Regional Gross Primary Production Research Articles

Dynamic process of ecosystem water use efficiency and response to drought in the Yellow River Basin, China

Ecosystem water use efficiency (WUE) is a crucial indicator of the impact of climate change on terrestrial ecosystems, reflecting the balance between biological processes (photosynthesis and transpiration) and physical processes (evapotranspiration). However, the response mechanisms and driving processes of WUE to drought remain to be further understood. In this study, we analyzed the spatial and temporal dynamics and response mechanisms of WUE in the Yellow River Basin (YRB) using data on Gross Primary Productivity (GPP), Evapotranspiration (ET) and Standardized Precipitation Evapotranspiration Index (SPEI), which revealed the cumulative effect of drought on WUE and assessed the ecosystem's resilience. The study results showed that (1) GPP, ET and WUE in the YRB exhibited a significant increasing trend, with 63.04 % of the area showing a marked increase in WUE. (2) GPP was the dominant factor influencing WUE in 65.36 % of the area, particularly in cropland and grassland, while ET was more influential in forested areas. Vapor pressure deficit (VPD) was identified as the principal driver affecting vegetation GPP in semi-arid and semi-humid regions of the YRB. In contrast, soil moisture (SM) was the limiting factor in arid areas. (3) 71.00 % of the WUE in the basin was affected by drought cumulative effects, with an average cumulative duration of 4.5 months. Arid regions experienced the most extended duration of 7.29 months, compared to 3.05 months in semi-humid regions. (4) 74.85 % of the regional ecosystems exhibited ecological resilience to drought, particularly in the source areas of the western basin of the YRB. Shrublands have the highest drought resilience among vegetation types, while grasslands have the lowest. The resilience of each climatic zone was in the order of semi-humid, semi-arid, and arid order. This study comprehensively analyzed of the spatial and temporal dynamics and response mechanisms of WUE in the YRB, offering a new perspective and scientific basis for understanding and predicting the ecosystem response to climate change.

Spatiotemporal variation and response of gross primary productivity to climate factors in forests in Qiannan state from 2000 to 2020

Accurate estimation of terrestrial gross primary productivity (GPP) is essential for quantifying the carbon exchange between the atmosphere and biosphere. Light use efficiency (LUE) models are widely used to estimate GPP at different spatial scales. However, difficulties in properly determining the maximum LUE (LUEmax) and downregulation of LUEmax into actual LUE result in uncertainties in the LUE-estimated GPP. The recently developed P model, a new LUE model, captures the adaptability of vegetation to the environment and simplifies parameterization. Site-level studies have proven the superior performance of the P model over LUE models. As a representative karst region with significant changes in forest cover in Southwest China, Qiannan is useful for exploring the spatiotemporal variation in forest GPP and its response to climate change for formulating forest management policies to address climate changes, e.g., global warming. Based on remote sensing and meteorological data, this study estimated the forest ecosystem GPP in Qiannan from 2000–2020 via the P model. This study explored the spatiotemporal changes in GPP in the study region over the past 20 years, used the Hurst index to predict future development trends from a time series perspective, and used partial correlation analysis to analyse the spatiotemporal GPP changes over the past 20 years in response to three factors: temperature, precipitation, and vapor pressure deficit (VPD). Our results showed that (1) the total amount of GPP and average GPP in Qiannan over the past 21 years (2000–2020) were 1.9 × 104 ± 2.0 × 103 MgC ha−1 year−1 and 1238.9 ± 107.9 gC m−2 year−1, respectively. The forest GPP generally increased at a rate of 6.1 gC m−2 year−1 from 2000 to 2020 in Qiannan, and this increase mainly occurred in the nongrowing season. (2) From 2000 to 2020, the forest GPP in Qiannan was higher in the southeast and lower in the northwest, indicating significant spatial heterogeneity. In the future, more than 70% of regional forest GPP will experience a weak increase in nonsustainability. (3) In Qiannan, forest GPP was positively correlated with both temperature and precipitation, with partial correlation coefficients of 0.10 and 0.11, respectively. However, the positive response of GPP to precipitation was approximately 70.47%, while that to temperature was 64.05%. Precipitation had a stronger restrictive effect on GPP than did temperature in this region, and GPP exhibited a negative correlation with VPD. The results showed that an increase in VPD inhibits GPP to some extent. Under rapid global change, the P model GPP provides new GPP data for global ecology studies, and the comparison of various stress factors allows for improvement of the GPP model in the future. The results of this study will aid in understanding the dynamic processes of terrestrial carbon. These findings are helpful for estimating and predicting the carbon budget of forest ecosystems in karst regions, clarifying the regional carbon absorption capacity, clarifying the main factors limiting vegetation growth in these regions, promoting sustainable regional forestry development and serving the “dual carbon goal.” This work has important guiding significance for policy formulation to mitigate climate change.

How the enhanced East Asian summer monsoon regulates total gross primary production in eastern China

Recognizing the relationship between gross primary production (GPP) and precipitation in eastern China, the East Asian Summer Monsoon (EASM) plays a crucial role in shaping GPP. Despite confirmation of the strong link between EASM and GPP, there remains a notable research gap in understanding the specific impact of the EASM on GPP in different regions of eastern China. Here we used simulations from Trends in Net Land–Atmosphere Carbon Exchanges (TRENDY) models from 1951 to 2010 and divided eastern China into five subregions for the study. We also used the New East Asian Summer Monsoon Index (NEWI) as a quantitative metric to distinguish between periods of strong and weak EASM. Building on this, this study aims to investigate the response of GPP in different subregions of eastern China. Regionally, under strengthened EASM years (1954, 1957, 1965, 1969, 1977, 1980, 1983, 1987, 1993 and 1998), East China experienced the most pronounced increase in GPP at 12 ± 21 (mean ± 1 sigma) gC m−2 mon−1 compared to the weak EASM years (1958, 1961, 1972, 1973,1978, 1981, 1985, 1994, 1997 and 2004). In contrast, Southwest China showed a decline in GPP at −4 ± 10 gC m−2 mon−1. Moreover, GPP also increased in Northeast and North China when EASM strengthened, while South China showed a decline in GPP. This indicated that GPP changed with monsoon intensity. According to the mechanism analysis, during strong EASM, there was intense moisture convergence through alterations in the atmospheric circulation field over East China and abundant precipitation, which further contributed to the increase in GPP. Downward solar radiation in Southwest China decreased with EASM enhancement, which suppressed GPP and hindered vegetation growth. Overall, the results highlight the importance of accurately predicting the impact of different EASM intensities of regional carbon fluxes.

Increased snow cover enhances gross primary productivity in cold and dry regions of the Tibetan Plateau

AbstractSnow cover is an important control element in the Tibetan Plateau (TP) ecosystem; however, the impact of snow cover changes on gross primary productivity (GPP) is largely unknown, particularly under complex geographical conditions. In this study, we investigated the impacts of snow cover changes on different seasonal GPP and their mechanisms in different geographical zones using multisource remote sensing data from 1982 to 2018. Snow cover significantly affected GPP by nearly 15% of the TP region, and snow cover days (SCDs) were the dominant snow cover indicators affecting GPP variations compared with snow water equivalent (SWE) and snow cover end date (SCED). In general, an increase in snow cover leads to a significant increase in GPP in regions with low precipitation and temperature, but limits the accumulation of GPP in humid and warm regions. Furthermore, from the humid to the arid zone, the “moisture effect” of snow cover (by altering soil moisture) plays an increasingly important role in regulating GPP variations with increasing drought levels. This study elucidates the importance of snow cover in regulating different seasonal GPP variations and significantly improves our insight into the response of vegetation carbon uptake to snow cover changes in the TP.

Quantifying effects of different types of droughts on gross primary production in China

AbstractThe increasing impact of drought stress on ecosystems with regard to climate change has received considerable attention, which has been demonstrated to have a time‐lag effect on ecosystems. However, the time‐lag effect on different types of droughts and gross primary production (GPP) at large regional scales remains unclear. Thus, using the GOSIF GPP product data of solar‐induced chlorophyll fluorescence, the meteorological drought index (standardized precipitation evapotranspiration index [SPEI]), and the agricultural drought index (soil moisture deficit index [SMDI]) based on the soil moisture data obtained from the Global Land Data Assimilation System, the time‐lag effect of regional drought and GPP and the contribution of drought to GPP variation were analysed in China from 2000 to 2018. Results showed that (1) China's annual GPP increased at an average rate of 9.26 g C m−2 y−1. With regard to the average rate for each climate zone, the largest increase of 30.44 g C m−2 y−1 was observed in the southern subtropical region, and the smallest increase (4.01 g C m−2 y−1) was observed in the Qinghai–Tibetan plateau, with about 53.11% of the regions showing a significant upward trend. In addition, the significantly declining regions were primarily concentrated in urban areas, accounting for about 2.26% of China. (2) SPEI and GPP had an average lag time of 6.28 months, whereas SMDI and GPP had an average lag time of 7.17 months. The comparison demonstrated that SMDI had a greater influence on GPP than SPEI. (3) The average contribution rate of meteorological drought and agricultural drought to the decrease of GPP was 79.37% and 87.06%, and their average contribution rate to the increase of GPP was 39.11% and 50.57%, respectively. This study will provide comprehensive understanding of the effects of drought on carbon and water cycles in terrestrial ecosystems with regard to climate change.

Spatial and temporal variations of gross primary production simulated by land surface model BCC_AVIM2.0

Gross primary production (GPP) is the largest flux and a crucial player in the terrestrial carbon cycle and has been studied extensively, yet large uncertainties remain in the spatiotemporal patterns of GPP in both observations and simulations. This study evaluates the performance of the second version of the Beijing Climate Center Atmosphere−Vegetation Interaction Model (BCC_AVIM2.0) in simulating GPP on multiple spatial and temporal scales in the Coupled Model Intercomparison Project Phase 6 (CMIP6) experiments. Model simulations driven by two meteorological datasets were compared with two observation-based GPP products covering 1982–2008. Spatial patterns of annual GPP show a significant latitudinal gradient in each dataset, increasing from cold (tundra) and dry (desert) biomes to warm (temperate) and humid (tropical rainforest) biomes. BCC_AVIM2.0 overestimates GPP in most parts of the globe, especially in boreal forest regions and Southeast China, while underestimating GPP in subhumid regions in eastern South America and tropical Africa. The four datasets broadly agree on the GPP seasonal cycle, but BCC_AVIM2.0 predicts an earlier beginning of spring growth and a larger amplitude of seasonal variations than those in the observations. The observation-based datasets exhibit slight interannual variability (IAV) and weak GPP linear trends, while the BCC_AVIM2.0 simulations demonstrate relatively large year-to-year variability and significant trends in the low-latitudes and temperate monsoon regions in North America and East Asia. Regarding the possible relationships between annual means of GPP and climate factors, BCC_AVIM2.0 predicts more extensive regions of the globe where the IAV of annual GPP is dominated by precipitation, especially in mid-to-high latitudes of the Northern Hemisphere and tropical Africa, while the observed GPP in the above regions is temperature- or radiation-dominant. The positive GPP biases due to earlier spring growth in boreal forest regions and negative GPP biases in off-equator tropical areas in the BCC_AVIM2.0 simulations imply that cold stress on biomes in boreal mid-to-high latitudes should be strengthened to restrain plant growth, while drought stress in low-latitude regions might be eased to enhance plant production in the future version of BCC_AVIM.

Observations of Satellite Land Surface Phenology Indicate That Maximum Leaf Greenness Is More Associated With Global Vegetation Productivity Than Growing Season Length

AbstractVegetation green leaf phenology directly impacts gross primary productivity (GPP) of terrestrial ecosystems. Satellite observations of land surface phenology (LSP) provide an important means to monitor the key timing of vegetation green leaf development. However, differences between satellite‐derived LSP proxies and in situ measurements of GPP make it difficult to quantify the impact of climate‐induced changes in green leaf phenology on annual GPP. Here, we used 1,110 site‐years of GPP measurements from eddy‐covariance towers in association with time series of satellite LSP observations from 2000 to 2014 to show that while satellite LSP explains a large proportion of variation in annual GPP, changes in green‐leaf‐based growing season length (GSL, leaf development period from spring to autumn) had less impact on annual GPP by ∼30% than GSL changes in GPP‐based photosynthetic duration. Further, maximum leaf greenness explained substantially more variance in annual GPP than green leaf GSL, highlighting the role of future vegetation greening trends on large‐scale carbon budgets. Site‐level variability contributes a substantial proportion of annual GPP variance in the model based on LSP metrics, suggesting the importance of local environmental factors altering regional GPP. We conclude that satellite LSP‐based inferences regarding large‐scale dynamics in GPP need to consider changes in both green leaf GSL and maximum greenness.

How well do light-use efficiency models capture large-scale drought impacts on vegetation productivity compared with data-driven estimates?

Remote sensing driven light-use efficiency (LUE) models are an important tool for estimating vegetation gross primary productivity (GPP) and assessing climate change impacts on the terrestrial carbon cycle, but their capacity in representing drought impact on GPP over large-scales is not fully explored. Here, we evaluated a suite of LUE models, i.e., VPM, TL-LUE, CASA, EC-LUE and MuSyQ, against two data-driven flux products (GOSIF-GPP and FluxSat), with the aids of environmental variables and eddy covariance flux measurements over North America. The two data-driven GPP products were firstly validated against with eddy covariance flux measurements, suggesting that they are able to reasonably indicate drought impacts at site scales. They were then used as a reference to evaluate these LUE models for indicating the drought impacts in 2011 and 2012 over North America at regional scales. In general, the simulated GPP by these models reasonably responded to droughts, but showed considerable differences in the seasonal anomalies for both amplitude and phase. Especially, these models consistently showed a clearly earlier trough (about 1 ∼ 2 months) of flux reduction than those in data-driven models over both entire drought-impacted regions and individual ecosystems (mostly pronounced for grasslands), indicating a likely misrepresentation of drought legacy effects, which needs to be considered in developing new-generation LUE models. Among the grass-dominated drought areas, TL-LUE and VPM performed best in indicating the impacts of the 2011 and 2012 droughts on regional GPP, respectively, while EC-LUE showed a significant underestimation of the impacts in both events, which may be attributed to the different representations of water stress factors in models. This study could provide some useful hints toward improving LUE models in characterizing drought impacts on regional GPP.

A two-stage light-use efficiency model for improving gross primary production estimation in agroecosystems

Accurate quantification of gross primary production (GPP) in agroecosystems not only improves our ability to understand the global carbon budget but also plays a critical role in human welfare and development. Light-use efficiency (LUE) models have been widely applied in estimating regional and global GPP due to their simple structure and clear physical basis. However, maximum LUE (), a key photosynthetic parameter in LUE models, has generally been treated as a constant, leading to common overestimation and underestimation of low and high magnitudes of GPP, respectively. Here, we propose a parsimonious and practical two-stage LUE (TS-LUE) model to improve GPP estimates by (a) considering seasonal variations of , and (b) separately re-parameterizing in the green-up and senescence stages. The TS-LUE model is inter-compared with state-of-the-art –static moderate resolution imaging spectroradiometer-GPP, eddy-covariance-LUE, and vegetation production models. Validation results at 14 FLUXNET sites for five crop species showed that: (a) the TS-LUE model significantly reduced the large bias at high- and low-level GPP as produced by the three –static LUE models for all crop types; and (b) the TS-LUE model generated daily GPP estimates in good agreement with in-situ measurements and was found to outperform the three –static LUE models. Especially compared to the well-known moderate resolution imaging spectroradiometer-GPP, the TS-LUE model could remarkably decrease the root mean square error (in gC m−2 d−1) by 24.2% and 35.4% (from 3.84 to 2.91 and 2.48) and could increase the coefficient of determination by 14.7% and 20% (from 0.75 to 0.86 and 0.9) when the leaf area index (LAI) and infrared reflectance of vegetation (NIRv) were used to re-parameterize the , respectively. The TS-LUE model provides a brand-new perspective on the re-parameterization of and indicates great potential for improving daily agroecosystem GPP estimates at a global scale.

Changes in Gross Primary Production in Response to Afforestation in the Hilly Loess Plateau of Northern Shaanxi, China

As a large-scale afforestation initiative, the Grain for Green (GFG) program was implemented for more than decade in the hilly Loess Plateau of Northern Shaanxi starting in 1999 and inevitably affected plant biomass production. This study evaluated the trend and magnitude of gross primary production (GPP) changes with VPM in the study area over 2000–2012 and quantitatively identified GFG’s contribution. The results indicate that the regional mean GPP was 529.6 to 854.7 g C m−2 yr−1, which significantly increased by 1.63% per year during the period. At the pixel level, 80% of the pixels presented an increasing trend in annual GPP, and in nearly half of the region, the increasing trend was statistically significant. Spatial analyses showed that the GPP increased more significantly in the gully areas of the middle north area, while no evident change trend was observed in the southern forestland areas. Statistical data from relevant yearbooks and interpreted data from high-resolution images show that the GFG program promoted vegetation restoration. Taking the increased area of forestland to indicate the achievements of GFG, we estimated, using regression analyses, that the afforestation contributed 40%–60% to the increased GPP in the region during 2000–2012. Overall, the ecosystem had a positive response to the Grain for Green (GFG) program in Northern Shaanxi.

Vegetation restoration dominated the variation of water use efficiency in China

Implementation of numerous ecological restoration programs over the last two decades has significantly increased vegetation coverage in China. However, the response of the coupled carbon and water cycles to the large-scale vegetation restoration and the underlying mechanisms remain unclear. This study analyzed the spatiotemporal patterns of regional gross primary productivity (GPP), evapotranspiration (ET) and water use efficiency (WUE) in China using the two-leaf light use efficiency (TL-LUE) and the Priestley Taylor Jet Propulsion Laboratory (PT-JPL) models during the 2000–2017 period. The influencing mechanisms of anthropogenic re-vegetation and climatic factors on regional WUE were also analyzed. Results indicate that (1) both the GPP and WUE showed a significant increasing tendency with inter-annual change rates of 10.53 g C m−2 yr−1 and 0.01 g C kg−1 H2O yr−1, respectively; while the inter-annual change rate of annual ET was 1.43 kg H2O m−2 yr−1. (2) Spatially, the regional GPP dominated WUE variation of more than 79.51% of the total area. (3) Among the influencing factors, the greening of vegetation driven by human activities (LAIh) explained 35.27% of the WUE variation in China, much more than the climatic factors over during the study period. At the regional scale, human-driven vegetation greening also dominated the increasing WUE at regional scale. These findings provide important information to support large-scale ecological restoration policy/decision making to improve ecosystem services in China.

Quantifying the contributions of climate change and human activities to vegetation dynamic in China based on multiple indices

Distinguishing the respective roles of climate change and anthropogenic activities can provide crucial information for sustainable management of the environment. Here, using the residual trend method (RESTREND), which measures the residue of the actual and potential trends of vegetation, we quantified the relative contributions of human activities (e.g., ecological restoration, overgrazing, and urbanization) and climate change (the warmer and wetter trend) to vegetation dynamics in China during 1988–2018 based on multiple vegetation indices, including the vegetation optical depth (Ku-VOD, C-VOD), normalized difference vegetation index (NDVI), and gross primary productivity (GPP). The results showed that the VOD, NDVI, and GPP exhibited overall increasing trends during 1988–2018. Human activities contributed >70% to the increases in NDVI and GPP in China, whereas a counterbalanced contribution of human activities and climate change was identified for the VOD dynamics (51% vs. 49%). Regions with high contributions from human activities to NDVI, GPP, and VOD were located in northeastern, southern, central, and northwestern China. In northern China, the positive impacts of human activities on NDVI (78%) and BEPS-GPP (83%) were greater than those of climate change. In contrast, human activities contributed 96% to the decrease in Ku-VOD over the same period. Before 2000, climate change promoted increases in GPP and NDVI in most regions of southern China. The increasing rates of GPP and NDVI accelerated after 2000 due to afforestation. However, human activities like overgrazing and urbanization have led to decreases in Ku-VOD in northern and southwestern China, and in C-VOD in northeastern, eastern, central, southwestern, and southern China. In all, the relative roles of climate and human factors varied in different regions when NDVI, GPP, or VOD were individually considered. Our results highlighted that the regional-scale vegetation conditions should be taken into full account to achieve sustainable management of ecosystems.

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.

Exceptional heat and atmospheric dryness amplified losses of primary production during the 2020 U.S. Southwest hot drought.

Earth's ecosystems are increasingly threatened by “hot drought,” which occurs when hot air temperatures coincide with precipitation deficits, intensifying the hydrological, physiological, and ecological effects of drought by enhancing evaporative losses of soil moisture (SM) and increasing plant stress due to higher vapor pressure deficit (VPD). Drought‐induced reductions in gross primary production (GPP) exert a major influence on the terrestrial carbon sink, but the extent to which hotter and atmospherically drier conditions will amplify the effects of precipitation deficits on Earth's carbon cycle remains largely unknown. During summer and autumn 2020, the U.S. Southwest experienced one of the most intense hot droughts on record, with record‐low precipitation and record‐high air temperature and VPD across the region. Here, we use this natural experiment to evaluate the effects of hot drought on GPP and further decompose those negative GPP anomalies into their constituent meteorological and hydrological drivers. We found a 122 Tg C (>25%) reduction in GPP below the 2015–2019 mean, by far the lowest regional GPP over the Soil Moisture Active Passive satellite record. Roughly half of the estimated GPP loss was attributable to low SM (likely a combination of record‐low precipitation and warming‐enhanced evaporative depletion), but record‐breaking VPD amplified the reduction of GPP, contributing roughly 40% of the GPP anomaly. Both air temperature and VPD are very likely to continue increasing over the next century, likely leading to more frequent and intense hot droughts and substantially enhancing drought‐induced GPP reductions.

Remotely Sensed Carbonyl Sulfide Constrains Model Estimates of Amazon Primary Productivity

AbstractUnderstanding the magnitude of tropical gross primary production (GPP) is critical for carbon cycle modeling and climate projections, but this quantity is poorly constrained at regional scales. Biospheric uptake of carbonyl sulfide (OCS) provides a promising approach to estimating regional GPP. Here, we simulate OCS concentrations driven by surface flux scenarios encompassing a wide range of GPP estimates for the Amazon basin. We compare the model output to satellite retrievals and find a regional GPP estimate of 2375 ± 914 g(C) m−2 yr−1, consistent with previous estimates, including the TRENDY model ensemble range of 1431–3812 g(C) m−2 yr−1.

Direct and Legacy Effects of Spring Temperature Anomalies on Seasonal Productivity in Northern Ecosystems

Warmer or cooler spring in northern high latitudes will, for the most part, directly impact gross primary productivity (GPP) of ecosystems, but also carry consequences for the upcoming seasonal GPP. Spatiotemporal patterns of these legacy effects are still largely unknown but important for improving our understanding of how plant phenology is associated with vegetation dynamics. In this study, impacts of spring temperature anomalies on spring, summer and autumn GPP were investigated, and the dominant drivers of summer and autumn GPP including air temperature, vapor pressure deficit and soil moisture have been explored for northern ecosystems (>30°N). Three remote sensing products of seasonal GPP (GOSIF-GPP, NIRv-GPP and FluxSat-GPP) over 2001–2018, all based on a spatial resolution of 0.05°, were employed. Our results indicate that legacy effects from spring temperature are most pronounced in summer, where they have stimulating effects on the Arctic ecosystem productivity. Spring warming likely lessens the harsh climatic constraints that govern the Arctic tundra and extends the growing season length. Further south, legacy effects are mainly negative. This strengthens the hypothesis that enhanced vegetation growth in spring will increase plant water demand and stress in summer and autumn. Soil moisture is the dominant control of summer GPP in temperate regions. However, the dominant meteorological variables controlling vegetation growth may differ depending on the GPP products, highlighting the need to address uncertainties among different methods of estimating GPP.

Estimation of Global Cropland Gross Primary Production from Satellite Observations by Integrating Water Availability Variable in Light-Use-Efficiency Model

Satellite-based models have been widely used to estimate gross primary production (GPP) of terrestrial ecosystems. Although they have many advantages for mapping spatiotemporal variations of regional or global GPP, the performance in agroecosystems is relatively poor. In this study, a light-use-efficiency model for cropland GPP estimation, named EF-LUE, driven by remote sensing data, was developed by integrating evaporative fraction (EF) as limiting factor accounting for soil water availability. Model parameters were optimized first using CO2 flux measurements by eddy covariance system from flux tower sites, and the optimized parameters were further spatially extrapolated according to climate zones for global cropland GPP estimation in 2001–2019. The major forcing datasets include the fraction of absorbed photosynthetically active radiation (FAPAR) data from the Copernicus Global Land Service System (CGLS) GEOV2 dataset, EF from the ETMonitor model, and meteorological forcing variables from ERA5 data. The EF-LUE model was first evaluated at flux tower site-level, and the results suggested that the proposed EF-LUE model and the LUE model without using water availability limiting factor, both driven by flux tower meteorology data, explained 82% and 74% of the temporal variations of GPP across crop sites, respectively. The overall KGE increased from 0.73 to 0.83, NSE increased from 0.73 to 0.81, and RMSE decreased from 2.87 to 2.39 g C m−2 d−1 in the estimated GPP after integrating EF in the LUE model. These improvements may be largely attributed to parameters optimized for different climatic zones and incorporating water availability limiting factor expressed by EF into the light-use-efficiency model. At global scale, the verification by GPP measurements from cropland flux tower sites showed that GPP estimated by the EF-LUE model driven by ERA5 reanalysis meteorological data and EF from ETMonitor had overall the highest R2, KGE, and NSE and the smallest RMSE over the four existing GPP datasets (MOD17 GPP, revised EC-LUE GPP, GOSIF GPP and PML-V2 GPP). The global GPP from the EF-LUE model could capture the significant negative GPP anomalies during drought or heat-wave events, indicating its ability to express the impacts of the water stress on cropland GPP.

Improving global gross primary productivity estimation by fusing multi-source data products

A reliable estimate of the gross primary productivity (GPP) of terrestrial vegetation is essential for both making decisions to address global climate change and understanding the global carbon balance. The lack of consistency in global terrestrial GPP estimates across various products leads to great uncertainty. In this study, we improve the quantification of global gross primary productivity by integrating multiple source GPP products without using any prior knowledge through the Bayesian-based Three-Cornered Hat (BTCH) method to generate a new weighted GPP data set. The fusion results demonstrate the superiority of weighted GPP, which greatly reduces the random error of individual datasets and fully takes advantage of the characteristics of multi-source data products. The weighted dataset can largely reproduce the interannual variation of regional GPP. Overall, the merging scheme based on the BTCH method can effectively generate a new GPP dataset that integrates information from multiple products and provides new ideas for GPP estimation on a global scale.

Adjusting solar-induced fluorescence to nadir-viewing provides a better proxy for GPP

Solar-induced fluorescence (SIF) provides key information for inferring terrestrial gross primary productivity (GPP). However, there is little research on analyzing the performance of nadir-adjusted SIF (SIFnadir) on GPP, compared to total SIF emitted by all leaves (SIFtotal) for reducing the viewing angle effects and estimating GPP. Besides, there have been controversial opinions on whether SIFtotal is better correlated to GPP than raw SIF observations (SIFobs). Here we systematically analyzed the relationship between raw/nadir/total SIF (i.e., SIFobs, SIFnadir and SIFtotal) and GPP and investigated the underlying mechanism, using multi-angular field measurements and eddy covariance data in wheat and corn crops at sub-daily scale. We further compared the performance of SIFobs, SIFnadir and SIFtotal in estimating GPP using the TROPOspheric Monitoring Instrument (TROPOMI) SIF and concurrent AmeriFlux measurements at daily scale. The results indicate that diurnal SIFnadir has stronger correlations to GPP than SIFobs for field measurements, with the increase of mean coefficient of determination (R2) by 0.05–0.07 for far-red band and 0.11–0.20 for red band. SIFnadir shows comparable performance with SIFtotal for both far-red and red bands. Although the viewing angle effects dominate the difference in estimating GPP between SIFnadir and SIFobs, the correlation between light use efficiency (LUE) and fesc further determines the different performance of SIFnadir and SIFtotal in estimating GPP. TROPOMI-based analysis further confirms that SIFnadir overall has higher correlations to AmeriFlux GPP than SIFobs for different plant functional types and shows similar performance with SIFtotal. Compared to SIFtotal, the estimation of SIFnadir independent of viewing angle effects does not require any canopy structure parameters, and thus offers promising potential for reliably estimating regional and global GPP.

Impacts of the 2012-2015 Californian drought on carbon, water and energy fluxes in the Californian Sierras: Results from an imaging spectrometry-constrained terrestrial biosphere model.

Accurate descriptions of current ecosystem composition are essential for improving terrestrial biosphere model predictions of how ecosystems are responding to climate variability and change. This study investigates how imaging spectrometry-derived ecosystem composition can constrain and improve terrestrial biosphere model predictions of regional-scale carbon, water and energy fluxes. Incorporating imaging spectrometry-derived composition of five plant functional types (Grasses/Shrubs, Oaks/Western Hardwoods, Western Pines, Fir/Cedar and High-elevation Pines) into the Ecosystem Demography (ED2) terrestrial biosphere model improves predictions of net ecosystem productivity (NEP) and gross primary productivity (GPP) across four flux towers of the Southern Sierra Critical Zone Observatory (SSCZO) spanning a 2250m elevational gradient in the western Sierra Nevada. NEP and GPP root-mean-square-errors were reduced by 23%-82% and 19%-89%, respectively, and water flux predictions improved at the mid-elevation pine (Soaproot), fir/cedar (P301) and high-elevation pine (Shorthair) flux tower sites, but not at the oak savanna (San Joaquin Experimental Range [SJER]) site. These improvements in carbon and water predictions are similar to those achieved with model initializations using ground-based inventory composition. The imaging spectrometry-constrained ED2model was then used to predict carbon, water and energy fluxes and above-ground biomass (AGB) dynamics over a 737km2 region to gain insight into the regional ecosystem impacts of the 2012-2015 Californian drought. The analysis indicates that the drought reduced regional NEP, GPP and transpiration by 83%, 40% and 33%, respectively, with the largest reductions occurring in the functionally diverse, high basal area mid-elevation forests. This was accompanied by a 54% decline in AGB growth in 2012, followed by a marked increase (823%) in AGB mortality in 2014, reflecting an approximately 10-fold increase in per capita tree mortality from ~55 treeskm-2 year-1 in 2010-2011, to ~535 treeskm-2 year-1 in 2014. These findings illustrate how imaging spectrometry estimates of ecosystem composition can constrain and improve terrestrial biosphere model predictions of regional carbon, water, and energy fluxes, and biomass dynamics.