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31 results for “Terrestrial productivity”
Foodscapes - A global clustering of terrestrial food production systems
<p>This repository contains the final outputs from the global <strong>Foodscape</strong> mapping exercise. In this project we aimed to identify broad homologues of foodscape classes, comparable in a minimum set of biophysical and management characteristics, would help to design possible interventions and leverage points for more sustainable agriculture.</p> <p>Provided are maps in spatial geotiff format (.tif) which can be opened in any typical GIS software such as ArcGIC or QGIS. The Resolution is 0.5' degree (WGS 84 or <em>longitude-latitude</em> projection). Both the foodscape map (<em>Foodscapes_combinedGEOTIFF_final.tif</em>) and a reclassified intensity map (<em>Foodscapes_combinedGEOTIFF_intensity.tif</em>) are provided. Accompanied with the tif files are .clr and .qml files, both of which provide layout information on colours used for each code combination in the global maps. The .qml file will be automatically loaded when opening the layer in QGIS.<br> In addition, in the Microsoft Excel sheet "<em>Foodscapes_combinedLEGEND_final.xlsx</em>" a legend with qualitative description and label of each class is provided.</p> <p>If you use this layer in any way, please cite this repository and the describing <a href="https://osf.io/puyzw/">preprint</a>.</p>
Data for "Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion"
<p>Data used in generating results for the paper <a href="https://doi.org/10.1029/2023JG007457">"Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion."</a></p> <ol> <li>VIIRS_MOD16_MOD17_tower_site_drivers_v9.h5</li> <li>MOD17_5km_global_simulation.zip</li> <li>VNP17_5km_global_simulation.zip</li> </ol> <p>[1] is an HDF5 file containing surface meteorological drivers, MODIS/ VIIRS vegetation fPAR and LAI, and other data necessary for calibrating and validating the MOD17 and VNP17 GPP models at FLUXNET towers. It also contains driver data and field-based NPP data for calibrating and validating MOD17/ VNP17 NPP models.</p> <p>[2] and [3] are the global, 5-km GPP and NPP simulations using the updated MOD17 parameters and new VNP17 model parameters. Other than their 5-km resolution, these global, annual GeoTIFF files are formatted the same as MOD17A3H data; <a href="https://lpdaac.usgs.gov/products/mod17a3hgfv061/">see the User Guide</a> for more information. The same scale factors apply to recover geophysical units: multiply the values by 0.0001 to obtain [kg C m-2 year-1].</p> <p>Please cite the peer-reviewed paper:</p> <blockquote> <p>Endsley, K.A., M. Zhao, J.S. Kimball, S. Devadiga. 2023. "Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion." <em>Journal of Geophysical Research: Biogeosciences</em> 128(9).</p> </blockquote>
Effects of anthropogenic activity on global terrestrial gross primary production (LAI and FAPAR)
<p>This data set contains the 100-member ensembles of monthly leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR) estimated using GIMMS3g LAI and FAPAR and d4PDF air temperature by the method described in Sasai et al. (2016) for historical and non-warming climates in 1951-2010/2011. Data is 0.5625-degree (640×320) 4-byte binary (.raw). Undefined value is -9999.</p> <p>For more details, please check the ReadmeVeg.pdf</p> <p>If you questions, please contact Irina Melnikova (irina.melnikova.russia@gmail.com)</p>
The role of terrestrial productivity in regulating aquatic dissolved organic carbon concentrations in boreal catchments
<p>The past decades have witnessed an increase in dissolved organic carbon (DOC) concentrations in the catchments of the Northern Hemisphere. Increases in terrestrial productivity may be a reason for the increases in DOC concentration. The aim of this study is to investigate the impacts of increased terrestrial productivity and changed hydrology following climate change on DOC concentrations. We tested and quantified the effects of gross primary production (GPP), ecosystem respiration (RE) and discharge on DOC concentrations in boreal catchments over three years. As catchment characteristics can regulate the extent of rising DOC concentrations caused by the regional or global environmental changes, we selected four catchments with different sizes (small, medium and large) and landscapes (forest, mire and forest-mire mixed). We applied multiple models: Wavelet coherence analysis detected the delay-effects of terrestrial productivity and discharge on aquatic DOC variations of boreal catchments; thereafter, the distributed-lag linear models (DLMs) quantified the contributions of each factor on DOC variations. Our results showed that the combined impacts of terrestrial productivity and discharge explained 62% of aquatic DOC variations on average across all sites, whereas discharge, GPP and RE accounted for 26%, 22% and 3%, respectively. GPP dominated the DOC variations in small catchments (<1 km<sup>2</sup>), but in large catchments, DOC variations were mainly dependent on discharge. The direction of the relation between GPP and discharge on DOC varied. Increasing RE always made a positive contribution to DOC concentration. This study demonstrated that terrestrial greening and changing hydrology caused by climate change did affect the DOC export from terrestrial to aquatic ecosystems, which improves the mechanistic understanding of surface water DOC regulation in boreal catchments under climate change.</p>
Production of Terrestrial Gamma-ray Flashes During the Early Stages of Lightning Flashes
<p>Supporting data to the paper "Production of Terrestrial Gamma-ray Flashes During the Early Stages of Lightning Flashes", Lindanger et al., 2022, JGR</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>
Interactive effects of global change factors on terrestrial net primary productivity are treatment length- and intensity-dependent
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Data from: Contrasting CO2 dynamics in seagrass meadows between organic carbon (OC)-rich reef and OC-poor terrestrial sediments: Implications for enhanced alkalinity production
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The role of terrestrial productivity in regulating aquatic dissolved organic carbon concentrations in boreal catchments
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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>
Data from: A global estimate of terrestrial net secondary production of primary consumers
Aim: Net Secondary production (NSP) emerges from the consumption of Net Primary Production (NPP) by any heterotrophic organism. There has been sporadic interest in the importance of NSP, but no global estimates have been produced. We revisit NSP and attempt a global estimate using contemporary NPP data combined with modern metabolic scaling theory for consumption rates. We distinguish between potential NSP as the amount of secondary production that could be supported by NPP, and realized NSP as the amount remaining after anthropogenic habitat disruption. Location: Global Time period: 2000-2014 Methods: We present a model of NSP implementing a Type II functional response for consumption rates wherein search efficiency and handling time are calculated based on consumer mass and ambient temperature. We solve this model for each 0.05-decimal-degree pixel in the global terrestrial biosphere using as data inputs NPP (MOD17A3) and land-surface temperature (MOD11C3). We aggregate estimates within global land cover classifications (MCD12C1) to obtain cover-specific and global estimates of NSP. We also correct our estimates based on declines in consumer abundance reported in Living Planet Report 2014. Results: We estimate potential NSP is 4.74 PgCy-1 globally (95%~CI~=~3.75--5.75). When we correct for global consumer population declines, realized NSP declines to 2.37 PgCy-1 (95%~CI~=~1.86--2.89), a loss of 50% in the rate of carbon flux through secondary consumers. Main Conclusions: Our estimates are sufficient to suggest that the flux of carbon through consumers is of a similar magnitude to many other fluxes critical to the global carbon cycle. We view this as a hypothesis to be tested that suggests NSP deserves significantly more attention in earth systems, macroecology, and biogeochemical research.
Data from: Species richness-productivity relationships of tropical terrestrial ferns at regional and local scales
1. The species richness-productivity relationship (SRPR), by which the species richness of habitats or ecosystems is related to the productivity of the ecosystem or the taxon, has been documented both on regional and local scales, but its generality, biological meaning, and underlying mechanisms remain debated. 2. We evaluated the SRPR and 3 mechanistic hypotheses using terrestrial ferns in 18 study plots along an elevational gradient (500-4000 m) in Ecuador. We measured annual increases in above-ground biomass of 6175 fern individuals from 91 species over 2 years, and estimated plot-level tree productivity from increases in above-ground woody biomass of 560 trees. Analyses were conducted by (a) comparing plots along the elevational gradient (regional scale) and (b) comparing plots within each elevational belt (local scale). 3. Fern diversity was related to the productivity of the fern assemblages, but not to above-ground productivity of the trees. At the regional scale, we found a positive relationship of fern species richness to fern productivity that appeared to be determined by an increase in the number of fern individuals and niche availability. In contrast, at the local scale this relationship was negative and likely driven by interspecific competition. 4. Synthesis: Plot diversity of ferns appears to be limited by the number of available niches and competition to occupy these niches. At the local scale, this is reflected in a negative SRPR probably driven by competition, whereas with increasing scale the positive influence of productivity emerges. This represents the first evidence that productivity and competition affect the diversity of tropical herb assemblages at the plot scale.
Datasets for "Reconciling global terrestrial evapotranspiration estimates from multi-product intercomparison and evaluation"
<p>Datasets for "Reconciling global terrestrial evapotranspiration estimates from multi-product intercomparison and evaluation"</p>
The main variables (soil moisture, net biome production and so on) of two experiments (CTRL and EXP) with the ORCHIDEE-MICT terrestrial biosphere model
<p class="MsoNormal"><span>Multiple linear regression (MLR) is widely used to attribute causes of the interannual variability (IAV) of land carbon uptake, yet, parameter estimation in MLR can be problematic if the predictors are strongly inter-correlated. Recently, Humphrey et al., (2021) used MLR method to conclude that the indirect effect of soil moisture (SM) via land-atmosphere coupling, rather than the direct effect of SM on photosynthesis and respiration, controls the IAV of NBP. Here we assess the validity of MLR as used by Humphrey et al. (2021) by comparing the true contribution of SM in a terrestrial biosphere model, derived from the difference between a control run (CTRL) and an experiment with prescribed climatological SM (EXP), with the MLR method applied to the CTRL outputs.</span></p> <p class="MsoNormal"><span><span>We ran two experiments (CTRL and EXP) with the ORCHIDEE-MICT</span><span> terrestrial biosphere model at 2º spatial resolution. The control (CTRL) run followed the protocol of "S3" experiment of TRENDY-v6</span><span>, forced by CRUNCEP-v8 climate forcing, increasing atmospheric CO<sub>2</sub> concentration, and varying land use maps. </span>Monthly outputs for the period 1960-2005 were used for analysis. For the EXP run, <a name="_Hlk107926231"></a>to remove the interannual variability (IAV) of soil moisture (SM) while keeping its seasonal cycle, a climatological monthly SM averaged for the years 1960-2005 simulated by the CTRL run was prescribed in the model. Note that the intrinsic time-step of hydrology and photosynthesis in ORCHIDEE-MICT is half-hourly, thus SM within the same month took the same monthly mean value. Other configurations in the EXP run were identical to the CTRL run. </span></p>
Data from: Estimating soil pCO2 using paleosol carbonates: implications for the relationship between primary productivity and faunal richness in ancient terrestrial ecosystems
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Data from: A global estimate of terrestrial net secondary production of primary consumers
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The main variables (soil moisture, net biome production and so on) of two experiments (CTRL and EXP) with the ORCHIDEE-MICT terrestrial biosphere model
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Data from: Species richness-productivity relationships of tropical terrestrial ferns at regional and local scales
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Gross primary production and ecosystem respiration measurments based on the Terrestrial Ecosystem Model (TEM)
Gross primary production and ecosystem respiration measurments based on the Terrestrial Ecosystem Model (TEM).
Drought risk of global terrestrial gross primary productivity in recent 40 years detected by a remote sensing-driven process model
<p class="Abstract"><span>Gross primary productivity (GPP) is the largest flux in the global terrestrial carbon cycle and affected by multiple factors. In recent decades, drought has significantly impacted global terrestrial GPP and been projected to occur with increasing frequency and intensity. However, the drought risk of global terrestrial GPP has not been well investigated. In this study, global terrestrial GPP over the period from 1981 to 2016 was simulated with the process-based Boreal Ecosystem Productivity Simulator (BEPS) model. Then, the drought risk of terrestrial GPP was quantified as the product of frequency of drought and reduction of GPP caused by drought, which were determined using the standardized precipitation evapotranspiration index (SPEI). During the study period, the drought risk of terrestrial GPP exhibited detectable spatial heterogeneity, high in southeastern United States, most of South America, southern Europe, central and eastern Africa, eastern and southeastern Asia, and eastern Australia. In these regions, the maximum reduction of GPP might be above 30% in drought years relative to that in normal years. The drought risk of GPP was low at high latitudes of the Northern Hemisphere, in which terrestrial GPP increased slightly in drought years. The spatial pattern of the drought risk of GPP simulated by the BEPS model was close to that of FLUXCOM GPP, which was scaled from tower observations with a machine learning algorithm forced by remote sensing and meteorological data. This study advances our understanding on the impact of drought on terrestrial GPP over the globe.</span></p>
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