Gross Primary Production Values Research Articles

Intercomparison of gross primary productivity in spatio-temporal distribution over a typical tropical region based on different datasets.

Gross Primary Productivity (GPP) plays a vital role in the carbon cycle of terrestrial ecosystems. For the purpose of assessing the performance of various GPP products in a typical tropical area, this study conducted an intercomparison of seven different GPP products over Hainan Island, China, and analyzed spatiotemporal characteristics of GPP in different elevation regions and vegetation types over study area. The results showed that although the GPP spatial distribution pattern over Hainan Island, as presented by different GPP products, is similar- showing that GPP values in the southwest are higher than those in the northeast- there are significant differences in the spatial distributions and magnitudes of each GPP product. The multi-annual mean GPP values from the seven GPP products exhibited similar temporal variation trends, with GOSIF being obviously higher than other GPP products. The GPP products indicated significant differences in different elevation and vegetation type groups. The GPP gap between GLASS AVHRR and BESS V2 decreases slightly with the increase in elevation. Most GPP products show a consistent change trend over time. There were also significant differences in the results of significance tests for the change trends of GPP products. Though GOSIF is significantly higher than other GPP products, it demonstrated a relatively high correlation with all GPP products, while AGPP exhibited relatively low correlations with all GPP products. The quantitatively comparative analysis of seven different GPP products in this study is valuable for improving GPP retrieval models and can also serve as a scientific reference for the application of GPP products in tropical region vegetation studies.

Spatial and Temporal Patterns of Vegetation Water Use Efficiency and Its Response to Climatic and Environmental Factors in Gansu Province

Vegetation water use efficiency (WUE) is a crucial indicator for elucidating the interconnections between the carbon and water cycles of ecosystems and for discerning the response of vegetation ecosystems to climate change. Gansu Province in northwestern China is facing significant ecological water-related challenges. However, the response of vegetation WUE to climate environmental factors in this region remains unclear. In this study, the MODIS vegetation gross primary productivity (GPP) and evapotranspiration (ET) datasets were used to calculate the vegetation WUE in Gansu Province and, combined with meteorological data, Theil–Sen median trend analysis and partial correlation analysis were used to determine the spatial and temporal characteristics of vegetation WUE in this region and its response to climate environmental factors. Finally, the random forest model was used to rank the importance of climate environmental factors. The results indicate the following: (1) The average values of vegetation WUE, GPP, and ET in Gansu Province from 2000 to 2020 were 1.46 gC·mm−1·m−2, 510.22 gC·m−2, and 343.68 mm, respectively, and their spatial distribution was high in the southeast and low in the northwest, which was closely related to the distribution of vegetation in the region. (2) Over the past 20 years, the vegetation WUE in this region showed a slowly decreasing trend in general, with a decrease rate of 16.57%. There were significant differences in the WUE of different vegetation types, among which forest WUE was the highest and grassland WUE the lowest. (3) The trend prediction of WUE in Gansu Province was performed by using the rescaled extreme difference method, and the Hurst index was 0.45, which means that the vegetation WUE in this region is expected to increase in the future. (4) In general, precipitation was the main factor influencing the change in vegetation WUE in Gansu Province, followed by vapor pressure deficit (VPD), temperature, and soil moisture. This study provides strategy support for the coupling process of vegetation ecosystems and the sustainable development of agriculture and animal husbandry in Gansu Province and has scientific reference value for promoting and planning the sustainable development of vegetation in arid and semi-arid areas.

Assessing Drought Impacts on Gross Primary Productivity of Rubber Plantations Using Flux Observations and Remote Sensing in China and Thailand

Rubber (Hevea brasiliensis Muell.) plantations are vital agricultural ecosystems in tropical regions. These plantations provide key industrial raw materials and sequester large amounts of carbon dioxide, playing a vital role in the global carbon cycle. Climate change has intensified droughts in Southeast Asia, negatively affecting rubber plantation growth. Limited in situ observations and short monitoring periods hinder accurate assessment of drought impacts on the gross primary productivity (GPP) of rubber plantations. This study used GPP data from flux observations at four rubber plantation sites in China and Thailand, along with solar-induced chlorophyll fluorescence (SIF), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRv), and photosynthetically active radiation (PAR) indices, to develop a robust GPP estimation model. The model reconstructed eight-day interval GPP data from 2001 to 2020 for the four sites. Finally, the study analyzed the seasonal drought impacts on GPP in these four regions. The results indicate that the GPP prediction model developed using SIF, EVI, NDVI, NIRv, and PAR has high accuracy and robustness. The model’s predictions have a relative root mean square error (rRMSE) of 0.22 compared to flux-observed GPP, with smaller errors in annual GPP predictions than the MOD17A3HGF model, thereby better reflecting the interannual variability in the GPP of rubber plantations. Drought significantly affects rubber plantation GPP, with impacts varying by region and season. In China and northern Thailand (NR site), short-term (3 months) and long-term (12 months) droughts during cool and warm dry seasons cause GPP declines of 4% to 29%. Other influencing factors may alleviate or offset GPP reductions caused by drought. During the rainy season across all four regions and the cool dry season with adequate rainfall in southern Thailand (SR site), mild droughts have negligible effects on GPP and may even slightly increase GPP values due to enhanced PAR. Overall, the study shows that drought significantly impacts rubber the GPP of rubber plantations, with effects varying by region and season. When assessing drought’s impact on rubber plantation GPP or carbon sequestration, it is essential to consider differences in drought thresholds within the climatic context.

Analysis of Carbon Flux Characteristics in Saline–Alkali Soil Under Global Warming

ABSTRACTThe carbon cycle of saline–alkali ecosystems will be affected to some extent in the context of future global warming. Therefore, we investigated the net ecosystem exchange (NEE) of three typical crops (wheat, maize and soybean) in the saline–alkaline land of the Yellow River Delta. To further investigate CO2 fluxes, NEE was decomposed into gross primary productivity (GPP) and ecosystem respiration (Re). In terms of seasonal variation, wheat and soybean were carbon sources in the early and late growth periods, and carbon sinks in the rest of the period, whereas maize was a carbon sink in the majority of the period, and maize had good carbon sink potential. The cumulative NEE during the growth periods for wheat, maize, and soybean were 414.86, 258.24 and 228.92 g cm−2, respectively, and the daily variation showed that the peak NEE values for the three crops preceded the peak values of both GPP and ecosystem respiration, occurring approximately at 12:00 a.m. In the correlation analysis, NEE and GPP of the three crops were well correlated with photosynthetic photon flux density and net radiation, whereas Re was significantly correlated with air temperature. Through a comparative analysis of CO2 fluxes within various agricultural ecosystems, our findings indicated that wheat demonstrated moderate carbon sequestration capabilities, whereas maize and soybean exhibited strong carbon sink characteristics. Notably, saline–alkali crops exhibited lower Re, whereas GPP levels remained at a moderate range. Therefore, under the global warming trend, the respiration of saline crops and soils will be affected and may change the original carbon sink into a carbon source. Hence, implementing suitable measures targeting saline–alkali areas, such as the establishment of an effective crop rotation system and the enhance saline–alkali land conditions, can reduce emissions of greenhouse gases, thus reducing the pressure of global warming and maintaining a stable carbon cycle in saline–alkali land.

Ecosystem level carbon and moisture fluxes from a high biomass fibre producing jute crop (Corchorus olitorius L): An eddy covariance-based analysis

ContextJute (Corchorus olitorius L) is the second-largest natural fibre producing crop in the world and plays a key role in the rural economy of South Asia particularly India and Bangladesh. It has diverse use due to its long length, lusture, biodegradability, high tensile strength, heat resistance and bendability. Jute is also fast-growing high biomass crop and quantification of the carbon / moisture exchanges is need of the hour towards its sustainability. ObjectiveThe study aims at quantitative estimation of the ecosystem level CO2 and H2O exchanges at diurnal, daily, and seasonal scales from jute crop cultivated in the alluvial soil of the Gangetic delta under a tropical hot and moist sub-humid climate during the years 2021–23. MethodsAn eddy covariance flux tower with suite of meteorological sensors were established at the jute growing farm to assess the high frequency ecosystem level exchanges of CO2, H2O and biometeorological parameters. Concurrent crop bio-physical parameters were also measured. The flux data were processed, quality checked, gap-filled and partitioned to construct seamless time-series of half-hourly fluxes for further analysis towards its dynamics, driving parameters, photosynthetic response and derived eco-physiological parameters. ResultsThe half-hourly CO2 fluxes exhibited significant variations across different growth stages of the jute crop, reaching peak values during the fibre development to maturity stages [mean Net Ecosystem CO2 Exchange (NEE) ranging from −20 to −22 µmol m−2 s−1; mean Gross Primary Production (GPP) ranging from 25 to 30 µmol m−2 s−1; and mean Ecosystem Respiration (Reco) ranging from 8 to 10 µmol m−2 s−1]. Seasonal GPP values was found to be high (902–1038 gC m−2) with moderate NEE value of −150 to −223 gC m−2. Seasonal mean Ecosystem Water Use Efficiency (EWUE) was high (2.5–2.7 gC kg−1 H2O) compared to other C3 crop. The daily Crop Coefficient (Kc) peaked at 1–1.2 during 90–110 Days After Sowing (DAS), indicative of high physiological activities. ConclusionDespite relatively short crop duration but high biomass (peak Leaf Area Index, LAI ∼6 m2m−2), the jute crop proved to be a robust gross CO2 sink. Due to the rapid growth, the jute crop allocates a significant portion of GPP as Reco (78–84 %), resulting in a relatively lower seasonal NEE. The high EWUE coupled with a net positive CO2 exchange positioned jute as a potential climate-resilient crop. This study provides valuable insights into the ecosystem exchanges of the jute crop, which in turn contribute to the global carbon and moisture budget.

Assessment of spongy moth infestation impacts on forest productivity and carbon loss using the Sentinel-2 satellite remote sensing and eddy covariance flux data

BackgroundDeciduous forests in eastern North America experienced a widespread and intense spongy moth (Lymantria dispar) infestation in 2021. This study quantified the impact of this spongy moth infestation on carbon (C) cycle in forests across the Great Lakes region in Canada, utilizing high-resolution (10 × 10 m2) Sentinel-2 satellite remote sensing images and eddy covariance (EC) flux data. Study results showed a significant reduction in leaf area index (LAI) and gross primary productivity (GPP) values in deciduous and mixed forests in the region in 2021.ResultsRemote sensing derived, growing season mean LAI values of deciduous (mixed) forests were 3.66 (3.18), 2.74 (2.64), and 3.53 (2.94) m2 m−2 in 2020, 2021 and 2022, respectively, indicating about 24 (14)% reduction in LAI, as compared to pre- and post-infestation years. Similarly, growing season GPP values in deciduous (mixed) forests were 1338 (1208), 868 (932), and 1367 (1175) g C m−2, respectively in 2020, 2021 and 2022, showing about 35 (22)% reduction in GPP in 2021 as compared to pre- and post-infestation years. This infestation induced reduction in GPP of deciduous and mixed forests, when upscaled to whole study area (178,000 km2), resulted in 21.1 (21.4) Mt of C loss as compared to 2020 (2022), respectively. It shows the large scale of C losses caused by this infestation in Canadian Great Lakes region.ConclusionsThe methods developed in this study offer valuable tools to assess and quantify natural disturbance impacts on the regional C balance of forest ecosystems by integrating field observations, high-resolution remote sensing data and models. Study results will also help in developing sustainable forest management practices to achieve net-zero C emission goals through nature-based climate change solutions.

Climate Warming Benefits Plant Growth but Not Net Carbon Uptake: Simulation of Alaska Tundra and Needle Leaf Forest Using LPJ-GUESS

Climate warming significantly impacts Arctic vegetation, yet its future role as a carbon sink or source is unclear. We analyzed vegetation growth and carbon exchange in Alaska’s tundra and needle leaf forests using the LPJ-GUESS model. The accuracy of the model is verified using linear regression of the measured data from 2004 to 2008, and the results are significantly correlated, which proves that the model is reliable, with R2 values of 0.51 and 0.46, respectively, for net ecosystem carbon exchange (NEE) at the tundra and needle leaf forest sites, and RMSE values of 22.85 and 23.40 gC/m2/yr for the tundra and needle forest sites, respectively. For the gross primary production (GPP), the R2 values were 0.66 and 0.85, and the RMSE values were 39.25 and 43.75 gC/m2/yr at the tundra and needle leaf forest sites, respectively. We simulated vegetation carbon exchanges for 1992–2014 and projected future exchanges for 2020–2100 using climate variables. Under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, GPP values increase with higher emissions, while the NEE showed great fluctuations without significant differences among the three pathways. Our results showed although climate warming can benefit vegetation growth, net carbon assimilation by vegetation may not increase accordingly in the future.

An inflection point-based method for estimating metrics of mangrove phenology combining climatic factors and Landsat NDVI time series

ABSTRACT The present research evaluated the prospects of utilizing rainfall and temperature combined with Landsat-8 derived HANTS (Harmonic Analysis of Time Series) reconstructed NDVI for estimating the metrics of the mangrove phenology. The selected period of the study was from 2013 to 2020 for the Pichavaram mangroves of Tamil Nadu. The NDVI and ERA5 (ECMWF Re-Analysis) datasets of rainfall and temperature were the input datasets for developing the new algorithm. The ‘z-score sum’ provided a measure of the cumulative impact of rainfall and temperature, displaying its most negative value coinciding with the peak positive value of the NDVI time series datasets. The algorithm developed for phenological metrics estimation identified the common inflection points of the z-score sum and NDVI curves. The temporal analysis of metrics revealed the average Length of Season (LoS) as 230 days. The metrics also identified the drought year 2016 with the shortest LoS and the least Gross Primary Productivity (GPP) values. The analysis showed the influences of the preceding year’s monsoon rainfall on the GPP values of the later part of the phenological cycle. The temperatures during the days of PoS were found to be the optimum temperature for the growth of mangroves.

Response of Ecosystem Carbon–Water Fluxes to Extreme Drought in West Asia

Global warming has resulted in increases in the intensity, frequency, and duration of drought in most land areas at the regional and global scales. Nevertheless, comprehensive understanding of how water use efficiency (WUE), gross primary production (GPP), and actual evapotranspiration (AET)-induced water losses respond to exceptional drought and whether the responses are influenced by drought severity (DS) is still limited. Herein, we assess the fluctuation in the standardized precipitation evapotranspiration index (SPEI) over the Middle East from 1982 to 2017 to detect the drought events and further examine standardized anomalies of GPP, WUE, and AET responses to multiyear exceptional droughts, which are separated into five groups designed to characterize the severity of extreme drought. The intensification of the five drought events (based on its DS) increased the WUE, decreased the GPP and AET from D5 to D1, where both the positive and negative variance among the DS group was statistically significant. The results showed that the positive values of standardized WUE with the corresponding values of the negative GPP and AET were dominant (44.3% of the study area), where the AET values decreased more than the GPP, and the WUE fluctuation in this region is mostly controlled by physical processes, i.e., evaporation. Drought’s consequences on ecosystem carbon-water interactions ranged significantly among eco-system types due to the unique hydrothermal conditions of each biome. Our study indicates that forthcoming droughts, along with heightened climate variability, pose increased risks to semi-arid and sub-humid ecosystems, potentially leading to biome restructuring, starting with low-productivity, water-sensitive grasslands. Our assessment of WUE enhances understanding of water-carbon cycle linkages and aids in projecting ecosystem responses to climate change.

Identification of soil properties influencing primary productivity of fish ponds under red and lateritic soil zones

Aim: The present study aimed to identify the key soil factors influencing the primary productivity of water in the fish ponds situated under low productive red and lateritic soil zones. Methodology: General properties of fish pond soils (n=42) from different red and lateritic soil zones of West Bengal, India were determined along with the gross primary productivity (GPP) values of corresponding pond waters. All the soil properties were correlated with GPP values and the soil factors which were significantly correlated with GPP of pond water were further used for step down regression analysis with regard to GPP.This helped to develop a minimum data set of the key soil factors which showed maximum influence on GPP. The efficiency of the regression equation, generated with the critical soil parameters, in predicting the GPP values of other ponds was also assessed through a study on the variations in calculated and on-farm GPP values of eight fish ponds. Results: Among all the studied soil properties, pH and availability of three major nutrients viz. N, P and K were observed to contribute 84.15% variations in gross production of primary fish food organisms in these pond environment. Of these 4 soil properties, again, pH and available P values of the pond soils appeared to be more important. A regression equation was also developed to predict the GPP of such pond water using soil pH and available P values only. This equation showed a precision range of 83.0 to 99.7% and was found to be statistically at par with the GPP values observed under actual pond condition. Interpretation: High efficiency in predictability of pond GPP values using the minimum data set of two key soil factors viz. soil pH and available P indicates that proper management of these productivity attributing soil properties will be particularly helpful to improve the primary productivity levels of the predominantly low yielding fish ponds under the red and lateritic soil zones. Key words: Fish ponds, Minimum data set, Productivity attributes, Red and lateritic soil zones, Soil factors

Functioning of a canopy-dominated intertidal community during emersion: highly productive but heterotrophic at the annual scale

In order to address lack of data regarding coastal carbon budgets, we estimated the annual metabolic carbon budget of an intertidal rocky reef macroalgal community during emersion. This budget is based on direct in situ measurements during emersion and establishes the seasonal variations of the photosynthetic parameters of such a community. CO2 fluxes were measured hourly to study the response of community gross primary productivity (GPP) to irradiance and the variation of GPP and community respiration (CR) over the emersion period at different times of the year. These were combined together with existing monthly measures of GPP and CR hourly rates to model the variations of these fluxes as a function of irradiance and the tidal cycle throughout an entire calendar year. Daily, monthly and annual values of GPP, CR and net primary productivity (NPP) were calculated with a relatively low sensitivity to any of the parameters used. While GPP fluxes show comparable orders of magnitude to those measured in other systems, higher CR fluxes lead to a heterotrophic system during emersion, both under measured (NPP = −299 gC m−2 year−1) and theoretical irradiances (NPP = −119 gC m−2 year−1). This heterotrophy is directly linked to the light availability, varying according to combined daily, tidal and seasonal cycles, and to temperature at the seasonal scale. Measurements performed in situ at the community scale integrated interactions that are otherwise absent at the individual scale. This gives access to aspects of the functioning that cannot be otherwise identified.

Quantitative assessment of the scale conversion from instantaneous to daily GPP under various sky conditions based on MODIS local overpassing time

ABSTRACT With the increasing frequency of extreme events, daily gross primary productivity (GPP) is necessary to be accurately assessed to determine when and how much it is affected. Fluctuating environmental conditions contribute to diverse diurnal photosynthetic patterns influenced by changing atmospheric factors, including solar radiation, CO2 concentrations, and leaf temperature. This complexity underscores the challenge of accurately estimating daily GPP. We quantitatively assessed three temporal upscaling approaches – cosine of the solar zenith angle, extraterrestrial solar irradiance, and photosynthetic active radiation(PAR) – under varying sky conditions. Additionally, we introduced a novel correction method for universal temporal upscaling ratios. Instantaneous GPP values from the FLUXNET2015 dataset, acquired around the MODIS overpassing time, were utilized. The upscaled daily GPP exhibited minimal deviation from the half-hourly GPPs collected between 11 am and 1 pm, with increased discrepancies for GPPs later in the afternoon. The PAR-based approach demonstrated superior accuracy in the afternoon, effectively capturing incoming radiation changes due to clouds. Instant environmental variables-GPP relationships were weak under clear skies but exhibited moderate-to-high positive correlations under cloudy conditions. Analyzing the impact of the diffuse ratio of incoming photosynthetic active radiation on instantaneous GPP revealed limited enhancement at short time scales. On a daily scale, all three temporal upscaling approaches, under different ranges of clearness index, consistently underestimated daily GPP. We proposed a novel correction method, normalizing the difference between maximum and minimum air temperature in a day, notably reduced errors by approximately 10.69 and 21.07 gC m−2 d−1 for cosine of the solar zenith angle and extraterrestrial solar irradiance-based temporal upscaling approaches. We recommend adopting the corrected temporal upscaling factor globally due to its simplicity and improved accuracy.

Gross primary production of Mediterranean watersheds: Using isotope mass balance approach to improve estimations

AbstractGlobal‐scale estimates of carbon fluxes from satellite data‐driven models are constrained by considerable uncertainties regarding Gross Primary Production (GPP) and the lack of the watershed‐scale measurements required for model calibration. Recently conducted global modelling efforts indicate that semiarid ecosystems dominate the increasing trends and inter‐annual variation of net CO2 exchange with the atmosphere, but semi‐arid regions have received little attention with regard to GPP estimation. In this study, we used the distinct isotope effect of transpiration and evaporation to calculate transpiration losses and subsequently CO2 uptake by terrestrial vegetation through the water and carbon cycle using the water use efficiency of plants. By studying two Mediterranean watersheds with contrasted environmental conditions over several hydrological years, we found a strong dependence of GPP on annual and seasonal water availability. The results demonstrated that when compared to GPP values obtained in worldwide biomes using biological methods, our isotope approach was validated, highlighting the limitations of satellite‐data‐driven models like MODIS in capturing the impact of water stress on photosynthesis and GPP estimates. These results encourage investigation of GPP by the isotope mass balance approach where direct carbon flux measurements are rare or absent in order to help to substantiate, modify or shed doubt on interpolated GPP for those regions and achieve consensus on global GPP estimates. Given the relevant role of semi‐arid ecosystems in the global carbon balance as well as the limitation of existing data sets, our improved method based on the isotope mass balance approach helps to obtain rapid and affordable estimates of GPP for semi‐arid ecosystems.

Sensitivities of Vegetation Gross Primary Production to Precipitation Frequency in the Northern Hemisphere from 1982 to 2015

Vegetation of the Northern Hemisphere plays a vital role in global ecosystems and the carbon cycle. Variations in precipitation profoundly affect vegetation productivity, plant growth, and species communities. Precipitation frequency directly controls soil moisture availability, which has an impact on the vegetation carbon sink. However, it is unclear how precipitation frequency affects the vegetation productivity of different land cover types in different seasons. In this study, the sensitivities of the gross primary production (GPP) of six vegetation types (forest, cropland, grassland, shrubland, tundra and barren land) in response to the frequency of five categories of precipitation (trace: 0.1–5 mm/day, small: 5–10 mm/day, moderate: 10–15 mm/day, heavy: 15–20 mm/day, and very heavy: >20 mm/day) were analyzed based on the XGBoost model. The results showed that, between 1982 and 2015, precipitation frequency declined in most land cover types but increased significantly in the pan-Arctic. Differences in the sensitivity to precipitation frequency were observed between seasons and precipitation categories in northern latitudes. The GPP values of forest and barren land vegetation were less sensitive to precipitation frequency than grassland, shrubland and tundra. This may be related to different vegetation community structures and underlying surfaces and gradually increasing drought resistance capability. The sensitivity to precipitation frequency declined for moderate and heavy precipitation in cropland, but it increased in winter. As the frequency of trace precipitation diminishes in winter, the sensitivity of each vegetation type reduces by an average of 0.03%/decade. Conversely, the sensitivities to small and moderate rain increase by 0.01%/decade and 0.02%/decade, respectively, for ecosystems such as cultivated land, forests, and shrubs. However, shrubs and tundra exhibit distinct behaviors, where shifts in precipitation frequency align directly with trends in sensitivity. These results show that the frequency of precipitation significantly affects vegetation productivity and has different sensitivities, and vegetation shows different feedback mechanisms in the face of environmental changes.

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.

Dynamics of major environmental disasters involving fire in the Brazilian Pantanal

The 2020 environmental catastrophe in Pantanal has highlighted the fragility of environmental policies and practices for managing and fighting fires in this biome. Therefore, it is essential to know the causes and circumstances that potentiate these fires. This study aimed to: (I) assess the relationship between fire foci and carbon absorption (GPP), precipitation, and carbon dioxide (CO2) flux; (ii) analyze vegetation recovery using the differenced normalized burn ratio (ΔNBR) in Brazilian Pantanal between 2001 and 2022; and (iii) identify priority areas, where the highest intensities of fire foci have occurred, in order to guide public policies in Brazil to maintain local conservation. To this purpose, fire foci were detected using data from the MODIS MOD14/MYD14 algorithm, annual precipitation with CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), and CO2 flux using the MODIS/MODO9A1 product, and Gross Primary Production (GPP) with the MODIS/MOD17A2 product. The severity of the burned area was also assessed using the ΔNBR index and the risk areas were determined using the averages of these images. During the time series studied, a total of 300,127 fire foci were detected throughout the Pantanal, where 2020 had the highest number of foci and the lowest accumulated precipitation. The years with the highest precipitation were 2014 and 2018. The year 2018 was also the second year with the highest GPP value. The Pettit test showed a trend for 2008 and 2011 as the points of change in the CO2 flux and GPP variables. Principal component analysis clustered fire foci and precipitation on opposite sides, as well as GPP and CO2 flux, while ΔNBR clustered HS, MHS and MLS classes with the years 2020, 2019, 2002 and 2021. There was a high negative correlation between fire foci × rainfall and GPP × CO2 flux. The years with the largest areas of High severity (HS), Moderate-high severity (MHS) and Moderate-low severity (MLS) classes were 2020 and 2019, respectively. The most vulnerable areas for severe fires were the municipalities of Cáceres, Poconé, and Corumbá. The major fire catastrophe in 2020 is correlated with the low precipitation in 2019, the high precipitation in 2018, and the increased GPP, as well government policies unfavorable to the environment.

Exploring carbon sequestration in broad-leaved Korean pine forests: Insights into photosynthetic and respiratory processes

A comprehensive understanding of carbon assimilation and sequestration in broad-leaved Korean pine forests is crucial for accurately estimating this significant aspect of temperate forests at a regional scale. In this study, we introduced a high-temporal resolution model designed for carbon assimilation insights at the plot scale, focusing on specific parameters such as leaf area dynamics, vertical leaf distribution, photosynthetically active radiation (PAR) fluctuations, and the photosynthetic traits of tree species. The findings reveal that most tree species in broad-leaved Korean pine forests exhibit an inverted U-shaped pattern in leaf area dynamics, with shorter leaf drop periods than leaf expansion events. Leaf distribution varies significantly among different canopy heights, with approximately 80 % of the leaves above 15 m. PAR decreases as canopy height decreases, with PAR at 25 m accounting for about 60 % of the PAR above the canopy. Our framework incorporates a leaf-scale light-response curve and empirical photosynthesis-temperature relationships to estimate forest carbon assimilation on daily and hourly scales accurately. Using the model, we assess the gross primary productivity (GPP), leaf net photosynthetic assimilation (LNPA), and carbon increment (ΔC) of broad-leaved Korean pine forests from 2017 to 2020. The results demonstrate GPP, LNPA, and ΔC values of 21.4 t·ha−1·a−1, 17.4 t·ha−1·a−1, and 4.0 t·ha−1·a−1, respectively. Regarding efficiency, GPP, LNPA, and ΔC per square meter of leaf per year are 179 g, 146 g, and 33 g, respectively. Notably, tree species in the canopy layer of the forest exhibit significantly higher efficiency than those in the understory layer. This research significantly contributes to our understanding of carbon cycling and the responses of forest ecosystems to climate change. Moreover, it provides a practical tool for forest management and the development of carbon sequestration strategies.

Diel variations in dissolved oxygen concentration and algal growth in the Laver pit lake, northern Sweden

The diel oxygen technique relying on automated in-situ measurements of dissolved oxygen was used as an indicator of seasonal and diurnal variations of photosynthesis in the circumneutral Laver pit lake in northern Sweden. From July to September 2017, surface water temperature, electrical conductivity, pH, and dissolved oxygen were continuously measured at a time resolution of 30 min using sensors mounted on a floating buoy. The data could be monitored in real-time using a browser-based software and permitted calculation of gross primary production (GPP), net ecosystem production (NEP), and respiration (R) in the lake. The dissolved O2 concentration showed a consistent pattern of diel variations that reached up to 0.5 mg L−1 during the warmer summer period (July–August). Towards the end of August these variations decreased in magnitude and remained at ∼0.1 mg L−1 throughout September. pH showed diel variations that mimicked those of dissolved O2, with maximum daily variations of 0.4–0.5 pH units during July and August. A seven-day moving average of GPP showed a peak during July to mid-August, and the maximum GPP value of 0.55 mg O2 L−1 day−1 is similar to those found in natural oligotrophic lakes. A phytoplankton sample showed a total biomass concentration of 24 μg L−1, with the species Chrysophyceae, Chlorophyta, and Bacillariophyta occurring in the water. Diel oxygen data indicated that respiration by autotrophs and respiration of autochthonous labile organic matter by heterotrophs dominated in the lake, as is often the case in lakes where planktonic primary production is the main supplier of labile organic carbon. A close coupling between R and GPP suggests that nearly all GPP was respired in the epilimnion. The study shows that the diel oxygen technique can be used for real-time monitoring of seasonal and diurnal variations of dissolved oxygen and pH in pit lakes. This would be a useful technique in pit lake remediation projects where fertilization is used to stimulate algal growth and metal sequestering by algae.

An Empirical Model of Gross Primary Productivity (GPP) and Relations between GPP and Its Driving Factors, Biogenic Volatile Organic Compounds in a Subtropical Conifer Plantation in China

Measurements of net ecosystem exchange (NEE), solar global radiation, photosynthetically active radiation (PAR) and meteorological parameters were carried out on a subtropical conifer plantation in China from 2013 to 2016. These observations were used to develop and evaluate an empirical model of gross primary production (GPP) (EMGPP) with 3-factor and 2-factor models. Using a 3-factor model, the simulated hourly GPP values were consistent with observations with a relative bias of 9.96% and normalized mean square error values of 0.07 mg CO2 m−2 s−1 for the scattering factor S/Q (S and Q are diffuse and global solar radiation) < 0.5 and 15.52% and 0.15 mg CO2 m−2 s−1 for S/Q ≥ 0.5. Validations of the EMGPP for hourly, daily, monthly, and annual GPP values were carried out and showed that both 3-factor and 2-factor EMGPP models can accurately capture diurnal, seasonal and interannual variations in GPP, but most simulated GPP overestimated the observed value. When the scattering factor is not available, the 2-factor EMGPP can be used. The EMGPP using 3-factor and 2-factor models was applied to simulate GPP under all sky conditions from 2013–2016, and the estimated GPP were in reasonable agreement with the measured values and showed systematic overestimations of 31% and 29% for mean hourly GPP and 41% and 29% for annual amounts, respectively. The sensitivity test demonstrated that GPP values were more sensitive to changes in PAR than to changes in water vapor and scattering factor at low S/Q, but were more sensitive to changes in water vapor than to PAR and S/Q at high S/Q. The sensitivity test revealed some mechanisms of GPP and its related processes, including the relationships between GPP and scattering of PAR, GPP and water vapor, which were in good agreement with other observations and model studies. An empirical model based on PAR energy balance can better describe the multiple interactions between GPP and its driving factors (PAR, water vapor, S/Q). The ratio of the emissions of biogenic volatile organic compounds (BVOCs) to net ecosystem exchange clearly varied between forests in different climate zones.

Regional‐Scale Wilting Point Estimation Using Satellite SIF, Radiative‐Transfer Inversion, and Soil‐Vegetation‐Atmosphere Transfer Simulation: A Grassland Study

AbstractAlthough water availability strongly controls gross primary production (GPP), the impact of soil moisture content (SMC) (wilting point) is poorly quantified on regional and global scales. In this study, we used 10 years of observations of solar‐induced chlorophyll fluorescence (SIF) from the Greenhouse gases Observing Satellite (GOSAT) satellite to estimate the wilting point of a semiarid grassland on the Mongolian Plateau. Radiative‐transfer model inversion and soil‐vegetation‐atmosphere transfer simulation were sequentially conducted to distinguish the drought impacts on plant physiology from the changes in the leaf‐canopy optical properties. We modified an existing inversion algorithm and the widely used Soil‐Canopy Observation of Photosynthesis and Energy fluxes model to adequately evaluate dryland features, for example, sparse canopy and strong convection. The modified model, with retrieved parameters and calibration to GOSAT SIF, predicted realistic GPP values. We found that (a) the SIF yield estimated from GOSAT showed a clear sigmoidal pattern in relation to drought, and the estimated wilting point matched ground‐based observations in the literature within ∼0.01 m3 m−3 for the SMC, (b) tuning the maximum carboxylation rate improved the SIF prediction after considering the changes in the leaf‐canopy optical properties, implying that GOSAT detected drought stress in leaf‐level photosynthesis, and (c) the surface energy balance significantly impacted the grassland's SIF; the modified model reproduced observed SIF well (mean bias = 0.004 mW m−2 nm−1 sr−1 in summer), whereas the original model predicted substantially low values under weak horizontal wind conditions. Some model‐observation mismatches in the SIF suggest that more research is needed for fluorescence parametrization (e.g., photoinhibition) and for additional observation constraints.