Improving the light use efficiency model for simulating terrestrial vegetation gross primary production by the inclusion of diffuse radiation across ecosystems in China

Spatiotemporal patterns and driving factors of gross primary productivity over the Mongolian Plateau steppe in the past 20 years

The Eurasian steppe is the largest temperate grassland in the world. The grassland of the Mongolian Plateau (MP) represents an important part of the Eurasian steppe with high climatic sensitivity. Gross primary productivity (GPP) is a key indicator of the grassland's production, status and dynamic on the MP. In this study, we calibrated and evaluated the grassland-specific light use efficiency model (GRASS-LUE) against the observed GPP collected from nine eddy covariance flux sites on the MP, and compared the performance with other four GPP products (MOD17, VPM, GLASS and GOSIF). GRASS-LUE with higher R2 (0.91) and lower root mean square error (RMSE = 0.99 gC m-2 day-1) showed a better performance compared to the four GPP products in terms of model accuracy and dynamic consistency, especially in typical and desert steppe. The parameters of the GRASS-LUE are more suitable for water-limited grassland could be the reason for its outstanding performance in typical and desert steppe. Mean grassland GPP derived from GRASS-LUE was higher in the east and lower in the west of the MP. Grassland GPP was on average 205 gC m-2 over the MP between 2001 and 2020 with mean annual total GPP of 322 TgC yr-1. 30 % of the MP steppe showed a significant GPP increase. Growing season precipitation is the main factor affecting GPP of the MP steppe across regions. Anthropogenic factors (livestock density and population density) had greater effect on GPP than growing season temperature in pastoral counties in IM that take grazing as one of main industries. These findings can inform the status and trend of the productivity of MP steppe and help government and scientific research institutions to understand the drivers for spatial pattern of grassland GPP on the MP.

A water stress factor based on normalized difference water index substantially improved the accuracy of light use efficiency model for arid and semi-arid grasslands

High-accuracy simulation of gross primary productivity (GPP) is crucial for the monitoring and evaluation of the ecosystem services and the adaptive management of grassland. The light use efficiency (LUE) model is one of the most widely-used methods to simulate GPP, given its simple structure and low input requirements. Current LUE models are less applicable to grasslands than other vegetation types and have lower overall estimation accuracy in arid and semi-arid regions. A grassland-specific light use efficiency model (GRASS-LUE), which optimizes three important parameters (the fraction of absorbed photosynthetically active radiation FPAR, optimum temperature Topt and water stress factor f(W)), has been developed to improve the accuracy of GPP simulation for grasslands along aridity gradients. GPP simulated by the GRASS-LUE agreed well with the eddy covariance (EC) GPP estimates for grasslands along the aridity gradient at 8-day (coefficient of determination (R2) = 0.85, Bias = -0.67 gC m-2 day-1), monthly (R2 = 0.88, Bias = -22.33 gC m-2 month-1) and annual time scales (R2 = 0.95, Bias = -118.91 gC m-2 year-1). Compared with five state-of-the-art GPP products (PML, MOD17, rEC-LUE, VPM and BESS), GRASS-LUE had the best and most stable performance in reproducing EC GPP, especially for semi-arid grassland, with the highest global performance indicator (GPI) value. Sensitivity tests further revealed that: 1) modifying f(W) to be based on the Normalized Difference Water Index (NDWI) substantially improved the model accuracy for arid and semi-arid grasslands and 2) using the minimum of temperature and water stress factors (i.e., min⁡(f(W),f(T))) to represent environmental stress in GRASS-LUE was better than that from the multiplication of temperature and water stress factors (i.e., f(W)×f(T)).

Effect of diffuse fraction on gross primary productivity and light use efficiency in a warm-temperate mixed plantation

Diffuse radiation (I _f) is one of important variables determining photosynthetic rate and carbon uptake of forest ecosystems. However, the responses of gross primary productivity (GPP) and light use efficiency (LUE) to diffuse fraction (DF) are still poorly understood. We used a 6-year dataset of carbon flux at a warm-temperate mixed plantation site in North China to explore the impacts of DF on GPP and LUE. During 2011-2017, ecosystem apparent quantum yield (α) and photosynthesis at photosynthetically active radiation (PAR) of 1800 µmol m^-2 s^-1 (P ₁₈₀₀) on cloudy days were 63% and 17% higher than on clear days, respectively. Under lower vapor pressure deficit (VPD) and air temperature (T _a) conditions, canopy photosynthesis was significantly higher on cloudy skies than on clear skies. On half-hourly scale, increased DF enhanced α and P ₁₈₀₀. Daily GPP peaked at a median DF (=0.5), while daily LUE significantly increased with DF (p<0.01). Both GPP and LUE were mainly controlled directly by DF and PAR. DF had an indirect effect on LUE and GPP mainly through PAR. At high DF levels (>0.5), the increase in LUE did not make GPP enhancement. The direct effect of DF on GPP and LUE under lower T _a and VPD was more sensitive than under higher T _a and VPD. When DF was incorporated into the Michaelis-Menten model, it performed well in the GPP estimation, and the determination coefficient increased by 32.61% and the root mean square error decreased by 25.74%. These findings highlight the importance of incorporating DF into carbon sequestration estimation in North China.

Effects of Aerosols on Gross Primary Production from Ecosystems to the Globe

Aerosols affect the gross primary productivity (GPP) of plants by absorbing and scattering solar radiation. However, it is still an open question whether and to what extent the effects of aerosol on the diffuse fraction (Df) can enhance GPP globally. We quantified the aerosol diffuse fertilization effect (DFE) and incorporated it into a light use efficiency (LUE) model, EC-LUE. The new model is driven by aerosol optical depth (AOD) data and is referred to as AOD-LUE. The eddy correlation variance (EC) of the FLUXNET2015 dataset was used to calibrate and validate the model. The results showed that the newly developed AOD-LUE model improved the performance in simulating GPP across all ecosystem types (R2 from 0.6 to 0.68), with the highest performance for mixed forest (average R2 from 0.71 to 0.77) and evergreen broadleaf forest (average R2 from 0.34 to 0.45). The maximum LUE of diffuse photosynthetic active radiation (PAR) (3.61 g C m−2 MJ−1) was larger than that of direct PAR (1.68 g C m−2 MJ−1) through parameter optimization, indicating that the aerosol DFE seriously affects the estimation of GPP, and the separation of diffuse PAR and direct PAR in the GPP model is necessary. In addition, we used AOD-LUE to quantify the impact of aerosol on GPP. Specifically, aerosols impaired GPP in closed shrub (CSH) by 6.45% but enhanced the GPP of grassland (GRA) and deciduous broadleaf forest (DBF) by 3.19% and 2.63%, respectively. Our study stresses the importance of understanding aerosol-radiation interactions and incorporating aerosol effects into regional and global GPP models.

Effects of clouds and aerosols on ecosystem exchange, water and light use efficiency in a humid region orchard

Investigating the patterns of water and carbon dynamics in agro-ecosystems in response to clouds and aerosols can shed new insights in understanding the biophysical impacts of climate change on crop productivity and water consumption. In this study, the effects of clouds and aerosols as well as other environmental factors on ecosystem water and carbon fluxes were examined based on three-year eddy covariance measurements under different sky conditions (quantified as the clearness index, K_t, i.e., the ratio of global solar radiation to extraterrestrial solar radiation) in a kiwifruit plantation in the humid Sichuan Basin of China. Results showed that evapotranspiration (ET) and canopy transpiration (T_c, measured by sap flow sensors) increased, while ecosystem light use efficiency (eLUE) and ecosystem water use efficiency (eWUE) decreased with increasing K_t. GPP presented a parabolic relationship with increasing K_t. The path analysis revealed that surface conductance (G_s) and canopy conductance (G_c) were the most dominant variables directly regulated carbon (GPP) and water (ET and T_c) fluxes. The effect path of K_t on ET and T_c was converted from through diffuse photosynthetic active radiation (PAR_dif) to direct PAR (PAR_dir) when the sky became clearer. The effect path of K_t on GPP was primarily through PAR_dif under different sky conditions. The declined eWUE with increasing K_t was caused by the different responses of GPP and ET to PAR_dir under clear skies. The declined eLUE resulted from the sharp decrease in GPP/PAR_dir, which surpassed the slight increase of GPP/PAR_dif with increasing PAR. The Priestley-Taylor Jet Propulsion Laboratory ET model (PT-JPL) incorporating K_t with an exponential function produced more reliable T_c estimates but minor improvement in ET. Further, the LUE-GPP model incorporating K_t with a linear function obtained much better GPP estimates. Our study shed light on how sky conditions modulate water and carbon dynamics between the biosphere and atmosphere, highlighting the necessity of the inclusion of sky conditions for better modeling regional water and carbon budgets.

Diffuse sunlight and cosmic rays: Missing pieces of the forest growth change attribution puzzle?

Forest growth changes have been a matter of intense research efforts since the 1980s. Owing to the variety of their environmental causes - mainly atmospheric CO₂ increase, atmospheric N deposition, changes in temperature and water availability, and their interactions - their interpretation has remained challenging. Recent isolated researches suggest further effects of neglected environmental factors, namely changes in the diffuse fraction of light, more efficient to photosynthesis, and galactic cosmic rays (GCR), both emphasized in this Discussion paper. With growing awareness of GCR influence on global cloudiness (the cosmoclimatologic theory by H. Svensmark), GCR may thus cause trends in diffuse-light, and distinguishing between their direct/indirect influences on forest growth remains uncertain. This link between cosmic rays and diffuse sunlight also forms an alternative explanation to the geological evidence of a negative correlation between GCR and atmospheric CO₂ concentration over the past 500 Myr. After a careful scrutiny of this literature and of key contributions in the field, we draw research options to progress further in this attribution. These include i) observational strategies intending to build on differences in the spatio-temporal dynamics of environmental growth factors, ranging from quasi-experiments to meta-analyses, ii) simulation strategies intending to quantify environmental factor's effects based on process-based ecosystem modelling, in a context where progresses for accounting for diffuse-light fraction are ongoing. Also, the hunt for tree-ring based proxies of GCR may offer the perspective of testing the GCR hypothesis on fully coupled forest growth samples.

Identifying the dominant climate-driven uncertainties in modeling gross primary productivity

Accurate simulation of gross primary productivity (GPP) is essential for estimating the global carbon budget. However, GPP modeling is subject to various sources of uncertainties, among which the impacts of biases in climate forcing data have not been well quantified. Here, using a well-validated vegetation model, we compare site-level simulations using either ground-based meteorology or assimilated reanalyses to identify climate-driven uncertainties in the predicted GPP at 91 FLUXNET sites. Simulations yield the lowest root mean square errors (RMSE) in GPP relative to observations when all site-level meteorology and CO₂ concentrations are used. Sensitivity tests conducted with Modern-Era Retrospective Analysis (MERRA) reanalyses increase GPP RMSE by 30%. Replacement of site-level CO₂ with global annual average values provides limited contributions to these changes. In contrast, GPP uncertainties increase almost linearly with the biases in meteorology. Among all factors, photosynthetically active radiation (PAR), especially diffuse PAR, plays dominant roles in modulating GPP uncertainties. Simulations using all MERRA forcings but with site-level diffuse PAR help reduce over 50% of the climate-driven biases in GPP. Our study reveals that biases in meteorological forcings, especially the variabilities at diurnal to seasonal time scales, can induce significant uncertainties in the simulated GPP at FLUXET sites. We suggest cautions in simulating global GPP using climate reanalyses for dynamic global vegetation models and urgent improvements in climatic variability in reanalyses data, especially for diffuse radiation.

Environmental factors modulate the diffuse fertilization effect on gross primary productivity across Chinese ecosystems

Diffuse radiation allocated by cloud cover and aerosols can promote vegetation photosynthesis, which is known as the diffuse fertilization effect (DFE). As an important uncertain factor regulating the DFE, understanding the role of environmental conditions in the response of terrestrial ecosystems to diffuse radiation is vital for quantitative and intensive studies. By using a light use efficiency model and statistical methods with satellite data and ChinaFLUX observation data, the optimal environmental range of DFE was estimated, the indirect role of vapor pressure deficit (VPD) and air temperature (T_a) on DFE was explored, and the relative contribution of diffuse photosynthetically active radiation (PAR_dif) on gross primary productivity (GPP) was analyzed across Chinese ecosystems under different sky conditions. The results showed that the DFE increased with leaf area index (LAI), but distributed a unimodal curve along with VPD and T_a, both of which had an optimum range that was lower in the forest (or cropland) and higher in the grass (or desert) ecosystem. When considering the co-effect of VPD and T_a, the strongest positive effect of DFE was found at 0-5 h Pa and 20-25 °C. Based on path analysis, PAR_dif promoted GPP and served as the main controlling factor in forest ecosystems predominantly through a direct pathway from half-hourly to the daily scale, while T_a and VPD occupied the dominant position at single-canopy ecosystem sites. When the aerosol optical depth (AOD) increased, the relative contribution of PAR_dif increased in multiple-canopy ecosystems and decreased in single-canopy ecosystems; when the sky conditions changed from sunny to cloudy, the relative contribution of PAR_dif was higher in the forest ecosystem and increased significantly in the grass ecosystem. These findings offer a more comprehensive understanding of the environmental effects of regulating DFE on GPP across ecosystems.

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.

Modeling the Effects of Global and Diffuse Radiation on Terrestrial Gross Primary Productivity in China Based on a Two-Leaf Light Use Efficiency Model

Solar radiation significantly affects terrestrial gross primary productivity (GPP). However, the relationship between GPP and solar radiation is nonlinear because it is affected by diffuse radiation. Solar radiation has undergone a shift from darker to brighter values over the past 30 years in China. However, the effects on GPP of variation in solar radiation because of changes in diffuse radiation are unclear. In this study, national global radiation in conjunction with other meteorological data and remotely sensed data were used as input into a two-leaf light use efficiency model (TL-LUE) that simulated GPP separately for sunlit and shaded leaves for the period from 1981 to 2012. The results showed that the nationwide annual global radiation experienced a significant reduction (2.18 MJ m−2 y−1; p < 0.05) from 1981 to 2012, decreasing by 1.3% over this 32-year interval. However, the nationwide annual diffuse radiation increased significantly (p < 0.05). The reduction in global radiation from 1981 to 2012 decreased the average annual GPP of terrestrial ecosystems in China by 0.09 Pg C y−1, whereas the gain in diffuse radiation from 1981 to 2012 increased the average annual GPP in China by about 50%. Therefore, the increase in canopy light use efficiency under higher diffuse radiation only partially offsets the loss of GPP caused by lower global radiation.

Integrating SIF and Clearness Index to Improve Maize GPP Estimation Using Continuous Tower-Based Observations

Solar-induced chlorophyll fluorescence (SIF) has been proven to be well correlated with vegetation photosynthesis. Although multiple studies have found that SIF demonstrates a strong correlation with gross primary production (GPP), SIF-based GPP estimation at different temporal scales has not been well explored. In this study, we aimed to investigate the quality of GPP estimates produced using the far-red SIF retrieved at 760 nm (SIF760) based on continuous tower-based observations of a maize field made during 2017 and 2018, and to explore the responses of GPP and SIF to different meteorological conditions, such as the amount of photosynthetically active radiation (PAR), the clearness index (CI, representing the weather condition), the air temperature (AT), and the vapor pressure deficit (VPD). Firstly, our results showed that the SIF760 tracked GPP well at both diurnal and seasonal scales, and that SIF760 was more linearly correlated to PAR than GPP was. Therefore, the SIF760-GPP relationship was clearly a hyperbolic relationship. For instantaneous observations made within a period of half an hour, the R2 value was 0.66 in 2017 and 2018. Based on daily mean observations, the R2 value was 0.82 and 0.76 in 2017 and 2018, respectively. and had an R2 value of 0.66 (2017) and 0.66 (2018) for instantaneous observations made within a period of half an hour and 0.82 (2017) and 0.76 (2018) for daily mean observations. Secondly, it was found that the SIF760-GPP relationship varied with the environmental conditions, with the CI being the dominant factor. At both diurnal and seasonal scales, the ratio of GPP to SIF760 decreased noticeably as the CI increased. Finally, the SIF760-based GPP models with and without the inclusion of CI were trained using 70% of daily observations from 2017 and 2018 and the models were validated using the remaining 30% of the dataset. For both linear and non-linear models, the inclusion of the CI greatly improved the SIF760-based GPP estimates based on daily mean observations: the value of R2 increased from 0.71 to 0.82 for the linear model and from 0.82 to 0.87 for the non-linear model. The validation results confirmed that the SIF760-based GPP estimation was improved greatly by including the CI, giving a higher R2 and a lower RMSE. These values improved from R2 = 0.66 and RMSE = 7.02 mw/m2/nm/sr to R2 = 0.76 and RMSE = 6.36 mw/m2/nm/sr for the linear model, and from R2 = 0.71 and RMSE = 4.76 mw/m2/nm/sr to R2 = 0.78 and RMSE = 3.50 mw/m2/nm/sr for the non-linear model. Therefore, our results demonstrated that SIF760 is a reliable proxy for GPP and that SIF760-based GPP estimation can be greatly improved by integrating the CI with SIF760. These findings will be useful in the remote sensing of vegetation GPP using satellite, airborne, and tower-based SIF data because the CI is usually an easily accessible meteorological variable.

Light use efficiency of a warm-temperate mixed plantation in north China

Light use efficiency (LUE) is one of the important parameters on calculating terrestrial gross primary productivity (GPP) and net primary productivity (NPP). Based on 5-year (2006-2010) carbon flux and climatic variable data of a mixed plantation in north China, the seasonal and interannual variation of LUE was investigated and the biophysical controls were examined. Our results show that LUE had a distinct seasonal course, and peaked in the vigorous growing season with a value of 0.92-1.27 g C MJ^-1. During the period of 2006-2010, annual mean LUE ranged between 0.54 and 0.62 g C MJ^-1, and it was linearly correlated with annual GPP. In the growing season, LUE was significantly linked with the water availability variables (including monthly mean vapor pressure deficit (VPD), precipitation, evaporative fraction (EF), and the ratio of precipitation to evapotranspiration (P/ET)) and canopy conductance (g _c). However, EF was a better estimator of LUE compared with other biophysical variables. LUE decreased with an increase of the clearness index (CI), indicating that LUE was higher under cloudy sky conditions than that under sunny sky conditions in the mixed plantation.

Taking climate, land use, and social economy into estimation of carbon budget in the Guanzhong-Tianshui Economic Region of China

Carbon sequestration is an indispensable ecosystem service provided by soil and vegetation, so mapping and valuing the carbon budget by considering both ecological and social factors is an important trend in evaluating ecosystem services. In this work, we established multiple scenarios to evaluate the impacts of land use change, population growth, carbon emission per capita, and carbon markets on carbon budget. We quantified carbon sinks (aboveground and belowground) under different scenarios, using the Carnegie-Ames-Stanford Approach (CASA) model and an improved carbon cycle process model, and studied carbon sources caused by human activities by analyzing the spatial distribution of human population and carbon emission per capita. We also assessed the net present value (NPV) for carbon budgets under different carbon price and discount rate scenarios using NPV model. Our results indicate that the carbon budget of Guanzhong-Tianshui Economic Region is surplus: Carbon sinks range from 1.50 × 10¹⁰ to 1.54 × 10¹⁰ t, while carbon sources caused by human activities range from 2.76 × 10⁵ to 7.60 × 10⁵ t. And the NPV for carbon deficits range from 3.20 × 10¹¹ RMB to 1.52 × 10¹² RMB. From the perspective of ecological management, deforestation, urban sprawl, population growth, and excessive carbon consumption are considered as the main challenges in balancing carbon sources and sinks. Levying carbon tax would be a considerable option when decision maker develops carbon emission reduction policies. Our results provide a scientific and credible reference for harmonious and sustainable development in the Guanzhong-Tianshui Economic Region of China.