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Yearly CO2 emissions from anthropogenic land use change by main driver (2014-2023)
<h3>Background</h3> <p>Human-induced land use change (LUC), driven by activities such as forestry, logging, and the production of agricultural commodities (e.g. fruits, nuts, and meat) significantly impacts the Global Commons, encompassing the climate system, ice sheets, land biosphere, oceans, and the ozone layer. The convertion of natural forests into areas dedicated to these activities lead to disrupted ecosystems (Foley et al. 2005), severely degraded biodiversity (Newbold et al. 2015), and the release of substantial amounts of greenhouse gases (GHGs) into the atmosphere (Hong et al. 2021), further exacerbating climate change and ocean acidification (Doney et al. 2009). The expansion of the agricultural frontier is identified as the predominant direct cause of deforestation globally, with other industries like timber and mining also playing significant roles (Curtis et al. 2018). To achieve global climate targets, forestry, and other land use GHG emissions must decrease along a nonlinear trajectory and reach carbon neutrality by 2050 (Rockström et al. 2017). However, to successfully address this road map, improving our understanding of deforestation drivers is urgently needed.</p> <h3>Summary</h3> <p>This dataset is the result of data processing performed to estimate the extent to which commodities and other agricultural products have replaced forests, while mapping the CO2 emission impact making use of the best available spatially explicit data. Results are reported globally for 52 products at national level, as well as agroecological and thermal zones (FAO & IIASA) and a 50km cell vector grid.</p> <p>In order to detect spatially-explicit deforestation drivers, the current extent of commodities and agricultural products was overlapped with global annual tree cover loss in the 10-year period from 2014 to 2023. Carbon stocks in the deforested areas were then assumed to have been emmited into the atmosphere. Recent, detailed crop and pasture maps for relevant commodities were used whenever available, and coarser resolution datasets were used as supplements when needed. Operations were performed in Google Earth Engine.</p> <h3>Datasets used</h3> <p><em>Forest and biomass carbon distribution</em></p> <p>The <a href="https://earthenginepartners.appspot.com/science-2013-global-forest">Global Forest Change</a> dataset (Hansen et al., 2013) is used to estimate deforestation between 2014 and 2023. This tree cover loss dataset measures the first instance of complete removal of tree cover canopy at a 30-meter resolution for all woody vegetation over 5 meters in height.</p> <p>The <a href="https://data-gis.unep-wcmc.org/portal/home/item.html?id=374a99fc76574f72bb8c71af7b428d0a">WCMC Above and Below Ground Biomass Carbon Density </a>(Soto-Navarro et al., 2020), for reference year 2010 at 300m pixel, is overlapped with resulting deforested areas pixels to dermine the biomass carbon present in the areas before deforestation.</p> <p><em>Generalized deforestation drivers</em></p> <p><a href="https://data.globalforestwatch.org/documents/ff304784a9f04ac4a45a40f60bae5b26/about">Tree cover loss by dominant driver</a> (Curtis et al., 2022) in 2023 is used to determine wide categories of deforestation drivers (commodities, shifting agriculture, forestry, wildfire and urbanization). Pixels indicating deforestation in the Global Forest Change dataset (Hansen et al., 2013) that overlap the commodities and shifting agriculture pixels from this dataset (Curtis et al., 2022) have their drivers further detailed with the data sources listed in the below.</p> <p><a href="http://www.earthstat.org/">EarthStat</a> pasture areas layer (Ramankutty et al., 2008) is used to identify areas for which specific livestock categories are to be defined. The project provides pasture areas for reference year 2000 at ~10km resolution.</p> <p><em>Detailed deforestation drivers</em></p> <p>The <a href="https://earthobservations.org/geoglam.php">Group on Earth Observations Global Agricultural Monitoring</a> (GEOGLAM) commodity distibution layer (Becker-Reshef et al., 2023) is used to identify specific commodities (winter wheat, spring wheat, maize, rice and soybean) to deforestation pixels pertaining to the "commodities" class. The ressource provides commodity distribution mapping at 5km pixel resolution. Values are provided as percentage of pixel area occupied by given crop.</p> <p>The <a href="https://mapspam.info/">Spatial Production Allocation Model (SPAM)</a> physical area layer (You et al., 2014) for reference year 2020 is used to detail drivers pertaining to the "shifting agriculture" class. The dataset covers 46 crops and crop groups at ~9km pixel resolution. Values are provided as percentage of pixel area occupied by given crop or crop group.</p> <p>The <a href="https://www.fao.org/livestock-systems/global-distributions/en/">Gridded Livestock of the World (GLW3)</a> (Gilbert et al., 2022) is used to determine which species (cattle, goat, sheep or horse) of livestock is raised in areas identified as pasture in the EarthStat layer and pertaining to the "commodities" class. The project provides livestock distribution for reference year 2015 at ~9km resolution. Values are provided as number of individuals located within the pixel. Values were converted into percentage of pixel area covered by grazing field for given species based on species density thresholds.</p> <h3>Data processing</h3> <p>Most of data processing takes place in Google Earth Engine, with scripts redacted in javascript. In summary, two strategies were implemented:</p> <p><strong>Proportional driver distribution strategy</strong>: When deforestation pixels (Hansen et al., 2013) overlapped with pixels from at least one of the detailed deforestation drivers data sources, the driver describe in the latter were associated with that deforested area. Whenever more than one of these data sources had non-null pixels overlapping the area, a proportional distribution was assumed (i.e. if SPAM indicated 100% of the area to be covered by cowpea crops, GEOGLAM 100% by maize, and GLW3 100% by cattle grazing fields, the pixel is assumed to have 33.3% of its deforested area associated with each of these drivers).</p> <p><strong>Main driver strategy</strong>: When deforestation pixels did not overlap with any non-null pixels from any of the detailed drivers sources, the pixel is assumed to have the entirety of its deforested area associated with one single main driver resulting from a crop-livestock mosaic. The mosaic is created by taking the highest value from each of the crop or livestock distribution rasters, and then assigning the raster category to be the new pixel value, ultimately creating a category raster layer containing the main crop, crop group or livestock species occupying that pixel area. Null or zero values in this mosaic are filled-in by nearest neighbour analysis, to a limit of 20 pixels expansion. This was enough to ensure that all deforestation pixels had at least one detailed driver with which it could be associated. The logic behind this operation resides in the fact that the deforestation layer (Hansen et al., 2013) has a larger temporal coverage (with the more recent data point being the reference year 2023), while the detailed driver layers can be as old as reference year 2015. This means we're assuming the main deforestation drivers continued to expand their limits to neighbouring areas during the years for which no data is available.</p> <p>Resulting rasters from both strategies are put together and a zonal statistics operation is performed in order to populate the vector grid cells.</p> <h3><strong>Files</strong></h3> <p>This repository contains the following files:</p> <ul> <li><em>deforested_area_by_LUC_driver_2014_2023</em>.CSV contains the deforested area (hectares) and the corresponding driver in each grid cell (idenfied by the id field) in each year, in CSV text format.</li> <li><em>carbon_emissions_by_LUC_driver_2014_2023</em>.CSV contains the carbon emitted (Mg CO2 eq.) and the corresponding driver in each grid cell (idenfied by the id field) in each year, in CSV text format.</li> <li><em>spatial_grid</em>.gpkg contains the raw 50km cell grid, with identification of country (iso3 and name fields), region, and FAO agroecological zone (zone field) and thermal zone (thermal field), in Geopackage format. In order to visualize the data in a map, the user will need to join one of the csv files to this geopackage file by basing the join on the 'id' field.</li> <li><em>summary_showcase</em>.png is an image showcasing maps created using the database, as well as a diagram showing the datasets used to create the final dataset.</li> </ul> <h3><strong>How to cite</strong></h3> <p>Iablonovski, G.; Berthet, E. C.; Roberts, S. (2024). Yearly CO2 emissions from anthropogenic land use change by main driver (2014-2023) [Data set]. Zenodo. https://zenodo.org/doi/10.5281/zenodo.13308514</p> <h3>Authors and contact</h3> <p>Authors: Guilherme Iablonovski*, Etienne Charles Berthet, Sophie Roberts</p> <p>*Corresponding author: Guilherme Iablonovski (guilherme.iablonovski@unsdsn.org)</p>
Lotic Intersite Nitrogen eXperiment II (LINX II): a cross-site study of the effects of anthropogenic land use change on nitrate uptake and retention in 72 streams across 8 different biomes (2003 – 2006).
The LINX II (Lotic Intersite Nitrogen eXperiment) project was designed to quantify the rates and mechanisms of nitrate retention in streams using stable isotope tracer additions. The study encompassed 72 stream reaches spread across 8 North American biomes. Within each biome, 9 streams were selected in three watershed land-use categories: 3 reference, 3 agricultural, and 3 urbanized. The core of the study was a 24-hour release of 15N- labeled nitrate. Prior to the isotope addition, physical, chemical and biological characteristics of the stream were measured. The measurements included, but were not limited to, dissolved nutrient concentrations, dissolved conservative tracer additions (to quantify hydraulic and hyporheic retention, velocity and discharge), standing stocks of primary uptake biota (including suspended and benthic particulate materials) as well as channel dimensions, photosynthetically active radiation, and water temperature. During the isotope release, whole stream rates of ecosystem metabolism were quantified (including quantification of re-aeration coefficients using tracer gas additions), and concentrations of 15N-labeled NO3, NH4, N2 and N2O were measured. Immediately following the isotope addition, 15N uptake by aquatic organisms was quantified by sampling biomass components on the stream bed. The data generated from these 72 stream reaches were used to develop a stream nitrogen retention model for each biome, which was expanded to entire drainage networks to predict nitrogen fluxes. The LINX II study demonstrated how biotic uptake of nitrate and denitrification increased with increasing nitrate concentrations. However, the efficiency of total uptake and denitrification actually declined with increasing nitrate concentrations (such as those seen on agricultural or urbanized streams), yielding higher rates of dissolved nitrogen exports downstream. The datasets presented here consist of the primary data collected by the LINX II study participants.
Figure 7 in A conservation palaeobiological approach to assess faunal response of threatened biota under natural and anthropogenic environmental change
Figure 7. Box plots show the distribution of relative abundances of the associations across the six facies types.
Figure 3 in A conservation palaeobiological approach to assess faunal response of threatened biota under natural and anthropogenic environmental change
Figure 3. Sampling locations of three expeditions in 2015–2017. The first (September–October 2015) and second expeditions (July 2016) were searching for living Pontocaspian molluscs in particular. The third expedition consisted of six transects sampled by GeoEcoMar in 2017 in search of any living mollusc. In the legend w/wo means with or without.
Figure 1 in A conservation palaeobiological approach to assess faunal response of threatened biota under natural and anthropogenic environmental change
Figure 1. Location of study area along the Romanian Black Sea coast with core locations indicated by black stars. (a) Danube Delta and RSL (modified after Vespremeanu-Stroe et al., 2017). (b) RSL bathymetry with location of study cores (modified after Dimitriu et al., 2008). Two current marine outlets are indicated by white arrows; a third outlet (Gura Portiţa) was closed in the 1970s and is indicated by a dashed white arrow.
Figure 6 in A conservation palaeobiological approach to assess faunal response of threatened biota under natural and anthropogenic environmental change
Figure 6. Overview of the 20 most abundant mollusc species grouped according to the results of Kendall's W coefficient of concordance (for k = 4). (a) Valvata piscinalis (RGM.1309841, Core C7, depth 6 cm). (b) Dreissena polymorpha (RGM.1309827, C7 – 6 cm). (c) Dreissena bugensis (RGM.1309846, C5 – 18 cm). (d) Adacna fragilis (RGM.1309835, C2 – 18 cm). (e) Monodacna colorata s.l. (RGM.1309823, C7 – 14 cm). (f) Rissoa membranacea (RGM.1309830, C3 – 48 cm). (g) Hypanis plicata (RGM.1309845, C9 – 3 cm). (h) Clathrocaspia knipowitschii (RGM.1309843, C11 – 102 cm). (i) Mytilaster minimus (RGM.1309838, C3 – 24 cm). (j) Ecrobia maritima (RGM.1309831, C3 – 48 cm). (k) Cerastoderma glaucum (RGM.1309844, C13 – 24 cm). (l) Abra segmentum (RGM.1309821, C1 – 48 cm). (m) Parthenia interstincta (RGM.1309832, C3 – 48 cm). (n) Lentidium mediterraneum (RGM.1309837, C4 – 12 cm). (o) Retusa truncatula (RGM.1309828, C2 – 42 cm). (p) Gyraulus crista (RGM.1309840, C5 – 54 cm). (q) Potamopyrgus antipodarum (RGM.1309836, C2 – 18 cm). (r) Lithoglyphus naticoides (RGM.1309842, C5 – 18 cm). (s) Theodoxus fluviatilis (RGM.1309826, C11 – 66 cm). (t) T. fluviatilis (RGM.1309824, C11 – 78 cm). (u) Theodoxus danubialis (RGM.1309839, C3 – 24 cm). (v) T. danubialis (RGM.1309834, C2 – 30 cm). Scale bars are 1 mm.
Figure 2 in A conservation palaeobiological approach to assess faunal response of threatened biota under natural and anthropogenic environmental change
Figure 2. Overview of core data. From left to right each core: core photograph, lithology, facies, fauna relative abundance per species group based on origin, evolution and estimated palaeosalinities.
Figure 5 in A conservation palaeobiological approach to assess faunal response of threatened biota under natural and anthropogenic environmental change
Figure 5. NMDS ordination plot of species compositions across samples grouped into lake regions (stress = 0.173). Optimum salinity and grain size were fitted as two-dimensional smooth surfaces to illustrate the associations with species composition. Species are marked with numbers: 1 – Planorbis planorbis, 2 – Clathrocaspia knipowitschii, 3 – Potamopyrgus antipodarum, 4 – Theodoxus danubialis, 5 – Planorbarius corneus, 6 – Abra segmentum, 7 – Rissoa membranacea, 8 – Valvata macrostoma, 9 – Hypanis plicata, 10 – Mytilaster minimus, 11 – Ecrobia maritima, 12 – Parthenia interstincta, 13 – Cerastoderma glaucum and 14 – Lentidium mediterraneum.
Figure 8 in A conservation palaeobiological approach to assess faunal response of threatened biota under natural and anthropogenic environmental change
Figure 8. Snapshot reconstructions of the evolution of the RSL and their mollusc biota. The names of major sand barriers are indicated in yellow, while those of deltaic lobes are in black. The names in parentheses and italic font are currently inactive lobes. Pie charts indicate the relative abundance of the three associations in the time interval of ±50 years of the indicated snapshot: blue – Association I (freshwater); green – Association II (Pontocaspian), orange – Association III (marine), grey – rest of the group. Water colours indicate a salinity gradient: blue is Black Sea influence (18 psu); green is river influence (0 psu). Note the freshening of the system and according changes in species associations with the decreasing influence of mesohaline waters from the Black Sea.
Figure 4 in A conservation palaeobiological approach to assess faunal response of threatened biota under natural and anthropogenic environmental change
Figure 4. Spatiotemporal salinity variations in the RSL. For each sample, the salinity was calculated by weighted averaging of the species' optimum salinities. Salinity categories adapted from Strydom et al. (2003). Snapshots referred to in the Discussion are indicated with letters A–F.
Data for "Archetypal flow regime change classes and their associations with anthropogenic drivers of global streamflow alterations"
<p>Data repository for "Archetypal flow regime change classes and their associations with anthropogenic drivers of global streamflow alterations"</p>
Fig. 3 in Histological Changes In Common Toad, Bufo Bufo (Anura, Bufonidae), Liver Tissue Under Conditions Of Anthropogenically Transformed Ecosystems
Fig. 3. Adipose dystrophy and hypopigmentation of Fig. 4. Well-seen adipose dystrophy combined with common toad's liver. Vacuol and pigment remain- protein dystrophy and a foci of necrosis in the middle ings are seen in melano-macrophagal aggregations. of the picture. Destroyed cells and pigment remain- Hematoxyline-eosine staining, ×200. ings are seen in melano-macrophagal aggregations. Hematoxyline-eosine staining, ×200.
Fig. 1 in Histological Changes In Common Toad, Bufo Bufo (Anura, Bufonidae), Liver Tissue Under Conditions Of Anthropogenically Transformed Ecosystems
Fig. 1. Protein dystrophy in common toad. Protein Fig. 2. Foci of necrosis in common toad liver tissue. granules are visible inside hepatocytes. Hematoxy- Destructed cells are seen. Hematoxyline-eosine stainline-eosine staining, ×200. ing, ×200.
Fig. 7 in Histological Changes In Common Toad, Bufo Bufo (Anura, Bufonidae), Liver Tissue Under Conditions Of Anthropogenically Transformed Ecosystems
Fig. 7. Histological changes in liver tissue of common toads from breeding population in NNP "Holosiivskyi", %.
Fig. 5 in Histological Changes In Common Toad, Bufo Bufo (Anura, Bufonidae), Liver Tissue Under Conditions Of Anthropogenically Transformed Ecosystems
Fig. 5. Manifestation of protein dystrophy and hyper- Fig. 6. Abnormally big melano-macrophagal aggrepigmentation in common toad liver tissue. Hematox- gation surrounded by lymphoid infiltrate (inflammayline-eosine staining, ×200. tion) and hyperpigmentation in common toad's liver
Data for "Opposing changes in Indian Summer Monsoon Rainfall variability produced by orbital and anthropogenic forcing"
<p>The dataset for the CAM5 and LBM experiments is presented in the manuscript titled "Opposing changes in Indian summer monsoon rainfall variability produced by orbital and anthropogenic forcing. And the proxy data for the paper.</p>
Research data related to the article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems"
<p><strong>Research Data related to the article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems" by Seibert et al. (2024) published in <em>Earth's Future</em></strong></p> <p>Dear reader,</p> <p>reasearch data are provided for the research article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems" by Seibert et al. (2024) published in <em>Earth's Future</em>. The authors hope that the research data allows for a better understanding of the modeling workflow. Questions regarding the modeling approach etc. can be directed to the authors, see contact details below.</p> <p>The research data covers the following files:</p> <ul> <li>iMOD-Python (Visser and Bootsma, 2019) scripts to create the iMOD-WQ (Verkaik et al., 2021) input files for the n=566 model variants. Subfolders for each model variant and corresponding files are stored in the subfolder 'model_variants'. An overview regarding the set-up of the model variants is presented in the .xlsx spreadsheet 'model_variants_overview.xlsx' in the folder 'model_variants'.</li> <li>Base data files, used as input files to iMOD-WQ (Verkaik et al., 2021), stored in the subfolder 'imod_input'. However, in most cases no consent for re-distribution of these data sets exists, and they cannot be made freely available through this publication. Please, consider the corresponding meta-data files and/or get in touch with one of the authors for further information.</li> <li>Post-processed model output data, which was further used for model evaluation, stored in the subfolder 'model_output'.</li> <li>Figure files and the corresponding .py scripts, stored in the subfolder 'figures'.</li> </ul> <p>Meta-data files are provided with data files in the different subfolders for clarification.</p> <p>iMOD-WQ (Verkaik et al., 2021) input and .run-files were executed on the University Oldenburg High-Performance Cluster 'Rosa', funded by DFG through its Major Research Instrumentation Program, INST 184/225-1 FUGG, and the Ministry of Science and Culture (MWK) of the Lower Saxony State.</p> <p>Further information on the iMOD suite can be found here: https://deltares.github.io/iMOD-Documentation/</p> <p>The DFG is thanked for SALTSA project funding (DFG project number MA 3274/9-1) within the Priority Programme ‘Regional Sea Level Change and Society (SeaLevel)’. Research related to this article further benefited from funding of the projects WAKOS (BMBF; support code 01LR2003E) and the DFG research unit FOR 5094: The dynamic deep subsurface of high-energy beaches (DynaDeep).</p> <p>Literature:</p> <p>Verkaik, J., Hughes, J. D., van Walsum, P. E. V., Oude Essink, G. H. P., Lin, H. X., & Bierkens, M. F. P. (2021). Distributed memory parallel groundwater modeling for the Netherlands Hydrological Instrument. Environmental Modelling & Software, 143, p.105092.</p> <p>Visser, M., & Bootsma, H. (2019). iMOD-Python: Work with iMOD MODFLOW models in Python. Retrieved from https://imod.xyz/</p> <p>Seibert, S. L., Greskowiak, J., Oude Essink, G. H. P., & Massmann, G. (2024). Understanding climate change and anthropogenic impacts on the salinization of low‐lying coastal groundwater systems. Earth's Future, 12, e2024EF004737. https://doi.org/10.1029/2024EF004737<br><br><strong>Contact one of the authors if you have further questions</strong>: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de), Gualbert H.P. Oude Essink (Gualbert.OudeEssink@deltares.nl) or Gudrun Massmann (gudrun.massmann@uol.de)</p>
Figure 3 in Phytoplankton adaptation strategies under the influence of climatic changes and anthropogenic pressure on the Black Sea coastal ecosystems on the example Sevastopol Bay
Figure 3. Multiannual dynamics: a, d – concentrations of chlorophyll a (1), phytoplankton biomass (2) and water temperature (3), b, e – concentrations of nitrates (1), silicon (2), phosphates (3) and ammonium (4), c, f- diatoms contribution (1), dinoflagellates (2) and coccolithophorides (3) in the total phytoplankton biomass in Sevastopol Bay in summer and autumn.
Figure 2 in Phytoplankton adaptation strategies under the influence of climatic changes and anthropogenic pressure on the Black Sea coastal ecosystems on the example Sevastopol Bay
Figure 2. Multiannual dynamics: a, d – concentrations of chlorophyll a (1), phytoplankton biomass (2) and water temperature (3), b, e – concentrations of nitrates (1), silicon (2), phosphates (3) and ammonium (4), c, f- diatoms contribution (1), dinoflagellates (2) and coccolithophorides (3) in the total phytoplankton biomass in Sevastopol Bay in winter and spring
Changes to anthropogenic pressures on reach-scale rivers in South and Southeast Asia from 1990 to 2014
<p>The data documented the spatiotemporal anthropogenic pressures on reach-scale rivers across South and Southeast Asia from 1990-2014. The data used to produce the results reported in the manuscript "Changes to anthropogenic pressures on reach-scale rivers in South and Southeast Asia from 1990 to 2014".</p> <p>The attributes of the data are explained as follows:</p> <p>(1) "lengthkm": the length (km) of the river reach.</p> <p>(2) "order": stream order of the river reach, from Lin et al. (2019).</p> <p>(3) "nnCCI92": the anthropogenic pressures induced by irrigation in 1992.</p> <p>(4) "nnCCI00": the anthropogenic pressures induced by irrigation in 2000.</p> <p>(5) "nnCCI14": the anthropogenic pressures induced by irrigation in 2014.</p> <p>(6) "nnGHSL90": the anthropogenic pressures induced by human usage in 1990.</p> <p>(7) "nnGHSL00": the anthropogenic pressures induced by human usage in 2000.</p> <p>(8) "nnGHSL14": the anthropogenic pressures induced by human usage in 2014.</p> <p>(9) "sum90": the total anthropogenic pressures on river reaches in 1990.</p> <p>(10) "sum00": the total anthropogenic pressures on river reaches in 2000.</p> <p>(11) "sum14": the total anthropogenic pressures on river reaches in 2014.</p> <p>(12) "mean9014": the average anthropogenic pressures on river reaches from 1990-2014.</p> <p>(13)"domin14": dominant pressure indicator impacting river reaches in 2014. "1" for healthy rivers, "2" for "both human usage and irrigation", "3" for "irrigation", "4" for "human usage".</p> <p><strong>Version 2 updates the anthropogenic pressures on rivers considering the proportion of irrigation taken from groundwater.</strong></p>
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