warming hiatus between 1998 and 2012, while its impacts on global phenology remains unclear. Here we use long-term satellite and FLUXNET records to examine phenology trends in the northern hemisphere before and during the warming hiatus. Our results based on the satellite record show that the phenology change rate slowed down during the warming hiatus. The analysis of the long-term FLUXNET measurements, mainly within the warming hiatus, shows that there were no widespread advancing (or delaying) trends in spring (or autumn) phenology. The lack of widespread phenology trends partly led to the lack of widespread trends in spring and autumn carbon fluxes. Our findings have significant implications for understanding the responses of phenology to climate change and the climate-carbon feedbacks.
The present study analyses the impacts of past and future climate change on extreme weather events for southern parts of Canada from 1981 to 2100. A set of precipitation and temperature‐based indices were computed using the downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) multi‐model ensemble projections at 8 km resolution over the 21st Century for two representative concentration pathway (RCP) scenarios: RCP4.5 and RCP8.5. The results show that this region is expected to experience stronger warming and a higher increase in precipitation extremes in future. Generally, projected changes in minimum temperature will be greater than changes in maximum temperature, as shown by respective indices. A decrease in frost days and an increase in warm nights will be expected. By 2100 there will be no cool nights and cool days. Daily minimum and maximum temperatures will increase by 12 and 7°C, respectively, under the RCP8.5 scenario, when compared with the reference period 1981–2000. The highest warming in minimum temperature and decrease in cool nights and days will occur in Ontario and Quebec provinces close to the Great Lakes and Hudson Bay. The highest warming in maximum temperature will occur in the southern parts of Alberta and Saskatchewan. Annual total precipitation is expected to increase by about 16% and the occurrence of heavy precipitation events by five days. The highest increase in annual total precipitation will occur in the northern parts of Ontario and Quebec and in western British Columbia.
Abstract Carotenoid pigments play an important role in the seasonal regulation of photosynthesis and photoprotection of overwintering conifers. Because the seasonal changes in the rate of photosynthetic CO2 assimilation are linked to changes in carotenoid pigment composition, it has been suggested that carotenoid sensitive vegetation indices might be used to track the otherwise “invisible” phenology of photosynthesis of conifer forests through remote sensing of leaf spectral reflectance. In this study we aimed to assess differences in the seasonal regulation of photosynthesis and the associated variation of carotenoids and chlorophylls at the leaf-scale for eastern white pine, red maple and white oak, in order to understand if photosynthetic and photoprotective processes are adequately represented by different vegetation indices over the course of the year. For this purpose we measured maximum rates of CO2 assimilation (Amax), quantified photosynthetic pigments, estimated photochemical and non-photochemical quenching processes via chlorophyll fluorescence and determined leaf spectral reflectance in pine, maple and oak trees over the course of two years. Seasonal variation in Amax, used here as a proxy for photosynthetic phenology, and photosynthetic pigments were adequately represented by the normalized difference vegetation index (NDVI) for the deciduous trees. For pine, NDVI overestimated photosynthetic activity for most of the year and was hence not able to represent photosynthetic phenology, due to the fact that needle chlorophyll content shows only little variation over the course of the year. By contrast, using the photochemical reflectance index (PRI) and the chlorophyll/carotenoid index (CCI), which both detect variations in carotenoids, we were able to observe an improved representation of the seasonal variation of CO2 assimilation and photosynthetic phenology for the two deciduous and the conifer species. Based on the accurate detection of the seasonal regulation of leaf-scale photosynthetic activity for all three species, we conclude that carotenoid-sensitive vegetation indices are promising tools to improve monitoring of phenology in both deciduous and conifer forests.
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Solar‐induced chlorophyll fluorescence exhibits a universal relationship with gross primary productivity across a wide variety of biomes
Jingfeng Xiao,
Xing Li,
Binbin He,
M. Altaf Arain,
Jason Beringer,
Ankur R. Desai,
Carmen Emmel,
David Y. Hollinger,
Alisa Krasnova,
Ivan Mammarella,
Steffen M. Noe,
Penélope Serrano-Ortíz,
Camilo Rey‐Sánchez,
Adrian V. Rocha,
Andrej Varlagin
Global Change Biology, Volume 25, Issue 4
In our recent study in Global Change Biology (Li et al., ), we examined the relationship between solar-induced chlorophyll fluorescence (SIF) measured from the Orbiting Carbon Observatory-2 (OCO-2) and gross primary productivity (GPP) derived from eddy covariance flux towers across the globe, and we discovered that there is a nearly universal relationship between SIF and GPP across a wide variety of biomes. This finding reveals the tremendous potential of SIF for accurately mapping terrestrial photosynthesis globally.
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Trends of actual and potential evapotranspiration based on Bouchet’s complementary concept in a cold and arid steppe site of Northeastern Asia
Guangyong You,
M. Altaf Arain,
Shusen Wang,
Naifeng Lin,
Dan Wu,
Shawn McKenzie,
Changxin Zou,
Bo Liu,
Xiao‐Hua Zhang,
Jixi Gao
Agricultural and Forest Meteorology, Volume 279
Abstract Due to complex natural water flux processes and the ambiguous explanation of Bouchet’s complementary theory, site-level investigations on evapotranspiration (ET) and related climate variables assist in understanding the regional hydrological response to climate change. In this study, site specific empirical parameters were incorporated in the Bouchet’s complementary relationship (CR) and potential and actual ET were estimated by CR method and subsequently validated by 6 years of ground-based vapor flux observations. Time series analysis, correlation analysis and principal regression analysis were conducted to reveal the characteristics of climate change and the controlling factor(s) of the variations of potential ET and actual ET. The results show that this region is exhibiting a combined warming and drying trend over the past decades with two change points that occurred in 1993 and in 2000. Potential ET was predominantly influenced by temperature and vapor pressure deficit, while actual ET was mostly influenced by vegetation activity. Potential ET was found to be increasing concurrently with declining actual ET to constitute nearly a symmetric complementary relationship over the past decades. This study help to enhance our understanding of the regional hydrological response to climate change. Further studies are needed to partition the actual ET into transpiration and other components and to reveal the role of vegetation activity in determining regional ET as well as water balance.
2018
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Quantifying the effect of forest age in annual net forest carbon balance
Simon Besnard,
Nuno Carvalhais,
M. Altaf Arain,
T. Andrew Black,
Sytze de Bruin,
Nina Buchmann,
Alessandro Cescatti,
Jiquan Chen,
J.G.P.W. Clevers,
Ankur R. Desai,
Christopher M. Gough,
Kateřina Havránková,
Martin Herold,
Lukas Hörtnagl,
Martin Jung,
Alexander Knohl,
Bart Kruijt,
Lenka Krupková,
B. E. Law,
Anders Lindroth,
Asko Noormets,
Olivier Roupsard,
R. Steinbrecher,
Andrej Varlagin,
Caroline Vincke,
Markus Reichstein
Environmental Research Letters, Volume 13, Issue 12
Forests dominate carbon (C) exchanges between the terrestrial biosphere and the atmosphere on land. In the long term, the net carbon flux between forests and the atmosphere has been significantly impacted by changes in forest cover area and structure due to ecological disturbances and management activities. Current empirical approaches for estimating net ecosystem productivity (NEP) rarely consider forest age as a predictor, which represents variation in physiological processes that can respond differently to environmental drivers, and regrowth following disturbance. Here, we conduct an observational synthesis to empirically determine to what extent climate, soil properties, nitrogen deposition, forest age and management influence the spatial and interannual variability of forest NEP across 126 forest eddy-covariance flux sites worldwide. The empirical models explained up to 62% and 71% of spatio-temporal and across-site variability of annual NEP, respectively. An investigation of model structures revealed that forest age was a dominant factor of NEP spatio-temporal variability in both space and time at the global scale as compared to abiotic factors, such as nutrient availability, soil characteristics and climate. These findings emphasize the importance of forest age in quantifying spatio-temporal variation in NEP using empirical approaches.
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Carbon, water and energy exchange dynamics of a young pine plantation forest during the initial fourteen years of growth
Felix C.C. Chan,
M. Altaf Arain,
Myroslava Khomik,
Jason Brodeur,
Matthias Peichl,
Natalia Restrepo‐Coupé,
Robin Thorne,
Eric Beamesderfer,
Shawn McKenzie,
Bing Xu,
Holly Croft,
M. R. Pejam,
Janelle Trant,
Michelle Kula,
Rachel A. Skubel
Forest Ecology and Management, Volume 410
Abstract This study presents the energy, water, and carbon (C) flux dynamics of a young afforested temperate white pine (Pinus strobus L.) forest in southern Ontario, Canada during the initial fourteen years (2003–2016) of establishment. Energy fluxes, namely, net radiation (Rn), latent heat (LE), and sensible heat (H) flux increased over time, due to canopy development. Annual values of ground heat flux (G) peaked in 2007 and then gradually declined in response to canopy closure. The forest became a consistent C-sink only 5 years after establishment owing in part to low respiratory fluxes from the former agricultural, sandy soils with low residual soil organic matter. Mean annual values of gross ecosystem productivity (GEP), ecosystem respiration (RE), and net ecosystem productivity (NEP) ranged from 494 to 1913, 515 to 1774 and −126 to 216 g C m−2 year−1 respectively, over the study period. Annual evapotranspiration (ET) values ranged from 328 to 429 mm year−1 over the same period. Water use efficiency (WUE) increased with stand age with a mean WUE value of 3.92 g C kg−1 H2O from 2008 to 2016. Multivariable linear regression analysis conducted using observed data suggested that the overall, C and water dynamics of the stand were primarily driven by radiation and temperature, both of which explained 77%, 48%, 28%, and 76% of the variability in GEP, RE, NEP, and ET, respectively. However, late summer droughts, which were prevalent in the region, reduced NEP. The reduction in NEP was enhanced when summer drought events were accompanied by increased heat such as those in 2005, 2012 and 2016. This study contributes to our understanding of the energy, water and C dynamics of afforested temperate conifer plantations and how these forests may respond to changing climate conditions during the crucial initial stage of their life cycle. Our findings also demonstrate the potential of pine plantation stands to sequester atmospheric CO2 in eastern North America.
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Temporal Dynamics of Aerodynamic Canopy Height Derived From Eddy Covariance Momentum Flux Data Across North American Flux Networks
Housen Chu,
Dennis Baldocchi,
C. Poindexter,
Michael Abraha,
Ankur R. Desai,
Gil Bohrer,
M. Altaf Arain,
Timothy J. Griffis,
Peter D. Blanken,
T. L. O’Halloran,
R. Quinn Thomas,
Quan Zhang,
Sean P. Burns,
J. M. Frank,
Christian Dold,
Shannon E. Brown,
T. Andrew Black,
Christopher M. Gough,
B. E. Law,
Xuhui Lee,
Jiquan Chen,
David E. Reed,
W. J. Massman,
Kenneth L. Clark,
Jerry L. Hatfield,
John H. Prueger,
Rosvel Bracho,
John M. Baker,
Timothy A. Martin
Geophysical Research Letters, Volume 45, Issue 17
Author(s): Chu, H; Baldocchi, DD; Poindexter, C; Abraha, M; Desai, AR; Bohrer, G; Arain, MA; Griffis, T; Blanken, PD; O'Halloran, TL; Thomas, RQ; Zhang, Q; Burns, SP; Frank, JM; Christian, D; Brown, S; Black, TA; Gough, CM; Law, BE; Lee, X; Chen, J; Reed, DE; Massman, WJ; Clark, K; Hatfield, J; Prueger, J; Bracho, R; Baker, JM; Martin, TA | Abstract: Aerodynamic canopy height (ha) is the effective height of vegetation canopy for its influence on atmospheric fluxes and is a key parameter of surface-atmosphere coupling. However, methods to estimate ha from data are limited. This synthesis evaluates the applicability and robustness of the calculation of ha from eddy covariance momentum-flux data. At 69 forest sites, annual ha robustly predicted site-to-site and year-to-year differences in canopy heights (R2n=n0.88, 111nsite-years). At 23 cropland/grassland sites, weekly ha successfully captured the dynamics of vegetation canopies over growing seasons (R2ngn0.70 in 74nsite-years). Our results demonstrate the potential of flux-derived ha determination for tracking the seasonal, interannual, and/or decadal dynamics of vegetation canopies including growth, harvest, land use change, and disturbance. The large-scale and time-varying ha derived from flux networks worldwide provides a new benchmark for regional and global Earth system models and satellite remote sensing of canopy structure.
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Solar‐induced chlorophyll fluorescence is strongly correlated with terrestrial photosynthesis for a wide variety of biomes: First global analysis based on OCO‐2 and flux tower observations
Xing Li,
Jingfeng Xiao,
Binbin He,
M. Altaf Arain,
Jason Beringer,
Ankur R. Desai,
Carmen Emmel,
David Y. Hollinger,
Alisa Krasnova,
Ivan Mammarella,
Steffen M. Noe,
Penélope Serrano-Ortíz,
Camilo Rey‐Sánchez,
Adrian V. Rocha,
Andrej Varlagin
Global Change Biology, Volume 24, Issue 9
Solar-induced chlorophyll fluorescence (SIF) has been increasingly used as a proxy for terrestrial gross primary productivity (GPP). Previous work mainly evaluated the relationship between satellite-observed SIF and gridded GPP products both based on coarse spatial resolutions. Finer resolution SIF (1.3 km × 2.25 km) measured from the Orbiting Carbon Observatory-2 (OCO-2) provides the first opportunity to examine the SIF–GPP relationship at the ecosystem scale using flux tower GPP data. However, it remains unclear how strong the relationship is for each biome and whether a robust, universal relationship exists across a variety of biomes. Here we conducted the first global analysis of the relationship between OCO-2 SIF and tower GPP for a total of 64 flux sites across the globe encompassing eight major biomes. OCO-2 SIF showed strong correlations with tower GPP at both midday and daily timescales, with the strongest relationship observed for daily SIF at the 757 nm (R2 = 0.72, p < 0.0001). Strong linear relationships between SIF and GPP were consistently found for all biomes (R2 = 0.57–0.79, p < 0.0001) except evergreen broadleaf forests (R2 = 0.16, p < 0.05) at the daily timescale. A higher slope was found for C4 grasslands and croplands than for C3 ecosystems. The generally consistent slope of the relationship among biomes suggests a nearly universal rather than biome-specific SIF–GPP relationship, and this finding is an important distinction and simplification compared to previous results. SIF was mainly driven by absorbed photosynthetically active radiation and was also influenced by environmental stresses (temperature and water stresses) that determine photosynthetic light use efficiency. OCO-2 SIF generally had a better performance for predicting GPP than satellite-derived vegetation indices and a light use efficiency model. The universal SIF–GPP relationship can potentially lead to more accurate GPP estimates regionally or globally. Our findings revealed the remarkable ability of finer resolution SIF observations from OCO-2 and other new or future missions (e.g., TROPOMI, FLEX) for estimating terrestrial photosynthesis across a wide variety of biomes and identified their potential and limitations for ecosystem functioning and carbon cycle studies.
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Comparison of Big‐Leaf, Two‐Big‐Leaf, and Two‐Leaf Upscaling Schemes for Evapotranspiration Estimation Using Coupled Carbon‐Water Modeling
Xiangzhong Luo,
Jing M. Chen,
Jane Liu,
T. Andrew Black,
Holly Croft,
R. M. Staebler,
Liming He,
M. Altaf Arain,
Bin Chen,
Gang Mo,
Alemu Gonsamo,
Harry McCaughey
Journal of Geophysical Research: Biogeosciences, Volume 123, Issue 1
Author(s): Luo, X; Chen, JM; Liu, J; Black, TA; Croft, H; Staebler, R; He, L; Arain, MA; Chen, B; Mo, G; Gonsamo, A; McCaughey, H | Abstract: Evapotranspiration (ET) is commonly estimated using the Penman-Monteith equation, which assumes that the plant canopy is a big leaf (BL) and the water flux from vegetation is regulated by canopy stomatal conductance (Gs). However, BL has been found to be unsuitable for terrestrial biosphere models built on the carbon-water coupling principle because it fails to capture daily variations of gross primary productivity (GPP). A two-big-leaf scheme (TBL) and a two-leaf scheme (TL) that stratify a canopy into sunlit and shaded leaves have been developed to address this issue. However, there is a lack of comparison of these upscaling schemes for ET estimation, especially on the difference between TBL and TL. We find that TL shows strong performance (r2n=n0.71, root-mean-square errorn=n0.05nmm/h) in estimating ET at nine eddy covariance towers in Canada. BL simulates lower annual ET and GPP than TL and TBL. The biases of estimated ET and GPP increase with leaf area index (LAI) in BL and TBL, and the biases of TL show no trends with LAI. BL miscalculates the portions of light-saturated and light-unsaturated leaves in the canopy, incurring negative biases in its flux estimation. TBL and TL showed improved yet different GPP and ET estimations. This difference is attributed to the lower Gs and intercellular CO2 concentration simulated in TBL compared to their counterparts in TL. We suggest to use TL for ET modeling to avoid the uncertainty propagated from the artificial upscaling of leaf-level processes to the canopy scale in BL and TBL.
Abstract Tree growth rings from three specimens in two different aged (14- and 77-year old) white pine plantation forests were analyzed for stable carbon isotope ratios to identify both short- and long-term variations in physiological response to changing environmental conditions. Three isotopic (δ13Ccorr) time series records were constructed from whole wood samples extracted from paths parallel to the growth rings in each forest. These δ13Ccorr records were corrected for the long-term anthropogenically induced CO2 and compared to historical climate (temperature, precipitation) data from 1935 to 2016. High resolution inter-annual variations in trees in each stand displayed similar intra-annual cycles in δ13Ccorr, demonstrating the seasonal physiological response of these forests to environmental stressors. In both stands, growing season temperature acted as a significant control (p
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Impacts of droughts and extreme-temperature events on gross primary production and ecosystem respiration: a systematic assessment across ecosystems and climate zones
J. von Buttlar,
Jakob Zscheischler,
Anja Rammig,
Sebastian Sippel,
Markus Reichstein,
Alexander Knohl,
Martin Jung,
Olaf Menzer,
M. Altaf Arain,
Nina Buchmann,
Alessandro Cescatti,
Damiano Gianelle,
Gerard Kiely,
B. E. Law,
Vincenzo Magliulo,
Hank A. Margolis,
Harry McCaughey,
Lutz Merbold,
Mirco Migliavacca,
Leonardo Montagnani,
Walter C. Oechel,
Marian Pavelka,
Matthias Peichl,
Serge Rambal,
A. Raschi,
Russell L. Scott,
Francesco Primo Vaccari,
Eva van Gorsel,
Andrej Varlagin,
Georg Wohlfahrt,
Miguel D. Mahecha
Biogeosciences, Volume 15, Issue 5
Abstract. Extreme climatic events, such as droughts and heat stress, induce anomalies in ecosystem–atmosphere CO2 fluxes, such as gross primary production (GPP) and ecosystem respiration (Reco), and, hence, can change the net ecosystem carbon balance. However, despite our increasing understanding of the underlying mechanisms, the magnitudes of the impacts of different types of extremes on GPP and Reco within and between ecosystems remain poorly predicted. Here we aim to identify the major factors controlling the amplitude of extreme-event impacts on GPP, Reco, and the resulting net ecosystem production (NEP). We focus on the impacts of heat and drought and their combination. We identified hydrometeorological extreme events in consistently downscaled water availability and temperature measurements over a 30-year time period. We then used FLUXNET eddy covariance flux measurements to estimate the CO2 flux anomalies during these extreme events across dominant vegetation types and climate zones. Overall, our results indicate that short-term heat extremes increased respiration more strongly than they downregulated GPP, resulting in a moderate reduction in the ecosystem's carbon sink potential. In the absence of heat stress, droughts tended to have smaller and similarly dampening effects on both GPP and Reco and, hence, often resulted in neutral NEP responses. The combination of drought and heat typically led to a strong decrease in GPP, whereas heat and drought impacts on respiration partially offset each other. Taken together, compound heat and drought events led to the strongest C sink reduction compared to any single-factor extreme. A key insight of this paper, however, is that duration matters most: for heat stress during droughts, the magnitude of impacts systematically increased with duration, whereas under heat stress without drought, the response of Reco over time turned from an initial increase to a downregulation after about 2 weeks. This confirms earlier theories that not only the magnitude but also the duration of an extreme event determines its impact. Our study corroborates the results of several local site-level case studies but as a novelty generalizes these findings on the global scale. Specifically, we find that the different response functions of the two antipodal land–atmosphere fluxes GPP and Reco can also result in increasing NEP during certain extreme conditions. Apparently counterintuitive findings of this kind bear great potential for scrutinizing the mechanisms implemented in state-of-the-art terrestrial biosphere models and provide a benchmark for future model development and testing.
2017
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Impacts of droughts and extreme temperature events on gross primary production and ecosystem respiration: a systematic assessment across ecosystems and climate zones
J. von Buttlar,
Jakob Zscheischler,
Anja Rammig,
Sebastian Sippel,
Markus Reichstein,
Alexander Knohl,
Martin Jung,
Olaf Menzer,
M. Altaf Arain,
Nina Buchmann,
Alessandro Cescatti,
Damiano Gianelle,
Gerard Kieley,
B. E. Law,
Vincenzo Magliulo,
Hank A. Margolis,
Harry McCaughey,
Lutz Merbold,
Mirco Migliavacca,
Leonardo Montagnani,
Walter C. Oechel,
Marian Pavelka,
Matthias Peichl,
Serge Rambal,
A. Raschi,
Russell L. Scott,
Francesco Primo Vaccari,
Eva van Gorsel,
Andrej Varlagin,
Georg Wohlfahrt,
Miguel D. Mahecha
Abstract. Extreme climatic events, such as droughts and heat stress induce anomalies in ecosystem-atmosphere CO2 fluxes, such as gross primary production (GPP) and ecosystem respiration (Reco), and, hence, can change the net ecosystem carbon balance. However, despite our increasing understanding of the underlying mechanisms, the magnitudes of the impacts of different types of extremes on GPP and Reco within and between ecosystems remain poorly predicted. Here we aim to identify the major factors controlling the amplitude of extreme event impacts on GPP, Reco, and the resulting net ecosystem production (NEP). We focus on the impacts of heat and drought and their combination. We identified hydrometeorological extreme events in consistently downscaled water availability and temperature measurements over a 30 year time period. We then used FLUXNET eddy-covariance flux measurements to estimate the CO2 flux anomalies during these extreme events across dominant vegetation types and climate zones. Overall, our results indicate that short-term heat extremes increased respiration more strongly than they down-regulated GPP, resulting in a moderate reduction of the ecosystem’s carbon sink potential. In the absence of heat stress, droughts tended to have smaller and similarly dampening effects on both GPP and Reco, and, hence, often resulted in neutral NEP responses. The combination of drought and heat typically led to a strong decrease in GPP, whereas heat and drought impacts on respiration partially offset each other. Taken together, compound heat and drought events led to the strongest C sink reduction compared to any single-factor extreme. A key insight of this paper, however, is that duration matters most: for heat stress during droughts, the magnitude of impacts systematically increased with duration, whereas under heat stress without drought, the response of Reco over time turned from an initial increase to a down-regulation after about two weeks. This confirms earlier theories that not only the magnitude but also the duration of an extreme event determines its impact. Our study corroborates the results of several local site-level case studies, but as a novelty generalizes these findings at the global scale. Specifically, we find that the different response functions of the two antipodal land-atmosphere fluxes GPP and Reco can also result in increasing NEP during certain extreme conditions. Apparently counterintuitive findings of this kind bear great potential for scrutinizing the mechanisms implemented in state-of-the-art terrestrial biosphere models and provide a benchmark for future model development and testing.
