Effects of chlorophyll fluorescence on environment and gross primary productivity of moso bamboo during the leaf-expansion stage

Chlorophyll fluorescence is the long-wave light released by the residual energy absorbed by vegetation after photosynthesis and dissipation, which can directly and non-destructively reflect the photosynthetic state of plants from the perspective of the mechanism of photosynthetic process. Moso bamboo has a substantial carbon sequestration ability, and leaf-expansion stage is an important phenological period for carbon sequestration. Gross primary production (GPP) is a key parameter reflecting vegetation carbon sequestration process. However, the ability of chlorophyll fluorescence in moso bamboo to explain GPP changes is unclear. The research area of this study is located in the bamboo forest near the flux station of Anji County, Zhejiang Province, where an observation tower is built to monitor the carbon flux and meteorological change of bamboo forest. The chlorophyll fluorescence physiological parameters (Fp) and fluorescence yield (Fy) indices were measured and calculated for the leaves of newborn moso bamboo (I Du bamboo) and the old leaves of 4- to 5-year-old moso bamboo (Ⅲ Du bamboo) during the leaf-expansion stage. The chlorophyll fluorescence in response to the environment and its effect on carbon flux were analyzed. The results showed that: Fv/Fm, Y(II) and α of Ⅰ Du bamboo gradually increased, while Ⅲ Du bamboo gradually decreased, and FYint and FY687/FY738 of Ⅰ Du bamboo were higher than those of Ⅲ Du bamboo; moso bamboo was sensitive to changes in air temperature(Ta), relative humidity(RH), water vapor pressure(E), soil temperature(ST) and soil water content (SWC), the Fy indices of the upper, middle and lower layers were significantly correlated with Ta, E and ST; single or multiple vegetation indices were able to estimate the fluorescence yield indices well (all with R2 greater than 0.77); chlorophyll fluorescence (Fp and Fy indices) of Ⅰ Du bamboo and Ⅲ Du bamboo could explain 74.4% and 72.7% of the GPP variation, respectively; chlorophyll fluorescence and normalized differential vegetation index of the canopy (NDVIc) could estimate GPP well using random forest (Ⅰ Du bamboo: r = 0.929, RMSE = 0.069 g C·m-2; Ⅲ Du bamboo: r = 0.899, RMSE = 0.134 g C·m-2). The results of this study show that chlorophyll fluorescence can provide a basis for judging the response of moso bamboo to environmental changes and can well explain GPP. This study has important scientific significance for evaluating the potential mechanisms of growth, stress feedback and photosynthetic carbon sequestration of bamboo.

Estimating global annual gross primary production based on satellite-derived phenology and maximal carbon uptake capacity

The high uncertainty regarding global gross primary production (GPP) remains unresolved. This study explored the relationships between phenology, physiology, and annual GPP to provide viable alternatives for accurate estimation. A statistical model of integrated phenology and physiology (SMIPP) was developed using GPP data from 145 FLUXNET sites to estimate the annual GPP for various vegetation types. By employing the SMIPP model driven by satellite-derived datasets of the global carbon uptake period (CUP) and maximal carbon uptake capacity (GPPmax), the global annual GPP was estimated for the period from 2001-2018. The results demonstrated that the SMIPP model accurately predicted annual GPP, with relative root mean square error values ranging from 11.20‒19.29% for forest types and 20.49‒35.71% for non-forest types. However, wetlands, shrublands, and evergreen forests exhibited relatively low accuracies. The average, trend, and interannual variation of global GPP during 2001-2018 were 132.6 Pg C yr-1, 0.25 Pg C yr-2, and 1.57 Pg C yr-1, respectively. They were within the ranges estimated in other global GPP products. Sensitivity analysis revealed that GPPmax had comparable effects to CUP in high-latitude regions but significantly greater impacts at the global scale, with sensitivity coefficients of 0.85 ± 0.23 for GPPmax and 0.46 ± 0.28 for CUP. This study provides a simple and practical method for estimating global annual GPP and highlights the influence of GPPmax and CUP on global-scale annual GPP.

An optimal ensemble of the CoLM for simulating the carbon and water fluxes over typical forests in China

Stomatal conductance (gs) and compensatory water uptake (CWU) are crucial processes in land surface models, as they directly influence the exchange of carbon and water fluxes between terrestrial ecosystems and the atmosphere. In this study, we integrated a new stomatal scheme derived from optimal stomatal theory (Medlyn's gs model), and an empirical CWU scheme into the Common Land Model (CoLM). Assessing the impacts on modeling gross primary productivity (GPP) and latent flux (LE) through observations obtained from eddy covariance (EC) measurements at three forest sites in China. Our results show that replacing the Ball-Berry's gs model (termed BB) with Medlyn's gs model (termed MED) did not bring about significant changes (had neutral impacts) in the performance of CoLM simulations at three forest sites. Considering the climate factors of annual mean precipitation to optimize key fitting parameters in gs exhibited improvement in model simulations. The average coefficient of determination (R2) achieved to 0.65 for GPP and LE at three sites, and the normalized root mean squared error (NRMSE) decreased from 0.83 to 0.77 at those sites. Besides, incorporating CWU into the model improved its performance. The R2 increased to 0.84 and RMSE decreased to 4.84 μmol m-2 s-1 for GPP, and the R2 increased to 0.62 and RMSE decreased to 55.64 W m-2 for LE. Therefore, modifying the model process of both contributed more to enhancing the model simulations than relying solely on one of these functions. Our study highlights that the response of plant functional types (PFTs) to water stress can be effectively represented in gs models when coupled with biochemical capacity to quantify carbon and water fluxes in forest ecosystems or other ecosystems.

Dry-season length affects the annual ecosystem carbon balance of a temperate semi-arid shrubland

Semi-arid ecosystems have been shown to dominate over tropical forests in determining the trend and interannual variability of land carbon (C) sink. However, the magnitude and variability of ecosystem C balance remain largely uncertain for temperate semi-arid shrublands at the decadal scale. Using eddy-covariance and micro-meteorological measurements, we quantified the interannual variation in net ecosystem production (NEP) and its components, gross primary production (GPP) and ecosystem respiration (Reco, i.e., the sum of autotrophic and heterotrophic respiration), in a semi-arid shrubland of the Mu Us Desert, northern China during 2012-2022. This shrubland was an overall weak C sink over the 11 years (NEP = 12 ± 46 g C m-2 yr-1, mean ± SD). Annual NEP ranged from -66 to 77 g C m-2 yr-1, with the ecosystem frequently switching between being an annual C sink and a C source. GPP was twice as sensitive as Reco to prolonged dry seasons, leading to a close negative relationship between annual NEP and dry-season length (R2 = 0.80, P < 0.01). Annual GPP (R2 = 0.51, P = 0.01) and NEP (R2 = 0.58, P < 0.01) were positively correlated with annual rainfall. Negative annual NEP (the ecosystem being a C source) tended to occur when the dry season exceeded 50 d yr-1 or rainfall dropped below 280 mm yr-1. Increases in dry-season length strengthened the effects of low soil moisture relative to high vapor pressure deficit in constraining NEP. Both GPP and NEP were more closely correlated with C uptake amplitude (annual maximum daily values) than with C uptake period. These findings indicate that dry-season extension under climate change may reduce the long-term C sequestration in semi-arid shrublands. Plant species adapted to prolonged dry seasons should be used in ecosystem restoration in the studied area to enhance ecosystem functions.

Vegetation canopy structure mediates the response of gross primary production to environmental drivers across multiple temporal scales

Gross primary production (GPP) is a critical component of the global carbon cycle and plays a significant role in the terrestrial carbon budget. The impact of environmental factors on GPP can occur through both direct (by influencing photosynthetic efficiency) and indirect (through the modulation of vegetation structure) pathways, but the extent to which these mechanisms contribute has been seldom quantified. In this study, we used structural equation modeling and observations from the FLUXNET network to investigate the direct and indirect effects of environmental factors on terrestrial ecosystem GPP at multiple temporal scales. We found that canopy structure, represented by leaf area index (LAI), is a crucial intermediate factor in the GPP response to environmental drivers. Environmental factors affect GPP indirectly by altering canopy structure, and the relative proportion of indirect effects decreased with increasing LAI. The study also identified different effects of environmental factors on GPP across time scales. At the half-hourly time scale, radiation was the primary driver of GPP. In contrast, the influences of temperature and vapor pressure deficit took on greater prominence at longer time scales. About half of the total effect of temperature on GPP was indirect through the regulation of canopy structure, and the indirect effect increased with increasing time scale (GPPNT-based models: 0.135 (half-hourly) vs. 0.171 (daily) vs. 0.189 (weekly) vs. 0.217 (monthly); GPPDT-based models: 0.139 vs. 0.170 vs. 0.187 vs. 0.215; all values were reported in gC m-2 d-1 °C-1, P < 0.001); while the indirect effect of radiation on GPP was comparatively lower, accounting for less than a quarter of the total effect. Furthermore, we observed a direct, negative-to-positive impact of precipitation on GPP across timescales. These findings provide crucial information on the interplay between environmental factors and LAI on GPP and enable a deeper understanding of the driving mechanisms of GPP.

Exploring the spatio-temporal patterns of global mangrove gross primary production and quantifying the factors affecting its estimation, 1996-2020

Mangrove ecosystems, as an important component of "Blue Carbon", play a curial role on global carbon cycling; however, the lack of the global estimates of mangrove ecosystem gross primary production (GPP) and the underlying environmental controls on its estimation remain a gap in knowledge. In this study, we utilized global mangrove eddy covariance data and applied Gaussian Process Regression (GPR) to estimate GPP for global mangrove ecosystems, aiming to elucidate the factors influencing these estimates. The optimal GPR achieved favorable estimation performance through cross-validation (R2 = 0.90, RMSE = 0.92 gC/m2/day, WI = 0.86). Over the study period, the globally annual averaged GPP was 2054.53 ± 38.51 gC/m2/yr (comparable to that of evergreen broadleaf forests and exceeds the GPP of most other plant function types), amounting to a total of 304.82 ± 7.71TgC/yr, hotspots exceeding 3000 gC/m2/yr observed near the equator. The analysis revealed a decline in global mangrove GPP during 1996-2020 of -0.89 TgC/yr. Human activities (changes in mangrove cover area) played a relatively consistent role in contributing to this decrease. Conversely, variations in external environmental conditions showed distinct inter-annual differences in their impact. The spatio-temporal distribution patterns of mangrove ecosystems GPP (e.g., the bimodal annual pattern, latitudinal gradients, etc.) demonstrated the regulatory influence of external environmental conditions on GPP estimates. The model ensemble attribution analysis indicated that the fraction of absorbed photosynthetically active radiation exerted the dominant control on GPP estimations, while temperature, salinity, and humidity acted as secondary constraints. The findings of this study provide valuable insights for monitoring, modeling, and managing mangrove ecosystems GPP; and underscore the critical role of mangroves in global carbon sequestration. By quantifying the influences of environmental factors, we enhance our understanding of mangrove carbon cycling estimates, thereby helping sustain of these disproportionately productive ecosystems.

Detection and attribution of an anomaly in terrestrial photosynthesis in Europe during the COVID-19 lockdown

Carbon dioxide (CO2) uptake by plant photosynthesis, referred to as gross primary production (GPP) at the ecosystem level, is sensitive to environmental factors, including pollutant exposure, pollutant uptake, and changes in the scattering of solar shortwave irradiance (SWin) - the energy source for photosynthesis. The 2020 spring lockdown due to COVID-19 resulted in improved air quality and atmospheric transparency, providing a unique opportunity to assess the impact of air pollutants on terrestrial ecosystem functioning. However, detecting these effects can be challenging as GPP is influenced by other meteorological drivers and management practices. Based on data collected from 44 European ecosystem-scale CO2 flux monitoring stations, we observed significant changes in spring GPP at 34 sites during 2020 compared to 2015-2019. Among these, 14 sites showed an increase in GPP associated with higher SWin, 10 sites had lower GPP linked to atmospheric and soil dryness, and seven sites were subjected to management practices. The remaining three sites exhibited varying dynamics, with one experiencing colder and rainier weather resulting in lower GPP, and two showing higher GPP associated with earlier spring melts. Analysis using the regional atmospheric chemical transport model (LOTOS-EUROS) indicated that the ozone (O3) concentration remained relatively unchanged at the research sites, making it unlikely that O3 exposure was the dominant factor driving the primary production anomaly. In contrast, SWin increased by 9.4 % at 36 sites, suggesting enhanced GPP possibly due to reduced aerosol optical depth and cloudiness. Our findings indicate that air pollution and cloudiness may weaken the terrestrial carbon sink by up to 16 %. Accurate and continuous ground-based observations are crucial for detecting and attributing subtle changes in terrestrial ecosystem functioning in response to environmental and anthropogenic drivers.

Soil moisture dominates the variation of gross primary productivity during hot drought in drylands

The frequency and severity of hot drought will increase in the future due to impact of climate change and human activities, threatening the sustainability of terrestrial ecosystems and human societies. Hot drought is a typical type of drought event, high vapor pressure deficit (VPD) and low soil moisture (SM) are its main characteristics of hot drought, with increasing water stress on vegetation and exacerbating hydrological drought and ecosystem risks. However, our understanding of the effects of high VPD and low SM on vegetation productivity is limited, because these two variables are strongly coupled and influenced by other climatic drivers. The southwestern United States experienced one of the most severe hot drought events on record in 2020. In this study, we used SM and gross primary productivity (GPP) datasets from Soil Moisture Active and Passive (SMAP), as well as VPD and other meteorological datasets from gridMET. We decoupled the effects of different meteorological factors on GPP at monthly and daily scales using partial correlation analysis, partial least squares regression, and binning methods. We found that SM anomalies contribute more to GPP anomalies than VPD anomalies at monthly and daily scales. Especially at the daily scale, as the decoupled SM anomalies increased, the GPP anomalies increased. However, there is no significant change in GPP anomalies as VPD increases. For all the vegetation types and arid zones, SM dominated the variation in GPP, followed by VPD or maximum temperature. At the flux tower scale, decoupled soil water content (SWC) also dominated changes in GPP, compared to VPD. In the next century, hot drought will occur frequently in dryland regions, where GPP is one of the highest uncertainties in terrestrial ecosystems. Our study has important implications for identifying the strong coupling of meteorological factors and their impact on vegetation under climate change.

Evaluating gross primary productivity over 9 ChinaFlux sites based on random forest regression models, remote sensing, and eddy covariance data

Accurate modeling of Gross Primary Productivity (GPP) in terrestrial ecosystems is a major challenge in quantifying the carbon cycle. Many light use efficiency (LUE) models have been developed, but the variables and algorithms used for environmental constraints in different models vary importantly. It is still unclear whether the models can be further improved by machine learning methods and the combination of different variables. Here, we have developed a series of RFR-LUE models, which used the random forest regression (RFR) algorithm based on variables of LUE models, to explore the potential of estimating site-level GPP. Based on remote sensing indices, eddy covariance and meteorological data, we applied RFR-LUE models to evaluate the effects of different variables combined on GPP on daily, 8-day, 16-day and monthly scales, respectively. Cross-validation analyses revealed performances of RFR-LUE models varied significantly among sites with R² of 0.52-0.97. Slopes of the regression relationship between simulated and observed GPP ranged from 0.59 to 0.95. Most models performed better in capturing the temporal changes and magnitude of GPP in mixed forests and evergreen needle-leaf forests than in evergreen broadleaf forests and grasslands. Performances were improved at the longer temporal scale, with the average R² for four-time resolutions of 0.81, 0.87, 0.88, and 0.90, respectively. Additionally, the importance of the variables showed that temperature and vegetation indices were critical variables for RFR-LUE models, followed by radiation and moisture variables. The importance of moisture variables was higher in non-forests than in forests. A comparison with four GPP products indicated that RFR-LUE model predicted GPP better matcher observed GPP across sites. The study provided an approach to deriving GPP fluxes and evaluating the extent to which variables affect GPP estimation. It may be used for predicting vegetation GPP at the regional scales and for calibration and evaluation of land surface process models.

Development of subsurface chlorophyll maximum layer and its contribution to the primary productivity of water column in a large subtropical reservoir

The phenomenon of subsurface chlorophyll maximum (SCM) layer emerging at a certain water depth is commonly found in stratified water bodies. Also, it is a crucial contributing region to the primary productivity of the water column. Currently, there is a lack of concern about the occurrence of SCM phenomena in studies targeting inland water bodies such as natural lakes and artificial reservoirs. This led to a significant underestimation of the level of primary productivity in these water bodies and their trophic state. In this study, a subtropical reservoir (the Xinanjiang Reservoir, XAJR) was investigated, to understand the characteristics of SCM layer in deep-large reservoir and its contribution to the primary productivity of the water column. Water sampling were conducted from September 2020 to August 2021, and in September 2022. Buoy station data for this reservoir between 2019 and 2021 were also collected. Based on the detailed observations of the water column profile in riverine area (X1), transitional area (X2), and central area (X3 and X4) of this reservoir, it was found that there was an obvious SCM phenomenon, which was closely related to the characteristics of seasonal thermal stratification. The SCM layer of XAJR appeared at depth around 3-5 m underwater from May to August, and as the thermal stratification strength increased, so did the depth and thickness of the SCM layer. It was estimated that gross primary productivity of euphotic layer of XAJR ranged from 347.9 to 4508.6 mgC·m^-2·d^-1. The average primary productivity level of the SCM layer reached 1411.7mgC·m^-2·d^-1, accounting for about 40-90% of the gross primary productivity of euphotic layer. This study contributes to a better understanding of the factors influencing changes in the development of the SCM layer in large reservoirs, as well as its critical role in the inland water carbon cycle.

Impacts of vegetation restoration on water resources and carbon sequestration in the mountainous area of Haihe River basin, China

The mountainous region of the Haihe River basin (MHRB) plays an important role in the water resource supply of its nearby mega-cities, including Beijing and Tianjin, and large areas of cropland. With the implementation of afforestation projects in recent decades, vegetation and carbon (C) uptake have greatly increased in the MHRB. In addition, the annual runoff has significantly declined, threatening regional water security. The trade-off relationship between water yield and C uptake in the MHRB remains unknown. This study employed a biogeochemical model (Biome-BGC) to simulate the natural vegetation dynamics and gross primary productivity (GPP) during 1982-2019 driven by climate forcing. A distributed hydrological model (geomorphology-based hydrological model, GBHM) was adopted to assess the impact of vegetation restoration on the hydrological processes. The results indicated that the leaf area index in the MHRB increased significantly (P < 0.01) during 1982-2019, which led to evapotranspiration increase and runoff (R) reduction. Under the influence of vegetation restoration, both the GPP and the water use efficiency (WUE) increased significantly in the MHRB during 2000-2019, however, the improvement of WUE decreased with the aridity index increasing. Our results showed that vegetation restoration can improve C sequestration efficiency in the MHRB and that the trade-off between water yield and C sequestration should be considered in planning ecological projects to achieve C neutrality.

Long-term effects of rewetting and drought on GPP in a temperate peatland based on satellite remote sensing data

Rewetting previously drained peatlands restores the critical function of peatlands as long-term carbon storages and sinks currently threatened by climate change and additional human-induced disturbances. Understanding and projecting the restoration process by rewetting, however, currently face a pressing challenge, the lack of consistent and gap-free records of important carbon cycling indicators of peatlands such as the gross primary production (GPP) over long term. In this study, we reconstructed the GPP in a rewetted peatland called Zarnekow (Fluxnet-ID: DE-Zrk) in Germany from 2000 to 2020 by combining long-term satellite observations and limited-term tower-based eddy covariance (EC) measurements based on Random Forest regression models. The R² between the reconstructed data and EC data was 0.6. The reasonable reconstruction of long-term GPP enabled trend analysis that identified two distinct periods of decreasing/increasing in GPP due to rewetting and droughts. Rewetting in the winter of 2004 and 2005 stabilized GPP after a decreasing period. A drought in 2018 significantly increased GPP, and GPP remained high over the following two years. Furthermore, the month-specific trends show significant seasonality at this site, specifically, an increasing trend over the 21 years in the growing-season months of June to August and a decreasing trend in the other months. The most important variables for satellite-based estimates of GPP at this site include total evapotranspiration, land surface temperature, enhanced vegetation index and near-infrared reflectance vegetation index. Long-term analyses of carbon fluxes through the combination of satellite observations and EC measurements provide crucial insights into the restoration of carbon sequestration functions in rewetted peatlands.

Ecosystem carbon dioxide exchange and water use efficiency in a triple-cropping rice paddy in Southern India: A two-year field observation

The lowland tropical triple-cropping rice system has unique characteristics that affect the hydrological, nutrient, and atmospheric environments. To better understand the ecosystem carbon and water dynamics of a triple-cropping rice paddy from the perspective of sustainability, ecosystem-level CO2 flux and ecosystem water use efficiency (eWUE) were observed using eddy covariance over 2 years (2016-2018) at an experimental field site in southern India, and gross primary production (GPP) and ecosystem respiration (RE) were derived using the flux partitioning technique. Results showed that among the three crop seasons per year, GPP and RE were higher (887.2 and 570.2 g C m-2, respectively) in Thaladi (October-January: wet season) than in Kuruvai (June-September: dry season; 773.4 and 568.9 g C m-2, respectively) and summer rice (February-May; 694.0 and 499.7 g C m-2, respectively) owing to the longer growing season. Triple-cropping meant that the quasi-annual GPP of 2598 g C m-2 (i.e., the total value for the three consecutive seasons, including the corresponding fallow periods) was much greater than the quasi-annual RE of 1974 g C m-2. Consequently, the net ecosystem production value was positive (624 g C m-2). Evapotranspiration was also high on the annual scale (1681 mm); that is, 48 % greater than mean annual precipitation (1139 mm). Analysis revealed that Thaladi had higher eWUE (2.21 g C (kg H2O)-1) than that of Kuruvai (1.46 g C (kg H2O)-1) and summer rice (1.57 g C (kg H2O)-1) owing to decreased water loss in cloudy weather. Intense solar radiation is generally recognized as advantageous for crop growth in most regions, but not for Kuruvai and summer rice, when too strong solar radiation increases loss of water unused for photosynthesis. The findings indicate that water-saving techniques should be targeted on the Kuruvai and summer rice seasons.

Environmental factors contributing to variations in CO2 flux over a barley-rice double-cropping paddy field in the Korean Peninsula

Understanding the CO₂ flux over agricultural crop fields is critical because the temporal cycle is driven by both ecological environment and anthropogenic change. We analyzed the net ecosystem exchange of CO₂ measured over a barley-rice double-cropping field using the eddy covariance method for 5 years. We conducted gap-filling based on u_*-threshold criteria and partitioned the net ecosystem exchange into gross primary production and respiration. The relative importance analysis of solar radiation, temperature, soil heat flux, soil water content, and vapor deficit revealed that solar radiation and temperature were the dominant contributors to net ecosystem exchange. The annual variation in the net ecosystem exchange followed a bimodal pattern driven by CO₂ uptake by both barley and rice, displaying two negative peaks in late April and mid-August. The elongation stages of the crops exhibited the highest flux. Gross primary production and respiration were closely related to solar radiation and nighttime temperature, respectively. The relative importance of the other environmental variables was affected by the cultivation season and irrigation water. In the period of rice cultivation, respiration was approximately 3 µmol m^-2 s^-1 higher during rice drainage than during the flooded period. The accumulated net ecosystem production was estimated to be 315 gC m^-2 and 349 gC m^-2 for the barley and rice growing periods, respectively, and 649 gC m^-2 for the annual total. These values are comparable with the results of other studies on barley-rice double-cropping fields.

Daytime and nighttime warming has no opposite effects on vegetation phenology and productivity in the northern hemisphere

Over the past 50 years, global land surface air temperature has been rising at a much higher rate at night than during the day. Understanding plant responses to the asymmetric daytime and nighttime warming in the context of climate change has been a hot topic in global change biology and global ecology. It has been debatable whether the asymmetric warming has opposite effects on vegetation activity (e.g., phenology, productivity). Here we settle the debate by scrutinizing the underpinnings of different statistical methods and revealing how the misuse or improper use of these methods could mischaracterize the effects of asymmetric warming with in situ and satellite observations. The use of the ordinary least square (OLS) methods including both daytime (T_max) and nighttime (T_min) temperature in the multiple regression models could overlook the multicollinearity problem and yield the misinterpretations that T_max and T_min had opposite effects on spring phenology, autumn phenology, gross primary production (GPP), and normalized difference vegetation index (NDVI). However, when the OLS methods were applied with T_max and T_min included in separate models or alternatively the ridge regression (RR) method with properly selected ridge parameter was used, the effects of T_max and T_min on vegetation activity were generally in the same direction. The use of the RR method with improperly selected ridge parameter could also mischaracterize the effects of asymmetric warming. Our findings show that daytime and nighttime warming has no opposite effects on vegetation phenology and productivity in the northern hemisphere, and properly dealing with the multicollinearity problem is critical for understanding the effects of asymmetric warming on vegetation activity.

Inter-comparisons of mean, trend and interannual variability of global terrestrial gross primary production retrieved from remote sensing approach

Many models were established to estimate gross primary production (GPP) of terrestrial ecosystems based on vegetation light use efficiency (LUE). Analysing the spatial-temporal variations of global terrestrial GPP became capable with the increasing length of satellite data. Previous studies mainly focused on evaluating the model performance or investigating the mean, the temporal trend or the interannual variability (IAV) of global terrestrial GPP based on one single or multiple models, which is difficult to identify common merits of a same cluster of GPP models. This study compared eight satellite-based LEU-type GPP models in capturing the mean, temporal trend and IAV of global GPP concurrently. Our results showed that current common-used models based on LUE methodology estimated global mean GPP ranging from 128.5 to 158.3 Pg C year^-1, and global mean IAV ranging from 0.1 to 0.35, but the trends ranging from -0.22 to 0.51 Pg C year^-1. In the context of plant functional types (PFTs) and climate classifications, no consistent feature for either of the mean, trend or IAV of GPP are identified among eight models. Future studies should integrate the latest advances on the mechanisms and associated environmental factors into models and consolidate performance of models to better understand the evolutions of terrestrial ecosystem functioning.

Intercomparison of global terrestrial carbon fluxes estimated by MODIS and Earth system models

Earth system models (ESMs) have been widely used to simulate global terrestrial carbon fluxes, including gross primary production (GPP) and net primary production (NPP). Assessment of such GPP and NPP products can be valuable for understanding the efficacy of certain ESMs in simulating the global carbon cycle and future climate impacts. In this work, we studied the model performance of 22 ESMs participating in the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) by comparing historical GPP and NPP simulations with satellite data from MODIS and further evaluating potential model improvement from CMIP5 to CMIP6. In CMIP6, the average global total GPP and NPP estimated by the 22 ESMs are 16% and 13% higher than MODIS data, respectively. The multi-model ensembles (MME) of the 22 ESMs can fairly reproduce the spatial distribution, zonal distribution and seasonal variations of both GPP and NPP from MODIS. They perform much better in simulating GPP and NPP for grasslands, wetlands, croplands and other biomes than forests. However, there are noticeable differences among individual ESM simulations in terms of overall fluxes, temporal and spatial flux distributions, and fluxes by biome and region. The MME consistently outperforms all individual models in nearly every respect. Even though several ESMs have been improved in CMIP6 relative to CMIP5, there is still much work to be done to improve individual ESM and overall CMIP performance. Future work needs to focus on more comprehensive model mechanisms and parametrizations, higher resolution and more reasonable coupling of land surface schemes and atmospheric/oceanic schemes.

A decreasing carbon allocation to belowground autotrophic respiration in global forest ecosystems

Belowground autotrophic respiration (RA_soil) depends on carbohydrates from photosynthesis flowing to roots and rhizospheres, and is one of the most important but least understood components in forest carbon cycling. Carbon allocation plays an important role in forest carbon cycling and reflects forest adaptation to changing environmental conditions. However, carbon allocation to RA_soil has not been fully examined at the global scale. To fill this knowledge gap, we first used a Random Forest algorithm to predict the spatio-temporal patterns of RA_soil from 1981 to 2017 based on the most updated Global Soil Respiration Database (v5) with global environmental variables; calculated carbon allocation from photosynthesis to RA_soil (CA_B) as a fraction of gross primary production; and assessed its temporal and spatial patterns in global forest ecosystems. Globally, mean RA_soil from forests was 8.9 ± 0.08 Pg C yr^-1 (mean ± standard deviation) from 1981 to 2017 and increased significantly at a rate of 0.006 Pg C yr^-2, paralleling broader soil respiration changes and suggesting increasing carbon respired by roots. Mean CA_B was 0.243 ± 0.016 and decreased over time. The temporal trend of CA_B varied greatly in space, reflecting uneven responses of CA_B to environmental changes. Combined with carbon use efficiency, our CA_B results offer a completely independent approach to quantify global aboveground autotropic respiration spatially and temporally, and could provide crucial insights into carbon flux partitioning and global carbon cycling under climate change.

Is satellite Sun-Induced Chlorophyll Fluorescence more indicative than vegetation indices under drought condition?

Droughts represent one of the most severe abiotic stress factors that could result in great crop yield loss. Numerous vegetation indices have been proposed for monitoring the vegetation condition under stress and assessing drought impacts on yield loss. However, the understanding and comparison between traditional vegetation indices (VIs) and the newly emerging satellite Sun-Induced Chlorophyll Fluorescence (SIF) for monitoring vegetation condition is still limited especially under drought stress and at multiple spatial scales. In this study, the potential of satellite observation SIF for monitoring corn response to drought was investigated based on the 2012 drought in the US Corn Belt. The standardized precipitation evapotranspiration index (SPEI) was used here to quantify drought. We found that all SPEI were above -1, except for July (-1.27), August (-1.39) and September (-1.14) in 2012, indicating the severity of this drought. We examined the relationship between satellite measurements of SIF, SIF_yield, VIs (e.g., NDVI and EVI) and SPEI. Results indicated that SIF_yield was sensitive to drought and SIF captured the stress more accurately both at the regional and state scales for the US Corn Belt. Quantitatively, SIF_yield had a high correlation with SPEI (r = 0.987, p < 0.05) over the entire Corn Belt, and it indicated losses in response to drought approximately one month earlier than SIF/NDVI/EVI. Furthermore, our results demonstrated that SIF could be trusted as an effective indicator to study the relationship between GPP (R² ≥ 0.8664, p < 0.01) under drought conditions across the Corn Belt. This study highlighted the advantage of using satellite SIF observations to monitor the drought stress on crop growth especially GPP at regional scale.

Improving modeling of ecosystem gross primary productivity through re-optimizing temperature restrictions on photosynthesis

The terrestrial ecosystem gross primary productivity (GPP) plays an important role in the global carbon cycle and ecosystem functions. However, the estimates of GPP still have large uncertainties due to insufficient understanding of the photosynthesis-temperature relationship and maximum light use efficiency (LUE_max). We used satellite-derived proxies of GPP to derive optimum, minimum, and maximum temperature for photosynthesis at the ecosystem scale, which was then used to construct a new temperature stress expression. This study improves the MODIS-based light use efficiency model through coupling the optimized LUE_max with the new proposed temperature stress expression. The new model (R² = 0.81, RMSE = 17.8 gC m^-2 (16 d)^-1) performed better than the MODIS GPP products (R² = 0.67, RMSE = 30.4 gC m^-2 (16 d)^-1), especially for evergreen broadleaf forests and croplands. The mean annual GPP over China is 5.7 ± 0.27 PgC, and the GPP significantly increased by 0.046 ± 0.006 PgC year^-1 during 2001-2018. This study provides a potential method for future projections of terrestrial ecosystem functioning.

Would the obtainable gross primary productivity (GPP) products stand up? A critical assessment of 45 global GPP products

Gross primary productivity (GPP) is a vital variable of the global carbon cycle, but the quantification of global GPP is subject to significant uncertainty due to the lack of direct observations at a global scale. Here, we evaluated and compared 45 GPP products in terms of their applicability to different vegetation types at various spatiotemporal scales. The results show that 44 GPP products and obsGPP (Model Tree Ensemble GPP derived from observations and named obsGPP) have similar global patterns with correlation coefficients greater than 0.8 except for NGT, where GOSIF, RS, and BESS are prominent. GPP products have the greatest variation in Suriname, with a mean 75th and 25th percentile difference value of 0.4748 (normalized), and we recommend RS, SDGVM and LPJ-wsl as they provide GPP estimates close to the average GPP. In terms of seasonal estimations, considerable disagreement occurs among the GPP products in winter, with a range from 118.76 to 314.95 gC/m²/season, among which JULES has the closest GPP value to the average GPP estimation. For studies concerning vegetation types preference is given to the LUE average GPP. The 45 GPP products are more consistent on grasslands but, have obvious differences for savannas. All GPP products have their own specific spatiotemporal scales, such as global or national scales or different seasons and different vegetation types (forest, grasslands, etc.). This study provides guidelines for selecting GPP products.

Area-ratio Fraunhofer line depth (aFLD) method approach to estimate solar-induced chlorophyll fluorescence in low spectral resolution spectra in a cool-temperate deciduous broadleaf forest

Solar-induced chlorophyll fluorescence (SIF) emissions were estimated by the "area-ratio Fraunhofer line depth (aFLD) method", a new retrieval methodology in spectra from a low spectral resolution (SR) spectroradiometer (MS-700: full width half maximum (FWHM) of 10 nm and spectral sampling interval of 3.3 nm), assisted with a scaling to reference SIF detected from high SR spectrum. The sparse pixels of a spectrum of low SR misses detecting the minimum of the O₂A absorption band around at 760 nm, which makes the SIF detection by conventional FLD methods lose accuracy considerably. To overcome this, the aFLD method uses the definite integral of spectra over a wide interval between 750 and 780 nm. The integration of the spectrum is insusceptible to the change in shape of the depression curve, leading to higher accuracy of the aFLD method. Daily SIF, calculated by the aFLD method using the spectra obtained with MS-700, was scaled to reference daily SIF calculated by the spectral fitting method using the spectra obtained from August to December 2019 with an ultrafine SR spectroradiometer (QE Pro, FWHM = 0.24 nm). As a result, SIF calculated from MS-700 spectra by aFLD method was strongly correlated with the reference SIF from QE Pro spectra (r² = 0.81) and was successfully scaled. Then, the scaled 11-year SIF from MS-700 at a deciduous broadleaf forest showed the correlation with GPP at multiple time steps: daily, monthly, and yearly, consistently during 2008-2018. The comparison of aFLD-derived SIF with the global Orbiting Carbon Observatory-2 (OCO-2) SIF data set (GOSIF) showed high correlation on monthly values during 2008-2017 (r² = 0.85). The combining approach of the aFLD method with a scaling to reference SIF successfully detected long-term canopy SIF emissions, which has great potential to provide essential information on ecosystem-level photosynthesis.

Physiological and environmental control on ecosystem water use efficiency in response to drought across the northern hemisphere

Drought, a natural hydrometeorological phenomenon, has been more frequent and more widespread due to climate change. Water availability strongly regulates the coupling (or trade-off) between carbon uptake via photosynthesis and water loss through transpiration, known as water-use efficiency (WUE). Understanding the effects of drought on WUE across different vegetation types and along the wet to dry gradient is paramount to achieving better understanding of ecosystem functioning in response to climate change. We explored the physiological and environmental control on ecosystem WUE in response to drought using observations for 44 eddy covariance flux sites in the Northern Hemisphere. We quantified the response of WUE to drought and the relative contributions of gross primary production (GPP) and evapotranspiration (ET) to the variations of WUE. We also examined the control of physiological and environmental factors on monthly WUE under different moisture conditions. Cropland had a peak WUE value under moderate drought conditions, while grassland, deciduous broadleaf forest (DBF), evergreen broadleaf forest (EBF), and evergreen needleleaf forest (ENF) had peak WUE under slight drought conditions. WUE was mainly driven by GPP for cropland, grassland, DBF, and ENF but was mainly driven by ET for EBF. Vapor pressure deficit (VPD) and canopy conductance (G_c) were the most important factors regulating WUE. Moreover, WUE had negative responses to air temperature, precipitation, and VPD but had a positive response to G_c and ecosystem respiration. Our findings highlight the different effects of biotic and abiotic factors on WUE among different vegetation types and the important roles of VPD and G_c in controlling ecosystem WUE in response to drought.

Ecosystem metabolism regulates seasonal bioaccumulation of metals in atyid shrimp (Neocaridina denticulata) in a tropical brackish wetland

Natural dissolved organic matter (DOM) forms the base of aquatic food webs and is a key environmental factor that affects the bioavailability of metals for aquatic organisms. Aquatic communities are naturally exposed simultaneously to environments containing a mixture of metals and varying DOM levels and compositions. However, the exact effect of DOM on metal bioaccumulation is difficult to predict due to temporal and spatial variations in sources, production, and consumption of DOM, and to interactions between DOM and metals. Ecosystem metabolism describes the process of organic carbon production and consumption and, therefore, the trophic status of ecosystems. However, whether and how ecosystem metabolism determines the seasonality of metal bioaccumulation remains unclear. The present study used in-situ water quality sondes and discrete field samplings to establish the relationship between the seasonality of ecosystem metabolism; related environmental and limnological regulators; the metal speciation and concentration in bulk water and sediments; and their metal bioaccumulation. The target population consisted of atyid shrimp (Neocaridina denticulata) in a brackish constructed wetland in tropical Taiwan was sampled between August 2014 and November 2015. Metal bioaccumulation displayed distinct seasonal patterns that peaked in summer (Cu, Cd, Cr, Zn, Mn, and Se) or winter (Pb and Ni). The in situ production (gross primary production) and heterotrophic consumption (ecosystem respiration) of organic matter significantly decreased with increasing waterborne DOM levels in this heterotrophic wetland. Both dissolved free metals bioavailable for respiratory surfaces (As, Zn, Cu, and Cr) and insoluble metals available for dietary intake (Mn and Ni) decreased with increasing DOM, as well as with decreasing gross primary production and ecosystem respiration. Seasonal variations of metal bioaccumulation also paralleled the transition in wetland trophic status, which reflected the effect of potential qualitative changes in the wetland DOM pool. Bioaccumulation of most metals displayed strong correlations with gross primary production, ecosystem respiration, and wetland trophic status. Our findings demonstrated that ecosystem metabolism can play a key mediating role in the seasonality of metal bioaccumulation in atyid shrimp, as it links the variation and interaction between DOM level/source, the speciation/bioavailability, and the uptake efficiency for metals by aquatic organisms. This study contributes to the temporal-specific risk assessment of aquatic metal exposure in regional environmental settings. It also reveals ecosystem-specific spectra in the context of changes in climate and environment.

Comment on "On the calculation of lake metabolic rates: Diel O2 and 18/16O technique" by Peeters et al. [Water Res. 165 2019, 114990]

Quantifying metabolic rates in lakes and other aquatic ecosystems is a complex task, as methods are continually evolving and are not currently standardized. Recently, Peeters et al. presented a valuable simulated dataset that advances the field by comparing the strengths and limitations of individual and combined metabolic techniques. The authors conclude that calculating metabolic rates from point sampling and mass balancing of surface water oxygen concentration and isotope composition is flawed, because the technique does not capture sub-daily patterns of metabolic variability, which they argue invalidates past applications and interpretations. These conclusions are inconsistent with how the method has been used, and are based on a biased construction of scenarios and interpretation of model results, especially because their parameterization of the stable isotopic model employs input values that appear unrepresentative of most lake conditions. Here, we establish that 1) empirical evidence supports the isotopic approach's suitability to approximate daily or longer metabolic patterns in most lakes. 2) The authors' own simulations show agreement between metabolic estimates from point isotopic measurements and average metabolic rates under most scenarios. 3) The authors' invalidation of isotopic measurements are based on the most extreme model deviations observed in simulated hypereutrophic environments. While we welcome a critical evaluation of the isotopic approach, we argue that isotopic model uncertainty needs to be placed within an appropriate context. We emphasize that isotopic sampling and steady state metabolic modelling has a key role to play in constraining metabolic patterns in the global lake landscape, but that the research questions addressed with the method need to be commensurate with the limitations and uncertainties of the approach.

Large increases of paddy rice area, gross primary production, and grain production in Northeast China during 2000-2017

China is the largest rice producer and consumer in the world. Accurate estimations of paddy rice planting area and rice grain production is important for feeding the increasing population in China. However, Southern China had substantial losses in paddy rice area over the last three decades in those regions where paddy rice has traditionally been produced. Several studies have shown increased paddy rice area in Northeast China. Here we document the annual dynamics of paddy rice area, gross primary production (GPP), and grain production in Northeast China (Heilongjiang, Jilin and Liaoning provinces) during 2000-2017 using agricultural statistical data, satellite images, and model simulations. Annual maps derived from satellite images show that paddy rice area in Northeast China has increased by 3.68 million ha from 2000 to 2017, which is more than the total combined paddy rice area of North Korea, South Korea, and Japan. Approximately 82% of paddy rice pixels had an increase in annual GPP during 2000-2017. The expansion of paddy rice area slowed down substantially since 2015. Annual GPP from those paddy rice fields cultivated continuously over the 18 years were moderately higher than that from other paddy rice fields, which suggested that improved management practices could increase grain production in the region. There was a strong linear relationship between annual GPP and annual rice grain production in Northeast China by province and year, which illustrates the potential of using satellite-based data-driven model to track and assess grain production of paddy rice in the region. Northeast China is clearly an emerging rice production base and plays an increasing role in crop production and food security in China. However, many challenges for the further expansion and sustainable cultivation of paddy rice in Northeast China remain.

Response of vegetation carbon uptake to snow-induced phenological and physiological changes across temperate China

Snow cover, which is undergoing significant change along with global climate change, has considerable impacts on the functioning of terrestrial ecosystems. However, how snow cover change affects the vegetation gross primary production (GPP) in temperate regions still requires in-depth exploration. In this study, we investigated how changes in the winter snow depth (WSD) and snowmelt date (SMD) affect spring GPP and summer GPP through their influences on the start date of the growing season (SGS) and the maximum daily GPP (GPP_max), respectively, across temperate China from 2001 to 2015, based on both in situ measurements and satellite products (i.e., GLASS GPP, WestDC snow depth and GLEAM soil moisture). Soil moisture is identified as an important factor in the snow-GPP relationship in temperate China. Since most of temperate China is water-limited, thicker snow cover along with later snowmelt generally resulted in earlier SGS via a significant increase in soil moisture (47% of the area), which lengthened the growth period and enhanced spring carbon uptake in these areas. However, in wetter regions (7% of the area), thicker snow cover with later snowmelt would be more likely to delay the SGS, thus reducing spring GPP. Moreover, although the direct impact mechanisms of snow cover dynamics on summer GPP have not been identified, the snow-induced SGS change was found to have delayed effects on summer photosynthesis capacity, as earlier SGS increased the GPP_max, and thus summer GPP. However, the photosynthesis enhanced by earlier SGS meanwhile increased the plant water consumption, which would bring water stress and reduce summer GPP if the subsequent precipitation is unable to compensate for the water consumption. Our findings on the effects of snow cover change on carbon uptake would provide the basic mechanisms for assessing how future climate change will affect ecosystem productivity.

Long-term trend in vegetation gross primary production, phenology and their relationships inferred from the FLUXNET data

Climate-induced changes in plant phenology and physiology plays an important role in control of carbon exchange between terrestrial ecosystems and the atmosphere. Based on dataset during 1997-2014 from 41 flux tower sites (440 site-years) across the northern hemisphere, relationships between long-term trends in start of growing season (SOS), end of growing season (EOS), length of growing season (LOS), maximal gross primary production (GPP_max), and seasonal and annual gross primary production (GPP) were analyzed. Statistical Models of Integrated Phenology and Physiology (SMIPP) were built for predicting the long-term trends in annual GPP. Results showed that SOS advanced and EOS delayed for forest sites, while both SOS and EOS for grassland (GRA) sites delayed. Long-term trends in SOS and EOS of evergreen needle-leaf forests (ENF) sites were greater than those of deciduous broadleaf forests (DBF) sites. Seasonal and annual GPP for forest sites increased, among which long-term trend in annual GPP of ENF sites was the largest. Spring GPP of GRA sites decreased, but annual GPP increased. Strong relationships between long-term trends in phenological and physiological indicators and seasonal GPP were found. Long-term trend in GPP_max had the highest relationship with long-term trend in annual GPP for forest sites, but long-term trend in SOS was the most related to long-term trend in annual GPP for GRA sites. Increases in spring and autumn GPP due to a one-day advance in SOS and delay in EOS for DBF sites were greater than ENF sites. Delay in EOS resulted in more carbon sequestration than advance in SOS for forest sites, while advance in SOS significantly increased spring GPP for GRA sites. The SMIPP model driven by long-term trends in LOS and GPP_max had stronger explanatory power for predicting long-term trend in annual GPP than the SMIPP model driven by long-term trends in SOS, EOS, and GPP_max. Long-term trend in annual GPP was accurately predicted by using the SMIPP model, while long-term trend in annual GPP for GRA sites was more difficult to be captured than the forest sites. Drought and disturbance effects on phenology and physiology were major factors for model uncertainty. This study is helpful to understand changes in phenology and carbon uptake and their differences among different vegetation types and provides a potential way for predicting annual rate of change in carbon uptake through vegetation photosynthesis at a global scale.

Evaluating and comparing remote sensing terrestrial GPP models for their response to climate variability and CO2 trends

Remote sensing (RS)-based models play an important role in estimating and monitoring terrestrial ecosystem gross primary productivity (GPP). Several RS-based GPP models have been developed using different criteria, yet the sensitivities to environmental factors vary among models; thus, a comparison of model sensitivity is necessary for analyzing and interpreting results and for choosing suitable models. In this study, we globally evaluated and compared the sensitivities of 14 RS-based models (2 process-, 4 vegetation-index-, 5 light-use-efficiency, and 3 machine-learning-based models) and benchmarked them against GPP responses to climatic factors measured at flux sites and to elevated CO₂ concentrations measured at free-air CO₂ enrichment experiment sites. The results demonstrated that the models with relatively high sensitivity to increasing atmospheric CO₂ concentrations showed a higher increasing GPP trend. The fundamental difference in the CO₂ effect in the models' algorithm either considers the effect of CO₂ through changes in greenness indices (nine models) or introduces the influences on photosynthesis (three models). The overall effects of temperature and radiation, in terms of both magnitude and sign, vary among the models, while the models respond relatively consistently to variations in precipitation. Spatially, larger differences among model sensitivity to climatic factors occur in the tropics; at high latitudes, models have a consistent and obvious positive response to variations in temperature and radiation, and precipitation significantly enhances the GPP in mid-latitudes. Compared with the results calculated by flux-site measurements, the model performance differed substantially among different sites. However, the sensitivities of most models are basically within the confidence interval of the flux-site results. In general, the comparison revealed that models differed substantially in the effect of environmental regulations, particularly CO₂ fertilization and water stress, on GPP, and none of the models performed consistently better across the different ecosystems and under the various external conditions.

Response of plant production to growing/non-growing season asymmetric warming in an alpine meadow of the Northern Tibetan Plateau

A field growing/non-growing season asymmetric warming experiment (C: control, i.e., no warming in the entire year; GLNG: growing season warming lower than non-growing season warming; GHNG: growing season warming higher than non-growing season warming) was conducted in an alpine meadow of the Northern Tibetan Plateau in early June 2015. The effects of growing/non-growing season asymmetric warming on the normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), aboveground biomass (AGB) and gross primary production (GPP) in 2015-2017 were examined. The 'GLNG' and 'GHNG' treatments significantly increased the annual mean air temperature (T_a) by 2.95 °C and 2.76 °C, and the vapor pressure deficit (VPD) by 0.23 kPa and 0.28 kPa but significantly reduced the annual mean soil moisture (SM) by 0.02 m³ m^-3 and 0.02 m³ m^-3 respectively; however, changes in the annual mean T_a, VPD and SM were the same between the 'GLNG' and 'GHNG' treatments over the three years in 2015-2017. There were no significant differences in the SAVI and GPP among the 'C', 'GLNG' and 'GHNG' treatments over the three growing seasons in 2015-2017. The 'GLNG' and 'GHNG' treatments did not significantly affect the NDVI and AGB compared to 'C', whereas the NDVI and AGB under the 'GLNG' treatment were significantly greater than those under the 'GHNG' treatment over the three growing seasons in 2015-2017. The significant differences in NDVI and AGB between the 'GLNG' and 'GHNG' treatments may be attributed to the different effects under the 'GLNG' and 'GHNG' treatments on the non-growing season T_a, growing season water availability and soil nitrogen availability. Therefore, the non-growing season with a higher warming magnitude may have stronger effects on the aboveground plant production than did the growing season with a higher warming magnitude in the alpine meadow of the Northern Tibetan Plateau.

Spatial-temporal consistency between gross primary productivity and solar-induced chlorophyll fluorescence of vegetation in China during 2007-2014

Accurately estimating spatial-temporal patterns of gross primary production (GPP) is important for the global carbon cycle. Satellite-based light use efficiency (LUE) models are regarded as an efficient tool in simulating spatial-temporal dynamics of GPP. However, the accuracy assessment of GPP simulations from LUE models at both spatial and temporal scales remains a challenge. In this study, we simulated GPP of vegetation in China during 2007-2014 using a LUE model (Vegetation Photosynthesis Model, VPM) based on MODIS (moderate-resolution imaging spectroradiometer) images with 8-day temporal and 500-m spatial resolutions and NCEP (National Center for Environmental Prediction) climate data. Global Ozone Monitoring Instrument 2 (GOME-2) solar-induced chlorophyll fluorescence (SIF) data were used to compare with VPM simulated GPP (GPP_VPM) temporally and spatially using linear correlation analysis. Significant positive linear correlations exist between monthly GPP_VPM and SIF data over a single year (2010) and multiple years (2007-2014) in most areas of China. GPP_VPM is also significantly positive correlated with GOME-2 SIF (R² > 0.43) spatially for seasonal scales. However, poor consistency was detected between GPP_VPM and SIF data at yearly scale. GPP dynamic trends have high spatial-temporal variation in China during 2007-2014. Temperature, leaf area index (LAI), and precipitation are the most important factors influence GPP_VPM in the regions of East Qinghai-Tibet Plateau, Loss Plateau, and Southwestern China, respectively. The results of this study indicate that GPP_VPM is temporally and spatially in line with GOME-2 SIF data, and space-borne SIF data have great potential for evaluating LUE-based GPP models.

Net ecosystem exchange of CO2 and H2O fluxes from irrigated grain sorghum and maize in the Texas High Plains

Net ecosystem exchange (NEE) of carbon dioxide (CO₂) and water vapor (H₂O) fluxes from irrigated grain sorghum (Sorghum bicolor L. Moench) and maize (Zea mays L.) fields in the Texas High Plains were quantified using the eddy covariance (EC) technique during 2014-2016 growing seasons and examined in terms of relevant controlling climatic variables. Eddy covariance measured evapotranspiration (ET_EC) was also compared against lysimeter measured ET (ET_Lys). Daily peak (7-day averages) NEE reached approximately -12 g C m^-2 for sorghum and -14.78 g C m^-2 for maize. Daily peak (7-day averages) ET_EC reached approximately 6.5 mm for sorghum and 7.3 mm for maize. Higher leaf area index (5.7 vs 4-4.5 m² m^-2) and grain yield (14 vs 8-9 t ha^-1) of maize compared to sorghum caused larger magnitudes of NEE and ET_EC in maize. Comparisons of ET_EC and ET_Lys showed a strong agreement (R² = 0.93-0.96), while the EC system underestimated ET by 15-24% as compared to lysimeter without any corrections or energy balance adjustments. Both NEE and ET_EC were not inhibited by climatic variables during peak photosynthetic period even though diurnal peak values (~2-weeks average) of photosynthetic photon flux density (PPFD), air temperature (T_a), and vapor pressure deficit (VPD) had reached over 2000 μmol m^-2 s^-1, 30 °C, and 2.5 kPa, respectively, indicating well adaptation of both C₄ crops in the Texas High Plains under irrigation. However, more sensitivity of NEE and H₂O fluxes beyond threshold T_a and VPD for maize than for sorghum indicated higher adaptability of sorghum for the region. These findings provide baseline information on CO₂ fluxes and ET for a minimally studied grain sorghum and offer a robust geographic comparison for maize outside the United States Corn Belt. However, longer-term measurements are required for assessing carbon and water dynamics of these globally important agro-ecosystems.

Effects of increasing atmospheric CO2 on the marine phytoplankton and bacterial metabolism during a bloom: A coastal mesocosm study

Increases of atmospheric CO₂ concentrations due to human activity and associated effects on aquatic ecosystems are recognized as an environmental issue at a global scale. Growing attention is being paid to CO₂ enrichment effects under multiple stresses or fluctuating environmental conditions in order to extrapolate from laboratory-scale experiments to natural systems. We carried out a mesocosm experiment in coastal water with an assemblage of three model phytoplankton species and their associated bacteria under the influence of elevated CO₂ concentrations. Net community production and the metabolic characteristics of the phytoplankton and bacteria were monitored to elucidate how these organisms responded to CO₂ enrichment during the course of the algal bloom. We found that CO₂ enrichment (1000μatm) significantly enhanced gross primary production and the ratio of photosynthesis to chlorophyll a by approximately 38% and 39%, respectively, during the early stationary phase of the algal bloom. Although there were few effects on bulk bacterial production, a significant decrease of bulk bacterial respiration (up to 31%) at elevated CO₂ resulted in an increase of bacterial growth efficiency. The implication is that an elevation of CO₂ concentrations leads to a reduction of bacterial carbon demand and enhances carbon transfer efficiency through the microbial loop, with a greater proportion of fixed carbon being allocated to bacterial biomass and less being lost as CO₂. The contemporaneous responses of phytoplankton and bacterial metabolism to CO₂ enrichment increased net community production by about 45%, an increase that would have profound implications for the carbon cycle in coastal marine ecosystems.

Influence of plankton metabolism and mixing depth on CO2 dynamics in an Amazon floodplain lake

We investigated plankton metabolism and its influence on carbon dioxide (CO₂) dynamics in a central Amazon floodplain lake (Janauacá, 3°23' S, 60°18' W) from September 2015 to May 2016, including a period with exceptional drought. We made diel measurements of CO₂ emissions to the atmosphere with floating chambers and depth profiles of temperature and CO₂ partial pressure (pCO₂) at two sites with differing wind exposure and proximity to vegetated habitats. Dissolved oxygen (DO) concentrations were monitored continuously during day and night in clear and dark chambers with autonomous optical sensors to evaluate plankton metabolism. Overnight community respiration (CR), and gross primary production (GPP) rates were higher in clear chambers and positively correlated with chlorophyll-a (Chl-a). CO₂ air-water fluxes varied over 24-h periods with changes in thermal structure and metabolism. Most net daily CO₂ fluxes during low water and mid-rising water at the wind exposed site were into the lake as a result of high rates of photosynthesis. All other measurements indicated net daily release to the atmosphere. Average GPP rates (6.8gCm^-2d^-1) were high compared with other studies in Amazon floodplain lakes. The growth of herbaceous plants on exposed sediment during an exceptional drought led to large carbon inputs when these areas were flooded, enhancing CR, pCO₂, and CO₂ fluxes. During the period when the submerged herbaceous vegetation decayed phytoplankton abundance increased and photosynthetic uptake of CO₂ occurred. While planktonic metabolism was often autotrophic (GPP:CR>1), CO₂ out-gassing occurred during most periods investigated indicating other inputs of carbon such as sediments or soils and wetland plants.

Performance of a two-leaf light use efficiency model for mapping gross primary productivity against remotely sensed sun-induced chlorophyll fluorescence data

Estimating terrestrial gross primary production is an important task when studying the carbon cycle. In this study, the ability of a two-leaf light use efficiency model to simulate regional gross primary production in China was validated using satellite Global Ozone Monitoring Instrument - 2 sun-induced chlorophyll fluorescence data. The two-leaf light use efficiency model was used to estimate daily gross primary production in China's terrestrial ecosystems with 500-m resolution for the period from 2007 to 2014. Gross primary production simulated with the two-leaf light use efficiency model was resampled to a spatial resolution of 0.5° and then compared with sun-induced chlorophyll fluorescence. During the study period, sun-induced chlorophyll fluorescence and gross primary production simulated by the two-leaf light use efficiency model exhibited similar spatial and temporal patterns in China. The correlation coefficient between sun-induced chlorophyll fluorescence and monthly gross primary production simulated by the two-leaf light use efficiency model was significant (p<0.05, n=96) in 88.9% of vegetated areas in China (average value 0.78) and varied among vegetation types. The interannual variations in monthly sun-induced chlorophyll fluorescence and gross primary production simulated by the two-leaf light use efficiency model were similar in spring and autumn in most vegetated regions, but dissimilar in winter and summer. The spatial variability of sun-induced chlorophyll fluorescence and gross primary production simulated by the two-leaf light use efficiency model was similar in spring, summer, and autumn. The proportion of spatial variations of sun-induced chlorophyll fluorescence and annual gross primary production simulated by the two-leaf light use efficiency model explained by ranged from 0.76 (2011) to 0.80 (2013) during the study period. Overall, the two-leaf light use efficiency model was capable of capturing spatial and temporal variations in gross primary production in China. However, the model needs further improvement to better simulate gross primary production in summer.

Higher assimilation than respiration sensitivity to drought for a desert ecosystem in Central Asia

Responses of ecosystem assimilation and respiration to global climate change vary considerably among terrestrial ecosystems constrained by both biotic and abiotic factors. In this study, net CO₂ exchange between ecosystem and atmosphere (NEE) was measured over a 4-year period (2013-2016) using eddy covariance technology in a desert ecosystem in Central Asia. Ecosystem assimilation (gross primary production, GPP) and respiration (R_eco) were derived from NEE by fitting light response curves to NEE data based on day- and nighttime data, and their responses to soil water content (SWC) and evaporative fraction (EF) were assessed during the growing season. Results indicated that both GPP and R_eco linearly decreased with declining SWC, with the sensitivity of GPP to SWC being 3.8 times higher than that of R_eco during the entire growing season. As a result, ecosystem CO₂ sequestration capacity decreased from 4.00μmolm^-2s^-1 to 1.00μmolm^-2s^-1, with increasing soil drought_. On a seasonal scale, significant correlation between GPP and SWC was only found in spring while that between R_eco and SWC was found in all growing seasons with the sensitivity increasing steadily from spring to autumn. EF had a low correlation with SWC, GPP and R_eco (R²=0.03, 0.02, 0.05, respectively), indicating that EF was not a good proxy for soil drought and energy partitioning was not tightly coupled to ecosystem carbon exchanges in this desert ecosystem. The study deepens our knowledge of ecosystem carbon exchange and its response to drought as well as its coupling with ecosystem energy partitioning in an extreme dry desert. The information is critical for better assessing carbon sequestration capacity in dryland, and for understanding its feedback to climate change.

Growing season variability in carbon dioxide exchange of irrigated and rainfed soybean in the southern United States

Measurement of carbon dynamics of soybean (Glycine max L.) ecosystems outside Corn Belt of the United States (U.S.) is lacking. This study examines the seasonal variability of net ecosystem CO₂ exchange (NEE) and its components (gross primary production, GPP and ecosystem respiration, ER), and relevant controlling environmental factors between rainfed (El Reno, Oklahoma) and irrigated (Stoneville, Mississippi) soybean fields in the southern U.S. during the 2016 growing season. Grain yield was about 1.6tha^-1 for rainfed soybean and 4.9tha^-1 for irrigated soybean. The magnitudes of diurnal NEE (~2-weeks average) reached seasonal peak values of -23.18 and -34.78μmolm^-2s^-1 in rainfed and irrigated soybean, respectively, approximately two months after planting (i.e., during peak growth). Similar thresholds of air temperature (T_a, slightly over 30°C) and vapor pressure deficit (VPD, ~2.5kPa) for NEE were observed at both sites. Daily (7-day average) NEE, GPP, and ER reached seasonal peak values of -4.55, 13.54, and 9.95gCm^-2d^-1 in rainfed soybean and -7.48, 18.13, and 14.93gCm^-2d^-1 in irrigated soybean, respectively. The growing season (DOY 132-243) NEE, GPP, and ER totals were -54, 783, and 729gCm^-2, respectively, in rainfed soybean. Similarly, cumulative NEE, GPP, and ER totals for DOY 163-256 (flux measurement was initiated on DOY 163, missing first 45days after planting) were -291, 1239, and 948gCm^-2, respectively, in irrigated soybean. Rainfed soybean was a net carbon sink for only two months, while irrigated soybean appeared to be a net carbon sink for about three months. However, grain yield and the magnitudes and seasonal sums of CO₂ fluxes for irrigated soybean in this study were comparable to those for soybean in the U.S. Corn Belt, but they were lower for rainfed soybean.

Evaluating carbon fluxes of global forest ecosystems by using an individual tree-based model FORCCHN

The carbon budget of forest ecosystems, an important component of the terrestrial carbon cycle, needs to be accurately quantified and predicted by ecological models. As a preamble to apply the model to estimate global carbon uptake by forest ecosystems, we used the CO₂ flux measurements from 37 forest eddy-covariance sites to examine the individual tree-based FORCCHN model's performance globally. In these initial tests, the FORCCHN model simulated gross primary production (GPP), ecosystem respiration (ER) and net ecosystem production (NEP) with correlations of 0.72, 0.70 and 0.53, respectively, across all forest biomes. The model underestimated GPP and slightly overestimated ER across most of the eddy-covariance sites. An underestimation of NEP arose primarily from the lower GPP estimates. Model performance was better in capturing both the temporal changes and magnitude of carbon fluxes in deciduous broadleaf forest than in evergreen broadleaf forest, and it performed less well for sites in Mediterranean climate. We then applied the model to estimate the carbon fluxes of forest ecosystems on global scale over 1982-2011. This application of FORCCHN gave a total GPP of 59.41±5.67 and an ER of 57.21±5.32PgCyr^-1 for global forest ecosystems during 1982-2011. The forest ecosystems over this same period contributed a large carbon storage, with total NEP being 2.20±0.64PgCyr^-1. These values are comparable to and reinforce estimates reported in other studies. This analysis highlights individual tree-based model FORCCHN could be used to evaluate carbon fluxes of forest ecosystems on global scale.

Stream primary producers relate positively to watershed natural gas measures in north-central Arkansas streams

Construction of unconventional natural gas (UNG) infrastructure (e.g., well pads, pipelines) is an increasingly common anthropogenic stressor that increases potential sediment erosion. Increased sediment inputs into nearby streams may decrease autotrophic processes through burial and scour, or sediment bound nutrients could have a positive effect through alleviating potential nutrient limitations. Ten streams with varying catchment UNG well densities (0-3.6 wells/km(2)) were sampled during winter and spring of 2010 and 2011 to examine relationships between landscape scale disturbances associated with UNG activity and stream periphyton [chlorophyll a (Chl a)] and gross primary production (GPP). Local scale variables including light availability and water column physicochemical variables were measured for each study site. Correlation analyses examined the relationships of autotrophic processes and local scale variables with the landscape scale variables percent pasture land use and UNG metrics (well density and well pad inverse flow path length). Both GPP and Chl a were primarily positively associated with the UNG activity metrics during most sample periods; however, neither landscape variables nor response variables correlated well with local scale factors. These positive correlations do not confirm causation, but they do suggest that it is possible that UNG development can alleviate one or more limiting factors on autotrophic production within these streams. A secondary manipulative study was used to examine the link between nutrient limitation and algal growth across a gradient of streams impacted by natural gas activity. Nitrogen limitation was common among minimally impacted stream reaches and was alleviated in streams with high UNG activity. These data provide evidence that UNG may stimulate the primary production of Fayetteville shale streams via alleviation of N-limitation. Restricting UNG activities from the riparian zone along with better enforcement of best management practices should help reduce these possible impacts of UNG activities on stream autotrophic processes.

Productivity, absorbed photosynthetically active radiation, and light use efficiency in crops: implications for remote sensing of crop primary production

Vegetation productivity metrics such as gross primary production (GPP) at the canopy scale are greatly affected by the efficiency of using absorbed radiation for photosynthesis, or light use efficiency (LUE). Thus, close investigation of the relationships between canopy GPP and photosynthetically active radiation absorbed by vegetation is the basis for quantification of LUE. We used multiyear observations over irrigated and rainfed contrasting C3 (soybean) and C4 (maize) crops having different physiology, leaf structure, and canopy architecture to establish the relationships between canopy GPP and radiation absorbed by vegetation and quantify LUE. Although multiple LUE definitions are reported in the literature, we used a definition of efficiency of light use by photosynthetically active "green" vegetation (LUE(green)) based on radiation absorbed by "green" photosynthetically active vegetation on a daily basis. We quantified, irreversible slowly changing seasonal (constitutive) and rapidly day-to-day changing (facultative) LUE(green), as well as sensitivity of LUE(green) to the magnitude of incident radiation and drought events. Large (2-3-fold) variation of daily LUE(green) over the course of a growing season that is governed by crop physiological and phenological status was observed. The day-to-day variations of LUE(green) oscillated with magnitude 10-15% around the seasonal LUE(green) trend and appeared to be closely related to day-to-day variations of magnitude and composition of incident radiation. Our results show the high variability of LUE(green) between C3 and C4 crop species (1.43 g C/MJ vs. 2.24 g C/MJ, respectively), as well as within single crop species (i.e., maize or soybean). This implies that assuming LUE(green) as a constant value in GPP models is not warranted for the crops studied, and brings unpredictable uncertainties of remote GPP estimation, which should be accounted for in LUE models. The uncertainty of GPP estimation due to facultative and constitutive changes in LUE(green) can be considered as a critical component of the total error budget in the context of remotely sensed based estimations of GPP. The quantitative framework of LUE(green) estimation presented here offers a way of characterizing LUE(green) in plants that can be used to assess their phenological and physiological status and vulnerability to drought under current and future climatic conditions and is essential for calibration and validation of globally applied LUE algorithms.

Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges

Chlorophyll a fluorescence (ChlF) has been used for decades to study the organization, functioning, and physiology of photosynthesis at the leaf and subcellular levels. ChlF is now measurable from remote sensing platforms. This provides a new optical means to track photosynthesis and gross primary productivity of terrestrial ecosystems. Importantly, the spatiotemporal and methodological context of the new applications is dramatically different compared with most of the available ChlF literature, which raises a number of important considerations. Although we have a good mechanistic understanding of the processes that control the ChlF signal over the short term, the seasonal link between ChlF and photosynthesis remains obscure. Additionally, while the current understanding of in vivo ChlF is based on pulse amplitude-modulated (PAM) measurements, remote sensing applications are based on the measurement of the passive solar-induced chlorophyll fluorescence (SIF), which entails important differences and new challenges that remain to be solved. In this review we introduce and revisit the physical, physiological, and methodological factors that control the leaf-level ChlF signal in the context of the new remote sensing applications. Specifically, we present the basis of photosynthetic acclimation and its optical signals, we introduce the physical and physiological basis of ChlF from the molecular to the leaf level and beyond, and we introduce and compare PAM and SIF methodology. Finally, we evaluate and identify the challenges that still remain to be answered in order to consolidate our mechanistic understanding of the remotely sensed SIF signal.