Vegetation Photosynthesis Model Research Articles

Long‐Term Response of Peatland Carbon Exchange to Climatic Changes in the Hudson Bay Lowlands

AbstractNorthern peatlands have been a persistent net sink of atmospheric carbon (C) due to the greater rates of gross primary production (GPP) compared to ecosystem respiration (ER). Global warming has raised concerns about the C sink strength of northern environments. In the vast peatlands of the Hudson Bay Lowlands (HBL) region of Canada, warming‐induced changes in sea ice dynamics over the Bay have altered its advective influence on the adjacent lowlands. Despite our knowledge of the short‐term C exchange in these peatlands, there remain uncertainties in the long‐term combined response of GPP and ER to climate change. In this study, the satellite‐data‐driven Vegetation Photosynthesis and Respiration Model was employed to investigate the response of peatland GPP, ER, and net ecosystem exchange to temperature and moisture changes. The results show contrasting net CO2 exchange at the two peatland sites over the last 20 years, with the fen acting as a net C source (+24 g C m−2) to the atmosphere and the bog serving as a net C sink (−130 g C m−2). There is ample evidence that a warmer and wetter climate enhanced GPP more than ER, while cooler temperatures weakened the peatland net C sink, regardless of the moisture conditions. Additionally, the advective influence of Hudson Bay on the lowlands produced markedly different C dynamics between offshore and onshore winds, with higher respiration rates (12%–26%) during offshore winds. We discuss the implications for peatland C balance under more frequent onshore winds in the region.

Is marginalised cultivated land on the Tibetan Plateau suitable for production or ecology? An example from the northeastern Tibetan Plateau

AbstractIn the ecologically fragile alpine area of the Tibetan Plateau, some marginalised cultivated lands have more serious conflicts related to agricultural production and ecological protection than others; thus, it is of practical significance to explore the rational use of marginalised cultivated lands. In this study, Xining City and Haidong City in the northeastern part of the Tibetan Plateau were taken as the study area. Marginalised cultivated land was identified, and the vegetation photosynthesis model was used to evaluate its production function. Ecological function was evaluated in terms of soil retention, landscape connectivity and habitat maintenance. The rational use pathways of marginalised cultivated land were derived through statistical analysis and aggregation model analysis. The results showed that there were 183,489.19 ha of marginalised cultivated land in the study area; the average value of production function of marginalised cultivated land was approximately 70% of that of nonmarginalised cultivated land, and its various ecological functions reached 93.64%–117.98% of that of nonmarginalised cultivated land. In general, the distribution of production functions decreases from northwest to southeast, and the ecological functions are high in the middle and low in the surroundings. A comprehensive analysis of the production and ecological functions of marginalised cultivated land can provide a reference basis for the rational formulation of cultivated land protection policies and ecological protection policies.

Gaps between Rice Actual and Potential Yields Based on the VPM and GAEZ Models in Heilongjiang Province, China

Heilongjiang Province is a significant region for grain production and serves as a crucial commodity grain production base in China. In recent years, due to the threat of declining cropland quality and quantity, coupled with the increasingly prominent demand for grain, there is an urgent need to enhance rice yields in Heilongjiang Province. It is imperative to accurately identify the gaps between actual and potential grain yields and effectively implement yield-enhancing measures in regions with significant yield gaps. This study aimed to determine the rice reproductive periods of Heilongjiang Province for 2000, 2010, and 2020, estimate the rice actual yields using the Vegetation Photosynthesis Model (VPM), simulate the rice potential yields based on the Global Agro-Ecological Zones (GAEZ) Model, and then identify the rice yield gaps at the pixel level by calculating the rice absolute yield gap (AYG) and relative yield gap (RYG). Additionally, yield-enhancing measures were proposed for regions with significant yield gaps. The results were as follows. (1) The rice reproductive periods of Heilongjiang Province for 2000, 2010, and 2020 were determined as days 153~249, days 145~249, and days 137~249. (2) The mean rice actual yield and potential yields decreased by 1222 and 5941 kg ha−1 during the 2000–2020 period, respectively, and the total actual and potential production increased by 3.75 and 1.70 million tons in Heilongjiang Province, respectively. (3) The rice AYG and RYG in the Sanjiang Plain region, such as Jixi City, Hegang City, and Jiamusi City were relatively large compared to other regions for the three years, and the rice yield gaps continued to decrease during the 2000–2020 period. (4) With regard to the Sanjiang Plain region with a large rice yield gap, this study proposes measures to narrow the rice yield gap by establishing ecological protection forests on cropland, transforming low- and middle-yielding fields, increasing agricultural science and technology inputs, selecting better rice cultivars, etc., which are important for ensuring food security.

Simulation of gross primary productivity and impact of drought in Liulin watershed of Taihang mountains over 2000–2020

As the largest flux in the global terrestrial carbon cycle, Gross Primary Productivity (GPP) is a key factor for the terrestrial carbon budget. Based on the eddy flux tower data, remote sensing data and the meteorological precipitation data, we calibrated three parameters of maximum light use efficiency, optimum temperature, and maximum temperature in the Vegetation Photosynthesis Model (VPM) to reproduce the GPP spatiotemporal characteristic in Liulin watershed of Taihang Mountains. We further analyzed the time lag effect and cumulative effect of the standard precipitation index (SPI) on GPP by the Pearson correlation coefficients. Results show that VPM captures the GPP characteristics of eddy flux tower observation in Liulin watershed. During the calibration period (R2 = 0.94, RMSE = 0.47 g C m-2·d-1) and validation period (R2 = 0.91, RMSE = 0.60 g C m-2 d-1), the adjusted VPM model successfully reproduced the temporal dynamics of site-observed GPP. From 2000 to 2020, VPM model can effectively reproduce the long-term variations in GPP (R2 = 0.69 vs. NIRv_GPP), with the GPP ranging from 446.4 to 828.1 g C m-2 y-1 and an average value of 545.6 g C m-2 y-1 in Liulin watershed. The GPP exhibited an increasing trend from 2000 to 2020. A mutation in the GPP occurred in 2010, with an average annual increase of 13.9 g C m-2 y-1 from 2000 to 2010 (p < 0.05), followed by an average annual increase of 10.22 g C m-2 y-1 from 2011 to 2020 (p > 0.05). The SPI effectively monitored drought events in the Liulin watershed. With a cumulative effect of 9 months and a lag effect of 6–7 months, the time lag and cumulative effects of drought had a long-term effect on GPP. It is imperative to have long-term eddy flux tower data and meteorological data to better evaluate the spatiotemporal dynamics of GPP and the impact of drought.

Improved Modeling of Gross Primary Production and Transpiration of Sugarcane Plantations with Time-Series Landsat and Sentinel-2 Images

Sugarcane croplands account for ~70% of global sugar production and ~60% of global ethanol production. Monitoring and predicting gross primary production (GPP) and transpiration (T) in these fields is crucial to improve crop yield estimation and management. While moderate-spatial-resolution (MSR, hundreds of meters) satellite images have been employed in several models to estimate GPP and T, the potential of high-spatial-resolution (HSR, tens of meters) imagery has been considered in only a few publications, and it is underexplored in sugarcane fields. Our study evaluated the efficacy of MSR and HSR satellite images in predicting daily GPP and T for sugarcane plantations at two sites equipped with eddy flux towers: Louisiana, USA (subtropical climate) and Sao Paulo, Brazil (tropical climate). We employed the Vegetation Photosynthesis Model (VPM) and Vegetation Transpiration Model (VTM) with C4 photosynthesis pathway, integrating vegetation index data derived from satellite images and on-ground weather data, to calculate daily GPP and T. The seasonal dynamics of vegetation indices from both MSR images (MODIS sensor, 500 m) and HSR images (Landsat, 30 m; Sentinel-2, 10 m) tracked well with the GPP seasonality from the EC flux towers. The enhanced vegetation index (EVI) from the HSR images had a stronger correlation with the tower-based GPP. Our findings underscored the potential of HSR imagery for estimating GPP and T in smaller sugarcane plantations.

Evaluation of Original and Water Stress-Incorporated Modified Weather Research and Forecasting Vegetation Photosynthesis and Respiration Model in Simulating CO2 Flux and Concentration Variability over the Tibetan Plateau

Evaluation of Original and Water Stress-Incorporated Modified Weather Research and Forecasting Vegetation Photosynthesis and Respiration Model in Simulating CO2 Flux and Concentration Variability over the Tibetan Plateau

A comparison of moderate and high spatial resolution satellite data for modeling gross primary production and transpiration of native prairie, alfalfa, and winter wheat

Although agroecosystems have a significant potential to offset carbon dioxide (CO2), the amount of CO2 captured can vary significantly depending on management practices. Accurate estimation of gross primary production (GPP) and transpiration (T) of agroecosystems at the field scale are essential for the study of food security and water resource management. To date, the carbon and water fluxes data products for commercial agroecosystems are limited, mostly at the moderate spatial resolution (MSR, hundreds of meters), which cannot be used to assess the temporal dynamics of GPP and T at the field scale. This study used the vegetation photosynthesis model (VPM) and vegetation transpiration model (VTM) to estimate field-level daily GPP (GPPVPM) and T (TVTM), respectively, in native prairie, alfalfa (Medicago sativa L.), and winter wheat (Triticum aestivum L.) in central Oklahoma, USA. We evaluated the reliability and advantages of vegetation indices (enhanced vegetation index, EVI and land surface water index, LSWI) in monitoring the land surface phenology using moderate spatial resolution data from Moderate Resolution Imaging Spectroradiometer (MODIS) and high spatial resolution (HSR, tens of meters) data from Landsat and Sentinel-2. The accuracy of GPPVPM and TVTM estimates at different spatial scales was evaluated using GPP (GPPEC) and evapotranspiration (ETEC) from the eddy flux tower sites, respectively. Results demonstrate the capacity of VPM and VTM to estimate the field-level carbon and water flux dynamics and their responses to weather conditions. The use of HSR vegetation indices helped to address certain challenges faced by MSR indices, especially in capturing the crop phenology in smaller areas with conservation measures or disturbances. The findings highlight the importance of using HSR GPP estimates to reduce uncertainty in quantifying CO2 fluxes for croplands and grasslands. The findings also demonstrate the ability of the models to track field-level vegetation phenology, carbon uptake, and water use in agroecosystems under different management practices.

Quantifying the Impact of COVID‐19 Pandemic on the Spatiotemporal Changes of CO2 Concentrations in the Yangtze River Delta, China

AbstractWhile the reduction in anthropogenic emissions due to Coronavirus disease 2019 (COVID‐19) lockdown in China and its impact on air quality have been reported extensively, its impact on ambient carbon dioxide (CO2) concentrations is still yet to be assessed. In this study, the impact of emission reductions on spatiotemporal changes of CO2 concentrations during the COVID‐19 pandemic was quantified in the Yangtze River Delta region (YRD), using high‐resolution dynamic emission inventory and the Weather Research and Forecasting model coupled with the Vegetation Photosynthesis and Respiration Model (WRF‐VPRM). The simulated CO2 concentrations from dynamic emission inventory shows a better agreement with surface observations compared with the Open‐source Data Inventory for Anthropogenic CO2 and Emission Database for Global Atmospheric Research emission, providing confidence in the quantification of CO2 concentrations variations. Our results show that emission reductions during the COVID‐19 pandemic lead to a CO2 decrease by 4.6 ppmv (−1.1%) in Shanghai and 3.1 ppmv (−0.7%) in YRD region. For the column‐averaged CO2 concentrations (denoted as XCO2), it also decreases by 0.20 ppmv (−0.05%) in Shanghai and 0.15 ppmv (−0.04%) in YRD region. Furthermore, emission reductions from transportation and industry are major contributors to the decline in CO2 concentrations at the near surface, accounting for 45.8% (41.1%) and 34.9% (41.0%) in Shanghai (YRD). Our study deepens the understanding of the response of CO2 concentrations to different sectors, which is helpful for emission management and climate adaption policies.

Effect of atmospheric conditions and VPRM parameters on high-resolution regional CO2 simulations over East Asia

Atmospheric CO2 concentrations are largely affected by the surface CO2 flux and atmospheric wind. To estimate atmospheric CO2 concentrations over East Asia, the effects of atmospheric conditions and the parameters of Vegetation Photosynthesis and Respiration Model (VPRM) that simulates biogenic CO2 concentrations were evaluated using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model. The VPRM in WRF-Chem requires parameter optimization for the experimental period and region. Total six experiments with two atmospheric fields (final analysis; FNL and fifth generation of European Centre for Medium-range Weather Forecasts atmospheric reanalysis; ERA5) and three VPRM parameter tables (US, Li, and Dayalu) were conducted to investigate the appropriate atmospheric field and VPRM parameter table for East Asia. For validation, two types of wind observations (SYNOP and SONDE) and two types of CO2 observations (surface CO2 observations and OCO-2 XCO2 observations) were used. The experiments using FNL showed a lower RMSE for surface winds, whereas those using ERA5 showed a lower RMSE for upper-air winds. On average, the surface wind RMSE in the experiments using FNL was lower than that using ERA5. With respect to surface CO2 observations, the experiments using the Li table showed relatively lower RMSEs compared to those using other tables. With respect to OCO-2 XCO2 observations, the Li table with FNL showed lower RMSEs than other combinations. Overall, the combination of the Li table and FNL was the most appropriate for simulating CO2 concentrations in East Asia using WRF-Chem with VPRM.

Spatiotemporal variation in sensitivity of urban vegetation growth and greenness to vegetation water content: Evidence from Chinese megacities

Understanding the sensitivity of vegetation growth and greenness to vegetation water content change is crucial for elucidating the mechanism of terrestrial ecosystems response to water availability change caused by climate change. Nevertheless, we still have limited knowledge of such aspects in urban in different climatic contexts under the influence of human activities. In this study, we employed Google Earth Engine (GEE), remote sensing satellite imagery, meteorological data, and Vegetation Photosynthesis Model (VPM) to explore the spatiotemporal pattern of vegetation growth and greenness sensitivity to vegetation water content in three megacities (Beijing, Shanghai, and Guangzhou) located in eastern China from 2001 to 2020. We found a significant increase (slope > 0, p < 0.05) in the sensitivity of urban vegetation growth and greenness to vegetation water content (SLSWI). This indicates the increasing dependence of urban vegetation ecosystems on vegetation water resources. Moreover, evident spatial heterogeneity was observed in both SLSWI and the trends of SLSWI, and spatial heterogeneity in SLSWI and the trends of SLSWI was also present among identical vegetation types within the same city. Additionally, both SLSWI of vegetation growth and greenness and the trend of SLSWI showed obvious spatial distribution differences (e.g., standard deviations of trends in SLSWI of open evergreen needle-leaved forest of GPP is 14.36 × 10−2 and standard deviations of trends in SLSWI of open evergreen needle-leaved forest of EVI is 10.16 × 10−2), closely associated with factors such as vegetation type, climatic conditions, and anthropogenic influences.

Spatio-temporal dynamics of water use efficiency over forest ecosystems using time series satellite data and carbon flux measurements

The significance of forests in limiting the consequences of climate change and maintaining the biogeochemical cycle is immense. Gross primary productivity (GPP) and evapotranspiration (ET) are the two most important measures of carbon and water fluxes in the forest ecosystem. The ratio between these two parameters is termed water use efficiency (WUE). In this study, the WUE was estimated for two contrasting forest ecosystems: a moist deciduous forest at the Barkot Flux Site (BFS) and a young mixed dry deciduous forest plantation at the Haldwani Flux Site (HFS), Uttarakhand, India. The Vegetation Photosynthesis Model (VPM) was utilized to estimate GPP, whilst the Mapping Evapo-Transpiration at High Resolution with Internal Calibration (METRIC) approach was employed to evaluate ET using Landsat 8 OLI satellite data from 2016 to 2018. Concurrent measurements of WUE as the ratio of GPP to ET, obtained from eddy covariance (EC) towers, were used for validation purpose. For both the sites, the maximum and minimum GPP were reported in October and December-January, respectively, whereas, the months of April-May and December had the highest and lowest ET, respectively. WUE remained elevated during November-December and reached its lowest point in April-May at both sites. The METRIC model estimated ET and EC-based GPP had reasonably good agreement with R2 values of 0.76 and 0.71, respectively. Consequently, the simulated WUE also matched closely with the observed WUE (R2 = 0.62). Accurate quantification of spatial WUE using the GPP and ET obtained from remote sensing-based VPM and METRIC models provided insight into its variability in different vegetation compositions. Further, the availability of long-term satellite data may pave the way for an improved understanding of forest ecosystems' response to water limitation in changing climate.

Recurrent forest fires, emission of atmospheric pollutants (GHGs) and degradation of tropical dry deciduous forest ecosystem services

Forest fires threaten to biodiversity, ecosystem productivity, multiple ecosystem services, and it influences the emissions of large amounts of greenhouse gases into the atmosphere. This scientific study has been conducted at Ayodhya hill range of dry deciduous forest of Chota Nagpur plateau (India).The principal objectives of this research are (1) to measure the terrestrial ecosystem productivity by Vegetation Photosynthesis Model (VPM); (2) to estimate the greenhouse gases (GHGs) emission through forest fire following IPCC guidelines; and (3) to quantify the ecosystem service value and degradation of ecosystem services (ESs) by specific indices and focus group discussions (FGDs). Results show that biophysical, climatic and environmental factors notably affect the growth of ESs. A significant reduction of net primary production (NPP) and biomass has been measured in fire month (100.71 and 223.59 gC m−2 month−1) and values of spectral indices also show negative trend during fire month (-0.1279 to −0.2104) respectively. Total 294.15 g, 1.44 g, 21.03 g, 0.0099 g and 0.0231 g of CO2, CH4, CO, NO2, and NOX have been emitted respectively through forest fire from this hill range during forest burning period (March 2021). This study also revealed that average (18.50%) forest dependency or relative forest income (RFI) has been fallen in the recent years due to recurrent forest fires, execution of different developmental works and deforestation. The effective management of forest resources (through payment for ESs and willingness to pay approaches) is highly necessary in strengthening the rural economy and welfare of indigenous tribal people.

On the Large Variation in Atmospheric CO2 Concentration at Shangdianzi GAW Station during Two Dust Storm Events in March 2021

Dust storms have large impacts on air quality and meteorological elements; however, their relationships with atmospheric greenhouse gases (e.g., CO2) and radiation components remain uncertain. In this study, the co-variation of dust and CO2 concentrations and its possible influencing mechanism are examined using observations at the Shangdianzi (SDZ) regional Global Atmosphere Watch (GAW) station along with simulations of the Vegetation Photosynthesis and Respiration Model coupled with the Weather Research and Forecasting model (WRF-VPRM), during two dust storm events on 15 and 28 March 2021. During these events, hourly CO2 concentrations decreased by 40–50 ppm at SDZ while dust concentrations increased to 1240.6 and 712.4 µg m−3. The elevated dust increased diffusive shortwave irradiance by 50–60% and decreased direct shortwave irradiance by ~60% along with clouds. The dust events were attributed to the passages of two cold front systems over northern China. At SDZ, during the frontal passages, wind speed increased by 3–6 m s−1, and relative humidity decreased by 50–60%. The CO2 variations associated with the frontal systems were captured by the WRF-VPRM despite the overestimated surface CO2 level at SDZ. Biogenic CO2 flux plays an indistinctive role in the large CO2 variation at SDZ, as it is weak during the non-growing season. The cold fronts pushed polluted air southeastward over the North China Plain and replaced it with low-CO2 air from Northwest China, leading to the decline in CO2. These findings demonstrate that mesoscale synoptic conditions significantly affect the regional transport and dispersion of CO2, which can influence the prediction of terrestrial carbon balance on a regional scale.

Phenology-Based Maximum Light Use Efficiency for Modeling Gross Primary Production across Typical Terrestrial Ecosystems

The maximum light use efficiency (LUE) (ε0) is a key essential parameter of the LUE model, and its accurate estimation is crucial for quantifying gross primary production (GPP) and better understanding the global carbon budget. Currently, a comprehensive understanding of the potential of seasonal variations of ε0 in GPP estimation across different plant functional types (PFTs) is still lacking. In this study, we used a phenology-based strategy for the estimation of ε0 to find the optimal photosynthetic responses of the parameter in different phenological stages. The start and end of growing season (SOS and EOS) from time series vegetation indices and the camera-derived greenness index were extracted across seven PFT flux sites using the methods of the hybrid generalized additive model (HGAM) and double logistic function (DLF). Optimal extractions of SOS and EOS were evaluated, and the ε0 was estimated from flux site observations during the optimal phenological stages with the light response equation. Coupled with other obligatory parameters of the LUE model, phenology-based GPP (GPPphe-based) was estimated over 21 site-years and compared with vegetation photosynthesis model (VPM)-based GPP (GPPVPM) and eddy covariance-measured GPP (GPPEC). Generally, GPPphe-based basically tracked both the seasonal dynamics and inter-annual variation of GPPEC well, especially at forest, cropland, and wetland flux sites. The R2 between GPPphe-based and GPPEC was stable between 0.85 and 0.95 in forest ecosystems, between 0.75 and 0.85 in cropland ecosystems, and around 0.9 in wetland ecosystems. Furthermore, we found that GPPphe-based was significantly improved compared to GPPVPM in cropland, grassland, and wetland ecosystems, implying that phenology-based ε0 is more appropriate in the GPP estimation of herbaceous plants. In addition, we found that GPPphe-based was significantly improved over GPPVPM in cropland, grassland, and wetland ecosystems, and the R2 between GPPphe-based and GPPEC was improved by up to 0.11 in cropland ecosystems and 0.05 in wetland ecosystems compared to GPPVPM, and RMSE was reduced by up to 5.90 and 2.11 g C m−2 8 day−1, respectively, implying that phenology-based ε0 in herbaceous plants is more appropriate for GPP estimation. This work highlights the potential of phenology-based ε0 in understanding the seasonal variation of vegetation photosynthesis and production.

Climate and Management Practices Jointly Control Vegetation Phenology in Native and Introduced Prairie Pastures

Climate, human disturbances, and management practices jointly control the spatial and temporal patterns of land surface phenology. However, most studies solely focus on analyzing the climatic controls on the inter-annual variability and trends in vegetation phenology. Investigating the main and interacting effects of management practices and climate might be crucial in determining vegetation phenology and productivity. This study examined the impacts of climate and management practices on vegetation phenology and productivity in adjacent native and introduced prairie pastures, which have detailed long-term management records, by combining climate, management, and satellite remote sensing data (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat). Modeled gross primary production (GPP) using vegetation photosynthesis model (VPM) was also included to investigate the dynamics of productivity. When comparing the impacts of the same management practices on different pastures, we used paired comparison, namely, comparing the native and introduced prairies side by side in the same year. The interactions of management practices and climate were investigated through comparing years with similar management but different climate (e.g., years with rainfall or not following baling events) in the same pasture. Results showed that air temperature (Ta) was an important factor in determining the start of the season (SOS) and the length of the season (LOS). Total rainfall (RF) during the annual growing season (AGS, derived from vegetation indices (VIs)) had the largest explanatory power (R2 = 0.53) in explaining the variations in the seasonal sums of VIs. The variations in GPP were better explained by RF (R2 = 0.43) than Ta (R2 = 0.14). Using the thermal growing season (March–October) or AGS climate factors did not show large differences in determining the relationships between phenology, GPP, and climate factors. Drought shortened the LOS and decreased GPP. In terms of management practices, grazing generally reduced the VIs and burning induced early greening-up and enhanced vegetation growth. Drought plus other management practices (e.g., grazing or baling) greatly affected vegetation phenology and suppressed GPP. The negative impacts (i.e., removal of biomass) of grazing on vegetation was compensated by enhanced vegetation growth after good RF. This study demonstrated that the interactions of climate and management practices could be positive (burning plus baling in a good RF year) or negative (grazing/baling plus drought), and can significantly affect vegetation phenology and production.

Temporal and spatial variations and influencing factors of gross primary productivity in Changbai Mountain Nature Reserve, China.

Changbai Mountain Nature Reserve (CNR) is a typical temperate forest ecosystem, and gross primary production (GPP) of which is closely related to topography and climate change. Research on the spatio-temporal variations and influencing factors of GPP in the CNR is of great significance for assessing growth status of vegetation and the quality of ecological environment. We calculated GPP in CNR using the vegetation photosynthesis model (VPM), and analyzed the influences of slope, altitude, temperature, precipitation, and total radiation. The results showed that the range of annual average GPP in CNR was 63-1706 g C·m-2·a-1 from 2000 to 2020 and that GPP decreased with the increases of altitude. Temperature played the most important role in driving the spatial varia-tion of GPP, with a significant positive correlation with GPP. During the study period, the overall annual GPP showed a significant increase trend in CNR, with an average annual increase of 13 g C·m-2·a-1. The areas with increase of annual GPP accounted for 79.9% of the total area, and the area proportion of annual GPP increase differed in each plant functional type. Annual precipitation was significantly negatively correlated with GPP in 43.2% of CNR, while annual mean temperature and annual total radiation were significantly positively correlated with GPP in 47.2% and 82.4% of CNR. GPP would increase continuously in CNR under the scenario of future global warming.

Improving the Estimation of Gross Primary Productivity across Global Biomes by Modeling Light Use Efficiency through Machine Learning

Estimating gross primary productivity (GPP) is important for simulating the subsequent carbon cycle elements and assessing the capacity of terrestrial ecosystems to support the sustainable development of human society. Light use efficiency (LUE) models were widely used to estimate GPP due to their concise model structures. However, quantifying LUEmax (maximum light use efficiency) and representing the responses of photosynthesis to environmental factors are still subject to large uncertainties, which lead to substantial errors in GPP simulations. In this study, we developed a hybrid model based on machine learning and a LUE model for GPP estimates. This hybrid model was built by targeting LUE with a machine learning approach, namely multi-layer perceptron (MLP), and then, estimating GPP within a LUE model framework with the MLP-based LUE and other required inputs. We trained the hybrid LUE (H-LUE) model and then, compared it against two conventional LUE models, the vegetation photosynthesis model (VPM) and vegetation photosynthesis and respiration model (VPRM), regarding GPP estimation, using tower-based daily-scale observations from 180 flux sites that cover nine different plant function types (PFTs). The results revealed better performance (R2 = 0.86 and RMSE = 1.79 gC m−2 d−1 on the test dataset) of the H-LUE model compared to the VPM and VPRM. Evaluations of the three models under four different extreme conditions consistently revealed better performance of the H-LUE model, indicating greater adaptability of the model to varied environments in the context of climate change. Furthermore, we also found that the H-LUE model can reasonably represent the responses of the LUE to meteorological variables. Our study revealed the reliable and robust performance of the developed hybrid LUE when simulating GPP across global biomes, providing references for developing better hybrid GPP models.

Effect of meteorological data assimilation using 3DVAR on high-resolution simulations of atmospheric CO2 concentrations in East Asia

In this study, the effects of meteorological data assimilation (DA) on high-resolution CO2 concentration simulations in East Asia were investigated using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and three-dimensional variational data assimilation (3DVAR). Experiments with and without meteorological DA were performed for June and December 2018. To determine an appropriate DA cycling interval, two experiments were performed at cycling intervals of 6 and 12 h. Compared to the meteorological fields simulated from experiment without DA, those simulated from experiments with DA were more similar to atmospheric observations. The CO2 concentrations simulated from the experiment with a 6 h DA cycling interval were highly similar to the observed CO2 concentrations. Compared to the root mean square error (RMSE) of the experiment without DA, the RMSE of simulated CO2 concentrations in the experiment with a 6 h DA interval decreased by 8.19% in June and 1.43% in December with respect to surface CO2 concentration observations, and decreased by 2.52% in June and 0.47% in December against column-averaged CO2 concentration (XCO2) observations of the Orbiting Carbon Observatory 2 (OCO-2). The improved 2 m temperature forecasts by DA enhanced biogenic CO2 simulations using the Vegetation Photosynthesis and Respiration Model (VPRM) in WRF-Chem. The improved wind speed and direction forecasts by DA led to improved simulations of the horizontal transport of CO2 concentrations and planetary boundary layer height. Overall, enhanced meteorological forecasts by meteorological DA using 3DVAR improved the accuracy of high-resolution CO2 concentration simulations using WRF-Chem in East Asia.

Very high-resolution Net Ecosystem Exchange over India using Vegetation Photosynthesis and Respiration Model (VPRM) simulations

The terrestrial biosphere has a crucial role in controlling the rate of CO2 accumulation in the atmosphere. The quantification of Net Ecosystem Exchange (NEE) is critical to determine whether the regional terrestrial ecosystem is a net sink or source of CO2. This study uses a satellite-data derived light-use-efficiency model (the Vegetation Photosynthesis and Respiration Model, VPRM) to compute the biospheric CO2 fluxes over India from 2011–2020. A very high-resolution Land Use-Land Cover (LULC) data from the IRS-P6 satellite and MODIS derived surface reflectance are utilised to compute the NEE over distinct vegetation types at ∼9km× 9 km. The VPRM model for the Indian region captures the spatial pattern and seasonal features of NEE over the country. The magnitude of annual mean NEE over India from the VPRM model is relatively high compared to that from Carbon Tracker, FLUXCOM, and Soil Moisture Active Passive Level-4 carbon data products. However, the VPRM model simulated NEE agrees with the NEE observations from the eddy covariance estimates, which are accepted as the ground truth. The fusion of high-resolution LULC and surface reflectance data in VPRM suggests that the average NEE over the Indian region during 2011–2020 is −0.16 ± 0.02 PgC yr−1. Among this, the contribution from Gross Ecosystem Exchange is about −0.47 ± 0.02 PgC yr−1, and Respiration is 0.31 ± 0.01 PgC yr−1. The maximum biospheric CO2 absorption is during the post-monsoon season, and the minimum is during the pre-monsoon season. The very high spatial resolution and heterogeneity resolved in the NEE data derived in this work offer the first data product of this kind for a variety of NEE analyses over India.

Terrestrial CO2 exchange diagnosis using a peatland-optimized vegetation photosynthesis and respiration model (VPRM) for the Hudson Bay Lowlands

Satellite-based light use efficiency (LUE) models have been widely used to estimate gross primary production in various terrestrial ecosystems such as forests and croplands, but northern peatlands have received less attention. In particular, the Hudson Bay Lowlands (HBL) which is a massive peatland-rich region in Canada has been largely ignored in previous LUE-based studies. These peatland ecosystems have accumulated large stocks of organic carbon over many millennia, and play a vital role in the global carbon cycle. In this study, we used the satellite data-driven Vegetation Photosynthesis and Respiration Model (VPRM) to examine the suitability of LUE models for carbon flux diagnosis in the HBL. VPRM was driven alternately with the satellite-derived enhanced vegetation index (EVI) and solar-induced chlorophyll fluorescence (SIF). The model parameter values were constrained by eddy covariance (EC) tower observations from the Churchill fen and Attawapiskat River bog sites. The main objectives of the study were to (i) investigate if site-specific parameter optimization improved NEE estimates, (ii) determine which satellite-based proxy of photosynthesis produced more reliable estimates of peatland net carbon exchange, and (iii) examine how LUE and other model parameters vary within and between the study sites. The results indicate that the VPRM mean diurnal and monthly estimates of NEE had significant strong agreements with EC tower fluxes at the two study sites. A comparison of the site-optimized VPRM against a generic peatland-optimized version of the model revealed that the site-optimized VPRM provided better estimates of NEE only during the calibration period at the Churchill fen. The diurnal and seasonal cycles of peatland carbon exchange were better captured by the SIF-driven VPRM, demonstrating that SIF is a more accurate proxy for photosynthesis compared to EVI. Our study suggests that satellite-based LUE models have the potential to be applied on a larger scale to the HBL region.