Mean Annual Gross Primary Production Research Articles

Beyond the visible: Accounting for ultraviolet and far-red radiation in vegetation productivity and surface energy budgets.

Photosynthetically active radiation (PAR) is typically defined as light with a wavelength within 400-700 nm. However, ultra-violet (UV) radiation within 280-400 nm and far-red (FR) radiation within 700-750 nm can also excite photosystems, though not as efficiently as PAR. Vegetation and land surface models (LSMs) typically do not explicitly account for UV's contribution to energy budgets or photosynthesis, nor FR's contribution to photosynthesis. However, whether neglecting UV and FR has significant impacts remains unknown. We explored how canopy radiative transfer (RT) and photosynthesis are impacted when explicitly implementing UV in the canopy RT model and accounting for UV and FR in the photosynthesis models within a next-generation LSM that can simulate hyperspectral canopy RT. We validated our improvements using photosynthesis measurements from plants under different light sources and intensities and surface reflection from an eddy-covariance tower. Our model simulations suggested that at the whole plant level, after accounting for UV and FR explicitly, chlorophyll content, leaf area index (LAI), clumping index, and solar radiation all impact the modeling of gross primary productivity (GPP). At the global scale, mean annual GPP within a grid would increase by up to 7.3% and the increase is proportional to LAI; globally integrated GPP increases by 4.6 PgC year-1 (3.8% of the GPP without accounting for UV + FR). Further, using PAR to proxy UV could overestimate surface albedo by more than 0.1, particularly in the boreal forests. Our results highlight the importance of improving UV and FR in canopy RT and photosynthesis modeling and the necessity to implement hyperspectral or multispectral canopy RT schemes in future vegetation and LSMs.

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.

Evaluating the effectiveness of different variable retention harvesting treatments on forest carbon uptake using remote sensing

Variable retention harvesting (VRH) is an ecologically based forest management practice applied to emulate natural post-disturbance residual canopy structure and increase the biodiversity and resilience of a regenerating stand. The pattern and density of canopy retention also influences the productivity, mortality, and carbon (C) sequestration rates of residual trees. In this study, we used high-resolution satellite and drone remote sensing data to evaluate the effect of five different VRH treatments on growth and C uptake of a 90-year-old red pine (Pinus resinosa Ait.) plantation forest in southern Ontario, Canada. The treatments included four different combinations of harvesting intensity residual tree distribution including 33% aggregate retention (33A), 55% aggregate retention (55A), 33% dispersed retention (33D), and 55% dispersed retention (55D) and unharvested control (CN) each replicated four times in 1 ha plots. Satellite- and drone-derived normal difference vegetation index (NDVI) and gross primary productivity (GPP) were estimated for each treatment during the growing seasons from 2010 to 2020. Over this period, observed mean daily NDVI values ranged from 0.25 to 0.86 among treatments, where the 55D treatment consistently showed the highest NDVI values. Overall, the highest mean daily GPP values were observed in the CN treatment, followed by the 55D, 55A, 33D, and 33A treatments. Remote sensing-derived mean annual GPP for the entire 20 ha study site was 1651 ± 89 g C m−2 year−1, with a range of 1407–1864 g C m−2 year−1 from 2010 to 2020. Satellite-derived annual GPP values were linearly related with observed annual GPP (R2 = 0.88, p = 0.032) measured using the eddy covariance technique in an adjacent white pine plantation of similar age and height over the study period. Study results suggested that VRH treatments that create a uniformly dispersed residual canopy retaining more than half of the initial basal area (e.g. 55D) is a viable forest management practice where optimizing forest growth and C uptake is a primary management objective. Our study will help forest managers and researchers to develop methodologies to evaluate the effectiveness of forest management practices, tailor them to achieve climate mitigation and adaptation goals and to develop forest management pathways for nature-based climate solutions.

Urbanization expands the fluctuating difference in gross primary productivity between urban and rural areas from 2000 to 2018 in China

Urban and rural vegetation are affected by both climate change and human activities, but the role of urbanization in vegetation productivity is unclear given the dual impacts. Here, we delineated urban area (UA) and rural area (RA), quantified the relative impacts of climate change and human activities on gross primary production (GPP) in 34 major cities (MCs) in China from 2000 to 2018, and analyzed the intrinsic impacts of urbanization on GPP. First, we found that the total urban impervious surface coverage (ISC) of the 34 MCs increased by 13.25 % and the mean annual GPP increased by 211 gC m−2 during the study period. GPP increased significantly in urban core areas, but decreased significantly in urban expansion areas, which was mainly due to a large amount of vegetation loss due to land use conversion. Second, the variability of GPP in UA was generally lower than in RA. Both climate change and human activities had a positive impact on GPP in UA and RA in the 34 MCs, of which the contribution was 49 % and 51 % in UA, and 76 % and 24 % in RA, respectively. Third, under climate change and human activities, the increase in GPP offset 4.96 % and 12.35 % of the impact of land use conversion on GPP in 2000 and 2018, respectively, which indicated that the offset strengthened over time. These findings emphasize the role of human activities in promoting carbon sequestration in urban vegetation, which is crucial for better understanding the processes and mechanisms of urban carbon cycles. Decision-makers can manage urban vegetation based on vegetation carbon sequestration potential as regions urbanize, aiding comprehensive decision-making.

Climate and management impacts on the spatiotemporal dynamics of water-carbon fluxes in the North China Plain

Evapotranspiration (ET) and gross primary production (GPP) over agroecosystems could be noticeably altered by climate change and human activities. A better understanding of the impacts on water-carbon interactions and the underlying mechanisms, are crucial to improve water use efficiency (WUE). Comparing to 23 site-years of observations, regional ET dataset and statistical yields, the Remote Sensing-based cropland Ecohydrological Model (ReSEM) was validated to estimate the spatiotemporal dynamics of ET, GPP and WUE for dryland agroecosystems in the North China Plain (NCP) from 2001 to 2018. Spatially, the distributions of mean annual ET, GPP and WUE were comparable which changed obviously in different climatic conditions while varied slightly between various irrigation areas. The significantly increased WUE occurred in 38.6 % cropland, mainly located in the humid south regions with less irrigation requirement and the central sub-humid areas along the Yellow River with access to surface irrigation. The averaged increasing trend of WUE in the heavily irrigated semi-arid areas was relatively low. The temporal dynamic of regional mean WUE indicated a significantly increased rate of 0.026 gC kg−1H2O per year during 2001–2018. The spatiotemporal trends of WUE were mainly resulted from the significantly increased GPP rather than ET. The experimental simulations demonstrated that agricultural management dominated the interannual trend of WUE, whose relative contribution (79.5 %) was obviously larger than that of air CO2 concentration (40.2 %) and climate variable change (−19.7 %). Besides, the effects of agricultural management on WUE were further divided based on the classified six cropping systems, 82.4 % of which was attributed to the management of winter wheat-summer maize rotation system. This study sheds light on the improvement of WUE for dryland agroecosystem under changing environments and highlights the dominant impact of agricultural management.

Pixel-level parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes with an efficient model–data fusion method and satellite-derived LAI and GPP data

Abstract. Inaccurate parameter estimation is a significant source of uncertainty in complex terrestrial biosphere models. Model parameters may have large spatial variability, even within a vegetation type. Model uncertainty from parameters can be significantly reduced by model–data fusion (MDF), which, however, is difficult to implement over a large region with traditional methods due to the high computational cost. This study proposed a hybrid modeling approach that couples a terrestrial biosphere model with a data-driven machine learning method, which is able to consider both satellite information and the physical mechanisms. We developed a two-step framework to estimate the essential parameters of the revised Integrated Biosphere Simulator (IBIS) pixel by pixel using the satellite-derived leaf area index (LAI) and gross primary productivity (GPP) products as “true values.” The first step was to estimate the optimal parameters for each sample using a modified adaptive surrogate modeling algorithm (MASM). We applied the Gaussian process regression algorithm (GPR) as a surrogate model to learn the relationship between model parameters and errors. In our second step, we built an extreme gradient boosting (XGBoost) model between the optimized parameters and local environmental variables. The trained XGBoost model was then used to predict optimal parameters spatially across the deciduous forests in the eastern United States. The results showed that the parameters were highly variable spatially and quite different from the default values over forests, and the simulation errors of the GPP and LAI could be markedly reduced with the optimized parameters. The effectiveness of the optimized model in estimating GPP, ecosystem respiration (ER), and net ecosystem exchange (NEE) were also tested through site validation. The optimized model reduced the root mean square error (RMSE) from 7.03 to 6.22 gC m−2 d−1 for GPP, 2.65 to 2.11 gC m−2 d−1 for ER, and 4.45 to 4.38 gC m−2 d−1 for NEE. The mean annual GPP, ER, and NEE of the region from 2000 to 2019 were 5.79, 4.60, and −1.19 Pg yr−1, respectively. The strategy used in this study requires only a few hundred model runs to calibrate regional parameters and is readily applicable to other complex terrestrial biosphere models with different spatial resolutions. Our study also emphasizes the necessity of pixel-level parameter calibration and the value of remote sensing products for per-pixel parameter optimization.

Environmental controls on water use efficiency in a hilly tea plantation in southeast China

Knowledge of water use efficiency (WUE) at the ecosystem level is imperative as it is a critical ecophysiological index reflecting the coupling processes between carbon sequestration and water consumption. However, the temporal patterns of WUE and their underlying regulating factors during different seasons in tea plantations, which are under intense pruning practice, remain poorly understood. Based on carbon and water vapor flux data obtained by the eddy covariance technique in a subtropical hilly tea plantation in southeast China during 2014–2018, this study analyzed the seasonal variations in gross primary productivity (GPP), evapotranspiration (ET) and ecosystem WUE, and discussed the response of GPP, ET and WUE to environmental variables during different seasons. The results showed that the mean annual GPP, ET and WUE ranged from 1426.61 ~ 1896.05 g C m−2, 607.05 ~ 805.83 mm and 2.22 ~ 2.68 g C kg−1 H2O, respectively. At the daily time scale, GPP and ET responded significantly and positively to variations in air temperature (Ta), water vapor pressure deficit (VPD), and net radiation (Rn) in both the pruning season and late growing season, but daily WUE had a significantly negative response to variations in Ta, VPD and Rn. Rn was identified as the dominant factor in regulating GPP and ET during both the pruning season and late growing season, while the dominant factor controlling daily variations in WUE was VPD during the pruning season and Ta during the late growing season. Such results were mainly due to the divergent responses of carbon assimilation and water loss to different environmental variations and management strategies. These findings highlighted the importance of understanding the coupled processes between carbon assimilation and water loss in tea plantations.

Influence of modifications (from AoB2015 to v0.5) in the Vegetation Optimality Model

Abstract. The Vegetation Optimality Model (VOM, Schymanski et al., 2009, 2015) is an optimality-based, coupled water–vegetation model that predicts vegetation properties and behaviour based on optimality theory rather than calibrating vegetation properties or prescribing them based on observations, as most conventional models do. Several updates to previous applications of the VOM have been made for the study in the accompanying paper of Nijzink et al. (2022), where we assess whether optimality theory can alleviate common shortcomings of conventional models, as identified in a previous model inter-comparison study along the North Australian Tropical Transect (NATT, Whitley et al., 2016). Therefore, we assess in this technical paper how the updates to the model and input data would have affected the original results of Schymanski et al. (2015), and we implemented these changes one at a time. The model updates included extended input data, the use of variable atmospheric CO2 levels, modified soil properties, implementation of free drainage conditions, and the addition of grass rooting depths to the optimized vegetation properties. A systematic assessment of these changes was carried out by adding each individual modification to the original version of the VOM at the flux tower site of Howard Springs, Australia. The analysis revealed that the implemented changes affected the simulation of mean annual evapotranspiration (ET) and gross primary productivity (GPP) by no more than 20 %, with the largest effects caused by the newly imposed free drainage conditions and modified soil texture. Free drainage conditions led to an underestimation of ET and GPP in comparison with the results of Schymanski et al. (2015), whereas more fine-grained soil textures increased the water storage in the soil and resulted in increased GPP. Although part of the effect of free drainage was compensated for by the updated soil texture, when combining all changes, the resulting effect on the simulated fluxes was still dominated by the effect of implementing free drainage conditions. Eventually, the relative error for the mean annual ET, in comparison with flux tower observations, changed from an 8.4 % overestimation to an 10.2 % underestimation, whereas the relative errors for the mean annual GPP remained similar, with an overestimation that slightly reduced from 17.8 % to 14.7 %. The sensitivity to free drainage conditions suggests that a realistic representation of groundwater dynamics is very important for predicting ET and GPP at a tropical open-forest savanna site as investigated here. The modest changes in model outputs highlighted the robustness of the optimization approach that is central to the VOM architecture.

Exploring Populus phenological response to climate change using observational data and ecosystem modelling

The length of the growing season for deciduous trees in temperate and boreal forests is determined by the timing of bud burst and autumn senescence. It is generally assumed that a warmer climate leads to a longer growing season due to earlier bud burst and delayed autumn senescence and thereby increased gross primary production (GPP) of forests. In this study, we analysed past (1873–1951) and current (2008–2020) phenological observations on bud burst and senescence from aspen trees (Populus tremula) grown in Sweden. The observations indicated a reduction in temperature sensitivity of bud burst between the time periods, likely associated with warmer winters and reduced exposure to chilling. The phenological observations were used in the evaluation of an ecosystem model. Biases in modelling spring and autumn leaf cover development influenced the seasonal and annual GPP estimates. The overestimation of the modelled GPP was more pronounced in spring than in autumn, reflecting the GPP limitations by leaf cover development in spring, and by daylength and temperature conditions in autumn. Calibration of the spring phenology parameters, using the accumulated temperature sums and chilling days at the observed timing of bud burst, significantly improved model performance compared to the original parameterisation. The calibrated ecosystem model projections representing RCP8.5 suggested 15 days earlier timing of bud burst and enhanced mean annual GPP by the end of the century compared with current climate conditions in Sweden.

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 (LUEmax). 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 LUEmax 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.

Spatiotemporal dynamics in assimilated-LAI phenology and its impact on subtropical bamboo forest productivity

Phenology has a significant effect on forest growth and directly affects the forest ecosystem carbon cycle. Bamboo forests possess strong carbon sequestration capacities. However, it is not clear whether variations in phenology increase or decrease carbon uptake and storage in subtropical bamboo forests. We first extracted the length of the growing season (LOS) by coupling a data assimilation algorithm and MODIS leaf area index (LAI) data, and then the LOS was used to drive the integrated terrestrial ecosystem carbon-budget (InTEC) model to simulate gross primary productivity (GPP) and net ecosystem productivity (NEP) in Zhejiang Province from 2001 to 2017. Our results showed that the LOS estimation using the assimilated LAI time series was more reliable than that of the MODIS LAI and enhanced vegetation index (EVI). The annual average LOS increased on average by 0.76 day yr−1 from 2001 to 2017. The GPP and NEP simulations based on the LAI assimilation-based phenology indicated that bamboo forest ecosystems possess strong carbon sequestration capacities and act as carbon sinks, with mean annual GPP and NEP values of 434.74 ± 257.93 g C m−2 yr−1 and 141.42 ± 82.54 g C m−2 yr−1, respectively, during 2001–2017. An increase of one day in the regional annual LOS increases the annual average GPP and NEP by 1.34 g C m−2 yr−1 and 0.75 g C m−2 yr−1, respectively. Moreover, the interannual variation of NEP was significantly correlated with precipitation and temperature, whereas GPP was not. Our results demonstrated that phenology extraction based on LAI data assimilation should play an important role in the simulation of bamboo forest productivity with ecological process models. The variation in phenology induced by climate change can strengthen the bamboo forest carbon sink, which is of great significance for subtropical forests coping with climate change in the future.

Montane forest productivity across a semiarid climatic gradient.

High-elevation montane forests are disproportionately important to carbon sequestration in semiarid climates where low elevations are dry and characterized by low carbon density ecosystems. However, these ecosystems are increasingly threatened by climate change with seasonal implications for photosynthesis and forest growth. As a result, we leveraged eddy covariance data from six evergreen conifer forest sites in the semiarid western United States to extrapolate the status of carbon sequestration within a framework of projected warming and drying. At colder locations, the seasonal evolution of gross primary productivity (GPP) was characterized by a single broad maximum during the summer that corresponded to snow melt-derived moisture and a transition from winter dormancy to spring activity. Conversely, winter dormancy was transient at warmer locations, and GPP was responsive to both winter and summer precipitation such that two distinct GPP maxima were separated by a period of foresummer drought. This resulted in a predictable sequence of primary limiting factors to GPP beginning with air temperature in winter and proceeding to moisture and leaf area during the summer. Due to counteracting winter (positive) and summer (negative) GPP responses to warming, leaf area index and moisture availability were the best predictors of annual GPP differences across sites. Overall, mean annual GPP was greatest at the warmest site due to persistent vegetation photosynthetic activity throughout the winter. These results indicate that the trajectory of this region's carbon sequestration will be sensitive to reduced or delayed summer precipitation, especially if coupled to snow drought and earlier soil moisture recession, but summer precipitation changes remain highly uncertain. Given the demonstrated potential for seasonally offsetting responses to warming, we project that decadal semiarid montane forest carbon sequestration will remain relatively stable in the absence of severe disturbance.

A carbon sink-driven approach to estimate gross primary production from microwave satellite observations

Global estimation of Gross Primary Production (GPP) - the uptake of atmospheric carbon dioxide by plants through photosynthesis - is commonly based on optical satellite remote sensing data. This presents a source-driven approach since it uses the amount of absorbed light, the main driver of photosynthesis, as a proxy for GPP. Vegetation Optical Depth (VOD) estimates obtained from microwave sensors provide an alternative and independent data source to estimate GPP on a global scale, which may complement existing GPP products. Recent studies have shown that VOD is related to aboveground biomass, and that both VOD and temporal changes in VOD relate to GPP. In this study, we build upon this concept and propose a model for estimating GPP from VOD. Since the model is driven by vegetation biomass, as observed through VOD, it presents a carbon sink-driven approach to quantify GPP and, therefore, is conceptually different from common source-driven approaches. The model developed in this study uses single frequencies from active or passive microwave VOD retrievals from C-, X- and Ku-band (Advanced Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer for Earth Observation (AMSR-E)) to estimate GPP at the global scale. We assessed the ability for temporal and spatial extrapolation of the model using global GPP from FLUXCOM and in situ GPP from FLUXNET. We further performed upscaling of in situ GPP based on different VOD data sets and compared these estimates with the FLUXCOM and MODerate-resolution Imaging Spectroradiometer (MODIS) GPP products. Our results show that the model developed for individual grid cells using VOD and change in VOD as input performs well in predicting temporal patterns in GPP for all VOD data sets. For spatial extrapolation of the model, however, additional input variables are needed to represent the spatial variability of the VOD-GPP relationship due to differences in vegetation type. As additional input variable, we included the grid cell median VOD (as a proxy for vegetation cover), which increased the model performance during cross validation. Mean annual GPP obtained for AMSR-E X-band data tends to overestimate mean annual GPP for FLUXCOM and MODIS but shows comparable latitudinal patterns. Overall, our findings demonstrate the potential of VOD for estimating GPP. The sink-driven approach provides additional information about GPP independent of optical data, which may contribute to our knowledge about the carbon source-sink balance in different ecosystems.

Changes in productivity and carbon storage of grasslands in China under future global warming scenarios of 1.5°C and 2°C

Abstract Aims The impacts of future global warming of 1.5°C and 2°C on the productivity and carbon (C) storage of grasslands in China are not clear yet, although grasslands in China support ~45 million agricultural populations and more than 238 million livestock populations, and are sensitive to global warming. Methods This study used a process-based terrestrial ecosystem model named ORCHIDEE to simulate C cycle of alpine meadows and temperate grasslands in China. This model was driven by high-resolution (0.5° × 0.5°) climate of global specific warming levels (SWL) of 1.5°C and 2°C (warmer than pre-industrial level), which is downscaled by EC-EARTH3-HR v3.1 with sea surface temperature and sea-ice concentration as boundary conditions from IPSL-CM5-LR (low spatial resolution, 2.5° × 1.5°) Earth system model (ESM). Important Findings Compared with baseline (1971–2005), the mean annual air temperature over Chinese grasslands increased by 2.5°C and 3.7°C under SWL1.5 and SWL2, respectively. The increase in temperature in the alpine meadow was higher than that in the temperate grassland under both SWL1.5 and SWL2. Precipitation was also shown an increasing trend under SWL2 over most of the Chinese grasslands. Strong increases in gross primary productivity (GPP) were simulated in the Chinese grasslands, and the mean annual GPP (GPPMA) increased by 19.32% and 43.62% under SWL1.5 and SWL2, respectively. The C storage increased by 0.64 Pg C and 1.37 Pg C under SWL1.5 and SWL2 for 50 years simulations. The GPPMA was 0.670.390.88 (0.82) (model meanminmax (this study)), 0.850.451.24 (0.97) and 0.940.611.30 (1.17) Pg C year−1 under baseline, SWL1.5 and SWL2 modeled by four CMIP5 ESMs (phase 5 of the Coupled Model Inter-comparison Project Earth System Models). In contrast, the mean annual net biome productivity was −18.55−40.374.47 (−3.61),18.65−2.0364.03 (10.29) and 24.158.3838.77 (24.93) Tg C year−1 under baseline, SWL1.5 and SWL2 modeled by the four CMIP5 ESMs. Our results indicated that the Chinese grasslands would have higher productivity than the baseline and can mitigate climate change through increased C sequestration under future global warming of 1.5°C and 2°C with the increase of precipitation and the global increase of atmospheric CO2 concentration.

Evaluating the E3SM land model version 0 (ELMv0) at a temperate forest site using flux and soil water measurements

Abstract. Accurate simulations of soil respiration and carbon dioxide (CO2) fluxes are critical to project global biogeochemical cycles and the magnitude of carbon–climate feedbacks in Earth system models (ESMs). Currently, soil respiration is not represented well in ESMs, and few studies have attempted to address this deficiency. In this study, we evaluated the simulation of soil respiration in the Energy Exascale Earth System Model (E3SM) land model version 0 (ELMv0) using long-term observations from the Missouri Ozark AmeriFlux (MOFLUX) forest site in the central US. Simulations using the default model parameters underestimated soil water potential (SWP) during peak growing seasons and overestimated SWP during non-growing seasons and consequently underestimated annual soil respiration and gross primary production (GPP). A site-specific soil water retention curve greatly improved model simulations of SWP, GPP, and soil respiration. However, the model continued to underestimate the seasonal and interannual variabilities and the impact of the extreme drought in 2012. Potential reasons may include inadequate representations of vegetation mortality, the soil moisture function, and the dynamics of microbial organisms and soil macroinvertebrates. Our results indicate that the simulations of mean annual GPP and soil respiration can be significantly improved by better model representations of the soil water retention curve.

Enhanced gross primary production and evapotranspiration in juniper‐encroached grasslands

Woody plant encroachment (WPE) into grasslands has been occurring globally and may be accelerated by climate change in the future. This land cover change is expected to alter the carbon and water cycles, but it remains uncertain how and to what extent the carbon and water cycles may change with WPE into grasslands under current climate. In this study, we examined the difference of vegetation indices (VIs), evapotranspiration (ET), gross primary production (GPP), and solar-induced chlorophyll fluorescence (SIF) during 2000-2010 between grasslands and juniper-encroached grasslands. We also quantitatively assessed the changes of GPP and ET for grasslands with different proportions of juniper encroachment (JWPE). Our results suggested that JWPE increased the GPP, ET, greenness-related VIs, and SIF of grasslands. Mean annual GPP and ET were, respectively, ~55% and ~45% higher when grasslands were completely converted into juniper forests under contemporary climate during 2000-2010. The enhancement of annual GPP and ET for grasslands with JWPE varied over years ranging from about +20% GPP (~+30% for ET) in the wettest year (2007) to about twice as much GPP (~+55% for ET) in the severe drought year (2006) relative to grasslands without encroachment. Additionally, the differences in GPP and ET showed significant seasonal dynamics. During the peak growing season (May-August), GPP and ET for grasslands with JWPE were ~30% and ~40% higher on average. This analysis provided insights into how and to what degree carbon and water cycles were impacted by JWPE, which is vital to understanding how JWPE and ecological succession will affect the regional and global carbon and water budgets in the future.

Estimating terrestrial gross primary productivity in water limited ecosystems across Africa using the Southampton Carbon Flux (SCARF) model

The amount of carbon uptake by vegetation is an important component to understand the functioning of ecosystem processes and their response/feedback to climate. Recently, a new diagnostic model called the Southampton Carbon Flux (SCARF) Model driven by remote sensing data was developed to predict terrestrial gross primary productivity (GPP) and successfully applied in temperate regions. The model is based on the concept of quantum yield of plants and improves on the previous diagnostic models by (i) using the fraction of photosynthetic active radiation absorbed by the photosynthetic pigment (FAPARps) and (ii) using direct quantum yield by classifying the vegetation into C3 or C4 classes. In this paper, we calibrated and applied the model to evaluate GPP across various ecosystems in Africa. The performance of the model was evaluated using data from seven eddy covariance flux tower sites. Overall, the modelled GPP values showed good correlation (R>0.59, p<0.0001) with estimated flux tower GPP at most sites (except at a tropical rainforest site, R=0.38, p=0.02) in terms of their seasonality and absolute values. Mean daily GPP across the investigated period varied significantly across sites depending on the vegetation types from a minimum of 0.44gCm−2day−1 at the semi-arid and sub-humid savanna grassland sites to a maximum of 9.86gCm−2day−1 at the woodland and tropical rain forest sites. Generally, strong correlation is observed in savanna woodlands and grasslands where vegetation follows a prescribed seasonal cycle as determined by changes in canopy chlorophyll content and leaf area index. Finally, the mean annual GPP value for Africa predicted by the model was 35.25PgCyr−1. The good performance of the SCARF model in water-limited ecosystems across Africa extends its potential for global application.

Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes

Understanding the terrestrial carbon and water cycles is crucial for mitigation and adaptation for climate change. Leaf area index (LAI) is a key biophysical parameter in process-based ecosystem models for simulating gross primary productivity (GPP) and evapotranspiration (ET). The uncertainty in satellite-derived LAI products and their effects on the simulation of carbon and water fluxes at regional scales remain unclear. We evaluated three satellite-derived LAI products - MODIS (MCD15), GLASS, and Four-Scale Geometric Optical Model based LAI (FSGOM) over the period 2003–2012 using fine-resolution (30m) LAI data and field LAI measurements. GLASS had higher accuracy than FSGOM and MCD15 for forests, while FSGOM had higher accuracy than MCD15 and GLASS for grasslands. The three LAI products differed in magnitude, spatial patterns, and trends in LAI. We then examined the resulting discrepancies in simulated annual GPP and ET over China using a process-based, diagnostic terrestrial biosphere model. Mean annual total GPP for China's terrestrial ecosystems based on GLASS (6.32PgCyr−1) and FSGOM (6.15PgCyr−1) was 22.5% and 19.2% higher than that based on MCD15 (5.16PgCyr−1), respectively. Annual GPP based on GLASS and MCD15 increased over larger fractions of China's vegetated area (15.9% and 17.3%, respectively) than that based on FSGOM (12.6%). National annual ET based on GLASS (379.9mmyr−1) and FSGOM (374.4mmyr−1) was 7.9% and 6.3% higher than that based on MCD15 (352.1mmyr−1), respectively. Simulated ET increased in larger fractions of the vegetated area for GLASS (5.7%) and MCD15 (5.8%) than for FSGOM (3.9%). Our study shows that there were large discrepancies in LAI among satellite-derived LAI products and the biases of the LAI products could lead to substantial uncertainties in simulated carbon and water fluxes.

Spatiotemporal pattern of gross primary productivity and its covariation with climate in China over the last thirty years.

The uncertainties of China's gross primary productivity (GPP) estimates by global data-oriented products and ecosystem models justify a development of high-resolution data-oriented GPP dataset over China. We applied a machine learning algorithm developing a new GPP dataset for China with 0.1° spatial resolution and monthly temporal frequency based on eddy flux measurements from 40 sites in China and surrounding countries, most of which have not been explored in previous global GPP datasets. According to our estimates, mean annual GPP over China is 6.62±0.23PgC/year during 1982-2015 with a clear gradient from southeast to northwest. The trend of GPP estimated by this study (0.020±0.002PgC/year2 from 1982 to 2015) is almost two times of that estimated by the previous global dataset. The GPP increment is widely spread with 60% area showing significant increasing trend (p<.05), except for Inner Mongolia. Most ecosystem models overestimated the GPP magnitudes but underestimated the temporal trend of GPP. The monsoon affected eastern China, in particular the area surrounding Qinling Mountain, seems having larger contribution to interannual variability (IAV) of China's GPP than the semiarid northwestern China and Tibetan Plateau. At country scale, temperature is the dominant climatic driver for IAV of GPP. The area where IAV of GPP dominated by temperature is about 42%, while precipitation and solar radiation dominate 31% and 27% respectively over semiarid area and cold-wet area. Such spatial pattern was generally consistent with global GPP dataset, except over the Tibetan Plateau and northeastern forests, but not captured by most ecosystem models, highlighting future research needs to improve the modeling of ecosystem response to climate variations.

Sensitivity of near real-time MODIS gross primary productivity in terrestrial forest based on eddy covariance measurements

As an important product of Moderate Resolution Imaging Spectroradiometer (MODIS), MOD17A2 provides dramatic improvements in our ability to accurately and continuously monitor global terrestrial primary production, which is also significant in effort to advance scientific research and eco-environmental management. Over the past decades, forests have moderated climate change by sequestrating about one-quarter of the carbon emitted by human activities through fossil fuels burning and land use/land cover change. Thus, the carbon uptake by forests reduces the rate at which carbon accumulates in the atmosphere. However, the sensitivity of near real-time MODIS gross primary productivity (GPP) product is directly constrained by uncertainties in the modeling process, especially in complicated forest ecosystems. Although there have been plenty of studies to verify MODIS GPP with ground-based measurements using the eddy covariance (EC) technique, few have comprehensively validated the performance of MODIS estimates (Collection 5) across diverse forest types. Therefore, the present study examined the degree of correspondence between MODIS-derived GPP and EC-measured GPP at seasonal and interannual time scales for the main forest ecosystems, including evergreen broadleaf forest (EBF), evergreen needleleaf forest (ENF), deciduous broadleaf forest (DBF), and mixed forest (MF) relying on 16 flux towers with a total of 68 site-year datasets. Overall, site-specific evaluation of multi-year mean annual GPP estimates indicates that the current MOD17A2 product works highly effectively for MF and DBF, moderately effectively for ENF, and ineffectively for EBF. Except for tropical forest, MODIS estimates could capture the broad trends of GPP at 8-day time scale for all other sites surveyed. On the annual time scale, the best performance was observed in MF, followed by ENF, DBF, and EBF. Trend analyses also revealed the poor performance of MODIS GPP product in EBF and DBF. Thus, improvements in the sensitivity of MOD17A2 to forest productivity require continued efforts.