Variation In Gross Primary Production Research Articles

Macrophytes enhance reach-scale metabolism on a daily, seasonal and annual basis in agricultural lowland streams

In many lowland streams, macrophytes are highly abundant and play a key role in ecosystem structure and function. However, no studies on annual stream metabolism have been conducted in streams with significant macrophyte abundance, despite the well-known effect on both gross primary production (GPP) and ecosystem respiration (ER). Macrophyte abundance in temperate streams is strongly seasonal, with highest biomass during summer and lowest during winter. We expected that this phenological pattern would drive annual fluctuations in GPP and ER. We measured daily metabolism for one year in two stream reaches, one with and one without macrophytes. Our results demonstrated that annual, aggregated GPP and ER were 2.2 and 1.3 times higher in the macrophyte reach. Furthermore, while daily GPP was the same between the two reaches during winter where biomass was negligible, GPP was higher during spring, summer and fall for the macrophyte reach. The range in daily ER was more constrained during summer, but more variable during fall and winter in the macrophyte reach relative to the non-macrophyte reach. Macrophyte abundance and chlorophyll-a controlled 80% of the variation in annual GPP for the macrophyte reach. Similarly, 63% of the variation in annual ER was controlled by macrophyte abundance together with discharge in the macrophyte reach. Although macrophytes enhanced GPP on an annual and seasonal time scale in agricultural lowland streams, both reaches were heterotrophic (i.e., GPP < ER) reflecting high organic matter supply from the landscape and in-stream retention and decomposition of organic matter within the macrophyte beds.

Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017

Abstract. Satellite-based models have been widely used to simulate vegetation gross primary production (GPP) at the site, regional, or global scales in recent years. However, accurately reproducing the interannual variations in GPP remains a major challenge, and the long-term changes in GPP remain highly uncertain. In this study, we generated a long-term global GPP dataset at 0.05∘ latitude by 0.05∘ longitude and 8 d interval by revising a light use efficiency model (i.e., EC-LUE model). In the revised EC-LUE model, we integrated the regulations of several major environmental variables: atmospheric CO2 concentration, radiation components, and atmospheric vapor pressure deficit (VPD). These environmental variables showed substantial long-term changes, which could greatly impact the global vegetation productivity. Eddy covariance (EC) measurements at 95 towers from the FLUXNET2015 dataset, covering nine major ecosystem types around the globe, were used to calibrate and validate the model. In general, the revised EC-LUE model could effectively reproduce the spatial, seasonal, and annual variations in the tower-estimated GPP at most sites. The revised EC-LUE model could explain 71 % of the spatial variations in annual GPP over 95 sites. At more than 95 % of the sites, the correlation coefficients (R2) of seasonal changes between tower-estimated and model-simulated GPP are larger than 0.5. Particularly, the revised EC-LUE model improved the model performance in reproducing the interannual variations in GPP, and the averaged R2 between annual mean tower-estimated and model-simulated GPP is 0.44 over all 55 sites with observations longer than 5 years, which is significantly higher than those of the original EC-LUE model (R2=0.36) and other LUE models (R2 ranged from 0.06 to 0.30 with an average value of 0.16). At the global scale, GPP derived from light use efficiency models, machine learning models, and process-based biophysical models shows substantial differences in magnitude and interannual variations. The revised EC-LUE model quantified the mean global GPP from 1982 to 2017 as 106.2±2.9 Pg C yr−1 with the trend 0.15 Pg C yr−1. Sensitivity analysis indicated that GPP simulated by the revised EC-LUE model was sensitive to atmospheric CO2 concentration, VPD, and radiation. Over the period of 1982–2017, the CO2 fertilization effect on the global GPP (0.22±0.07 Pg C yr−1) could be partly offset by increased VPD (-0.17±0.06 Pg C yr−1). The long-term changes in the environmental variables could be well reflected in global GPP. Overall, the revised EC-LUE model is able to provide a reliable long-term estimate of global GPP. The GPP dataset is available at https://doi.org/10.6084/m9.figshare.8942336.v3 (Zheng et al., 2019).

Improvement of predicting ecosystem productivity by modifying carbon–water–nitrogen coupling processes in a temperate grassland

AbstractAimsPrediction of changes in ecosystem gross primary productivity (GPP) in response to climatic variability is a core mission in the field of global change ecology. However, it remains a big challenge for the model community to reproduce the interannual variation (IAV) of GPP in arid ecosystems. Accurate estimates of soil water content (SWC) and GPP sensitivity to SWC are the two most critical aspects for predicting the IAV of GPP in arid ecosystems.MethodsWe took a widely used model Biome-BGC as an example, to improve the model performances in a temperate grassland ecosystem. Firstly, we updated the estimation of SWC by modifying modules of evapotranspiration, SWC vertical profile and field capacity. Secondly, we modified the function of controlling water–nitrogen relation, which regulates the GPP–SWC sensitivity.Important FindingsThe original Biome-BGC overestimated the SWC and underestimated the IAV of GPP sensitivity, resulting in lower IAV of GPP than the observations, e.g. it largely underestimated the reduction of GPP in drought years. In comparison, the modified model accurately reproduced the observed seasonal and IAVs in SWC, especially in the surface layer. Simulated GPP–SWC sensitivity was also enhanced and became closer to the observations by optimizing parameter controlling nitrogen mineralization. Consequently, the model’s capability of reproducing IAV of GPP has been largely improved by the modifications. Our results demonstrate that SWC in the surface layer and the consequent effects on nitrogen availability should be among the first considerations for accurate modeling IAV of GPP in arid ecosystems.

Estimating Crop and Grass Productivity over the United States Using Satellite Solar-Induced Chlorophyll Fluorescence, Precipitation and Soil Moisture Data

This study investigates how gross primary production (GPP) estimates can be improved with the use of solar-induced chlorophyll fluorescence (SIF) based on the interdependence between SIF, precipitation, soil moisture and GPP itself. We have used multi-year datasets from Global Ozone Monitoring Experiment-2 (GOME-2), Tropical Rainfall Measuring Mission (TRMM), European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM), and FLUXNET observations from ten stations in the continental United States. We have employed a GPP quantification framework that makes use of two factors whose influence on the SIF–GPP relationship was not evaluated previously—namely, differential plant sensitivity to water supply at different stages of its lifecycle and spatial variability patterns in SIF that are in contrast to those of GPP, precipitation, and soil moisture. It was found that over the Great Plains and Texas, fluorescence emission levels lag behind precipitation events from about two weeks for grasses to four weeks for crops. The spatial variability of SIF and GPP is shown to be characterized by different patterns: SIF demonstrates less variation over the same spatial extent as compared to GPP, precipitation and soil moisture. Thus, using newly introduced SIF–precipitation lead–lag relationships, we estimate GPP using SIF, precipitation and soil moisture data for grasses and crops over the US by applying the multiple linear regression technique. Our GPP estimates capture the drought impact over the US better than those from Moderate Resolution Imaging Spectroradiometer (MODIS). During the drought year of 2011 over Texas, our GPP values show a decrease by 50–75 gC/m2/month, as opposed to the normal yielding year of 2007. In 2012, a drought year over the Great Plains, we observe a significant reduction in GPP, as compared to 2007. Hence, estimating GPP using specific SIF–GPP relationships, and information on different plant functional types (PFTs) and their interactions with precipitation and soil moisture over the Great Plains and Texas regions can help produce more reasonable GPP estimates.

Minimum carbon uptake controls the interannual variability of ecosystem productivity in tropical evergreen forests

Tropical evergreen forests contribute an important part to the interannual variability (IAV) of the global terrestrial gross primary productivity (GPP). Due to its year-round growing-season, high minimum carbon uptake (GPP min ) and dry season greening-up, the key processes driving the GPP variability across seasonal to interannual scale are still in debate. Here, we analyzed the time-series of FLUXCOM GPP (1980–2013), sun-induced fluorescence (SIF; 2001–2013) and site-level GPP measurements in three tropical evergreen forests regions (i.e., Amazon, Africa, and Southeast Asia). We decomposed the annual accumulated GPP into the basic and recurrent GPP, which represent the accumulated minimum and seasonal vegetation productivity, respectively. Then we quantified the proportion of each component and estimated the contribution to the IAV of GPP. We find that the basic GPP overwhelmed the recurrent GPP with the averaging ratio of 4.2:1 across the global tropical regions, and dominated the IAV of annual total GPP in 83.7% of the tropical evergreen forest areas. The high contribution of the basic GPP resulted from the great sensitivity of GPP min to rainfall changes among years. The decomposition of the basic and recurrent GPP sheds new light on the understanding of tropical GPP variability in responding to climate change at seasonal and annual scale. Our study highlights the critical role of the GPP min in shaping temporal dynamics of the annual GPP in tropical forests and emphasizes the importance of managing tropical forest of the shifting periods between wet-dry seasons in global tropical regions. • Visualizing the seasonal recurrence of tropical forests. • Revealing the role of the minimum carbon uptake in shaping the inter-annual variability of GPP. • Bridging the understandings of GPP variations at seasonal and inter-annual scales.

Vegetation structural change and CO2 fertilization more than offset gross primary production decline caused by reduced solar radiation in China

Terrestrial gross primary production (GPP) is the largest carbon flux from the atmosphere to the terrestrial ecosystems. However, how GPP dynamics respond to vegetation structural change (VSC), climate change, and rising CO2 concentration are still unclear. In this study, we developed a process-based model (i.e., Farquhar GPP Model) to characterize GPP dynamics in China from 2001 to 2016, and further identify the dominant drivers for GPP variation related to VSC, climate change, and rising atmospheric CO2 through model scenario design. During the study period, VSC showed an overwhelmingly positive impact on annual total GPP (i.e., 39.2 Tg C yr−1, P<0.001), followed by the fertilization effect of rising CO2 (i.e., 11.9 Tg C yr−1, P<0.001). However, climate variation would be expected to have an overall negative effect (i.e., -9.9 Tg C yr−1, P<0.001), which is primarily related to the decline of the incoming solar radiation over this period. Consequently, China's GPP showed a remarkable upward trend (i.e., 40.5 Tg C yr−1, P<0.001) during the study period. Our study highlighted the strong positive GPP contribution from VSC, which is mainly related to forest increase under the background of forest cover changes primarily from widespread forest conservation and restoration programs implemented in China since the late 1990s. Therefore, our study suggests that forest conservation and restoration could effectively increase terrestrial ecosystem GPP, despite the negative effects of climate change.

Detecting regional GPP variations with statistically downscaled solar-induced chlorophyll fluorescence (SIF) based on GOME-2 and MODIS data

ABSTRACT Solar-Induced chlorophyll Fluorescence (SIF) is associated with vegetation canopy photosynthesis and is potentially used to retrieve Gross Primary Productivity (GPP). However, the coarse resolutions of the currently available SIF satellite data limit their applications. To expand the applicability of the SIF dataset, a framework was developed to disaggregate the Global Ozone Monitoring Experiment-2 (GOME-2) SIF dataset, which was based on statistical relationships between SIF and remotely sensed measurements of the Normalized Difference Vegetation Index (NDVI), the fraction of absorbed photosynthetically active radiation (f PAR), the soil moisture index and Land Surface Temperature (LST). The statistical relationships were established within a zone of n × n pixels (n∈[1, 25]) with a moving window technique. The regression function established within n × n pixels with the smallest Root Mean Square Error (RMSE) and highest coefficient of determination (R 2) was selected for downscaling regression. Compared with the fixed window technique (n = 5) and theglobal regression model, the moving window technique presented low residuals and high R 2 values. Validated with flux-tower eddy covariance measurements, the GPP retrieved within the downscaled SIF data shows the potential to improve vegetation GPP prediction, and the downscaled SIF could trace the seasonal phenology of evergreen forests.

Nutrients influence seasonal metabolic patterns and total productivity of Arctic streams

AbstractThe seasonality of gross primary production (GPP) in streams is driven by multiple physical and chemical factors, yet incident light is often thought to be most important. In Arctic tundra streams, however, light is available in saturating amounts throughout the summer, but sharp declines in nutrient supply during the terrestrial growing season may constrain aquatic productivity. Given the opposing seasonality of these drivers, we hypothesized that “shoulder seasons”—spring and autumn—represent critical time windows when light and nutrients align to optimize rates of stream productivity in the Arctic. To test this, we measured annual patterns of GPP and biofilm accumulation in eight streams in Arctic Sweden. We found that the aquatic growing season length differed by 4 months across streams and was determined largely by the timing of ice‐off in spring. During the growing season, temporal variability in GPP for nitrogen (N) poor streams was correlated with inorganic N concentration, while in more N‐rich streams GPP was instead linked to changes in phosphorus and light. Annual GPP varied ninefold among streams and was enhanced by N availability, the length of ice‐free period, and low flood frequency. Finally, network scale estimates of GPP highlight the overall significance of the shoulder seasons, which accounted for 48% of annual productivity. We suggest that the timing of ice off and nutrient supply from land interact to regulate the annual metabolic regimes of nutrient poor, Arctic streams, leading to unexpected peaks in productivity that are offset from the terrestrial growing season.

Estimating Benthic Light Regimes Improves Predictions of Primary Production and constrains Light-Use Efficiency in Streams and Rivers

Light-use efficiency (LUE) describes conversion of incident light into gross primary production (GPP), combining the inherent photosynthetic efficiency of chloroplasts with light-capture ability of the autotrophic community. In lotic ecosystems, LUE is poorly constrained, in part because most studies neglect water-column attenuation. We hypothesized that rigorous quantification of benthic light would (1) improve GPP predictions and (2) constrain cross-site variation in LUE. We used a field-validated light model to successively attenuate open-sky irradiance through the riparian canopy and water column to estimate benthic light at 11 sites spanning discharge and dissolved-color gradients where we simultaneously calculated daily GPP. Our results indicate substantial water-column attenuation (up to 96% of stream-surface light), implying significant underestimation of LUE using stream-surface light alone. Benthic light dramatically improved GPP predictions, especially after considering mean-light conditions, which we suggest enumerates ecosystem light-capture ability due to biomass density. The model including mean-light effects explained 78% of GPP variation across sites and yielded a LUE identical to terrestrial ecosystems (1.9%). Interactions between daily and mean-light only slightly improved model fit (R2 = 0.80), implying higher LUE at sites with higher mean light, but notably reduced LUE variation across sites compared with individual site analyses. This suggests that better representation of benthic light regimes leads to LUE convergence. Our study supports use of a global river LUE to translate large-scale predictions of stream light regimes into expected GPP, from which disturbance and nutrient limitation effects can then be discerned.

Development of a topographic-corrected temperature and greenness model (TG) for improving GPP estimation over mountainous areas

The temperature and greenness model (TG) demonstrates that the combination of enhanced vegetation index and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) is feasible in obtaining gross primary productivity (GPP) at the landscape, regional, and global scales. However, the input LST data of TG is always available at a coarse resolution (~ 1 km), averaging a relatively large portion of the topographic characteristics. Hence, GPP simulated using the coarse spatial resolution LST data would suffer from unavoidable bias over mountainous areas. Considering the above limitation, this work proposed a mountainous temperature and greenness model (MTG) through integrating an elevation-corrected factor and a radiation-corrected factor with the current TG model. The proposed MTG model was validated at sixteen eddy covariance (EC) sites with apparent topography in the carbon footprint areas. Results showed that MTG-simulated GPP presented a better agreement with EC GPP than TG-simulated GPP, characterized by an increase of 0.06 in R2 and a decrease of 5.43 gC m − 2 8d−1 in root mean square error, suggesting that the MTG model had a better feasibility of capturing the GPP variations over mountainous areas than the TG model. The standard deviation of MTG-simulated GPP at the sixteen study sites varied between 3.29 and 22.79 gC m − 2 8d−1, highlighting the importance of considering topography within coarse pixels when obtaining GPP estimates over mountainous areas. Furthermore, results also indicated that the MTG-simulated GPP showed obvious responses to topography, suggesting that the MTG model could adequately characterize the topographic effects on plant photosynthesis. More specifically, MTG-simulated GPP increased when slope increased in the sunlit terrains, while it was found to have a lower value when slope increased in the shaded terrains. Our study suggests that incorporating topography information into current GPP models is a practical approach to improve GPP estimates over mountainous areas.

The response of CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; fluxes to the amplitude of diurnal temperature in alpine meadow during growing season from 2002 to 2016 at the southern foot of Qilian Mountains

Known as the “Third Pole” and “Early-warning region” of the world, the Qinghai-Tibetan Plateau (QTP) had an intense increase in surface temperature and marked change in the amplitude of diurnal temperature (ADT), which could have significant influences on the ecosystem carbon cycle. The increasing rate of the mean daily minimum temperature (MinTa) was about two times higher than that of maximum temperature (MaxTa) in the last five decades, and this asymmetric pattern has resulted in a smaller ADT, which substantially affected the plant phenology and vegetation productivity. The reduction in the ADT caused by global climate change would have a profound impact on the carbon balance of alpine ecosystems. However, the response of carbon budgets to the ADT over the QTP remained unclear. Here, we analyzed the 15-a growing seasonal (June−September) carbon fluxes (measured by the eddy covariance [EC] technique) in alpine meadow at the southern foot of Qilian Mountains, which is one of the most extensive vegetation types on the QTP. This study aimed to clarify how carbon fluxes respond to ADT at different temporal (daily, monthly and annual) scales in alpine meadow and to understand their potential response to future climate change. The results indicated that both the MaxTa and MinTa showed bell-shaped seasonal patterns, whereas the ADT failed to show an obvious trend during the growing season from 2002 to 2016. Besides, there was a non-significant increase in annual MaxTa, MinTa and ADT ( P >0.05). Meanwhile, daily gross primary productivity (GPP) and ecosystem respiration (Re) exhibited a single-peaked trend that increased and then decreased, whereas daily net ecosystem CO2 exchange (NEE) showed a v-shaped trend. The alpine meadow ecosystem is a carbon sink during the growing season, and the annual NEE, GPP, and Re were −230.4±17.3, 668.8±25.5, 438.3±27.5 g C m–2, respectively. Moreover, the annual GPP and Re of alpine meadow in the growing season showed a significant increase trend ( P P >0.05). Annual CO2 fluxes were not related annual ADT. Only annual MaxTa exerted significant influence on variations in annual GPP and Re. Interestingly, the slopes of GPP and Re with respect to MaxTa were similar, also indicating the little impact of MaxTa on annual NEE. On a monthly scale, ADT exerted a negligible influence on CO2 fluxes ( P >0.05), but there were significant correlations between MinTa and MaxTa with CO2 fluxes. On a daily scale, there was a significant quadratic relationship between daily NEE and ADT during the whole growing season ( P P

Synergistic use of SMAP and OCO-2 data in assessing the responses of ecosystem productivity to the 2018 U.S. drought

Soil moisture and gross primary productivity (GPP) estimates from the Soil Moisture Active Passive (SMAP) and solar-induced chlorophyll fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) provide new opportunities for understanding the relationship between soil moisture and terrestrial photosynthesis over large regions. Here we explored the potential of the synergistic use of SMAP and OCO-2 based data for monitoring the responses of ecosystem productivity to drought. We used complementary observational information on root-zone soil moisture and GPP (9 km) from SMAP and fine-resolution SIF (0.05°; GOSIF) derived from OCO-2 SIF soundings. We compared the spatial pattern and temporal evolution of anomalies of these variables over the conterminous U.S. during the 2018 drought, and examined to what extent they could characterize the drought-induced variations of flux tower GPP and crop yield data. Our results showed that SMAP GPP and GOSIF, both freely available online, could well capture the spatial extent and dynamics of the impacts of drought indicated by the U.S. Drought Monitor maps and the SMAP root-zone soil moisture deficit. Over the U.S. Southwest, monthly anomalies of soil moisture showed significant positive correlations with those of SMAP GPP (R2 = 0.44, p < 0.001) and GOSIF (R2 = 0.76, p < 0.001), demonstrating strong water availability constraints on plant productivity across dryland ecosystems. We further found that SMAP GPP and GOSIF captured the impact of drought on tower GPP and crop yield. Our results suggest that synergistic use of SMAP and OCO-2 data products can reveal the drought evolution and its impact on ecosystem productivity and carbon uptake at multiple spatial and temporal scales, and demonstrate the value of SMAP and OCO-2 for studying ecosystem function, carbon cycling, and climate change.

Evaluating Multi-Angle Photochemical Reflectance Index and Solar-Induced Fluorescence for the Estimation of Gross Primary Production in Maize

The photochemical reflectance index (PRI) has been suggested as an indicator of light use efficiency (LUE), and for use in the improvement of estimating gross primary production (GPP) in LUE models. Over the last two decades, solar-induced fluorescence (SIF) observations from remote sensing have been used to evaluate the distribution of GPP over a range of spatial and temporal scales. However, both PRI and SIF observations have been decoupled from photosynthesis under a variety of non-physiological factors, i.e., sun-view geometry and environmental variables. These observations are important for estimating GPP but rarely reported in the literature. In our study, multi-angle PRI and SIF observations were obtained during the 2018 growing season in a maize field. We evaluated a PRI-based LUE model for estimating GPP, and compared it with the direct estimation of GPP using concurrent SIF measurements. Our results showed that the observed PRI varied with view angles and that the averaged PRI from the multi-angle observations exhibited better performance than the single-angle observed PRI for estimating LUE. The PRI-based LUE model when compared to SIF, demonstrated a higher ability to capture the diurnal dynamics of GPP (the coefficient of determination (R2) = 0.71) than the seasonal changes (R2 = 0.44), while the seasonal GPP variations were better estimated by SIF (R2 = 0.50). Based on random forest analyses, relative humidity (RH) was the most important driver affecting diurnal GPP estimation using the PRI-based LUE model. The SIF-based linear model was most influenced by photosynthetically active radiation (PAR). The SIF-based linear model did not perform as well as the PRI-based LUE model under most environmental conditions, the exception being clear days (the ratio of direct and diffuse sky radiance &gt; 2). Our study confirms the utility of multi-angle PRI observations in the estimation of GPP in LUE models and suggests that the effects of changing environmental conditions should be taken into account for accurately estimating GPP with PRI and SIF observations.

Differential responses of native and managed prairie pastures to environmental variability and management practices

Future weather and climates, especially rainfall, are expected to have larger variability in the Southern Plains of the United States. However, the degree and timing of environmental variability that affect productivity of pastures managed differently have not been well studied. We examined the impacts of environmental variability on grassland productivity using 17 years of gross primary productivity (GPP) data for co-located native and managed prairie pastures in Oklahoma. We also considered the interactive effects of management factors and environmental variability into the regression models and identified the critical temporal windows of environmental variables (CWE) that influence annual variability in GPP. Managed pasture (MP) showed greater variability of GPP than did native pasture (NP), particularly with reduced GPP in drought years. The resilience of native prairies under unfavorable climate extremes was evident by lower GPP anomalies in NP than MP during the 2011–2012 drought. Although both pastures experienced the same degree of environmental variability, the CWE affecting GPP was significantly different between NP and MP due to the modulating impact of management practices on the responses of GPP. Not only the range but also the timings of the CWE were different between NP and MP as MP was more responsive to the spring temperature and fall rainfall. Our findings warrant the incorporation of MP as a different commodity from NP when accounting for the ecosystem responses to environmental variability in global climate models.

Spatio-temporal variability of gross primary productivity in moist and dry deciduous plant functional types of Northwest Himalayan foothills of India using temperature-greenness model

The present study aims to estimate the spatio-temporal variability of gross primary productivity (GPP) in moist and dry deciduous plant functional types (PFTs) of northwest Himalayan foothills of India using remote sensing-based Temperature-Greenness (TG) model and to study the response of GPP to environmental variables. TG model was implemented in Google Earth Engine platform using Moderate Resolution Imaging Spectroradiometer enhanced vegetation index (MOD13A2) and land surface temperature (MOD11A2) from 2001 to 2018. The mean monthly GPP ranged from 1.80 to 18.57 gCm−2day−1 in moist deciduous and from 0.20 to 12.06 gCm−2day−1 in dry deciduous PFTs. On site-scale validation with eddy covariance flux tower GPP, the modelled GPP showed R2=0.79 for moist deciduous and R2=0.77 for dry deciduous PFT. Leaf area index showed the highest correlation with the predicted GPP (r = 0.74 for moist and 0.83 for dry deciduous PFTs). The study revealed that TG model could predict the long-term forest GPP with minimum in-situ inputs.

Seasonal changes in primary production and respiration in a subtropical lake undergoing eutrophication.

The balance between gross primary production (GPP) and respiration (R) is frequently used to estimate the role of lakes in the carbon cycle. Seasonal changes in the carbon cycle of subtropical lakes are often underestimated, but changes in meteorological and limnological characteristics often follow the well-defined climatic seasons. Based on 1year's free-water dissolved oxygen and temperature measurements, we investigated the seasonal changes in primary production and respiration in subtropical Peri Lake in Southern Brazil, which is currently undergoing eutrophication. We expected that periods of high light availability and temperature would lead to a net autotrophic condition. Furthermore, we explored the seasonal coupling between GPP and R, expecting that different sources of organic matter would have different effects on the metabolic rates. We found that Peri Lake was predominately net heterotrophic (GPP <R). GPP was high during summer and autumn and low in winter, as was R, coinciding with the seasonal changes occurring in light and temperature. Light conditions were of essential importance for the variations in GPP, while respiration was fueled by both autochthonous and allochthonous organic matter. Constant external input of organic matter resulted in a generally low coupling between GPP and R. A tighter coupling between GPP and R was observed in spring as a result of higher productivity, while a decoupling in autumn was due to intensified allochthonous organic matter runoff caused by rainfall and wind. We found that higher productivity rates in summer did not shift the system to an autotrophic condition and that Peri Lake functioned as a carbon source, light and organic matter being the prime drivers for the metabolic rates.

Tracking Seasonal and Interannual Variability in Photosynthetic Downregulation in Response to Water Stress at a Temperate Deciduous Forest

AbstractThe understanding and modeling of photosynthetic dynamics affected by climate variability can be highly uncertain. In this paper, we examined a well‐characterized eddy covariance site in a drought‐prone temperate deciduous broadleaf forest combining tower measurements and satellite observations. We find that an increase in spring temperature usually leads to enhanced spring gross primary production (GPP), but a GPP reduction in late growing season due to water limitation. We evaluated how well a coupled fluorescence‐photosynthesis model (SCOPE) and satellite data sets track the interannual and seasonal variations of tower GPP from 2007 to 2016. In SCOPE, a simple stress factor scaling of Vcmax as a linear function of observed predawn leaf water potential (ψpd) shows a good agreement between modeled and measured interannual variations in both GPP and solar‐induced chlorophyll fluorescence (SIF) from the Global Ozone Monitoring Experiment‐2 (GOME‐2). The modeled and satellite‐observed changes in SIFyield are ~30% smaller than corresponding changes in light use efficiency (LUE) under severe stress, for which a common linear SIF to GPP scaling would underestimate the stress reduction in GPP. Overall, GOME‐2 SIF tracks interannual tower GPP variations better than satellite vegetations indices (VIs) representing canopy “greenness.” However, it is still challenging to attribute observed SIF variations unequivocally to greenness or physiological changes due to large GOME‐2 footprint. Higher‐resolution SIF data sets (e.g., TROPOMI) already show the potential to well capture the downregulation of late‐season GPP and could pave the way to better disentangle canopy structural and physiological changes in the future.

Impact of bedrock geochemistry on vegetation productivity depends on climate dryness in the Guizhou karst of China

The Guizhou karst area is one of the largest continuous areas of karst in the humid climate zone and is representative of karst landforms in China. Large portions of the karst system are characterized by extremely shallow soils underlain by weathered bedrock and water deficits are common. Although the distribution of ecosystem productivity is largely related to variations in the temperature and precipitation, the influence of the substrate in karst areas requires further exploration. We explored the relative importance of the bedrock geochemistry (characterized by the concentrations of Ca, Mg and Si) and climatic factors (temperature and precipitation) to explain the spatial variability in gross primary productivity (GPP) with various degrees of water deficit during the time period 2001–2015. Our results show that the impact of bedrock geochemistry is an important parameter in changing the original relationship between climate and the GPP. The bedrock geochemistry functioned as a “regulator” of the relation between climate and the GPP, which strengthened with decreasing climate favourability. The variations in GPP and surface water storage were significantly different when different elements (Ca, Mg or Si) were dominant. The Mg-rich regions showed the greatest annual variations in the GPP, whereas the Si-rich regions had the strongest surface water storage potential to support vegetation growth. The results of our study are important for systematically evaluating the effects of climate on vegetation productivity and provide a benchmark for global vegetation modelling predictions.

Estimation of Gross Primary Productivity (GPP) Phenology of a Short-Rotation Plantation Using Remotely Sensed Indices Derived from Sentinel-2 Images

This study aimed to understand which vegetation indices (VIs) are an ideal proxy for describing phenology and interannual variability of Gross Primary Productivity (GPP) in short-rotation coppice (SRC) plantations. Canopy structure- and chlorophyll-sensitive VIs derived from Sentinel-2 images were used to estimate the start and end of the growing season (SOS and EOS, respectively) during the period 2016–2018, for an SRC poplar (Populus spp.) plantation in Lochristi (Belgium). Three different filtering methods (Savitzky–Golay (SavGol), polynomial (Polyfit) and Harmonic Analysis of Time Series (HANTS)) and five SOS- and EOS threshold methods (first derivative function, 10% and 20% percentages and 10% and 20% percentiles) were applied to identify the optimal methods for the determination of phenophases. Our results showed that the MEdium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) had the best fit with GPP phenology, as derived from eddy covariance measurements, in identifying SOS- and EOS-dates. For SOS, the performance was only slightly better than for several other indices, whereas for EOS, MTCI performed markedly better. The relationship between SOS/EOS derived from GPP and VIs varied interannually. MTCI described best the seasonal pattern of the SRC plantation’s GPP (R2 = 0.52 when combining all three years). However, during the extreme dry year 2018, the Chlorophyll Red Edge Index performed slightly better in reproducing growing season GPP variability than MTCI (R2 = 0.59; R2 = 0.49, respectively). Regarding smoothing functions, Polyfit and HANTS methods showed the best (and very similar) performances. We further found that defining SOS as the date at which the 10% or 20% percentile occurred, yielded the best agreement between the VIs and the GPP; while for EOS the dates of the 10% percentile threshold came out as the best.

Growth stage-dependent responses of carbon fixation process of alpine grasslands to climate change over the Tibetan Plateau, China

Alpine grassland ecosystem on the Tibetan Plateau (TP) is highly sensitive to climate change and plays a critical role in terrestrial carbon cycle. However, the long-term responses of alpine grassland ecosystem carbon fluxes to climate change at different growth stages and their controlling mechanisms have not been well understood. In this study, we quantified the spatial and temporal variations of gross primary productivity (GPP) and net ecosystem productivity (NEP) of alpine grassland ecosystems from 1982 to 2015 based on an improved Biome-BGC model, and then we examined how different climate factors (i.e., soil moisture, temperature, vapor pressure deficit (VPD), and solar shortwave radiation) affected GPP and NEP dynamics at different growth stages (i.e., the early rapid growth, physiological maturity, and senescence stages) using partial correlation analysis. Furthermore, the relative contributions of different climate factors on GPP and NEP dynamics were also analyzed using path analysis model. The results showed that both GPP and NEP of alpine grassland ecosystems increased significantly during the past 34 years, and the responses of GPP and NEP to climate change and their dominant climate factors significantly varied at different growth stages. Specifically, temperature severely limited the productivity of most alpine grasslands at all growth stages, and soil moisture played an increasingly important role in vegetation carbon fixation before senescence stage, while the effects of VPD and solar shortwave radiation on vegetation carbon fixation were relatively weak. Our study suggested that a finer temporal scale (e.g., the growth stage basis) should be considered to reveal the complex relationships between climate change and the variations of ecosystem carbon fluxes, which could provide a new sight to understand the mechanisms of terrestrial ecosystem's responses to global climate change.