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101 results for “gross primary productivity”
Gross primary production responses to warming, elevated CO2 , and irrigation: quantifying the drivers of ecosystem physiology in a semiarid grassland
<p>Determining whether the terrestrial biosphere will be a source or sink of carbon (C) under a future climate of elevated CO<sub>2</sub> (eCO<sub>2</sub>) and warming requires accurate quantification of gross primary production (GPP), the largest flux of C in the global C cycle. We evaluated 6 years (2007–2012) of flux‐derived GPP data from the Prairie Heating and CO<sub>2</sub> Enrichment (PHACE) experiment, situated in a grassland in Wyoming, USA. The GPP data were used to calibrate a light response model whose basic formulation has been successfully used in a variety of ecosystems. The model was extended by modeling maximum photosynthetic rate (<i>A</i><sub>max</sub>) and light‐use efficiency (<i>Q</i>) as functions of soil water, air temperature, vapor pressure deficit, vegetation greenness, and nitrogen at current and antecedent (past) timescales. The model fits the observed GPP well (<i>R</i><sup>2</sup> = 0.79), which was confirmed by other model performance checks that compared different variants of the model (e.g. with and without antecedent effects). Stimulation of cumulative 6‐year GPP by warming (29%, <i>P</i> = 0.02) and eCO<sub>2</sub> (26%, <i>P</i> = 0.07) was primarily driven by enhanced C uptake during spring (129%, <i>P</i> = 0.001) and fall (124%, <i>P</i> = 0.001), respectively, which was consistent across years. Antecedent air temperature (Tair<sub>ant</sub>) and vapor pressure deficit (VPD<sub>ant</sub>) effects on <i>A</i><sub>max</sub> (over the past 3–4 days and 1–3 days, respectively) were the most significant predictors of temporal variability in GPP among most treatments. The importance of VPD<sub>ant</sub> suggests that atmospheric drought is important for predicting GPP under current and future climate; we highlight the need for experimental studies to identify the mechanisms underlying such antecedent effects. Finally, posterior estimates of cumulative GPP under control and eCO<sub>2</sub> treatments were tested as a benchmark against 12 terrestrial biosphere models (TBMs). The narrow uncertainties of these data‐driven GPP estimates suggest that they could be useful semi‐independent data streams for validating TBMs.</p>
The reasonable application of Bayesian multi-model averaging to produce gross primary production with high quality in China
<p>To reduce the uncertainties of output data from the Multi-scale Terrestrial Model Intercomparison Project (MsTMIP), Bayesian Model Averaging (BMA) was trained by observed GPP from ChinaFLUX and a set of monthly 0.5° by 0.5° GPP data from 1948 to 2010 for China was produced.</p>
A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies (1992–2020)
<p>Distinguishing gross primary production of sunlit and shaded leaves (GPP<sub>sun</sub> and GPP<sub>shade</sub>) is crucial for improving our understanding of the underlying mechanisms regulating long-term GPP variations. Here we produce a global 0.05°, 8-day dataset for GPP, GPP<sub>shade</sub> and GPP<sub>sun</sub> over 1992-2020 using an updated two-leaf light use efficiency model (TL-LUE), which is driven by the GLOBMAP leaf area index, CRUJRA meteorology, and ESA-CCI land cover. Our products estimate the mean annual totals of global GPP, GPP<sub>sun</sub>, and GPP<sub>shade</sub> over 1992-2020 at 125.0±3.8 (mean ± std) Pg C a<sup>-1</sup>, 50.5±1.2 Pg C a<sup>-1</sup>, and 74.5±2.6 Pg C a<sup>-1</sup>, respectively, in which EBF (evergreen broadleaf forest) and CRO (crops) contribute more than half of the totals. They show clear increasing trends over time, in which the trend of GPP (also GPP<sub>sun</sub> and GPP<sub>shade</sub>) for CRO is distinctively greatest, and that for DBF (deciduous broadleaf forest) is relatively large and GPP<sub>shade</sub> overwhelmingly outweighs GPP<sub>sun</sub>. This new dataset advances our in-depth understanding of large-scale carbon cycle processes and dynamics.</p>
eLUE-GPP (MODIS): A global gross primary productivity product based on ecosystem light-use-efficiency model and MODIS EVI
<p>Gross Primary Productivity (GPP) represents the cumulative amount of carbon dioxide (CO<sub>2</sub>) assimilated by green plants through photosynthesis at specific time intervals and spatial scales. It is the main component of the carbon exchange between the terrestrial biosphere and the atmosphere, and has a major influence on global climate and terrestrial ecosystem functioning. Over the last two decades, the continuous and reliable collection of global land surface variables by EOS-MODIS, and the parallel development of the eddy-covariance flux tower network (FLUXNET) have enabled the integration of MODIS observations with tower measurements for the calibration and validation of remote sensing models to obtain global GPP estimates. Despite the significant progress and success to date, current remote sensing GPP models based on the light use efficiency (LUE) concept share several limitations, including the difficulty in accurately predicting LUE variability and the associated use of land cover maps and look-up tables for biome specific maximum LUE, further down-regulated by coarse resolution interpolated meteorological data, which introduce significant uncertainties in the predicted GPP. To address the above limitations, here we applied a simple yet ecologically sound remote sensing GPP model based on the ecosystem light use efficiency (eLUE) concept, using the more than two decades of global MODIS Enhanced Vegetation Index (EVI) product and the publicly available FLUXDATA2015 dataset, to generate a global 5 km, 16-d GPP product (eLUE-GPP) from February 2000 to March 2024. Cross-validation with 202 flux tower sites (1494 site/year) showed favorable accuracy of eLUE-GPP (hereafter GPP<sub>eLUE</sub>) (<em>R</em><sup>2</sup> = 0.71, RMSE = 2.11 g C m<sup>-2</sup> d<sup>-1</sup>). The uncertainty associated with GPP<sub>eLUE</sub> is comparatively lower than that of the other global GPP datasets (MOD17, FluxSat, VPM, among others). We have also calculated the uncertainty analytically for each GPP estimate based on the law of error propagation, which allows quantification of the error budget in applications such as Earth system model benchmarking and atmospheric inversion. Our estimate of global total annual GPP, averaged over the period 2001-2023, was 138.46±13.92 Pg C yr<sup>-1</sup>. Furthermore, we found a significant increasing trend in global total annual GPP at a rate of 0.28±0.05 Pg C yr<sup>-1</sup> (<em>p</em> < 0.001) from 2001 to 2023, particularly in eastern Asia, northern India, Europe, eastern North America, and central South America. We expect that the eLUE-GPP product will enable a more accurate diagnostic analysis of the global carbon budget and thus contribute to climate change research.</p>
Pre-analysis and figure data for Lomax et al. (2024), Untangling the environmental drivers of gross primary productivity in African rangelands
<p>Data required to reproduce main analyses and main text figures for the following publication:</p> <p>Lomax, G. A., Powell, T. W. R., Lenton, T. M., Economou, T., and Cunliffe, A. M. (in press), Untangling the environmental drivers of gross primary productivity in African rangelands.</p> <p> </p> <p>File details:</p> <ol> <li>df_annual.csv - the full 19-year dataset of model variables for reproducing the main analysis (for Figures 2-3).</li> <li>df_multi_annual.csv - a smaller dataset of multi-annual mean and variability variables for reproducing the analysis behind Figure 4.</li> <li>Fig1_data.tif - raster dataset containing the four variables shown in Figure 1.</li> <li>Fig2_data.csv - model results for the main analysis underlying Figure 2.</li> <li>Fig3_data.csv - model results for the binned analysis underlying Figure 3.</li> <li>Fig4_data.csv - model results for the multi-annual analysis underlying Figure 4.</li> </ol>
The gross primary productivity and leaf area index from TRENDY v7 project
<p>The gross primary productivity and leaf area index from TRENDY v7 DGVMs S3, including:</p> <p>CABLE-POP</p> <p>CLASS-CTEM</p> <p>CLM5.0</p> <p>DLEM</p> <p>JSBACH</p> <p>JULES</p> <p>LPX</p> <p>OCN</p> <p>ORCHIDEE</p> <p>ORCHIDEE-CNP</p> <p>SDGVM</p> <p>SURFEX</p> <p>VISIT</p>
Related data to article "Environmental Drivers of Gross Primary Productivity and Light Use Efficiency of a Temperate Spruce Forest"
<p>Data related to the article "Environmental Drivers of Gross Primary Productivity and Light Use Efficiency of a Temperate Spruce Forest", currently (2022-12-05) under review for publication in JGR:Biogeosciences.</p>
Dataset for: Immediate and carry-over effects of late-spring frost and growing season drought on forest gross primary productivity capacity in the Northern Hemisphere
<p>Forests are increasingly exposed to extreme global warming-induced climatic events. However, the immediate and carry-over effects of extreme events on forests are still poorly understood. Gross primary productivity (GPP) capacity is regarded as a good proxy of the ecosystem's functional stability, reflecting its physiological response to its surroundings. Using eddy covariance data from 34 forest sites in the Northern Hemisphere, we analyzed the immediate and carry-over effects of late-spring frost (LSF) and growing season drought on needle-leaf and broadleaf forests. Path analysis was applied to reveal the plausible reasons behind the varied responses of forests to extreme events. The results show that LSF had clear immediate effects on the GPP capacity of both needle-leaf and broadleaf forests. However, GPP capacity in needle-leaf forests was more sensitive to drought than in broadleaf forests. There was no interaction between LSF and drought in either needle-leaf or broadleaf forests. Drought effects were still visible when LSF and drought coexisted in needle-leaf forests. Path analysis further showed that the response of GPP capacity to drought differed between needle-leaf and broadleaf forests, mainly due to the difference in the sensitivity of canopy conductance. Moreover, LSF had a more severe and long-lasting carry-over effect on forests than drought. These results enrich our understanding of the mechanisms of forest response to extreme events across forest types.</p>
Improving gross primary production estimation accuracy on the Qinghai-Tibet Plateau considering the effect of atmospheric CO2 fertilization
<p>This GPP dataset was generated by the improved GPP estimation model which introduced atmospheric CO<sub>2</sub> fertilization effect and canopy-to-leaf CO<sub>2</sub> concentration gradients into the CASA model. The dataset was provided in TIF format at a month interval. The valid value ranges from 0 to 1000, and the background filled value is set to NoData. The scale factor of the data is 1. Each TIF file represents a month GPP at a daily cumulative value (unit: g C m<sup>-2</sup> month<sup>-1</sup>).</p>
Datasets for "The direct and indirect effects of the environmental factors on global terrestrial gross primary productivity over the past four decades"
<p>The environmental changes can affect gross primary productivity (GPP) by altering not only the biogeochemical characteristics of the photosynthesis system (direct effects) but also the structure of the vegetation canopy (indirect effects). However, comprehensively quantifying the multi-pathway effects of environmental change on GPP is currently challenging. We proposed a framework to analyse the changes in global GPP by combining a nested machine-learning model and a theoretical photosynthesis model. We quantified direct and indirect effects of changes in key environmental factors (atmospheric CO2 concentration, temperature, solar radiation, vapor pressure deficit (VPD), and soil moisture) on global GPP from 1982 to 2020. The three datasets(RF_LAI, RF_GPP, and RF_GPPlai) are derived from LAI random forest model, GPP random forest model and hierarchical nested model respectively.</p>
Dataset for: Immediate and carry-over effects of late-spring frost and growing season drought on forest gross primary productivity capacity in the Northern Hemisphere
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Improved estimation of global gross primary productivity during 1981–2020 using the optimized P model
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A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies (1992–2020)
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eLUE-GPP (MODIS): A global gross primary productivity product based on ecosystem light-use-efficiency model and MODIS EVI
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Gross primary production responses to warming, elevated CO2 , and irrigation: quantifying the drivers of ecosystem physiology in a semiarid grassland
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Estimates of mixed lyer gross primary productivity and photophysiology based on underway Lagrangian measurements of the dissolved chlorophyll fluorescence of phytoplankton usung Fast Repetition Rate Fluorometry (FRRF)
GPP was estimated on the P1706 cruise based on the photo-physiology of the mixed-layer phytoplankton community measured by FRRF. Shipboard measurements were made using a bench-top FastAct 2+ Fast TRAKA instrument (Chelsea, UK) plumbed into the ship’s running seawater system. Photosynthesis versus irradiance (P vs. E) curves were run continuously on a ~45 min sampling interval.
Effects of anthropogenic activity on global terrestrial gross primary production (GPP)
<p>This data set contains the 100-member ensembles of monthly gross primary production (GPP) estimated using the biosphere model BEAMS and d4PDF data for historical and non-warming climates in 1951-2010/2011, 100-member ensembles of yearly GPP of the historical sensitivity experiment for 7 input variables in 1951-2010, yearly GPP of the extended CO<sub>2</sub> sensitivity experiment using four RCP scenarios in 1951-2300. Data is 0.5625-degree (640×320) 4-byte binary (.raw). Undefined value is -9999.</p> <p>For more details, please check the ReadmeGPP.pdf</p> <p>If you questions, please contact Irina Melnikova (irina.melnikova.russia@gmail.com)</p>
Seasonal Gross Primary Productivity (GPP) Imagery 2005-2020
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PML_V2 global evapotranspiration and gross primary production (2000.02-2023.12)
<h2>Summary</h2> <p>This data is an <strong>8-day 5km (0.05°)</strong> data aggregated from the latest <strong>8day 500m PML-V2 global evapotranspiration and gross primary production data</strong> in Google Earth Engine, available since 2000.2.26 to 2023 (latest and will update annually).</p> <p><strong>Notes</strong></p> <ul> <li> <p>8-day means an average of the variable for the 8 days (xx d-1).</p> </li> <li> <p>Land evapotranspiration (ET) can be computed as a sum of Ec, Ei, and Es, while in water, Penman evapotranspiration denotes actual evaporation (ET_water).</p> </li> <li> <p>In a 5km resolution, please do not add ET_water to land ET as they represent a different coverage of area within the 5km pixel. Please see the coverage ratio file for each variable.</p> </li> </ul> <table> <tbody> <tr> <th>BandName</th> <th>Units</th> <th>Scale</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>GPP</td> <td>gC m-2 d-1</td> <td>0.01</td> <td>Gross primary product</td> </tr> <tr> <td>Ec</td> <td>mm d-1</td> <td>0.01</td> <td>Vegetation transpiration</td> </tr> <tr> <td>Es</td> <td>mm d-1</td> <td>0.01</td> <td>Soil evaporation</td> </tr> <tr> <td>Ei</td> <td>mm d-1</td> <td>0.01</td> <td>Interception from vegetation canopy</td> </tr> <tr> <td>ET_water</td> <td>mm d-1</td> <td>0.01</td> <td>Water body, snow and ice evaporation. Penman <br>evapotranspiration is regarded as actual evaporation for them.</td> </tr> </tbody> </table> <h2>Changes</h2> <p>Here, this PML-V2 dataset denotes <strong>the latest update</strong> that follows the original implementation of Zhang et al., 2019, <strong>except with </strong>Terra LAI for longer temporal coverage and annual updates.</p> <ul> <li> <p>Temporal coverage lengthened to 2000.2-2023.12</p> </li> <li> <p>Using MODIS Terra LAI (MOD15A2H) with original wWhd smoother processing as in Kong et al., 2019</p> </li> <li> <p>Recalibrated with the new MODIS Terra LAI</p> </li> <li> <p>Other climatic forcing and MODIS input remain the same</p> </li> </ul> <h2>Google Earth Engine</h2> <p>Original 500m 8-day data in GEE</p> <p>https://developers.google.com/earth-engine/datasets/catalog/CAS_IGSNRR_PML_V2_v018</p> <h2>Methods</h2> <p>Penman-Monteith-Leuning Evapotranspiration V2 (PML_V2) products include evapotranspiration (ET), its three components, and gross primary product (GPP) at 500m and 8-day resolution during 2000-2017 and with spatial range from -60°S to 90°N. The major advantages of the PML_V2 products are:</p> <ol> <li> <p>coupled estimates of transpiration and GPP via canopy conductance (Gan et al., 2018; Zhang et al., 2019)</p> </li> <li> <p>partitioning ET into three components: transpiration from vegetation, direct evaporation from the soil and vaporization of intercepted rainfall from vegetation (Zhang et al., 2016).</p> </li> </ol> <p>The PML_V2 products perform well against observations at 95 flux sites across globe, and are similar to or noticeably better than major state-of-the-art ET and GPP products widely used by water and ecology science communities (Zhang et al., 2019).</p> <h2>References</h2> <ul> <li> <p>Zhang, Y., Kong, D., Gan, R., Chiew, F.H.S., McVicar, T.R., Zhang, Q., and Yang, Y., 2019. Coupled estimation of 500m and 8-day resolution global evapotranspiration and gross primary production in 2002-2017. Remote Sens. Environ. 222, 165-182, <a href="https://doi.org/10.1016/j.rse.2018.12.031">doi:10.1016/j.rse.2018.12.031</a></p> </li> <li> <p>Gan, R., Zhang, Y.Q., Shi, H., Yang, Y.T., Eamus, D., Cheng, L., Chiew, F.H.S., Yu, Q., 2018. Use of satellite leaf area index estimating evapotranspiration and gross assimilation for Australian ecosystems. Ecohydrology, <a href="https://doi.org/10.1002/eco.1974">doi:10.1002/eco.1974</a></p> </li> <li> <p>Zhang, Y., Peña-Arancibia, J.L., McVicar, T.R., Chiew, F.H.S., Vaze, J., Liu, C., Lu, X., Zheng, H., Wang, Y., Liu, Y.Y., Miralles, D.G., Pan, M., 2016. Multi-decadal trends in global terrestrial evapotranspiration and its components. Sci. Rep. 6, 19124. <a href="https://doi.org/10.1038/srep19124">doi:10.1038/srep19124</a></p> </li> </ul>
Gross Primary Productivity is More Sensitive to Accelerated Flash Droughts
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